From fe812cd4cb551193f9a6527c70ed0fb05b9959eb Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Tue, 30 Sep 2025 15:06:44 +0100 Subject: [PATCH 01/52] Udpate scripts --- .../slurm/grid_search_fft_m2l_1e6.slurm | 91 ++++++++++++++++++ .../slurm/grid_search_fft_m2l_1e7.slurm | 91 ++++++++++++++++++ hpc/archer2/slurm/weak_1_blas.slurm | 1 + hpc/archer2/slurm/weak_1_fft.slurm | 3 +- hpc/archer2/slurm/weak_2_blas.slurm | 1 + hpc/archer2/slurm/weak_2_fft.slurm | 3 +- hpc/archer2/slurm/weak_3_fft.slurm | 94 +++++++++++++++++++ 7 files changed, 282 insertions(+), 2 deletions(-) create mode 100644 hpc/archer2/slurm/grid_search_fft_m2l_1e6.slurm create mode 100644 hpc/archer2/slurm/grid_search_fft_m2l_1e7.slurm create mode 100644 hpc/archer2/slurm/weak_3_fft.slurm diff --git a/hpc/archer2/slurm/grid_search_fft_m2l_1e6.slurm b/hpc/archer2/slurm/grid_search_fft_m2l_1e6.slurm new file mode 100644 index 00000000..b3b29005 --- /dev/null +++ b/hpc/archer2/slurm/grid_search_fft_m2l_1e6.slurm @@ -0,0 +1,91 @@ +#!/bin/bash +# Here, we perform grid search where the number of MPI processes is set equal to the number of CCX units, each of which +# share an L3 cache, conist of 4 processors. There are 16 CCX regions per processor, and 32 per node + +#SBATCH --job-name=grid_search +#SBATCH --time=00:30:00 +#SBATCH --nodes=1 +#SBATCH --ntasks-per-node=32 +#SBATCH --cpus-per-task=4 + +#SBATCH --account=e738 +#SBATCH --partition=standard +#SBATCH --qos=standard +# Development environment for KiFMM + +# Restore AMD compiler env +module load PrgEnv-aocc +module load craype-network-ucx +module load cray-mpich-ucx + +# Home and work directories +export HOME="/home/e738/e738/skailasa" +export WORK="/work/e738/e738/skailasa" + +# Create a scratch directory for this run +export SCRATCH=${WORK}/grid_search_${SLURM_JOBID} + +# Load Spack +source $HOME/spack/share/spack/setup-env.sh +. "$HOME/.cargo/env" + +# Load BLAS +spack load openblas + +# Ensure Rust can find the Cray libraries +# export RUSTFLAGS="-L $(echo $CRAY_LD_LIBRARY_PATH)" +# export LD_LIBRARY_PATH=$CRAY_LD_LIBRARY_PATH:$LD_LIBRARY_PATH +export RUSTFLAGS="-L $(spack location -i openblas)/lib" +export LD_LIBRARY_PATH=$(spack location -i openblas)/lib:$LD_LIBRARY_PATH + +mkdir -p ${SCRATCH} +cd ${SCRATCH} + +# Pass variable to SRUN from SBATCH +export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK + +# Script being run, expected in the work directory +script_name="fmm_m2l_fft_mpi_f32" + +# Set simulation parameters +n_points=(250000 500000 1000000) # points per MPI process +n_tasks=32 +cpus_per_task=4 +global_depth=2 # Number of local roots matches the number of MPI processes, therefore the number of NUMA regions +local_depth=(4 5) +n_samples=(500) +block_size=(128) +n_threads=(4) # See if bandwidth saturates with different threading parameters for Rayon thread pool +export OMP_NUM_THREADS=1 # Need to set to 1 to avoid oversubsciption between Rayon and OpenMP + +# Create a CSV output file for analysis +export OUTPUT=${SCRATCH}/grid_search_fft_${SLURM_JOBID}.csv +touch ${OUTPUT} +echo " +experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ +source_tree,target_tree,source_domain,target_domain,layout,\ +ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\ +source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\ +displacement_map,metadata_creation,\ +expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples" >> ${OUTPUT} + +# Perform grid search +for i in ${!local_depth[@]}; do + for j in ${!n_samples[@]}; do + for k in ${!block_size[@]}; do + for l in ${!n_threads[@]}; do + for m in ${!n_points[@]}; do + experiment_id="${i}_${j}_${k}_${l}_${m}" + srun --ntasks=$n_tasks --cpus-per-task=$cpus_per_task --distribution=block:block --hint=nomultithread \ + "${WORK}/${script_name}" --id $experiment_id --n-points ${n_points[$m]} \ + --expansion-order 3 \ + --prune-empty \ + --global-depth $global_depth \ + --local-depth ${local_depth[$i]} \ + --n-samples ${n_samples[$j]} \ + --n-threads ${n_threads[$l]} >> ${OUTPUT} + done + done + done + done +done diff --git a/hpc/archer2/slurm/grid_search_fft_m2l_1e7.slurm b/hpc/archer2/slurm/grid_search_fft_m2l_1e7.slurm new file mode 100644 index 00000000..16a3fd6d --- /dev/null +++ b/hpc/archer2/slurm/grid_search_fft_m2l_1e7.slurm @@ -0,0 +1,91 @@ +#!/bin/bash +# Here, we perform grid search where the number of MPI processes is set equal to the number of CCX units, each of which +# share an L3 cache, conist of 4 processors. There are 16 CCX regions per processor, and 32 per node + +#SBATCH --job-name=grid_search +#SBATCH --time=00:30:00 +#SBATCH --nodes=1 +#SBATCH --ntasks-per-node=32 +#SBATCH --cpus-per-task=4 + +#SBATCH --account=e738 +#SBATCH --partition=standard +#SBATCH --qos=standard +# Development environment for KiFMM + +# Restore AMD compiler env +module load PrgEnv-aocc +module load craype-network-ucx +module load cray-mpich-ucx + +# Home and work directories +export HOME="/home/e738/e738/skailasa" +export WORK="/work/e738/e738/skailasa" + +# Create a scratch directory for this run +export SCRATCH=${WORK}/grid_search_${SLURM_JOBID} + +# Load Spack +source $HOME/spack/share/spack/setup-env.sh +. "$HOME/.cargo/env" + +# Load BLAS +spack load openblas + +# Ensure Rust can find the Cray libraries +# export RUSTFLAGS="-L $(echo $CRAY_LD_LIBRARY_PATH)" +# export LD_LIBRARY_PATH=$CRAY_LD_LIBRARY_PATH:$LD_LIBRARY_PATH +export RUSTFLAGS="-L $(spack location -i openblas)/lib" +export LD_LIBRARY_PATH=$(spack location -i openblas)/lib:$LD_LIBRARY_PATH + +mkdir -p ${SCRATCH} +cd ${SCRATCH} + +# Pass variable to SRUN from SBATCH +export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK + +# Script being run, expected in the work directory +script_name="fmm_m2l_fft_mpi_f32" + +# Set simulation parameters +n_points=(5000000 10000000) # points per MPI process +n_tasks=32 +cpus_per_task=4 +global_depth=2 # Number of local roots matches the number of MPI processes, therefore the number of NUMA regions +local_depth=(5 6) +n_samples=(500) +block_size=(128) +n_threads=(4) # See if bandwidth saturates with different threading parameters for Rayon thread pool +export OMP_NUM_THREADS=1 # Need to set to 1 to avoid oversubsciption between Rayon and OpenMP + +# Create a CSV output file for analysis +export OUTPUT=${SCRATCH}/grid_search_fft_${SLURM_JOBID}.csv +touch ${OUTPUT} +echo " +experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ +source_tree,target_tree,source_domain,target_domain,layout,\ +ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\ +source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\ +displacement_map,metadata_creation,\ +expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples" >> ${OUTPUT} + +# Perform grid search +for i in ${!local_depth[@]}; do + for j in ${!n_samples[@]}; do + for k in ${!block_size[@]}; do + for l in ${!n_threads[@]}; do + for m in ${!n_points[@]}; do + experiment_id="${i}_${j}_${k}_${l}_${m}" + srun --ntasks=$n_tasks --cpus-per-task=$cpus_per_task --distribution=block:block --hint=nomultithread \ + "${WORK}/${script_name}" --id $experiment_id --n-points ${n_points[$m]} \ + --expansion-order 3 \ + --prune-empty \ + --global-depth $global_depth \ + --local-depth ${local_depth[$i]} \ + --n-samples ${n_samples[$j]} \ + --n-threads ${n_threads[$l]} >> ${OUTPUT} + done + done + done + done +done diff --git a/hpc/archer2/slurm/weak_1_blas.slurm b/hpc/archer2/slurm/weak_1_blas.slurm index ab453a2e..8f20b49b 100644 --- a/hpc/archer2/slurm/weak_1_blas.slurm +++ b/hpc/archer2/slurm/weak_1_blas.slurm @@ -75,6 +75,7 @@ experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ source_tree,target_tree,source_domain,target_domain,layout,\ ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\ source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\ +displacement_map,metadata_creation,\ expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples" >> ${OUTPUT} # Perform weak scaling diff --git a/hpc/archer2/slurm/weak_1_fft.slurm b/hpc/archer2/slurm/weak_1_fft.slurm index c191b12a..f20f4654 100644 --- a/hpc/archer2/slurm/weak_1_fft.slurm +++ b/hpc/archer2/slurm/weak_1_fft.slurm @@ -69,13 +69,14 @@ cpus_per_task=4 export OMP_NUM_THREADS=1 # Need to set to 1 to avoid oversubsciption between Rayon and OpenMP # Create a CSV output file for analysis -export OUTPUT=${SCRATCH}/weak_blas_${SLURM_JOBID}.csv +export OUTPUT=${SCRATCH}/weak_fft_${SLURM_JOBID}.csv touch ${OUTPUT} echo " experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ source_tree,target_tree,source_domain,target_domain,layout,\ ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\ source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\ +displacement_map,metadata_creation,\ expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples" >> ${OUTPUT} # Perform weak scaling diff --git a/hpc/archer2/slurm/weak_2_blas.slurm b/hpc/archer2/slurm/weak_2_blas.slurm index b7d4b722..e30f9d61 100644 --- a/hpc/archer2/slurm/weak_2_blas.slurm +++ b/hpc/archer2/slurm/weak_2_blas.slurm @@ -75,6 +75,7 @@ experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ source_tree,target_tree,source_domain,target_domain,layout,\ ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\ source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\ +displacement_map,metadata_creation,\ expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples" >> ${OUTPUT} # Perform weak scaling diff --git a/hpc/archer2/slurm/weak_2_fft.slurm b/hpc/archer2/slurm/weak_2_fft.slurm index eab01c8e..e331a1a2 100644 --- a/hpc/archer2/slurm/weak_2_fft.slurm +++ b/hpc/archer2/slurm/weak_2_fft.slurm @@ -69,13 +69,14 @@ cpus_per_task=4 export OMP_NUM_THREADS=1 # Need to set to 1 to avoid oversubsciption between Rayon and OpenMP # Create a CSV output file for analysis -export OUTPUT=${SCRATCH}/weak_blas_${SLURM_JOBID}.csv +export OUTPUT=${SCRATCH}/weak_fft_${SLURM_JOBID}.csv touch ${OUTPUT} echo " experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ source_tree,target_tree,source_domain,target_domain,layout,\ ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\ source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\ +displacement_map,metadata_creation,\ expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples" >> ${OUTPUT} # Perform weak scaling diff --git a/hpc/archer2/slurm/weak_3_fft.slurm b/hpc/archer2/slurm/weak_3_fft.slurm new file mode 100644 index 00000000..60bb2f35 --- /dev/null +++ b/hpc/archer2/slurm/weak_3_fft.slurm @@ -0,0 +1,94 @@ +#!/bin/bash +# Here, we perform a weak scaling run where the number of MPI processes is set equal to the number of CCX units, each of which +# share an L3 cache, conist of 4 processors. There are 16 CCX regions per processor, and 32 per node + +# Running on just a single node, we try and examine the scaling performance as the number of tasks is increased +# just to see what a single node is capable of (in the best parameter settings found) + +# This should give a good baseline of what to expect when communication costs are minimal + +#SBATCH --job-name=weak_scaling_fft +#SBATCH --time=00:30:00 +#SBATCH --nodes=1024 +#SBATCH --ntasks-per-node=32 +#SBATCH --cpus-per-task=4 +#SBATCH --contiguous + +#SBATCH --account=e738 +#SBATCH --partition=standard +#SBATCH --qos=standard +# Development environment for KiFMM + +# Restore AMD compiler env +module load PrgEnv-aocc +module load craype-network-ucx +module load cray-mpich-ucx + +# Home and work directories +export HOME="/home/e738/e738/skailasa" +export WORK="/work/e738/e738/skailasa" + +# Create a scratch directory for this run +export SCRATCH=${WORK}/weak_${SLURM_JOBID} + +# Load Spack +source $HOME/spack/share/spack/setup-env.sh +. "$HOME/.cargo/env" + +# Load BLAS +spack load openblas + +# Ensure Rust can find the Cray libraries +# export RUSTFLAGS="-L $(echo $CRAY_LD_LIBRARY_PATH)" +# export LD_LIBRARY_PATH=$CRAY_LD_LIBRARY_PATH:$LD_LIBRARY_PATH +export RUSTFLAGS="-L $(spack location -i openblas)/lib" +export LD_LIBRARY_PATH=$(spack location -i openblas)/lib:$LD_LIBRARY_PATH + +mkdir -p ${SCRATCH} +cd ${SCRATCH} + +# Pass variable to SRUN from SBATCH +export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK + +# Script being run, expected in the work directory +script_name="fmm_m2l_fft_mpi_f32" + +# Set simulation parameters for FMM +expansion_order=3 +n_points=250000 # points per MPI process (n_tasks) +global_depth=(4 4 4) # Number of local roots +local_depth=4 +n_samples=5000 +block_size=128 + +# Set parameters for weak scaling run +n_tasks=(8192 16384 32768) +n_nodes=(256 512 1024) +n_threads=4 # See if bandwidth saturates with different threading parameters for Rayon thread pool +cpus_per_task=4 +export OMP_NUM_THREADS=1 # Need to set to 1 to avoid oversubsciption between Rayon and OpenMP + +# Create a CSV output file for analysis +export OUTPUT=${SCRATCH}/weak_fft_${SLURM_JOBID}.csv +touch ${OUTPUT} +echo " +experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ +source_tree,target_tree,source_domain,target_domain,layout,\ +ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\ +source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\ +displacement_map,metadata_creation,\ +expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples" >> ${OUTPUT} + +# Perform weak scaling +for i in ${!n_tasks[@]}; do + experiment_id="${i}" + srun --nodes=${n_nodes[$i]} --ntasks=${n_tasks[$i]} --cpus-per-task=$cpus_per_task --distribution=block:block --hint=nomultithread \ + "${WORK}/${script_name}" --id $experiment_id --n-points $n_points \ + --expansion-order $expansion_order \ + --prune-empty \ + --global-depth ${global_depth[$i]} \ + --local-depth $local_depth \ + --n-samples $n_samples \ + --block-size $block_size \ + --n-threads $n_threads >> ${OUTPUT} +done From 0fe59b5aba801666684bac71d2921ad2ac001847 Mon Sep 17 00:00:00 2001 From: srinath kailasa Date: Tue, 30 Sep 2025 16:33:00 +0100 Subject: [PATCH 02/52] Add grid search data --- .../data/grid_search/grid_search.ipynb | 198 ++++++++++++++++++ hpc/archer2/slurm/weak_1_fft.slurm | 6 +- kifmm/pyproject.toml | 2 +- 3 files changed, 202 insertions(+), 4 deletions(-) create mode 100644 hpc/archer2/data/grid_search/grid_search.ipynb diff --git a/hpc/archer2/data/grid_search/grid_search.ipynb b/hpc/archer2/data/grid_search/grid_search.ipynb new file mode 100644 index 00000000..1f0196fc --- /dev/null +++ b/hpc/archer2/data/grid_search/grid_search.ipynb @@ -0,0 +1,198 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 13, + "id": "b165b3a0-1743-44e5-adbd-fb1fb758e8a3", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "604d9bd3-c036-4e4e-bdb6-19a5193e8174", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('grid_search_fft_f32_1e6.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "37937fb4-276d-4a0e-9c31-602b8aade5c5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['experiment_id', 'rank', 'runtime', 'p2m', 'm2m', 'l2l', 'm2l', 'p2p',\n", + " 'source_tree', 'target_tree', 'source_domain', 'target_domain',\n", + " 'layout', 'ghost_exchange_v', 'ghost_exchange_v_runtime',\n", + " 'ghost_exchange_u', 'gather_global_fmm', 'scatter_global_fmm',\n", + " 'source_to_target_data', 'source_data', 'target_data', 'global_fmm',\n", + " 'ghost_fmm_v', 'ghost_fmm_u', 'displacement_map', 'metadata_creation',\n", + " 'expansion_order', 'n_points', 'local_depth', 'global_depth',\n", + " 'block_size', 'n_threads', 'n_samples'],\n", + " dtype='object')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "a58fcee5-281b-4c8e-892b-5a438cc32616", + "metadata": {}, + "outputs": [], + "source": [ + "experiments = df.groupby('experiment_id')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "cdd2452e-a4f0-406f-9f47-f09382f2f2c0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "n_points_per_rank, local_depth, runtime, m2l, p2p, source_tree\n", + "250000 4 652 483 148 2258\n", + "500000 4 946 484 438 4599\n", + "1000000 4 2078 479 1559 9261\n", + "250000 5 4315 4055 147 2453\n", + "500000 5 4434 4054 247 4881\n", + "1000000 5 4707 4092 479 9332\n" + ] + } + ], + "source": [ + "print(\"n_points_per_rank, local_depth, runtime, m2l, p2p, source_tree\")\n", + "for (k, i) in experiments.groups.items():\n", + " print(\n", + " df.iloc[i]['n_points'].min(), \n", + " df.iloc[i]['local_depth'].min(), \n", + " df.iloc[i]['runtime'].max(), \n", + " df.iloc[i]['m2l'].max(), \n", + " df.iloc[i]['p2p'].max(), \n", + " df.iloc[i]['source_tree'].max()\n", + " )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "e6b552bf-97cc-40f4-9a49-3348e36996bc", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('grid_search_fft_f32_1e7.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "36de7ef2-b49a-4917-9572-cf6dfde8c1d3", + "metadata": {}, + "outputs": [], + "source": [ + "experiments = df.groupby('experiment_id')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "96142445-b99f-4e44-a4a4-45f39f2d1652", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "n_points_per_rank, local_depth, runtime, m2l, p2p, source_tree\n", + "5000000 5 9557 3998 5351 47382\n", + "10000000 5 16051 2782 12970 94296\n", + "5000000 6 37248 33776 2351 48902\n" + ] + } + ], + "source": [ + "print(\"n_points_per_rank, local_depth, runtime, m2l, p2p, source_tree\")\n", + "for (k, i) in experiments.groups.items():\n", + " print(\n", + " df.iloc[i]['n_points'].min(), \n", + " df.iloc[i]['local_depth'].min(), \n", + " df.iloc[i]['runtime'].max(), \n", + " df.iloc[i]['m2l'].max(), \n", + " df.iloc[i]['p2p'].max(), \n", + " df.iloc[i]['source_tree'].max()\n", + " )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "84034538-3fb6-43be-84f9-258f5d0285fe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.56" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "512*5e6/1e9" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6fc681a0-670e-4e0e-af42-45e669e745da", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/hpc/archer2/slurm/weak_1_fft.slurm b/hpc/archer2/slurm/weak_1_fft.slurm index f20f4654..1561732f 100644 --- a/hpc/archer2/slurm/weak_1_fft.slurm +++ b/hpc/archer2/slurm/weak_1_fft.slurm @@ -55,10 +55,10 @@ script_name="fmm_m2l_fft_mpi_f32" # Set simulation parameters for FMM expansion_order=3 -n_points=250000 # points per MPI process (n_tasks) +n_points=5000000 # points per MPI process (n_tasks) global_depth=(1 2 2 2 3 3 3) # Number of local roots -local_depth=4 -n_samples=5000 +local_depth=5 +n_samples=500 block_size=128 # Set parameters for weak scaling run diff --git a/kifmm/pyproject.toml b/kifmm/pyproject.toml index 4d726e02..e38c05d3 100644 --- a/kifmm/pyproject.toml +++ b/kifmm/pyproject.toml @@ -26,7 +26,7 @@ dependencies = [ "cffi", "pandas==2.2.2", "seaborn==0.13.2", - "mayavi==4.8.1", +# "mayavi==4.8.1", "vtk==9.3.0", "pyqt5==5.15.10", "numpy-stl==3.1.1", From 2d54691c4c94a716826dcc75db69086a719dddea Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Wed, 1 Oct 2025 09:54:18 +0100 Subject: [PATCH 03/52] Missing comma --- scripts/src/bin/fmm_m2l_fft_mpi_f32.rs | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs b/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs index c463aeb4..595bbcfa 100644 --- a/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs +++ b/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs @@ -224,7 +224,7 @@ fn main() { println!( "{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},\ {:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?}, \ - {:?},{:?},{:?},{:?},{:?},{:?}, {:?}, {:?} \ + {:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?}, \ {:?},{:?},{:?},{:?},{:?},{:?},{:?}", id, multi_fmm.rank(), From bf1a96291e0641c303babfcd8c21e5459803d1f0 Mon Sep 17 00:00:00 2001 From: srinath kailasa Date: Wed, 1 Oct 2025 10:28:55 +0100 Subject: [PATCH 04/52] Some data --- .gitignore | 2 +- .../grid_search/grid_search_fft_f32_1e6.csv | 194 ++++ .../grid_search/grid_search_fft_f32_1e7.csv | 98 ++ .../data/ijhpca/Weak Scaling (16 Nodes).ipynb | 662 +++++++++++ hpc/archer2/data/ijhpca/fix.py | 12 + hpc/archer2/data/ijhpca/weak_fft_10984742.csv | 1018 +++++++++++++++++ hpc/archer2/data/ijhpca/weak_fft_10984754.csv | 1018 +++++++++++++++++ hpc/archer2/data/ijhpca/weak_fft_10984759.csv | 1018 +++++++++++++++++ 8 files changed, 4021 insertions(+), 1 deletion(-) create mode 100644 hpc/archer2/data/grid_search/grid_search_fft_f32_1e6.csv create mode 100644 hpc/archer2/data/grid_search/grid_search_fft_f32_1e7.csv create mode 100644 hpc/archer2/data/ijhpca/Weak Scaling (16 Nodes).ipynb create mode 100644 hpc/archer2/data/ijhpca/fix.py create mode 100644 hpc/archer2/data/ijhpca/weak_fft_10984742.csv create mode 100644 hpc/archer2/data/ijhpca/weak_fft_10984754.csv create mode 100644 hpc/archer2/data/ijhpca/weak_fft_10984759.csv diff --git a/.gitignore b/.gitignore index 77437de4..696dd937 100644 --- a/.gitignore +++ b/.gitignore @@ -19,7 +19,7 @@ Cargo.lock fftw-src/fftw-* # Data -*.csv +# *.csv # Python *.ipynb_checkpoints/ diff --git a/hpc/archer2/data/grid_search/grid_search_fft_f32_1e6.csv b/hpc/archer2/data/grid_search/grid_search_fft_f32_1e6.csv new file mode 100644 index 00000000..2505ea34 --- /dev/null +++ b/hpc/archer2/data/grid_search/grid_search_fft_f32_1e6.csv @@ -0,0 +1,194 @@ + +experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,source_tree,target_tree,source_domain,target_domain,layout,ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,displacement_map,metadata_creation,expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples +"0_0_0_0_0",0,237,6,0,0,158,46,2258,2266,5,4,1,74,14,16,3,0, 380,0,0,0,0,0, 1, 150, 3,250000,4,2,16,4,500 +"0_0_0_0_0",29,252,6,0,0,173,50,2258,2266,4,4,1,68,12,25,0,4, 383,1,0,0,0,0, 1, 151, 3,250000,4,2,16,4,500 +"0_0_0_0_0",5,454,6,0,1,328,97,2258,2266,6,3,1,60,10,34,0,4, 381,1,0,0,0,0, 2, 155, 3,250000,4,2,16,4,500 +"0_0_0_0_0",27,452,6,0,1,329,95,2258,2266,5,4,1,34,9,63,0,3, 383,1,0,0,0,0, 2, 155, 3,250000,4,2,16,4,500 +"0_0_0_0_0",18,453,6,0,0,333,95,2258,2266,5,3,1,31,8,66,0,3, 382,1,0,0,0,0, 2, 156, 3,250000,4,2,16,4,500 +"0_0_0_0_0",13,457,6,0,1,331,97,2258,2266,5,3,1,62,10,33,0,4, 382,0,0,0,0,0, 2, 154, 3,250000,4,2,16,4,500 +"0_0_0_0_0",9,457,6,0,1,329,98,2258,2266,5,3,1,62,10,33,0,4, 382,1,0,0,0,0, 2, 154, 3,250000,4,2,16,4,500 +"0_0_0_0_0",31,461,9,1,1,334,96,2258,2266,5,3,1,69,8,23,0,4, 383,1,0,0,0,0, 2, 150, 3,250000,4,2,16,4,500 +"0_0_0_0_0",22,457,6,0,1,332,98,2258,2266,4,3,1,32,9,65,0,3, 383,1,0,0,0,0, 2, 155, 3,250000,4,2,16,4,500 +"0_0_0_0_0",23,457,6,0,0,332,99,2258,2266,4,3,1,28,8,70,0,3, 383,1,0,0,0,0, 2, 157, 3,250000,4,2,16,4,500 +"0_0_0_0_0",19,458,6,0,0,327,105,2258,2266,5,3,1,28,8,70,0,2, 382,1,0,0,0,0, 2, 157, 3,250000,4,2,16,4,500 +"0_0_0_0_0",14,459,6,0,1,333,99,2258,2266,5,4,1,60,8,37,0,3, 382,1,0,0,0,0, 2, 156, 3,250000,4,2,16,4,500 +"0_0_0_0_0",7,460,6,0,1,328,104,2258,2266,5,4,1,58,8,39,0,4, 381,1,0,0,0,0, 2, 157, 3,250000,4,2,16,4,500 +"0_0_0_0_0",12,460,6,0,1,331,102,2258,2266,5,4,1,58,8,39,0,3, 382,1,0,0,0,0, 2, 157, 3,250000,4,2,16,4,500 +"0_0_0_0_0",6,460,6,0,1,332,101,2258,2266,5,3,1,60,9,37,0,3, 381,0,0,0,0,0, 2, 157, 3,250000,4,2,16,4,500 +"0_0_0_0_0",11,461,6,0,1,332,103,2258,2266,5,3,1,59,8,39,0,3, 381,0,0,0,0,0, 2, 157, 3,250000,4,2,16,4,500 +"0_0_0_0_0",15,463,6,0,1,333,103,2258,2266,5,4,1,57,9,39,0,3, 383,1,0,0,0,0, 2, 156, 3,250000,4,2,16,4,500 +"0_0_0_0_0",4,460,3,0,1,338,100,2258,2266,5,3,1,19,9,81,0,3, 381,1,0,0,0,0, 1, 161, 3,250000,4,2,16,4,500 +"0_0_0_0_0",26,462,6,0,1,338,99,2258,2266,4,4,1,26,6,73,0,3, 383,1,0,0,0,0, 2, 157, 3,250000,4,2,16,4,500 +"0_0_0_0_0",8,465,6,0,1,336,101,2258,2266,5,3,1,61,8,36,0,4, 381,1,0,0,0,0, 2, 157, 3,250000,4,2,16,4,500 +"0_0_0_0_0",1,464,6,0,1,341,97,2258,2266,6,4,1,31,7,67,0,3, 381,0,0,0,0,0, 2, 159, 3,250000,4,2,16,4,500 +"0_0_0_0_0",25,465,6,0,1,341,99,2258,2266,5,3,1,27,7,71,0,3, 383,1,0,0,0,0, 2, 156, 3,250000,4,2,16,4,500 +"0_0_0_0_0",20,466,6,0,1,341,99,2258,2266,4,3,1,26,8,72,0,2, 383,1,0,0,0,0, 2, 157, 3,250000,4,2,16,4,500 +"0_0_0_0_0",24,464,6,0,0,337,104,2258,2266,4,3,1,20,6,80,0,2, 383,1,0,0,0,0, 2, 158, 3,250000,4,2,16,4,500 +"0_0_0_0_0",2,466,6,0,1,343,99,2258,2266,5,4,1,23,8,77,0,2, 380,0,0,0,0,0, 2, 160, 3,250000,4,2,16,4,500 +"0_0_0_0_0",10,470,6,0,1,342,101,2258,2266,5,3,1,60,8,36,0,4, 382,1,0,0,0,0, 2, 156, 3,250000,4,2,16,4,500 +"0_0_0_0_0",3,467,7,0,1,341,102,2258,2266,5,3,1,24,6,76,0,1, 381,1,0,0,0,0, 2, 160, 3,250000,4,2,16,4,500 +"0_0_0_0_0",17,469,6,0,1,341,104,2258,2266,5,4,1,22,7,78,0,2, 383,1,0,0,0,0, 2, 158, 3,250000,4,2,16,4,500 +"0_0_0_0_0",21,469,6,0,0,342,103,2258,2266,5,4,1,23,7,76,0,2, 383,1,0,0,0,0, 2, 158, 3,250000,4,2,16,4,500 +"0_0_0_0_0",28,469,6,0,0,342,102,2258,2266,5,4,1,19,6,81,0,2, 383,1,0,0,0,0, 2, 158, 3,250000,4,2,16,4,500 +"0_0_0_0_0",30,651,6,0,1,480,148,2258,2266,4,4,1,18,5,83,0,1, 383,1,0,0,0,0, 3, 160, 3,250000,4,2,16,4,500 +"0_0_0_0_0",16,652,6,0,1,483,147,2258,2266,4,4,0,16,4,86,0,1, 383,1,0,0,0,0, 3, 161, 3,250000,4,2,16,4,500 +"0_0_0_0_1",17,354,8,0,0,171,150,4599,4509,8,7,1,190,12,48,0,5, 386,1,0,0,0,0, 1, 337, 3,500000,4,2,16,4,500 +"0_0_0_0_1",15,365,11,1,0,175,156,4599,4509,9,7,1,196,10,41,0,4, 385,1,0,0,0,0, 1, 338, 3,500000,4,2,16,4,500 +"0_0_0_0_1",0,639,8,0,0,321,283,4599,4509,9,8,0,199,11,39,4,0, 383,0,0,0,0,0, 2, 341, 3,500000,4,2,16,4,500 +"0_0_0_0_1",23,645,7,0,0,322,288,4599,4509,8,7,1,179,10,62,0,5, 385,1,0,0,0,0, 2, 340, 3,500000,4,2,16,4,500 +"0_0_0_0_1",9,647,8,0,1,328,283,4599,4509,8,7,1,195,11,43,0,5, 384,1,0,0,0,0, 2, 340, 3,500000,4,2,16,4,500 +"0_0_0_0_1",13,650,8,0,0,330,285,4599,4509,9,7,1,193,11,45,0,5, 384,0,0,0,0,0, 2, 339, 3,500000,4,2,16,4,500 +"0_0_0_0_1",4,653,8,0,1,329,289,4599,4509,9,7,1,199,10,39,0,5, 383,1,0,0,0,0, 2, 341, 3,500000,4,2,16,4,500 +"0_0_0_0_1",11,657,8,0,0,327,295,4599,4509,9,7,1,184,10,56,0,5, 384,0,0,0,0,0, 2, 341, 3,500000,4,2,16,4,500 +"0_0_0_0_1",18,655,7,0,0,328,295,4599,4509,9,7,0,172,9,71,0,3, 385,0,0,0,0,0, 2, 343, 3,500000,4,2,16,4,500 +"0_0_0_0_1",27,658,8,0,1,334,290,4599,4509,9,7,1,180,9,61,0,4, 386,1,0,0,0,0, 2, 340, 3,500000,4,2,16,4,500 +"0_0_0_0_1",2,660,8,0,1,322,303,4599,4509,9,7,0,185,10,54,0,4, 383,1,0,0,0,0, 2, 342, 3,500000,4,2,16,4,500 +"0_0_0_0_1",12,659,9,0,1,331,294,4599,4509,8,7,1,185,9,54,0,4, 384,0,0,0,0,0, 2, 341, 3,500000,4,2,16,4,500 +"0_0_0_0_1",8,660,8,0,0,326,298,4599,4509,10,7,1,187,10,53,0,5, 384,1,0,0,0,0, 2, 341, 3,500000,4,2,16,4,500 +"0_0_0_0_1",1,663,8,0,0,334,296,4599,4509,8,7,1,187,10,52,0,5, 383,1,0,0,0,0, 2, 342 ,3,500000,4,2,16,4,500 +"0_0_0_0_1",6,663,8,0,1,330,300,4599,4509,10,7,1,186,10,54,0,5, 383,1,0,0,0,0, 2, 342, 3,500000,4,2,16,4,500 +"0_0_0_0_1",24,660,8,0,1,333,295,4599,4509,9,8,0,174,8,69,0,4, 386,1,0,0,0,0, 2, 342 ,3,500000,4,2,16,4,500 +"0_0_0_0_1",22,662,7,0,0,331,299,4599,4509,9,7,0,173,9,69,0,3, 385,0,0,0,0,0, 2, 343 ,3,500000,4,2,16,4,500 +"0_0_0_0_1",19,666,7,0,0,332,300,4599,4509,9,7,1,179,10,61,0,4, 385,1,0,0,0,0, 2, 341, 3,500000,4,2,16,4,500 +"0_0_0_0_1",26,665,8,0,1,337,295,4599,4509,9,7,1,173,9,69,0,3, 386,1,0,0,0,0, 2, 342, 3,500000,4,2,16,4,500 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00000000..cf2dfb0c --- /dev/null +++ b/hpc/archer2/data/grid_search/grid_search_fft_f32_1e7.csv @@ -0,0 +1,98 @@ + +experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,source_tree,target_tree,source_domain,target_domain,layout,ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,displacement_map,metadata_creation,expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples +"0_0_0_0_0",0,3300,72,7,3,1355,1710,47381,47327,73,71,4,1860,90,192,26,0, 387,1,0,0,0,0, 10, 3460, 3,5000000,5,2,16,4,500 +"0_0_0_0_0",2,3325,72,6,4,1347,1747,47381,47327,75,71,4,1806,89,249,0,26, 386,1,0,0,0,0, 10, 3465, 3,5000000,5,2,16,4,500 +"0_0_0_0_0",31,6449,110,11,17,2744,3451,47382,47329,73,71,1,1842,28,223,0,25, 391,1,0,0,0,0, 19, 3471, 3,5000000,5,2,16,4,500 +"0_0_0_0_0",9,6487,69,7,10,2733,3516,47381,47330,74,71,1,1718,68,358,0,23, 387,1,0,0,0,0, 15, 3485, 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+"1_0_0_0_0",17,25388,437,70,174,22596,1546,48901,48812,79,71,1,682,487,1824,0,9, 384,1,0,0,0,0, 139, 5259, 3,5000000,6,2,16,4,500 +"1_0_0_0_0",4,37248,418,71,220,33776,2351,48901,48812,78,71,1,861,313,1777,0,1, 382,1,0,0,0,0, 208, 5392, 3,5000000,6,2,16,4,500 diff --git a/hpc/archer2/data/ijhpca/Weak Scaling (16 Nodes).ipynb b/hpc/archer2/data/ijhpca/Weak Scaling (16 Nodes).ipynb new file mode 100644 index 00000000..09986171 --- /dev/null +++ b/hpc/archer2/data/ijhpca/Weak Scaling (16 Nodes).ipynb @@ -0,0 +1,662 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "e74144a3-a310-4e35-8b6f-ceadad982fc0", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a15c010f-3256-4039-97b3-4ab6c4bb403e", + "metadata": {}, + "outputs": [], + "source": [ + "df_5e6 = pd.read_csv('weak_fft_10984742.csv')\n", + "df_1e6 = pd.read_csv('weak_fft_10984754.csv')\n", + "df_250k = pd.read_csv('weak_fft_10984759.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "6cbe52e4-fe73-477f-87aa-4ffd218c4542", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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experiment_idrankruntimep2mm2ml2lm2lp2psource_treetarget_tree...ghost_fmm_udisplacement_mapmetadata_creationexpansion_ordern_pointslocal_depthglobal_depthblock_sizen_threadsn_samples
0001210000029702935...00403250000411284500
1052410008414129702935...001513250000411284500
20132580016114329702934...011473250000411284500
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40432240016414329702934...011513250000411284500
\n", + "

5 rows × 33 columns

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" + ], + "text/plain": [ + " experiment_id rank runtime p2m m2m l2l m2l p2p source_tree \\\n", + "0 0 0 121 0 0 0 0 0 2970 \n", + "1 0 5 241 0 0 0 84 141 2970 \n", + "2 0 1 325 8 0 0 161 143 2970 \n", + "3 0 2 322 4 0 0 162 144 2970 \n", + "4 0 4 322 4 0 0 164 143 2970 \n", + "\n", + " target_tree ... ghost_fmm_u displacement_map metadata_creation \\\n", + "0 2935 ... 0 0 40 \n", + "1 2935 ... 0 0 151 \n", + "2 2934 ... 0 1 147 \n", + "3 2934 ... 0 1 151 \n", + "4 2934 ... 0 1 151 \n", + "\n", + " expansion_order n_points local_depth global_depth block_size \\\n", + "0 3 250000 4 1 128 \n", + "1 3 250000 4 1 128 \n", + "2 3 250000 4 1 128 \n", + "3 3 250000 4 1 128 \n", + "4 3 250000 4 1 128 \n", + "\n", + " n_threads n_samples \n", + "0 4 500 \n", + "1 4 500 \n", + "2 4 500 \n", + "3 4 500 \n", + "4 4 500 \n", + "\n", + "[5 rows x 33 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_250k.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "b5623876-8efb-48c1-9e93-a2f8459f2a67", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1016, 33)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_1e6.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "caf1a774-3904-4e65-95bf-f37f1180a3d6", + "metadata": {}, + "outputs": [], + "source": [ + "def runtime_vs_npoints(df):\n", + " npoints = []\n", + " runtime = []\n", + " for (id, experiment) in df.groupby('experiment_id'):\n", + " n = experiment['n_points'].max()*experiment.shape[0]\n", + " npoints.append(n)\n", + " r = experiment['runtime'].max()\n", + " runtime.append(r)\n", + " \n", + " npoints = np.array(npoints)\n", + " runtime = np.array(runtime)\n", + " \n", + " fig, ax = plt.subplots()\n", + " ax.plot(npoints, runtime, marker=\"o\")\n", + " ax.set_xscale('log')\n", + " \n", + " return (fig, ax, npoints, runtime)\n", + "\n", + "\n", + "def runtime_vs_nprocesses(df):\n", + " nprocs = []\n", + " runtime = []\n", + " for (id, experiment) in df.groupby('experiment_id'):\n", + " n = experiment.shape[0]\n", + " nprocs.append(n)\n", + " r = experiment['runtime'].max()\n", + " runtime.append(r)\n", + " \n", + " nprocs = np.array(nprocs)\n", + " runtime = np.array(runtime)\n", + " \n", + " fig, ax = plt.subplots()\n", + " ax.plot(nprocs, runtime, marker=\"o\")\n", + " # ax.set_xscale('log')\n", + " \n", + " return (fig, ax, nprocs, runtime)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "022c161a-007f-4f90-8f6d-3b73ed57dc00", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "(fig, ax, npoints_250k, runtime_250k) = runtime_vs_npoints(df_250k)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "eb5e74a3-f5b1-4188-8cc9-0887efacbb2e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "(fig, ax, nprocs_250k, runtime_250k) = runtime_vs_nprocesses(df_250k)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "4490d705-be26-482c-beda-918422da1555", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 8, 16, 32, 64, 128, 256, 512])" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nprocs_250k" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "36c22929-cc80-4167-9e28-47754e8d32cb", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "(fig, ax, npoints_1e6, runtime_1e6) = runtime(df_1e6)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "324b6d11-8713-4863-bff0-e0eb14db4d3a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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dO4fe3l55zOrVq0N+bnV1NWpra0edl9PphN1uD/mIJo1GwIrSwJYV83KIIi6crSrg2muSW1ZE4ZC2qhZMN8V1U85gEQ9y1qxZg3//93/Hvn378Nxzz+EPf/gD7rrrLni9XgBAR0cHcnNzQ75Hp9MhOzsbHR0d8pi8vLyQMdLnY42RHh/Js88+C7PZLH8UFRVN7sWOw8qbmJdDFC1SfZxlM8a/dC5dkzwQQBQeeasqjOtNaRGvk7Nhwwb53xcuXIhFixZh1qxZ2L9/P+64445I/7iwPPXUU9iyZYv8ud1uj3qgUxVIPq5v7oXT473hEVciGr/ufic+7fIfWAgryJnFa5JoIuqD2qckiqgfIZ85cyamTJmCTz/9FACQn5+Prq6ukDEejwc9PT1yHk9+fj46OztDxkifjzVmtFwgwJ8rZDKZQj6i7abcDEzJMMDp8eFYizXqP48oWRwJLJ3fkpeBrHT9GKOHzZqagamZvCaJwmEddOGTwJsKBjlB2tra0N3djWnT/K0NqqqqYLVa0dDQII/54IMP4PP5UFlZKY85cOAA3G63PKampgazZ89GVlaWPGbfvn0hP6umpgZVVVXRfklhEQRBPtHBHACiyBmrX9VoBEHgUXKiMB1t8b+pKJ2SjikZ8d2UM1jYQU5/fz8aGxvR2NgIAGhqakJjYyNaWlrQ39+PJ598EocOHUJzczP27duHL3zhC7jppptQXV0NAJg7dy7WrFmDhx56CHV1dfjTn/6EzZs3Y8OGDSgo8Pd7uv/++6HX67Fp0yacOnUKb7zxBl5++eWQrabHHnsMe/fuxYsvvoizZ89i+/btqK+vx+bNmyPwa4ksacuKQQ5R5NQ1+6+n5YFE4nCsnMUDAUThkJKOlybQKg4wgSCnvr4e5eXlKC8vBwBs2bIF5eXl2LZtG7RaLY4fP47Pf/7zuOWWW7Bp0yZUVFTgww8/hMEwHPm9/vrrmDNnDu644w7cfffdWLVqVUgNHLPZjPfffx9NTU2oqKjAE088gW3btoXU0lm5ciV27dqFnTt3oqysDL/+9a+xe/duLFiwYDK/j6iQbqjHWnsx5PIqPBuixNfncMvtUpZPIAlSatZ5rMWKQZcnonMjUqPhpOPECnLCTjz+zGc+A1EUR338vffeG/M5srOzsWvXrhuOWbRoET788MMbjrn33ntx7733jvnzlFaSk4ZpZiMu2xxouNiLVTdPUXpKRAmt4WIvfCJQnJ2GfLMx7O8vyk5DYVYq2nqHcKS5F392y9QozJJIHVweHz5qtQIAKhKkCKCEvatiIDgvh8dWiSZvIkfHr7WS1yTRuJy8ZIPT40NWWgpmTU1XejphYZATI1XsY0UUMVLScWWYScfBeCCAaHwapH5VJVkQhPhvyhmMQU6MSDfU42029DuZA0A0UQ63Fx+12gCEf7IqWNVM/7bxyXYbbIPuMUYTJa/6i/43FYlUBFDCICdGCrPSUJydBq9PlPvtEFH4Pmq1wuX1YWqmASU5aRN+nnyzETOnpsMnAoebuJpDNBJRFNFwMTFPVgEMcmJK2rJiDgDRxAXXx5ns0rl8lJzbyEQjutg9iKv9Lui1GiyYnhhNOYMxyIkh9rEimry6QNLxRI6OX0s6Ss68HKKRSUn+CwvNCdOUMxiDnBiSVnJOXbIzB4BoAjxen7x0Ppl8HMmKwDV5tqMPV/udk34+IrVJ5K0qgEFOTOWa/DkAoggcYg4AUdhOXbJj0OWFyajD7LzMST9fdroec6f5e9gd4gor0XWkIoCJ1K8qGIOcGFvJY6tEExZcH0ejicxRVvaxIhqZddCFTxOwKWcwBjkxJh1bZZBDFL7DE2zKeSN840E0MmmraubUdOQkUFPOYAxyYmzFTP/N+VxnH7qZA0A0bj6fiHppJSeCQc6y0mxoBKDp6gAuWYci9rxEia4+wfNxAAY5MZeTYcCcfH8uwaELrJdDNF6fXulH76AbqSlaLCiI3FFWkzEFCwstALiaQxRMelOxNMH6VQVjkKMA9rEiCp+0VbWkxAK9LrK3LublEIVyerz4qM1fWbwiwTqPB2OQowD2sSIKn1QpfDJNOUcjBTmHLnRDFMWIPz9RojnZbofL40N2uh4zpyRWU85gDHIUUFmaA0EALlwZQKfdofR0iOKeKIohlY4jbWlJNlK0AtqtQ2jpGYz48xMlmoZAv6pEbMoZjEGOAsxpKXJOAXMAiMbW1juEDrsDKVoB5UWRXzpP1WtRXux/Xm5ZEQH1zYmfdAwwyFEM83KIxk/Kx1k43YxUfXRKyzMvh8gvpClnAufjAAxyFFPFxoBE41YXqBC+vDQnaj9juI/VVeblUFJrujqA7gEX9LrEbMoZjEGOQpbNyIZWI6C1ZwitzAEguqEjzVK/qui9qywrMsOYosHVfhc+CVR5JUpGUn2cRdPNMOgSrylnMAY5Cskw6FBWGMjL4WoO0ai6+hxoujoAQQAqolivw6DTyie3Dn7KbWRKXg1SPk4UTjLGGoMcBUlbVoeYA0A0qiNN/hvunHwTzKkpUf1ZVczLIUL9RakIYGLn4wAMchQl9bE6eJ61OYhGI+XjVEbh6Pi1pLycQxe64fXxmqTk0zvgwvkrAwAStylnMAY5CqooyYJeq0GH3YHmbublEI2kTs7HiX6Qs6DAhEyDDnaHB2cu26P+84jijXSqatbUdGSl6xWezeQxyFFQql6LxcUWAKyXQzQS26AbZzv8wUY0Kh1fS6fVoDLQRJflHSgZHbmY+P2qgjHIUdhK1sshGlX9xR6IIjBzSjqmZhpi8jOrZg1vIxMlGynpOJH7VQVjkKMwqY8Ve+YQXa+uOXr9qkYjvfGoa+qB2+uL2c8lUprT48Xxdn9Tzlhec9HEIEdhi4stMOhYm4NoJNHsVzWa2XmZyE7XY9DlxfE2a8x+LpHSTrbb4PL4kJOux4ycNKWnExEMchQWXJuDeTlEw4ZcXpxo87+rjGWQo9EIWCHl5XzKa5KSh9SvKtGbcgZjkBMH2MeK6HrHWnrh8YmYZjaiMCs1pj+beTmUjI40q6NfVTAGOXFACnION/XAx9ocRACGm3IuL82O+btKKS+noaUXDrc3pj+bSAmiKOJoi7SSo458HIBBTlxYON2MdL0W1kE3znSwNgcRABxRIOlYMnNKOvJMBrg8PhwN1A0hUrMLVwfQM+CCQafBgukmpacTMQxy4kCKViPnHDAvhwj+4CLwrjIWlY6vJQjCcFdy9pajJCAdHS8rtCR8U85gDHLihLRlxSCHCDh5yQaH24fsdD1uys1QZA7sY0XJROpXpZb6OBIGOXFC6mN1uKkHHtbmoCQnHR1fquApDykv56NWK/qdHkXmQBQr9YFtWTU05QzGICdOzCswwWTUod/pwclLzMuh5KZEfZxrFWaloTg7DR6fKOcHEalRd78TF1TUlDMYg5w4odUIqJzJo+RE3qCgQskgBxhezeE2MqmZ1JTzptwMWNISvylnMAY5cYQ3VCLgXEcf+hwepOu1mDdN2VMerGFFyUAKcpapLB8HYJATV6Qban1zL1we5uVQcpJWcSpmZEOnVfYWJfWWO3XJDuugS9G5EEWLlI+jpvo4EgY5ceSWXH/PnCG3Fx+xZw4lKTkfJw7eVeaajLgpNwOiCBy6wLwcUh+He7h9itqSjgEGOXFFoxHkd47csqJkJIpiUKXjHIVn4ze8jcwtK1Kfk+02uLw+TMnQo0QlTTmDMciJMyuYA0BJrLl7EFf7ndBrNVhUaFZ6OgCCghwWBSQVOqLCppzBGOTEGemGerTFyp45lHTqmvyBxOIiC4wp8VF1tbI0B4IAfNzZjyt9TqWnQxRRDRelmlTqy8cBGOTEnZlT0pGbaQgpa0+ULOqa/P/PK310PFhWul4+5cXVHFITURTlk1Vq6jweLOwg58CBA1i3bh0KCgogCAJ279496thvfvObEAQB//RP/xTy9Z6eHmzcuBEmkwkWiwWbNm1Cf39/yJjjx4/j1ltvhdFoRFFREZ5//vnrnv/NN9/EnDlzYDQasXDhQrz77rvhvpy44++Zw7wcSk51zf7/55fFUZADMC+H1On8lQH0Drph0GkwvyA+tocjLewgZ2BgAGVlZXj11VdvOO6tt97CoUOHUFBQcN1jGzduxKlTp1BTU4M9e/bgwIEDePjhh+XH7XY77rzzTpSUlKChoQEvvPACtm/fjp07d8pjDh48iPvuuw+bNm3CsWPHsH79eqxfvx4nT54M9yXFHfaxomR02TaE1p4haIT4q7oqNetkHytSE2mrqqzIAr1OnRs7unC/4a677sJdd911wzHt7e341re+hffeew9r164NeezMmTPYu3cvjhw5gqVLlwIAfvKTn+Duu+/Gj3/8YxQUFOD111+Hy+XCv/7rv0Kv12P+/PlobGzEP/7jP8rB0Msvv4w1a9bgySefBAD86Ec/Qk1NDV555RXs2LFjxHk5nU44ncN76nZ7fLZPkPpYNbZaMeD0IN0Q9n8mooQjHR2fX2BGRpz9P7+sNBtajYCL3YNo6x1EYZb6TqFQ8pGSjtV4dFwS8dDN5/PhgQcewJNPPon58+df93htbS0sFosc4ADA6tWrodFocPjwYXnMbbfdBr1+uLx0dXU1zp07h97eXnnM6tWrQ567uroatbW1o87t2Wefhdlslj+Kioom9VqjpSg7FdMtqfD4RLlIE5HaxUO/qtFkGHTyaS+usJJaqD0fB4hCkPPcc89Bp9Ph29/+9oiPd3R0IDc3N+RrOp0O2dnZ6OjokMfk5eWFjJE+H2uM9PhInnrqKdhsNvmjtbU1vBcXI4IgsJw8JZ146Vc1GubKkZpc7Xei6WqgKWdxfF5zkRDRIKehoQEvv/wyXnvttbg8b28wGGAymUI+4pV0Qz3EGyolgZ4BFz7u9B8+WDYjPm+4wXk5oigqPBuiyZFWcW7Jy4A5LUXh2URPRIOcDz/8EF1dXSguLoZOp4NOp8PFixfxxBNPYMaMGQCA/Px8dHV1hXyfx+NBT08P8vPz5TGdnZ0hY6TPxxojPZ7opJWcE+022B1uhWdDFF3SKs7NuRnITo/PLsgVJVnQazXosDvQ3D2o9HSIJqVBxf2qgkU0yHnggQdw/PhxNDY2yh8FBQV48skn8d577wEAqqqqYLVa0dDQIH/fBx98AJ/Ph8rKSnnMgQMH4HYP/3GvqanB7NmzkZWVJY/Zt29fyM+vqalBVVVVJF+SYqaZUzEjJw0+EahjzxxSuSOBfJx4OzoezJiixZISCwBuI1Piq2+WigCqNx8HmECQ09/fLwcwANDU1ITGxka0tLQgJycHCxYsCPlISUlBfn4+Zs+eDQCYO3cu1qxZg4ceegh1dXX405/+hM2bN2PDhg3ycfP7778fer0emzZtwqlTp/DGG2/g5ZdfxpYtW+R5PPbYY9i7dy9efPFFnD17Ftu3b0d9fT02b94cgV9LfKgKLI+zABmpXV3ghlsZx0EOwKPkpA4Otxcn2gNNOVWcdAxMIMipr69HeXk5ysvLAQBbtmxBeXk5tm3bNu7neP311zFnzhzccccduPvuu7Fq1aqQGjhmsxnvv/8+mpqaUFFRgSeeeALbtm0LqaWzcuVK7Nq1Czt37kRZWRl+/etfY/fu3ViwYEG4LyluDScf84ZK6tXv9ODUJX85h3jNx5EE58r5fMzLocR0vM0Gt1fElAwDirPVXQ4h7GIUn/nMZ8JKumtubr7ua9nZ2di1a9cNv2/RokX48MMPbzjm3nvvxb333jvuuSQaqSP5mct29A64kBWnuQpEk3H0Yi+8PhGFWakosKQqPZ0bWlRoQWqKFt0DLnzc1Yc5+fF7eIFoNPWBIoDLZqizKWcwdZY4VImpmQbcnJsBADjcxNUcUqd4ro9zLb1OI+cNHfyU1yQlpoagzuNqxyAnzq3klhWpnJSPszzOt6okvCYpkfl8IhpapCKAiXHNTQaDnDjHPlakZk6PF42tVgCJsZIDDAc5hy90w+P1KTwbovBcuNoP66AbxhQN5heof7uVQU6cqyzNgSAAn3T1o6vPofR0iCLqeJsNLo8PUzIMKJ2SrvR0xmV+gRmZRh36ghKmiRKF1K+qrNCCFK36QwD1v8IEl5Wux9xAcuMh1sshlRnOx0mcBEitRsCKwKEAlnegRFPfrP5+VcEY5CSA4S0rFiAjdTnclFj5OBLm5VCiagicrEqGfByAQU5CYGNAUiOP14ejgdLy8VzpeCRSUcAjTT1weZiXQ4nhSp8Tzd2DEARgSTFXcihOLCvNhkYAmrsHcck6pPR0iCLizOU+9Ds9yDTqEq7ezC15GchJ12PI7cVHbValp0M0LnJTztxMmFPV25QzGIOcBGAypmBhoQUAV3NIPaSj48tmZEOrSYx8HIkgCFghbVmxXg4lCGmrqiJJ8nEABjkJo4qJjqQydYECl/HeymE0w3k5zJWjxCCdrFJ7U85gDHISRHC9nHDaahDFI1EU5RtuotTHuZaUl3OsxYohl1fh2RDdmMPtxalL/qacifrGYiIY5CSIZTOyoNMIaLcOobWHeTmU2M5f6UfPgAvGFA0WTjcrPZ0JmZGThmlmI1xen5zrQBSvPmq1wu0VkZtpQGFWfPeIiyQGOQkiTa/D4iILAC6PU+KTjo6XF2VBr0vM25AgCPIKK69Jinf1F4fr4yRKTapISMy7S5KSj5IzL4cS3JEEasp5I9KWFa9JinfSamNFSWJfc+FikJNAVjAvh1RAFMXhIoAJHuRIKznH22zoc7gVng3RyHw+EfWB04zJlHQMMMhJKEuK/Uv7XX1OnL8yoPR0iCakrXcIl20O6DQCyostSk9nUqZbUjEjJw1en4gjzWy7QvHp0yv9sDs8SE3RYl4SNOUMxiAngRhTtKgIVKnk8jglKikYWDDdjDS9TuHZTF5VYMuK9XIoXkn9qhYXJUdTzmDJ9WpVgH2sKNFJTTkrE3yrSlLFPlYU5+rlflXJtVUFMMhJOFLy8aELPfD5mJdDiUeqdJzo+TgSqVDn6ct29A64FJ4N0fWGk44Z5FCcW1RoQWqKFj0DLnzc1af0dIjCcqXPiQtXBiAIwFKVnPKYmmnALXkZAIBD3EamONPV58BFqSkngxyKd3qdRu7YzBwASjRSPs7svEyY09TTIFA6Ss4tK4o3DYF8nNl5mTAZ1XPNjReDnATEPlaUqOpUcnT8WiwKSPGqPom3qgAGOQmpSs7L6YaXeTmUQNQa5KwozYEgAOevDKDL7lB6OkQyKchJpn5VwRjkJKAFBSZkGnToc3hw+pJd6ekQjYvd4caZDv//r8tVdsM1p6VgQYG/BxdXWCleDLm8ONXub8rJlRxKGDqtRn4nzOVxShQNzb0QRX9jy1yTUenpRJx08pG5chQvPmqzwuMTkWdKrqacwRjkJKgq9rGiBKOWVg6jkdquHLzANx4UH6Sj40tLspOqKWcwBjkJSgpyjjT1wO31KTwborFJJ6vUmhuwbEY2dBoBrT1DaO0ZVHo6RPI1l6xbVQCDnIQ1N98ES1oKBlxeHG+zKT0dohtyuL043mYFAFSW5ig7mSjJMOhQVmQB4G+iS6Qkn0/E0SRPOgYY5CQsjUbAitLhU1ZE8exYixVur4h8kxFF2erNDVjJo+QUJz7p8jflTNNrMXdaptLTUQyDnATG2hyUKKSj48tK1Z0bENzHShRZ3oGUI/WrWlxkgS7JmnIGS95XrgLSu8b65l44PV6FZ0M0urpm/2qjWpOOJUuKs6DXadDV58T5KwNKT4eSmFTpeGkS5+MADHIS2k25GZiSYYDT40Nji1Xp6RCNyO314ehFKwD11ce5ljFFK/9R4clHUtKRwEpOhcqvubEwyElggiCELI8TxaOT7TYMub2wpKXg5twMpacTddIKay23kUkhXXYHWnuGIAhAebFF6ekoikFOgmMfK4p3wUfHNRr15uNIqgLNOmvPd8PHtiukAKmVw5x8U1I25QzGICfBSSs5x1p6MeRiXg7FH7lfVZIsmy8qNCNNr0XvoBtnO/qUng4loXrm48gY5CS4GTlpmGY2wu0V5eqWRPHC5xNxJHDDVXvSsSSFbVdIYQ2BfJylMxjkMMhJcIIgyFtWvKFSvPm4qw+2ITfS9FrMLzApPZ2YGc7L4TYyxdagy4NTgcbNyVzpWMIgRwXYx4rilbRVVVGSlVS1OlYG8nION/XAw7YrFEONrf6mnPkmI6Zb1Ft4c7yS566jYlKQc7zNhn6nR+HZEA1LtnwcydxpJphTU9Dv9OBEO9uuUOzI9XFmZKm68OZ4MchRgcKsNBRnp8HrE3Ek8EeFSGmiKIZUOk4mWo2AFTOlvByusFLs1F9k0nEwBjkqwaPkFG8udg+iq88JvVaDxYHGlclE2rJibzmKFZ9PxNEWaSUnud5YjIZBjkqwjxXFm7pAfZxFhWYYU7QKzyb2pOTjI809bLtCMfFxVx/6Ak055+Qnb1POYGEHOQcOHMC6detQUFAAQRCwe/fukMe3b9+OOXPmID09HVlZWVi9ejUOHz4cMqanpwcbN26EyWSCxWLBpk2b0N/fHzLm+PHjuPXWW2E0GlFUVITnn3/+urm8+eabmDNnDoxGIxYuXIh333033JejGlKQc+qSHbZBt8KzIQrKx0myrSqJ1HbF4WbbFYoNqT5OeXFyN+UMFvZvYWBgAGVlZXj11VdHfPyWW27BK6+8ghMnTuCPf/wjZsyYgTvvvBNXrlyRx2zcuBGnTp1CTU0N9uzZgwMHDuDhhx+WH7fb7bjzzjtRUlKChoYGvPDCC9i+fTt27twpjzl48CDuu+8+bNq0CceOHcP69euxfv16nDx5MtyXpAp5JiNmTk2HKAKHmrg8TsqTKh0na5DDtisUa/XN0mnG5LzmRiROAgDxrbfeuuEYm80mAhB///vfi6IoiqdPnxYBiEeOHJHH/O53vxMFQRDb29tFURTFn/70p2JWVpbodDrlMVu3bhVnz54tf/6Vr3xFXLt2bcjPqqysFL/xjW+Me/7S3Gw227i/J559/63jYsnWPeLT/3NS6alQkrtsHRJLtu4RS7+3R7QPuZSejmJ2Hb4olmzdI977s4NKT4WSwP/5h31iydY94oGPu5SeStSN9+93VNezXC4Xdu7cCbPZjLKyMgBAbW0tLBYLli5dKo9bvXo1NBqNvK1VW1uL2267DXq9Xh5TXV2Nc+fOobe3Vx6zevXqkJ9XXV2N2traUefjdDpht9tDPtSkaiYTHSk+SPk48wpMyEzi3jlSXs6x1l4MuljegaKn0+5AW+8QNAJQXsyTVZKoBDl79uxBRkYGjEYjXnrpJdTU1GDKFP8f4I6ODuTm5oaM1+l0yM7ORkdHhzwmLy8vZIz0+VhjpMdH8uyzz8JsNssfRUVFk3uhcUY6snq2ow/d/U6FZ0PJTCplsCzJT3gUZ6dhuiUVbq8o50sQRYP0/9ecfBMyDDqFZxM/ohLk3H777WhsbMTBgwexZs0afOUrX0FXV1c0flRYnnrqKdhsNvmjtbVV6SlFVE6GQc6oP3SB9XJIOVLScWWS5uNImJdDsVLPflUjikqQk56ejptuugkrVqzAL37xC+h0OvziF78AAOTn518X8Hg8HvT09CA/P18e09nZGTJG+nysMdLjIzEYDDCZTCEfarOCfaxIYdZBF851+rtvJ/tKDhDcx4rXJEWPtJLDflWhYnLGzOfzwen0b59UVVXBarWioaFBfvyDDz6Az+dDZWWlPObAgQNwu4ePQtfU1GD27NnIysqSx+zbty/k59TU1KCqqiraLyeurWQfK1KY1HV81tR05GQYFJ6N8qSVnBPtNtgdLO9AkTfg9OD0ZX+OKYsAhgo7yOnv70djYyMaGxsBAE1NTWhsbERLSwsGBgbwN3/zNzh06BAuXryIhoYG/NVf/RXa29tx7733AgDmzp2LNWvW4KGHHkJdXR3+9Kc/YfPmzdiwYQMKCgoAAPfffz/0ej02bdqEU6dO4Y033sDLL7+MLVu2yPN47LHHsHfvXrz44os4e/Ystm/fjvr6emzevDkCv5bEVVmaA0EALlwZQKfdofR0KAnVBUoYLC/NUXgm8WGaORUzp6TDJwJ13EamKPio1QqvT0SBmU05rxV2kFNfX4/y8nKUl5cDALZs2YLy8nJs27YNWq0WZ8+exT333INbbrkF69atQ3d3Nz788EPMnz9ffo7XX38dc+bMwR133IG7774bq1atCqmBYzab8f7776OpqQkVFRV44oknsG3btpBaOitXrsSuXbuwc+dOlJWV4de//jV2796NBQsWTOb3kfDMaSlYUGAGANQyB4AUUBdYyVleymVzyQrm5VAUSf2qKriKc52wU7A/85nPQBTFUR//zW9+M+ZzZGdnY9euXTccs2jRInz44Yc3HHPvvffKK0Q0rGpWDk6021B7vhvry6crPR1KIgNOD04Gum5zJWfYylk52HW4hblyFBVsyjk61n1WIalZ58ELvKFSbB1r8S+bT7ekctk8iHQggOUdKNK8PhHHLjLpeDQMclRoWWk2tBoBrT1DaO0ZVHo6lESG83G4bB5sCss7UJSc6+hDn9ODdDblHBGDHBXKMOiwqDCQl8NTVhRDh5O8KeeNDNfL4QorRU5DoD7OkpIsNuUcAX8jKiUdJT/EREeKEafHi8ZWKwDWxxnJyln+qu88EECRVM+tqhtikKNSUh+r2gvdN0wUJ4qUE202OD0+5KTrMWtqutLTiTvLS7OhEYALVwfQYWN5B4oMqQjgUnYeHxGDHJWqKMlCilbAZZsDzd3My6Hok5pyLi/NhiAICs8m/phTU7BwurSNzC0rmrwOmwPtVn9TzsXFFqWnE5cY5KhUql4rd6Ll8jjFQh2bco6pKrBldfBTXpM0eVK/qrnT2JRzNAxyVKyKfawoRrw+EQ1yEUAGOaMJbtbJbWSarOGtKubjjIZBjorJyccXenhDpag6c9mOPqcHmQYd5k5TX+PbSFk2Iws6jYB26xBae4aUng4luOHO43xjMRoGOSq2uNgCg06Dq/1OfNrVr/R0SMWkraqKGVnQapiPM5o0vQ7lgdwJrrDSZAw4PThzuQ8AsHQGV3JGwyBHxQw6rZwfwZ45FE1HmlkfZ7zkvBxekzQJja3D1cWnmVldfDQMclROygFg8jFFiyiK8krOci6bj2kl83IoAqR8HNbHuTEGOSon9cw51NQNn483VIq881cG0D3ggkGnwcJApW0aXTm3kSkChvNxGOTcCIMclVtUaEa6XgvroBtnOuxKT4dUSNqqWlxkgUGnVXg28Y/byDRZXp+IYy1WAFzJGQuDHJVL0WqwLJAnwS0rigZpq6qS+Tjjxm1kmoyzHXb0Oz3IMOgwJ5+nGW+EQU4SWMkbKkWRnI9TmqPwTBKHfE1e4DYyha8h0K+qvNjC04xjYJCTBKQ+VnVNPfB4fQrPhtSkrXcQ7dYhaDWCfDSaxrZwuhkZBh1sQ26cvsxtZAoP+1WNH4OcJDCvwASTUYc+pwcnL/GGSpEj5eMsmG5GOsvKj5tOq5GP23OFlcIlreQw6XhsDHKSgFYjoHImt6wo8uqaAq0ceLMN2/BRchYFpPG7ZB2SV08XF1mUnk7cY5CTJNjHiqKhrskfNDMfJ3xS8nFdUw/c3EamcaoPrOLMm2bi6uk4MMhJEitv8t9Q65t74fLwhkqTd7XfifNXBgD4ezJReObmm2BJS8GAy4vjbTalp0MJoiGwRcyj4+PDICdJ3JKbiex0PYbcXnzUZlV6OqQC9YGb7ey8TFjS9ArPJvFoNIK8wlrLFVYap3rm44SFQU6SCL2hMi+HJu9wE/tVTVZwiweisfQ7PTgTOI3Hk1XjwyAniaxgvRyKIOlk1TIGORMmNetsuNgLh9ur8Gwo3jW2WOETgemWVOSbjUpPJyEwyEki0kpOQwtvqDQ5fQ43TgfKEbAp58TNmpqOqZkGOD0+uUw/0WikNxbcqho/BjlJZNbUdORmGuDy+HC0pVfp6VACa7jYC58IlOSk8R3lJAiCEFSRnHk5dGPD9XH4xmK8GOQkEUEQ2DOHIkJq5bCMN9tJY14OjYfH68OxFqnSMVdyxotBTpJhHyuKhDomHUfMykBeTmOrFQNOj8KzoXh1tqMPAy4vMg063JKXqfR0EgaDnCQj9bFqbLVi0MUbKoXP4R6u68J8nMkryk5DYVYqPD5RzrkgupbclLMki005w8AgJ8kUZadiukW6oTIvh8LX2GqFy+tDbqYBJTlpSk9HFbjCSmOR6+NwqyosDHKSDPNyaLKOBG1VCQLfUUaCtGXFvBwajVR8k0FOeBjkJCFWWaXJqGtmPk6kSW88Tl6ywTboVng2FG/arUO4bHP4m3IWW5SeTkJhkJOEpBvqiXYb7A7eUGn8PF6fnBvAICdy8kxGzJqaDlEEDjdxNYdCSas48wtMSNOzKWc4GOQkoQJLKmbkpMEnAnUXmOhI43fqkh2DLi/MqSm4JZcnPCKpikfJaRTSGws25Qwfg5wkJZWTr73AGyqN33B9nCxoeMIjoqS8HObK0bXqm6WkY66ehotBTpJi8jFNBPNxomdFIFfuXGcfrvQ5FZ4NxYs+hxtnOwJNOdnOIWwMcpLUipn+P1KnL9vRO+BSeDaUCHxBdVxY6TjystP1mDvNBAA4xBVWCjgWaMpZlJ2KPBNbqISLQU6Sys004ubcDABMdKTx+aSrH9ZBN1JTtFgw3az0dFSJLR7oWsP1cfjGYiIY5CQxJjpSOKStqiUlFqRoeeuIBjbrpGs1XPRfd0w6nhjeqZIYq6xSOOR+VTNyFJ6Jei0vzYZWI6C5exDt1iGlp0MK8zfltAJgPs5EMchJYpWlORAE/zYEEx3pRkRRDKl0TNGRaUzBwsBWIN980NmOPgy6vMg06liyYYIY5CSxrHQ95ub7Ex15lJxupLVnCB12B1K0AspZcTWquMJKEinRf0kxSzZMFIOcJMej5DQeUnL6okILjClahWejblVBeTmiKCo8G1KSlHS8jFtVExZ2kHPgwAGsW7cOBQUFEAQBu3fvlh9zu93YunUrFi5ciPT0dBQUFOBrX/saLl26FPIcPT092LhxI0wmEywWCzZt2oT+/v6QMcePH8ett94Ko9GIoqIiPP/889fN5c0338ScOXNgNBqxcOFCvPvuu+G+nKTHPlY0Hjw6HjtLS7KRohVwyebAxe5BpadDChFFEQ3NUqVjXncTFXaQMzAwgLKyMrz66qvXPTY4OIijR4/ihz/8IY4ePYrf/OY3OHfuHD7/+c+HjNu4cSNOnTqFmpoa7NmzBwcOHMDDDz8sP26323HnnXeipKQEDQ0NeOGFF7B9+3bs3LlTHnPw4EHcd9992LRpE44dO4b169dj/fr1OHnyZLgvKaktn5kNjQA0dw/iEhMdaRRS0nEl83GiLlWvRXmx/507Tz4mr3arf4tYpxGwuMii9HQSlzgJAMS33nrrhmPq6upEAOLFixdFURTF06dPiwDEI0eOyGN+97vfiYIgiO3t7aIoiuJPf/pTMSsrS3Q6nfKYrVu3irNnz5Y//8pXviKuXbs25GdVVlaK3/jGN8Y9f5vNJgIQbTbbuL9HjT7/kw/Fkq17xF/Xtyo9FYpDnbYhsWTrHnHG9/aItiGX0tNJCi/VnBNLtu4RH329QempkEJ2H2sTS7buET//kw+VnkpcGu/f76jn5NhsNgiCAIvFAgCora2FxWLB0qVL5TGrV6+GRqPB4cOH5TG33XYb9Hq9PKa6uhrnzp1Db2+vPGb16tUhP6u6uhq1tbWjzsXpdMJut4d8EPtY0Y1J9XHm5ptgMqYoPJvkENzHSmReTlKq51ZVREQ1yHE4HNi6dSvuu+8+mEz+UzwdHR3Izc0NGafT6ZCdnY2Ojg55TF5eXsgY6fOxxkiPj+TZZ5+F2WyWP4qKiib3AlUiOPmYN1S6Vh2Pjsfc4iILjCkadA+48HFn/9jfQKoznAfHpOPJiFqQ43a78ZWvfAWiKOJnP/tZtH5MWJ566inYbDb5o7W1VekpxYWlJVnQaQS0W4fQ2sO8HArFICf29DqNnOR9kIcCko7d4ca5zj4AQAWDnEmJSpAjBTgXL15ETU2NvIoDAPn5+ejq6goZ7/F40NPTg/z8fHlMZ2dnyBjp87HGSI+PxGAwwGQyhXwQkG7QyYlttRd4Q6VhtsHhmy1PVsWWtGXF5OPkc6zFClEEirPTkJvJppyTEfEgRwpwPvnkE/z+979HTk5oCfiqqipYrVY0NDTIX/vggw/g8/lQWVkpjzlw4ADcbrc8pqamBrNnz0ZWVpY8Zt++fSHPXVNTg6qqqki/pKTAPlY0kvqLPRBFYObUdEzNNCg9naQiFQU8fKEbXh+3kZNJQ2Crain7VU1a2EFOf38/Ghsb0djYCABoampCY2MjWlpa4Ha78eUvfxn19fV4/fXX4fV60dHRgY6ODrhcLgDA3LlzsWbNGjz00EOoq6vDn/70J2zevBkbNmxAQUEBAOD++++HXq/Hpk2bcOrUKbzxxht4+eWXsWXLFnkejz32GPbu3YsXX3wRZ8+exfbt21FfX4/NmzdH4NeSfJiXQyMZ7lfFVZxYm19gQqZBB7vDg9OXeEgimUhFALlVNXlhBzn19fUoLy9HeXk5AGDLli0oLy/Htm3b0N7ejt/+9rdoa2vD4sWLMW3aNPnj4MGD8nO8/vrrmDNnDu644w7cfffdWLVqVUgNHLPZjPfffx9NTU2oqKjAE088gW3btoXU0lm5ciV27dqFnTt3oqysDL/+9a+xe/duLFiwYDK/j6S1pDgLep0GXX1OnL8yoPR0KE4cZj6OYnRaDSpnMi8n2biDm3LyZNWk6cL9hs985jM3fKc/nlWA7Oxs7Nq164ZjFi1ahA8//PCGY+69917ce++9Y/48GpsxRYuK4izUXuhG7YVu3JSbofSUSGGDLg9OttsAMB9HKVWzpuD3Z7pw8Hw3vvFns5SeDsXAmct2DLm9MBl1uJn34Ulj7yqSSVtWh5iXQ/AnP3p8IgrMRhRmpSo9naQk5eUcae6By+NTeDYUC8P1cdiUMxIY5JBMzsu50A0fEx2TXvDRcUHgzVYJs/MykZ2ux6DLi+NtVqWnQzHQEMjHWcrV04hgkEOyskILUlO06Blw4eOuPqWnQwqTgpxlzMdRjEYjyE10efJR/URRRP1F/3VXwZNVEcEgh2R6nQZLA9n8Bz/lDTWZuTw+HGv1v6NkU05lDZd3YPKx2rX1DqHT7oROI6Cs0KL0dFSBQQ6FWMk+VgTgRLsNDrcP2el6zJrK5EclSXk5Ry9a4XB7FZ4NRZO0ijN/uhmpeq3Cs1EHBjkUoooFyAhBW1UzspiPo7DSKenIMxng8vpwNJCvQeokJR0v41ZVxDDIoRALWICMENwckFtVShMEgS0eksRw0jGDnEhhkEMhdFqNXPiNfaySk9cnykFOZWnOGKMpFpiXo362oaCmnCwCGDEMcug67GOV3M519KHP4UGGQYe50zKVng5hOC/nozYb+p0ehWdD0XCspReiCJTkpLFPXAQxyKHrSEHOkaYeuL0sQJZs6pr8we2SkizotLxFxIPCrDQUZ6f5V9kC+VKkLtJWFY+ORxbvYHSdufkmWNJSMODy4nibTenpUIzVyVtVXDKPJyu5ZaVqzIOLDgY5dB2NRsCKQC7GIR4lTyqiKKKuKXDCgzfbuMJtZPVye31obLUCAJZyJSeiGOTQiOQWD7yhJpWmqwO42u+EXqfBokKz0tOhINI1efqyHb0DLoVnQ5F0+pIdDrcP5tQU1qWKMAY5NKKqoMaATg8LkCULacl8cZEFxhQWI4snuZlG3JybAVEEDjfxzYea1F9kU85oYZBDI7o5NwNTMvRwenxobLEqPR2KkcNSU05uVcUlrrCqUwP7VUUNgxwakSAIWMHGgEknuPM4xZ+VzMtRHVEU5UrHzMeJPAY5NCr2sUoul6xDaOsdgkbwHx+n+FNZmgNBAD7p6kdXn0Pp6VAEtPYMoavPiRStgLIii9LTUR0GOTQqaWm8scWKIRfzctROysdZMN2MDINO4dnQSLLS9Zg3zQSAW1ZqITXlXDDdzDy4KGCQQ6OakZOGaWYjXF6fXKiK1KuO+TgJYSXzclRFSjrmVlV0MMihUQmCgKpAXg77WKmf3Hmc+Thxjc061aWhWTpZxesuGhjk0A2tYKJjUugZcOGTrn4ALAIY75aVZkOrEdDSM4jWnkGlp0OTYBt04+MuqSknV3KigUEO3ZC0NH6cjQFVTcrHuTk3A9npeoVnQzeSYdChLFCokYcCEtvRQFPOGWzKGTUMcuiGCrPSUJSdysaAKsej44lFPvnIFdaEJiUdL+XqadQwyKExrZzJo+RqJ63kMMhJDMFFAUVRVHg2NFGsjxN9DHJoTKyyqm79Tg9Otvu7zTPISQwVJVnQazXosDvQdHVA6enQBLi9PnzUZgUALJ3BICdaGOTQmKQg5+QlG2yDboVnQ5HWcLEXPhEoyk7FNHOq0tOhcTCmaLGkxAKAhwIS1alAU05LWgpmTmFTzmhhkENjyjMZMXNqOhsDqpSUa8VTVYmFeTmJrT6wRVxRzKac0cQgh8alin2sVEtKOq7kVlVCkYsCXuiGz8e8nEQj5+PwzUVUMcihcZHeNR5i8rGqONxeNAbyApaX5ig7GQrLokIL0vRa9Ay4cK6zT+npUBhEURyudMx8nKhikEPjsmKm/93G2Y4+dPc7FZ4NRcrxNhtcHh+mZBgwIydN6elQGPQ6jbzFyBXWxNLSM4ir/U7otRosnG5WejqqxiCHxiUnw4A5+ZkAgEMXWC9HLeoCOVaVpdkQBOYFJJrhPlZsu5JIpK2qBdNNbMoZZQxyaNxWsI+V6tQFbrbLuGSekKSTj4cv9MDj9Sk8Gxqv4a0q5uNEG4McGrcq9rFSFY/Xhwa5CCDzcRLR/AIzMo069Dk9OHXJrvR0aJwaApWO2a8q+hjk0LitKM2BIAAXrgyg0+5Qejo0SWcu92HA5YXJqMPswFYkJRatRpBXWPnmIzFYB134uNPfDJeVjqOPQQ6NmzktBfMLTABYm0MNpJpHS2f4u1pTYlopr7ByGzkRHG3xb1XNnJKOnAw25Yw2BjkUFhYgUw825VQH6Zo80twDl4d5OfFOSjrmVlVsMMihsFTNHC5ARolLFEW5KScrHSe2W/IykJOuh8PtQ2OrVenp0BhYHye2GORQWJaV+rc2WnoG0dY7qPR0aII+7epH76AbxhTW6Uh0giAEHQrgllU8c3l8+CgQiFaU8M1FLDDIobBkGHRYVOj/o8gtq8RVF1jFWVKcBb2Ot4FEJ21ZMfk4vp28ZIPT40NWWgpmTU1XejpJgXc3Cpu8ZcUbasKqY1NOVZGSj4+19GLI5VV4NjSaBjkfh8U3Y4VBDoVNTj6+0A1RZGPARCOKIptyqkxJThqmmY1we0XUX2RF8ngl/bdhPk7sMMihsFWUZCFFK+CyzYHmbublJJq23iFctjmg0wgoL+bNVg2C83K4whqfRFFEg5R0zJNVMcMgh8KWqtfKfxx5Q0080irOwkIzUvXsm6MWzMuJbxe7B3G13wW9VoMFTPaPmbCDnAMHDmDdunUoKCiAIAjYvXt3yOO/+c1vcOeddyInJweCIKCxsfG653A4HHj00UeRk5ODjIwM3HPPPejs7AwZ09LSgrVr1yItLQ25ubl48skn4fF4Qsbs378fS5YsgcFgwE033YTXXnst3JdDE8Sj5InrSDPr46iRtJJzvM0Ku8Ot8GzoWtLR8YWFZjbljKGwg5yBgQGUlZXh1VdfHfXxVatW4bnnnhv1OR5//HG8/fbbePPNN/GHP/wBly5dwpe+9CX5ca/Xi7Vr18LlcuHgwYP4t3/7N7z22mvYtm2bPKapqQlr167F7bffjsbGRnznO9/B17/+dbz33nvhviSagOClceblJBa5CCCTjlVluiUVM3LS4BOBI03My4k39c3Mx1GCLtxvuOuuu3DXXXeN+vgDDzwAAGhubh7xcZvNhl/84hfYtWsXPvvZzwIAfvnLX2Lu3Lk4dOgQVqxYgffffx+nT5/G73//e+Tl5WHx4sX40Y9+hK1bt2L79u3Q6/XYsWMHSktL8eKLLwIA5s6diz/+8Y946aWXUF1dPeLPdjqdcDqd8ud2OxvaTVR5sQUGnQZX+534tKsfN+ex91Ei6Opz4MLVAQgCsJR1OlSnatYUNHe34OD5btwxN0/p6VAQuQggr7uYinlOTkNDA9xuN1avXi1/bc6cOSguLkZtbS0AoLa2FgsXLkRe3vBFWl1dDbvdjlOnTsljgp9DGiM9x0ieffZZmM1m+aOoqCiSLy2pGHRa+R0JcwASh1RSfnZeJsxpKQrPhiJtuI8Vr8l4Yh104dMuf1NOtnOIrZgHOR0dHdDr9bBYLCFfz8vLQ0dHhzwmOMCRHpceu9EYu92OoaGhEX/2U089BZvNJn+0trZG4iUlLfaxSjw8Oq5uUkfyM5ft6BlwKTwbkkinqmZOTUd2ul7h2SSXpDpdZTAYYDKZQj5o4qQb6qGmbvh8zMtJBMNNOXMUnglFw9RMA2YHto4P8VBA3Kjn0XHFxDzIyc/Ph8vlgtVqDfl6Z2cn8vPz5THXnraSPh9rjMlkQmpqapRmT8EWFZqRrtfCOujGmQ7mN8U729Dwf6dlpbzZqhX7WMUfqdIx83FiL+ZBTkVFBVJSUrBv3z75a+fOnUNLSwuqqqoAAFVVVThx4gS6urrkMTU1NTCZTJg3b548Jvg5pDHSc1D0pWg1WBbY9uCWVfxruNgDUQRKp6QjN9Oo9HQoSlgUML44PV40tlkB8GSVEsIOcvr7+9HY2CjXv2lqakJjYyNaWloAAD09PWhsbMTp06cB+AOYxsZGOZfGbDZj06ZN2LJlC/73f/8XDQ0N+Mu//EtUVVVhxYoVAIA777wT8+bNwwMPPICPPvoI7733Hn7wgx/g0UcfhcFgAAB885vfxIULF/Dd734XZ8+exU9/+lP893//Nx5//PFJ/1Jo/NjHKnHUNfnfTS7jjVbVVpTmQBCA81cG0Gl3KD2dpHey3Q6Xx4ecdD1Kp7ApZ6yFHeTU19ejvLwc5eXlAIAtW7agvLxcrmHz29/+FuXl5Vi7di0AYMOGDSgvL8eOHTvk53jppZfwuc99Dvfccw9uu+025Ofn4ze/+Y38uFarxZ49e6DValFVVYWvfvWr+NrXvoa//du/lceUlpbinXfeQU1NDcrKyvDiiy/i5z//+ajHxyk6pOTjuqYeeLw+hWdDN1LX5A9EmY+jbua0FCwo8FfU5ZsP5TUE+lUtKcliU04FCGISV3Kz2+0wm82w2WxMQp4gr09E+d++D7vDg92P/h8sLrIoPSUawZDLi0XPvAe3V8SH370dRdlpSk+JoujZd8/g/ztwAV9ZWojnv1ym9HSS2sP/Xo/3T3fiqbvm4Bt/Nkvp6ajGeP9+J9XpKoo8rUZAJbes4t6x1l64vSLyTUYUZjExX+2qWC8nLoQ05eQ2sSIY5NCksY9V/Bs+Op7NJfMksGxGNnQaAW29Q2jtGVR6OkmruXsQ3QMu6HVsyqkUBjk0adK7xiNNPXB5mJcTj6SmnMtYBDAppBt08tYxj5IrR7ruygrNMOjYlFMJDHJo0mbnZSI7XY8htxfHA0clKX64vT4cvWgFwErHyYRbVsqT6uNUsD6OYhjk0KRpNAJWzPRfxLyhxp+T7TYMub3ISkvBTVMzlJ4OxUhwkJPE50sUVR84WcVKx8phkEMRUcU+VnFLysdZOiMbGg3zcZLFkuIs6HUaXOlz4vyVAaWnk3R6B1zy751NOZXDIIciQko+bmjphcPtVXg2FIxNOZOTMUUrryDUMi8n5qRTVbOmpiOLTTkVwyCHImLW1HTkZhrg8vhwtKVX6elQgM8nDicdz2CQk2xWMi9HMUcu8rqLBwxyKCIEQZBzAA7xhho3znX2we7wIE2vxfwCFrxMNvI28oVu+HzMy4ml4aRjblUpiUEORYy0ZcV3jfFDWsWpKMmCTsvLPdksKjQjXa+FdXC4Az1Fn9PjxfF2GwB/Lhwph3c9ihipj1VjqxWDLo/CsyEAOCwVAeSNNimlaDVYHsjF4qGA2DnZbpObcs7IYQsVJTHIoYgpyk7FdEsqPD4RR5qZl6M0URRDKh1TcpLefHCFNXbqg7aqWGFcWQxyKGKC83L4rlF5F7sHcaXPCb1WgzI2Tk1a0jV5+EI33F5WJI+FevarihsMciii2McqfkirOGVFZhhTWFI+Wc2dZoI5NQUDLi9OBPJEKHpCm3JyBVVpDHIooqR3jSfarLA73ArPJrnVNXOrigBtUEVyrrBG34WrA+gZcMGg02BBAZtyKo1BDkVUgSUVM3LS4BP9DTtJOdJKDut00EpWJI8Z6eh4WaEFeh3/xCqN/wUo4tgYUHkdNgdaegahEVing4aLAh5p7oHTw4rk0ST1q6pgPk5cYJBDEcc+VsqTtqrmFZiQaUxReDaktJtyMzAlwwCnx4djLValp6NqctIx31zEBQY5FHHS/v/py3b0DrgUnk1yqmvyB5jLZ+QoPBOKB4IgsMVDDHT3O3GBTTnjCoMcirjcTCNuzs0AABxu4g1VCUea/O8mmXRMkpVyeQc264wW6VTVzbkZsKSxKWc8YJBDUcF6OcrpHXDhXGcfAGAZ8wIoQEo+PtbCiuTR0sD6OHGHQQ5FBftYKUfqV3VTbgZyMgwKz4biBSuSR5+Uj1NRwhXUeMEgh6JiRSDI+aSrH1f6nArPJrlIQQ6PjlOw4IrkB7llFXEOtxcn2gJNOZmPEzcY5FBUZKXrMXeaCQCrH8eaVB+nkvk4dA0pL+cQV1gj7mS7DS6vD1My9ChhU864wSCHomYl83JibsDpwclLdgBMOqbryRXJ222wDbEieSRJW4BLS7LZlDOOMMihqJHycg5xJSdmjrb0wusTMd2SigJLqtLToTgzzZyKmVPS4ROHV/woMhoCRQCZdBxfGORQ1CyfmQ2NADRdHcBl25DS00kK3KqisTAvJ/KCm3KyPk58YZBDUWMypmDhdH+DOm5ZxYbcr4pBDo2Cfawi7/yVAfQOumHQaTCfTTnjCoMciqoVrLIaM06PF8darQCYj0OjkyqSn+3ow9V+nnyMBGmrqqyITTnjDf9rUFQFv2sURVHh2ajbiTYbXB7/6Y6ZU9KVng7FqZwMA+bkZwJgvlyk1DezX1W8YpBDUbW0JAs6jYB26xBae5iXE02Hm4br4/B0B91IFVdYI0oqAsjaVPGHQQ5FVbpBh8VFFgBA7QUmOkaTlI/DrSoaC/NyIudqvxNNV/1NOZcUcyUn3jDIoahjH6vo8/qGT3fw3SSNZXkpTz5GinTd3ZKXAXNaisKzoWsxyKGoC+5jxbyc6Dhz2Y5+pweZBp1caZpoNOZUnnyMlAb2q4prDHIo6paUZEGv06Crz4kLgWVdiixpq2rpjCxoNczHobFVBbasmJczOfWBXnFMOo5PDHIo6owpWiwptgDgDTVaWB+HwhXcdoUrrBPjcHtxot3flJPbxPGJQQ7FhJToyMaAkSeKotx5nJWOabyWzshCitZ/8rGlZ1Dp6SSk4202uL0ipmYaUJTNNirxiEEOxYSUfHzoQjd8Pr5rjKTzVwbQPeCCQafBwukWpadDCSJNr0N5kX+LhSusE1N/cXirimUb4hODHIqJskILUlO06B5w4eOuPqWnoyrSVlV5MautUnhYkXxyGprZryre8Y5IMaHXaeTuvDzNEVnSVtXy0hyFZ0KJRsrL+cO5LvzPsXbUnu+Glyut4+LziWhoCVQ6Zj5O3GKQQzHDKqvRIRcB5I2WwnSlzwEAsDs8eOyNRtz3L4ew6rkPsPfkZYVnFv8uXO2HddANY4oG8wtYtiFeMcihmJGSjw9f4LvFSGnrHUS7dQg6jYAlJRalp0MJZO/Jy/j2fzZe9/UOmwOP/OooA50xHAlsVS0usiBFyz+l8Srs/zIHDhzAunXrUFBQAEEQsHv37pDHRVHEtm3bMG3aNKSmpmL16tX45JNPQsb09PRg48aNMJlMsFgs2LRpE/r7+0PGHD9+HLfeeiuMRiOKiorw/PPPXzeXN998E3PmzIHRaMTChQvx7rvvhvtyKIYWFJiQYdDB7vDg9CW70tNRBWmrav50M9L0OoVnQ4nC6xPxzNunMdJbDelrz7x9mm9GbmC4KSdXUONZ2EHOwMAAysrK8Oqrr474+PPPP49//ud/xo4dO3D48GGkp6ejuroaDodDHrNx40acOnUKNTU12LNnDw4cOICHH35Yftxut+POO+9ESUkJGhoa8MILL2D79u3YuXOnPObgwYO47777sGnTJhw7dgzr16/H+vXrcfLkyXBfEsWITquRjzizj1VkSFtVPDpO4ahr6sFlm2PUx0UAl20O3PHifnzjP+rxoz2n8cs/NaHmdCfOdviraye7hsDJqooZTDqOZ4I4iSpQgiDgrbfewvr16wH4V3EKCgrwxBNP4K//+q8BADabDXl5eXjttdewYcMGnDlzBvPmzcORI0ewdOlSAMDevXtx9913o62tDQUFBfjZz36G73//++jo6IBerwcAfO9738Pu3btx9uxZAMBf/MVfYGBgAHv27JHns2LFCixevBg7duwY1/ztdjvMZjNsNhtMJu6pxsLPP7yAv3vnDG6fPRW//MvlSk8n4d3x4n6cvzKAn39tKVbPy1N6OpQg/qexHY/9V+OknsOSloLCrFQUWtJQmJWKomz/Pwuz/P9MN6h3ZfFKnxPL/v73EASgcdudMKeyZ1Wsjffvd0T/L2xqakJHRwdWr14tf81sNqOyshK1tbXYsGEDamtrYbFY5AAHAFavXg2NRoPDhw/ji1/8Impra3HbbbfJAQ4AVFdX47nnnkNvby+ysrJQW1uLLVu2hPz86urq67bPgjmdTjidTvlzu51bJrG2ItDHqq6pB26vj3vZk3C134nzV/xtMpby3SSFITfTOK5x362+BRnGFLT1DqGtdxCtPf5/9g66YQ18nGwf+T6alZaCwqw0FGUPBz7BQVAib6/KTTlzMxngxLmI/l/W0dEBAMjLC31HmZeXJz/W0dGB3Nzc0EnodMjOzg4ZU1paet1zSI9lZWWho6Pjhj9nJM8++yyeeeaZCbwyipR500wwp6bANuTGiXYblhTzj/NEHQlsVc3Jz4QlTT/GaKJhy0uzMc1sRIfNMWJejgAg32zEN/7sphF7ofU7PWgPBD5tvUNo7fH/s83q/6d10I3eQTd6B21y24Nr5aTrQ4Ie6d+LslMx3ZKGVL02si86grhVlTgSN5SegKeeeipk9cdut6OoqEjBGSUfjUbAipnZeO9UJ2rPdzPImYS6QNIxe+ZQuLQaAU+vm4dHfnUUAhAS6EghzdPr5o3a7DXDoMPs/EzMzs8c8fE+hzuw+jMcCAUHRHaHB90DLnQPuPBR28hB0JQMPaYHBUBFWaHbYcYU5YIg6WTVMgY5cS+iQU5+fj4AoLOzE9OmTZO/3tnZicWLF8tjurq6Qr7P4/Ggp6dH/v78/Hx0dnaGjJE+H2uM9PhIDAYDDAbDBF4ZRdLKWVPkIOfR229SejoJS66Pw6RjmoA1C6bhZ19dgmfePh2ShJxvNuLpdfOwZsG0G3z3jWUaUzB3WgrmThs5V8I25A5ZCWrrHUKr9O89g+hzenC134Wr/S581God8TmmZBiuyQUaDoCmW6ITBHl9Iv746RWcaPPPSWqLQfErokFOaWkp8vPzsW/fPjmosdvtOHz4MB555BEAQFVVFaxWKxoaGlBRUQEA+OCDD+Dz+VBZWSmP+f73vw+3242UFP9+Z01NDWbPno2srCx5zL59+/Cd73xH/vk1NTWoqqqK5EuiKJCKAh5p7oHT44VBF7/L0vHK7nDjzGV/LgSDHJqoNQum4c/n5aOuqQddfQ7kZhqxvDR71BWcSDGnpsCcmoJ5oxTRsw25Q3KArl0V6nd6cLXfiav9TjSOEgTlZhqu2Q4bzg8qsBjDvu/sPXn5uoBww78cwvZJBoQUXWEHOf39/fj000/lz5uamtDY2Ijs7GwUFxfjO9/5Dv7u7/4ON998M0pLS/HDH/4QBQUF8gmsuXPnYs2aNXjooYewY8cOuN1ubN68GRs2bEBBQQEA4P7778czzzyDTZs2YevWrTh58iRefvllvPTSS/LPfeyxx/Bnf/ZnePHFF7F27Vr813/9F+rr60OOmVN8ujk3A1My9Lja70JjixWVM9mOIFwNF3vhE4GSnDTkmcaXREo0Eq1GkN94xAt/EGTG/ALzdY+JohgIgq4PgKSgaMDlRVefE119ThxtsY74M/JMBjkAunYrbNo1QdDek5fxyK+OXpe/1BkonPizry5hoBOnwg5y6uvrcfvtt8ufSzkuDz74IF577TV897vfxcDAAB5++GFYrVasWrUKe/fuhdE4fCN+/fXXsXnzZtxxxx3QaDS455578M///M/y42azGe+//z4effRRVFRUYMqUKdi2bVtILZ2VK1di165d+MEPfoC/+Zu/wc0334zdu3djwYIFE/pFUOwIgoAVM3Ow5/hl1F7oZpAzAWzlQMlKEARY0vSwpOmxYPrIQZB1MDQIag3KC2rtGcKQ24tOuxOddqd8Uir0ZwB5mUZ5G+z3ZzpHLZwowF848c/n5Ud9BYzCN6k6OYmOdXKU8/rhi/j+WyexvDQb//0NbjGG68s/O4j6i714/suL8JWlTJ4nGi9RFNE7ONJ22HBA5HD7wn7e/3xoRdytiKmZInVyiMZL6mPV2GLFkMsb18dF443D7cVHgcRHVjomCo8gCMhO1yM7XY9FhZbrHhdFEd0DLjnwef9UJ3770aUxn7erb/QK0qQcVmIjRczISUNepgEurw8/+eAT1J5n087xamy1wu0VkWcyoDg7TenpEKmKIAiYkmHA4iILPreoAPctLx7X9423wCLFFoMcUsR7pzpgd7gBAD/dfx73/cshrHruA3Y+HgcpH2fZjGwIAnMAiKJJKpw42pUmAJhmNvKUY5xikEMxJ51UGLp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experiment_idrankruntimep2mm2ml2lm2lp2psource_treetarget_tree...ghost_fmm_udisplacement_mapmetadata_creationexpansion_ordern_pointslocal_depthglobal_depthblock_sizen_threadsn_samples
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5 rows × 33 columns

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" + ], + "text/plain": [ + " experiment_id rank runtime p2m m2m l2l m2l p2p source_tree \\\n", + "0 0 2 21 9 0 0 0 0 6299 \n", + "1 0 1 1977 9 0 0 160 1784 6299 \n", + "2 0 3 1976 9 0 0 161 1784 6299 \n", + "3 0 6 1975 9 0 0 163 1783 6299 \n", + "4 0 4 1978 9 0 0 160 1788 6299 \n", + "\n", + " target_tree ... ghost_fmm_u displacement_map metadata_creation \\\n", + "0 11839 ... 0 0 534 \n", + "1 11839 ... 0 1 541 \n", + "2 11839 ... 0 1 543 \n", + "3 11839 ... 0 1 544 \n", + "4 11839 ... 0 1 544 \n", + "\n", + " expansion_order n_points local_depth global_depth block_size \\\n", + "0 3 1000000 4 1 128 \n", + "1 3 1000000 4 1 128 \n", + "2 3 1000000 4 1 128 \n", + "3 3 1000000 4 1 128 \n", + "4 3 1000000 4 1 128 \n", + "\n", + " n_threads n_samples \n", + "0 4 500 \n", + "1 4 500 \n", + "2 4 500 \n", + "3 4 500 \n", + "4 4 500 \n", + "\n", + "[5 rows x 33 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_1e6.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef7e67ce-b959-4e4e-bbfb-e511f9350a46", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/hpc/archer2/data/ijhpca/fix.py b/hpc/archer2/data/ijhpca/fix.py new file mode 100644 index 00000000..187ec418 --- /dev/null +++ b/hpc/archer2/data/ijhpca/fix.py @@ -0,0 +1,12 @@ +import re + +# Read file +with open("weak_fft_10984759.csv", "r") as f: + data = f.read() + +# Replace "digit + space + digit" with "digit, digit" +fixed_data = re.sub(r"(\d)\s+(\d)", r"\1,\2", data) + +# Write corrected CSV +with open("weak_fft_10984759.csv", "w") as f: + f.write(fixed_data) \ No newline at end of file diff --git a/hpc/archer2/data/ijhpca/weak_fft_10984742.csv b/hpc/archer2/data/ijhpca/weak_fft_10984742.csv new file mode 100644 index 00000000..b93860df --- /dev/null +++ b/hpc/archer2/data/ijhpca/weak_fft_10984742.csv @@ -0,0 +1,1018 @@ + +experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,source_tree,target_tree,source_domain,target_domain,layout,ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,displacement_map,metadata_creation,expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples +"0",2,0,0,0,0,0,0,60003,59929,73,73,2,0,0,0,0,0, 389,1,0,0,0,0, 0, 1027,3,5000000,5,1,128,4,500 +"0",5,159,51,3,0,0,0,60005,59927,76,73,2,1910,103,127,0,0, 388,1,0,0,0,0, 2, 3066,3,5000000,5,1,128,4,500 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1039,3,1000000,4,3,128,4,500 +"5",26,2136,11,0,1,481,1599,9382,9341,39,15,1,165,6,371,4,9, 408,2,0,0,0,0, 2, 1035,3,1000000,4,3,128,4,500 +"5",52,2139,11,0,1,485,1597,9382,9341,100,15,1,199,5,336,6,9, 405,2,0,0,0,0, 3, 1037,3,1000000,4,3,128,4,500 +"5",32,2152,16,1,1,489,1603,9383,9341,99,15,1,348,7,182,3,9, 407,1,0,0,0,0, 3, 1031,3,1000000,4,3,128,4,500 +"6",1,49,8,0,0,0,0,12543,12434,100,17,2,870,17,58,13,9, 444,1,1,0,0,0, 0, 1983,3,1000000,4,3,128,4,500 +"6",273,48,10,0,0,0,0,12543,12434,14,17,2,835,22,94,6,9, 414,2,1,0,0,0, 0, 2013,3,1000000,4,3,128,4,500 +"6",7,49,9,0,0,0,0,12543,12434,107,17,2,862,16,66,12,9, 443,1,1,0,0,0, 0, 1984,3,1000000,4,3,128,4,500 +"6",241,48,9,1,0,0,0,12543,12434,160,17,2,841,23,88,5,9, 417,1,1,0,0,0, 0, 2011,3,1000000,4,3,128,4,500 +"6",270,49,9,0,0,0,0,12543,12434,163,17,2,839,22,90,6,9, 414,2,1,0,0,0, 0, 2013,3,1000000,4,3,128,4,500 +"6",22,49,9,0,0,0,0,12543,12434,162,17,2,853,16,76,13,9, 442,1,1,0,0,0, 0, 1985,3,1000000,4,3,128,4,500 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1989,3,1000000,4,3,128,4,500 +"6",65,1938,9,0,0,167,1714,12543,12435,101,17,1,787,14,147,10,9, 436,1,1,0,0,0, 1, 1996,3,1000000,4,3,128,4,500 +"6",73,1938,9,0,0,166,1714,12543,12435,163,17,1,786,14,147,10,9, 437,1,1,0,0,0, 1, 1995,3,1000000,4,3,128,4,500 +"6",17,1935,9,0,0,169,1711,12543,12435,14,17,1,506,9,430,11,9, 443,1,1,0,0,0, 1, 1992,3,1000000,4,3,128,4,500 +"6",156,1938,9,0,0,167,1713,12543,12435,128,17,1,779,18,155,6,9, 428,2,1,0,0,0, 1, 2004,3,1000000,4,3,128,4,500 +"6",66,1939,9,0,0,168,1713,12543,12435,76,17,1,787,14,146,10,9, 438,1,1,0,0,0, 1, 1994,3,1000000,4,3,128,4,500 +"6",110,1935,9,0,0,172,1709,12543,12434,160,17,2,458,11,479,8,9, 433,1,1,0,0,0, 1, 2003,3,1000000,4,3,128,4,500 +"6",260,1935,10,0,0,168,1713,12543,12435,95,17,1,446,14,492,4,9, 414,1,1,0,0,0, 1, 2022,3,1000000,4,3,128,4,500 +"6",37,1936,9,0,0,173,1708,12543,12435,159,17,1,497,14,440,6,9, 441,2,1,0,0,0, 1, 1995,3,1000000,4,3,128,4,500 +"6",212,1940,9,0,0,168,1715,12543,12435,53,17,1,777,17,157,6,9, 422,1,1,0,0,0, 1, 2011,3,1000000,4,3,128,4,500 +"6",9,1941,9,0,0,167,1716,12543,12435,162,17,1,785,14,148,10,9, 442,1,1,0,0,0, 1, 1990,3,1000000,4,3,128,4,500 +"6",510,1941,9,0,0,172,1711,12543,12435,160,17,1,794,15,140,8,9, 390,1,1,0,0,0, 1, 2042,3,1000000,4,3,128,4,500 +"6",256,1936,9,0,0,168,1715,12543,12435,105,17,1,444,14,494,5,9, 416,1,1,0,0,0, 1, 2021,3,1000000,4,3,128,4,500 +"6",211,1941,9,0,0,168,1716,12543,12435,129,17,1,776,16,158,6,9, 422,2,1,0,0,0, 1, 2011,3,1000000,4,3,128,4,500 +"6",90,1938,9,0,0,172,1712,12543,12435,17,17,1,468,14,469,5,9, 435,1,1,0,0,0, 1, 2001,3,1000000,4,3,128,4,500 +"6",289,1937,9,0,0,170,1715,12542,12435,101,17,1,447,13,491,5,9, 413,1,0,0,0,0, 1, 2023,3,1000000,4,3,128,4,500 +"6",382,1937,9,0,0,170,1714,12543,12435,159,17,1,481,17,458,1,9, 403,1,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 +"6",179,1942,9,0,0,170,1714,12543,12435,161,17,1,813,13,121,10,9, 423,1,1,0,0,0, 1, 2009,3,1000000,4,3,128,4,500 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2032,3,1000000,4,3,128,4,500 +"6",421,1944,9,0,0,172,1715,12543,12435,159,17,1,774,17,160,6,9, 397,1,1,0,0,0, 1, 2035,3,1000000,4,3,128,4,500 +"6",407,1944,9,0,0,170,1717,12543,12435,36,17,1,801,13,134,10,9, 399,2,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 +"6",204,1945,9,0,0,169,1718,12543,12435,161,17,1,788,17,146,6,9, 423,2,1,0,0,0, 1, 2009,3,1000000,4,3,128,4,500 +"6",357,1941,9,0,0,172,1717,12543,12435,160,17,1,465,17,473,2,9, 405,1,1,0,0,0, 1, 2032,3,1000000,4,3,128,4,500 +"6",301,1943,9,0,0,172,1718,12543,12435,38,17,1,443,15,495,3,9, 412,1,1,0,0,0, 1, 2024,3,1000000,4,3,128,4,500 +"6",447,1942,9,0,0,175,1715,12543,12435,127,17,1,509,11,430,8,9, 394,1,1,0,0,0, 1, 2043,3,1000000,4,3,128,4,500 +"6",503,1947,9,0,0,172,1718,12543,12435,159,17,1,782,15,152,8,9, 390,2,0,0,0,0, 1, 2044,3,1000000,4,3,128,4,500 +"6",76,1944,9,0,0,172,1718,12543,12435,162,17,1,472,11,466,8,9, 435,1,1,0,0,0, 1, 2001,3,1000000,4,3,128,4,500 +"6",183,1948,9,0,0,169,1721,12543,12435,160,17,1,803,13,131,10,9, 425,1,1,0,0,0, 1, 2008,3,1000000,4,3,128,4,500 +"6",494,1947,8,0,0,173,1718,12543,12435,160,16,1,794,20,141,2,9, 390,1,1,0,0,0, 1, 2043,3,1000000,4,3,128,4,500 +"6",440,1949,9,0,0,171,1721,12543,12435,160,17,1,806,14,127,9,9, 397,1,1,0,0,0, 1, 2035,3,1000000,4,3,128,4,500 +"6",403,1949,10,0,0,169,1722,12543,12435,36,17,1,802,12,132,10,9, 400,1,1,0,0,0, 1, 2033,3,1000000,4,3,128,4,500 +"6",302,1946,9,0,0,173,1719,12543,12435,160,17,1,476,15,462,5,9, 412,1,1,0,0,0, 1, 2024,3,1000000,4,3,128,4,500 +"6",504,1949,10,0,0,171,1721,12543,12435,160,17,1,784,13,150,8,9, 389,1,1,0,0,0, 1, 2044,3,1000000,4,3,128,4,500 +"6",129,1950,9,0,0,169,1724,12543,12435,101,17,1,784,10,149,13,9, 429,1,1,0,0,0, 1, 2003,3,1000000,4,3,128,4,500 +"6",448,1948,9,0,0,175,1720,12543,12435,104,17,1,449,16,490,2,9, 395,1,1,0,0,0, 1, 2042,3,1000000,4,3,128,4,500 +"6",476,1948,10,0,0,175,1719,12543,12435,128,17,1,458,14,480,4,9, 394,1,1,0,0,0, 1, 2043,3,1000000,4,3,128,4,500 +"6",480,1948,9,0,0,176,1720,12543,12435,105,17,1,480,16,459,2,9, 392,2,1,0,0,0, 1, 2045,3,1000000,4,3,128,4,500 +"6",223,1949,9,0,0,171,1723,12543,12435,162,17,1,476,14,462,5,9, 418,1,1,0,0,0, 1, 2019,3,1000000,4,3,128,4,500 +"6",251,1955,9,0,0,170,1730,12543,12435,161,17,1,474,16,463,4,9, 415,1,1,0,0,0, 1, 2021,3,1000000,4,3,128,4,500 +"6",508,1959,10,0,0,182,1719,12543,12435,159,17,1,786,13,148,8,9, 389,1,1,0,0,0, 1, 2044,3,1000000,4,3,128,4,500 +"6",106,1957,9,0,0,171,1732,12543,12435,161,17,1,462,11,476,8,9, 433,1,1,0,0,0, 1, 2003,3,1000000,4,3,128,4,500 +"6",362,1958,9,0,0,170,1734,12543,12435,160,17,1,491,18,446,2,9, 403,1,1,0,0,0, 1, 2032,3,1000000,4,3,128,4,500 +"6",145,1963,9,0,0,169,1736,12543,12435,128,17,1,814,4,120,19,9, 428,1,1,0,0,0, 1, 2004,3,1000000,4,3,128,4,500 +"6",155,1966,9,0,0,167,1743,12543,12435,40,17,1,816,17,118,6,9, 426,2,1,0,0,0, 1, 2006,3,1000000,4,3,128,4,500 +"6",70,1970,0,0,0,94,1822,12542,12435,61,17,2,436,19,503,9,9, 437,2,1,0,0,0, 0, 2000,3,1000000,4,3,128,4,500 +"6",322,1977,0,0,0,95,1830,12542,12436,77,17,1,441,26,498,3,9, 410,1,1,0,0,0, 1, 2028,3,1000000,4,3,128,4,500 +"6",55,1980,0,0,0,94,1832,12542,12436,160,17,1,425,23,514,6,9, 439,1,1,0,0,0, 1, 1998,3,1000000,4,3,128,4,500 +"6",84,1983,0,0,0,95,1834,12542,12436,53,17,1,441,23,498,5,9, 436,1,1,0,0,0, 0, 2001,3,1000000,4,3,128,4,500 +"6",131,2046,9,0,0,169,1818,12543,12435,96,17,1,801,10,133,13,9, 430,1,1,0,0,0, 1, 2001,3,1000000,4,3,128,4,500 +"6",196,2048,9,0,0,167,1825,12543,12435,127,17,1,752,15,183,6,9, 421,1,1,0,0,0, 1, 2012,3,1000000,4,3,128,4,500 +"6",298,2047,9,0,0,169,1825,12543,12435,162,17,1,426,15,512,4,9, 411,1,1,0,0,0, 1, 2026,3,1000000,4,3,128,4,500 +"6",333,2048,9,0,0,170,1827,12543,12435,162,17,2,414,16,525,2,9, 407,1,0,0,0,0, 1, 2031,3,1000000,4,3,128,4,500 +"6",367,2054,9,0,0,164,1833,12543,12435,159,17,1,743,19,191,3,9, 402,1,1,0,0,0, 1, 2031,3,1000000,4,3,128,4,500 +"6",6,2055,9,0,0,168,1830,12543,12435,93,17,1,754,11,180,12,9, 443,1,1,0,0,0, 1, 1990,3,1000000,4,3,128,4,500 +"6",139,2054,9,0,0,168,1829,12543,12435,130,17,1,769,16,166,6,9, 428,2,1,0,0,0, 1, 2005,3,1000000,4,3,128,4,500 +"6",336,2056,18,0,0,169,1829,12543,12435,127,17,1,764,10,170,3,9, 407,2,1,0,0,0, 1, 2026,3,1000000,4,3,128,4,500 +"6",109,2052,9,0,0,169,1830,12543,12435,160,17,1,442,11,496,8,9, 431,1,1,0,0,0, 1, 2005,3,1000000,4,3,128,4,500 +"6",13,2056,9,0,0,166,1832,12543,12435,162,17,1,762,13,173,10,9, 442,1,1,0,0,0, 1, 1991,3,1000000,4,3,128,4,500 +"6",146,2055,9,0,0,168,1830,12543,12435,163,17,1,790,3,145,19,9, 427,2,1,0,0,0, 1, 2006,3,1000000,4,3,128,4,500 +"6",194,2056,9,0,0,168,1830,12543,12435,76,17,1,758,16,177,6,9, 421,1,1,0,0,0, 1, 2012,3,1000000,4,3,128,4,500 +"6",165,2056,9,0,0,168,1830,12543,12435,161,17,1,751,16,184,6,9, 425,1,1,0,0,0, 1, 2008,3,1000000,4,3,128,4,500 +"6",317,2052,9,0,0,172,1827,12543,12435,160,17,1,410,14,529,4,9, 410,1,1,0,0,0, 1, 2027,3,1000000,4,3,128,4,500 +"6",203,2055,9,0,0,169,1829,12543,12435,162,17,1,757,16,179,6,9, 420,1,1,0,0,0, 1, 2013,3,1000000,4,3,128,4,500 +"6",19,2057,9,0,0,167,1832,12543,12435,36,17,1,760,10,174,12,9, 441,1,0,0,0,0, 1, 1991,3,1000000,4,3,128,4,500 +"6",184,2056,9,0,0,167,1831,12543,12435,162,17,1,778,12,157,10,9, 423,2,0,0,0,0, 1, 2010,3,1000000,4,3,128,4,500 +"6",101,2053,9,0,0,172,1827,12543,12435,159,17,1,441,13,497,5,9, 432,1,1,0,0,0, 1, 2004,3,1000000,4,3,128,4,500 +"6",167,2057,9,0,0,171,1829,12543,12435,160,17,1,752,16,183,6,9, 424,2,1,0,0,0, 1, 2008,3,1000000,4,3,128,4,500 +"6",312,2057,18,0,0,173,1827,12543,12435,161,17,1,763,8,172,6,9, 408,1,1,0,0,0, 1, 2024,3,1000000,4,3,128,4,500 +"6",361,2053,9,0,0,169,1832,12543,12435,160,17,1,433,16,506,2,9, 403,2,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 +"6",360,2054,9,0,0,169,1830,12543,12434,160,17,2,440,17,499,2,9, 404,1,1,0,0,0, 1, 2033,3,1000000,4,3,128,4,500 +"6",458,2056,9,0,0,170,1830,12543,12435,161,17,1,743,18,192,3,9, 393,1,1,0,0,0, 1, 2041,3,1000000,4,3,128,4,500 +"6",375,2058,9,0,0,171,1830,12543,12435,159,17,1,745,19,190,3,9, 402,1,1,0,0,0, 1, 2031,3,1000000,4,3,128,4,500 +"6",506,2057,9,0,0,172,1828,12543,12435,41,17,1,756,14,178,8,9, 389,2,1,0,0,0, 1, 2044,3,1000000,4,3,128,4,500 +"6",159,2058,9,0,0,171,1829,12543,12435,163,17,1,755,16,180,6,9, 425,1,1,0,0,0, 1, 2008,3,1000000,4,3,128,4,500 +"6",105,2055,9,0,0,170,1832,12543,12435,161,17,1,449,11,489,8,9, 432,1,1,0,0,0, 1, 2005,3,1000000,4,3,128,4,500 +"6",215,2058,9,0,0,170,1831,12543,12435,128,17,1,736,17,199,4,9, 419,1,1,0,0,0, 1, 2015,3,1000000,4,3,128,4,500 +"6",153,2058,9,0,0,168,1833,12543,12435,94,17,1,788,16,146,6,9, 426,2,1,0,0,0, 1, 2007,3,1000000,4,3,128,4,500 +"6",412,2058,10,0,0,171,1831,12543,12435,128,17,1,750,15,185,6,9, 398,1,1,0,0,0, 1, 2036,3,1000000,4,3,128,4,500 +"6",435,2059,9,0,0,170,1831,12543,12435,160,17,1,802,13,132,10,9, 397,1,1,0,0,0, 1, 2035,3,1000000,4,3,128,4,500 +"6",374,2059,9,0,0,170,1831,12543,12435,160,17,1,744,20,191,2,9, 402,1,1,0,0,0, 1, 2031,3,1000000,4,3,128,4,500 +"6",326,2055,9,0,0,175,1828,12543,12434,94,16,2,435,16,504,2,9, 407,1,1,0,0,0, 1, 2029,3,1000000,4,3,128,4,500 +"6",80,2054,10,0,0,173,1830,12543,12435,128,17,1,433,13,506,4,9, 435,1,1,0,0,0, 1, 2003,3,1000000,4,3,128,4,500 +"6",5,2059,9,0,0,167,1835,12543,12435,162,17,1,761,12,173,11,9, 443,1,1,0,0,0, 1, 1990,3,1000000,4,3,128,4,500 +"6",417,2059,9,0,0,172,1830,12543,12435,101,17,1,746,17,188,6,9, 397,1,1,0,0,0, 1, 2036,3,1000000,4,3,128,4,500 +"6",192,2058,9,0,0,170,1833,12543,12435,105,17,1,743,16,192,5,9, 421,1,1,0,0,0, 1, 2012,3,1000000,4,3,128,4,500 +"6",193,2059,9,0,0,168,1833,12543,12435,101,17,1,757,16,178,6,9, 422,1,1,0,0,0, 1, 2012,3,1000000,4,3,128,4,500 +"6",34,2056,9,0,0,171,1831,12543,12435,76,17,1,433,13,505,5,9, 439,1,1,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 +"6",98,2056,9,0,0,172,1832,12543,12435,76,17,1,420,13,519,5,9, 434,2,1,0,0,0, 1, 2003,3,1000000,4,3,128,4,500 +"6",376,2060,9,0,0,168,1834,12543,12435,160,17,1,755,20,179,2,9, 402,2,1,0,0,0, 1, 2031,3,1000000,4,3,128,4,500 +"6",268,2056,9,0,0,173,1831,12543,12435,163,17,1,421,15,518,3,9, 415,1,1,0,0,0, 1, 2022,3,1000000,4,3,128,4,500 +"6",23,2057,9,0,0,168,1835,12543,12435,36,17,1,463,8,475,11,9, 442,1,1,0,0,0, 1, 1994,3,1000000,4,3,128,4,500 +"6",3,2061,9,0,0,167,1836,12543,12435,95,17,1,769,11,165,12,9, 443,1,1,0,0,0, 1, 1990,3,1000000,4,3,128,4,500 +"6",354,2056,9,0,0,173,1831,12543,12435,76,17,1,433,17,505,2,9, 404,1,1,0,0,0, 1, 2033,3,1000000,4,3,128,4,500 +"6",137,2060,9,0,0,168,1834,12543,12434,162,16,2,759,16,176,6,9, 428,1,1,0,0,0, 1, 2005,3,1000000,4,3,128,4,500 +"6",216,2060,9,0,0,170,1833,12543,12434,128,17,1,747,17,188,5,9, 419,1,1,0,0,0, 1, 2015,3,1000000,4,3,128,4,500 +"6",222,2056,9,0,0,172,1830,12543,12435,161,17,1,414,13,525,5,9, 418,1,1,0,0,0, 1, 2020,3,1000000,4,3,128,4,500 +"6",160,2060,9,0,0,169,1833,12543,12435,105,17,1,743,17,191,5,9, 425,1,1,0,0,0, 1, 2008,3,1000000,4,3,128,4,500 +"6",460,2059,9,0,0,171,1833,12543,12434,161,17,2,738,18,198,3,9, 393,1,1,0,0,0, 1, 2041,3,1000000,4,3,128,4,500 +"6",305,2057,9,1,0,171,1833,12543,12435,162,17,1,393,13,545,5,9, 409,2,1,0,0,0, 1, 2028,3,1000000,4,3,128,4,500 +"6",97,2057,9,0,0,172,1831,12543,12435,101,17,1,442,13,496,5,9, 432,1,1,0,0,0, 1, 2004,3,1000000,4,3,128,4,500 +"6",264,2056,9,0,0,173,1830,12543,12435,95,17,1,403,14,536,4,9, 414,2,1,0,0,0, 1, 2024,3,1000000,4,3,128,4,500 +"6",67,2061,9,0,0,169,1834,12543,12435,97,17,1,759,14,175,9,9, 436,1,0,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 +"6",163,2060,9,0,0,169,1836,12543,12435,98,17,1,765,16,170,6,9, 425,1,1,0,0,0, 1, 2008,3,1000000,4,3,128,4,500 +"6",191,2061,9,0,0,168,1835,12543,12435,127,17,1,754,16,181,6,9, 422,2,1,0,0,0, 1, 2011,3,1000000,4,3,128,4,500 +"6",83,2057,9,0,0,170,1835,12543,12435,129,17,1,456,14,482,5,9, 434,2,1,0,0,0, 1, 2002,3,1000000,4,3,128,4,500 +"6",132,2061,9,0,0,170,1833,12543,12435,95,17,1,772,9,162,13,9, 429,1,1,0,0,0, 1, 2004,3,1000000,4,3,128,4,500 +"6",262,2058,8,0,0,168,1837,12543,12434,93,17,2,437,14,501,5,9, 414,1,1,0,0,0, 1, 2022,3,1000000,4,3,128,4,500 +"6",53,2058,9,0,0,174,1831,12543,12435,161,17,1,431,15,506,4,9, 439,1,1,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 +"6",25,2058,9,0,0,171,1833,12543,12435,94,17,1,450,11,488,8,9, 441,1,1,0,0,0, 1, 1996,3,1000000,4,3,128,4,500 +"6",94,2057,9,0,0,173,1831,12543,12435,129,17,1,420,14,518,4,9, 435,1,1,0,0,0, 1, 2002,3,1000000,4,3,128,4,500 +"6",490,2059,9,0,0,172,1831,12543,12434,159,17,2,752,18,185,2,9, 390,1,1,0,0,0, 1, 2045,3,1000000,4,3,128,4,500 +"6",195,2060,9,0,0,169,1835,12543,12435,97,17,1,751,16,184,5,9, 421,1,1,0,0,0, 1, 2013,3,1000000,4,3,128,4,500 +"6",415,2060,9,0,0,171,1833,12543,12435,161,17,1,763,16,173,5,9, 398,2,1,0,0,0, 1, 2036,3,1000000,4,3,128,4,500 +"6",277,2057,9,0,0,171,1834,12543,12435,130,17,1,393,15,546,3,9, 414,2,1,0,0,0, 1, 2023,3,1000000,4,3,128,4,500 +"6",217,2060,9,0,0,169,1835,12543,12435,95,17,1,750,15,186,6,9, 419,1,1,0,0,0, 1, 2015,3,1000000,4,3,128,4,500 +"6",235,2058,9,0,0,168,1838,12543,12435,160,17,1,429,15,509,3,9, 419,1,1,0,0,0, 1, 2018,3,1000000,4,3,128,4,500 +"6",177,2062,9,0,0,172,1832,12543,12435,161,17,1,775,12,160,10,9, 423,1,1,0,0,0, 1, 2009,3,1000000,4,3,128,4,500 +"6",350,2058,9,0,0,172,1833,12542,12435,129,17,1,424,16,514,2,9, 407,1,1,0,0,0, 1, 2030,3,1000000,4,3,128,4,500 +"6",430,2061,9,0,0,170,1834,12543,12434,160,17,2,746,16,190,6,9, 396,1,1,0,0,0, 1, 2038,3,1000000,4,3,128,4,500 +"6",445,2062,9,0,0,170,1835,12543,12435,161,17,1,755,13,179,9,9, 394,1,1,0,0,0, 1, 2039,3,1000000,4,3,128,4,500 +"6",502,2061,9,0,0,173,1832,12543,12435,160,17,1,757,14,178,8,9, 390,1,1,0,0,0, 1, 2044,3,1000000,4,3,128,4,500 +"6",68,2062,9,0,0,168,1837,12543,12435,127,17,1,761,13,174,9,9, 436,2,0,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 +"6",41,2059,9,0,0,171,1836,12543,12435,161,17,1,442,14,496,5,9, 439,1,1,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 +"6",45,2059,9,0,0,172,1833,12543,12435,37,17,1,446,14,492,5,9, 439,1,1,0,0,0, 1, 1998,3,1000000,4,3,128,4,500 +"6",353,2058,9,0,0,172,1833,12543,12435,101,17,1,410,16,529,2,9, 404,1,1,0,0,0, 1, 2033,3,1000000,4,3,128,4,500 +"6",210,2062,9,0,0,169,1835,12543,12435,163,17,1,747,17,188,5,9, 420,1,1,0,0,0, 1, 2013,3,1000000,4,3,128,4,500 +"6",509,2062,9,0,0,173,1833,12543,12435,159,17,1,761,14,174,8,9, 389,2,1,0,0,0, 1, 2045,3,1000000,4,3,128,4,500 +"6",39,2059,9,0,0,173,1832,12543,12435,161,17,1,430,15,508,4,9, 440,1,1,0,0,0, 1, 1996,3,1000000,4,3,128,4,500 +"6",209,2061,9,0,0,166,1839,12543,12435,128,17,1,749,16,186,6,9, 420,1,1,0,0,0, 1, 2014,3,1000000,4,3,128,4,500 +"6",296,2059,9,0,0,172,1833,12543,12435,161,17,1,435,14,503,5,9, 411,2,1,0,0,0, 1, 2026,3,1000000,4,3,128,4,500 +"6",492,2061,10,0,0,173,1832,12543,12435,159,16,2,756,17,181,2,9, 391,1,0,0,0,0, 1, 2044,3,1000000,4,3,128,4,500 +"6",233,2059,9,0,0,171,1835,12543,12435,161,17,1,438,13,501,5,9, 419,1,1,0,0,0, 1, 2018,3,1000000,4,3,128,4,500 +"6",91,2059,9,0,0,172,1833,12543,12435,111,17,1,434,14,504,5,9, 433,1,1,0,0,0, 1, 2003,3,1000000,4,3,128,4,500 +"6",265,2059,9,0,0,174,1833,12543,12435,162,17,1,396,12,543,5,9, 416,1,1,0,0,0, 1, 2022,3,1000000,4,3,128,4,500 +"6",479,2060,9,0,0,175,1831,12543,12435,162,17,1,446,15,492,4,9, 390,1,1,0,0,0, 1, 2046,3,1000000,4,3,128,4,500 +"6",208,2062,9,0,0,168,1838,12543,12435,128,17,1,740,16,195,6,9, 420,1,1,0,0,0, 1, 2014,3,1000000,4,3,128,4,500 +"6",401,2064,9,0,0,171,1835,12543,12435,128,17,1,752,13,182,9,9, 399,1,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 +"6",181,2063,9,0,0,170,1836,12543,12435,160,17,1,757,12,177,9,9, 423,2,1,0,0,0, 1, 2011,3,1000000,4,3,128,4,500 +"6",373,2063,9,0,0,169,1836,12543,12435,160,17,1,743,19,192,3,9, 402,1,1,0,0,0, 1, 2031,3,1000000,4,3,128,4,500 +"6",380,2059,9,0,0,172,1836,12543,12435,160,16,1,442,17,497,1,9, 403,1,1,0,0,0, 0, 2035,3,1000000,4,3,128,4,500 +"6",385,2063,9,0,0,170,1837,12543,12435,101,17,1,754,16,181,6,9, 400,2,1,0,0,0, 1, 2033,3,1000000,4,3,128,4,500 +"6",237,2060,9,0,0,170,1837,12543,12435,160,17,1,412,16,527,3,9, 417,1,1,0,0,0, 1, 2020,3,1000000,4,3,128,4,500 +"6",389,2064,9,0,0,170,1836,12543,12435,161,17,1,750,17,185,6,9, 400,1,1,0,0,0, 1, 2033,3,1000000,4,3,128,4,500 +"6",257,2059,9,0,0,174,1834,12543,12435,101,17,1,406,13,533,5,9, 414,1,1,0,0,0, 1, 2023,3,1000000,4,3,128,4,500 +"6",149,2065,9,0,0,170,1837,12543,12435,131,16,1,787,4,147,19,9, 426,1,1,0,0,0, 1, 2006,3,1000000,4,3,128,4,500 +"6",247,2061,9,0,0,171,1836,12543,12435,160,17,1,426,15,513,3,9, 418,1,1,0,0,0, 1, 2019,3,1000000,4,3,128,4,500 +"6",269,2060,9,0,0,171,1836,12543,12435,162,17,1,416,14,522,4,9, 415,1,1,0,0,0, 1, 2022,3,1000000,4,3,128,4,500 +"6",436,2065,9,0,0,172,1834,12543,12435,53,17,1,777,13,158,9,9, 395,1,1,0,0,0, 1, 2038,3,1000000,4,3,128,4,500 +"6",321,2065,9,0,0,170,1838,12543,12435,101,17,1,739,19,196,3,9, 407,1,1,0,0,0, 1, 2026,3,1000000,4,3,128,4,500 +"6",140,2065,9,0,0,168,1839,12543,12435,162,17,1,752,16,183,6,9, 428,1,1,0,0,0, 1, 2005,3,1000000,4,3,128,4,500 +"6",253,2061,9,0,0,170,1837,12543,12435,160,17,1,408,13,531,5,9, 415,1,1,0,0,0, 1, 2022,3,1000000,4,3,128,4,500 +"6",423,2066,9,0,0,171,1834,12543,12435,159,17,1,780,18,153,6,9, 398,2,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 +"6",124,2065,10,0,0,170,1838,12543,12435,161,17,1,746,15,189,6,9, 429,1,1,0,0,0, 1, 2004,3,1000000,4,3,128,4,500 +"6",468,2060,9,0,0,174,1833,12543,12435,53,17,1,414,14,526,4,9, 395,1,1,0,0,0, 1, 2043,3,1000000,4,3,128,4,500 +"6",309,2062,9,0,0,171,1834,12543,12435,160,17,1,443,14,494,5,9, 411,1,1,0,0,0, 1, 2025,3,1000000,4,3,128,4,500 +"6",496,2065,9,0,0,175,1833,12543,12435,160,16,1,796,19,139,3,9, 391,1,1,0,0,0, 1, 2042,3,1000000,4,3,128,4,500 +"6",378,2062,9,0,0,172,1837,12543,12435,41,17,1,453,17,486,1,9, 403,1,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 +"6",507,2066,9,0,0,173,1836,12543,12435,160,17,1,757,15,178,8,9, 389,1,1,0,0,0, 1, 2045,3,1000000,4,3,128,4,500 +"6",206,2064,9,0,0,169,1839,12543,12435,162,17,1,747,16,189,6,9, 421,2,1,0,0,0, 1, 2013,3,1000000,4,3,128,4,500 +"6",433,2066,9,0,0,171,1838,12543,12435,161,16,1,776,13,159,9,9, 396,1,1,0,0,0, 1, 2037,3,1000000,4,3,128,4,500 +"6",405,2066,9,0,0,171,1837,12543,12435,130,17,1,751,13,183,9,9, 399,2,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 +"6",38,2062,9,0,0,174,1835,12543,12434,94,17,2,411,15,527,4,9, 439,2,1,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 +"6",138,2066,10,0,0,167,1841,12543,12435,163,17,1,757,15,178,6,9, 428,1,1,0,0,0, 1, 2005,3,1000000,4,3,128,4,500 +"6",161,2067,9,0,0,167,1842,12543,12435,100,17,1,742,17,192,6,9, 425,1,1,0,0,0, 1, 2008,3,1000000,4,3,128,4,500 +"6",243,2062,9,0,0,169,1840,12543,12435,160,17,1,433,15,505,3,9, 418,1,1,0,0,0, 1, 2019,3,1000000,4,3,128,4,500 +"6",364,2066,9,0,0,166,1842,12543,12435,160,17,1,750,19,185,3,9, 403,1,1,0,0,0, 1, 2030,3,1000000,4,3,128,4,500 +"6",221,2062,9,0,0,173,1835,12543,12435,68,17,1,433,14,506,4,9, 420,1,1,0,0,0, 1, 2017,3,1000000,4,3,128,4,500 +"6",102,2062,9,0,0,172,1837,12543,12435,94,17,1,429,13,510,5,9, 434,2,1,0,0,0, 1, 2003,3,1000000,4,3,128,4,500 +"6",133,2066,9,0,0,168,1841,12543,12435,163,17,1,748,9,186,13,9, 428,1,1,0,0,0, 1, 2004,3,1000000,4,3,128,4,500 +"6",77,2063,8,0,0,169,1839,12543,12435,162,17,1,461,11,477,8,9, 435,1,1,0,0,0, 1, 2002,3,1000000,4,3,128,4,500 +"6",325,2062,9,0,0,173,1836,12542,12435,162,17,1,419,16,520,2,9, 409,1,1,0,0,0, 1, 2028,3,1000000,4,3,128,4,500 +"6",26,2062,9,0,0,169,1840,12543,12435,41,17,1,433,9,506,9,9, 441,1,1,0,0,0, 1, 1996,3,1000000,4,3,128,4,500 +"6",275,2062,9,0,0,169,1839,12543,12435,36,17,1,422,14,517,4,9, 412,1,1,0,0,0, 1, 2025,3,1000000,4,3,128,4,500 +"6",381,2061,9,0,0,173,1836,12543,12435,160,17,1,424,16,516,1,9, 401,1,1,0,0,0, 1, 2038,3,1000000,4,3,128,4,500 +"6",391,2066,9,0,0,170,1839,12543,12435,129,17,1,751,17,183,6,9, 400,2,0,0,0,0, 1, 2033,3,1000000,4,3,128,4,500 +"6",135,2067,9,0,0,168,1841,12543,12435,128,17,1,763,9,171,13,9, 428,2,0,0,0,0, 1, 2004,3,1000000,4,3,128,4,500 +"6",319,2063,9,0,0,174,1836,12543,12435,127,17,1,417,14,521,4,9, 407,2,1,0,0,0, 1, 2030,3,1000000,4,3,128,4,500 +"6",429,2067,9,0,1,172,1837,12543,12434,37,17,1,746,16,189,5,9, 396,2,1,0,0,0, 1, 2037,3,1000000,4,3,128,4,500 +"6",27,2064,9,0,0,174,1837,12543,12435,40,17,1,450,11,488,8,9, 440,1,1,0,0,0, 1, 1996,3,1000000,4,3,128,4,500 +"6",318,2064,9,0,0,172,1838,12543,12435,160,17,1,426,15,512,5,9, 408,1,1,0,0,0, 1, 2028,3,1000000,4,3,128,4,500 +"6",424,2067,9,0,0,172,1838,12543,12435,159,16,1,756,16,179,6,9, 396,1,1,0,0,0, 1, 2037,3,1000000,4,3,128,4,500 +"6",254,2064,9,0,0,170,1839,12543,12435,160,17,1,426,15,512,3,9, 417,1,1,0,0,0, 1, 2020,3,1000000,4,3,128,4,500 +"6",303,2064,8,0,0,170,1839,12543,12435,162,17,1,420,14,518,5,9, 410,1,1,0,0,0, 1, 2027,3,1000000,4,3,128,4,500 +"6",51,2065,9,0,0,170,1840,12543,12435,161,17,1,446,14,491,5,9, 440,2,1,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 +"6",122,2068,9,0,0,167,1844,12543,12435,41,17,1,750,16,185,6,9, 430,1,1,0,0,0, 1, 2004,3,1000000,4,3,128,4,500 +"6",346,2064,9,0,0,172,1840,12543,12435,17,17,1,436,16,502,2,9, 407,1,1,0,0,0, 1, 2030,3,1000000,4,3,128,4,500 +"6",128,2068,9,0,0,170,1840,12543,12435,105,17,1,743,16,192,6,9, 429,2,1,0,0,0, 1, 2005,3,1000000,4,3,128,4,500 +"6",419,2068,9,0,0,171,1840,12543,12435,97,17,1,752,16,183,6,9, 396,2,1,0,0,0, 1, 2037,3,1000000,4,3,128,4,500 +"6",304,2065,9,0,0,171,1839,12543,12435,160,17,1,425,14,513,5,9, 410,2,0,0,0,0, 1, 2026,3,1000000,4,3,128,4,500 +"6",416,2068,9,0,0,173,1839,12543,12435,105,17,1,760,16,176,6,9, 396,2,1,0,0,0, 1, 2037,3,1000000,4,3,128,4,500 +"6",352,2064,9,0,0,167,1844,12543,12435,104,17,1,439,15,501,2,9, 405,1,1,0,0,0, 1, 2033,3,1000000,4,3,128,4,500 +"6",486,2067,9,0,0,175,1838,12542,12435,94,17,1,754,18,182,2,9, 390,2,1,0,0,0, 1, 2044,3,1000000,4,3,128,4,500 +"6",329,2065,9,0,0,174,1838,12543,12435,162,17,1,410,16,529,2,9, 408,1,1,0,0,0, 1, 2029,3,1000000,4,3,128,4,500 +"6",297,2065,9,0,0,172,1840,12543,12435,161,17,1,410,13,529,5,9, 411,2,1,0,0,0, 1, 2026,3,1000000,4,3,128,4,500 +"6",443,2070,9,0,0,172,1840,12543,12435,161,17,1,759,13,176,9,9, 394,1,1,0,0,0, 1, 2038,3,1000000,4,3,128,4,500 +"6",475,2066,9,0,0,176,1837,12543,12435,128,17,1,442,14,497,4,9, 393,1,1,0,0,0, 1, 2044,3,1000000,4,3,128,4,500 +"6",151,2070,9,0,0,169,1843,12543,12435,36,17,1,816,4,118,19,9, 426,1,1,0,0,0, 1, 2006,3,1000000,4,3,128,4,500 +"6",332,2066,9,0,0,171,1841,12543,12435,162,16,1,435,17,503,2,9, 407,1,1,0,0,0, 1, 2030,3,1000000,4,3,128,4,500 +"6",255,2067,9,0,0,171,1841,12543,12435,160,17,1,465,16,473,3,9, 415,1,1,0,0,0, 1, 2022,3,1000000,4,3,128,4,500 +"6",307,2066,9,0,0,172,1840,12543,12435,161,17,1,404,14,534,5,9, 411,2,1,0,0,0, 1, 2026,3,1000000,4,3,128,4,500 +"6",130,2070,9,0,0,170,1842,12543,12434,76,17,1,762,9,172,13,9, 428,1,1,0,0,0, 1, 2004,3,1000000,4,3,128,4,500 +"6",359,2066,9,0,0,172,1840,12543,12435,160,17,1,424,17,515,2,9, 403,1,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 +"6",501,2070,9,0,0,172,1840,12543,12435,160,17,1,756,15,179,8,9, 390,1,1,0,0,0, 1, 2043,3,1000000,4,3,128,4,500 +"6",348,2067,9,0,0,171,1843,12543,12435,127,17,1,439,16,499,2,9, 407,1,1,0,0,0, 1, 2030,3,1000000,4,3,128,4,500 +"6",462,2065,9,0,0,174,1839,12543,12435,161,17,1,438,15,501,2,9, 395,1,1,0,0,0, 1, 2044,3,1000000,4,3,128,4,500 +"6",261,2067,9,0,0,170,1844,12543,12435,162,17,1,405,14,534,4,9, 416,2,1,0,0,0, 1, 2021,3,1000000,4,3,128,4,500 +"6",485,2069,9,0,0,173,1840,12543,12435,158,17,1,752,18,185,3,9, 390,1,1,0,0,0, 1, 2044,3,1000000,4,3,128,4,500 +"6",500,2070,9,0,0,171,1842,12543,12435,53,17,1,758,13,178,8,9, 390,2,1,0,0,0, 1, 2044,3,1000000,4,3,128,4,500 +"6",49,2068,9,0,0,171,1843,12543,12435,161,17,1,440,14,498,5,9, 440,1,1,0,0,0, 1, 1996,3,1000000,4,3,128,4,500 +"6",96,2067,9,0,0,174,1841,12543,12435,105,17,1,423,12,516,5,9, 433,1,1,0,0,0, 1, 2005,3,1000000,4,3,128,4,500 +"6",310,2068,9,0,0,175,1841,12543,12435,160,17,1,425,14,513,5,9, 409,1,1,0,0,0, 1, 2028,3,1000000,4,3,128,4,500 +"6",42,2069,9,0,0,173,1842,12543,12435,161,17,1,422,13,516,5,9, 440,1,1,0,0,0, 1, 1996,3,1000000,4,3,128,4,500 +"6",422,2072,9,0,0,171,1844,12543,12434,94,17,1,750,17,185,5,9, 396,1,1,0,0,0, 1, 2037,3,1000000,4,3,128,4,500 +"6",474,2072,18,0,0,177,1838,12543,12435,17,17,1,749,9,186,4,9, 391,1,1,0,0,0, 1, 2042,3,1000000,4,3,128,4,500 +"6",478,2069,9,0,0,179,1838,12543,12435,162,17,1,408,14,531,4,9, 392,1,1,0,0,0, 1, 2045,3,1000000,4,3,128,4,500 +"6",295,2070,9,0,0,173,1841,12543,12435,160,17,1,460,14,478,5,9, 411,1,1,0,0,0, 1, 2026,3,1000000,4,3,128,4,500 +"6",291,2069,9,0,0,172,1845,12543,12435,97,17,1,420,13,519,5,9, 411,1,0,0,0,0, 1, 2026,3,1000000,4,3,128,4,500 +"6",35,2070,9,0,0,174,1843,12543,12435,97,17,1,442,13,496,5,9, 441,1,1,0,0,0, 1, 1996,3,1000000,4,3,128,4,500 +"6",464,2068,9,0,0,175,1842,12543,12435,128,17,1,415,14,525,2,9, 395,1,1,0,0,0, 1, 2044,3,1000000,4,3,128,4,500 +"6",69,2074,18,0,0,178,1839,12543,12435,162,17,1,779,4,155,9,9, 437,1,1,0,0,0, 1, 1996,3,1000000,4,3,128,4,500 +"6",112,2071,9,0,0,173,1846,12543,12435,160,17,1,420,13,519,5,9, 433,1,1,0,0,0, 1, 2005,3,1000000,4,3,128,4,500 +"6",126,2076,9,0,0,168,1847,12543,12435,161,17,1,750,17,184,6,9, 429,2,1,0,0,0, 1, 2004,3,1000000,4,3,128,4,500 +"6",487,2074,9,0,0,175,1842,12543,12435,159,17,1,756,18,180,2,9, 391,1,1,0,0,0, 1, 2044,3,1000000,4,3,128,4,500 +"6",108,2071,9,0,0,172,1847,12543,12435,160,17,1,422,10,517,8,9, 432,1,1,0,0,0, 1, 2006,3,1000000,4,3,128,4,500 +"6",467,2072,9,0,0,174,1845,12543,12435,129,17,1,437,14,501,4,9, 392,2,1,0,0,0, 1, 2045,3,1000000,4,3,128,4,500 +"6",20,2079,9,0,0,168,1854,12543,12435,52,17,1,752,11,182,12,9, 441,1,1,0,0,0, 1, 1991,3,1000000,4,3,128,4,500 +"6",279,2076,9,0,0,170,1853,12543,12435,37,17,1,391,14,547,4,9, 414,1,1,0,0,0, 1, 2023,3,1000000,4,3,128,4,500 +"6",75,2081,18,0,0,174,1849,12543,12435,162,17,1,780,5,154,9,9, 435,1,1,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 +"6",28,2077,9,0,0,171,1853,12543,12435,97,17,1,415,8,525,9,9, 442,1,1,0,0,0, 1, 1996,3,1000000,4,3,128,4,500 +"6",201,2085,9,0,0,168,1861,12543,12435,162,17,1,759,16,176,6,9, 421,1,1,0,0,0, 1, 2013,3,1000000,4,3,128,4,500 +"6",404,2085,9,0,0,194,1834,12543,12434,53,17,2,776,12,159,10,9, 399,1,1,0,0,0, 1, 2035,3,1000000,4,3,128,4,500 +"6",320,2086,18,0,0,171,1858,12543,12435,104,17,1,764,7,170,6,9, 408,1,1,0,0,0, 1, 2025,3,1000000,4,3,128,4,500 +"6",258,2086,9,0,0,182,1851,12543,12435,76,17,1,446,12,492,5,9, 415,2,1,0,0,0, 1, 2022,3,1000000,4,3,128,4,500 +"6",472,2102,0,0,0,96,1952,12542,12436,128,17,1,445,24,495,5,9, 394,1,1,0,0,0, 0, 2043,3,1000000,4,3,128,4,500 +"6",290,2103,8,0,0,171,1877,12543,12435,76,17,1,427,14,511,5,9, 411,1,1,0,0,0, 1, 2025,3,1000000,4,3,128,4,500 +"6",481,2104,0,0,0,99,1955,12542,12436,100,17,1,406,24,535,2,9, 393,1,1,0,0,0, 0, 2047,3,1000000,4,3,128,4,500 +"6",455,2107,0,0,0,96,1958,12542,12436,128,17,1,373,24,568,2,9, 395,2,1,0,0,0, 0, 2044,3,1000000,4,3,128,4,500 +"6",377,2110,0,0,0,95,1961,12542,12436,160,17,2,393,26,547,2,9, 403,2,1,0,0,0, 0, 2035,3,1000000,4,3,128,4,500 +"6",331,2112,0,0,0,96,1963,12542,12436,162,17,1,391,25,549,2,9, 409,1,1,0,0,0, 0, 2030,3,1000000,4,3,128,4,500 +"6",236,2115,0,0,0,93,1969,12542,12436,161,17,1,383,23,557,5,9, 419,1,1,0,0,0, 0, 2019,3,1000000,4,3,128,4,500 +"6",121,2116,0,0,0,95,1968,12542,12436,160,17,1,399,22,541,5,9, 431,2,1,0,0,0, 0, 2007,3,1000000,4,3,128,4,500 +"6",81,2118,0,0,0,94,1969,12542,12436,128,17,1,444,23,495,5,9, 436,1,1,0,0,0, 0, 2001,3,1000000,4,3,128,4,500 +"6",313,2118,0,0,0,96,1969,12542,12436,160,17,1,377,22,563,5,9, 411,2,1,0,0,0, 0, 2028,3,1000000,4,3,128,4,500 +"6",337,2119,0,0,0,93,1973,12542,12436,128,17,1,370,25,570,2,9, 408,1,1,0,0,0, 0, 2031,3,1000000,4,3,128,4,500 +"6",328,2120,0,0,0,95,1972,12542,12436,127,17,1,388,25,552,2,9, 408,1,1,0,0,0, 0, 2030,3,1000000,4,3,128,4,500 +"6",46,2126,9,0,0,173,1900,12543,12435,161,17,1,419,13,519,5,9, 440,1,1,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 +"6",432,2171,9,0,0,172,1941,12543,12434,160,17,2,754,16,181,6,9, 395,2,0,0,0,0, 1, 2038,3,1000000,4,3,128,4,500 +"6",188,2171,9,0,0,170,1944,12543,12435,160,17,1,740,18,195,4,9, 422,1,1,0,0,0, 1, 2011,3,1000000,4,3,128,4,500 +"6",197,2171,9,0,0,166,1950,12543,12435,162,17,1,711,15,225,5,9, 421,2,1,0,0,0, 1, 2014,3,1000000,4,3,128,4,500 +"6",198,2173,9,0,0,169,1948,12543,12435,94,17,1,715,16,221,5,9, 421,1,1,0,0,0, 1, 2013,3,1000000,4,3,128,4,500 +"6",495,2172,9,0,0,172,1944,12543,12435,160,17,1,757,18,179,2,9, 390,1,1,0,0,0, 1, 2045,3,1000000,4,3,128,4,500 +"6",471,2171,9,0,0,175,1942,12543,12435,128,17,1,453,14,486,4,9, 392,1,1,0,0,0, 1, 2045,3,1000000,4,3,128,4,500 +"6",205,2174,9,0,0,167,1951,12543,12435,162,16,1,717,15,219,5,9, 420,1,1,0,0,0, 1, 2014,3,1000000,4,3,128,4,500 +"6",187,2177,9,0,0,167,1954,12543,12435,160,17,1,753,16,181,6,9, 423,2,0,0,0,0, 1, 2011,3,1000000,4,3,128,4,500 +"6",185,2178,9,0,0,167,1954,12543,12435,161,17,1,726,16,210,5,9, 422,1,1,0,0,0, 1, 2012,3,1000000,4,3,128,4,500 +"6",154,2179,9,0,0,170,1951,12543,12435,41,17,1,753,16,182,6,9, 426,1,1,0,0,0, 1, 2007,3,1000000,4,3,128,4,500 +"6",402,2178,9,0,0,171,1951,12543,12435,162,17,1,725,13,211,8,9, 398,1,1,0,0,0, 1, 2035,3,1000000,4,3,128,4,500 +"6",47,2176,9,0,0,171,1952,12543,12435,160,17,1,391,13,548,5,9, 438,1,1,0,0,0, 1, 1999,3,1000000,4,3,128,4,500 +"6",125,2180,9,0,0,170,1952,12543,12435,160,17,1,721,16,215,5,9, 429,1,1,0,0,0, 1, 2005,3,1000000,4,3,128,4,500 +"6",59,2180,9,0,0,169,1953,12543,12435,161,17,1,711,15,224,6,9, 437,1,1,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 +"6",438,2180,9,0,0,170,1952,12543,12435,160,17,1,778,13,157,9,9, 395,1,1,0,0,0, 1, 2038,3,1000000,4,3,128,4,500 +"6",499,2180,9,0,0,177,1947,12543,12435,159,17,1,759,14,176,8,9, 390,2,1,0,0,0, 1, 2044,3,1000000,4,3,128,4,500 +"6",238,2177,9,0,0,169,1955,12543,12435,161,17,1,390,15,550,3,9, 416,2,1,0,0,0, 1, 2021,3,1000000,4,3,128,4,500 +"6",213,2180,9,0,0,169,1954,12543,12435,162,17,1,710,17,226,3,9, 419,1,1,0,0,0, 1, 2015,3,1000000,4,3,128,4,500 +"6",371,2181,9,0,0,170,1955,12543,12435,160,17,1,708,19,228,3,9, 402,1,1,0,0,0, 1, 2032,3,1000000,4,3,128,4,500 +"6",396,2181,9,0,0,171,1953,12543,12435,162,16,1,717,16,219,5,9, 400,2,1,0,0,0, 1, 2035,3,1000000,4,3,128,4,500 +"6",484,2180,10,0,0,172,1952,12543,12435,159,16,1,732,17,205,2,9, 390,1,1,0,0,0, 1, 2045,3,1000000,4,3,128,4,500 +"6",411,2182,9,0,0,171,1954,12543,12435,40,17,1,764,16,171,6,9, 398,2,1,0,0,0, 1, 2035,3,1000000,4,3,128,4,500 +"6",113,2178,9,0,0,169,1955,12543,12435,160,17,1,394,13,545,4,9, 431,1,1,0,0,0, 1, 2007,3,1000000,4,3,128,4,500 +"6",143,2182,9,0,0,167,1958,12543,12435,129,17,1,727,16,208,5,9, 427,1,1,0,0,0, 1, 2007,3,1000000,4,3,128,4,500 +"6",57,2182,9,0,0,171,1957,12543,12435,161,17,1,711,15,225,6,9, 437,2,1,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 +"6",431,2183,9,0,0,171,1955,12543,12435,161,17,1,752,16,183,6,9, 396,2,1,0,0,0, 1, 2038,3,1000000,4,3,128,4,500 +"6",104,2179,9,0,0,171,1955,12543,12435,160,17,1,379,13,560,5,9, 432,1,1,0,0,0, 1, 2006,3,1000000,4,3,128,4,500 +"6",123,2183,9,0,0,170,1956,12543,12435,161,17,1,719,15,217,5,9, 429,1,1,0,0,0, 1, 2005,3,1000000,4,3,128,4,500 +"6",10,2184,9,0,0,166,1960,12543,12435,162,17,1,761,12,173,10,9, 442,1,1,0,0,0, 1, 1991,3,1000000,4,3,128,4,500 +"6",250,2180,9,0,0,170,1958,12543,12435,41,17,1,431,14,508,4,9, 417,1,1,0,0,0, 1, 2020,3,1000000,4,3,128,4,500 +"6",229,2179,9,0,0,169,1959,12543,12435,161,17,1,374,12,566,5,9, 418,1,1,0,0,0, 1, 2020,3,1000000,4,3,128,4,500 +"6",61,2183,9,0,0,170,1958,12543,12435,160,17,1,718,15,217,6,9, 437,1,1,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 +"6",207,2183,9,0,0,170,1957,12543,12435,162,17,1,717,15,219,5,9, 420,1,1,0,0,0, 1, 2014,3,1000000,4,3,128,4,500 +"6",134,2184,9,0,0,170,1956,12543,12435,94,17,1,724,9,211,13,9, 428,1,1,0,0,0, 1, 2006,3,1000000,4,3,128,4,500 +"6",115,2180,9,0,0,173,1954,12543,12435,160,17,1,388,13,552,4,9, 431,1,1,0,0,0, 1, 2008,3,1000000,4,3,128,4,500 +"6",107,2181,9,0,0,173,1955,12543,12435,160,17,1,398,10,541,8,9, 432,1,1,0,0,0, 1, 2006,3,1000000,4,3,128,4,500 +"6",231,2180,9,0,0,173,1954,12543,12435,160,17,1,378,14,562,2,9, 418,1,1,0,0,0, 1, 2020,3,1000000,4,3,128,4,500 +"6",178,2184,9,0,0,171,1957,12543,12435,128,17,1,749,11,187,10,9, 423,1,1,0,0,0, 1, 2011,3,1000000,4,3,128,4,500 +"6",227,2180,9,0,0,171,1957,12543,12435,97,17,1,374,14,565,3,9, 418,1,1,0,0,0, 1, 2020,3,1000000,4,3,128,4,500 +"6",48,2181,9,0,0,172,1956,12543,12435,161,16,1,380,13,560,5,9, 438,1,1,0,0,0, 1, 2000,3,1000000,4,3,128,4,500 +"6",111,2181,9,0,0,172,1956,12543,12435,160,17,1,396,10,543,8,9, 431,1,1,0,0,0, 1, 2007,3,1000000,4,3,128,4,500 +"6",446,2184,18,0,0,176,1953,12543,12435,160,17,1,755,3,181,8,9, 395,1,1,0,0,0, 1, 2039,3,1000000,4,3,128,4,500 +"6",54,2181,9,0,0,173,1956,12543,12435,160,17,1,367,14,573,3,9, 437,1,1,0,0,0, 1, 2000,3,1000000,4,3,128,4,500 +"6",16,2181,9,0,0,171,1957,12543,12435,36,17,1,405,9,534,9,9, 442,2,1,0,0,0, 1, 1996,3,1000000,4,3,128,4,500 +"6",176,2185,9,0,0,170,1958,12543,12435,161,17,1,718,17,218,4,9, 423,1,1,0,0,0, 1, 2011,3,1000000,4,3,128,4,500 +"6",118,2181,9,0,0,173,1957,12543,12435,160,17,1,367,13,573,4,9, 430,1,1,0,0,0, 1, 2009,3,1000000,4,3,128,4,500 +"6",459,2184,9,0,0,173,1956,12542,12435,161,17,1,715,17,222,3,9, 393,1,1,0,0,0, 1, 2043,3,1000000,4,3,128,4,500 +"6",71,2186,9,0,0,169,1959,12543,12435,128,17,1,728,13,207,8,9, 436,2,1,0,0,0, 1, 1998,3,1000000,4,3,128,4,500 +"6",168,2186,9,0,0,169,1959,12542,12435,161,17,1,725,15,210,6,9, 424,1,1,0,0,0, 1, 2009,3,1000000,4,3,128,4,500 +"6",338,2181,9,0,0,172,1958,12543,12435,163,17,1,396,15,544,2,9, 406,1,1,0,0,0, 1, 2033,3,1000000,4,3,128,4,500 +"6",370,2186,9,0,0,168,1960,12543,12435,160,17,1,720,19,216,3,9, 402,1,1,0,0,0, 1, 2032,3,1000000,4,3,128,4,500 +"6",274,2182,9,0,0,172,1958,12543,12435,163,17,1,394,14,545,3,9, 413,1,1,0,0,0, 1, 2025,3,1000000,4,3,128,4,500 +"6",164,2186,9,0,0,168,1961,12543,12435,161,17,1,735,15,201,6,9, 424,1,1,0,0,0, 1, 2009,3,1000000,4,3,128,4,500 +"6",355,2182,9,0,0,173,1957,12543,12435,98,17,1,364,16,575,2,9, 404,1,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 +"6",60,2186,9,0,0,172,1959,12543,12435,160,17,1,712,15,224,6,9, 437,1,1,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 +"6",395,2185,9,0,0,172,1958,12543,12435,130,17,1,724,15,212,5,9, 399,2,1,0,0,0, 1, 2035,3,1000000,4,3,128,4,500 +"6",400,2185,9,0,0,171,1958,12543,12435,37,17,1,728,15,209,5,9, 399,2,1,0,0,0, 1, 2036,3,1000000,4,3,128,4,500 +"6",232,2182,9,0,0,172,1958,12543,12435,161,17,1,374,13,566,3,9, 417,1,1,0,0,0, 1, 2022,3,1000000,4,3,128,4,500 +"6",152,2187,9,0,0,170,1960,12543,12435,95,17,1,790,3,144,19,9, 426,2,1,0,0,0, 1, 2007,3,1000000,4,3,128,4,500 +"6",175,2186,9,0,0,170,1960,12543,12435,161,17,1,726,15,209,5,9, 423,1,1,0,0,0, 1, 2011,3,1000000,4,3,128,4,500 +"6",220,2182,8,0,0,174,1956,12543,12435,128,17,1,387,14,553,4,9, 419,1,1,0,0,0, 1, 2020,3,1000000,4,3,128,4,500 +"6",170,2186,9,0,0,170,1960,12543,12435,161,17,1,708,15,227,5,9, 424,1,1,0,0,0, 1, 2010,3,1000000,4,3,128,4,500 +"6",174,2186,9,0,0,168,1962,12543,12435,161,17,1,709,15,227,5,9, 423,1,1,0,0,0, 1, 2011,3,1000000,4,3,128,4,500 +"6",85,2187,18,0,0,172,1959,12543,12435,162,17,1,749,6,186,6,9, 434,1,1,0,0,0, 1, 2000,3,1000000,4,3,128,4,500 +"6",56,2187,9,0,0,168,1961,12543,12435,160,17,1,707,17,228,5,9, 437,1,1,0,0,0, 1, 1996,3,1000000,4,3,128,4,500 +"6",234,2182,9,0,0,173,1958,12543,12435,161,17,1,370,14,571,2,9, 417,1,1,0,0,0, 0, 2022,3,1000000,4,3,128,4,500 +"6",226,2182,9,0,0,170,1960,12543,12435,77,17,1,363,12,577,4,9, 418,1,1,0,0,0, 1, 2021,3,1000000,4,3,128,4,500 +"6",349,2182,12,0,0,174,1957,12543,12435,68,17,1,375,12,565,2,9, 405,1,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 +"6",418,2187,9,0,0,172,1958,12543,12435,76,17,1,712,16,224,5,9, 396,1,1,0,0,0, 1, 2038,3,1000000,4,3,128,4,500 +"6",225,2183,9,0,0,170,1961,12543,12435,101,17,1,382,12,558,4,9, 418,1,1,0,0,0, 1, 2020,3,1000000,4,3,128,4,500 +"6",477,2183,9,0,0,175,1956,12543,12435,68,17,1,403,13,536,4,9, 392,1,1,0,0,0, 1, 2046,3,1000000,4,3,128,4,500 +"6",392,2187,11,0,0,170,1960,12543,12435,96,17,1,723,14,212,6,9, 400,2,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 +"6",103,2184,9,0,0,172,1959,12543,12435,160,17,1,398,13,541,5,9, 432,1,1,0,0,0, 1, 2006,3,1000000,4,3,128,4,500 +"6",171,2187,9,0,0,170,1961,12543,12435,160,17,1,720,15,216,5,9, 424,1,1,0,0,0, 1, 2010,3,1000000,4,3,128,4,500 +"6",173,2187,9,0,0,168,1962,12542,12435,38,17,1,721,15,214,5,9, 424,1,1,0,0,0, 1, 2011,3,1000000,4,3,128,4,500 +"6",29,2185,9,0,0,172,1960,12543,12435,35,17,1,407,11,532,7,9, 441,1,1,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 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2028,3,1000000,4,3,128,4,500 +"6",390,2189,9,0,0,173,1961,12543,12435,94,17,1,708,16,227,5,9, 400,1,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 +"6",450,2184,9,0,0,176,1957,12543,12435,76,17,1,372,14,569,2,9, 393,1,1,0,0,0, 1, 2046,3,1000000,4,3,128,4,500 +"6",182,2190,9,0,0,171,1961,12543,12435,162,17,1,731,13,204,9,9, 422,1,1,0,0,0, 1, 2011,3,1000000,4,3,128,4,500 +"6",456,2188,9,0,0,173,1960,12543,12435,127,17,1,719,17,218,3,9, 393,1,1,0,0,0, 1, 2042,3,1000000,4,3,128,4,500 +"6",142,2189,9,0,0,168,1963,12543,12435,162,17,1,717,16,219,5,9, 427,1,1,0,0,0, 1, 2007,3,1000000,4,3,128,4,500 +"6",498,2189,9,0,0,172,1961,12543,12435,160,17,1,732,13,204,8,9, 389,1,1,0,0,0, 1, 2045,3,1000000,4,3,128,4,500 +"6",489,2188,10,0,0,173,1959,12543,12435,159,17,1,726,17,211,2,9, 390,1,1,0,0,0, 1, 2045,3,1000000,4,3,128,4,500 +"6",393,2188,9,0,0,170,1962,12542,12435,162,17,1,708,15,229,5,9, 400,1,1,0,0,0, 1, 2035,3,1000000,4,3,128,4,500 +"6",493,2189,9,0,0,174,1960,12543,12435,160,17,1,756,19,180,2,9, 390,1,1,0,0,0, 1, 2044,3,1000000,4,3,128,4,500 +"6",397,2189,9,0,0,170,1962,12543,12435,162,17,1,706,15,230,5,9, 400,1,1,0,0,0, 1, 2035,3,1000000,4,3,128,4,500 +"6",150,2190,9,0,0,172,1961,12543,12435,162,17,1,761,3,175,19,9, 426,2,1,0,0,0, 1, 2008,3,1000000,4,3,128,4,500 +"6",292,2186,9,0,0,172,1961,12543,12435,160,17,1,393,13,546,5,9, 410,2,1,0,0,0, 1, 2027,3,1000000,4,3,128,4,500 +"6",413,2189,10,0,0,171,1962,12543,12435,35,17,1,733,14,203,5,9, 398,1,1,0,0,0, 1, 2037,3,1000000,4,3,128,4,500 +"6",351,2186,9,0,0,174,1959,12543,12435,163,17,1,379,16,561,1,9, 404,1,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 +"6",505,2190,9,0,0,173,1961,12543,12435,160,17,1,733,13,202,8,9, 388,1,1,0,0,0, 1, 2046,3,1000000,4,3,128,4,500 +"6",246,2186,9,0,0,171,1962,12543,12435,161,17,1,393,15,547,3,9, 416,1,1,0,0,0, 1, 2022,3,1000000,4,3,128,4,500 +"6",230,2186,9,0,0,174,1960,12543,12435,94,17,1,360,12,580,5,9, 418,1,1,0,0,0, 1, 2021,3,1000000,4,3,128,4,500 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2033,3,1000000,4,3,128,4,500 +"6",224,2186,9,0,0,168,1966,12543,12435,104,17,1,360,12,580,4,9, 418,1,0,0,0,0, 1, 2021,3,1000000,4,3,128,4,500 +"6",95,2187,9,0,0,173,1961,12543,12435,161,17,1,394,13,545,5,9, 433,1,1,0,0,0, 1, 2005,3,1000000,4,3,128,4,500 +"6",347,2186,9,0,0,174,1960,12543,12435,111,17,1,374,16,566,2,9, 405,1,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 +"6",281,2187,9,0,0,174,1961,12543,12435,94,17,1,361,12,579,5,9, 411,1,1,0,0,0, 1, 2027,3,1000000,4,3,128,4,500 +"6",141,2190,9,0,0,169,1964,12543,12435,162,17,1,722,15,215,5,9, 427,2,1,0,0,0, 1, 2008,3,1000000,4,3,128,4,500 +"6",426,2191,9,0,0,171,1964,12543,12435,159,17,1,709,15,226,5,9, 395,1,1,0,0,0, 1, 2039,3,1000000,4,3,128,4,500 +"6",406,2191,9,0,0,170,1964,12543,12435,162,17,1,730,12,205,9,9, 398,1,1,0,0,0, 1, 2036,3,1000000,4,3,128,4,500 +"6",263,2187,9,0,0,172,1963,12543,12435,107,17,1,390,13,549,5,9, 413,1,1,0,0,0, 1, 2024,3,1000000,4,3,128,4,500 +"6",463,2186,9,0,0,177,1959,12543,12435,162,17,1,414,15,527,2,9, 392,1,1,0,0,0, 1, 2046,3,1000000,4,3,128,4,500 +"6",442,2191,9,0,0,172,1962,12543,12435,41,17,1,732,13,204,8,9, 394,1,1,0,0,0, 1, 2040,3,1000000,4,3,128,4,500 +"6",473,2191,9,0,0,173,1962,12543,12435,94,17,1,719,16,217,4,9, 392,1,1,0,0,0, 1, 2042,3,1000000,4,3,128,4,500 +"6",324,2188,9,0,0,172,1964,12543,12435,127,17,1,400,16,540,1,9, 407,1,1,0,0,0, 1, 2031,3,1000000,4,3,128,4,500 +"6",399,2191,9,0,0,172,1963,12543,12435,129,17,1,727,15,209,5,9, 399,1,1,0,0,0, 1, 2036,3,1000000,4,3,128,4,500 +"6",449,2186,9,0,0,177,1960,12543,12435,101,17,1,394,14,547,2,9, 394,1,1,0,0,0, 1, 2046,3,1000000,4,3,128,4,500 +"6",93,2188,9,0,0,174,1960,12543,12435,68,17,1,399,13,540,4,9, 433,1,1,0,0,0, 1, 2005,3,1000000,4,3,128,4,500 +"6",408,2193,9,0,0,171,1964,12543,12435,96,17,1,777,13,158,10,9, 399,2,0,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 +"6",340,2188,9,0,0,172,1963,12543,12435,53,17,1,389,16,551,2,9, 406,1,1,0,0,0, 1, 2032,3,1000000,4,3,128,4,500 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2003,3,1000000,4,3,128,4,500 +"6",116,2189,9,0,0,173,1964,12543,12435,53,17,1,374,12,566,5,9, 431,1,1,0,0,0, 1, 2008,3,1000000,4,3,128,4,500 +"6",410,2193,9,0,0,171,1965,12543,12435,41,17,1,721,15,216,5,9, 398,1,1,0,0,0, 1, 2037,3,1000000,4,3,128,4,500 +"6",40,2190,9,0,0,174,1963,12543,12435,160,17,1,387,13,552,5,9, 439,1,1,0,0,0, 1, 1999,3,1000000,4,3,128,4,500 +"6",285,2189,9,0,0,172,1965,12543,12435,36,17,1,363,12,577,5,9, 411,1,1,0,0,0, 1, 2027,3,1000000,4,3,128,4,500 +"6",287,2189,9,0,0,174,1964,12543,12435,162,17,1,355,14,585,3,9, 411,2,1,0,0,0, 1, 2028,3,1000000,4,3,128,4,500 +"6",63,2194,9,0,0,173,1965,12543,12435,127,17,1,709,16,226,6,9, 437,1,1,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 +"6",200,2193,9,0,0,169,1966,12543,12435,127,17,1,713,17,223,4,9, 421,1,1,0,0,0, 1, 2014,3,1000000,4,3,128,4,500 +"6",252,2190,9,0,0,175,1962,12543,12435,161,17,1,370,13,570,4,9, 415,2,1,0,0,0, 1, 2023,3,1000000,4,3,128,4,500 +"6",482,2192,9,0,0,173,1964,12543,12435,76,17,1,726,17,211,2,9, 390,1,1,0,0,0, 1, 2045,3,1000000,4,3,128,4,500 +"6",245,2189,9,0,0,172,1965,12543,12435,160,17,1,375,14,565,2,9, 416,1,1,0,0,0, 1, 2023,3,1000000,4,3,128,4,500 +"6",169,2194,9,0,0,171,1966,12543,12435,161,17,1,721,16,214,5,9, 424,1,1,0,0,0, 1, 2010,3,1000000,4,3,128,4,500 +"6",24,2190,9,0,0,170,1967,12543,12435,94,17,1,389,9,551,9,9, 441,1,1,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 +"6",99,2190,9,0,0,172,1964,12543,12435,97,17,1,389,12,550,5,9, 432,1,1,0,0,0, 1, 2005,3,1000000,4,3,128,4,500 +"6",356,2191,9,0,0,172,1966,12543,12435,160,16,1,389,16,551,2,9, 404,1,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 +"6",454,2189,9,0,0,174,1964,12543,12435,94,17,1,371,14,570,2,9, 393,1,1,0,0,0, 1, 2046,3,1000000,4,3,128,4,500 +"6",114,2190,9,0,0,173,1965,12543,12435,161,17,1,367,13,573,4,9, 431,1,1,0,0,0, 1, 2008,3,1000000,4,3,128,4,500 +"6",414,2194,9,0,0,171,1966,12543,12435,162,17,1,727,15,210,5,9, 398,1,1,0,0,0, 1, 2037,3,1000000,4,3,128,4,500 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393,1,1,0,0,0, 0, 2046,3,1000000,4,3,128,4,500 +"6",82,2201,18,0,0,171,1974,12543,12435,162,17,1,760,7,174,6,9, 435,1,1,0,0,0, 1, 1998,3,1000000,4,3,128,4,500 +"6",117,2197,9,0,0,173,1972,12543,12435,160,17,1,392,13,548,4,9, 430,1,1,0,0,0, 1, 2008,3,1000000,4,3,128,4,500 +"6",214,2200,9,0,0,170,1974,12543,12435,162,17,1,715,17,221,4,9, 419,1,1,0,0,0, 1, 2015,3,1000000,4,3,128,4,500 +"6",315,2197,9,0,0,173,1972,12543,12435,160,17,1,381,14,559,3,9, 408,1,1,0,0,0, 1, 2030,3,1000000,4,3,128,4,500 +"6",497,2201,9,0,0,173,1971,12543,12435,159,17,1,726,14,210,7,9, 389,1,1,0,0,0, 1, 2045,3,1000000,4,3,128,4,500 +"6",199,2200,9,0,0,169,1974,12543,12435,128,17,1,714,17,222,4,9, 421,1,1,0,0,0, 1, 2014,3,1000000,4,3,128,4,500 +"6",50,2197,9,0,0,174,1970,12543,12435,128,17,1,385,13,554,5,9, 438,1,1,0,0,0, 1, 2000,3,1000000,4,3,128,4,500 +"6",267,2198,9,0,0,172,1973,12543,12435,118,17,1,379,15,560,3,9, 414,2,1,0,0,0, 1, 2024,3,1000000,4,3,128,4,500 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417,2,1,0,0,0, 1, 2030,3,1000000,4,3,128,4,500 +"6",249,4305,9,0,1,328,3921,12543,12435,160,17,1,57,7,891,2,9, 418,1,1,0,0,0, 1, 2028,3,1000000,4,3,128,4,500 +"6",368,4325,9,0,0,331,3934,12543,12435,161,17,1,321,12,622,1,9, 404,1,1,0,0,0, 1, 2038,3,1000000,4,3,128,4,500 diff --git a/hpc/archer2/data/ijhpca/weak_fft_10984759.csv b/hpc/archer2/data/ijhpca/weak_fft_10984759.csv new file mode 100644 index 00000000..8aa63b13 --- /dev/null +++ b/hpc/archer2/data/ijhpca/weak_fft_10984759.csv @@ -0,0 +1,1018 @@ + +experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,source_tree,target_tree,source_domain,target_domain,layout,ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,displacement_map,metadata_creation,expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples +"0",0,121,0,0,0,0,0,2970,2935,4,3,0,0,0,0,121,0, 386,0,0,0,0,0, 0, 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231,3,250000,4,2,128,4,500 +"3",11,344,4,0,0,172,151,2909,2983,5,3,1,35,7,124,0,3, 391,1,0,0,0,0, 1, 231,3,250000,4,2,128,4,500 +"3",31,344,4,0,0,172,151,2909,2983,5,3,1,35,7,124,0,3, 389,1,0,0,0,0, 1, 234,3,250000,4,2,128,4,500 +"3",44,348,4,0,0,171,151,2909,2983,6,4,1,113,11,43,1,1, 387,1,0,0,0,0, 1, 232,3,250000,4,2,128,4,500 +"3",23,346,4,0,0,172,152,2909,2983,8,3,1,37,8,122,0,3, 390,1,0,0,0,0, 1, 232,3,250000,4,2,128,4,500 +"3",30,345,4,0,0,172,150,2909,2983,4,3,1,30,7,129,0,3, 389,1,0,0,0,0, 1, 234,3,250000,4,2,128,4,500 +"3",6,345,4,0,0,170,153,2909,2983,4,3,1,27,6,132,0,3, 392,2,0,0,0,0, 1, 231,3,250000,4,2,128,4,500 +"3",16,345,4,0,0,172,152,2909,2983,7,3,1,25,7,135,0,3, 390,1,0,0,0,0, 0, 232,3,250000,4,2,128,4,500 +"3",21,346,4,0,0,173,152,2909,2983,5,3,1,32,7,127,0,3, 390,1,0,0,0,0, 1, 232,3,250000,4,2,128,4,500 +"3",48,347,4,0,0,175,151,2909,2983,5,3,1,26,6,134,2,1, 387,1,0,0,0,0, 1, 237,3,250000,4,2,128,4,500 +"3",61,351,4,0,0,170,155,2909,2983,5,3,1,115,10,41,1,1, 385,2,0,0,0,0, 1, 234,3,250000,4,2,128,4,500 +"3",20,348,4,0,0,171,156,2909,2983,6,3,1,35,7,124,0,3, 390,1,0,0,0,0, 1, 233,3,250000,4,2,128,4,500 +"3",58,352,4,0,0,170,156,2909,2983,6,3,1,114,10,42,2,1, 385,1,0,0,0,0, 1, 234,3,250000,4,2,128,4,500 +"3",8,349,4,0,0,171,158,2909,2983,7,3,1,22,6,138,0,3, 391,1,0,0,0,0, 1, 232,3,250000,4,2,128,4,500 +"3",34,353,5,0,0,176,151,2909,2983,6,3,1,114,8,42,2,1, 388,1,0,0,0,0, 1, 231,3,250000,4,2,128,4,500 +"3",29,349,4,0,0,172,157,2909,2983,6,3,1,27,6,134,0,3, 388,1,0,0,0,0, 1, 235,3,250000,4,2,128,4,500 +"3",24,350,4,0,0,171,158,2909,2983,5,3,1,32,6,128,0,3, 389,1,0,0,0,0, 1, 234,3,250000,4,2,128,4,500 +"3",18,350,4,0,0,172,156,2909,2983,6,3,1,27,7,133,0,3, 390,1,0,0,0,0, 1, 232,3,250000,4,2,128,4,500 +"3",15,350,4,0,0,172,157,2909,2983,6,3,1,24,6,136,0,3, 391,1,0,0,0,0, 1, 232,3,250000,4,2,128,4,500 +"3",54,355,4,0,0,171,157,2909,2983,5,3,1,115,11,41,2,1, 386,1,0,0,0,0, 1, 233,3,250000,4,2,128,4,500 +"3",43,354,4,0,0,171,158,2909,2983,6,3,1,108,10,49,1,1, 387,1,0,0,0,0, 1, 233,3,250000,4,2,128,4,500 +"3",51,353,4,0,0,174,156,2909,2983,5,3,1,37,8,123,1,1, 386,1,0,0,0,0, 1, 236,3,250000,4,2,128,4,500 +"3",39,352,4,0,0,175,155,2909,2983,5,3,1,36,7,123,2,1, 387,1,0,0,0,0, 1, 235,3,250000,4,2,128,4,500 +"3",22,352,4,0,0,173,158,2909,2983,5,3,1,19,6,141,0,3, 390,1,0,0,0,0, 0, 234,3,250000,4,2,128,4,500 +"3",49,353,4,0,0,176,156,2909,2983,6,3,1,25,6,136,1,1, 386,1,0,0,0,0, 1, 237,3,250000,4,2,128,4,500 +"3",63,643,8,0,0,326,291,2909,2984,5,3,1,116,4,41,1,1, 384,1,0,0,0,0, 2, 236,3,250000,4,2,128,4,500 +"3",26,648,4,0,0,338,289,2909,2984,6,3,1,21,4,142,0,3, 389,1,0,0,0,0, 1, 236,3,250000,4,2,128,4,500 +"3",59,658,4,0,0,337,298,2909,2984,5,3,1,32,5,130,0,1, 385,1,0,0,0,0, 1, 240,3,250000,4,2,128,4,500 +"3",33,661,4,0,0,339,301,2909,2984,5,3,1,29,4,133,1,1, 388,1,0,0,0,0, 1, 238,3,250000,4,2,128,4,500 +"3",56,673,4,0,0,341,312,2909,2984,6,3,1,15,4,149,0,1, 385,1,0,0,0,0, 1, 242,3,250000,4,2,128,4,500 +"3",46,677,4,0,1,341,314,2909,2984,6,3,1,25,3,137,1,1, 387,1,0,0,0,0, 1, 239,3,250000,4,2,128,4,500 +"4",107,601,10,1,1,484,57,2008,1954,51,4,1,99,24,30,6,9, 385,1,0,0,0,0, 3, 254,3,250000,4,3,128,4,500 +"4",4,605,10,1,1,487,56,2007,1954,50,3,1,102,23,24,9,9, 397,1,1,0,0,0, 3, 239,3,250000,4,3,128,4,500 +"4",19,602,10,1,1,487,57,2007,1954,49,3,1,99,20,30,9,9, 395,1,0,0,0,0, 3, 245,3,250000,4,3,128,4,500 +"4",10,602,10,1,1,485,58,2008,1954,50,3,1,99,23,30,6,9, 396,1,0,0,0,0, 3, 244,3,250000,4,3,128,4,500 +"4",125,605,10,1,1,488,57,2007,1954,51,4,1,101,18,28,12,9, 382,1,0,0,0,0, 3, 257,3,250000,4,3,128,4,500 +"4",31,601,7,1,1,489,60,2007,1954,51,3,1,76,22,60,4,9, 394,1,0,0,0,0, 3, 252,3,250000,4,3,128,4,500 +"4",88,601,10,1,1,493,58,2008,1954,51,4,1,76,18,59,4,9, 387,1,1,0,0,0, 4, 258,3,250000,4,3,128,4,500 +"4",43,609,10,1,1,495,59,2007,1954,52,3,1,93,21,38,5,9, 393,1,0,0,0,0, 4, 249,3,250000,4,3,128,4,500 +"4",112,612,10,1,1,498,59,2007,1954,50,3,1,95,23,36,3,9, 384,1,1,0,0,0, 3, 258,3,250000,4,3,128,4,500 +"4",27,778,10,1,1,653,76,2007,1954,50,3,1,61,15,77,4,9, 394,1,1,0,0,0, 4, 255,3,250000,4,3,128,4,500 +"4",0,787,8,1,2,658,73,2007,1954,50,3,1,88,14,44,15,0, 373,1,1,0,0,0, 5, 265,3,250000,4,3,128,4,500 +"4",34,780,8,1,2,656,76,2007,1954,50,3,1,60,13,80,7,9, 393,1,0,0,0,0, 4, 257,3,250000,4,3,128,4,500 +"4",11,782,10,1,2,655,78,2008,1954,50,3,1,75,14,63,4,9, 396,1,1,0,0,0, 4, 253,3,250000,4,3,128,4,500 +"4",26,781,10,1,1,658,75,2007,1954,50,3,1,58,14,82,3,9, 394,1,0,0,0,0, 4, 256,3,250000,4,3,128,4,500 +"4",29,779,10,1,1,654,79,2007,1954,50,4,1,55,14,87,1,9, 394,1,0,0,0,0, 4, 258,3,250000,4,3,128,4,500 +"4",79,779,10,1,2,658,75,2007,1954,50,3,1,38,13,104,3,9, 388,1,0,0,0,0, 4, 264,3,250000,4,3,128,4,500 +"4",127,794,10,1,2,660,75,2007,1954,50,4,1,103,17,24,13,9, 382,1,1,0,0,0, 4, 256,3,250000,4,3,128,4,500 +"4",42,784,10,1,1,655,80,2007,1954,51,3,1,76,14,64,4,9, 393,1,1,0,0,0, 4, 257,3,250000,4,3,128,4,500 +"4",12,782,7,1,2,657,78,2007,1954,50,3,1,71,16,70,4,9, 395,1,1,0,0,0, 4, 256,3,250000,4,3,128,4,500 +"4",105,793,10,1,2,661,74,2007,1954,51,3,1,101,19,29,9,9, 385,1,0,0,0,0, 4, 255,3,250000,4,3,128,4,500 +"4",87,783,10,1,1,659,77,2007,1954,50,4,1,55,13,85,3,9, 388,1,1,0,0,0, 4, 263,3,250000,4,3,128,4,500 +"4",72,783,12,2,1,661,76,2007,1954,50,3,1,57,12,83,2,9, 389,1,0,0,0,0, 5, 261,3,250000,4,3,128,4,500 +"4",73,789,10,1,2,663,72,2007,1954,51,4,1,66,16,69,7,9, 389,1,0,0,0,0, 4, 256,3,250000,4,3,128,4,500 +"4",37,786,7,1,2,661,76,2007,1954,51,3,1,63,14,75,8,9, 393,1,1,0,0,0, 4, 256,3,250000,4,3,128,4,500 +"4",69,787,10,1,1,663,76,2007,1954,50,4,1,56,12,84,5,9, 389,1,0,0,0,0, 4, 261,3,250000,4,3,128,4,500 +"4",24,790,10,1,2,664,76,2007,1954,50,4,1,98,15,40,4,9, 394,1,1,0,0,0, 4, 254,3,250000,4,3,128,4,500 +"4",28,785,10,1,2,660,79,2007,1954,51,4,1,53,12,90,3,9, 394,1,0,0,0,0, 4, 260,3,250000,4,3,128,4,500 +"4",98,790,10,1,1,661,80,2007,1954,50,3,1,75,15,64,4,9, 386,1,1,0,0,0, 4, 264,3,250000,4,3,128,4,500 +"4",90,795,12,2,2,666,76,2007,1954,51,3,1,77,15,57,4,9, 387,1,1,0,0,0, 4, 258,3,250000,4,3,128,4,500 +"4",76,788,10,1,1,664,78,2008,1954,50,3,1,55,13,86,3,9, 389,1,1,0,0,0, 4, 263,3,250000,4,3,128,4,500 +"4",52,797,10,1,2,666,76,2007,1954,50,3,1,96,15,37,9,9, 391,1,0,0,0,0, 4, 252,3,250000,4,3,128,4,500 +"4",113,789,10,1,2,663,79,2007,1954,51,3,1,71,13,69,3,9, 384,1,1,0,0,0, 4, 267,3,250000,4,3,128,4,500 +"4",80,785,10,1,1,665,77,2007,1954,50,4,1,31,11,114,2,9, 389,1,1,0,0,0, 4, 266,3,250000,4,3,128,4,500 +"4",2,799,10,1,2,669,73,2007,1954,50,3,1,99,16,32,10,9, 396,1,1,0,0,0, 4, 245,3,250000,4,3,128,4,500 +"4",54,799,10,1,2,669,73,2007,1954,51,4,1,97,14,35,12,9, 392,1,1,0,0,0, 4, 250,3,250000,4,3,128,4,500 +"4",104,798,10,1,1,664,79,2007,1954,50,4,1,95,16,37,9,9, 385,1,0,0,0,0, 4, 258,3,250000,4,3,128,4,500 +"4",86,789,10,1,1,665,78,2007,1954,51,3,1,54,14,88,1,9, 388,1,0,0,0,0, 4, 265,3,250000,4,3,128,4,500 +"4",70,788,10,1,1,664,79,2007,1954,51,3,1,51,14,92,1,9, 389,1,0,0,0,0, 4, 264,3,250000,4,3,128,4,500 +"4",66,787,8,1,1,667,78,2007,1954,50,3,1,31,13,113,2,9, 390,1,0,0,0,0, 4, 265,3,250000,4,3,128,4,500 +"4",45,798,10,1,1,667,77,2007,1954,51,3,1,95,14,38,10,9, 392,1,1,0,0,0, 4, 251,3,250000,4,3,128,4,500 +"4",74,790,10,1,1,668,77,2007,1954,50,3,1,54,13,87,3,9, 389,1,0,0,0,0, 4, 263,3,250000,4,3,128,4,500 +"4",1,801,13,2,2,668,76,2007,1954,50,4,1,103,9,27,13,9, 396,1,0,0,0,0, 5, 245,3,250000,4,3,128,4,500 +"4",82,788,10,1,1,667,78,2007,1954,51,3,1,34,13,110,1,9, 388,1,0,0,0,0, 4, 265,3,250000,4,3,128,4,500 +"4",84,789,13,2,2,664,79,2007,1954,50,4,1,51,10,92,1,9, 388,1,1,0,0,0, 4, 265,3,250000,4,3,128,4,500 +"4",7,794,10,1,2,665,80,2007,1954,50,4,1,77,11,62,7,9, 396,1,0,0,0,0, 4, 253,3,250000,4,3,128,4,500 +"4",3,801,10,1,2,666,79,2007,1954,50,4,1,97,15,35,9,9, 396,1,0,0,0,0, 4, 246,3,250000,4,3,128,4,500 +"4",17,800,10,1,1,668,77,2007,1954,50,3,1,94,15,39,9,9, 396,1,1,0,0,0, 4, 248,3,250000,4,3,128,4,500 +"4",100,799,10,1,2,670,76,2007,1954,51,4,1,79,18,55,5,9, 385,1,1,0,0,0, 4, 259,3,250000,4,3,128,4,500 +"4",103,801,10,1,1,667,78,2007,1954,51,3,1,96,14,37,11,9, 385,1,0,0,0,0, 4, 257,3,250000,4,3,128,4,500 +"4",16,800,10,1,1,668,78,2008,1954,50,4,1,94,18,40,5,9, 396,1,0,0,0,0, 4, 249,3,250000,4,3,128,4,500 +"4",123,802,10,1,1,672,75,2007,1954,50,3,1,94,20,39,5,9, 383,1,1,0,0,0, 4, 260,3,250000,4,3,128,4,500 +"4",81,792,10,1,1,670,77,2008,1954,51,3,1,33,14,110,1,9, 388,1,1,0,0,0, 4, 265,3,250000,4,3,128,4,500 +"4",60,795,10,1,2,667,79,2007,1954,50,4,1,75,13,65,4,9, 391,1,0,0,0,0, 4, 260,3,250000,4,3,128,4,500 +"4",91,805,10,1,1,672,75,2007,1954,50,4,1,82,21,49,5,9, 387,1,0,0,0,0, 4, 254,3,250000,4,3,128,4,500 +"4",75,797,10,1,1,673,75,2008,1954,50,4,1,64,15,74,4,9, 389,1,0,0,0,0, 4, 260,3,250000,4,3,128,4,500 +"4",120,795,10,1,1,668,80,2007,1954,50,4,1,72,13,68,4,9, 383,1,0,0,0,0, 4, 268,3,250000,4,3,128,4,500 +"4",8,797,10,1,1,672,76,2008,1954,50,4,1,78,12,60,8,9, 396,1,1,0,0,0, 4, 253,3,250000,4,3,128,4,500 +"4",85,794,10,1,1,669,79,2008,1954,51,4,1,52,14,90,1,9, 388,1,1,0,0,0, 4, 265,3,250000,4,3,128,4,500 +"4",55,804,10,1,1,673,75,2007,1954,51,4,1,97,13,35,13,9, 391,1,1,0,0,0, 4, 251,3,250000,4,3,128,4,500 +"4",40,799,10,1,2,669,80,2008,1954,50,3,1,77,12,60,8,9, 393,1,1,0,0,0, 4, 255,3,250000,4,3,128,4,500 +"4",83,795,10,1,2,670,79,2008,1954,52,4,1,36,14,106,2,9, 388,2,0,0,0,0, 4, 264,3,250000,4,3,128,4,500 +"4",59,797,7,1,1,671,78,2007,1954,51,3,1,73,18,67,3,9, 391,1,0,0,0,0, 4, 259,3,250000,4,3,128,4,500 +"4",77,800,10,1,2,675,74,2007,1954,4,4,1,64,13,74,6,9, 388,1,0,0,0,0, 4, 260,3,250000,4,3,128,4,500 +"4",102,806,10,1,2,673,77,2007,1954,50,4,1,97,12,36,12,9, 385,1,1,0,0,0, 4, 257,3,250000,4,3,128,4,500 +"4",21,802,11,1,1,672,78,2007,1954,50,4,1,95,13,42,6,9, 395,1,0,0,0,0, 4, 253,3,250000,4,3,128,4,500 +"4",68,797,10,1,2,674,77,2007,1954,51,4,1,53,12,88,3,9, 389,1,0,0,0,0, 4, 262,3,250000,4,3,128,4,500 +"4",53,805,10,1,2,672,79,2007,1954,50,3,1,95,12,39,10,9, 391,2,0,0,0,0, 4, 253,3,250000,4,3,128,4,500 +"4",92,799,10,1,1,671,81,2008,1954,50,3,1,75,14,65,4,9, 387,1,1,0,0,0, 4, 263,3,250000,4,3,128,4,500 +"4",56,802,10,1,2,674,77,2007,1954,50,3,1,96,15,42,4,9, 391,1,0,0,0,0, 4, 257,3,250000,4,3,128,4,500 +"4",5,802,10,1,1,672,79,2007,1954,50,3,1,80,12,58,8,9, 397,1,0,0,0,0, 4, 251,3,250000,4,3,128,4,500 +"4",121,805,12,2,2,673,79,2007,1954,50,3,1,94,15,40,5,9, 383,1,0,0,0,0, 5, 262,3,250000,4,3,128,4,500 +"4",14,802,11,1,2,672,80,2007,1954,50,4,1,92,14,47,4,9, 396,1,1,0,0,0, 4, 253,3,250000,4,3,128,4,500 +"4",39,803,10,1,2,674,78,2008,1954,51,3,1,80,12,57,9,9, 393,1,0,0,0,0, 4, 254,3,250000,4,3,128,4,500 +"4",6,802,10,1,2,676,77,2007,1954,50,3,1,77,11,61,8,9, 396,1,1,0,0,0, 4, 252,3,250000,4,3,128,4,500 +"4",93,807,10,1,1,678,76,2007,1954,50,4,1,80,18,54,4,9, 387,1,1,0,0,0, 4, 258,3,250000,4,3,128,4,500 +"4",67,801,12,2,2,672,80,2007,1954,51,3,1,57,12,83,2,9, 390,1,1,0,0,0, 4, 261,3,250000,4,3,128,4,500 +"4",46,805,10,1,2,675,79,2007,1954,50,3,1,95,12,42,8,9, 392,1,1,0,0,0, 4, 255,3,250000,4,3,128,4,500 +"4",101,811,13,2,1,677,77,2007,1954,51,4,1,101,9,30,14,9, 385,1,1,0,0,0, 4, 256,3,250000,4,3,128,4,500 +"4",119,805,8,1,1,678,77,2007,1954,50,3,1,79,14,59,9,9, 383,1,0,0,0,0, 4, 265,3,250000,4,3,128,4,500 +"4",41,805,10,1,2,676,79,2008,1954,50,3,1,77,12,60,8,9, 393,1,0,0,0,0, 4, 255,3,250000,4,3,128,4,500 +"4",106,808,10,1,2,675,79,2007,1954,50,3,1,94,18,41,5,9, 385,1,1,0,0,0, 4, 260,3,250000,4,3,128,4,500 +"4",64,805,10,1,1,681,74,2007,1954,51,4,1,79,12,58,8,9, 390,1,0,0,0,0, 4, 258,3,250000,4,3,128,4,500 +"4",109,809,10,1,2,679,75,2007,1954,51,3,1,85,10,49,13,9, 384,1,1,0,0,0, 4, 260,3,250000,4,3,128,4,500 +"4",115,803,7,1,2,677,77,2007,1954,51,4,1,75,18,65,3,9, 383,1,1,0,0,0, 4, 267,3,250000,4,3,128,4,500 +"4",118,809,10,1,2,678,78,2007,1954,50,3,1,95,13,39,10,9, 383,1,1,0,0,0, 4, 261,3,250000,4,3,128,4,500 +"4",108,805,10,1,2,675,79,2007,1954,50,3,1,80,16,59,4,9, 384,1,1,0,0,0, 4, 264,3,250000,4,3,128,4,500 +"4",36,810,12,2,1,684,72,2007,1954,50,3,1,85,12,47,9,9, 393,1,1,0,0,0, 5, 249,3,250000,4,3,128,4,500 +"4",15,805,10,1,2,676,79,2007,1954,50,3,1,91,14,47,4,9, 396,1,1,0,0,0, 4, 253,3,250000,4,3,128,4,500 +"4",35,803,10,1,1,677,80,2007,1954,53,3,1,57,10,84,6,9, 393,1,0,0,0,0, 4, 258,3,250000,4,3,128,4,500 +"4",47,808,10,1,2,677,80,2007,1954,50,3,1,95,12,41,9,9, 392,1,0,0,0,0, 4, 255,3,250000,4,3,128,4,500 +"4",71,797,7,1,1,676,80,2007,1954,50,3,1,31,14,115,0,9, 389,1,1,0,0,0, 4, 268,3,250000,4,3,128,4,500 +"4",51,810,10,1,2,679,78,2008,1954,51,3,1,95,15,39,8,9, 392,1,1,0,0,0, 4, 253,3,250000,4,3,128,4,500 +"4",20,810,10,1,2,681,76,2007,1954,50,4,1,94,16,39,7,9, 395,1,1,0,0,0, 5, 249,3,250000,4,3,128,4,500 +"4",22,811,10,1,1,680,76,2007,1954,50,3,1,94,17,39,8,9, 395,1,0,0,0,0, 4, 249,3,250000,4,3,128,4,500 +"4",116,808,10,1,2,679,79,2007,1954,51,4,1,96,15,41,4,9, 383,1,0,0,0,0, 4, 264,3,250000,4,3,128,4,500 +"4",23,812,10,1,2,682,76,2008,1954,50,3,1,95,17,39,7,9, 395,1,0,0,0,0, 4, 249,3,250000,4,3,128,4,500 +"4",97,809,10,1,2,679,79,2007,1954,50,4,1,77,13,60,7,9, 386,1,1,0,0,0, 4, 262,3,250000,4,3,128,4,500 +"4",114,805,10,1,1,681,78,2007,1954,50,3,1,72,14,69,3,9, 384,1,1,0,0,0, 4, 267,3,250000,4,3,128,4,500 +"4",63,809,10,1,1,681,77,2007,1954,51,3,1,80,10,57,10,9, 390,1,0,0,0,0, 4, 257,3,250000,4,3,128,4,500 +"4",117,810,10,1,1,681,78,2008,1954,51,3,1,96,11,41,10,9, 383,1,1,0,0,0, 4, 264,3,250000,4,3,128,4,500 +"4",38,814,12,2,2,683,76,2007,1954,50,4,1,83,13,49,8,9, 393,1,1,0,0,0, 4, 250,3,250000,4,3,128,4,500 +"4",94,809,11,1,2,682,78,2007,1954,50,4,1,77,15,61,4,9, 387,1,0,0,0,0, 4, 261,3,250000,4,3,128,4,500 +"4",44,810,10,1,2,680,79,2007,1954,51,3,1,96,16,41,4,9, 392,1,1,0,0,0, 4, 255,3,250000,4,3,128,4,500 +"4",33,811,10,1,2,681,79,2007,1954,51,3,1,80,14,57,7,9, 393,1,1,0,0,0, 4, 254,3,250000,4,3,128,4,500 +"4",122,811,10,1,2,681,79,2007,1954,51,4,1,92,17,45,4,9, 383,1,0,0,0,0, 4, 264,3,250000,4,3,128,4,500 +"4",30,809,13,2,1,681,80,2007,1954,50,3,1,79,13,60,3,9, 394,1,0,0,0,0, 4, 255,3,250000,4,3,128,4,500 +"4",95,816,10,1,2,683,76,2007,1954,50,4,1,81,20,52,5,9, 387,1,1,0,0,0, 4, 256,3,250000,4,3,128,4,500 +"4",62,810,10,1,1,680,80,2007,1954,50,3,1,78,10,60,9,9, 391,1,1,0,0,0, 4, 258,3,250000,4,3,128,4,500 +"4",99,810,10,1,2,681,80,2007,1954,51,3,1,77,15,62,3,9, 385,1,1,0,0,0, 4, 263,3,250000,4,3,128,4,500 +"4",96,812,10,1,2,683,78,2007,1954,50,4,1,76,16,62,4,9, 386,1,1,0,0,0, 4, 262,3,250000,4,3,128,4,500 +"4",78,804,8,1,1,684,79,2007,1954,50,3,1,31,11,116,2,9, 388,1,1,0,0,0, 4, 269,3,250000,4,3,128,4,500 +"4",57,813,10,1,2,684,80,2007,1954,51,3,1,91,15,48,3,9, 391,1,1,0,0,0, 4, 258,3,250000,4,3,128,4,500 +"4",61,813,10,1,1,685,79,2007,1954,50,3,1,78,9,60,9,9, 390,1,1,0,0,0, 4, 259,3,250000,4,3,128,4,500 +"4",48,819,10,1,2,679,85,2007,1954,51,3,1,95,15,39,9,9, 392,1,0,0,0,0, 4, 252,3,250000,4,3,128,4,500 +"4",13,815,10,1,2,686,79,2007,1954,4,4,1,92,11,45,9,9, 396,1,1,0,0,0, 5, 251,3,250000,4,3,128,4,500 +"4",110,816,10,1,1,688,77,2007,1954,50,3,1,80,10,59,8,9, 384,1,0,0,0,0, 4, 265,3,250000,4,3,128,4,500 +"4",49,818,10,1,2,687,80,2007,1954,50,3,1,94,13,43,8,9, 392,1,1,0,0,0, 4, 256,3,250000,4,3,128,4,500 +"4",58,818,10,1,2,689,80,2007,1954,50,3,1,94,15,44,3,9, 391,1,1,0,0,0, 4, 258,3,250000,4,3,128,4,500 +"4",50,826,10,1,2,697,75,2008,1954,52,4,1,96,15,38,9,9, 392,1,1,0,0,0, 4, 252,3,250000,4,3,128,4,500 +"4",18,937,10,1,2,794,92,2007,1954,50,3,1,82,11,56,8,9, 395,1,1,0,0,0, 5, 253,3,250000,4,3,128,4,500 +"4",65,934,10,1,3,792,95,2007,1954,51,3,1,57,10,86,4,9, 390,2,0,0,0,0, 4, 263,3,250000,4,3,128,4,500 +"4",111,942,10,1,2,798,94,2007,1954,51,3,1,84,9,54,9,9, 384,1,1,0,0,0, 5, 264,3,250000,4,3,128,4,500 +"4",9,943,10,1,2,798,96,2007,1954,50,3,1,76,10,64,8,9, 396,1,0,0,0,0, 4, 254,3,250000,4,3,128,4,500 +"4",126,948,12,2,2,799,95,2007,1954,50,3,1,97,9,38,10,9, 382,1,1,0,0,0, 5, 263,3,250000,4,3,128,4,500 +"4",89,942,10,1,2,802,94,2007,1954,51,4,1,60,12,82,3,9, 387,1,0,0,0,0, 4, 265,3,250000,4,3,128,4,500 +"4",25,942,7,1,2,799,98,2007,1954,50,4,1,52,13,92,2,9, 394,1,1,0,0,0, 4, 261,3,250000,4,3,128,4,500 +"4",124,947,10,1,2,803,97,2007,1954,51,4,1,76,12,65,4,9, 383,1,0,0,0,0, 5, 269,3,250000,4,3,128,4,500 +"4",32,950,13,2,2,807,94,2007,1954,51,4,1,80,10,60,4,9, 393,1,1,0,0,0, 5, 257,3,250000,4,3,128,4,500 +"5",144,261,6,0,0,167,46,2365,2384,85,4,1,120,21,25,4,9, 397,1,0,0,0,0, 1, 309,3,250000,4,3,128,4,500 +"5",37,270,6,0,0,172,50,2365,2384,84,4,1,122,17,23,8,9, 409,1,1,0,0,0, 1, 297,3,250000,4,3,128,4,500 +"5",29,270,6,0,0,170,53,2365,2384,5,4,1,118,19,28,5,9, 410,1,1,0,0,0, 1, 297,3,250000,4,3,128,4,500 +"5",208,271,6,0,0,175,50,2365,2384,84,4,1,117,20,29,5,9, 390,1,1,0,0,0, 1, 316,3,250000,4,3,128,4,500 +"5",142,271,6,0,0,173,52,2365,2384,85,4,1,113,20,34,4,9, 397,1,0,0,0,0, 1, 310,3,250000,4,3,128,4,500 +"5",56,272,6,0,0,173,53,2365,2384,84,4,1,118,19,28,5,9, 407,1,1,0,0,0, 1, 300,3,250000,4,3,128,4,500 +"5",39,274,6,0,0,173,53,2365,2384,60,4,1,118,17,28,7,9, 409,1,1,0,0,0, 1, 298,3,250000,4,3,128,4,500 +"5",113,273,6,0,0,174,53,2365,2384,85,4,1,116,19,30,5,9, 401,1,1,0,0,0, 1, 307,3,250000,4,3,128,4,500 +"5",51,275,6,0,0,175,53,2365,2384,85,4,1,118,19,27,5,9, 408,1,1,0,0,0, 1, 299,3,250000,4,3,128,4,500 +"5",196,271,6,0,0,175,53,2365,2384,84,4,1,77,16,74,4,9, 391,1,1,0,0,0, 1, 320,3,250000,4,3,128,4,500 +"5",254,276,6,0,0,177,52,2365,2384,85,4,1,119,16,27,8,9, 385,1,0,0,0,0, 1, 321,3,250000,4,3,128,4,500 +"5",0,465,6,0,0,327,90,2365,2384,68,4,1,125,14,21,10,0, 376,1,1,0,0,0, 2, 327,3,250000,4,3,128,4,500 +"5",73,463,7,0,0,326,94,2365,2384,85,4,1,82,8,69,10,9, 404,1,1,0,0,0, 2, 308,3,250000,4,3,128,4,500 +"5",36,470,6,0,1,330,92,2365,2384,84,4,1,121,15,25,8,9, 409,1,0,0,0,0, 2, 298,3,250000,4,3,128,4,500 +"5",96,468,6,0,0,326,100,2365,2384,84,4,1,110,11,41,8,9, 402,1,0,0,0,0, 2, 310,3,250000,4,3,128,4,500 +"5",148,469,8,0,0,325,100,2365,2384,85,4,1,111,13,40,4,9, 397,1,1,0,0,0, 2, 314,3,250000,4,3,128,4,500 +"5",13,472,7,0,0,326,99,2365,2384,60,4,1,114,12,34,8,9, 412,1,1,0,0,0, 2, 297,3,250000,4,3,128,4,500 +"5",77,468,6,0,0,330,96,2365,2384,60,4,1,78,8,73,10,9, 404,1,0,0,0,0, 2, 308,3,250000,4,3,128,4,500 +"5",12,472,6,0,1,328,99,2365,2384,60,4,1,114,13,34,8,9, 412,1,1,0,0,0, 2, 298,3,250000,4,3,128,4,500 +"5",105,473,6,0,0,330,97,2365,2384,84,4,1,113,10,36,10,9, 401,1,0,0,0,0, 2, 309,3,250000,4,3,128,4,500 +"5",151,470,6,0,1,331,96,2365,2384,37,4,1,81,14,71,4,9, 397,1,0,0,0,0, 2, 316,3,250000,4,3,128,4,500 +"5",253,471,6,0,0,331,96,2365,2384,53,4,1,83,12,68,6,9, 385,1,0,0,0,0, 2, 327,3,250000,4,3,128,4,500 +"5",182,472,7,0,1,336,93,2365,2384,84,4,1,86,11,64,8,9, 393,1,1,0,0,0, 2, 318,3,250000,4,3,128,4,500 +"5",178,475,9,1,0,334,94,2365,2384,84,4,1,118,14,29,5,9, 394,1,0,0,0,0, 2, 314,3,250000,4,3,128,4,500 +"5",164,471,6,0,1,333,96,2365,2384,84,4,1,79,14,73,4,9, 395,1,1,0,0,0, 2, 317,3,250000,4,3,128,4,500 +"5",6,475,6,0,0,330,98,2365,2384,7,4,1,114,14,34,8,9, 412,1,1,0,0,0, 2, 297,3,250000,4,3,128,4,500 +"5",211,475,9,1,0,333,96,2365,2384,84,4,1,115,14,33,4,9, 390,1,1,0,0,0, 2, 319,3,250000,4,3,128,4,500 +"5",22,475,6,0,0,330,100,2365,2384,37,4,1,113,16,35,5,9, 411,1,1,0,0,0, 2, 298,3,250000,4,3,128,4,500 +"5",20,476,6,0,1,331,99,2365,2384,85,4,1,114,17,35,5,9, 411,1,0,0,0,0, 2, 298,3,250000,4,3,128,4,500 +"5",101,475,6,0,0,334,96,2365,2384,85,4,1,113,12,36,9,9, 402,1,1,0,0,0, 2, 308,3,250000,4,3,128,4,500 +"5",108,474,6,0,0,331,99,2365,2384,61,4,1,112,8,38,10,9, 401,1,1,0,0,0, 2, 310,3,250000,4,3,128,4,500 +"5",4,478,6,0,1,336,94,2365,2384,85,4,1,119,14,28,8,9, 412,1,1,0,0,0, 2, 296,3,250000,4,3,128,4,500 +"5",125,475,6,0,0,331,100,2365,2384,53,4,1,110,8,40,11,9, 400,1,0,0,0,0, 2, 311,3,250000,4,3,128,4,500 +"5",8,478,6,0,0,334,96,2365,2384,85,4,1,119,13,28,9,9, 412,1,1,0,0,0, 2, 297,3,250000,4,3,128,4,500 +"5",65,474,6,0,1,335,96,2365,2384,85,4,1,79,10,73,8,9, 405,1,1,0,0,0, 2, 307,3,250000,4,3,128,4,500 +"5",91,475,6,0,1,332,99,2365,2384,84,4,1,108,9,43,10,9, 403,1,1,0,0,0, 2, 308,3,250000,4,3,128,4,500 +"5",46,472,8,1,1,332,99,2365,2384,84,4,1,74,7,80,6,9, 408,1,1,0,0,0, 2, 306,3,250000,4,3,128,4,500 +"5",145,475,6,0,1,330,100,2365,2384,38,4,1,111,14,40,4,9, 397,1,1,0,0,0, 2, 315,3,250000,4,3,128,4,500 +"5",146,475,6,0,0,334,97,2365,2384,85,4,1,113,15,37,4,9, 397,1,0,0,0,0, 2, 314,3,250000,4,3,128,4,500 +"5",34,478,6,0,0,333,99,2365,2384,64,4,1,113,15,35,6,9, 409,1,0,0,0,0, 2, 300,3,250000,4,3,128,4,500 +"5",97,476,6,0,1,332,100,2365,2384,84,4,1,110,11,40,8,9, 402,1,1,0,0,0, 2, 309,3,250000,4,3,128,4,500 +"5",110,476,6,1,0,335,96,2365,2384,84,4,1,114,7,36,11,9, 401,1,0,0,0,0, 2, 310,3,250000,4,3,128,4,500 +"5",106,476,6,0,1,332,100,2365,2384,84,4,1,110,9,40,10,9, 401,1,1,0,0,0, 2, 310,3,250000,4,3,128,4,500 +"5",251,476,6,0,1,335,97,2365,2384,84,4,1,112,13,37,7,9, 386,1,1,0,0,0, 2, 325,3,250000,4,3,128,4,500 +"5",10,477,6,0,0,330,102,2365,2384,86,4,1,114,11,36,9,9, 411,1,1,0,0,0, 2, 299,3,250000,4,3,128,4,500 +"5",55,477,6,0,0,329,104,2365,2384,57,4,1,111,13,39,6,9, 407,1,1,0,0,0, 2, 303,3,250000,4,3,128,4,500 +"5",124,476,6,0,1,329,103,2365,2384,84,4,1,108,9,43,10,9, 400,1,1,0,0,0, 1, 313,3,250000,4,3,128,4,500 +"5",1,480,6,0,1,337,96,2365,2384,47,4,1,118,13,29,9,9, 412,1,1,0,0,0, 2, 296,3,250000,4,3,128,4,500 +"5",153,476,6,0,0,330,103,2365,2384,85,4,1,107,14,44,4,9, 396,1,0,0,0,0, 2, 316,3,250000,4,3,128,4,500 +"5",2,481,6,0,1,338,96,2365,2384,36,4,1,118,13,29,9,9, 413,1,1,0,0,0, 2, 295,3,250000,4,3,128,4,500 +"5",162,475,6,0,1,335,99,2365,2384,64,4,1,74,13,78,4,9, 396,1,0,0,0,0, 2, 318,3,250000,4,3,128,4,500 +"5",117,477,7,0,0,331,103,2365,2384,85,4,1,108,9,43,8,9, 400,1,1,0,0,0, 2, 311,3,250000,4,3,128,4,500 +"5",109,480,6,0,1,339,95,2365,2384,60,4,1,117,10,31,12,9, 401,1,1,0,0,0, 2, 308,3,250000,4,3,128,4,500 +"5",209,477,6,0,1,330,103,2365,2384,68,4,1,106,14,46,4,9, 390,1,1,0,0,0, 2, 322,3,250000,4,3,128,4,500 +"5",160,476,6,0,1,335,99,2365,2384,84,4,1,73,13,80,4,9, 395,1,0,0,0,0, 2, 318,3,250000,4,3,128,4,500 +"5",223,480,9,1,0,336,98,2365,2384,84,4,1,116,10,33,8,9, 389,1,0,0,0,0, 2, 320,3,250000,4,3,128,4,500 +"5",17,479,6,0,1,329,105,2365,2384,37,4,1,113,16,37,4,9, 411,1,1,0,0,0, 2, 300,3,250000,4,3,128,4,500 +"5",93,477,7,0,0,329,106,2365,2384,54,4,1,108,7,43,10,9, 403,1,0,0,0,0, 2, 309,3,250000,4,3,128,4,500 +"5",210,478,6,0,1,336,99,2365,2384,84,4,1,108,15,43,4,9, 390,1,1,0,0,0, 2, 322,3,250000,4,3,128,4,500 +"5",205,476,6,0,1,335,100,2365,2384,60,4,1,78,10,75,6,9, 391,1,1,0,0,0, 2, 323,3,250000,4,3,128,4,500 +"5",3,479,6,0,0,333,101,2365,2384,86,4,1,114,11,36,9,9, 412,1,1,0,0,0, 2, 298,3,250000,4,3,128,4,500 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2, 299,3,250000,4,3,128,4,500 +"5",191,477,6,0,1,337,99,2365,2384,85,4,1,73,14,80,3,9, 392,1,0,0,0,0, 1, 322,3,250000,4,3,128,4,500 +"5",121,479,7,0,1,330,106,2365,2384,84,4,1,109,7,43,10,9, 400,1,1,0,0,0, 2, 312,3,250000,4,3,128,4,500 +"5",74,478,6,0,1,336,100,2365,2384,85,4,1,78,8,75,9,9, 404,1,1,0,0,0, 2, 309,3,250000,4,3,128,4,500 +"5",250,479,6,0,1,335,100,2365,2384,84,4,1,110,12,41,6,9, 386,1,0,0,0,0, 2, 326,3,250000,4,3,128,4,500 +"5",18,483,6,0,0,336,100,2365,2384,85,4,1,115,17,32,5,9, 411,1,1,0,0,0, 2, 297,3,250000,4,3,128,4,500 +"5",152,477,3,0,0,337,100,2365,2384,85,4,1,72,15,82,3,9, 396,1,1,0,0,0, 1, 319,3,250000,4,3,128,4,500 +"5",24,481,6,0,0,334,102,2365,2384,85,4,1,114,15,36,5,9, 410,1,0,0,0,0, 2, 301,3,250000,4,3,128,4,500 +"5",219,480,6,0,1,343,94,2365,2384,84,4,1,81,8,71,10,9, 389,1,1,0,0,0, 2, 323,3,250000,4,3,128,4,500 +"5",229,480,6,0,1,332,105,2365,2384,84,4,1,107,11,44,7,9, 388,1,1,0,0,0, 2, 324,3,250000,4,3,128,4,500 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310,3,250000,4,3,128,4,500 +"5",90,481,6,0,0,338,100,2365,2384,21,4,1,109,9,43,9,9, 403,1,1,0,0,0, 2, 309,3,250000,4,3,128,4,500 +"5",243,482,6,0,1,338,100,2365,2384,84,4,1,111,15,40,4,9, 387,1,1,0,0,0, 2, 325,3,250000,4,3,128,4,500 +"5",203,481,6,0,0,342,96,2365,2384,60,4,1,78,9,74,9,9, 391,1,1,0,0,0, 2, 322,3,250000,4,3,128,4,500 +"5",26,482,6,0,0,334,104,2365,2384,20,4,1,113,15,38,4,9, 410,1,1,0,0,0, 2, 301,3,250000,4,3,128,4,500 +"5",189,480,6,0,1,337,102,2365,2384,53,4,1,72,11,81,6,9, 393,1,1,0,0,0, 2, 321,3,250000,4,3,128,4,500 +"5",233,482,6,0,1,336,103,2365,2384,84,4,1,108,12,44,7,9, 388,1,1,0,0,0, 2, 325,3,250000,4,3,128,4,500 +"5",102,482,6,0,0,334,105,2365,2384,61,4,1,111,11,40,8,9, 402,1,1,0,0,0, 2, 310,3,250000,4,3,128,4,500 +"5",130,481,6,0,1,342,97,2365,2384,37,4,1,78,14,73,4,9, 398,1,1,0,0,0, 2, 314,3,250000,4,3,128,4,500 +"5",63,480,6,0,1,338,101,2365,2384,85,4,1,70,10,83,7,9, 406,1,1,0,0,0, 1, 308,3,250000,4,3,128,4,500 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2, 308,3,250000,4,3,128,4,500 +"5",239,483,7,0,0,340,100,2365,2384,84,4,1,110,12,40,6,9, 387,1,0,0,0,0, 2, 324,3,250000,4,3,128,4,500 +"5",119,483,6,0,0,333,106,2365,2384,57,4,1,107,10,44,8,9, 400,1,1,0,0,0, 2, 312,3,250000,4,3,128,4,500 +"5",118,482,6,0,0,336,104,2365,2384,84,4,1,107,10,45,8,9, 400,1,1,0,0,0, 2, 312,3,250000,4,3,128,4,500 +"5",170,479,6,0,0,334,105,2365,2384,85,4,1,67,14,88,1,9, 395,1,1,0,0,0, 2, 321,3,250000,4,3,128,4,500 +"5",159,482,3,0,0,341,99,2365,2384,85,4,1,79,17,74,4,9, 395,1,0,0,0,0, 1, 318,3,250000,4,3,128,4,500 +"5",27,484,6,0,1,337,103,2365,2384,85,4,1,111,15,39,4,9, 410,1,1,0,0,0, 2, 301,3,250000,4,3,128,4,500 +"5",246,484,6,0,1,333,106,2365,2384,83,4,1,108,13,42,7,9, 386,1,1,0,0,0, 2, 325,3,250000,4,3,128,4,500 +"5",80,481,3,0,1,339,101,2365,2384,86,4,1,72,16,82,3,9, 404,1,1,0,0,0, 1, 311,3,250000,4,3,128,4,500 +"5",188,481,6,0,1,336,104,2365,2384,84,4,1,71,13,83,3,9, 393,1,0,0,0,0, 2, 322,3,250000,4,3,128,4,500 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304,3,250000,4,3,128,4,500 +"5",242,483,6,0,1,334,106,2365,2384,84,4,1,107,15,45,3,9, 387,1,1,0,0,0, 2, 326,3,250000,4,3,128,4,500 +"5",137,482,6,0,1,339,102,2365,2384,85,4,1,69,13,84,4,9, 397,1,1,0,0,0, 2, 316,3,250000,4,3,128,4,500 +"5",234,483,6,0,1,337,103,2365,2384,84,4,1,106,11,45,7,9, 388,1,1,0,0,0, 2, 325,3,250000,4,3,128,4,500 +"5",186,482,6,0,1,342,98,2365,2384,84,4,1,75,13,78,3,9, 393,1,0,0,0,0, 2, 321,3,250000,4,3,128,4,500 +"5",54,488,6,0,0,342,99,2365,2384,84,4,1,115,16,33,7,9, 407,1,0,0,0,0, 2, 301,3,250000,4,3,128,4,500 +"5",9,488,6,0,0,342,99,2365,2384,85,4,1,117,13,31,9,9, 412,1,1,0,0,0, 2, 297,3,250000,4,3,128,4,500 +"5",158,486,9,1,1,336,105,2365,2384,85,4,1,113,14,35,3,9, 396,1,0,0,0,0, 2, 314,3,250000,4,3,128,4,500 +"5",214,483,7,1,0,335,106,2365,2384,38,4,1,107,12,45,3,9, 390,1,1,0,0,0, 2, 323,3,250000,4,3,128,4,500 +"5",85,483,6,0,1,335,105,2365,2384,38,4,1,105,13,48,4,9, 403,1,1,0,0,0, 2, 310,3,250000,4,3,128,4,500 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319,3,250000,4,3,128,4,500 +"5",216,482,6,0,1,342,99,2365,2384,85,4,1,71,13,83,3,9, 389,1,1,0,0,0, 2, 326,3,250000,4,3,128,4,500 +"5",58,479,6,0,0,336,105,2365,2384,85,4,1,66,10,92,2,9, 407,1,1,0,0,0, 2, 312,3,250000,4,3,128,4,500 +"5",103,485,6,1,1,336,105,2365,2384,60,4,1,108,10,44,8,9, 402,1,0,0,0,0, 2, 310,3,250000,4,3,128,4,500 +"5",83,485,6,0,1,336,105,2365,2384,85,4,1,105,15,46,4,9, 404,1,0,0,0,0, 2, 308,3,250000,4,3,128,4,500 +"5",147,489,6,0,1,341,100,2365,2384,85,4,1,114,17,34,5,9, 397,1,1,0,0,0, 2, 312,3,250000,4,3,128,4,500 +"5",92,485,6,0,1,336,106,2365,2384,38,4,1,108,8,44,10,9, 403,1,1,0,0,0, 2, 310,3,250000,4,3,128,4,500 +"5",231,485,7,0,0,337,105,2365,2384,60,4,1,107,12,44,6,9, 388,1,1,0,0,0, 2, 324,3,250000,4,3,128,4,500 +"5",176,484,6,0,0,343,99,2365,2384,84,4,1,76,14,77,3,9, 394,1,1,0,0,0, 2, 319,3,250000,4,3,128,4,500 +"5",165,488,9,1,1,338,104,2365,2384,84,4,1,113,10,36,7,9, 395,1,1,0,0,0, 2, 314,3,250000,4,3,128,4,500 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2, 317,3,250000,4,3,128,4,500 +"5",82,481,3,0,1,341,102,2365,2384,84,4,1,67,14,90,3,9, 403,1,1,0,0,0, 1, 314,3,250000,4,3,128,4,500 +"5",173,485,6,0,1,341,102,2365,2384,61,4,1,72,11,81,6,9, 395,1,1,0,0,0, 2, 319,3,250000,4,3,128,4,500 +"5",41,485,7,0,0,344,99,2365,2384,84,4,1,75,9,78,7,9, 408,1,0,0,0,0, 2, 305,3,250000,4,3,128,4,500 +"5",227,485,6,0,1,336,106,2365,2384,84,4,1,102,14,51,3,9, 388,1,1,0,0,0, 2, 325,3,250000,4,3,128,4,500 +"5",52,483,6,0,1,339,105,2365,2384,84,4,1,65,12,89,4,9, 407,1,1,0,0,0, 2, 308,3,250000,4,3,128,4,500 +"5",49,485,6,0,1,338,105,2365,2384,85,4,1,72,13,81,3,9, 408,1,1,0,0,0, 2, 306,3,250000,4,3,128,4,500 +"5",61,485,7,0,1,341,102,2365,2384,53,4,1,71,9,82,6,9, 406,1,1,0,0,0, 2, 308,3,250000,4,3,128,4,500 +"5",66,485,6,0,1,345,99,2365,2384,64,4,1,73,9,81,8,9, 405,1,1,0,0,0, 2, 309,3,250000,4,3,128,4,500 +"5",201,486,6,0,0,347,97,2365,2384,84,4,1,79,9,73,9,9, 391,1,1,0,0,0, 2, 322,3,250000,4,3,128,4,500 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324,3,250000,4,3,128,4,500 +"5",184,485,6,0,1,338,106,2365,2384,84,4,1,67,14,88,2,9, 393,1,0,0,0,0, 2, 322,3,250000,4,3,128,4,500 +"5",99,487,6,0,1,338,106,2365,2384,84,4,1,105,10,47,8,9, 402,1,1,0,0,0, 2, 311,3,250000,4,3,128,4,500 +"5",220,485,6,0,1,345,99,2365,2384,39,4,1,71,9,83,6,9, 389,1,1,0,0,0, 2, 326,3,250000,4,3,128,4,500 +"5",122,487,6,0,0,339,105,2365,2384,84,4,1,106,8,46,10,9, 400,1,1,0,0,0, 2, 313,3,250000,4,3,128,4,500 +"5",131,487,6,0,1,342,102,2365,2384,85,4,1,74,14,79,3,9, 399,1,0,0,0,0, 2, 315,3,250000,4,3,128,4,500 +"5",47,486,7,0,1,342,102,2365,2384,84,4,1,68,9,86,6,9, 408,1,1,0,0,0, 2, 306,3,250000,4,3,128,4,500 +"5",62,485,6,0,1,343,102,2365,2384,84,4,1,70,9,85,6,9, 406,1,1,0,0,0, 2, 309,3,250000,4,3,128,4,500 +"5",136,486,6,0,1,340,105,2365,2384,85,4,1,69,14,85,2,9, 398,1,0,0,0,0, 2, 317,3,250000,4,3,128,4,500 +"5",217,487,7,0,1,346,100,2365,2384,84,4,1,71,6,82,9,9, 389,1,1,0,0,0, 2, 325,3,250000,4,3,128,4,500 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312,3,250000,4,3,128,4,500 +"5",224,487,6,0,1,347,99,2365,2384,84,4,1,72,13,82,2,9, 388,1,1,0,0,0, 2, 327,3,250000,4,3,128,4,500 +"5",206,486,6,0,1,341,104,2365,2384,84,4,1,66,9,89,6,9, 391,1,0,0,0,0, 2, 325,3,250000,4,3,128,4,500 +"5",72,487,6,0,0,340,104,2365,2384,84,4,1,71,8,83,8,9, 404,1,1,0,0,0, 2, 310,3,250000,4,3,128,4,500 +"5",112,487,6,0,0,342,105,2365,2384,85,4,1,71,12,83,4,9, 401,1,1,0,0,0, 2, 314,3,250000,4,3,128,4,500 +"5",133,488,6,0,0,340,107,2365,2384,85,4,1,70,12,83,4,9, 398,1,1,0,0,0, 2, 316,3,250000,4,3,128,4,500 +"5",42,487,6,0,1,343,104,2365,2384,84,4,1,70,11,85,4,9, 408,1,1,0,0,0, 2, 307,3,250000,4,3,128,4,500 +"5",135,488,6,0,1,340,106,2365,2384,35,4,1,71,14,83,1,9, 398,1,1,0,0,0, 2, 317,3,250000,4,3,128,4,500 +"5",31,485,7,0,0,341,106,2365,2384,85,4,1,68,9,89,3,9, 409,1,1,0,0,0, 2, 308,3,250000,4,3,128,4,500 +"5",140,487,6,0,0,342,105,2365,2384,61,4,1,67,14,88,1,9, 398,1,0,0,0,0, 2, 318,3,250000,4,3,128,4,500 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400,1,1,0,0,0, 1, 488,3,250000,4,3,128,4,500 +"6",265,668,4,0,0,340,297,3189,3172,89,4,1,29,5,132,1,9, 412,2,1,0,0,0, 1, 475,3,250000,4,3,128,4,500 +"6",377,680,4,0,0,344,305,3189,3172,134,4,1,22,5,140,1,9, 400,1,1,0,0,0, 1, 488,3,250000,4,3,128,4,500 +"6",406,683,4,0,0,341,312,3189,3172,87,4,1,31,5,132,0,9, 397,1,1,0,0,0, 2, 492,3,250000,4,3,128,4,500 From 9ef20146277208021065784d6d43c0cdb3f52cf5 Mon Sep 17 00:00:00 2001 From: srinath kailasa Date: Wed, 1 Oct 2025 14:09:29 +0100 Subject: [PATCH 05/52] Notebook --- .../data/ijhpca/Weak Scaling (16 Nodes).ipynb | 427 ++++++++---------- 1 file changed, 194 insertions(+), 233 deletions(-) diff --git a/hpc/archer2/data/ijhpca/Weak Scaling (16 Nodes).ipynb b/hpc/archer2/data/ijhpca/Weak Scaling (16 Nodes).ipynb index 09986171..297c2874 100644 --- a/hpc/archer2/data/ijhpca/Weak Scaling (16 Nodes).ipynb +++ b/hpc/archer2/data/ijhpca/Weak Scaling (16 Nodes).ipynb @@ -264,7 +264,18 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 100, + "id": "3bff612b-6a2b-449e-a301-cb45048cf545", + "metadata": {}, + "outputs": [], + "source": [ + "n_tasks=np.array([8, 16, 32, 64, 128, 256, 512])\n", + "n_nodes=np.array([1, 1, 1, 2, 4, 8, 16])" + ] + }, + { + "cell_type": "code", + "execution_count": 208, "id": "caf1a774-3904-4e65-95bf-f37f1180a3d6", "metadata": {}, "outputs": [], @@ -273,7 +284,7 @@ " npoints = []\n", " runtime = []\n", " for (id, experiment) in df.groupby('experiment_id'):\n", - " n = experiment['n_points'].max()*experiment.shape[0]\n", + " n = experiment['n_points'].max() * experiment.shape[0]\n", " npoints.append(n)\n", " r = experiment['runtime'].max()\n", " runtime.append(r)\n", @@ -281,41 +292,119 @@ " npoints = np.array(npoints)\n", " runtime = np.array(runtime)\n", " \n", + " # unique processor counts for each experiment (same length as npoints)\n", + " nprocs = np.array([n_tasks[i] for i in range(len(npoints))])\n", + "\n", + " # sort everything by npoints for consistent region layout\n", + " sort_idx = np.argsort(npoints)\n", + " npoints = npoints[sort_idx]\n", + " runtime = runtime[sort_idx]\n", + " nprocs = nprocs[sort_idx]\n", + "\n", " fig, ax = plt.subplots()\n", " ax.plot(npoints, runtime, marker=\"o\")\n", " ax.set_xscale('log')\n", - " \n", - " return (fig, ax, npoints, runtime)\n", + " plt.ylim(np.min(runtime) - 10)\n", + "\n", + " # Work in log space for region boundaries\n", + " log_x = np.log10(npoints)\n", + " y0, y1 = ax.get_ylim()\n", + "\n", + " # Midpoints between data points (log space)\n", + " mids = (log_x[:-1] + log_x[1:]) / 2\n", + " # Region edges = start, mids, end\n", + " region_edges = np.concatenate([[log_x[0]], mids, [log_x[-1]]])\n", + "\n", + " # Shade per-point region\n", + " for i in range(len(npoints)):\n", + " left = 10**region_edges[i]\n", + " right = 10**region_edges[i+1]\n", + "\n", + " ax.axvspan(left, right, alpha=0.15,\n", + " color='orange' if i % 2 == 0 else 'lightblue')\n", + "\n", + " # Label at the data point’s x position\n", + " ax.text(npoints[i], (y0 + y1) / 2, f\"{nprocs[i]} ranks\",\n", + " ha='center', va='center', rotation=90,\n", + " bbox=dict(facecolor='white', alpha=0.6, edgecolor='none'))\n", + "\n", + " ax.set_title(f\"Weak Scaling to {npoints.max():.2e} points\")\n", + " ax.set_xlabel('Number of Points')\n", + " ax.set_ylabel('Runtime (ms)')\n", + "\n", + " return fig, ax, nprocs, runtime\n", + "\n", "\n", "\n", "def runtime_vs_nprocesses(df):\n", - " nprocs = []\n", + " nranks = []\n", " runtime = []\n", " for (id, experiment) in df.groupby('experiment_id'):\n", " n = experiment.shape[0]\n", - " nprocs.append(n)\n", - " r = experiment['runtime'].max()\n", + " nranks.append(n)\n", + " r = experiment['runtime'].mean()\n", " runtime.append(r)\n", " \n", - " nprocs = np.array(nprocs)\n", + " nranks = np.array(nranks)\n", " runtime = np.array(runtime)\n", - " \n", + "\n", + " # unique sorted processor counts\n", + " nprocs = np.unique([n_nodes[i] for i in range(len(nranks))])\n", + " boundaries = nprocs * 64\n", + "\n", " fig, ax = plt.subplots()\n", - " ax.plot(nprocs, runtime, marker=\"o\")\n", - " # ax.set_xscale('log')\n", - " \n", - " return (fig, ax, nprocs, runtime)" + " ax.plot(nranks, runtime, marker=\"o\")\n", + " ax.set_xscale('log')\n", + " plt.ylim(np.min(runtime)-10)\n", + "\n", + " # Region edges (start, between boundaries, end)\n", + " region_edges = np.concatenate([[nranks.min()], boundaries, [nranks.max()]])\n", + "\n", + " # Shade regions for each processor count\n", + " y0, y1 = ax.get_ylim()\n", + "\n", + " # shrink factor in log space (e.g. 5% inside)\n", + " shrink = 0.1 \n", + "\n", + " for i in range(len(nprocs)):\n", + " left = region_edges[i]\n", + " right = region_edges[i+1]\n", + "\n", + " # shift inward in log space\n", + " log_left, log_right = np.log10(left), np.log10(right)\n", + " log_pad = (log_right - log_left) * shrink\n", + " log_left_adj = log_left - log_pad\n", + " log_right_adj = log_right - log_pad\n", + "\n", + " left_adj = 10**log_left_adj\n", + " right_adj = 10**log_right_adj\n", + "\n", + " ax.axvspan(left_adj, right_adj, alpha=0.15,\n", + " color='orange' if i % 2 == 0 else 'lightblue')\n", + "\n", + " # label in the log-midpoint\n", + " xmid = 10**((log_left + log_right) / 2)\n", + " ax.text(xmid, (y0 + y1) / 2, f\"{nprocs[i]} nodes\",\n", + " ha='center', va='center', rotation=90,\n", + " bbox=dict(facecolor='white', alpha=0.6, edgecolor='none'))\n", + "\n", + " ax.set_xlabel('Number of Ranks')\n", + " ax.set_ylabel('Runtime (ms)')\n", + "\n", + " return fig, ax, nprocs, runtime\n", + "\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 209, "id": "022c161a-007f-4f90-8f6d-3b73ed57dc00", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -325,18 +414,39 @@ } ], "source": [ - "(fig, ax, npoints_250k, runtime_250k) = runtime_vs_npoints(df_250k)" + "(fig, ax, nprocs_250k, runtime_250k) = runtime_vs_npoints(df_250k)" ] }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 210, + "id": "3de223dc-2c25-41f3-9ed9-f295efc63c34", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 8, 16, 32, 64, 128, 256, 512])" + ] + }, + "execution_count": 210, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n_tasks" + ] + }, + { + "cell_type": "code", + "execution_count": 211, "id": "eb5e74a3-f5b1-4188-8cc9-0887efacbb2e", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -351,34 +461,47 @@ }, { "cell_type": "code", - "execution_count": 60, - "id": "4490d705-be26-482c-beda-918422da1555", + "execution_count": 212, + "id": "698e8d04-6885-4eff-86f3-e58a2b944579", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 8, 16, 32, 64, 128, 256, 512])" + "(
,\n", + " ,\n", + " array([ 8, 16, 32, 64, 128, 256, 512]),\n", + " array([4046, 1599, 2010, 4134, 1660, 2152, 4325]))" ] }, - "execution_count": 60, + "execution_count": 212, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", 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cc6IaCquvlNehsk5ntdgIsYTTRVXQ6o1QuEsQEeAhdDguzy4SokmTJmHbtm3Yu3cv2rZty9+uVCqh1WpRUVFhdn5RURGUSiV/zj9XnXHX73SOQqG46egQAMhkMigUCrOLvTEYWcxLK8HNJse42+btKqHpM+KQTqnU0BqM8HF3Q1u/m79P/8nHQ4o2DeeeomkzYuf4+qE2PrTDvR0QNCFiWRaTJk3C5s2bsWfPHnTo0MHsePfu3eHm5obdu3fzt+Xl5eHSpUtITk4GACQnJyMnJwfFxcX8OWlpaVAoFIiOjubPufExuHO4x3BURy/XNRoZuhELoFCtx9HLdbYLihAL4eqHosMUzfqw4Bo0Uj8iYu+u1w/5ChsIASBwQjRx4kT897//xbp16+Dt7Q2VSgWVSoW6OtMHuI+PD8aPH49p06Zh7969yMjIwHPPPYfk5GT06dMHADBo0CBER0djzJgxyMrKwo4dO/DWW29h4sSJkMlkAICXXnoJ58+fx2uvvYZTp07hs88+w//+9z9MnTpVsNduCcXVBoueR4g94VaYxTSzWR1fR0QjRMTOZV+5PkJEhCdoQvT555+jsrISAwYMQGhoKH/ZsGEDf87SpUsxfPhwjBo1Cv3794dSqcSmTZv442KxGNu2bYNYLEZycjKefvppPPPMM5g/fz5/TocOHfDLL78gLS0NCQkJ+Oijj/DVV185/JL7YK+m7Yrc1PMIsSfcCFFMMz8somiEiDiAyjodLpSaeu41ddEAsS5BV5mx7J1rW+RyOVasWIEVK1bc8pyIiAj8+uuvt32cAQMG4O+//252jPasV7g7Qr0lUFXpb1pHxABQKiToFd60+gtC7EVVvQ5nS0wLAmLCmjdCxG3yevFaLarr9fCS281iWkJ43B59bf3c4e8pFTgaAthJUTVpGbGIwZwHggCYkp8bcdfnpARBLKJiPeJYcq5WgmWBMF85Arxkzbqvv6cUSh85AJo2I/aL+g/ZH0qIHNzgSC98/kgolN7m34KV3hJ8/kgo9SEiDomrrUhoYbEp1RERe5dD9UN2hxIiJzA40gsHJ7bHmifCwA0GrX+6DSVDxGFx9UPxLUyIqI6I2DOWZWnLDjtECZGTEIsY3NvRE9EhpumF4yqNwBER0nLXE6KWfXumESJizwoq6lFWo4VEzPDNRInwKCFyMvGhpoQoq5ASIuKYiqvqUVBZD4Zpeofqf+J6EeWX1qBWc+teXYQIIedKBQCgW4g3ZG60CtheUELkZBLDTMWkWQX1AkdCSMtkXzZNJXQJ9oKXrGUrxAK9ZQj2loFlgTxVlSXDI6TVsqmg2i5RQuRk4kNNCVGOqp627CAOKavh23NLC6o5fB0RTZsRO0MrzOwTJUROpkugFB5uDGq0LM5f0wodDiHNlsnVD4X7tupxuDoiKqwm9kRnuGGHeyqotiuUEDkZsYhBrJLqiIhjYlmWn05IbO0IESVExA6dKaqGRm+Et1yCCH/a4d6eUELkhBKojog4qIvXalFZp4NUIkI3ZetW33AjROdLqlGnpf38iH3gCqrj2vhARE1z7QolRE6IqyPKLqSEiDgWrn4oJkwBqaR1f56CvWXw95TCyAKni6iwmtgHbgSU9i+zP5QQOaGEhoToRJEGGr1R4GgIaTqufqi1BdUAwDAM9SMidocrqG5pjy1iPZQQOaFwXwn83EXQGYFTxVRYTRwH15AxIdwyHxbUsZrYk6p6HfIbdringmr7QwmRE2IYhp82ozoi4ih0BiOONyQulhghAkxTbwBwkhIiYgdyr5p+D9vQDvd2iRIiJ8UXVlMdEXEQeaoqaPRGKOQStA/wtMhjcivNzhZXQ6OjwmoirBsLqon9oYTISSVwW3gU0NJ74hi4gur4tr4WW30T6iOHr4cb9EYWZ4urLfKYhLQUNWS0b5QQOSluyuzcNS2qNVRYTeyfpeuHANP0MVdHdJymzYiAbtzhngqq7RMlRE4qyEuCNgoJWJi28SDE3nHLkS1VP8ShlWbEHhRW1uNatRYSEYPIhiSd2BdKiJwYNWgkjqJGo+d7BSW2csuOf6KVZsQecAl/lxBvyGmHe7tECZETi2+oI8qmLTyIncu9WgkjCygVcgQr5BZ9bG6E6ExRFXTUl4sIJIevkaPpMntFCZETo6X3xFHwO9xbsH6I09bPHd5yCXQGFmdLqLCaCCOXCqrtHiVETiwuVAYGwFW1HiXVeqHDIeSWsrj6IQtPlwENHatDqR8REY7OYMQJfod7SojsFSVETsxbJkanAFPzL9rXjNgzboVZa3e4vxWuHxHVEREhnC2uRr3OtMO9pXpsEcujhMjJJYQ19COiOiJip0qrNbhSXgfAehte0kozIiSu/1BMGO1wb88oIXJyCVRHROxcdkP9UKcgTyjkblZ5Dm6lWZ6qCnoDFVYT26KGjI6BEiInxy29zy6sB8uyAkdDSGNZl61XP8Rp5+8BT5kYGr0R5xs21yTEVmiFmWOghMjJRQZL4SYCyuuMuFJJhdXE/nArzCzdf+hGIhGDSCXVERHbq67X80m4taaEiWVQQuTkZBIRokNMdUSZNG1G7AzLsnxBdbyVCqo5fB0RJUTEhnKvVoJlgTBfOQK9ZEKHQ26DEiIXwPUjopVmxN5cKa9Dea0ObmIGUaHeVn0uWmlGhJB7lasf8hU2EHJHlBC5gHjawoPYqcyG0aHoUAVkEutuZxATdr2w2mCkejpiG9yWHXFtaLrM3lFC5AISG7bwyFFpoKcPAmJHru9w72v154oI8IS7VIw6nQEXqLCa2ADLsnxBNa0ws3+UELmAjgFSeEoZ1OlYnC3VCh0OIbwsfvWNr9WfSyxiEKk0TcudoH5ExAZUlfUordZCLGL41g/EflFC5ALEIgZxSqojIvZFbzAi96opMUm0wh5mN8N9KFEdEbGFnIb6oa4hXnCX0g739k7QhGj//v148MEHERYWBoZhsGXLFrPjRUVFePbZZxEWFgYPDw8MHjwYZ86cMTunvr4eEydOREBAALy8vDBq1CgUFRWZnXPp0iUMGzYMHh4eCA4OxowZM6DXu9YSdK4fEa00I/biTHE16nQGeMkk6BjoZZPnpJVmxJauN2T0FTYQ0iSCJkQ1NTVISEjAihUrGh1jWRYjR47E+fPn8dNPP+Hvv/9GREQEUlJSUFNzff5/6tSp2Lp1KzZu3Ih9+/ahoKAAjzzyCH/cYDBg2LBh0Gq1+OOPP7BmzRp88803mD17tk1eo71IaKgjyqYtPIiduL7c3nbbGXArzU6p1DBSPR2xsmzqUO1QJEI++ZAhQzBkyJCbHjtz5gwOHz6M3NxcxMTEAAA+//xzKJVKfP/993j++edRWVmJVatWYd26dRg4cCAA4Ouvv0ZUVBQOHz6MPn36YOfOnThx4gR27dqFkJAQJCYm4p133sHMmTMxd+5cSKVSm71eIXErzU4Va1CvN0IuodlSIixb1g9xOgZ6QiYRoUZjwKWyWrQPpI02iXXoDUZ+JJJWmDkGu/1U1GhMIxlyuZy/TSQSQSaT4eDBgwCAjIwM6HQ6pKSk8OdERkaiXbt2SE9PBwCkp6cjLi4OISEh/DmpqalQq9U4fvz4bZ9frVabXRxZG4UEgR5i6I3AiSIaJSLC47bssFX9EABIxCJ04wqradqMWNHZG6aEO1Di7RDsNiHiEptZs2ahvLwcWq0WixYtwpUrV1BYWAgAUKlUkEql8PX1NbtvSEgIVCoVf86NyRB3nDt2KwsWLICPjw9/CQ8Pt+Crsz2GYRDfsPN9dgElRERYdVoD8oqqANhmyf2NuGkz2vmeWBNXUB3TRkE73DsIu02I3NzcsGnTJpw+fRr+/v7w8PDA3r17MWTIEIhE1g971qxZqKys5C+XL1+2+nNaG9exOotWmhGBHS80NUcM9pZBqZDf+Q4WFE0rzYgNcAXVtpwSJq0jaA3RnXTv3h2ZmZmorKyEVqtFUFAQevfujR49egAAlEoltFotKioqzEaJioqKoFQq+XOOHj1q9rjcKjTunJuRyWSQyZxr35kE6lhN7ETmDR8WDGPbb8/RN4wQsSxr8+cnroFLiGKpfshh2O0I0Y18fHwQFBSEM2fO4NixY3jooYcAmBImNzc37N69mz83Ly8Ply5dQnJyMgAgOTkZOTk5KC4u5s9JS0uDQqFAdHS0bV+IwBIaRojOl+lQWW8QOBriyrKv2Lb/0I06BXnBTcygql6PK+V1Nn9+4vyq6/U4V1INwLSKkjgGQUeIqqurcfbsWf56fn4+MjMz4e/vj3bt2mHjxo0ICgpCu3btkJOTg1deeQUjR47EoEGDAJgSpfHjx2PatGnw9/eHQqHA5MmTkZycjD59+gAABg0ahOjoaIwZMwaLFy+GSqXCW2+9hYkTJzrdCNCd+HuIEe4rweUKPXILNbing4fQIREXldWQENm6fggA3CQidA3xxvECNU4UqBHuT+8DYlknCkw73If6yBHo7VqfM45M0BGiY8eOISkpCUlJSQCAadOmISkpie8RVFhYiDFjxiAyMhJTpkzBmDFj8P3335s9xtKlSzF8+HCMGjUK/fv3h1KpxKZNm/jjYrEY27Ztg1gsRnJyMp5++mk888wzmD9/vu1eqB3h6ogyqY6ICKS8zoiLZaaRmfg2voLEwBVWUx0RsQbqP+SYBB0hGjBgAFj21s3RpkyZgilTptz2MeRyOVasWHHT5o6ciIgI/Prrry2O05kkhsnxy8lqZFMdERFIlsrUJb5DoCd8PNwEiSGaVpoRK+JWmFH/IcfiEDVExHK4ESLqWE2Ekt2QECUI+O35xpVmt/tSRkhzmXa4py07HBElRC4mVimDiAEKq/Qornat/dyIfcgq1AEQpn6I0yXEGxIRg8o6HQorabSUWE6RWoOSKg3EIoYfiSSOgRIiF+MpFaFLoGm7Elp+T2yNZVl+ykzIhEgqEaFzsGlDWaojIpaU07AlTZdg2uHe0VBC5IL4Bo2UEBEbu6o2oLSWhUTE8NNWQommwmpiBXz/ISqodjiUELmghIYtPLKojojYGFe7Fqn0gtxN2G/PtNKMWEM233SUEiJHQwmRC0rgC6vrqaCU2FRWoRYAkNBW+NoKboSK61hNSGvpDUY+waaCasdDCZEL6hYsg1TMoLLeiIvlOqHDIS4ks2GESMgVZpyuSm+IRQzKarQoUtNoKWm9cyU1qNMZ4CkT0w73DogSIhckFTOIDuGmzaiOiNiGwcgiR2U/I0RyNzE6Bpk+tKgfEbEErqA6JswHYtrh3uFQQuSiErk6ogL6Zkxs49w1LWp1LDzcgM5B9vHtOSqU6oiI5eRQh2qHRgmRi4q/oY6IEFvIbFjVGBcisZtvz3zHakqIiAXwHaopIXJIlBC5qIQwU0KUq9JAb6SCUmJ93GhkYqgw23XcDN+xmqbMSCvVavQ4V8ztcO8rbDCkRSghclEd/N3gLROhXs/idIlW6HCIC+Dq1eKVgm6haKZbqDcYBiipMnUXJqSljheoYWSBEIUcQbTDvUOihMhFiRgGcUqujoimzYh11euNOFXcsMLMjhIiD6mEXw1EhdWkNbIbCqqp/5DjooTIhXHTZlRHRKztRJEGeiMQ6CFCG4V9/dmhjtXEEnKpfsjh2ddfJmJTXIPGTBohIlbG1Q8lhMrAMPZRUM2JuqFBIyEtlU0rzBweJUQujNvC43SJFnU6o8DREGfGTcvGh0oFjqQxGiEirVWkrkexWgMRA8H36CMtRwmRC1N6SxDkKYaBBY6rqKCUWA83LZsQan/FppFK0weYqrIeZTW0wIA0H9d/qHOwFzxk9lMjR5qHEiIXxjAMX0dEHauJtVTWGXC+zLRFTIIdjhB5ySVoH+ABgPoRkZa53pDRV9hASKtQQuTirm/0SiNExDqyVaZkO8LPDX7uwu5wfyvczvdUR0RaIodWmDkFSohcXEIYLb0n1sUVVMfb4XQZJ5q28CAtZDCyON7wexNLCZFDo4TIxXFbeFwo16GiziBwNMQZcdOx3PSsPeJGiKhjNWmucyXVqNUa4CEVo1OQl9DhkFaghMjF+bqLEeFn2kqB+hERS2NZFplXTb9XiaF2nBA1jBBdLa9DZS0VVpOmy22oH4oJU9jNHn2kZSghIvzKH6ojIpamqtKjpMYAMQPEKO13ykzh7oa2fu4AgJOFVQJHQxxJNhVUOw1KiAjiw6hBI7EOrn6oa5AU7m72/eeG+hGRlqCCaudh33+hiE1wUxlZBfVgWVbgaIgz4eqHEu24fohDHatJc9Vq9DjbsMM9FVQ7PkqICGKUMogZoKTGAFWVXuhwiBPhVi/ac0E1h0aISHNxO9wHK2QIUdj/7zi5PUqICNzdROgaZGqYl0V1RMRCjCzL16XF23FBNYdLiC6V1aKqXidwNMQRcBu60nSZc6CEiAC4/g2eVpoRSzl/TYdqrRFyCcMn3PbM10OKMF/T++AUFVaTJuA7VLfxFTYQYhHN3nQlPz8fBw4cwMWLF1FbW4ugoCAkJSUhOTkZcrn9fwskN5cQKsf6TDU1aCQWw9UPxSllkDjIcuSoUAUKKupxokCNnh38hQ6H2Dna4d65NDkhWrt2LZYvX45jx44hJCQEYWFhcHd3R1lZGc6dOwe5XI7Ro0dj5syZiIiIsGbMxAriw65v4WFkWYgYx/gAI/bLkeqHONFhCuw+WUwNGskdFavrUaSuh4gx9SAijq9JCVFSUhKkUimeffZZ/PjjjwgPDzc7rtFokJ6ejvXr16NHjx747LPP8Nhjj1klYGIdXQOlkEkYVGmMyC/ToVOA/U9xEPvGJUSOUD/E4VeaUWE1uYOchvqhTrTDvdNo0k9x4cKFSE1NveVxmUyGAQMGYMCAAXjvvfdw4cIFS8VHbMRNzCBWKUPGlXpkF9ZTQkRaRaM34kSRqaDaEZbcc7gtPC5cq0GNRg9P+qAjt8DVD1FBtfNoUlH17ZKhfwoICED37t1bHBARTvwN/YgIaY1TxVrojICfuwjhvo6TVAR6yRCskIFlgTwVFVaTW+MSotg2lBA5i2avMvvrr7+Qk5PDX//pp58wcuRIvPHGG9Bqm7cH0P79+/Hggw8iLCwMDMNgy5YtZserq6sxadIktG3bFu7u7oiOjsbKlSvNzqmvr8fEiRMREBAALy8vjBo1CkVFRWbnXLp0CcOGDYOHhweCg4MxY8YM6PXUb+efErmd72npPWmlG+uHGAerR6N+RORODEaWX3JPW3Y4j2YnRC+++CJOnz4NADh//jyeeOIJeHh4YOPGjXjttdea9Vg1NTVISEjAihUrbnp82rRp2L59O/773//i5MmTePXVVzFp0iT8/PPP/DlTp07F1q1bsXHjRuzbtw8FBQV45JFH+OMGgwHDhg2DVqvFH3/8gTVr1uCbb77B7Nmzm/vSnR43QnRcpYHWQB2rSctlFjpe/RAnmjpWkzvIL61BrdYAd6kYnYNph3tn0eyx7NOnTyMxMREAsHHjRvTv3x/r1q3DoUOH8MQTT2DZsmVNfqwhQ4ZgyJAhtzz+xx9/YOzYsRgwYAAAYMKECfjPf/6Do0ePYsSIEaisrMSqVauwbt06DBw4EADw9ddfIyoqCocPH0afPn2wc+dOnDhxArt27UJISAgSExPxzjvvYObMmZg7dy6kUqqV4bT3c4NCLoK63ojTJRrEKh3vw4zYh+wCx6sf4tAIEbkTbv8yh93hXh5s/eeoL7b+c1hYs0eIWJaF0WgEAOzatQtDhw4FAISHh6O0tNSiwd199934+eefcfXqVbAsi7179+L06dMYNGgQACAjIwM6nQ4pKSn8fSIjI9GuXTukp6cDANLT0xEXF4eQkBD+nNTUVKjVahw/fvyWz63RaKBWq80uzo5hGCTwdUQ0bUZaRl1vwLlrpunz+FD73eH+VriVZudLqlGnNQgcDbFHfP8hqh9yKs1OiHr06IF3330X3333Hfbt24dhw4YBMDVsvDHpsIRPPvkE0dHRaNu2LaRSKQYPHowVK1agf//+AACVSgWpVApfX1+z+4WEhEClUvHn/DMu7jp3zs0sWLAAPj4+/OWfrQacVUIoV0dEhdWkZXJVGrAA2vpIEOjpOAXVnGCFHIFeUhipsJrcQg41ZHRKzU6Ili1bhr/++guTJk3Cm2++ic6dOwMAfvjhB9x9990WDe6TTz7B4cOH8fPPPyMjIwMfffQRJk6ciF27dln0eW5m1qxZqKys5C+XL1+2+nPaA75BI600Iy2UyRVUO2D9ECeK6ojILdRqr+9wTwXVzqXZX9/i4+PNVplxPvjgA4jFYosEBQB1dXV44403sHnzZn4UKj4+HpmZmfjwww+RkpICpVIJrVaLiooKs1GioqIiKJVKAIBSqcTRo0fNHptbhcadczMymQwymeMN97cW9yF2ulSLWq0RHlIX3O6O5tdbhdsPz5E6VMvE5r/n0x7oiiGxSrQP9Gx0rDU0BqPFHstiXPj3vSU/2yo9i3ceioHcTYwIf487nm+XP3NyU616p1dXV/P1NVqtFnV1dZaKCzqdDjqdDiKReYhisZivYerevTvc3Nywe/du/nheXh4uXbqE5ORkAEBycjJycnJQXHz9DZmWlgaFQoHo6GiLxessQrwlUHpLYGRNUx+ENBdXf+ZICdE/+XqaFluU19Ku98RcWY2pPs7fkxbkOJsWbe46adIk/P7776ivvz6twrIsGIaBwdD0IsTq6mqcPXvW7LEzMzPh7++Pdu3a4d5778WMGTPg7u6OiIgI7Nu3D99++y2WLFkCAPDx8cH48eMxbdo0+Pv7Q6FQYPLkyUhOTkafPn0AAIMGDUJ0dDTGjBmDxYsXQ6VS4a233sLEiRNdcgSoKeJDZVBV6ZFVWI9e7dyFDoc4kKIqPQqr9BAxQKzScd9f/h5uAAB1nQ4GI+uYK4mIVVyjhMhpNTshevrpp8GyLFavXo2QkJBWNV07duwY7rvvPv76tGnTAABjx47FN998g/Xr12PWrFkYPXo0ysrKEBERgffeew8vvfQSf5+lS5dCJBJh1KhR0Gg0SE1NxWeffcYfF4vF2LZtG15++WUkJyfD09MTY8eOxfz581sct7NLCJNj5+ka6lhNmo0rxu8SKIWnA0+3ukvFkEpE0OqNUNfp4EcffqRBeUNCFOBCvxMGgwE5uScQ0S4cfn6+QodjNc1OiLKyspCRkYFu3bq1+skHDBgAlr11A0ClUomvv/76to8hl8uxYsWKWzZ3BICIiAj8+uuvLY7T1fBL72mlGWmmbAfc4f7mGPh5SFGkrkd5rZYSIgIAqNcZUKs17XLgzL8Tr06fhbjYaIx/bgwMBgPuTRmGP9KPwsPDA9s2r8eAe/sKHaJVNPsrXM+ePV1mxZWrimtYen+5Qo+yWurDQpqO2/bFkVeYcfw8GuqIaqiOiJhw9UMKdzdILFhsb29+2PwzEuJjAQBbt21H/oVLOJV9FFOnvIw357wjcHTW0+wRoq+++govvfQSrl69itjYWLi5uZkdj4+Pt1hwRBg+cjE6+rvhfJkO2YX1GNDJU+iQiAMwsiw/qpgQ5rj1Qxw/T9PftvLa5u3RSJxXWbVr1A+Vll6DMsS0+vDX7Wl47JGH0LVrZ4wb+zSWf/ofgaOznmYnRCUlJTh37hyee+45/jaGYVpUVE3sV0KYHOfLdMgqoIToTtRqNfbsPYBuXTsjKqr1U8mO6kKZDup6I2QSBt2CHD8h8m0YIaqs08FoZCG6obDaYDAgNycH7SIi4OfnJ1SIgnCVepJ/UqvV2PrzT/AICsddET2FDseqQkKCcOJkHkJDldietguff/wRAKC2rhZiJx4Za/YrGzduHJKSkpCeno7z588jPz/f7L/EOXCbcmYX0tL7f3r8qefw6WdfADD1y+qRPBCPj34O8T364sfNP9/h3s6L6z8UEyKDm9jxV2V5ysRwk4jAsiymvPIKvl69CoApIUi57z707tkDndpHYN/vvwsbqJW9On0WVn39HQDw9SR39b4X4Z1i8fu+gwJHZz1PPfEvfNZQm1pXV4fk3r3wzrQJmPX0YPyx6xeBo7Ou554ZjcdHP4fYpLvBMAxS7h8AADhyNAOR3boKG5wVNTshunjxIhYtWoTevXujffv2iIiIMLsQ58DtQZVVUH/bwndXtP/gH+jX19TnavNP28CyLCqKL+DjJQvx7oIPBY5OOJlO0H/IHMPXEf28ZTPi4xMAANu2bcWFC/nIPn4CU155FXPeflvIIK3OVetJDh44gL59TcXDP23ZDIPRiC93ZWPs9Hn4+MPFAkdnXXPffh1frfwYE8aPxaG92/kWNWKxGK//+1Vhg7OiZidEAwcORFZWljViIXYkJkQGiQgorTWgQK0XOhy7Ulmphn/DNMn2nbsx6uEH4eHhgWFDBuHMWdcdJeXqhxKdoH6I49fQj6j82jWENHS23/7bb3jk0UfRtWtXjH3uOeTmNu7c70xuV0+Sk3tS4Oisp7KyEn7+/gCAnTt2IGXICMjk7rjvgUE4e/aMwNFZ36OPPISpr/wfAgMD+NvGjnkSD40YKmBU1tXshOjBBx/E1KlTMXfuXPz444/4+eefzS7EOcjdRHwdCPUjMhfetg3Sj/yJmpoabN+5G4NSBgIAyisqIJc7TzLQHFoDi+MNnc3jnWCFGYerI/INCMTJEydgMBiQtmMH7r8/BQBQV1tr0S2L7BFXT2IwGLA9bRceaJg+cfZ6krbh4ThyOB01NTXYuWMH4nv3AwCIdLWQy53nd/xmDAYD3nn/A7TpEA0v/7Y4f/4CAODtue/x06fOqNlF1VxTxJs1NqSiaueSECbD8SINsgo1GBrlLXQ4duPVyS9h9NgJ8PLyRLvwtnxPjv0H0hEX65rbwZwu0UBrYKGQi9Dez+3Od3AQ3AhRv2GPYfSTT0AZGgqGYXB/iikhOnr0CLp1ixQyRKvj6klClUqXqieZPOUVjB0zBl5eXghv1w7t43pBXa9HXuZRxMbGCR2eVb238COs+e57LH5/Ll54+VX+9tiYKCz7ZCXGPzdGuOCsqNkJEbePGHF+CaFyrPtbzRfLEpP/e+l59OrZHZevXMUD9w/g99vr2CEC7859U+DohHHjDvet6V5vbzxlEkjEIjzy/KsY2r8XrhUXYtSjj5rVVPx75msCR2ldc99+HbExUbh8+SoeG/WQy9STvPTyy+jZsyeuXLmMAQNTsOtMJQAgpltn3HePc+908O1/1+OLz5bh/oH34qVJ0/nbE+JicSrvtICRWVezEyLiOrji2JxCDYwsC5ETfdC1Vo/uSYiPi0F+/kV06tQBEokEw4amCh2WYLgNXROdpqDahGEY+Hm4oaRKg76DhqN9oKfZHo5jnhkrYHS28+gjDwGA2WsfO+ZJocKxme49eiAuPh5/Hc+DXucFT3cZhj/0IADn/lt4taAQnTt1aHS7kTVCp3PemtImTQCvX7++yQ94+fJlHDp0qMUBEfvROVAKdzcG1Vojzl+jbr2c2tpajH9xMjx8wxCTlIxLl64AACa/+hoWfrBU4OiEwdWZcasTnYmvhxRGgwEfLXofHdqFw99HwbcYmTt7Nr8c31m5aj1JbW0tXnzhefh6e2Fgn+64VlQAf08pXn3lFXywaJHQ4VlVdFQ3HDiU3uj2Hzb9jKRE550ubFJC9PnnnyMqKgqLFy/GyZONVxVUVlbi119/xVNPPYW77roL165ds3igxPYkIgZxDTuWZ1JhNW/WW/ORlZ2L39O2mhVXpgwcgA0bNwsYmTCqNUacKTV18HWeJffX+Xm6Ycs3n+Lnjd/j/YULIZVe71IcExuDr1etFjA663tv4Uf45tt1WPz+XLPXHhsTha+cOCF66803kJ2djbTdeyBtmCb095Ri4P33Y+PG/wkcnXXNfuM1THr1NSz6cBmMRiM2bdmKF15+Be8t/Aiz33DeKeImJUT79u3DokWLkJaWhtjYWCgUCnTp0gVxcXFo27YtAgICMG7cOLRr1w65ubkYMWKEteMmNnK9QSMlRJwtW3/Bp8sWo+89yWb1MjHRkTjX8O3ZleSq6sECCFNIEOzlfLPwfh5SHPxtE8a//j6eePIps1VlcfEJyMs7JWB01sfVk4x+8nGz1+7s9SRbf/oJy5Z/jHv69gXbMEXm7ylFdHQMzp87J3B01vXQiKHYuul77Nq9D56eHpg9fwFOnsrD1k3f44GU+4QOz2qa/NdrxIgRGDFiBEpLS3Hw4EFcvHgRdXV1CAwMRFJSEpKSkvjiUuI8uISIlt5fV1JyDcFBQY1ur6mtgSuWWTnThq434yWToKxEhaA2EaiqN6+fYI1G6HTOPZ3sqvUkJSUlCAoORr3u+sppP08pVDU1TrVw4Fb69b0bab+51oh3s7/OBQYGYuTIkVYIhdgjrkj2ZLEWGr0RMgklvT26J+KX33Zi8sQJAMD/cfxq9XdI7t1LyNAEwdcPOVFDxhsxDIOITl2Rl/knyu8237x6048/IjExSaDIbIOrJ4mIaGd2u7PXk3Tv3gO//forHn56PADAS+4GN7EIq1evQu8+fQSOjliD841vE4sK95XAz12E8jojThVrnbJGpLnen/82hox4HCdOnoJer8fyT1fixMk8/HH4KPbt2iZ0eDbH73DvpCNEAPDiKzMwZ/pEiOrKYTQasWXzZpw5nYf/fvcdNv/k3A1pZ7/xGsY+/zKuFhTy9SR5Z87i2/+ux7bNTV9w42jmv/suRgwfhkPHMmEw6LFz49dYv/RtHE5Px649e4UOz+L8Qto3eeSrTJVv5WiEQQkRuS2GYRAfKse+87XILqynhAhA33uSkXl0PxZ+uAxxsdHYuWsv7kqKR/r+HYiLjRE6PJsqqdbjaqUeDIA4J1xhxnlo5EioWTm2ffMpPD09MX/uHCQl3YVNW35CygMPCB2eVXH1JPPf+4CvJ7krKd7p60nu6dsXRzP+wvQ35yG8UyT+OrQPvXp2x/6DhxAb53wjY8s+XMD//7VrZXh34YdIfWAgP+qdfuQodqTtwduzZggVotUxLO3c2SRqtRo+Pj6orKyEQqGw3AMbNEB5JiD2BMTSO54uhCX7ruHjQ2V4NN4bHw5XCh2O9cmDrf8c9cXWfw4b2H2mGuM3FqJLoBRpE5qwubNBCxhqAL9EQHznBMpgZFGh0UEiYqzaB0t2hy0o1HU67DyugkQkwkNJYS2uIdEYmt7Y1qivB1uWCW93H4jdrJhs2uHvu0GvNf3cfbpCJLHea7/Tz51lWfyUWQC9wYiU6BB+K5fmcMSf+ah/PYP77u2LSf83wez2Tz/7Arv27MOWH9be+Tma+zeumX8bmqOpn980QkTuKCGM29NMI3AkwlGr1U0+16IJs53jfiecsf/QjbzlEohFDPRGI6o1enjLnWd7EnLdP9/n1fU6qNWVEDMMGJ0CavX1xSXO/D7fkbYHi96b2+j2wYNS8Ppbztulu8UJkVarRX5+Pjp16gSJhPIqZ8atNDtbqkW1xggvmesVVvsGN31+3VDnOn24ru9w73xTqSGBAWY/c52BBQv2pqNVqpJSW4dnVa5aTxIc4N/odbO4eV/qOq3zri4MCPDDT1t/xfSpk8xu/2nrrwgI8BMoKutrdiZTW1uLyZMnY82aNQCA06dPo2PHjpg8eTLatGmD119/3eJBEmEFeUnQRiHBVbUeOap6JEd4CB2Sze3duZX//wsXL+H1t+bh2TFPIrl3TwBA+pE/sea/67HgndlChWhzLMteL6h2woTowyVL+P+/dq0M7737LmJ69cfdyckI8/PAkcPpSNu5E7PefEvAKK3DVetJdu7azf//xYsXMHPm67h7yCjc2/cetA/yxJH0w/jvd9/inffeEzBK65v39iw8/9IU/L7/IHr36gEAOHL0GLbv3I0vP18ucHTW0+waoldeeQWHDh3CsmXLMHjwYGRnZ6Njx4746aefMHfuXPz999/WilVQrlxDBAAv/1iI3/KqMWtgIF7s47zfEADccX79/tSH8Py4MXjyX4+a3b5u/UZ8sWoNfk9rwkozJ6ghuliuxb2fX4RUzCD3350gFTdhRMFBa4j+9dijiOtxNyLvfwxB3jLc2830O/LZihXYs3sXftjUtH4tVE/SNPZQQ5T6wAPomToKiQOGoXfHAIT7m74Irv9+HVZ9+RXS9uxp0nM44s8cMCVAH6/4D06eMjXfjIrsiikTX+QTpDtywBqiZs99bNmyBZ9++in69u1r3qU3JgbnnLx7pyuL5+uIqEFj+pE/0eOuxr1netyVhKN//iVARMLIbKgfigqRNi0ZcmBpO3di2NAhAIDyWh2475GDUlOxZ/fu293V4e1I24PBg1Ia3T54UAp27dknQES2ceRwOpSdTKtGA7yuf1m9q3sP/PnnUaHCspnevXpg7Zov8deRffjryD6sXfNl05MhB9XshKikpATBwY2zyxoX6d7pqrgeM1m0hQfC27bBl6vXNLr9q6+/RXjbNgJEJAxuO5dEJ+4/xAkICMDetF8hEjHQG4yo0Zg6NG/9+WcEBAQIHJ11cfUk/+Ts9SShbdpiz5bvIXMTw0N6fcuSr1etQtvwcAEjsw2DwYAfN/+Mdxd8iHcXfIjNP22DwWC48x0dWLNriHr06IFffvkFkydPBnBDl96vvkJycrJloyN2Iy5UBgbA1Uo9Smv0CPR03UL6pR+8h1FPjMVvO3bx35iO/pmBM2fP48f1jRMlZ8WNFjpj/dA/vT1nDl6aMAHd++5Eu24JOObvgbycv7Bzxw58/p//CB2eVblqPcm/Z7+HV14YgxNH9+GXfncDAP7880+cPXMG6/+3UeDorOvs2fMYNvJfuHK1AN26dgYALFi8FOFt2+CXLRvQ6SZbuTiDZn+qvf/++xgyZAhOnDhh6tK7fDlOnDiBP/74A/v2Oe/wqavzlonRKUCKs9e0yC7UYGBn102Ihg4ZhDPHj+Gz/6zCqbwzAIAHhw3GSy88h/DwtgJHZxs6A4tcVcMeZi6QED0z9llERkbhnUUf4djv25HjJkZSfAz27tuPXr17Cx2eVT37zFOIiuyKj1f8B5u2mBYXREV2xcG9vzn1FEpMnwH4aOPvyNzxP1y7alpJN2zYcLzw4osId/IRoinTZqJjhwik798Jf3/TKOC1a2V4+tkJmDJtJn756X8CR2gdzf5U69u3LzIzM7Fw4ULExcVh586duOuuu5Ceno44J+zeSa6LD5Ph7DUtsgrqMbCzp9DhCKpt2zZ434VWlP3T6VIt6vUsvGUidPB3jZ48vXr3xsf/WY2Mi+UIVsjRv2vjDX6dVe9ePbDWiZOfmymr1iIgOBRz33kXIQrnT/pvtO/AHzh84HoyBAABAf5Y+N4c3DNgiICRWVeLvuZ36tQJX375paVjIXYuMVSOTTlVVEcEoKKiEqu+/g4n80wrMGKiIjHu2dHw8fERODLbyOY2dA2VWXX1lz0xGAw4uOsXbD+QAbGIQXlKHwx/cATEYvGd7+zgDAYDtvz8C7/iKCY6EiOGD3Ha167RGVCr1aOmqhL//eJ/OHP6FAAgKjoGzz73nNO/z2UyKaqqqhvdXl1dA6nUeb8AtXjeo7i4GMXFxTAazZcUxsfH3+IexNHFN0yNZBdowLKsyxbRH8v4G6nDR8Hd3R29etwFAFjy8Qq8t+gj7PxlE+5KShA4Qutz5v5DN3P27FmMHPEgrl65guDwDgALbP32M7QND8eWn7eiU6dOQodoNa5YT1JWo8X5k9lY/OozUHh5okdPU7+xj5ctxaIF7+OX37Yj6a67BI7SeoYPTcWE/3sVq/7zCXr17A7AVDf20qRpGDHceUeImt2HKCMjA2PHjsXJkyfxz7syDOO0Veiu3ocIADR6I2I/PAedETjwf+0R7uuk3xTu0KOj38Ah6NypI778fDnfpV2v1+P5l6bgfP4F7N/deEVOIw7eh2jwVxdxqliL/4wKRWo3r6bf0UH7EI0YPgwsy2LNd//FXyodKmq1iPQDZk15ESKRCD9tbULvKThmT5qhIx4Dy7JYu+bLRvUkIpGoafUkDtaH6PjVSjw5IhWdOnXCxnVrzN7nL014Afnn87H799+b9ByO+DOvqKjE2PEvY+sv2+HmZvo7r9frMWL4EHzz1YqmjZA5YB+iZo8QjRs3Dl27dsWqVasQEhLisqMErkgmESEqRIbsQg2yCuqdNyG6g2MZmfjys+VmW9ZIJBK8Nn0KeiQPFDAy26jVGnG6RAvAdUaIDuzfjwOH/oC/vz/81GWoqNUCcgXee38BBvTvJ3R4VuWK9STXarQ4fyobH338WaP3+fR/z+A7djsrX18f/PTjOpw5cw6n8rjGjN3QuXNHgSOzrmYnROfPn8ePP/6Izp07WyMeYufiQ+WmhKiwHsOjvYUORxAKhTcuXb6CyMiuZrdfvnwV3t7NGC1xUMeLNDCyQIiXGEpv11htKJPJUFVVBQDw85Qiv7QG5TVaiKurIZXa98hua7laPQnLsiiv0cLdwxvVZapGxy9fvgxvb9f429elSyd06eK808H/1OzGjPfffz+ysrIs8uT79+/Hgw8+iLCwMDAMgy1btpgdZxjmppcPPviAP6esrAyjR4+GQqGAr68vxo8fj+pq8zdvdnY2+vXrB7lcjvDwcCxevNgi8bsibkSA2+XcFf3r0Ycx/qXJ2LBxEy5fvoLLl69g/f9+xPMvT8GTj48SOjyrc6X+Q5yhw4bh/15+CUePHIGPuwQsy+LokSOYNPH/MPzBB4UOz6q4epIjR4+BZVmwLIvDR/502nqSao0eOoMRyQ8Mx/TJL2Pj/zbg8uXLuHz5Mv63YT1efnECHn/iCaHDtCqDwYBVX3+Hp555HimDR2Jg6gizi7Nq9te7r776CmPHjkVubi5iY2P5+UXOiBFN/8eqqalBQkICxo0bh0ceeaTR8cLCQrPrv/32G8aPH49Ro65/6IwePRqFhYVIS0uDTqfDc889hwkTJmDdunUATHOHgwYNQkpKClauXImcnByMGzcOvr6+mDDBfG8ecmcJoaa53VxVPQxGFmKR602ZfrjoHTAMg2fGvQy93tSx2M3NDS9PGIeF780RODrry3TBhGjJsuUY/9yz6N/3Hri5ucHIAgaDHkOHDcdHS5cJHZ5VfbxkEcaOfxnJ/Qc1qidZ/tGCO9zb8ZTVmKaDJ82ah+2rvTHu2WfN3ucTXnwJ7y1wvtd9o1emvY5vvvsew4YMQmxMlMuUxjS7qHrr1q0YM2YM1Gp14wdrRVE1wzDYvHkzRo4cectzRo4ciaqqKuxu2Dvo5MmTiI6Oxp9//okePUw9MrZv346hQ4fiypUrCAsLw+eff44333wTKpWKH9p+/fXXsWXLFpw6darJ8VFRtYnByCJ+yTnUaFnseL4dugVbsfBPKE3Y+BAAamtrce68qWFbp44d4OHh0fTncOCi6n6f5eNyhR7/fbIN+nZoxmsGHLaomnPmzBnknTqFzEvl8A3rgEfu64EwX/cmP48jFthyWlVP4kBF1X9fKse54mp0CfFGQrgvamtrcb5hn86OnTo1730Ox/yZB4Z1wrerPsfQIYNa/hyuUFQ9efJkPP3003j77bcREhLSqiCbo6ioCL/88gvWrLm+NUJ6ejp8fX35ZAgAUlJSIBKJcOTIETz88MNIT09H//79zeb5U1NTsWjRIpSXl8PP7+Z78Wg0Gmg016eFbpYAuiKxiEGsUo4jl+qQVVjvnAlRE3l4eCAuNkboMGyqrNaAyxWmb8txoa73s+/SpQu6dOkC5YUyXGioI2pOQuTIXKWepKzaNELk72n6zPDw8ECsizUdlkql6NzJuQuob6bZCdG1a9cwdepUmyZDALBmzRp4e3ubTa2pVKpGG81KJBL4+/tDpVLx53ToYN4ng4tdpVLdMiFasGAB5s2bZ8mX4DQSwxoSogINHnf+ljuN1NTUYOEHy7B77z4UF5c26sV1Pi9TmMBsgOs/1NHfDT5y52zKdzMGgwHfrvkGe/fsQXFxCWo0WlTX6yGViODrLsWOXbuEDtFqDAYDvvl23fXfd9b8933Pjp8FiszyDEYWFXU6AIAMOsydPdv0My9p3HMv78xZIUK0iemvTMTyT1fi0+UfuMx0GdCChOiRRx7B3r17bd6IbPXq1Rg9ejTkctvULcyaNQvTpk3jr6vVaqffv6ap4htGBrJdtGP18y9Nwb4Df2DMU48jVKl0qT8YXEF1ogvVDwHAtKmv4rs1azBk6FDExMZAozPicnktJCIGHQKde2WhK9WTVNRqwbIsZBIxpk9+GQf278dTo5+GMjTUqV/3Px384zD27juA33bsQkx0ZKNa4U3/+06gyKyr2QlR165dMWvWLBw8eBBxcXGN/qGmTJliseA4Bw4cQF5eHjZs2GB2u1KpRHGx+TylXq9HWVkZlEolf05RUZHZOdx17pybkclkkMlcb0qgKbhi2pPFGtTrjZBLmr1Y0aH9tmMXftmyAffc3UfoUGwui9+yw7USoo0bNmDt9+sxZOhQAKaRhM1/XQEADE8IEzI0q1u/cRP+t3Z16+pJHARXUO3vKcWO7dux5eetuPueewSOyvZ8fX3w8EPDhQ7D5lq0yszLywv79u1rtLs9wzBWSYhWrVqF7t27IyHBfH4mOTkZFRUVyMjIQPfupvbie/bsgdFoRO+GHaiTk5Px5ptvQqfT8clbWloaunXrdsvpMnJ7bRQSBHiIca3WgJNFWiS1ca0PRz9fX/i74O8Oy7LILnSdHe5vJJVK0emG3mtiEQOFuxvUdTqU12gR6sR1RK5UT3JjQuTr5wc/f3+BIxLG11+uEDoEQTT7q31+fv4tL+fPn2/WY1VXVyMzMxOZmZn8Y2dmZuLSpUv8OWq1Ghs3bsTzzz/f6P5RUVEYPHgwXnjhBRw9ehSHDh3CpEmT8MQTTyAszPSt7amnnoJUKsX48eNx/PhxbNiwAcuXLzebDiPNwzAMP23GjRi4knfmvoHZ8xegtrZW6FBs6kqlHtdqDXATAVEh9r8i0pJemToNn37ysdl2Rb4epn+D8lqtUGHZBFdP0swFyQ6JT4i8pJg7bx7mz53jcu9zVyZom9ljx47hvvvu469zScrYsWPxzTffAADWr18PlmXx5JNP3vQx1q5di0mTJuH++++HSCTCqFGj8PHHH/PHfXx8sHPnTkycOBHdu3dHYGAgZs+eTT2IWikhTI6952pdso7oo2UrcO78BYSEd0P7iPBG08Z/Hdl3i3s6Ni75jQqRudw06R+HDmLf779jx/btiI6OhpubG6o1elTW6iCXirH7t61Ch2g1rlJPotEbUKMxraD095Bi2dKlOH/uHMLDQhHRvn2j133kz2NChEmsqEkJ0bRp0/DOO+/A09PzjiMrS5YsafKTDxgw4I7fOiZMmHDb5MXf359vwngr8fHxOHDgQJPjIneW0FBDkumCI0QjRwwTOgRBcMmvq9UPAYCvry8e+kePNJnOAJ2oHmKRcyeHrlJPwi2395K7wU0iwogRDwkcEbG1JiVEf//9N3Q6Hf//hHBTZufLdFDXG6BwoSXYc96aKXQIgsgscM36IQD4ctXqRrfpDUZs+fsqAECjM0Dm5pzvAVepJ+GmywIa+g+9NXu2kOEQATQpIdq7d+9N/5+4rgBPCdr6SHClUo9clQZ3t29mx2LiUPRGFjmqhiX3LtiQ8WYkYhG85G6ortehvFYLpY/zFla7ghsLqolravZY77hx4/hdn29UU1ODcePGWSQo4hi4kQJXnDZzNWdLtajTsfCSitAxgD4wOH4eprqS8lqdwJGQ1mFRVnu9oNpVXblyFaWl1/jrBw7+gdFjX0C/gUPw9LMTkH74qIDRWV+zE6I1a9agrq6u0e11dXX49ttvLRIUcQxcHZErFla7Gu5nHKuUueSGvrfi17DSrMLJV5o5u6p6PXR6I0QiBj5ytzvfwUmNemIsDh/5EwDw08+/YsADD6K6ugb3JPdGbW0d7k0Zjm2/bBc4Sutp8ioztVoNlmXBsiyqqqrMOkYbDAb8+uuvjbbRIM6NGyHKKtDc4Uzi6Fxxh/um8GuYXimvoREiR8ZNl/l6SCFy4YT/+IlTiImOAgAs+GAp3n/nbcz896v88U8/+wKz5y/A8GGDBYrQupqcEPn6+oJhGDAMg65duzY6zjAM7f3lYmKVMogYoLBKj+JqPYK9BO3iQKyIS3oTw6h+6Ea+DVNmtVo9tHoDpBLnLKy+EcuyTreNxT8Lql2VRCJGVbWpJCb/wkUMSU0xOz4k9QHMfNN5P+ebPGW2d+9e7N69GyzL4ocffsCePXv4y8GDB3Hp0iW8+eab1oyV2BlPqQidG+pJXLFB4z9dvnwF4yZMEjoMi6vXGXGq2HVXmNXV1eHQwYM4eeJEo2MGnRZHd24B4Dp1RDLvEJw8mSd0GBb1z4Lqz1aswLhnx+J/G9YDANb+9zskxMUiLiYab7/5JvR6vWCxWtO9/e7B9xt+BAAkJcTj932HzI7v3XcAbcJChQjNJpr8lf7ee+8FYOomHR4eDpGT994gTZMQJsfpUi2yCzV4oKtzb3J5J2Xl5Vjz3fdY/cWnQodiUceLNDCwQKCnGKHerjUKePr0aQwfMhiXLl0CwzC4556++G7dOoSGmj4UKisr8cncafhu0EhU1GoRonCehHHajJt/wTUYDFj44TIENGxrseSD92wZlsUZjCwqGpJZf08pFrz3Hj768AOkPPAAZkyfjksXL2HJRx9iyiuvQiQS4ePly+Dm5obZc+cKG7gVLHx3DvrdPxQFhSr0vacP3pzzLv7M+AtRkV2Rd/osNmzcjJWfNr3XoKNp9l+3iIgIVFRU4OjRoyguLobRaDQ7/swzz1gsOGL/4kNl2JjtGivNft76622Pn8+/aKNIbOvGHe6dbarkTt6c9TqiY2Lwx5GjqKiowL+nTcWA/v2QtnsP2rVrZ3aus9URLfvkcyTEx8LX18fsdpZlcfLUaXh6ejjF7wO3w71UIoKnTIxvv12Dr1avxsiHH0F2Vhb69OqJVV9/jSefGg0A6BbZDbNef90pE6KoqG44cmAX3pr7LhZ/9DFqamqw9vuNkEgk6NkjCeu/W4WRDzlvY9pmJ0Rbt27F6NGjUV1dDYVCYfaGYBiGEiIXk9gwhZKjqnfK2oIbjXzsaTAMc9vu6s74+rO4DV1dsEP14fR0/LZjJwIDAxEYGIjNP/2MyZMm4v4B92LHrt3w9PTkz3W2Pc3ef+dtfPHVGny06F0MvK8/f7ubZxC++WoFoqMiBYzOcrifm7+nDACDwoIC3NW9BwAgPiEBIpEICQmJ/PmJSXehsKBAgEhto1OnDvj+u1VgWRbFxSUwGo0IDAxotHWJM2r2vNf06dMxbtw4VFdXo6KiAuXl5fylrKzMGjESO9YtWAapmEFFnRGXKpzrG/I/hYYqsWnDdzDWl9304ux7mCW4YEF1XV0dxJLr3xsZhsGnKz7D0OHD8cDA+3Dm9Gn+WI3GtHTbWbw+Yyo2rF2NlydPx79nvs3vVuBsrlWbF1SHKJV8vdiZM2dgMBhw8uT1+rETx48jyAVWVDMMg5CQYISGKl0iGQJakBBdvXoVU6ZMgYcHdSYmgFTMIDrE9EGZ6eTL77snJSDj78xbHmcYON2O4BV1BlwoN30QuuIeZt26ReKvjMabeC7/+BMMHzECox4eCQDwkJqSJmfrR9Szx13IOLwXJaWl6JF8H3KPn3C6UdB/FlQ/+eRTGP/cs3j5xQkYPnQIpv97Bl5/7TV88Z+V+PKL/2DyxP/DQw+NFDBi4TjrwhFOsxOi1NRUHDtGu/yS6xIatnJw9gaNM6ZNxt19et3yeOdOHbF3p3Ptes79TNv7ucHX3fmXlP/TQyNHYsP69Tc9tvzjT/D4E0+AZVn4eTpvx2ovLy+sWfU5Zr02FSlDHobBYBA6JIu5cYd7rqfU7LlzMXnKK1CpVBg//nm88957eG/BAixeuBBz3n4b9w0ciLnz5wsZtmC4hSPOqtk1RMOGDcOMGTNw4sQJxMXFNRpKGzFihMWCI44hIUwOZFQ6/dL7fn3vvu1xT09P3Nv/HhtFYxtZLryhKwC89vrreA2v3/L4J5+uwCefrsCpQjWultc5XR3RjZ54fBT63t0HGX9lIaJduNDhWER5DbfDvQRSiWl8QCQSYeasWWbnPf6vJ/D4v56weXy25qoLRzjNToheeOEFAMD8m2TIDMM41bcH0jTcVEquSgO9kYXEhTu9OpushhGiBNrQ9bZ8+T3NnDchAoC2bdugbds2QodhMdeny+j3G3DdhSOcZk+ZGY3GW14oGXJNHQPc4C0ToV7P4nSJc38guBKWZWnLjibi9jSrrtdDZ3CewmpnxxVU+3u6RtHwnbjqwhEOdVckrSZiGMQpXaOOyJUUVulRWmOAmAFiQugb9O3I3MRw5wurna+OyBmxLMuP6AXQCBEA11w4cqNmT5ndbKrsRrNnz25xMMRxxYfJ8cfFOmQV1OOJRJ8734HYPa4mLDJYBrkbfXe6Ez8PN9Rp9aio1SLImz5g7d3lslpouR3u3WmECDAtHKmpqb3lcWdcOHKjZidEmzdvNruu0+mQn58PiUSCTp06UULkohIb6oi4Jn4Orb5Y6AjsQiZfUO38H+4aC0xzHb1Qhs/2nsPwhFAsGBVvgahsxEV/37OvVOKNzbmIb+uDYQlhgMF5Rz6ayhUXjtyo2QnR33//3eg2tVqNZ599Fg8//LBFgiKOJ77hQzOvWIN6nZFGFJxANl9QTfVDTRETZhoZPVmgFjgS0hQ5VyoBAHFtaUSbmFjkU0uhUGDevHl4++23LfFwxAGFeksQ5CmGgTVtBkocm8HIIqfQtZfcN1dUmAIAkF9ag1qtc+6G7kwoISL/ZLGv8ZWVlaisrLTUwxEHwzAM/8Hp7P2IXMH5a1pUa43wcGPQJVAqdDgOIchbhiBvGYwscFpVJXQ45Da0eiNOqUwjefFtfYUNhtiNZk+Zffzxx2bXWZZFYWEhvvvuOwwZMsRigRHHEx8qw64zNc5RR+TiMht+hrFKGcTUV6rJokIVKKkqwfECNRLb+QkdDrmFPJUaOgMLXw83tPVzFzocYieanRAtXbrU7LpIJEJQUBDGjh2LWf/o7klcCzdCREvvHV829R9qkegwBfafLsHJQqojsmfZDdNlsW18nLrR4C25aCH9nTQ7IcrPz7/lsbq6ulYFQxxbvNL04ZlfpkNlnQE+Lrj3lbPIooLqFokKNdURnaDCarvG1Q/FU/0QuYFFaog0Gg2WLFmCDh06WOLhiIPy8xAjws/UzyNbRaNEjqpeb8TJIiqobonohsLq8yU1qNdR5357lXuVCqpJY01OiDQaDWbNmoUePXrg7rvvxpYtWwAAq1evRocOHbB06VJMnTrVWnESBxHfsOcVtykocTwni7TQGQF/dzHa+jR7ENmlhShk8PeUwmBkqbDaTlXWanHxmqn5YFwbSojIdU1OiGbPno3PP/8c7du3x4ULF/DYY49hwoQJWLZsGZYsWYILFy5g5syZ1oyVOAB+pRnVETksvv9QmMw16ytagWEYftqM6ojsU07D6FBEgAd8PGgFJbmuyV//Nm7ciG+//RYjRoxAbm4u4uPjodfrkZWVRX80CY+rOcmmpfcOK4sKqlslOkyBQ2dLqY7ITuXcUFBNyI2aPEJ05coVdO/eHQAQGxsLmUyGqVOnUjJEzMSEyCBmgKJqA1RV1JzOEdEO963D1RHRCJF9yqaGjOQWmpwQGQwGSKXXhxclEgm8vLysEhRxXB5SEboEmX5PqEGj46msN+B8mWm3dlph1jLclNmZ4mpo9a3fI41YDsuyfEE1rTAj/9TkKTOWZfHss89CJjMVzdbX1+Oll16Cp6en2XmbNm2ybITE4SSEynGqWIuswnqkdqOk2ZHkNjRkDPeVwN+D2ia0RJivHD7ubqis0+FMcRW/xxkR3pXyOlTU6uAmZtBNqRA6HGJnmpwQjR071uz6008/bfFgiHNICJNjQ5aa6ogcUCb1H2o1rrD68PlrOFGgpoTIjmRfqQAARCoVkEpoA2pirskJ0ddff23xJ9+/fz8++OADZGRkoLCwEJs3b8bIkSPNzjl58iRmzpyJffv2Qa/XIzo6Gj/++CPatWsHwDRSNX36dKxfvx4ajQapqan47LPPEBISwj/GpUuX8PLLL2Pv3r3w8vLC2LFjsWDBAkgktKTYGvil94UaGFkWIqozcxjcNGci1Q+1SnSYKSGine/tC23oSm5H0BS5pqYGCQkJWLFixU2Pnzt3Dn379kVkZCR+//13ZGdn4+2334Zcfv2P9dSpU7F161Zs3LgR+/btQ0FBAR555BH+uMFgwLBhw6DVavHHH39gzZo1+OabbzB79myrvz5X1S1IBpmEQZXGiAsN9SjEMXAJUTyNELUKt/M9rTSzL5QQkdsRdIhkyJAht90Q9s0338TQoUOxePFi/rZOnTrx/19ZWYlVq1Zh3bp1GDhwIADTSFZUVBQOHz6MPn36YOfOnThx4gR27dqFkJAQJCYm4p133sHMmTMxd+5cs0JxYhluYgYxITL8dbUeWYX16BhA/8aOQFWlR1G1ASLGtKkrabmYhoTodFEVdAYj3MQ0PSM0nd6IUw3NMqmgmtyM3b5LjUYjfvnlF3Tt2hWpqakIDg5G7969+Q7ZAJCRkQGdToeUlBT+tsjISLRr1w7p6ekAgPT0dMTFxZlNoaWmpkKtVuP48eO3fH6NRgO1Wm12IU3HN2ikjtUOgxsd6hokhYfUbv80OIS2fu7wlkugM7A4V1wtdDgEQF5RFbR6I3zc3RDu7yF0OMQO2e1fveLiYlRXV2PhwoUYPHgwdu7ciYcffhiPPPII9u3bBwBQqVSQSqXw9fU1u29ISAhUKhV/zo3JEHecO3YrCxYsgI+PD38JDw+34KtzfgkNdUTZ1LHaYXDdxal+qPWoY7X94QqqXXaHe3JHdpsQGY2m/h0PPfQQpk6disTERLz++usYPnw4Vq5cafXnnzVrFiorK/nL5cuXrf6czoQbITpepIHOwAocDWkKqh+yLC4hOk51RHYh94rp50DTZeRW7DYhCgwMhEQiQXR0tNntUVFRuHTpEgBAqVRCq9WioqLC7JyioiIolUr+nKKiokbHuWO3IpPJoFAozC6k6dr7uUEhF0GjZ5FXQtNm9s7IssgupB3uLYnvWE0JkV3IaRghooJqcit2mxBJpVL07NkTeXl5ZrefPn0aERERAIDu3bvDzc0Nu3fv5o/n5eXh0qVLSE5OBgAkJycjJycHxcXF/DlpaWlQKBSNki1iOQzDIL6hMJfqiOxffpkOVRoj5BIGXQOpCN4Som4orNYbqGO1kCrrdLjQsMM97WFGbkXQVWbV1dU4e/Ysfz0/Px+ZmZnw9/dHu3btMGPGDPzrX/9C//79cd9992H79u3YunUrfv/9dwCAj48Pxo8fj2nTpsHf3x8KhQKTJ09GcnIy+vTpAwAYNGgQoqOjMWbMGCxevBgqlQpvvfUWJk6cyHfdJtaRECbHwQt1yC6sx2jQHyF7xk2XxSplcBNTfYUlRPh7wEMqRq3WgPzSGnQJ8RY6JJeV27DcPtzfHX6elPCTmxN0hOjYsWNISkpCUlISAGDatGlISkriewQ9/PDDWLlyJRYvXoy4uDh89dVX+PHHH9G3b1/+MZYuXYrhw4dj1KhR6N+/P5RKpdn2IWKxGNu2bYNYLEZycjKefvppPPPMM5g/f75tX6wL4mpRaE8z+0f1Q5YnEjGIDKV+RPYg5yrXf8hX2ECIXRN0hGjAgAFg2dsX3I4bNw7jxo275XG5XI4VK1bcsrkjAERERODXX39tcZykZbjVSqdLtajVGmkptx3Lovohq4gOU+Cvi+U4WajGQ0lthA7HZXENGamgmtwOfUIRqwnxliDESwwjC+SqqI7IXmkNLE4UmX4+iWE0jWxJ0TRCJDiWZfmCaqofIrdDCRGxKm7EgfoR2a9TxRpoDSx83UVo5+smdDhOhVtpdkpVBYOR2k8I4Up5HcprdZCIGUQqqY6L3BolRMSquF3Tsyghsls31g9RwzrLah/oCXc3Meq0Bly8ViN0OC4pt6F+KFLpDZmbWOBoiD2jhIhYFW3hYf/4DtVUUG1xYhGDbg2jEjRtJozsK1RQTZqGEiJiVXENW3hcqtChvNYgcDTkZrhkNYHqh6yC60dECZEw+B3uqX6I3AElRMSqfORidPA31aXQtJn9qdYYcbZUC4CW3FsL37Ga9jSzOZ3eyP+7U4dqcieUEBGr4+qIqLDa/uSo6sECaKOQIMhL0C4cTuvGTV6NVFhtU6eLTTvcK9wliAigHe7J7VFCRKwuPpS28LBXXEE19R+ynk5BnpBJRKjRGHC5vFbocFzKjdNltGCA3AklRMTq+MLqwvo7NuIktnW9fogSImuRiEXoGkKF1ULgEiLqP0SaghIiYnUxITJIREBpjQEFar3Q4ZAbcHVdCaFUUG1NfB0RJUQ2lc13qPYVNhDiECghIlYndxOhW5DpA5fqiOxHcbUeBWo9GACxShohsiZaaWZ76jodLpSaej/FUkE1aQJKiIhN8HVEhVRHZC+45LRLoBReMvpTYE3cCNGJQjVNG9sI15CxrZ87/GmHe9IE9FeQ2EQi36CRRojsBdUP2U7nIC+4iRlU1etxpbxO6HBcAl9QTaNDpIkoISI2Ed/woZtTqIGRviHbhUx+hRnVD1mbm0SELg2F1dSPyDaoISNpLkqIiE10CZRCLmFQrTXi/DWd0OG4PJZl+SmzBGrIaBNcPyKqI7I+lmWRc5W27CDNQwkRsQmJiEGckqsjomkzoV0s16Gy3gipmEG3YBohsgVaaWY7BRX1KKvRQiJmEBVKO9yTpqGEiNhMPNUR2Q0uKY1RyiAVU8M6W6DCatvJuVIBAOgWQjvck6ajhIjYDDc1QyNEwsvkCqppusxmugR7QSJiUFGrg6qS3gPWxPUfouX2pDkoISI2wxXvnizSQmugb8hC4uuHqKDaZmRuYnQK9gJAdUTWlsM3ZKSEiDQdJUTEZtr5usHXXQStgcWpYupHJBSdgUWuikaIhHDjtBmxDp3hxh3ufYUNhjgUSoiIzTAMg/hQqiMSWl6JBho9C4VchPb+bkKH41JopZn1nSmqhkZvhLdcggh/2uGeNB0lRMSmEqmOSHBZN9QPiWgHcJuKvmELDyqstg6uoDq2jQ9EIvr9Jk1HCRGxqfiGmpXsApoyEwpXPxRPG7raXNcQb4gYoKxGi+Iqeg9YQzZ1qCYtRAkRsSluyuxMqRbVGqPA0bimLL5DNdUP2Zq7VIyOQabCaupHZB3cHmZUUE2aixIiYlPBXhKEKSRgAeSqaNrM1mq0Rpwu1QK4vr8csa0bp82IZVXV65DP7XBPW3aQZqKEiNgcN1VDO9/bXq5KAyMLhHpLEOwlETocl8R3rKaVZhaXe1UNlgXa+LojwIumhEnzUEJEbI6bqsmmlWY2R/VDwqOVZtbDFVRT/RBpCUqIiM1xvW8yaaWZzWVS/ZDgIpXeYBiguEqDUiqstqjrG7pSQkSajxIiYnOxShkYAFcr9bhWoxc6HJfCFVRT/ZBwPGQStA/wBEANGi2JZVnqUE1ahRIiYnMKuRgdA0wNAbOpjshmrtXocaVSDwampJQIh68jomkziymsrMe1ai0kIgaRDdOShDQHJUREENyUTSbVEdkMl3x2DHCDQk47gAuJryOiESKL4foPdQnxhpx2uCctQAkREQRXR5RNdUQ2Q/VD9oNGiCwvl6bLSCsJmhDt378fDz74IMLCwsAwDLZs2WJ2/NlnnwXDMGaXwYMHm51TVlaG0aNHQ6FQwNfXF+PHj0d1dbXZOdnZ2ejXrx/kcjnCw8OxePFia780cgfX9zTT0BYGNsJtl5JIG7oKLjLUG4Bpmqe8RitwNM6BVpiR1hI0IaqpqUFCQgJWrFhxy3MGDx6MwsJC/vL999+bHR89ejSOHz+OtLQ0bNu2Dfv378eECRP442q1GoMGDUJERAQyMjLwwQcfYO7cufjiiy+s9rrInUWFSOEmAsrqDLhSSYXV1sayLHWotiPecjdEBJg2HqV+RK2nMxj56UdKiEhLCdqZbciQIRgyZMhtz5HJZFAqlTc9dvLkSWzfvh1//vknevToAQD45JNPMHToUHz44YcICwvD2rVrodVqsXr1akilUsTExCAzMxNLliwxS5yIbcklIkQGy5Cj0iCroB7hvrTrujVdqdSjvM4INxEQGSwVOhwCUx3RxWu1OFGgxt2dA4UOx6GdLa5Gvc60wz23go+Q5rL7GqLff/8dwcHB6NatG15++WVcu3aNP5aeng5fX18+GQKAlJQUiEQiHDlyhD+nf//+kEqvfwikpqYiLy8P5eXltnshpBG+QSPVEVkdVz8UHSKDTGL3b3uXwG/hQSNErcYtt48Jox3uScvZde/+wYMH45FHHkGHDh1w7tw5vPHGGxgyZAjS09MhFouhUqkQHBxsdh+JRAJ/f3+oVCoAgEqlQocOHczOCQkJ4Y/5+fnd9Lk1Gg00mutLwtVq+qNlaVy35MwCWnpvbTRdZn+oY7Xl5NAO98QC7DoheuKJJ/j/j4uLQ3x8PDp16oTff/8d999/v1Wfe8GCBZg3b55Vn8PVcc0Bc1X1MBhZiOmbndVQQmR/ohpGiK6W16GyTgcfd5o2bikqqCaW4FBj5x07dkRgYCDOnj0LAFAqlSguLjY7R6/Xo6ysjK87UiqVKCoqMjuHu36r2iQAmDVrFiorK/nL5cuXLflSCIBOAVJ4uDGo1bE4d41W2liL3sgit8g0CpdAK8zsho+7G9r4uQOg5fetUV2vx/mGHe4pISKt4VAJ0ZUrV3Dt2jWEhoYCAJKTk1FRUYGMjAz+nD179sBoNKJ37978Ofv374dOp+PPSUtLQ7du3W45XQaYirkVCoXZhViWWMQgLpQaNFrbmVIt6nQsvKQivkM4sQ98PyKqI2qx4wWVYFkgzFeOQNrhnrSCoAlRdXU1MjMzkZmZCQDIz89HZmYmLl26hOrqasyYMQOHDx/GhQsXsHv3bjz00EPo3LkzUlNTAQBRUVEYPHgwXnjhBRw9ehSHDh3CpEmT8MQTTyAsLAwA8NRTT0EqlWL8+PE4fvw4NmzYgOXLl2PatGlCvWxyg4SGOiLawsN6uOmy+FAZRAxNS9qTaKojarXr9UO+wgZCHJ6gCdGxY8eQlJSEpKQkAMC0adOQlJSE2bNnQywWIzs7GyNGjEDXrl0xfvx4dO/eHQcOHIBMdv1bwNq1axEZGYn7778fQ4cORd++fc16DPn4+GDnzp3Iz89H9+7dMX36dMyePZuW3NuJ6w0aaYTIWqh+yH7RCFHrcVt2xLWh6TLSOoIWVQ8YMOC2XYp37Nhxx8fw9/fHunXrbntOfHw8Dhw40Oz4iPVxH9KnijWo1xshpyXhFpdVSPVD9opbaXbxWi2q6/Xwktv1Ohe7Y9rhvgIA1Q+R1qNPHyKotj4S+LuLoTMCJ4uosNrS6nRG5BU3JERhVF9hb/w8pQj1MSWqNErUfEXqepRWayEWMXxySUhLUUJEBMUwDP9BTQ0aLe+4SgMDCwR7iaH0ptEHexRF02Ytxk2XdQ3xgruUdrgnrUMJEREc1RFZT2bh9fohhgqq7RIVVrccFVQTS6KEiAiOa9CYRSNEFpfNFVRT/ZDdohGiluNGiGKpoJpYACVERHDcFh7nr+mgrjcIHI1z4QuqqX7IbnErzfJLa1Cr0QscjePQG4x8Q8t4KqgmFkAJERFcgKcEbX0kYAHkqqgfkaWU1xpwsdzUkDReSSNE9irQS4ZgbxlYFshTVQkdjsM4V1KNOp0BXjIJOgTSDvek9SghInaBm9KhaTPL4f4tO/q7wcedCk7tGTdKRDvfNx03XRbTRkE73BOLoISI2IX4MCqstjRu1V481Q/ZvSgqrG62HGrISCyMEiJiF2gLD8vLKuDqhyghsnf8CBElRE1GK8yIpVFCROxCrFIOBkCBWo/iaiosbS2WZW/YsoMKqu0dt9LsfEk16rS0sOBOajR6nCupBkAF1cRyKCEidsFLJkKXQCkAatBoCVfVepTWGiARAdEhlBDZu2BvGQK8pDCywOkiKqy+k+NXTTvch/rIEehNv9/EMighInYjnutYXUDTZq3F9R+KDJbR/nAOgGGubz1B/YjujO8/RKNDxILoLyWxG9xKs0waIWo12tDV8VAdUdPlXDUlRPFUUE0siBIiYje44t/swnqwLCtwNI4tk+qHHA63hcdJSojuKJcKqokVUEJE7EZksAxSMYOKOiMuVeiEDsdhGYwsclWmhCiRVpg5DG6E6GxxNTQ6Kqy+FVVlPYqrNKYd7sO8hQ6HOBFKiIjdkIoZRIWYCquzqI6oxc5d06JGy8LDjUGnAKnQ4ZAmUvrI4evhBr2RxdniaqHDsVs5VyoAAJ2DveAhlQgbDHEqlBARu0Idq1uPmy6LC5VDTB18HQbDMPy02XGaNrul6/2HqH6IWBYlRMSu8HVE1LG6xbjRNZouczxcPyJaaXZrfEE1JUTEwighInaFGyHKUWmgN1JhdUtwfZy47t/EcdBKs9szGFl+9IwKqomlUUJE7ErHADd4SUWo17M4U6oVOhyHU6834mSxaYQonkaIHA7Xi+hMURV0BqPA0dgXg5HFlr+vok5rgEwiQjt/D6FDIk6GEiJiV0QMgzhuXzOaNmu2E0Ua6I1AoIcYbRRUcOpo2vq5w1sugc7A4mxxjdDh2I1dJ4qQumQf5v50HACg0RsxdNl+7DpRJHBkxJlQQkTsDldHlEkJUbPduKErw1BBtaO5sbD6pIpWmgGmZGja+kwUqc1XnharNZi2PpOSImIxlBARu8PVvtDO983H1Q/FU/2Qw4rmC6spITIYWSz89SRuVk3I3bbo11MwUL0hsQBKiIjd4UaIThVrUK+jOormuL7DPdUPOarrK81ok9e/LlU0Ghm6EQtApa7HXxfLbRcUcVqUEBG7E+otQaCnGAYWOF5Eo0RNVVlnwPkyU4dv2sPMcXEjRKeLalx+pWVxVdMWVpRU0d8J0nqUEBG7wzDM9QaNVEfUZNkN23VE+LnBz0MscDSkpcL9POApE0OjNyK/zHVHSM+W6rD64KUmnRvkTVPEpPUoISJ2iduUlOqImi67oaCa6occm0jE8MvvT5S43p5mtVojFv1ejqe+L8HZktuvtGMAKBVy3BXhZ5vgiFOjhIjYpXjawqPZMgupfshZcAnRKRdLiNJOV+OBLy7ii6NqGIzAfd0CMfvBaDAwJT834q7PHBpJW9QQi6BGJcQucVNm+WU6VNYb4COnKaDbYVkWmVcbdrin+iGHx3esLnaNhOhqpQ5z00qQdto0ItRGIca/7/XBwKRYiCQy+HlKsfDXk2YF1iEKOWYOjURKdIhQYRMnQwkRsUt+HmK083XDpQodcgo16NuButLejqpKj5IaA8QMEKOkKTNHxyVEeaUGGIwsnPXrgM7AYvWfFVh24BrqdCwkIuD53n6Y2NsLWvZ6MpgSHYL7IoPx18VylFRpEOQtw10RfjQyRCyKEiJitxLCZLhUoUNWQT0lRHeQ1VBr1TVICnc3mgl3dBEBnnB3E6FOZ8T5Mj0iQ4WOyPKOXa7Dm9uLkVdiWknWK1yOdwcHo2uQDAa9FlqN+eiYWMSgZwd/IUIlLoL+chK7lUB1RE3GrcajHe6dg1jEoJvSCwBwvMi59vQrrzVg5i9FePS7K8gr0cLPXYQPhodgw9Nt0TWIRjeJcGiEiNgtrjiYlt7fGTVkdD7Rod7IvKxGbpEWo4QOxgJYlsXGbDUW7ClFeZ2pncATiQrMHBBIbSKIXRB0hGj//v148MEHERYWBoZhsGXLllue+9JLL4FhGCxbtszs9rKyMowePRoKhQK+vr4YP348qqvNW95nZ2ejX79+kMvlCA8Px+LFi63waoilxYTIIGKAomoDVFV6ocOxW0aWRY7q+h5mxDlEhTrPCNHpEg3+9d8reO2XYpTXGdEtSIofxrTFwqEhlAwRuyFoQlRTU4OEhASsWLHitudt3rwZhw8fRlhYWKNjo0ePxvHjx5GWloZt27Zh//79mDBhAn9crVZj0KBBiIiIQEZGBj744APMnTsXX3zxhcVfD7EsD6kIXQOlAGiU6HbOX9OhSmOEXMKgS8O/F3F8UaHeAIATxVoYWcfsWF2rNWLh3lIMXXUJRy/Xw92NwayBgdg2rh16hLsLHR4hZgSdMhsyZAiGDBly23OuXr2KyZMnY8eOHRg2bJjZsZMnT2L79u34888/0aNHDwDAJ598gqFDh+LDDz9EWFgY1q5dC61Wi9WrV0MqlSImJgaZmZlYsmSJWeJE7FNCmBynSrTILqxHajcvocOxS1yNVZxSBgmtunEaHQI9IJcA1VoWF8p06BjgWMnu7jPVmL2zBFcrTaO7D3T1xNwHgtDGx03gyAi5ObsuqjYajRgzZgxmzJiBmJiYRsfT09Ph6+vLJ0MAkJKSApFIhCNHjvDn9O/fH1Lp9T8mqampyMvLQ3n5rTcE1Gg0UKvVZhdie/G0hccdUf2Qc5KIGHQNME0ncVOijuBqpQ4TfijA+I2FuFqpRxuFBF8+GoovHw2jZIjYNbtOiBYtWgSJRIIpU6bc9LhKpUJwcLDZbRKJBP7+/lCpVPw5ISHmjbu469w5N7NgwQL4+Pjwl/Dw8Na8FNJC/BYeKg1YB502sDZKiJxXZLApITqusv8vBDoDiy8Ol+OBLy5i5+kaSETAS8l+SJsQgQe60ugusX92u8osIyMDy5cvx19//QWGsf00wKxZszBt2jT+ulqtpqRIAN2CZJBJGKjrjbhQrkMHf8eaNrAmg5HFoQu1yG0YPYgNoSXLziY6yDFGiDKu1OHN34pxqqGnUM+2pp5C3YLpd5I4DrsdITpw4ACKi4vRrl07SCQSSCQSXLx4EdOnT0f79u0BAEqlEsXFxWb30+v1KCsrg1Kp5M8pKioyO4e7zp1zMzKZDAqFwuxCbM9NzCCm4YOeps2u236qGn1XXMAz6wtgaBg4e3LdFWw/VX37OxKHEtmQEOXa6Qhpea0Br/9ahFHfXsGphp5Ci4cFY8OYtpQMEYdjtwnRmDFjkJ2djczMTP4SFhaGGTNmYMeOHQCA5ORkVFRUICMjg7/fnj17YDQa0bt3b/6c/fv3Q6fT8eekpaWhW7du8POjHZIdwfWNXu37W7KtbD9VjZc3FaLwH60IiqoMeHlTISVFTqSTvwhSMVClMeJShe7Od7ARrqfQ/f+5iPWZpvrKxxMU2P1iezye4AORAKP6hLSWoFNm1dXVOHv2LH89Pz8fmZmZ8Pf3R7t27RAQEGB2vpubG5RKJbp16wYAiIqKwuDBg/HCCy9g5cqV0Ol0mDRpEp544gl+if5TTz2FefPmYfz48Zg5cyZyc3OxfPlyLF261HYvlLRKYhiNEHEMRhbz0kpws7ECFqYdwOftKsEDXT1pnycn4CZm0C1IihyVFrkqDSL8hJ8yPlOiwZvbS3D0ch0AoGugFO8NCUZPWkZPHJygI0THjh1DUlISkpKSAADTpk1DUlISZs+e3eTHWLt2LSIjI3H//fdj6NCh6Nu3r1mPIR8fH+zcuRP5+fno3r07pk+fjtmzZ9OSewfCjRAdL9JAZ7C/aQNbOnq5rtHI0I1YAIVqPf9hRRxfbIgpCRK6jqhOZ8SivaUYsuoSjl6u43sK/TK+HSVDxCkIOkI0YMCAZs2LX7hwodFt/v7+WLdu3W3vFx8fjwMHDjQ3PGIn2vu7wVsmQpXGiLwSDWKVrruaqrjacOeTmnEesX8xDQnRcQETot1nqjFnZwmuNPQUSuniibmDgtCWltETJ2K3q8wI4YgYBgmhMhy8UIfsQtdNiKo1Ruw527T6oGAv2g7BWVwfIaoHy7I2XXVboNZh3s4S7DhdAwAIU0gwd1AQBtEyeuKEKCEiDiE+TI6DF+qQVVCPp5J8hA7HpgxGFj/mqLH492sorbn9yA8DQKmQoBdNYTiNroFucBMBFXVGXFXrbTIqozOw+OZYBZbuv4ZaHQuJCBjfyw9T+vrDU2q3a3EIaRVKiIhDSOBXmrlWYfXhi7V4Z1cpjheZpkva+7lhSKQnVqZXAIBZcTU3bjAnJYgKqp2ITMKga5AMx4s0yC3UWD0hyrhShze3F+NUsamnUI+GnkKRtIyeODlKiIhD4LownynRolZrhIeTf0u9VK7Dgj2l+C3PNEXmLRPhlb7+eKaHL6RiBgmh7piXVmJWYK1USDAnJQiDI2k6w9nEKhsSIlW91X6+FXUGLNpbiu8bltH7uovwxsBAPBqvoGX0xCVQQkQcgtJbghAvMYqqDThepHHaVS1VGgNW/FGO1UcroDWwEDHAU0k+mNrPHwGe19+ugyO98EBXTxy9XIfiagOCvcToFe5OI0NOKlYpw4Ys66w0Y1kWP+ZU4f3dpSirM03JPhavwKyBgfD3oFo04jooISIOIz5MjrTTNcgqrHe6hMhgNDW6+/D3ayitNX0o9W3vjrdTgm7Z8VcsYpAc4WHLMIlAuIUEXMdqSxVWnynR4K0dJThy6XpPoXcHB6NXO+d6fxHSFJQQEYeRGNqQEDlZg8Y/LtTinV0lONlQs9HR3w1v3h+IgZ09BdnHj9ifqGApxAxwrdYAVZUeoYrW1RHV6Yz45FAZvjhcDr0RkEsYvNLPH+N7+UEqpt854pooISIOI76hY3W2k2zhcbFci/d3l/JLmhVyU53QmO6+9KFEzMjdROgSKMWpElPH6tYkRHvO1mD2jmK+p9D9nU09hcJ9qacQcW2UEBGHEd8wbXCxXIfyWgP8HLS+QV3P1QmVQ2cExAww+i4fvNovgGo2yC3FhspwqkSLHJUGD7SgD1ChWod5aSXYnne9p9CcB4IwqCuNRBICUEJEHIiPuxgd/N2QX6ZDtqoe93b0FDqkZjEYWazPVGPJ/mu41lAn1K+DB95OCUTXIFrSTG4vNkSOH1DV7I7VeiOLb/6swNID11CjZSFmgPG9fPFKvwDqKUTIDSghIg4lPlSO/DIdsgocKyE6lG+qEzpVcr1O6O2UIAzo5EHfzkmTxIWakuYcVdNr6DKu1OGt7cV8fVr3hp5CUdRTiJBGKCEiDiUhVIafjlc5TB1RfpkW7+0uxa4zpmkKH7kIr/YLwNN3+cCN6oRIM0QFyyBiTPvUFVfrEex16z/fN+spNOu+QDyWQD2FCLkVSoiIQ+EaNGYW2H5fp+aorDfgk4NlWHOsgq8TGtPdF6/284evO9UJkebzkIrQKUCKM6WmwuqBnRv/+WZZFptyTT2FuGnZR+O9Meu+QLM+VoSQxugdQhxKTIgMYgYorTGgsEqPsFYuP7Y0vZHF+sxKLNlXxje5u6+TB968PwidA6UCR0ccXUyIKSH6IbsS7m6MWTPOs6VavLm9mO8p1KWhp1Bv6ilESJNQQkQcitxNhG7BMpwo0iC7QGNXCdGB/Bq8u6sUeQ11Qp0DpHgrJRADOjlOrROxX9tPVWP32VoAwK+navDrqRqEekvw+sAAnC7R4ovDplWLcgmDKX398Xxv6ilESHNQQkQcTkKoKSHKLLTevk7Ncf6aFu/vuV4n5OsuwrR+AXgyieqEiGVsP1WNlzcVmm3mCwCFVXq88lMRf516ChHScpQQEYeTECbH95lqZAvcsbqyzoCPD5nqhPRGQCJqqBPq6w8fqhMiFmIwspiXVtIoGbqRiAFWPKzE4G5edltXR4i9o4SIOJz4UFNhdY5KAyPL2nzVjN7IYt1flVh64BrK64wAgIGdPfDGQKoTIpb35xUNCqv0tz3HyAK+7mJKhghpBUqIiMPpGiSFXMKgSmPE+Ws6myYh+86b6oTOlJrqhLoEmuqEHKknEnEsxTWGpp1X3bTzCCE3RwkRcTgSEYNYpQzHrtQjq7DeJgnR2VIt3ttdgr3nTEWtfu4iTOtvqhOSiOhbObGeYM+mTb8Ge9E0LSGtQQkRcUgJYXIcu1KP7IJ6jIpTWO15KuoMWHagDP/963qd0LM9fDH5HqoTIrbRs60Mod4SqKr0N60jYgAoFRL0Cqfl9YS0BiVExCFxdURZVupYrTOwWNtQJ1RZb6oTSuniiTcGBqJjANUJEdsRixjMeSAIL28qBAOYJUXc2OSclCC+HxEhpGUoISIOKTHMtBfTiSINtAbWov1W9p6rwbu7SnDumg4A0C1IirdTgtC3g4fFnoOQ5hgc6YXPHwnFvLQSswJrpUKCOSlBdtF+ghBHRwkRcUjtfN3g6y5CRZ0RecUaxDWMGLXGmRIN3t1din3nTXVC/u5iTL83AP9KVFCdEBHc4EgvPNDVE0cv16G42oBgL7FZp2pCSOtQQkQcEsMwiA+VY//5WmQW1LcqISqvNWDZgWv471+VMLCAmwh4rqcvJt7jDx851QkR+yEWMUiOoJFKQqyBEiLisBIaEqKW7nyvM7D4LqMCyw6WQd1QJzSoq6lOqL0/1QkRQogroYSIOKz4UFMdUVYzO1azLIu952rx7q4SnC8z1QlFBksxOyUId7enb9+EEOKKKCEiDishzDRNdqZUi2qNEV4y0R3vc7pEg3d2leJAvqlOKMBDjH/fG4DHExRUi0EIIS6MEiLisIK9JAj1lqCwSo9cVT363Ka2oqzWgKX7r2Ht35UwsoBUzJjqhO72g4LqhAghxOVRQkQcWkKYDIV5emQXam6aEGkNLL7NqMDyA2Wo0pjqhAZ388SsgYGI8KM6IUIIISaUEBGHFquUYXteDX49VYW4UBm/DJllWew+W4P3dpciv6FOKDpEhrdTAmmVDiGEkEYoISIOa/upaqz+swIAkFmgwZNrryLUW4LxvXyx91wNDl2oAwAEeoox494APBpPdUKEEEJujhIi4pC2n6rGy5sKG+3tVFilx7u7SwGY6oTG9/LF/93tB28Z1QkRQgi5tTsvy7Gi/fv348EHH0RYWBgYhsGWLVvMjs+dOxeRkZHw9PSEn58fUlJScOTIEbNzysrKMHr0aCgUCvj6+mL8+PGorq42Oyc7Oxv9+vWDXC5HeHg4Fi9ebO2XRqzIYGQxL63kphtdcuQSBjtfaIeZ9wVSMkQIIeSOBE2IampqkJCQgBUrVtz0eNeuXfHpp58iJycHBw8eRPv27TFo0CCUlJTw54wePRrHjx9HWloatm3bhv3792PChAn8cbVajUGDBiEiIgIZGRn44IMPMHfuXHzxxRdWf33EOo5erjPbz+lm6vXsHc8hhBBCOIJOmQ0ZMgRDhgy55fGnnnrK7PqSJUuwatUqZGdn4/7778fJkyexfft2/Pnnn+jRowcA4JNPPsHQoUPx4YcfIiwsDGvXroVWq8Xq1ashlUoRExODzMxMLFmyxCxxIo6juNpg0fMIIYQQQUeImkOr1eKLL76Aj48PEhISAADp6enw9fXlkyEASElJgUgk4qfW0tPT0b9/f0il15dYp6amIi8vD+Xl5bd8Po1GA7VabXYh9iHYq2lTYE09jxBCCLH7hGjbtm3w8vKCXC7H0qVLkZaWhsDAQACASqVCcHCw2fkSiQT+/v5QqVT8OSEhIWbncNe5c25mwYIF8PHx4S/h4eGWfFmkFXqFuyPUW4JbrRdjAIQqJOgV7m7LsAghhDgwu19ldt999yEzMxOlpaX48ssv8fjjj+PIkSONEiFLmzVrFqZNm8Zfr6ysRLt27Sw/UmTQAFU1AKMBGGoU2FT/7uOGadtMP4sbi6uZhuv/7h2AmspbjwASgbBagNUDYjUglt3xdIORRZVGB4YBRIxrtUww6uvBVNWA1RogbsK/lTMxGLWo0jFgoIZI4jp/F135Z97cvw3NwX1us+ztluI4QELk6emJzp07o3PnzujTpw+6dOmCVatWYdasWVAqlSguLjY7X6/Xo6ysDEqlEgCgVCpRVFRkdg53nTvnZmQyGWSy6z8U7h+URoocw6PLhI6AEEKIPamqqoKPj88tj9t9QvRPRqMRGo0GAJCcnIyKigpkZGSge/fuAIA9e/bAaDSid+/e/DlvvvkmdDod3NzcAABpaWno1q0b/Pz8mvy8YWFhuHz5Mry9vcG04Jtqz5498eeff1r9fs05/07nqtVqhIeH4/Lly1AoFE2OwVm09GdmLbaKx9LPY4nHo/eP46H3j/08nqu/f1iWRVVVFcLCwm57nqAJUXV1Nc6ePctfz8/PR2ZmJvz9/REQEID33nsPI0aMQGhoKEpLS7FixQpcvXoVjz32GAAgKioKgwcPxgsvvICVK1dCp9Nh0qRJeOKJJ/gX/tRTT2HevHkYP348Zs6cidzcXCxfvhxLly5tVqwikQht27Zt8WsVi8Ut+qE2937NOb+p5yoUCpf8g97Sn5m12CoeSz+PJR6P3j+Oh94/9vN49P7BbUeGOIImRMeOHcN9993HX+dqdsaOHYuVK1fi1KlTWLNmDUpLSxEQEICePXviwIEDiImJ4e+zdu1aTJo0Cffffz9EIhFGjRqFjz/+mD/u4+ODnTt3YuLEiejevTsCAwMxe/Zsmy+5nzhxok3u15zzWxqTq7C3fx9bxWPp57HE49H7x/HY278PvX+sfz9Hf/8w7J2qjIjLUqvV8PHxQWVlpV190yPEEdD7h5CWE+L9Y/fL7olwZDIZ5syZY1ZcTghpGnr/ENJyQrx/aISIEEIIIS6PRogIIYQQ4vIoISKEEEKIy6OEiBBCCCEujxIiQgghhLg8SogIIYQQ4vIoISIt8vDDD8PPzw+PPvqo0KEQ4nAuX76MAQMGIDo6GvHx8di4caPQIRHiMCoqKtCjRw8kJiYiNjYWX375pUUel5bdkxb5/fffUVVVhTVr1uCHH34QOhxCHEphYSGKioqQmJgIlUqF7t274/Tp0/D09BQ6NELsnsFggEajgYeHB2pqahAbG4tjx44hICCgVY9LI0SkRQYMGABvb2+hwyDEIYWGhiIxMREAoFQqERgYiLKyMmGDIsRBiMVieHh4AAA0Gg1YloUlxnYoIXJB+/fvx4MPPoiwsDAwDIMtW7Y0OmfFihVo37495HI5evfujaNHj9o+UELslCXfQxkZGTAYDAgPD7dy1ITYB0u8fyoqKpCQkIC2bdtixowZCAwMbHVclBC5oJqaGiQkJGDFihU3Pb5hwwZMmzYNc+bMwV9//YWEhASkpqaiuLjYxpESYp8s9R4qKyvDM888gy+++MIWYRNiFyzx/vH19UVWVhby8/Oxbt06FBUVtT4wlrg0AOzmzZvNbuvVqxc7ceJE/rrBYGDDwsLYBQsWmJ23d+9edtSoUbYIkxC71dL3UH19PduvXz/222+/tVWohNid1nwGcV5++WV248aNrY6FRoiIGa1Wi4yMDKSkpPC3iUQipKSkID09XcDICHEMTXkPsSyLZ599FgMHDsSYMWOECpUQu9OU909RURGqqqoAAJWVldi/fz+6devW6uemhIiYKS0thcFgQEhIiNntISEhUKlU/PWUlBQ89thj+PXXX9G2bVtKlghp0JT30KFDh7BhwwZs2bIFiYmJSExMRE5OjhDhEmJXmvL+uXjxIvr164eEhAT069cPkydPRlxcXKufW9LqRyAuadeuXUKHQIjD6tu3L4xGo9BhEOKQevXqhczMTIs/Lo0QETOBgYEQi8WNCtSKioqgVCoFiooQx0HvIUJaTsj3DyVExIxUKkX37t2xe/du/jaj0Yjdu3cjOTlZwMgIcQz0HiKk5YR8/9CUmQuqrq7G2bNn+ev5+fnIzMyEv78/2rVrh2nTpmHs2LHo0aMHevXqhWXLlqGmpgbPPfecgFETYj/oPURIy9nt+6fV69SIw9m7dy8LoNFl7Nix/DmffPIJ265dO1YqlbK9evViDx8+LFzAhNgZeg8R0nL2+v6hvcwIIYQQ4vKohogQQgghLo8SIkIIIYS4PEqICCGEEOLyKCEihBBCiMujhIgQQgghLo8SIkIIIYS4PEqICCGEEOLyKCEihBBCiMujhIgQYrcuXLgAhmGssrN1S506dQp9+vSBXC5HYmKi0OGAYRhs2bJF6DAIcXiUEBFCbunZZ58FwzBYuHCh2e1btmwBwzACRSWsOXPmwNPTE3l5eWYbUN6I+3djGAZubm7o0KEDXnvtNdTX19s4WkJIU1FCRAi5LblcjkWLFqG8vFzoUCxGq9W2+L7nzp1D3759ERERgYCAgFueN3jwYBQWFuL8+fNYunQp/vOf/2DOnDktfl5CiHVRQkQIua2UlBQolUosWLDglufMnTu30fTRsmXL0L59e/76s88+i5EjR+L9999HSEgIfH19MX/+fOj1esyYMQP+/v5o27Ytvv7660aPf+rUKdx9992Qy+WIjY3Fvn37zI7n5uZiyJAh8PLyQkhICMaMGYPS0lL++IABAzBp0iS8+uqrCAwMRGpq6k1fh9FoxPz589G2bVvIZDIkJiZi+/bt/HGGYZCRkYH58+eDYRjMnTv3lv8mMpkMSqUS4eHhGDlyJFJSUpCWlsYfv3btGp588km0adMGHh4eiIuLw/fff2/2GAMGDMCUKVPw2muvwd/fH0ql8rbPCZhGsEJDQ5GdnQ0A+Oyzz9ClSxfI5XKEhITg0Ucfve39CXFVlBARQm5LLBbj/fffxyeffIIrV6606rH27NmDgoIC7N+/H0uWLMGcOXMwfPhw+Pn54ciRI3jppZfw4osvNnqeGTNmYPr06fj777+RnJyMBx98ENeuXQMAVFRUYODAgUhKSsKxY8ewfft2FBUV4fHHHzd7jDVr1kAqleLQoUNYuXLlTeNbvnw5PvroI3z44YfIzs5GamoqRowYgTNnzgAACgsLERMTg+nTp6OwsBD//ve/m/S6c3Nz8ccff0AqlfK31dfXo3v37vjll1+Qm5uLCRMmYMyYMTh69GijuD09PXHkyBEsXrwY8+fPN0usOCzLYvLkyfj2229x4MABxMfH49ixY5gyZQrmz5+PvLw8bN++Hf37929SzIS4HJYQQm5h7Nix7EMPPcSyLMv26dOHHTduHMuyLLt582b2xj8fc+bMYRMSEszuu3TpUjYiIsLssSIiIliDwcDf1q1bN7Zfv378db1ez3p6erLff/89y7Ism5+fzwJgFy5cyJ+j0+nYtm3bsosWLWJZlmXfeecddtCgQWbPffnyZRYAm5eXx7Isy957771sUlLSHV9vWFgY+95775nd1rNnT/b//u//+OsJCQnsnDlzbvs4Y8eOZcViMevp6cnKZDIWACsSidgffvjhtvcbNmwYO336dP76vffey/bt27dRPDNnzuSvA2A3btzIPvXUU2xUVBR75coV/tiPP/7IKhQKVq1W3/Z5CSEsKxE0GyOEOIxFixZh4MCBTR4VuZmYmBiIRNcHpkNCQhAbG8tfF4vFCAgIQHFxsdn9kpOT+f+XSCTo0aMHTp48CQDIysrC3r174eXl1ej5zp07h65duwIAunfvftvY1Go1CgoKcM8995jdfs899yArK6uJr/C6++67D59//jlqamqwdOlSSCQSjBo1ij9uMBjw/vvv43//+x+uXr0KrVYLjUYDDw8Ps8eJj483ux4aGtro32fq1KmQyWQ4fPgwAgMD+dsfeOABREREoGPHjhg8eDAGDx6Mhx9+uNFzEEJoyowQ0kT9+/dHamoqZs2a1eiYSCQCy7Jmt+l0ukbnubm5mV3nVmH98zaj0djkuKqrq/Hggw8iMzPT7HLmzBmz6SFPT88mP6YleHp6onPnzkhISMDq1atx5MgRrFq1ij/+wQcfYPny5Zg5cyb27t2LzMxMpKamNir4bsq/zwMPPICrV69ix44dZrd7e3vjr7/+wvfff4/Q0FDMnj0bCQkJqKiosOyLJcQJUEJECGmyhQsXYuvWrUhPTze7PSgoCCqVyiwpsmTvoMOHD/P/r9frkZGRgaioKADAXXfdhePHj6N9+/bo3Lmz2aU5SZBCoUBYWBgOHTpkdvuhQ4cQHR3dqvhFIhHeeOMNvPXWW6irq+Mf96GHHsLTTz+NhIQEdOzYEadPn27R448YMQLr1q3D888/j/Xr15sdk0gkSElJweLFi5GdnY0LFy5gz549rXo9hDgjSogIIU0WFxeH0aNH4+OPPza7fcCAASgpKcHixYtx7tw5rFixAr/99pvFnnfFihXYvHkzTp06hYkTJ6K8vBzjxo0DAEycOBFlZWV48skn8eeff+LcuXPYsWMHnnvuORgMhmY9z4wZM7Bo0SJs2LABeXl5eP3115GZmYlXXnml1a/hscceg1gsxooVKwAAXbp0QVpaGv744w+cPHkSL774IoqKilr8+A8//DC+++47PPfcc/jhhx8AANu2bcPHH3+MzMxMXLx4Ed9++y2MRiO6devW6tdDiLOhhIgQ0izz589vNGUTFRWFzz77DCtWrEBCQgKOHj3aqlqjf1q4cCEWLlyIhIQEHDx4ED///DNfK8ON6hgMBgwaNAhxcXF49dVX4evra1av1BRTpkzBtGnTMH36dMTFxWH79u34+eef0aVLl1a/BolEgkmTJmHx4sWoqanBW2+9hbvuugupqakYMGAAlEolRo4c2arnePTRR7FmzRqMGTMGmzZtgq+vLzZt2oSBAwciKioKK1euxPfff4+YmJhWvx5CnA3D/nPinxBCCCHExdAIESGEEEJcHiVEhBBCCHF5lBARQgghxOVRQkQIIYQQl0cJESGEEEJcHiVEhBBCCHF5lBARQgghxOVRQkQIIYQQl0cJESGEEEJcHiVEhBBCCHF5lBARQgghxOVRQkQIIYQQl/f/utfF8KaxdYAAAAAASUVORK5CYII=", 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" ] @@ -388,18 +511,39 @@ } ], "source": [ - "(fig, ax, npoints_1e6, runtime_1e6) = runtime(df_1e6)" + "(fig, ax, nprocs_250k, runtime_1e6) = runtime_vs_nprocesses(df_1e6)" ] }, { "cell_type": "code", - "execution_count": 53, - "id": "324b6d11-8713-4863-bff0-e0eb14db4d3a", + "execution_count": 214, + "id": "81c4b69a-4ba2-4207-a093-ed34e25efe7d", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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5 rows × 33 columns

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" - ], "text/plain": [ - " experiment_id rank runtime p2m m2m l2l m2l p2p source_tree \\\n", - "0 0 2 21 9 0 0 0 0 6299 \n", - "1 0 1 1977 9 0 0 160 1784 6299 \n", - "2 0 3 1976 9 0 0 161 1784 6299 \n", - "3 0 6 1975 9 0 0 163 1783 6299 \n", - "4 0 4 1978 9 0 0 160 1788 6299 \n", - "\n", - " target_tree ... ghost_fmm_u displacement_map metadata_creation \\\n", - "0 11839 ... 0 0 534 \n", - "1 11839 ... 0 1 541 \n", - "2 11839 ... 0 1 543 \n", - "3 11839 ... 0 1 544 \n", - "4 11839 ... 0 1 544 \n", - "\n", - " expansion_order n_points local_depth global_depth block_size \\\n", - "0 3 1000000 4 1 128 \n", - "1 3 1000000 4 1 128 \n", - "2 3 1000000 4 1 128 \n", - "3 3 1000000 4 1 128 \n", - "4 3 1000000 4 1 128 \n", - "\n", - " n_threads n_samples \n", - "0 4 500 \n", - "1 4 500 \n", - "2 4 500 \n", - "3 4 500 \n", - "4 4 500 \n", - "\n", - "[5 rows x 33 columns]" + "(
,\n", + " ,\n", + " array([ 8, 16, 32, 64, 128, 256, 512]),\n", + " array([15229, 9749, 9664, 15484, 10043, 9867, 15925]))" ] }, - "execution_count": 6, + "execution_count": 216, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "df_1e6.head()" + "runtime_vs_npoints(df_5e6)" ] }, { "cell_type": "code", "execution_count": null, - "id": "ef7e67ce-b959-4e4e-bbfb-e511f9350a46", + "id": "52a10342-9c99-4ac3-8b0d-b2ab1702fb6b", "metadata": {}, "outputs": [], "source": [] From f87ab0ca9122b914ed6543238b2ad44d71b18ea6 Mon Sep 17 00:00:00 2001 From: srinath kailasa Date: Thu, 2 Oct 2025 10:20:45 +0100 Subject: [PATCH 06/52] More data including block:cyclic runs --- .../data/ijhpca/Weak Scaling (16 Nodes).ipynb | 133 +-- hpc/archer2/data/ijhpca/fix.py | 4 +- hpc/archer2/data/ijhpca/weak_fft_11004445.csv | 1018 +++++++++++++++++ 3 files changed, 1076 insertions(+), 79 deletions(-) create mode 100644 hpc/archer2/data/ijhpca/weak_fft_11004445.csv diff --git a/hpc/archer2/data/ijhpca/Weak Scaling (16 Nodes).ipynb b/hpc/archer2/data/ijhpca/Weak Scaling (16 Nodes).ipynb index 297c2874..6d08f88c 100644 --- a/hpc/archer2/data/ijhpca/Weak Scaling (16 Nodes).ipynb +++ b/hpc/archer2/data/ijhpca/Weak Scaling (16 Nodes).ipynb @@ -14,19 +14,19 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "id": "a15c010f-3256-4039-97b3-4ab6c4bb403e", "metadata": {}, "outputs": [], "source": [ "df_5e6 = pd.read_csv('weak_fft_10984742.csv')\n", "df_1e6 = pd.read_csv('weak_fft_10984754.csv')\n", - "df_250k = pd.read_csv('weak_fft_10984759.csv')" + "df_250k = pd.read_csv('weak_fft_11004445.csv')" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 10, "id": "6cbe52e4-fe73-477f-87aa-4ffd218c4542", "metadata": {}, "outputs": [ @@ -78,19 +78,19 @@ " \n", " 0\n", " 0\n", - " 0\n", - " 121\n", - " 0\n", + " 1\n", + " 18\n", + " 4\n", " 0\n", " 0\n", " 0\n", " 0\n", - " 2970\n", + " 2980\n", " 2935\n", " ...\n", " 0\n", " 0\n", - " 40\n", + " 160\n", " 3\n", " 250000\n", " 4\n", @@ -102,19 +102,19 @@ " \n", " 1\n", " 0\n", - " 5\n", - " 241\n", + " 0\n", + " 244\n", " 0\n", " 0\n", " 0\n", - " 84\n", - " 141\n", - " 2970\n", + " 82\n", + " 143\n", + " 2980\n", " 2935\n", " ...\n", " 0\n", - " 0\n", - " 151\n", + " 1\n", + " 168\n", " 3\n", " 250000\n", " 4\n", @@ -126,19 +126,19 @@ " \n", " 2\n", " 0\n", - " 1\n", - " 325\n", - " 8\n", + " 2\n", + " 327\n", + " 4\n", " 0\n", " 0\n", " 161\n", - " 143\n", - " 2970\n", - " 2934\n", + " 144\n", + " 2980\n", + " 2935\n", " ...\n", " 0\n", " 1\n", - " 147\n", + " 164\n", " 3\n", " 250000\n", " 4\n", @@ -150,19 +150,19 @@ " \n", " 3\n", " 0\n", - " 2\n", - " 322\n", + " 3\n", + " 328\n", " 4\n", " 0\n", " 0\n", " 162\n", - " 144\n", - " 2970\n", - " 2934\n", + " 143\n", + " 2980\n", + " 2935\n", " ...\n", " 0\n", - " 1\n", - " 151\n", + " 0\n", + " 163\n", " 3\n", " 250000\n", " 4\n", @@ -174,19 +174,19 @@ " \n", " 4\n", " 0\n", - " 4\n", - " 322\n", - " 4\n", + " 7\n", + " 328\n", + " 8\n", " 0\n", " 0\n", - " 164\n", + " 163\n", " 143\n", - " 2970\n", - " 2934\n", + " 2981\n", + " 2935\n", " ...\n", " 0\n", " 1\n", - " 151\n", + " 164\n", " 3\n", " 250000\n", " 4\n", @@ -202,18 +202,18 @@ ], "text/plain": [ " experiment_id rank runtime p2m m2m l2l m2l p2p source_tree \\\n", - "0 0 0 121 0 0 0 0 0 2970 \n", - "1 0 5 241 0 0 0 84 141 2970 \n", - "2 0 1 325 8 0 0 161 143 2970 \n", - "3 0 2 322 4 0 0 162 144 2970 \n", - "4 0 4 322 4 0 0 164 143 2970 \n", + "0 0 1 18 4 0 0 0 0 2980 \n", + "1 0 0 244 0 0 0 82 143 2980 \n", + "2 0 2 327 4 0 0 161 144 2980 \n", + "3 0 3 328 4 0 0 162 143 2980 \n", + "4 0 7 328 8 0 0 163 143 2981 \n", "\n", " target_tree ... ghost_fmm_u displacement_map metadata_creation \\\n", - "0 2935 ... 0 0 40 \n", - "1 2935 ... 0 0 151 \n", - "2 2934 ... 0 1 147 \n", - "3 2934 ... 0 1 151 \n", - "4 2934 ... 0 1 151 \n", + "0 2935 ... 0 0 160 \n", + "1 2935 ... 0 1 168 \n", + "2 2935 ... 0 1 164 \n", + "3 2935 ... 0 0 163 \n", + "4 2935 ... 0 1 164 \n", "\n", " expansion_order n_points local_depth global_depth block_size \\\n", "0 3 250000 4 1 128 \n", @@ -232,7 +232,7 @@ "[5 rows x 33 columns]" ] }, - "execution_count": 21, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -243,7 +243,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 11, "id": "b5623876-8efb-48c1-9e93-a2f8459f2a67", "metadata": {}, "outputs": [ @@ -253,7 +253,7 @@ "(1016, 33)" ] }, - "execution_count": 25, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -264,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 12, "id": "3bff612b-6a2b-449e-a301-cb45048cf545", "metadata": {}, "outputs": [], @@ -275,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 208, + "execution_count": 16, "id": "caf1a774-3904-4e65-95bf-f37f1180a3d6", "metadata": {}, "outputs": [], @@ -286,7 +286,7 @@ " for (id, experiment) in df.groupby('experiment_id'):\n", " n = experiment['n_points'].max() * experiment.shape[0]\n", " npoints.append(n)\n", - " r = experiment['runtime'].max()\n", + " r = experiment['runtime'].mean()\n", " runtime.append(r)\n", " \n", " npoints = np.array(npoints)\n", @@ -398,13 +398,13 @@ }, { "cell_type": "code", - "execution_count": 209, + "execution_count": 17, "id": "022c161a-007f-4f90-8f6d-3b73ed57dc00", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -419,34 +419,13 @@ }, { "cell_type": "code", - "execution_count": 210, - "id": "3de223dc-2c25-41f3-9ed9-f295efc63c34", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 8, 16, 32, 64, 128, 256, 512])" - ] - }, - "execution_count": 210, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "n_tasks" - ] - }, - { - "cell_type": "code", - "execution_count": 211, + "execution_count": 18, "id": "eb5e74a3-f5b1-4188-8cc9-0887efacbb2e", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] diff --git a/hpc/archer2/data/ijhpca/fix.py b/hpc/archer2/data/ijhpca/fix.py index 187ec418..a0ba80cc 100644 --- a/hpc/archer2/data/ijhpca/fix.py +++ b/hpc/archer2/data/ijhpca/fix.py @@ -1,12 +1,12 @@ import re # Read file -with open("weak_fft_10984759.csv", "r") as f: +with open("weak_fft_11004445.csv", "r") as f: data = f.read() # Replace "digit + space + digit" with "digit, digit" fixed_data = re.sub(r"(\d)\s+(\d)", r"\1,\2", data) # Write corrected CSV -with open("weak_fft_10984759.csv", "w") as f: +with open("weak_fft_11004445.csv", "w") as f: f.write(fixed_data) \ No newline at end of file diff --git a/hpc/archer2/data/ijhpca/weak_fft_11004445.csv b/hpc/archer2/data/ijhpca/weak_fft_11004445.csv new file mode 100644 index 00000000..b68f14d1 --- /dev/null +++ b/hpc/archer2/data/ijhpca/weak_fft_11004445.csv @@ -0,0 +1,1018 @@ + +experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,source_tree,target_tree,source_domain,target_domain,layout,ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,displacement_map,metadata_creation,expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples +"0",1,18,4,0,0,0,0,2980,2935,4,4,0,112,13,11,0,0, 381,1,0,0,0,0, 0, 160,3,250000,4,1,128,4,500 +"0",0,244,0,0,0,82,143,2980,2935,5,3,0,32,11,97,1,0, 381,0,0,0,0,0, 1, 168,3,250000,4,1,128,4,500 +"0",2,327,4,0,0,161,144,2980,2935,5,4,0,102,9,24,0,1, 381,1,0,0,0,0, 1, 164,3,250000,4,1,128,4,500 +"0",3,328,4,0,0,162,143,2980,2935,5,4,0,102,10,24,0,2, 382,1,0,0,0,0, 0, 163,3,250000,4,1,128,4,500 +"0",7,328,8,0,0,163,143,2981,2935,4,3,0,105,6,21,0,1, 381,1,0,0,0,0, 1, 164,3,250000,4,1,128,4,500 +"0",5,327,4,0,0,164,143,2981,2935,4,3,0,27,8,103,0,0, 381,1,0,0,0,0, 0, 167,3,250000,4,1,128,4,500 +"0",6,327,4,0,0,164,144,2981,2935,4,3,0,26,7,104,0,0, 381,1,0,0,0,0, 1, 168,3,250000,4,1,128,4,500 +"0",4,633,4,0,0,321,294,2981,2935,4,3,0,11,3,123,0,0, 381,1,0,0,0,0, 2, 172,3,250000,4,1,128,4,500 +"1",0,588,8,1,1,488,57,1954,1952,4,3,0,45,20,17,4,0, 382,0,0,0,0,0, 3, 136,3,250000,4,2,128,4,500 +"1",10,585,8,1,1,486,60,1954,1952,4,3,0,40,18,25,0,4, 383,1,0,0,0,0, 3, 140,3,250000,4,2,128,4,500 +"1",9,777,10,1,1,661,75,1954,1952,4,3,0,46,15,18,0,5, 383,1,0,0,0,0, 4, 139,3,250000,4,2,128,4,500 +"1",13,777,10,1,2,667,75,1954,1952,4,3,0,26,11,44,0,3, 383,1,0,0,0,0, 4, 144,3,250000,4,2,128,4,500 +"1",12,783,13,2,2,671,76,1954,1952,4,3,0,40,9,28,0,3, 383,1,0,0,0,0, 4, 143,3,250000,4,2,128,4,500 +"1",11,786,10,1,2,669,79,1954,1952,4,3,0,42,12,26,0,4, 383,1,0,0,0,0, 4, 142,3,250000,4,2,128,4,500 +"1",14,789,8,1,2,680,79,1954,1952,4,3,0,23,11,52,0,1, 383,1,0,0,0,0, 4, 149,3,250000,4,2,128,4,500 +"1",2,799,13,2,2,684,78,1954,1952,4,3,0,43,10,25,0,3, 382,1,0,0,0,0, 4, 143,3,250000,4,2,128,4,500 +"1",1,800,10,1,2,685,78,1954,1952,4,3,0,39,11,29,0,3, 382,1,0,0,0,0, 4, 144,3,250000,4,2,128,4,500 +"1",3,800,10,1,1,690,77,1954,1952,4,3,0,24,11,46,0,3, 382,1,0,0,0,0, 4, 146,3,250000,4,2,128,4,500 +"1",4,802,10,1,2,690,78,1954,1952,4,3,0,24,9,46,0,4, 382,1,0,0,0,0, 4, 145,3,250000,4,2,128,4,500 +"1",7,802,10,1,2,690,77,1954,1952,5,3,0,24,11,47,0,2, 383,1,0,0,0,0, 4, 145,3,250000,4,2,128,4,500 +"1",5,803,10,1,2,693,77,1954,1952,4,3,0,23,11,48,0,2, 382,1,0,0,0,0, 4, 145,3,250000,4,2,128,4,500 +"1",6,806,10,1,2,693,78,1954,1952,5,3,0,22,10,49,0,3, 383,1,0,0,0,0, 4, 145,3,250000,4,2,128,4,500 +"1",15,939,13,2,2,806,95,1954,1952,4,3,0,46,9,22,0,4, 383,1,0,0,0,0, 5, 141,3,250000,4,2,128,4,500 +"1",8,931,10,1,2,802,99,1954,1952,4,3,0,23,7,53,0,1, 383,1,0,0,0,0, 4, 150,3,250000,4,2,128,4,500 +"2",0,243,6,0,0,160,47,2256,2320,4,3,1,75,17,15,4,0, 381,0,0,0,0,0, 1, 156,3,250000,4,2,128,4,500 +"2",11,254,6,0,0,171,48,2257,2320,5,3,1,70,17,21,0,5, 383,1,0,0,0,0, 1, 156,3,250000,4,2,128,4,500 +"2",7,255,6,0,0,171,53,2257,2320,6,3,1,66,15,28,0,4, 382,1,0,0,0,0, 1, 159,3,250000,4,2,128,4,500 +"2",13,457,6,0,0,327,97,2257,2320,5,3,1,63,13,31,0,4, 383,1,0,0,0,0, 2, 159,3,250000,4,2,128,4,500 +"2",22,456,6,0,1,326,100,2257,2320,5,3,1,61,12,36,0,4, 384,1,0,0,0,0, 2, 160,3,250000,4,2,128,4,500 +"2",12,456,6,0,0,325,102,2256,2320,5,3,1,60,11,38,0,3, 383,1,0,0,0,0, 2, 162,3,250000,4,2,128,4,500 +"2",27,459,6,0,0,330,97,2257,2320,4,3,1,64,13,31,0,4, 384,1,0,0,0,0, 2, 158,3,250000,4,2,128,4,500 +"2",23,459,6,0,1,329,97,2257,2320,4,3,1,64,13,31,0,5, 384,1,0,0,0,0, 2, 158,3,250000,4,2,128,4,500 +"2",29,457,6,0,1,326,101,2257,2320,5,3,1,60,12,37,0,3, 384,1,0,0,0,0, 2, 160,3,250000,4,2,128,4,500 +"2",18,459,6,0,0,330,99,2257,2320,5,3,1,61,11,36,0,4, 384,1,0,0,0,0, 2, 161,3,250000,4,2,128,4,500 +"2",17,460,6,0,1,329,101,2257,2320,5,3,1,60,11,37,0,4, 384,1,0,0,0,0, 2, 161,3,250000,4,2,128,4,500 +"2",9,460,6,0,0,336,95,2257,2320,5,3,1,35,12,62,0,3, 383,1,0,0,0,0, 2, 162,3,250000,4,2,128,4,500 +"2",2,460,3,0,0,332,99,2256,2320,5,3,1,33,13,66,0,4, 382,1,0,0,0,0, 1, 165,3,250000,4,2,128,4,500 +"2",19,464,6,0,1,330,101,2257,2320,5,3,1,65,13,30,0,4, 384,1,0,0,0,0, 2, 159,3,250000,4,2,128,4,500 +"2",30,461,6,0,1,332,98,2257,2320,4,3,1,64,11,33,0,4, 385,1,0,0,0,0, 2, 161,3,250000,4,2,128,4,500 +"2",16,464,6,0,1,335,97,2256,2320,5,3,1,65,12,31,0,4, 384,1,0,0,0,0, 2, 159,3,250000,4,2,128,4,500 +"2",26,463,6,0,1,331,101,2257,2320,4,3,1,62,11,35,0,4, 384,1,0,0,0,0, 2, 160,3,250000,4,2,128,4,500 +"2",20,463,8,0,0,332,101,2257,2320,6,3,1,60,8,38,0,4, 384,1,0,0,0,0, 2, 161,3,250000,4,2,128,4,500 +"2",6,464,6,0,1,336,98,2257,2320,6,3,1,29,11,69,0,2, 382,1,0,0,0,0, 2, 164,3,250000,4,2,128,4,500 +"2",21,465,6,0,1,333,101,2257,2320,6,3,1,61,12,37,0,3, 384,1,0,0,0,0, 2, 161,3,250000,4,2,128,4,500 +"2",15,465,6,0,1,335,101,2256,2320,5,3,1,60,11,38,0,3, 383,1,0,0,0,0, 2, 162,3,250000,4,2,128,4,500 +"2",24,466,6,0,1,334,101,2257,2320,5,3,1,61,11,36,0,4, 384,1,0,0,0,0, 2, 160,3,250000,4,2,128,4,500 +"2",4,466,6,0,0,340,97,2256,2320,5,3,1,36,12,62,0,3, 382,1,0,0,0,0, 2, 163,3,250000,4,2,128,4,500 +"2",14,467,6,0,1,331,105,2256,2320,5,3,1,60,12,37,0,4, 383,1,0,0,0,0, 2, 161,3,250000,4,2,128,4,500 +"2",28,466,6,0,1,333,104,2257,2320,5,3,1,60,10,38,0,4, 384,1,0,0,0,0, 2, 161,3,250000,4,2,128,4,500 +"2",25,467,6,0,1,334,103,2256,2320,5,3,1,60,11,38,0,4, 384,1,0,0,0,0, 2, 161,3,250000,4,2,128,4,500 +"2",5,468,6,0,1,340,99,2257,2320,4,3,1,31,11,68,0,2, 382,1,0,0,0,0, 2, 163,3,250000,4,2,128,4,500 +"2",3,469,6,0,1,338,103,2257,2320,6,3,1,26,11,74,0,2, 382,1,0,0,0,0, 2, 166,3,250000,4,2,128,4,500 +"2",10,470,6,0,1,338,104,2256,2320,5,3,1,22,10,78,0,2, 383,1,0,0,0,0, 2, 165,3,250000,4,2,128,4,500 +"2",1,655,6,0,1,477,150,2256,2320,7,3,1,24,9,77,0,2, 382,1,0,0,0,0, 3, 166,3,250000,4,2,128,4,500 +"2",31,661,9,1,1,483,145,2257,2320,4,3,1,71,7,25,0,5, 384,1,0,0,0,0, 3, 159,3,250000,4,2,128,4,500 +"2",8,658,6,0,1,483,151,2257,2320,5,3,1,19,5,86,0,1, 383,1,0,0,0,0, 3, 170,3,250000,4,2,128,4,500 +"3",11,164,0,0,0,0,0,3018,2921,8,4,1,0,0,0,162,1, 388,2,0,0,0,0, 0, 69,3,250000,4,2,128,4,500 +"3",53,23,4,0,0,0,0,3018,2921,7,3,1,122,13,18,3,1, 382,2,0,0,0,0, 0, 215,3,250000,4,2,128,4,500 +"3",1,257,0,0,0,94,142,3018,2921,7,3,1,39,10,109,2,1, 388,1,0,0,0,0, 0, 218,3,250000,4,2,128,4,500 +"3",33,259,0,0,0,89,150,3018,2921,10,4,1,28,10,121,1,1, 384,2,0,0,0,0, 0, 223,3,250000,4,2,128,4,500 +"3",19,261,0,0,0,94,146,3018,2921,9,4,1,41,11,107,1,1, 387,1,0,0,0,0, 0, 219,3,250000,4,2,128,4,500 +"3",0,325,4,0,0,157,142,3018,2921,8,4,1,116,9,28,2,0, 371,1,1,0,0,0, 1, 229,3,250000,4,2,128,4,500 +"3",55,330,4,0,0,168,137,3018,2921,8,4,1,109,9,34,2,1, 382,2,0,0,0,0, 1, 219,3,250000,4,2,128,4,500 +"3",27,332,4,0,0,167,139,3018,2921,7,4,1,114,9,29,2,1, 386,1,0,0,0,0, 1, 215,3,250000,4,2,128,4,500 +"3",9,333,4,0,0,168,139,3018,2921,8,3,1,117,9,27,2,1, 388,1,0,0,0,0, 1, 213,3,250000,4,2,128,4,500 +"3",45,330,4,0,0,170,138,3018,2921,8,3,1,37,7,109,2,1, 383,2,0,0,0,0, 1, 221,3,250000,4,2,128,4,500 +"3",36,331,4,0,0,171,139,3018,2921,8,3,1,31,6,116,1,1, 385,2,0,0,0,0, 1, 220,3,250000,4,2,128,4,500 +"3",12,336,4,0,0,167,144,3018,2921,7,3,1,111,9,33,2,1, 388,2,0,0,0,0, 1, 214,3,250000,4,2,128,4,500 +"3",41,332,4,0,0,170,143,3018,2921,8,3,1,33,5,115,1,1, 384,2,0,0,0,0, 1, 222,3,250000,4,2,128,4,500 +"3",25,337,5,0,0,168,144,3018,2921,9,4,1,111,7,33,2,1, 386,1,0,0,0,0, 1, 216,3,250000,4,2,128,4,500 +"3",60,337,4,0,0,169,144,3018,2921,8,3,1,107,9,36,2,1, 381,1,0,0,0,0, 1, 220,3,250000,4,2,128,4,500 +"3",4,337,4,0,0,168,144,3018,2921,10,3,1,109,9,35,2,1, 388,1,0,0,0,0, 1, 213,3,250000,4,2,128,4,500 +"3",62,333,4,0,0,170,143,3018,2921,8,4,1,33,6,116,1,1, 381,2,0,0,0,0, 1, 225,3,250000,4,2,128,4,500 +"3",2,333,4,0,0,170,144,3018,2921,7,3,1,37,5,111,1,1, 388,2,0,0,0,0, 1, 218,3,250000,4,2,128,4,500 +"3",32,338,4,0,0,167,146,3018,2921,8,4,1,107,9,37,2,1, 384,1,0,0,0,0, 1, 217,3,250000,4,2,128,4,500 +"3",8,337,4,0,0,169,144,3018,2921,7,4,1,111,8,33,2,1, 388,1,0,0,0,0, 1, 214,3,250000,4,2,128,4,500 +"3",56,338,4,0,0,169,143,3018,2921,8,4,1,107,9,36,2,1, 382,2,0,0,0,0, 1, 220,3,250000,4,2,128,4,500 +"3",22,339,4,0,0,169,145,3018,2921,6,4,1,107,9,37,2,1, 386,1,0,0,0,0, 1, 215,3,250000,4,2,128,4,500 +"3",14,339,4,0,0,170,145,3018,2921,6,3,1,107,8,37,2,1, 388,2,0,0,0,0, 1, 214,3,250000,4,2,128,4,500 +"3",47,335,4,0,0,171,144,3018,2921,8,4,1,32,5,116,1,1, 384,2,0,0,0,0, 1, 223,3,250000,4,2,128,4,500 +"3",52,336,4,0,0,172,145,3018,2921,10,4,1,35,5,114,1,1, 382,2,0,0,0,0, 1, 224,3,250000,4,2,128,4,500 +"3",31,341,4,0,0,167,149,3018,2921,6,3,1,107,9,37,2,1, 385,1,0,0,0,0, 1, 217,3,250000,4,2,128,4,500 +"3",29,341,4,0,0,168,150,3018,2921,7,4,1,103,8,42,2,1, 386,1,0,0,0,0, 1, 217,3,250000,4,2,128,4,500 +"3",37,338,4,0,0,173,144,3018,2921,8,4,1,31,6,117,1,1, 384,1,0,0,0,0, 1, 222,3,250000,4,2,128,4,500 +"3",44,338,4,0,0,173,146,3018,2921,7,3,1,24,5,124,1,1, 384,1,0,0,0,0, 1, 223,3,250000,4,2,128,4,500 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222,3,250000,4,2,128,4,500 +"3",16,345,4,0,0,170,151,3018,2921,7,4,1,104,8,41,2,1, 387,1,1,0,0,0, 1, 216,3,250000,4,2,128,4,500 +"3",10,345,4,0,0,169,152,3018,2921,8,4,1,106,8,39,2,1, 388,2,0,0,0,0, 1, 215,3,250000,4,2,128,4,500 +"3",48,341,4,0,0,171,151,3018,2921,7,3,1,14,4,136,1,1, 383,1,0,0,0,0, 1, 224,3,250000,4,2,128,4,500 +"3",34,342,4,0,0,172,150,3018,2921,7,4,1,22,5,128,1,1, 384,2,0,0,0,0, 1, 223,3,250000,4,2,128,4,500 +"3",49,341,4,0,0,166,156,3018,2921,8,4,1,14,4,136,1,1, 383,2,0,0,0,0, 1, 225,3,250000,4,2,128,4,500 +"3",61,346,8,0,0,172,150,3018,2921,9,4,1,106,4,39,2,1, 381,1,0,0,0,0, 1, 221,3,250000,4,2,128,4,500 +"3",50,343,4,0,0,171,150,3018,2921,8,4,1,26,5,123,1,1, 383,1,0,0,0,0, 1, 223,3,250000,4,2,128,4,500 +"3",40,342,4,0,0,172,150,3018,2921,8,3,1,18,5,131,1,1, 384,2,0,0,0,0, 1, 223,3,250000,4,2,128,4,500 +"3",43,344,4,0,0,173,151,3018,2921,8,3,1,23,5,126,1,1, 384,2,0,0,0,0, 1, 223,3,250000,4,2,128,4,500 +"3",7,348,4,0,0,168,158,3018,2921,8,4,1,97,7,49,1,1, 388,2,0,0,0,0, 1, 216,3,250000,4,2,128,4,500 +"3",39,345,4,0,0,170,155,3018,2921,8,3,1,23,5,125,1,1, 384,2,0,0,0,0, 1, 222,3,250000,4,2,128,4,500 +"3",15,349,4,0,0,170,157,3018,2921,7,3,1,99,7,47,2,1, 387,1,0,0,0,0, 1, 217,3,250000,4,2,128,4,500 +"3",26,351,4,0,0,170,156,3018,2921,5,3,1,110,9,34,2,1, 386,2,0,0,0,0, 1, 216,3,250000,4,2,128,4,500 +"3",59,350,4,0,0,171,155,3018,2921,8,3,1,103,8,42,2,1, 382,1,0,0,0,0, 1, 221,3,250000,4,2,128,4,500 +"3",57,349,4,0,0,170,156,3018,2921,7,3,1,98,7,48,2,1, 382,1,0,0,0,0, 1, 222,3,250000,4,2,128,4,500 +"3",51,347,4,0,0,172,156,3018,2921,7,3,1,21,5,128,1,1, 383,1,0,0,0,0, 1, 224,3,250000,4,2,128,4,500 +"3",3,351,8,0,0,171,156,3018,2921,8,4,1,110,4,35,1,1, 388,1,0,0,0,0, 1, 215,3,250000,4,2,128,4,500 +"3",28,350,4,0,0,170,158,3018,2921,7,4,1,98,7,48,2,1, 386,2,0,0,0,0, 1, 218,3,250000,4,2,128,4,500 +"3",35,348,4,0,0,171,157,3018,2921,7,3,1,19,4,131,1,1, 384,2,0,0,0,0, 1, 223,3,250000,4,2,128,4,500 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208,3,250000,4,3,128,4,500 +"4",86,596,10,1,1,489,59,2027,1994,4,3,1,53,17,50,3,9, 388,1,0,0,0,0, 4, 217,3,250000,4,3,128,4,500 +"4",114,602,7,1,1,492,59,2027,1994,5,3,1,69,23,33,3,9, 385,1,0,0,0,0, 3, 218,3,250000,4,3,128,4,500 +"4",30,601,10,1,1,491,58,2027,1994,4,3,1,70,17,32,5,9, 395,1,0,0,0,0, 3, 208,3,250000,4,3,128,4,500 +"4",9,605,10,1,1,491,59,2027,1994,5,3,1,73,16,26,9,9, 397,1,1,0,0,0, 3, 204,3,250000,4,3,128,4,500 +"4",76,604,10,1,1,492,59,2027,1994,4,3,1,70,16,31,8,9, 389,1,0,0,0,0, 3, 213,3,250000,4,3,128,4,500 +"4",120,603,7,1,1,493,59,2027,1994,5,3,1,68,21,34,4,9, 384,1,0,0,0,0, 3, 219,3,250000,4,3,128,4,500 +"4",110,606,10,1,1,492,60,2027,1994,5,3,1,72,18,28,6,9, 385,1,0,0,0,0, 4, 217,3,250000,4,3,128,4,500 +"4",71,605,10,1,1,493,59,2027,1994,4,3,1,70,18,31,5,9, 390,1,0,0,0,0, 3, 213,3,250000,4,3,128,4,500 +"4",99,605,10,1,1,494,59,2027,1994,6,3,1,70,17,32,5,9, 386,1,0,0,0,0, 3, 217,3,250000,4,3,128,4,500 +"4",106,604,10,1,1,495,59,2027,1994,5,3,1,68,17,34,4,9, 385,1,0,0,0,0, 3, 219,3,250000,4,3,128,4,500 +"4",112,607,10,1,1,496,59,2027,1994,4,3,1,70,18,32,4,9, 385,1,0,0,0,0, 4, 218,3,250000,4,3,128,4,500 +"4",104,611,10,1,1,497,60,2027,1994,5,3,1,71,18,29,6,9, 386,1,0,0,0,0, 4, 216,3,250000,4,3,128,4,500 +"4",0,784,10,1,2,655,72,2027,1994,6,3,1,77,15,21,11,0, 373,1,1,0,0,0, 4, 222,3,250000,4,3,128,4,500 +"4",68,776,7,1,1,654,77,2027,1994,6,3,1,47,12,63,5,9, 390,1,0,0,0,0, 3, 222,3,250000,4,3,128,4,500 +"4",17,787,10,1,1,660,77,2027,1994,6,3,1,71,13,31,8,9, 396,1,0,0,0,0, 4, 208,3,250000,4,3,128,4,500 +"4",84,778,7,1,2,658,80,2027,1994,5,3,1,28,14,85,0,9, 388,1,0,0,0,0, 4, 227,3,250000,4,3,128,4,500 +"4",2,792,10,1,2,662,76,2027,1994,6,3,1,73,15,27,9,9, 397,1,0,0,0,0, 4, 204,3,250000,4,3,128,4,500 +"4",69,781,10,1,1,661,77,2027,1994,6,4,1,47,11,64,3,9, 390,1,0,0,0,0, 4, 222,3,250000,4,3,128,4,500 +"4",1,791,10,1,1,661,77,2027,1994,5,3,1,73,14,29,8,9, 397,1,0,0,0,0, 4, 206,3,250000,4,3,128,4,500 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212,3,250000,4,3,128,4,500 +"4",79,790,10,1,2,668,76,2027,1994,6,3,1,56,13,51,3,9, 389,1,0,0,0,0, 4, 220,3,250000,4,3,128,4,500 +"4",13,794,10,1,1,664,79,2027,1994,5,3,1,70,13,33,8,9, 397,1,1,0,0,0, 4, 208,3,250000,4,3,128,4,500 +"4",108,793,10,1,2,668,76,2027,1994,4,3,1,70,14,34,5,9, 385,1,0,0,0,0, 4, 221,3,250000,4,3,128,4,500 +"4",16,798,10,1,2,664,79,2027,1994,5,4,1,75,14,24,10,9, 396,1,1,0,0,0, 4, 205,3,250000,4,3,128,4,500 +"4",21,789,7,1,2,663,80,2027,1994,5,3,1,45,12,64,6,9, 396,1,0,0,0,0, 4, 215,3,250000,4,3,128,4,500 +"4",75,794,10,1,2,670,75,2027,1994,4,3,1,70,15,33,5,9, 390,1,0,0,0,0, 4, 215,3,250000,4,3,128,4,500 +"4",45,797,10,1,2,668,77,2027,1994,5,3,1,73,15,28,8,9, 393,1,0,0,0,0, 4, 210,3,250000,4,3,128,4,500 +"4",8,791,10,1,1,669,75,2027,1994,4,4,1,56,10,52,6,9, 397,1,0,0,0,0, 4, 212,3,250000,4,3,128,4,500 +"4",46,796,10,1,1,667,78,2027,1994,6,3,1,70,14,33,6,9, 393,1,0,0,0,0, 4, 211,3,250000,4,3,128,4,500 +"4",12,796,10,1,1,666,79,2027,1994,7,3,1,70,13,32,8,9, 397,1,0,0,0,0, 4, 207,3,250000,4,3,128,4,500 +"4",3,796,10,1,2,666,79,2027,1994,4,3,1,71,13,31,8,9, 397,1,1,0,0,0, 4, 207,3,250000,4,3,128,4,500 +"4",31,792,10,1,2,669,77,2027,1994,5,3,1,68,13,39,4,9, 395,1,0,0,0,0, 4, 214,3,250000,4,3,128,4,500 +"4",107,795,10,1,2,669,76,2027,1994,4,3,1,68,15,36,4,9, 385,1,1,0,0,0, 4, 221,3,250000,4,3,128,4,500 +"4",60,797,10,1,1,669,77,2027,1994,5,3,1,71,15,32,6,9, 391,1,1,0,0,0, 4, 214,3,250000,4,3,128,4,500 +"4",91,798,10,1,1,670,75,2027,1994,4,3,1,57,17,45,5,9, 388,1,0,0,0,0, 4, 216,3,250000,4,3,128,4,500 +"4",20,799,10,1,1,669,77,2027,1994,5,3,1,73,14,29,8,9, 396,1,0,0,0,0, 4, 207,3,250000,4,3,128,4,500 +"4",123,800,13,2,1,671,76,2027,1994,5,3,1,77,14,24,5,9, 384,1,0,0,0,0, 4, 219,3,250000,4,3,128,4,500 +"4",25,793,10,1,2,670,77,2027,1994,5,3,1,53,13,55,3,9, 395,1,1,0,0,0, 4, 213,3,250000,4,3,128,4,500 +"4",77,797,10,1,2,673,74,2027,1994,4,3,1,56,14,47,6,9, 389,1,0,0,0,0, 4, 216,3,250000,4,3,128,4,500 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217,3,250000,4,3,128,4,500 +"4",38,800,10,1,2,676,76,2027,1994,5,3,1,58,13,46,7,9, 394,1,0,0,0,0, 4, 212,3,250000,4,3,128,4,500 +"4",50,803,10,1,2,674,76,2027,1994,5,3,1,77,15,24,8,9, 393,1,0,0,0,0, 4, 210,3,250000,4,3,128,4,500 +"4",35,796,10,1,2,669,80,2027,1994,4,3,1,52,11,57,4,9, 394,1,0,0,0,0, 4, 216,3,250000,4,3,128,4,500 +"4",51,801,10,1,1,671,79,2027,1994,5,3,1,70,15,33,6,9, 392,1,0,0,0,0, 4, 212,3,250000,4,3,128,4,500 +"4",37,800,10,1,2,674,77,2027,1994,6,3,1,55,13,49,6,9, 394,1,1,0,0,0, 4, 212,3,250000,4,3,128,4,500 +"4",95,803,14,2,2,678,74,2027,1994,4,3,1,75,12,26,5,9, 387,1,0,0,0,0, 5, 216,3,250000,4,3,128,4,500 +"4",52,804,12,2,2,677,74,2027,1994,5,3,1,77,13,23,7,9, 392,1,0,0,0,0, 4, 210,3,250000,4,3,128,4,500 +"4",102,798,7,1,1,671,80,2027,1994,4,4,1,54,15,53,4,9, 386,1,0,0,0,0, 4, 224,3,250000,4,3,128,4,500 +"4",24,798,10,1,1,672,80,2027,1994,5,4,1,54,13,54,3,9, 395,1,0,0,0,0, 4, 214,3,250000,4,3,128,4,500 +"4",73,803,12,2,1,677,75,2027,1994,5,3,1,75,13,27,5,9, 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226,3,250000,4,3,128,4,500 +"4",64,943,10,1,3,804,94,2027,1994,5,4,1,57,8,52,7,9, 390,1,0,0,0,0, 4, 221,3,250000,4,3,128,4,500 +"4",113,939,10,1,2,797,99,2027,1994,5,3,1,45,8,68,2,9, 385,1,0,0,0,0, 5, 230,3,250000,4,3,128,4,500 +"4",61,940,10,1,2,801,97,2027,1994,7,3,1,47,7,66,3,9, 391,1,1,0,0,0, 4, 224,3,250000,4,3,128,4,500 +"4",34,941,10,1,2,802,95,2027,1994,5,3,1,50,7,62,4,9, 394,1,0,0,0,0, 5, 219,3,250000,4,3,128,4,500 +"4",19,944,10,1,2,802,96,2027,1994,5,3,1,56,6,52,9,9, 399,1,0,0,0,0, 4, 211,3,250000,4,3,128,4,500 +"4",105,946,10,1,2,802,98,2027,1994,5,3,1,55,8,53,7,9, 385,1,0,0,0,0, 5, 225,3,250000,4,3,128,4,500 +"4",122,942,10,1,2,802,98,2027,1994,4,3,1,46,8,67,2,9, 384,1,0,0,0,0, 5, 231,3,250000,4,3,128,4,500 +"4",87,940,10,1,2,801,98,2027,1994,5,3,1,28,8,86,0,9, 393,1,0,0,0,0, 4, 224,3,250000,4,3,128,4,500 +"5",134,267,6,0,0,172,49,2383,2357,67,3,1,104,18,23,5,9, 394,1,1,0,0,0, 1, 340,3,250000,4,3,128,4,500 +"5",76,268,6,0,0,174,49,2383,2357,65,3,1,106,15,21,8,9, 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335,3,250000,4,3,128,4,500 +"5",110,476,6,0,1,331,103,2383,2357,65,3,1,96,11,37,6,9, 397,1,1,0,0,0, 2, 342,3,250000,4,3,128,4,500 +"5",166,480,6,0,1,340,95,2383,2357,67,3,1,97,12,32,9,9, 390,1,1,0,0,0, 2, 346,3,250000,4,3,128,4,500 +"5",128,480,6,0,1,337,98,2383,2357,68,3,1,99,14,30,7,9, 395,1,0,0,0,0, 1, 342,3,250000,4,3,128,4,500 +"5",41,474,6,0,1,335,99,2383,2357,65,4,1,61,7,73,7,9, 405,1,1,0,0,0, 2, 337,3,250000,4,3,128,4,500 +"5",10,478,6,0,1,333,102,2383,2357,66,3,1,97,5,34,13,9, 408,1,1,0,0,0, 2, 331,3,250000,4,3,128,4,500 +"5",180,474,6,0,0,336,99,2383,2357,65,3,1,56,9,79,5,9, 389,2,1,0,0,0, 2, 353,3,250000,4,3,128,4,500 +"5",168,476,7,0,0,329,106,2383,2357,65,4,1,90,11,42,5,9, 390,1,1,0,0,0, 2, 350,3,250000,4,3,128,4,500 +"5",46,478,6,0,0,331,105,2383,2357,65,3,1,96,10,36,8,9, 404,1,1,0,0,0, 2, 335,3,250000,4,3,128,4,500 +"5",88,476,6,0,0,332,103,2383,2357,65,4,1,88,12,46,4,9, 400,1,1,0,0,0, 2, 341,3,250000,4,3,128,4,500 +"5",11,477,6,0,1,333,102,2383,2357,66,3,1,98,4,34,13,9, 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331,3,250000,4,3,128,4,500 +"5",57,477,6,0,0,332,105,2383,2357,66,4,1,93,10,40,6,9, 403,1,1,0,0,0, 2, 337,3,250000,4,3,128,4,500 +"5",16,481,6,0,1,339,98,2383,2357,67,3,1,102,14,27,6,9, 407,1,1,0,0,0, 2, 329,3,250000,4,3,128,4,500 +"5",192,479,6,0,1,334,103,2383,2357,65,3,1,92,15,40,3,9, 388,1,1,0,0,0, 2, 351,3,250000,4,3,128,4,500 +"5",127,482,6,0,0,338,99,2383,2357,66,3,1,97,14,33,6,9, 395,1,1,0,0,0, 1, 341,3,250000,4,3,128,4,500 +"5",201,476,6,0,1,335,101,2383,2357,65,3,1,54,11,81,3,9, 387,1,1,0,0,0, 2, 355,3,250000,4,3,128,4,500 +"5",129,480,6,0,0,333,105,2383,2357,66,3,1,94,14,37,4,9, 394,1,0,0,0,0, 2, 344,3,250000,4,3,128,4,500 +"5",215,479,6,0,1,331,106,2383,2357,65,3,1,90,13,43,4,9, 386,1,1,0,0,0, 2, 354,3,250000,4,3,128,4,500 +"5",155,481,6,0,1,340,96,2383,2357,54,3,1,98,15,32,5,9, 392,1,1,0,0,0, 2, 345,3,250000,4,3,128,4,500 +"5",107,478,6,0,0,334,103,2383,2357,66,3,1,91,11,42,6,9, 398,1,1,0,0,0, 2, 342,3,250000,4,3,128,4,500 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427,1,1,0,0,0, 2, 1362,3,250000,4,3,128,4,500 +"6",100,688,4,0,0,341,315,3190,3329,133,4,1,35,7,155,1,9, 427,1,1,0,0,0, 1, 1362,3,250000,4,3,128,4,500 +"6",58,688,4,0,0,342,314,3190,3329,75,4,1,30,6,161,1,9, 431,1,1,0,0,0, 1, 1358,3,250000,4,3,128,4,500 From e5c0cfaadea21242579a60712cedba10c5e15490 Mon Sep 17 00:00:00 2001 From: srinath kailasa Date: Thu, 2 Oct 2025 13:42:25 +0100 Subject: [PATCH 07/52] Add strong scaling script --- hpc/archer2/slurm/strong_1_nodes_fft.slurm | 76 ++++++++++++++++++++++ hpc/archer2/slurm/weak_1_fft.slurm | 56 +++++++--------- scripts/src/bin/fmm_m2l_fft_mpi_f32.rs | 2 +- 3 files changed, 102 insertions(+), 32 deletions(-) create mode 100644 hpc/archer2/slurm/strong_1_nodes_fft.slurm diff --git a/hpc/archer2/slurm/strong_1_nodes_fft.slurm b/hpc/archer2/slurm/strong_1_nodes_fft.slurm new file mode 100644 index 00000000..698d165c --- /dev/null +++ b/hpc/archer2/slurm/strong_1_nodes_fft.slurm @@ -0,0 +1,76 @@ +#!/bin/bash +# Here, we perform a strong scaling run where the number of MPI processes are tied to CCX units, each of which +# share an L3 cache, consist of 4 cores. There are 16 CCX regions per processor, and 32 per node (i.e. two AMD EPYC Rome units per node) +# We start with a large (>2.5B) point problem on a single fully utilised node, and gradually double the number of nodes to identify +# the saturation point (in terms of nodes) + +#SBATCH --job-name=strong_scaling_fft +#SBATCH --time=00:30:00 +#SBATCH --nodes=256 +#SBATCH --ntasks-per-node=32 +#SBATCH --cpus-per-task=4 +#SBATCH --contiguous + +#SBATCH --account=e738 +#SBATCH --partition=standard +#SBATCH --qos=standard + +# Home and work directories +export HOME="/home/e738/e738/skailasa" +export WORK="/work/e738/e738/skailasa" +TOTAL_POINTS=2048000000 # 2.048B points = 64M * 32 tasks (1 node) and stays constant + +# Script being run, expected in the work directory +script_name="fmm_m2l_fft_mpi_f32" # must exist in $WORK + +# Set simulation parameters for FMM +expansion_order=3 +global_depth=(2 2 3 3 3 4 4 4 5) # Number of local roots +n_samples=500 +block_size=128 + +# Set parameters for strong scaling run +n_points=(64000000 32000000 16000000 8000000 4000000 2000000 1000000 500000 250000) +local_depth=(6 6 5 5 5 4 4 4 3) +n_tasks=(32 64 128 256 512 1024 2048 4096 8192) # Equal to the number of local trees +n_nodes=(1 2 4 8 16 32 64 128 256) +n_threads=4 # See if bandwidth saturates with different threading parameters for Rayon thread pool +cpus_per_task=4 + +# Create a scratch directory for this run +last_nodes=${n_nodes[@]: -1} +export SCRATCH=${WORK}/strong_fft_p=${TOTAL_POINTS}_n=${last_nodes}_${SLURM_JOBID} + +mkdir -p ${SCRATCH} +cd ${SCRATCH} + +# Pass variable to SRUN from SBATCH +export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK + +export OMP_NUM_THREADS=1 # Need to set to 1 to avoid oversubsciption between Rayon and OpenMP + +# Create a CSV output file for analysis +export OUTPUT=${SCRATCH}/strong_fft_p=${TOTAL_POINTS}_n=${last_nodes}_${SLURM_JOBID}.csv + +# Create a CSV output file for analysis +touch ${OUTPUT} +echo " +experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ +source_tree,target_tree,source_domain,target_domain,layout,\ +ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\ +source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\ +displacement_map,metadata_creation,\ +expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples" >> ${OUTPUT} + +for i in ${!n_tasks[@]}; do + + experiment_id="${i}" + srun --nodes=${n_nodes[$i]} --ntasks=${n_tasks[$i]} --cpus-per-task=$cpus_per_task --distribution=block:block --hint=nomultithread \ + --expansion-order $expansion_order \ + --prune-empty \ + --global-depth ${global_depth[$i]} \ + --local-depth ${local_depth[$i]} \ + --n-samples $n_samples \ + --block-size $block_size \ + --n-threads $n_threads >> ${OUTPUT} +done diff --git a/hpc/archer2/slurm/weak_1_fft.slurm b/hpc/archer2/slurm/weak_1_fft.slurm index 1561732f..44243e7d 100644 --- a/hpc/archer2/slurm/weak_1_fft.slurm +++ b/hpc/archer2/slurm/weak_1_fft.slurm @@ -1,11 +1,6 @@ #!/bin/bash -# Here, we perform a weak scaling run where the number of MPI processes is set equal to the number of CCX units, each of which -# share an L3 cache, conist of 4 processors. There are 16 CCX regions per processor, and 32 per node - -# Running on just a single node, we try and examine the scaling performance as the number of tasks is increased -# just to see what a single node is capable of (in the best parameter settings found) - -# This should give a good baseline of what to expect when communication costs are minimal +# Here, we perform a weak scaling run where the number of MPI processes are tied to CCX units, each of which +# share an L3 cache, consist of 4 processors. There are 16 CCX regions per processor, and 32 per node (i.e. two AMD EPYC Rome units per node) #SBATCH --job-name=weak_scaling_fft #SBATCH --time=00:30:00 @@ -17,7 +12,6 @@ #SBATCH --account=e738 #SBATCH --partition=standard #SBATCH --qos=standard -# Development environment for KiFMM # Restore AMD compiler env module load PrgEnv-aocc @@ -28,28 +22,6 @@ module load cray-mpich-ucx export HOME="/home/e738/e738/skailasa" export WORK="/work/e738/e738/skailasa" -# Create a scratch directory for this run -export SCRATCH=${WORK}/weak_${SLURM_JOBID} - -# Load Spack -source $HOME/spack/share/spack/setup-env.sh -. "$HOME/.cargo/env" - -# Load BLAS -spack load openblas - -# Ensure Rust can find the Cray libraries -# export RUSTFLAGS="-L $(echo $CRAY_LD_LIBRARY_PATH)" -# export LD_LIBRARY_PATH=$CRAY_LD_LIBRARY_PATH:$LD_LIBRARY_PATH -export RUSTFLAGS="-L $(spack location -i openblas)/lib" -export LD_LIBRARY_PATH=$(spack location -i openblas)/lib:$LD_LIBRARY_PATH - -mkdir -p ${SCRATCH} -cd ${SCRATCH} - -# Pass variable to SRUN from SBATCH -export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK - # Script being run, expected in the work directory script_name="fmm_m2l_fft_mpi_f32" @@ -66,6 +38,29 @@ n_tasks=(8 16 32 64 128 256 512) n_nodes=(1 1 1 2 4 8 16) n_threads=4 # See if bandwidth saturates with different threading parameters for Rayon thread pool cpus_per_task=4 + +# Create a scratch directory for this run +export SCRATCH=${WORK}/weak_fft_p=${n_points}_n=${n_nodes[-1]}_${SLURM_JOBID} + +## Load Spack +#source $HOME/spack/share/spack/setup-env.sh +#. "$HOME/.cargo/env" +# +## Load BLAS +#spack load openblas +# +## Ensure Rust can find the Cray libraries +## export RUSTFLAGS="-L $(echo $CRAY_LD_LIBRARY_PATH)" +## export LD_LIBRARY_PATH=$CRAY_LD_LIBRARY_PATH:$LD_LIBRARY_PATH +#export RUSTFLAGS="-L $(spack location -i openblas)/lib" +#export LD_LIBRARY_PATH=$(spack location -i openblas)/lib:$LD_LIBRARY_PATH + +mkdir -p ${SCRATCH} +cd ${SCRATCH} + +# Pass variable to SRUN from SBATCH +export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK + export OMP_NUM_THREADS=1 # Need to set to 1 to avoid oversubsciption between Rayon and OpenMP # Create a CSV output file for analysis @@ -92,4 +87,3 @@ for i in ${!n_tasks[@]}; do --block-size $block_size \ --n-threads $n_threads >> ${OUTPUT} done - diff --git a/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs b/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs index c463aeb4..595bbcfa 100644 --- a/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs +++ b/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs @@ -224,7 +224,7 @@ fn main() { println!( "{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},\ {:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?}, \ - {:?},{:?},{:?},{:?},{:?},{:?}, {:?}, {:?} \ + {:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?}, \ {:?},{:?},{:?},{:?},{:?},{:?},{:?}", id, multi_fmm.rank(), From 515e3feeff01f93f0f995288f351a4b1402cc46e Mon Sep 17 00:00:00 2001 From: srinath kailasa Date: Thu, 2 Oct 2025 13:46:05 +0100 Subject: [PATCH 08/52] Fix slurm script --- hpc/archer2/slurm/strong_1_nodes_fft.slurm | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/hpc/archer2/slurm/strong_1_nodes_fft.slurm b/hpc/archer2/slurm/strong_1_nodes_fft.slurm index 698d165c..7a9f472c 100644 --- a/hpc/archer2/slurm/strong_1_nodes_fft.slurm +++ b/hpc/archer2/slurm/strong_1_nodes_fft.slurm @@ -5,7 +5,7 @@ # the saturation point (in terms of nodes) #SBATCH --job-name=strong_scaling_fft -#SBATCH --time=00:30:00 +#SBATCH --time=01:00:00 #SBATCH --nodes=256 #SBATCH --ntasks-per-node=32 #SBATCH --cpus-per-task=4 @@ -66,6 +66,7 @@ for i in ${!n_tasks[@]}; do experiment_id="${i}" srun --nodes=${n_nodes[$i]} --ntasks=${n_tasks[$i]} --cpus-per-task=$cpus_per_task --distribution=block:block --hint=nomultithread \ + "${WORK}/${script_name}" --id $experiment_id --n-points ${n_points[$i]} \ --expansion-order $expansion_order \ --prune-empty \ --global-depth ${global_depth[$i]} \ From d2c1c9fed3caec3ca25c0b71cb8c127ddf5562dc Mon Sep 17 00:00:00 2001 From: srinath kailasa Date: Thu, 2 Oct 2025 14:30:08 +0100 Subject: [PATCH 09/52] Reduce local depth --- hpc/archer2/slurm/strong_1_nodes_fft.slurm | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/hpc/archer2/slurm/strong_1_nodes_fft.slurm b/hpc/archer2/slurm/strong_1_nodes_fft.slurm index 7a9f472c..83bc8136 100644 --- a/hpc/archer2/slurm/strong_1_nodes_fft.slurm +++ b/hpc/archer2/slurm/strong_1_nodes_fft.slurm @@ -15,10 +15,15 @@ #SBATCH --partition=standard #SBATCH --qos=standard +# Restore AMD compiler env +module load PrgEnv-aocc +module load craype-network-ucx +module load cray-mpich-ucx + # Home and work directories export HOME="/home/e738/e738/skailasa" export WORK="/work/e738/e738/skailasa" -TOTAL_POINTS=2048000000 # 2.048B points = 64M * 32 tasks (1 node) and stays constant +TOTAL_POINTS=1.024B # 1.024B points = 32M * 32 tasks (1 node) and stays constant # Script being run, expected in the work directory script_name="fmm_m2l_fft_mpi_f32" # must exist in $WORK @@ -30,8 +35,8 @@ n_samples=500 block_size=128 # Set parameters for strong scaling run -n_points=(64000000 32000000 16000000 8000000 4000000 2000000 1000000 500000 250000) -local_depth=(6 6 5 5 5 4 4 4 3) +n_points=(32000000 16000000 8000000 4000000 2000000 1000000 500000 250000 125000) +local_depth=(5 5 4 4 4 3 3 3 3) n_tasks=(32 64 128 256 512 1024 2048 4096 8192) # Equal to the number of local trees n_nodes=(1 2 4 8 16 32 64 128 256) n_threads=4 # See if bandwidth saturates with different threading parameters for Rayon thread pool From b519d4ef4400235ee91ff136b6c847e7e878fa48 Mon Sep 17 00:00:00 2001 From: srinath kailasa Date: Thu, 2 Oct 2025 14:33:01 +0100 Subject: [PATCH 10/52] Add small script changes --- hpc/archer2/slurm/strong_1_nodes_fft.slurm | 2 +- hpc/archer2/slurm/weak_1_fft.slurm | 7 ++++--- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/hpc/archer2/slurm/strong_1_nodes_fft.slurm b/hpc/archer2/slurm/strong_1_nodes_fft.slurm index 83bc8136..a4535026 100644 --- a/hpc/archer2/slurm/strong_1_nodes_fft.slurm +++ b/hpc/archer2/slurm/strong_1_nodes_fft.slurm @@ -44,7 +44,7 @@ cpus_per_task=4 # Create a scratch directory for this run last_nodes=${n_nodes[@]: -1} -export SCRATCH=${WORK}/strong_fft_p=${TOTAL_POINTS}_n=${last_nodes}_${SLURM_JOBID} +export SCRATCH=${WORK}/strong_fft_n=${TOTAL_POINTS}_p=${last_nodes}_${SLURM_JOBID} mkdir -p ${SCRATCH} cd ${SCRATCH} diff --git a/hpc/archer2/slurm/weak_1_fft.slurm b/hpc/archer2/slurm/weak_1_fft.slurm index 44243e7d..8594c607 100644 --- a/hpc/archer2/slurm/weak_1_fft.slurm +++ b/hpc/archer2/slurm/weak_1_fft.slurm @@ -27,9 +27,9 @@ script_name="fmm_m2l_fft_mpi_f32" # Set simulation parameters for FMM expansion_order=3 -n_points=5000000 # points per MPI process (n_tasks) +n_points=250000 # points per MPI process (n_tasks) global_depth=(1 2 2 2 3 3 3) # Number of local roots -local_depth=5 +local_depth=4 n_samples=500 block_size=128 @@ -40,7 +40,8 @@ n_threads=4 # See if bandwidth saturates with different threading parameters for cpus_per_task=4 # Create a scratch directory for this run -export SCRATCH=${WORK}/weak_fft_p=${n_points}_n=${n_nodes[-1]}_${SLURM_JOBID} +last_nodes=${n_nodes[@]: -1} +export SCRATCH=${WORK}/weak_fft_n=${n_points}_p=${last_nodes}_${SLURM_JOBID} ## Load Spack #source $HOME/spack/share/spack/setup-env.sh From 78f2984d4c8871ca489bbc74219277298eb128a5 Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Thu, 2 Oct 2025 14:36:25 +0100 Subject: [PATCH 11/52] Add strong scaling notebook --- hpc/archer2/data/ijhpca/Strong Scaling.ipynb | 211 +++++++++++++++++++ 1 file changed, 211 insertions(+) create mode 100644 hpc/archer2/data/ijhpca/Strong Scaling.ipynb diff --git a/hpc/archer2/data/ijhpca/Strong Scaling.ipynb b/hpc/archer2/data/ijhpca/Strong Scaling.ipynb new file mode 100644 index 00000000..2aad2d3a --- /dev/null +++ b/hpc/archer2/data/ijhpca/Strong Scaling.ipynb @@ -0,0 +1,211 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 123, + "id": "2137b1d9-7f65-410a-b79d-7ed81dcf9b46", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "id": "20e41bec-f758-4668-aef5-cada4ba8652b", + "metadata": {}, + "outputs": [], + "source": [ + "def global_depth(rank):\n", + " \"\"\"\n", + " will return the first power of 8 greater than rank, the power corresponding to the level\n", + " \"\"\"\n", + " level = 1\n", + " loop = True\n", + "\n", + " while loop:\n", + "\n", + " if rank <= 8**level:\n", + " loop = False\n", + " result = level\n", + " else:\n", + " level += 1\n", + " \n", + " return result\n", + "\n", + "v_global_depth = np.vectorize(global_depth)\n", + "\n", + "\n", + "def local_depth(points_per_rank, max_points_per_leaf, min_local_depth):\n", + "\n", + " local_level = min_local_depth\n", + " loop = True\n", + "\n", + " while loop:\n", + " n_leaves = 8**local_level # leaves in local tree\n", + " points_per_leaf = points_per_rank / n_leaves\n", + " if points_per_leaf <= max_points_per_leaf:\n", + " loop = False\n", + " result = local_level\n", + " else:\n", + " local_level += 1\n", + " \n", + " return result\n", + "\n", + "v_local_depth = np.vectorize(local_depth)\n", + "\n", + "def pow2(x): return np.log2(x).is_integer()" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "id": "f0abc6a1-73c9-4608-80f7-43c6c64c0257", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total problem size n=1.024B\n", + "global depth [2 2 3 3 3 4 4 4 5]\n", + "local depth [5 5 4 4 4 3 3 3 3]\n", + "points per rank [32000000 16000000 8000000 4000000 2000000 1000000 500000 250000\n", + " 125000]\n" + ] + } + ], + "source": [ + "max_nodes = 256\n", + "min_nodes = 1\n", + "ranks_per_node=32 # ranks on 1 node, such that 1 per CCX region\n", + "total_ranks = max_nodes*ranks_per_node # 16 nodes, 32 ranks per node (1 per CCX region)\n", + "min_points_per_rank = 125e3\n", + "n = min_points_per_rank*total_ranks # total problem size\n", + "print(f\"Total problem size n={n/1e9}B\")\n", + "\n", + "# Double number of nodes used until we reach max_nodes\n", + "n_nodes = np.array(list(filter(pow2, [i for i in range(min_nodes, max_nodes+1)])))\n", + "\n", + "points_per_node = [n]\n", + "for i in range(len(n_nodes)-1):\n", + " p = points_per_node[i]/2\n", + " points_per_node.append(p)\n", + "points_per_node = np.array(points_per_node)\n", + " \n", + "total_ranks = n_nodes*32\n", + "points_per_rank = np.int64(points_per_node/ranks_per_node)\n", + "\n", + "\n", + "# The global depth is a function of the ranks per node (as will need enough global leaves to cover this many ranks (local trees))\n", + "global_depth = v_global_depth(total_ranks)\n", + "print(f\"global depth {global_depth}\")\n", + "\n", + "# Calculate max threshold for points per leaf from largest scale problem\n", + "min_local_depth = 3\n", + "max_points_per_leaf = points_per_rank[-1] / 8**min_local_depth\n", + "\n", + "\n", + "local_depth = v_local_depth(points_per_rank, max_points_per_leaf = 2000, min_local_depth=min_local_depth)\n", + "print(f\"local depth {local_depth}\")\n", + "\n", + "print(f\"points per rank {points_per_rank}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "id": "7e4cee87-4ec2-43b7-aae5-473898e26188", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1024000000, 1024000000, 1024000000, 1024000000, 1024000000,\n", + " 1024000000, 1024000000, 1024000000, 1024000000])" + ] + }, + "execution_count": 137, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total_ranks * points_per_rank" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "id": "1e9f65ac-7c18-42c7-ba6c-9c405be10761", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192])" + ] + }, + "execution_count": 139, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total_ranks" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "id": "f9d7f392-ae99-4f18-a1a1-595a3b422e07", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1, 2, 4, 8, 16, 32, 64, 128, 256])" + ] + }, + "execution_count": 140, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n_nodes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23d20266-53e5-40a5-9561-fcc5347a011c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.17" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From e5a0f90c8b7d5fb7afb757d789fc6ff9b579eabf Mon Sep 17 00:00:00 2001 From: srinath kailasa Date: Thu, 2 Oct 2025 14:38:54 +0100 Subject: [PATCH 12/52] Add weak scaling non-contiguous data --- .../weak_fft_11018099_non_contiguous.csv | 1018 +++++++++++++++++ 1 file changed, 1018 insertions(+) create mode 100644 hpc/archer2/data/ijhpca/weak_fft_11018099_non_contiguous.csv diff --git a/hpc/archer2/data/ijhpca/weak_fft_11018099_non_contiguous.csv b/hpc/archer2/data/ijhpca/weak_fft_11018099_non_contiguous.csv new file mode 100644 index 00000000..a812204a --- /dev/null +++ b/hpc/archer2/data/ijhpca/weak_fft_11018099_non_contiguous.csv @@ -0,0 +1,1018 @@ + +experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,source_tree,target_tree,source_domain,target_domain,layout,ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,displacement_map,metadata_creation,expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples +"0",0,125,0,0,0,0,0,2996,2993,4,4,0,0,0,0,125,0, 378,0,0,0,0,0,0,34, 3,250000,4,1,128,4,500 +"0",4,252,0,0,0,84,151,2997,2993,4,3,0,15,9,98,0,1, 379,1,0,0,0,0,0,148, 3,250000,4,1,128,4,500 +"0",1,321,4,0,0,160,142,2996,2992,4,4,0,87,7,23,0,1, 379,1,0,0,0,0,1,145, 3,250000,4,1,128,4,500 +"0",2,322,4,0,0,159,143,2997,2992,4,3,0,87,7,23,0,1, 379,1,0,0,0,0,1,144, 3,250000,4,1,128,4,500 +"0",5,322,4,0,0,159,142,2996,2992,4,4,0,87,7,23,0,1, 379,1,0,0,0,0,1,144, 3,250000,4,1,128,4,500 +"0",6,323,8,0,0,160,143,2997,2992,4,3,0,85,3,24,0,1, 379,1,0,0,0,0,1,144, 3,250000,4,1,128,4,500 +"0",3,331,8,0,0,162,149,2997,2992,4,3,0,90,3,19,0,1, 379,1,0,0,0,0,1,144, 3,250000,4,1,128,4,500 +"0",7,619,4,0,0,317,285,2997,2993,4,3,0,10,2,106,0,0, 379,1,0,0,0,0,1,150, 3,250000,4,1,128,4,500 +"1",2,578,10,1,1,480,57,1951,1924,5,3,0,48,17,15,0,5, 380,0,0,0,0,0,3,131, 3,250000,4,2,128,4,500 +"1",15,763,10,1,1,646,76,1951,1924,4,3,0,46,15,18,0,5, 381,1,0,0,0,0,4,132, 3,250000,4,2,128,4,500 +"1",0,762,7,1,2,651,74,1951,1924,5,3,0,30,15,39,3,0, 380,0,0,0,0,0,4,137, 3,250000,4,2,128,4,500 +"1",13,769,10,1,1,659,73,1951,1924,4,3,0,31,13,38,0,3, 381,1,0,0,0,0,4,137, 3,250000,4,2,128,4,500 +"1",9,771,10,1,2,661,74,1951,1924,5,3,0,41,12,28,0,4, 381,1,0,0,0,0,4,137, 3,250000,4,2,128,4,500 +"1",1,776,12,2,2,660,77,1951,1924,4,3,0,45,11,23,0,3, 380,0,0,0,0,0,4,137, 3,250000,4,2,128,4,500 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3,250000,4,2,128,4,500 +"3",61,338,4,0,0,174,143,3054,3052,4,4,1,45,7,110,0,1, 382,2,1,0,0,0,1,232, 3,250000,4,2,128,4,500 +"3",50,343,4,0,0,171,146,3054,3053,4,4,1,117,10,34,2,1, 384,1,0,0,0,0,1,226, 3,250000,4,2,128,4,500 +"3",56,338,4,0,0,174,143,3054,3052,4,4,1,38,7,118,0,1, 383,2,0,0,0,0,1,232, 3,250000,4,2,128,4,500 +"3",6,343,4,0,0,168,150,3054,3052,5,4,1,113,8,40,0,3, 389,2,1,0,0,0,1,223, 3,250000,4,2,128,4,500 +"3",17,344,4,0,0,169,150,3054,3052,7,4,1,118,9,34,0,3, 388,2,0,0,0,0,1,224, 3,250000,4,2,128,4,500 +"3",29,344,4,0,0,169,149,3054,3052,5,4,1,113,8,40,0,3, 387,2,0,0,0,0,1,226, 3,250000,4,2,128,4,500 +"3",20,344,4,0,0,167,152,3054,3052,5,4,1,112,9,40,0,3, 388,2,0,0,0,0,1,224, 3,250000,4,2,128,4,500 +"3",5,344,4,0,0,169,150,3054,3052,6,4,1,114,8,38,0,3, 389,2,0,0,0,0,1,223, 3,250000,4,2,128,4,500 +"3",23,345,4,0,0,170,151,3054,3052,5,4,1,113,8,40,0,3, 387,2,1,0,0,0,1,225, 3,250000,4,2,128,4,500 +"3",44,347,8,0,0,174,146,3054,3052,4,4,1,116,5,35,2,1, 385,2,0,0,0,0,1,226, 3,250000,4,2,128,4,500 +"3",30,346,4,0,0,170,151,3054,3052,5,4,1,113,9,39,0,3, 387,2,0,0,0,0,1,225, 3,250000,4,2,128,4,500 +"3",12,345,4,0,0,170,151,3054,3052,5,4,1,115,8,38,0,3, 389,2,0,0,0,0,1,224, 3,250000,4,2,128,4,500 +"3",10,346,4,0,0,167,155,3054,3052,4,4,1,117,8,36,0,3, 389,1,1,0,0,0,1,223, 3,250000,4,2,128,4,500 +"3",40,347,4,0,0,171,151,3054,3053,5,4,1,112,8,40,2,1, 385,2,1,0,0,0,1,227, 3,250000,4,2,128,4,500 +"3",34,346,4,0,0,172,151,3054,3052,4,4,1,113,8,40,2,1, 385,1,1,0,0,0,1,227, 3,250000,4,2,128,4,500 +"3",39,347,4,0,0,172,150,3054,3052,5,4,1,112,9,40,2,1, 385,2,0,0,0,0,1,227, 3,250000,4,2,128,4,500 +"3",46,347,4,0,0,172,150,3054,3053,5,4,1,112,9,40,2,1, 385,2,1,0,0,0,0,227, 3,250000,4,2,128,4,500 +"3",3,348,4,0,0,167,156,3054,3052,5,4,1,117,9,35,0,3, 389,2,0,0,0,0,1,222, 3,250000,4,2,128,4,500 +"3",19,349,5,0,0,168,156,3054,3052,6,4,1,121,8,30,0,3, 388,2,0,0,0,0,1,223, 3,250000,4,2,128,4,500 +"3",15,348,4,0,0,173,152,3054,3052,5,4,1,113,8,39,0,3, 389,2,0,0,0,0,1,223, 3,250000,4,2,128,4,500 +"3",7,348,4,0,0,169,156,3054,3052,5,4,1,108,7,45,0,3, 389,2,0,0,0,0,1,223, 3,250000,4,2,128,4,500 +"3",48,350,4,0,0,173,153,3054,3052,5,4,1,112,9,40,2,1, 385,1,1,0,0,0,1,227, 3,250000,4,2,128,4,500 +"3",43,347,4,0,0,176,151,3054,3052,4,4,1,42,7,115,0,1, 385,2,0,0,0,0,1,230, 3,250000,4,2,128,4,500 +"3",52,346,4,0,0,176,151,3054,3052,5,4,1,30,5,127,1,1, 384,1,0,0,0,0,1,233, 3,250000,4,2,128,4,500 +"3",59,351,4,0,0,171,155,3054,3052,4,4,1,111,9,41,2,1, 383,2,0,0,0,0,1,229, 3,250000,4,2,128,4,500 +"3",24,351,4,0,0,172,156,3054,3052,5,4,1,116,8,36,0,3, 387,2,0,0,0,0,1,225, 3,250000,4,2,128,4,500 +"3",54,348,4,0,0,177,151,3054,3052,5,4,1,28,6,128,1,1, 383,1,0,0,0,0,1,232, 3,250000,4,2,128,4,500 +"3",21,352,4,0,0,171,158,3054,3052,5,4,1,109,7,45,0,3, 388,2,0,0,0,0,1,226, 3,250000,4,2,128,4,500 +"3",28,352,4,0,0,172,158,3054,3052,5,4,1,107,8,46,0,3, 387,2,0,0,0,0,1,226, 3,250000,4,2,128,4,500 +"3",33,354,4,0,0,172,157,3054,3052,5,4,1,113,9,40,2,1, 386,1,1,0,0,0,1,226, 3,250000,4,2,128,4,500 +"3",62,351,4,0,0,173,156,3054,3052,4,4,1,41,7,115,0,1, 382,2,0,0,0,0,1,233, 3,250000,4,2,128,4,500 +"3",35,353,4,0,0,172,158,3054,3052,5,4,1,107,8,47,2,1, 385,1,1,0,0,0,1,227, 3,250000,4,2,128,4,500 +"3",14,353,4,0,0,171,159,3054,3052,5,4,1,107,8,46,0,3, 389,2,0,0,0,0,1,224, 3,250000,4,2,128,4,500 +"3",49,354,4,0,0,173,158,3054,3052,4,4,1,107,8,47,1,1, 384,2,0,0,0,0,1,229, 3,250000,4,2,128,4,500 +"3",57,352,4,0,0,176,156,3054,3052,5,4,1,26,6,131,0,1, 383,2,0,0,0,0,1,233, 3,250000,4,2,128,4,500 +"3",60,672,4,0,0,340,312,3054,3053,5,4,1,14,3,146,0,1, 382,1,1,0,0,0,1,237, 3,250000,4,2,128,4,500 +"4",76,595,8,1,1,485,59,2065,1980,10,6,1,61,18,55,6,9, 388,1,1,0,0,0,3,224, 3,250000,4,3,128,4,500 +"4",110,600,10,1,1,485,58,2065,1980,10,5,1,81,19,30,8,9, 384,1,1,0,0,0,4,223, 3,250000,4,3,128,4,500 +"4",72,600,10,1,1,490,58,2065,1980,11,6,1,60,19,53,5,9, 388,1,1,0,0,0,4,221, 3,250000,4,3,128,4,500 +"4",14,600,10,1,1,488,59,2065,1980,11,6,1,78,20,35,4,9, 395,1,1,0,0,0,3,214, 3,250000,4,3,128,4,500 +"4",10,603,10,1,1,489,58,2065,1980,11,6,1,82,19,29,7,9, 396,1,1,0,0,0,3,211, 3,250000,4,3,128,4,500 +"4",19,604,10,1,1,490,57,2065,1980,8,6,1,85,21,25,7,9, 395,1,0,0,0,0,4,212, 3,250000,4,3,128,4,500 +"4",95,600,10,1,1,490,57,2065,1980,10,5,1,62,18,52,6,9, 385,1,0,0,0,0,3,225, 3,250000,4,3,128,4,500 +"4",5,603,10,1,1,489,57,2065,1980,11,5,1,82,20,30,7,9, 396,1,1,0,0,0,3,211, 3,250000,4,3,128,4,500 +"4",124,603,10,1,1,491,58,2065,1980,10,6,1,78,19,34,6,9, 382,1,0,0,0,0,4,226, 3,250000,4,3,128,4,500 +"4",107,605,10,1,1,493,58,2065,1980,11,6,1,64,18,49,6,9, 384,1,0,0,0,0,4,225, 3,250000,4,3,128,4,500 +"4",99,602,7,1,1,492,60,2065,1980,11,5,1,60,19,58,4,9, 385,1,0,0,0,0,3,229, 3,250000,4,3,128,4,500 +"4",0,778,10,1,2,648,71,2065,1980,11,6,1,87,17,22,11,0, 372,1,1,0,0,0,4,229, 3,250000,4,3,128,4,500 +"4",18,788,10,1,2,653,76,2065,1980,12,5,1,87,20,23,8,9, 395,1,0,0,0,0,4,211, 3,250000,4,3,128,4,500 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3,250000,4,3,128,4,500 +"4",23,789,10,1,1,661,77,2065,1980,12,6,1,77,17,37,5,9, 394,1,0,0,0,0,5,216, 3,250000,4,3,128,4,500 +"4",67,783,10,1,2,658,78,2065,1980,10,6,1,58,13,63,3,9, 388,1,0,0,0,0,4,229, 3,250000,4,3,128,4,500 +"4",3,791,10,1,2,660,78,2065,1980,8,6,1,80,16,33,7,9, 396,1,0,0,0,0,4,214, 3,250000,4,3,128,4,500 +"4",40,781,10,1,1,658,79,2065,1980,11,6,1,36,12,88,1,9, 392,1,0,0,0,0,4,227, 3,250000,4,3,128,4,500 +"4",32,783,10,1,2,663,75,2065,1980,10,6,1,40,12,82,3,9, 393,1,1,0,0,0,4,225, 3,250000,4,3,128,4,500 +"4",111,793,10,1,1,664,75,2065,1980,11,6,1,84,16,28,8,9, 384,1,0,0,0,0,4,225, 3,250000,4,3,128,4,500 +"4",52,784,10,1,2,664,75,2065,1980,11,6,1,41,11,81,4,9, 391,1,0,0,0,0,4,227, 3,250000,4,3,128,4,500 +"4",50,783,10,1,2,662,76,2065,1980,12,6,1,42,11,80,4,9, 391,1,1,0,0,0,5,227, 3,250000,4,3,128,4,500 +"4",33,783,10,1,2,661,79,2065,1980,10,6,1,37,11,87,2,9, 393,1,0,0,0,0,4,227, 3,250000,4,3,128,4,500 +"4",48,784,10,1,2,663,76,2065,1980,11,5,1,36,11,87,3,9, 392,1,1,0,0,0,4,228, 3,250000,4,3,128,4,500 +"4",25,793,10,1,2,663,77,2065,1980,10,5,1,79,18,35,6,9, 394,1,0,0,0,0,4,216, 3,250000,4,3,128,4,500 +"4",43,782,10,1,2,660,80,2065,1980,10,5,1,34,11,91,0,9, 392,1,0,0,0,0,4,230, 3,250000,4,3,128,4,500 +"4",39,784,10,1,2,662,79,2065,1980,11,5,1,39,11,85,3,9, 392,1,0,0,0,0,4,228, 3,250000,4,3,128,4,500 +"4",84,783,8,1,1,660,80,2065,1981,10,5,1,33,13,93,2,9, 387,1,0,0,0,0,4,235, 3,250000,4,3,128,4,500 +"4",69,787,12,2,2,662,79,2065,1980,11,6,1,55,10,66,3,9, 388,1,0,0,0,0,4,229, 3,250000,4,3,128,4,500 +"4",15,786,10,1,2,662,79,2065,1980,9,5,1,55,11,67,3,9, 395,2,0,0,0,0,4,223, 3,250000,4,3,128,4,500 +"4",1,796,10,1,2,663,76,2065,1980,8,5,1,83,14,30,10,9, 396,1,1,0,0,0,4,213, 3,250000,4,3,128,4,500 +"4",88,786,10,1,1,664,77,2065,1980,10,5,1,40,11,82,4,9, 386,1,0,0,0,0,4,232, 3,250000,4,3,128,4,500 +"4",44,786,10,1,2,663,78,2065,1980,10,5,1,38,13,84,2,9, 392,1,1,0,0,0,4,227, 3,250000,4,3,128,4,500 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3,250000,4,3,128,4,500 +"4",22,799,10,1,2,669,75,2065,1981,9,6,1,84,18,29,7,9, 394,1,0,0,0,0,4,214, 3,250000,4,3,128,4,500 +"4",57,786,10,1,1,665,79,2065,1980,11,6,1,33,11,93,0,9, 390,1,0,0,0,0,4,232, 3,250000,4,3,128,4,500 +"4",87,788,10,1,1,667,77,2065,1980,12,6,1,39,11,84,2,9, 387,1,1,0,0,0,4,233, 3,250000,4,3,128,4,500 +"4",98,790,10,1,2,666,78,2065,1980,10,5,1,54,13,68,2,9, 385,2,0,0,0,0,4,234, 3,250000,4,3,128,4,500 +"4",80,798,10,1,1,668,77,2065,1980,10,6,1,79,18,35,6,9, 387,1,0,0,0,0,4,222, 3,250000,4,3,128,4,500 +"4",63,793,10,1,1,670,74,2065,1980,10,5,1,63,13,55,5,9, 389,2,0,0,0,0,4,225, 3,250000,4,3,128,4,500 +"4",85,786,10,1,2,667,79,2065,1980,11,5,1,33,10,92,1,9, 387,1,0,0,0,0,4,235, 3,250000,4,3,128,4,500 +"4",73,800,10,1,1,674,72,2065,1981,10,5,1,67,17,46,7,9, 388,1,1,0,0,0,4,221, 3,250000,4,3,128,4,500 +"4",109,795,10,1,2,673,74,2065,1980,10,5,1,63,12,55,7,9, 384,1,1,0,0,0,4,231, 3,250000,4,3,128,4,500 +"4",4,801,10,1,1,669,76,2065,1980,10,5,1,84,18,28,7,9, 396,1,1,0,0,0,4,211, 3,250000,4,3,128,4,500 +"4",29,788,10,1,1,665,81,2065,1980,10,6,1,33,11,92,1,9, 394,1,0,0,0,0,4,228, 3,250000,4,3,128,4,500 +"4",120,792,10,1,1,665,80,2065,1980,10,5,1,59,12,62,4,9, 383,1,0,0,0,0,4,234, 3,250000,4,3,128,4,500 +"4",71,790,10,1,2,668,79,2065,1980,10,5,1,36,12,89,1,9, 388,1,1,0,0,0,4,232, 3,250000,4,3,128,4,500 +"4",26,802,10,1,2,671,76,2065,1980,11,5,1,83,18,29,6,9, 394,1,0,0,0,0,4,214, 3,250000,4,3,128,4,500 +"4",97,792,10,1,1,668,79,2065,1981,11,6,1,55,10,67,4,9, 385,1,0,0,0,0,5,233, 3,250000,4,3,128,4,500 +"4",46,791,10,1,1,670,77,2065,1980,10,6,1,36,12,88,1,9, 391,1,0,0,0,0,4,228, 3,250000,4,3,128,4,500 +"4",28,790,10,1,2,669,79,2065,1980,11,6,1,34,11,91,1,9, 394,1,1,0,0,0,4,228, 3,250000,4,3,128,4,500 +"4",53,792,10,1,2,671,77,2065,1980,11,5,1,39,11,84,3,9, 391,1,0,0,0,0,4,228, 3,250000,4,3,128,4,500 +"4",70,790,7,1,1,668,80,2065,1981,10,5,1,34,13,91,2,9, 389,1,0,0,0,0,4,233, 3,250000,4,3,128,4,500 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3,250000,4,3,128,4,500 +"4",62,801,10,1,2,676,75,2065,1980,11,6,1,60,13,59,5,9, 389,1,0,0,0,0,4,225, 3,250000,4,3,128,4,500 +"4",106,795,10,1,2,673,80,2065,1980,10,5,1,38,10,86,3,9, 384,2,0,0,0,0,4,236, 3,250000,4,3,128,4,500 +"4",77,808,13,2,1,678,74,2065,1980,11,5,1,84,16,27,6,9, 387,1,0,0,0,0,4,220, 3,250000,4,3,128,4,500 +"4",90,804,10,1,2,676,77,2065,1980,10,5,1,64,16,51,6,9, 386,2,0,0,0,0,4,225, 3,250000,4,3,128,4,500 +"4",96,800,10,1,1,673,80,2065,1980,10,5,1,59,14,61,2,9, 385,1,0,0,0,0,4,232, 3,250000,4,3,128,4,500 +"4",65,801,10,1,2,674,80,2065,1980,10,6,1,59,14,61,3,9, 388,1,1,0,0,0,4,228, 3,250000,4,3,128,4,500 +"4",100,805,10,1,1,678,76,2065,1981,11,5,1,60,16,57,5,9, 385,1,0,0,0,0,4,228, 3,250000,4,3,128,4,500 +"4",119,806,10,1,2,678,76,2065,1980,10,5,1,63,16,52,6,9, 383,1,0,0,0,0,4,228, 3,250000,4,3,128,4,500 +"4",75,802,10,1,2,679,76,2065,1980,10,5,1,62,13,56,6,9, 388,1,0,0,0,0,4,227, 3,250000,4,3,128,4,500 +"4",91,803,10,1,2,680,74,2065,1980,10,6,1,63,13,55,5,9, 386,1,0,0,0,0,4,228, 3,250000,4,3,128,4,500 +"4",104,802,10,1,1,674,80,2065,1980,10,5,1,57,13,62,5,9, 384,1,0,0,0,0,4,231, 3,250000,4,3,128,4,500 +"4",115,803,10,1,2,678,77,2065,1980,10,6,1,62,13,57,5,9, 383,1,0,0,0,0,4,232, 3,250000,4,3,128,4,500 +"4",116,803,10,1,2,678,78,2065,1980,11,6,1,61,13,59,5,9, 383,1,0,0,0,0,4,233, 3,250000,4,3,128,4,500 +"4",55,802,12,2,2,681,76,2065,1980,12,5,1,61,9,60,4,9, 390,1,0,0,0,0,4,226, 3,250000,4,3,128,4,500 +"4",49,798,10,1,1,677,78,2065,1980,10,6,1,36,11,89,2,9, 391,1,0,0,0,0,4,230, 3,250000,4,3,128,4,500 +"4",121,808,10,1,1,682,76,2065,1980,11,5,1,64,16,51,5,9, 382,1,0,0,0,0,4,229, 3,250000,4,3,128,4,500 +"4",112,804,8,1,2,675,81,2065,1980,10,5,1,58,16,63,3,9, 383,1,0,0,0,0,4,234, 3,250000,4,3,128,4,500 +"4",64,807,10,1,2,679,79,2065,1980,10,6,1,59,13,59,6,9, 388,1,1,0,0,0,4,226, 3,250000,4,3,128,4,500 +"4",78,805,7,1,2,677,81,2065,1980,10,5,1,57,16,64,4,9, 387,1,0,0,0,0,4,229, 3,250000,4,3,128,4,500 +"4",59,803,12,2,2,677,81,2065,1980,11,5,1,58,9,64,3,9, 390,1,0,0,0,0,4,228, 3,250000,4,3,128,4,500 +"4",101,810,10,1,2,683,76,2065,1980,11,5,1,62,16,53,6,9, 385,1,0,0,0,0,4,227, 3,250000,4,3,128,4,500 +"4",102,806,10,1,2,682,77,2065,1980,11,5,1,61,13,58,4,9, 385,1,0,0,0,0,5,231, 3,250000,4,3,128,4,500 +"4",126,810,10,1,1,679,79,2065,1980,11,5,1,65,13,51,8,9, 381,1,0,0,0,0,4,230, 3,250000,4,3,128,4,500 +"4",35,806,13,2,2,681,79,2065,1980,11,6,1,59,11,62,3,9, 393,1,1,0,0,0,4,224, 3,250000,4,3,128,4,500 +"4",118,810,10,1,2,683,77,2065,1980,11,5,1,63,15,54,5,9, 383,1,0,0,0,0,4,230, 3,250000,4,3,128,4,500 +"4",60,807,10,1,2,680,79,2065,1980,10,5,1,58,12,62,4,9, 390,1,0,0,0,0,4,226, 3,250000,4,3,128,4,500 +"4",114,808,10,1,2,680,80,2065,1980,12,5,1,61,13,58,4,9, 383,1,0,0,0,0,4,232, 3,250000,4,3,128,4,500 +"4",123,813,10,1,2,683,76,2065,1980,10,5,1,68,17,46,6,9, 382,1,0,0,0,0,4,228, 3,250000,4,3,128,4,500 +"4",103,809,10,1,2,682,79,2065,1980,6,5,1,62,13,57,5,9, 385,1,0,0,0,0,5,230, 3,250000,4,3,128,4,500 +"4",89,809,12,2,2,684,76,2065,1980,10,6,1,65,10,54,4,9, 386,1,0,0,0,0,5,228, 3,250000,4,3,128,4,500 +"4",82,811,10,1,2,687,76,2065,1980,10,5,1,64,14,55,4,9, 387,1,1,0,0,0,4,228, 3,250000,4,3,128,4,500 +"4",122,812,10,1,2,685,78,2065,1980,11,5,1,63,14,55,4,9, 382,1,0,0,0,0,4,232, 3,250000,4,3,128,4,500 +"4",93,812,12,2,2,687,79,2065,1980,10,5,1,61,11,59,3,9, 386,1,0,0,0,0,5,230, 3,250000,4,3,128,4,500 +"4",113,812,10,1,1,685,79,2065,1980,10,6,1,59,13,62,3,9, 383,1,0,0,0,0,4,233, 3,250000,4,3,128,4,500 +"4",66,816,10,1,2,688,80,2065,1980,11,6,1,59,13,61,3,9, 388,1,1,0,0,0,4,227, 3,250000,4,3,128,4,500 +"4",117,821,10,1,2,694,79,2065,1980,10,5,1,62,14,57,5,9, 383,1,0,0,0,0,4,232, 3,250000,4,3,128,4,500 +"4",11,934,10,1,2,791,97,2065,1981,9,6,1,57,10,66,4,9, 396,1,0,0,0,0,5,223, 3,250000,4,3,128,4,500 +"4",27,933,7,1,2,794,96,2065,1981,12,5,1,39,11,88,3,9, 394,1,0,0,0,0,4,229, 3,250000,4,3,128,4,500 +"4",81,936,10,1,2,794,97,2065,1981,10,5,1,56,10,67,3,9, 390,1,0,0,0,0,5,230, 3,250000,4,3,128,4,500 +"4",94,940,10,1,2,801,95,2065,1981,12,6,1,44,10,80,2,9, 386,2,0,0,0,0,4,234, 3,250000,4,3,128,4,500 +"4",17,943,10,1,2,795,101,2065,1981,11,5,1,61,11,60,4,9, 400,1,0,0,0,0,4,218, 3,250000,4,3,128,4,500 +"4",105,940,10,1,2,803,94,2065,1981,10,5,1,39,9,86,4,9, 387,1,0,0,0,0,4,234, 3,250000,4,3,128,4,500 +"4",20,944,10,1,2,801,98,2065,1981,8,5,1,60,8,62,5,9, 394,1,1,0,0,0,5,224, 3,250000,4,3,128,4,500 +"4",125,950,10,1,2,807,94,2065,1981,12,6,1,65,11,54,7,9, 382,1,0,0,0,0,4,233, 3,250000,4,3,128,4,500 +"4",6,949,10,1,2,806,96,2065,1981,11,5,1,56,9,67,5,9, 396,1,1,0,0,0,5,223, 3,250000,4,3,128,4,500 +"4",108,953,12,2,2,809,96,2065,1980,10,6,1,64,9,56,5,9, 384,1,0,0,0,0,5,232, 3,250000,4,3,128,4,500 +"4",74,953,13,2,2,806,99,2065,1980,11,5,1,60,9,61,3,9, 388,1,1,0,0,0,5,229, 3,250000,4,3,128,4,500 +"5",27,267,6,0,0,171,49,2356,2400,138,3,1,129,18,26,7,9, 408,1,1,0,0,0,1,375, 3,250000,4,3,128,4,500 +"5",24,268,6,0,0,172,49,2356,2400,139,4,1,130,17,24,7,9, 408,1,1,0,0,0,1,375, 3,250000,4,3,128,4,500 +"5",49,269,6,0,0,170,53,2356,2400,139,4,1,125,18,31,6,9, 405,1,0,0,0,0,1,379, 3,250000,4,3,128,4,500 +"5",88,269,7,0,0,173,50,2356,2400,138,4,1,125,17,30,6,9, 400,1,1,0,0,0,1,383, 3,250000,4,3,128,4,500 +"5",57,271,6,0,0,172,53,2356,2400,138,3,1,124,17,31,6,9, 404,1,1,0,0,0,1,380, 3,250000,4,3,128,4,500 +"5",63,271,6,0,0,172,53,2356,2400,138,3,1,123,19,33,5,9, 403,1,0,0,0,0,1,381, 3,250000,4,3,128,4,500 +"5",42,272,6,0,0,172,52,2356,2400,139,3,1,126,17,29,7,9, 406,1,0,0,0,0,1,378, 3,250000,4,3,128,4,500 +"5",102,271,6,0,0,172,53,2356,2400,139,3,1,126,17,30,6,9, 399,1,1,0,0,0,1,386, 3,250000,4,3,128,4,500 +"5",250,273,6,0,0,174,52,2356,2400,138,3,1,126,14,30,9,9, 382,1,1,0,0,0,1,401, 3,250000,4,3,128,4,500 +"5",84,273,6,0,0,174,52,2356,2400,139,4,1,125,18,30,6,9, 401,1,1,0,0,0,1,383, 3,250000,4,3,128,4,500 +"5",252,272,6,0,0,174,53,2356,2400,138,3,1,123,14,33,9,9, 382,1,1,0,0,0,1,403, 3,250000,4,3,128,4,500 +"5",113,273,6,0,0,173,53,2356,2400,139,3,1,125,18,30,5,9, 397,1,1,0,0,0,1,386, 3,250000,4,3,128,4,500 +"5",35,273,6,0,0,174,53,2356,2400,138,4,1,126,16,29,7,9, 407,1,0,0,0,0,1,377, 3,250000,4,3,128,4,500 +"5",140,273,6,0,0,174,52,2356,2400,138,4,1,121,17,35,6,9, 394,1,1,0,0,0,1,390, 3,250000,4,3,128,4,500 +"5",202,274,3,0,0,179,52,2356,2400,139,3,1,84,17,75,5,9, 388,1,0,0,0,0,1,400, 3,250000,4,3,128,4,500 +"5",0,459,6,0,1,320,92,2356,2400,138,3,1,133,15,22,8,0, 372,1,1,0,0,0,2,408, 3,250000,4,3,128,4,500 +"5",146,467,6,0,0,333,91,2356,2400,139,4,1,93,14,66,5,9, 394,1,1,0,0,0,2,394, 3,250000,4,3,128,4,500 +"5",73,468,6,0,1,333,91,2356,2400,139,3,1,98,12,61,7,9, 402,1,1,0,0,0,2,385, 3,250000,4,3,128,4,500 +"5",37,467,7,0,1,331,94,2356,2400,138,3,1,92,10,69,6,9, 407,1,1,0,0,0,2,383, 3,250000,4,3,128,4,500 +"5",128,471,7,1,0,333,94,2356,2400,138,4,1,122,12,36,6,9, 395,1,1,0,0,0,2,391, 3,250000,4,3,128,4,500 +"5",68,468,6,0,0,328,99,2356,2400,139,3,1,85,12,77,4,9, 403,1,0,0,0,0,2,389, 3,250000,4,3,128,4,500 +"5",219,471,6,0,1,334,94,2356,2400,138,3,1,94,11,65,8,9, 387,1,1,0,0,0,2,401, 3,250000,4,3,128,4,500 +"5",5,474,6,0,0,329,99,2356,2400,4,4,1,122,14,35,7,9, 410,1,0,0,0,0,2,376, 3,250000,4,3,128,4,500 +"5",12,474,6,0,0,330,99,2356,2400,140,4,1,124,13,34,7,9, 409,1,1,0,0,0,2,378, 3,250000,4,3,128,4,500 +"5",76,475,6,0,1,330,99,2356,2400,139,4,1,122,15,36,6,9, 402,1,1,0,0,0,2,384, 3,250000,4,3,128,4,500 +"5",33,471,6,0,1,333,96,2356,2400,138,4,1,89,11,72,5,9, 407,2,0,0,0,0,2,383, 3,250000,4,3,128,4,500 +"5",6,475,6,0,0,331,98,2356,2400,140,3,1,123,13,35,7,9, 410,1,1,0,0,0,2,376, 3,250000,4,3,128,4,500 +"5",48,475,6,0,1,331,99,2356,2400,138,4,1,120,15,37,5,9, 405,1,1,0,0,0,2,381, 3,250000,4,3,128,4,500 +"5",40,475,6,0,1,330,99,2356,2400,138,4,1,122,14,36,7,9, 406,1,0,0,0,0,2,380, 3,250000,4,3,128,4,500 +"5",147,477,9,1,1,333,97,2356,2400,139,4,1,125,13,31,5,9, 393,1,1,0,0,0,2,391, 3,250000,4,3,128,4,500 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3,250000,4,3,128,4,500 +"5",2,478,7,0,0,337,96,2356,2400,140,3,1,128,12,29,8,9, 410,1,1,0,0,0,2,376, 3,250000,4,3,128,4,500 +"5",253,476,6,0,1,331,100,2356,2400,138,3,1,116,8,43,11,9, 381,1,1,0,0,0,2,406, 3,250000,4,3,128,4,500 +"5",247,474,6,0,1,336,96,2356,2400,138,4,1,89,13,72,4,9, 383,1,1,0,0,0,2,407, 3,250000,4,3,128,4,500 +"5",237,475,6,0,1,336,97,2356,2400,138,3,1,88,11,73,7,9, 384,1,1,0,0,0,2,406, 3,250000,4,3,128,4,500 +"5",41,478,6,0,0,334,99,2356,2400,138,4,1,123,15,35,6,9, 406,1,1,0,0,0,2,380, 3,250000,4,3,128,4,500 +"5",101,479,6,0,0,336,97,2356,2400,139,3,1,123,16,34,6,9, 399,1,1,0,0,0,2,386, 3,250000,4,3,128,4,500 +"5",72,475,6,0,1,335,98,2356,2400,139,4,1,88,13,73,4,9, 402,1,0,0,0,0,2,387, 3,250000,4,3,128,4,500 +"5",249,474,6,0,1,333,99,2356,2400,138,3,1,84,9,77,8,9, 382,1,0,0,0,0,2,408, 3,250000,4,3,128,4,500 +"5",79,479,6,0,1,333,100,2356,2400,139,3,1,121,15,37,6,9, 402,1,0,0,0,0,2,384, 3,250000,4,3,128,4,500 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396,1,1,0,0,0,2,392, 3,250000,4,3,128,4,500 +"5",132,475,7,1,0,335,99,2356,2400,138,4,1,80,12,82,3,9, 395,1,1,0,0,0,2,395, 3,250000,4,3,128,4,500 +"5",77,482,6,0,1,339,94,2356,2400,139,4,1,127,16,28,7,9, 402,1,1,0,0,0,2,383, 3,250000,4,3,128,4,500 +"5",47,478,6,0,0,331,103,2356,2400,138,4,1,119,14,41,5,9, 406,1,1,0,0,0,2,382, 3,250000,4,3,128,4,500 +"5",65,481,6,0,0,339,96,2356,2400,139,3,1,124,15,33,7,9, 402,1,1,0,0,0,2,383, 3,250000,4,3,128,4,500 +"5",97,476,6,0,1,335,100,2356,2400,139,3,1,84,13,78,3,9, 399,1,1,0,0,0,2,392, 3,250000,4,3,128,4,500 +"5",46,478,6,0,0,333,102,2356,2400,140,3,1,119,14,40,5,9, 406,1,0,0,0,0,2,382, 3,250000,4,3,128,4,500 +"5",126,478,6,0,1,335,100,2356,2400,139,3,1,117,13,42,6,9, 396,1,1,0,0,0,2,392, 3,250000,4,3,128,4,500 +"5",82,479,6,0,0,330,105,2356,2400,140,4,1,119,13,40,6,9, 401,1,1,0,0,0,2,386, 3,250000,4,3,128,4,500 +"5",210,477,6,0,1,336,98,2356,2400,140,4,1,84,13,77,5,9, 387,1,1,0,0,0,2,402, 3,250000,4,3,128,4,500 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3,250000,4,3,128,4,500 +"5",55,480,6,0,1,334,102,2356,2400,139,3,1,116,14,44,5,9, 404,1,0,0,0,0,2,384, 3,250000,4,3,128,4,500 +"5",52,483,6,0,1,337,99,2356,2400,138,4,1,123,16,33,6,9, 404,1,0,0,0,0,1,381, 3,250000,4,3,128,4,500 +"5",151,482,6,0,0,337,100,2356,2400,139,3,1,117,15,41,6,9, 394,1,1,0,0,0,2,392, 3,250000,4,3,128,4,500 +"5",123,480,6,0,1,335,102,2356,2400,138,4,1,114,12,47,5,9, 396,1,1,0,0,0,2,393, 3,250000,4,3,128,4,500 +"5",110,482,6,0,1,332,104,2356,2400,139,4,1,120,14,39,6,9, 398,1,0,0,0,0,2,389, 3,250000,4,3,128,4,500 +"5",51,480,6,0,1,332,105,2356,2400,139,3,1,114,14,46,4,9, 405,1,0,0,0,0,2,384, 3,250000,4,3,128,4,500 +"5",90,478,3,0,0,338,100,2356,2400,140,4,1,80,13,83,6,9, 400,1,1,0,0,0,1,392, 3,250000,4,3,128,4,500 +"5",74,479,6,0,0,332,105,2356,2400,139,3,1,81,10,81,6,9, 402,1,1,0,0,0,2,389, 3,250000,4,3,128,4,500 +"5",36,478,6,0,1,339,98,2356,2400,138,4,1,80,11,83,5,9, 406,1,0,0,0,0,2,385, 3,250000,4,3,128,4,500 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3,250000,4,3,128,4,500 +"5",60,477,6,0,1,333,105,2356,2400,138,3,1,73,10,94,2,9, 403,1,1,0,0,0,2,392, 3,250000,4,3,128,4,500 +"5",230,481,6,0,1,338,102,2356,2400,139,3,1,76,12,87,4,9, 384,1,0,0,0,0,1,407, 3,250000,4,3,128,4,500 +"5",142,481,6,0,1,335,105,2356,2400,139,3,1,109,13,54,3,9, 394,1,1,0,0,0,2,396, 3,250000,4,3,128,4,500 +"5",143,482,6,0,1,335,105,2356,2400,139,3,1,108,13,54,4,9, 394,1,1,0,0,0,2,396, 3,250000,4,3,128,4,500 +"5",203,482,6,0,0,340,99,2356,2400,138,4,1,86,12,75,6,9, 388,1,1,0,0,0,2,401, 3,250000,4,3,128,4,500 +"5",91,484,9,1,1,336,105,2356,2400,140,4,1,120,8,39,7,9, 400,1,0,0,0,0,2,387, 3,250000,4,3,128,4,500 +"5",215,482,6,0,0,336,104,2356,2400,138,3,1,84,13,77,4,9, 387,1,0,0,0,0,2,403, 3,250000,4,3,128,4,500 +"5",141,481,6,0,0,335,105,2356,2400,118,4,1,108,13,54,3,9, 394,1,1,0,0,0,2,396, 3,250000,4,3,128,4,500 +"5",69,482,7,0,1,341,100,2356,2400,125,3,1,87,10,74,5,9, 403,1,1,0,0,0,2,387, 3,250000,4,3,128,4,500 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386,1,1,0,0,0,1,1236, 3,250000,4,3,128,4,500 +"6",234,356,4,0,0,173,147,3220,3190,120,4,1,30,7,131,6,9, 413,1,1,0,0,0,1,1212, 3,250000,4,3,128,4,500 +"6",255,360,8,0,0,172,148,3220,3190,120,4,1,119,6,38,7,9, 411,1,1,0,0,0,1,1210, 3,250000,4,3,128,4,500 +"6",490,360,4,0,0,174,148,3220,3190,120,4,1,121,8,35,9,9, 386,2,1,0,0,0,1,1236, 3,250000,4,3,128,4,500 +"6",222,355,4,0,0,173,149,3220,3190,121,4,1,25,7,136,6,9, 414,1,1,0,0,0,1,1212, 3,250000,4,3,128,4,500 +"6",350,359,4,0,0,172,148,3220,3190,121,4,1,119,9,38,7,9, 400,1,1,0,0,0,1,1222, 3,250000,4,3,128,4,500 +"6",424,359,4,0,0,173,148,3220,3190,120,4,1,117,13,40,4,9, 392,1,1,0,0,0,1,1230, 3,250000,4,3,128,4,500 +"6",475,359,4,0,0,172,148,3220,3190,70,4,1,118,8,39,8,9, 388,1,1,0,0,0,1,1234, 3,250000,4,3,128,4,500 +"6",137,359,4,0,0,171,149,3220,3190,121,4,1,113,10,45,5,9, 424,1,1,0,0,0,1,1199, 3,250000,4,3,128,4,500 +"6",329,360,4,0,0,173,148,3220,3190,120,4,1,117,10,39,7,9, 402,1,1,0,0,0,1,1219, 3,250000,4,3,128,4,500 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390,1,1,0,0,0,1,1238, 3,250000,4,3,128,4,500 +"6",448,366,4,0,0,177,156,3220,3190,120,4,1,22,9,140,2,9, 390,1,1,0,0,0,1,1238, 3,250000,4,3,128,4,500 +"6",281,370,4,0,0,176,158,3220,3190,121,4,1,108,10,50,6,9, 408,2,1,0,0,0,1,1215, 3,250000,4,3,128,4,500 +"6",449,366,4,0,0,175,157,3220,3190,109,4,1,24,9,138,2,9, 390,1,1,0,0,0,1,1237, 3,250000,4,3,128,4,500 +"6",370,370,4,0,0,176,157,3220,3190,120,4,1,112,7,45,9,9, 398,1,1,0,0,0,1,1225, 3,250000,4,3,128,4,500 +"6",420,370,4,0,0,175,157,3220,3190,120,4,1,110,13,47,3,9, 392,1,1,0,0,0,1,1230, 3,250000,4,3,128,4,500 +"6",174,370,4,0,0,170,161,3220,3190,120,4,1,111,13,48,3,9, 420,1,1,0,0,0,1,1204, 3,250000,4,3,128,4,500 +"6",425,370,4,0,0,175,158,3220,3190,120,4,1,110,12,48,3,10, 392,1,1,0,0,0,1,1231, 3,250000,4,3,128,4,500 +"6",135,366,4,0,0,176,158,3220,3190,121,4,1,27,8,135,4,9, 424,1,1,0,0,0,1,1203, 3,250000,4,3,128,4,500 +"6",315,370,4,0,0,174,157,3220,3190,70,4,1,110,9,48,6,9, 404,1,1,0,0,0,1,1219, 3,250000,4,3,128,4,500 +"6",483,370,4,0,0,174,158,3220,3190,119,4,1,110,12,48,3,9, 386,1,1,0,0,0,1,1237, 3,250000,4,3,128,4,500 +"6",313,367,4,0,0,177,157,3220,3190,119,4,1,31,7,130,5,9, 404,1,1,0,0,0,1,1222, 3,250000,4,3,128,4,500 +"6",280,370,4,0,0,175,158,3220,3190,121,4,1,109,11,49,5,9, 408,1,1,0,0,0,1,1215, 3,250000,4,3,128,4,500 +"6",477,370,4,0,0,174,158,3220,3190,72,4,1,112,7,46,8,9, 387,1,1,0,0,0,1,1236, 3,250000,4,3,128,4,500 +"6",356,371,4,0,0,174,158,3220,3190,119,4,1,110,7,48,9,9, 400,1,1,0,0,0,1,1223, 3,250000,4,3,128,4,500 +"6",379,371,4,0,0,177,157,3220,3190,70,4,1,113,6,45,9,9, 397,1,1,0,0,0,1,1226, 3,250000,4,3,128,4,500 +"6",437,368,4,0,0,174,158,3220,3190,59,4,1,40,11,120,3,9, 391,1,1,0,0,0,1,1234, 3,250000,4,3,128,4,500 +"6",342,371,4,0,0,173,159,3220,3190,120,4,1,111,9,47,7,9, 401,1,1,0,0,0,1,1222, 3,250000,4,3,128,4,500 +"6",268,367,4,0,0,175,158,3220,3190,79,4,1,30,5,132,7,9, 409,1,1,0,0,0,1,1218, 3,250000,4,3,128,4,500 +"6",441,367,4,0,0,177,157,3220,3190,119,4,1,27,10,135,2,9, 391,1,1,0,0,0,1,1236, 3,250000,4,3,128,4,500 +"6",337,370,4,0,0,174,159,3220,3190,74,4,1,110,8,49,7,9, 402,1,1,0,0,0,1,1222, 3,250000,4,3,128,4,500 +"6",434,371,4,0,0,174,159,3220,3190,121,4,1,110,12,47,4,9, 391,1,1,0,0,0,1,1232, 3,250000,4,3,128,4,500 +"6",440,367,4,0,0,177,158,3220,3190,120,4,1,25,9,137,3,9, 391,1,1,0,0,0,0,1236, 3,250000,4,3,128,4,500 +"6",451,368,4,0,0,174,159,3220,3190,120,4,1,23,11,139,1,9, 390,1,1,0,0,0,1,1237, 3,250000,4,3,128,4,500 +"6",278,371,8,0,0,176,158,3220,3190,71,4,1,108,6,49,5,9, 409,1,1,0,0,0,1,1215, 3,250000,4,3,128,4,500 +"6",372,371,4,0,0,178,157,3220,3190,120,4,1,113,7,44,9,9, 398,1,1,0,0,0,1,1225, 3,250000,4,3,128,4,500 +"6",285,371,5,0,0,175,158,3220,3190,70,4,1,108,8,50,6,9, 407,1,1,0,0,0,1,1216, 3,250000,4,3,128,4,500 +"6",267,368,5,0,0,176,157,3220,3190,74,4,1,25,5,137,6,9, 409,1,1,0,0,0,1,1217, 3,250000,4,3,128,4,500 +"6",271,372,8,0,0,176,158,3220,3190,120,4,1,113,4,44,7,9, 409,1,1,0,0,0,1,1213, 3,250000,4,3,128,4,500 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417,1,1,0,0,0,1,1213, 3,250000,4,3,128,4,500 +"6",416,671,4,0,0,344,295,3220,3190,120,4,1,24,6,141,1,9, 393,1,1,0,0,0,1,1238, 3,250000,4,3,128,4,500 +"6",178,675,3,0,1,339,303,3220,3190,121,4,1,15,6,150,2,9, 420,1,1,0,0,0,1,1211, 3,250000,4,3,128,4,500 +"6",123,676,4,0,1,336,305,3220,3190,70,4,1,28,4,136,5,9, 425,1,1,0,0,0,1,1204, 3,250000,4,3,128,4,500 +"6",182,689,7,0,0,339,313,3220,3190,120,4,1,98,5,64,2,9, 419,1,1,0,0,0,2,1208, 3,250000,4,3,128,4,500 +"6",412,687,4,0,0,343,314,3220,3190,120,4,1,18,5,150,0,9, 394,1,1,0,0,0,1,1239, 3,250000,4,3,128,4,500 +"6",410,691,4,0,0,345,316,3220,3190,120,4,1,20,5,147,0,9, 394,1,1,0,0,0,1,1239, 3,250000,4,3,128,4,500 From c481708473a8781713f1c3a30d837d813f3f466b Mon Sep 17 00:00:00 2001 From: srinath kailasa Date: Thu, 2 Oct 2025 15:15:05 +0100 Subject: [PATCH 13/52] Pin to CCD for strong scaling --- hpc/archer2/slurm/strong_1_nodes_fft.slurm | 24 +++++++++++----------- 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/hpc/archer2/slurm/strong_1_nodes_fft.slurm b/hpc/archer2/slurm/strong_1_nodes_fft.slurm index a4535026..76083463 100644 --- a/hpc/archer2/slurm/strong_1_nodes_fft.slurm +++ b/hpc/archer2/slurm/strong_1_nodes_fft.slurm @@ -6,9 +6,9 @@ #SBATCH --job-name=strong_scaling_fft #SBATCH --time=01:00:00 -#SBATCH --nodes=256 -#SBATCH --ntasks-per-node=32 -#SBATCH --cpus-per-task=4 +#SBATCH --nodes=128 +#SBATCH --ntasks-per-node=16 +#SBATCH --cpus-per-task=8 #SBATCH --contiguous #SBATCH --account=e738 @@ -23,24 +23,24 @@ module load cray-mpich-ucx # Home and work directories export HOME="/home/e738/e738/skailasa" export WORK="/work/e738/e738/skailasa" -TOTAL_POINTS=1.024B # 1.024B points = 32M * 32 tasks (1 node) and stays constant +TOTAL_POINTS=153600000 # 150M points # Script being run, expected in the work directory script_name="fmm_m2l_fft_mpi_f32" # must exist in $WORK # Set simulation parameters for FMM expansion_order=3 -global_depth=(2 2 3 3 3 4 4 4 5) # Number of local roots +global_depth=(2 2 2 3 3 3 4 4) # Number of local roots n_samples=500 block_size=128 # Set parameters for strong scaling run -n_points=(32000000 16000000 8000000 4000000 2000000 1000000 500000 250000 125000) -local_depth=(5 5 4 4 4 3 3 3 3) -n_tasks=(32 64 128 256 512 1024 2048 4096 8192) # Equal to the number of local trees -n_nodes=(1 2 4 8 16 32 64 128 256) -n_threads=4 # See if bandwidth saturates with different threading parameters for Rayon thread pool -cpus_per_task=4 +n_points=(9600000 4800000 2400000 1200000 600000 300000 150000 75000) +local_depth=(5 4 4 4 3 3 3 3) +n_tasks=(16 32 64 128 256 512 1024 2048) # Equal to the number of local trees +n_nodes=(1 2 4 8 16 32 64 128) +n_threads=8 # See if bandwidth saturates with different threading parameters for Rayon thread pool +cpus_per_task=8 # Create a scratch directory for this run last_nodes=${n_nodes[@]: -1} @@ -78,5 +78,5 @@ for i in ${!n_tasks[@]}; do --local-depth ${local_depth[$i]} \ --n-samples $n_samples \ --block-size $block_size \ - --n-threads $n_threads >> ${OUTPUT} + --n-threads $n_threads >> ${OUTPUT} 2> ${SCRATCH}/err_run_${i}.log done From 93cbf9ce48a2e3c23b78eb4cda001313e3c946b9 Mon Sep 17 00:00:00 2001 From: srinath kailasa Date: Thu, 2 Oct 2025 15:45:18 +0100 Subject: [PATCH 14/52] First strong scaling data --- .../strong_fft_p=153600000_n=128_11018635.csv | 2034 +++++++++++++++++ hpc/archer2/slurm/strong_1_nodes_fft.slurm | 9 +- 2 files changed, 2039 insertions(+), 4 deletions(-) create mode 100644 hpc/archer2/data/ijhpca/strong_fft_p=153600000_n=128_11018635.csv diff --git a/hpc/archer2/data/ijhpca/strong_fft_p=153600000_n=128_11018635.csv b/hpc/archer2/data/ijhpca/strong_fft_p=153600000_n=128_11018635.csv new file mode 100644 index 00000000..3654593a --- /dev/null +++ b/hpc/archer2/data/ijhpca/strong_fft_p=153600000_n=128_11018635.csv @@ -0,0 +1,2034 @@ + +experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,source_tree,target_tree,source_domain,target_domain,layout,ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,displacement_map,metadata_creation,expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples +"0",14,6784,77,13,27,3928,2556,74311,73812,121,120,2,906,115,399,0,21, 381,0,0,0,0,0,29,3207, 3,9600000,5,2,128,8,500 +"0",4,8776,69,13,24,5233,3273,74311,73813,122,120,1,234,90,1110,0,13, 380,0,0,0,0,0,31,3247, 3,9600000,5,2,128,8,500 +"0",6,8795,69,13,27,5260,3267,74311,73813,123,120,1,250,85,1095,0,12, 380,0,0,0,0,0,36,3248, 3,9600000,5,2,128,8,500 +"0",0,8837,71,13,33,5255,3271,74311,73813,122,120,0,1024,109,282,23,0, 380,0,0,0,0,0,38,3208, 3,9600000,5,2,128,8,500 +"0",11,8845,88,16,32,5270,3264,74312,73812,123,120,0,1026,90,276,0,24, 381,0,0,0,0,0,40,3205, 3,9600000,5,2,128,8,500 +"0",2,8809,81,13,38,5258,3272,74311,73813,123,120,0,226,80,1118,0,6, 380,1,0,0,0,0,37,3247, 3,9600000,5,2,128,8,500 +"0",13,8831,69,13,25,5298,3266,74311,73813,122,120,0,259,90,1083,0,11, 381,1,0,0,0,0,36,3244, 3,9600000,5,2,128,8,500 +"0",9,8906,107,17,39,5322,3266,74312,73813,122,120,0,1030,78,272,0,17, 380,1,0,0,0,0,39,3205, 3,9600000,5,2,128,8,500 +"0",12,8874,53,10,22,5243,3378,74310,73814,122,120,0,122,101,1236,0,6, 381,0,0,0,0,0,34,3260, 3,9600000,5,2,128,8,500 +"0",8,8937,111,14,40,5266,3405,74311,73813,122,120,0,130,37,1227,0,1, 380,0,0,0,0,0,39,3260, 3,9600000,5,2,128,8,500 +"0",7,8948,84,13,40,5296,3378,74311,73813,123,120,1,165,69,1187,0,5, 381,1,0,0,0,0,37,3255, 3,9600000,5,2,128,8,500 +"0",1,8961,62,11,39,5315,3372,74310,73814,124,120,0,127,95,1229,0,6, 380,1,0,0,0,0,33,3258, 3,9600000,5,2,128,8,500 +"0",5,8994,70,13,27,5354,3379,74311,73813,121,120,0,146,87,1205,0,4, 380,1,0,0,0,0,36,3255, 3,9600000,5,2,128,8,500 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3,4800000,4,2,128,8,500 +"1",19,10142,22,0,1,322,9749,42275,43841,63,62,0,998,12,500,0,3, 389,1,0,0,0,0,3,2274, 3,4800000,4,2,128,8,500 +"1",1,10140,11,0,1,319,9754,42273,43843,65,62,0,230,22,1272,0,3, 384,1,0,0,0,0,2,2281, 3,4800000,4,2,128,8,500 +"1",27,10162,22,0,1,320,9771,42275,43841,63,62,0,989,12,509,0,3, 388,1,0,0,0,0,2,2276, 3,4800000,4,2,128,8,500 +"1",16,10164,19,0,1,321,9772,42275,43841,63,62,0,986,15,513,0,3, 389,1,0,0,0,0,2,2275, 3,4800000,4,2,128,8,500 +"1",5,10169,19,0,1,325,9774,42275,43841,64,62,1,994,13,504,0,5, 384,1,0,0,0,0,2,2279, 3,4800000,4,2,128,8,500 +"1",6,10354,19,1,1,321,9963,42275,43841,65,62,1,922,12,578,0,5, 385,1,0,0,0,0,2,2280, 3,4800000,4,2,128,8,500 +"1",30,10363,25,0,1,326,9970,42275,43841,64,62,0,272,7,1230,0,1, 388,1,0,0,0,0,2,2280, 3,4800000,4,2,128,8,500 +"1",22,10372,21,1,1,323,9980,42275,43841,64,62,1,910,12,590,0,2, 387,1,0,0,0,0,3,2278, 3,4800000,4,2,128,8,500 +"1",12,10376,19,0,1,329,9979,42275,43841,64,62,1,238,13,1263,0,2, 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3,4800000,4,2,128,8,500 +"2",0,1453,0,0,0,0,0,28136,28657,34,32,2,0,0,0,1451,0, 371,1,0,0,0,0,0,627, 3,2400000,4,2,128,8,500 +"2",7,39,14,0,0,0,0,28137,28656,34,32,2,1318,19,95,2,2, 387,1,0,0,0,0,0,2025, 3,2400000,4,2,128,8,500 +"2",14,38,10,0,0,0,0,28137,28656,34,32,2,1317,22,97,2,2, 386,1,0,0,0,0,0,2027, 3,2400000,4,2,128,8,500 +"2",17,38,16,0,0,0,0,28137,28656,34,32,2,1327,17,87,2,2, 385,1,0,0,0,0,1,2028, 3,2400000,4,2,128,8,500 +"2",62,39,11,0,0,0,0,28136,28656,33,32,2,1316,21,97,2,2, 379,1,0,0,0,0,0,2034, 3,2400000,4,2,128,8,500 +"2",22,39,11,0,0,0,0,28137,28656,34,32,2,1318,22,95,2,2, 384,1,0,0,0,0,0,2029, 3,2400000,4,2,128,8,500 +"2",30,39,14,0,0,0,0,28137,28656,34,32,2,1315,19,99,2,2, 383,1,0,0,0,0,0,2030, 3,2400000,4,2,128,8,500 +"2",9,4871,10,0,0,160,4660,28137,28657,33,32,1,1177,19,240,2,2, 387,1,0,0,0,0,1,2029, 3,2400000,4,2,128,8,500 +"2",54,4867,11,0,0,164,4656,28137,28658,33,32,1,397,15,1025,1,2, 380,1,0,0,0,0,1,2041, 3,2400000,4,2,128,8,500 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3,2400000,4,2,128,8,500 +"2",38,5091,10,0,0,163,4879,28137,28657,33,32,1,1125,17,294,2,2, 383,1,0,0,0,0,1,2036, 3,2400000,4,2,128,8,500 +"2",59,5090,11,0,0,165,4880,28136,28657,34,32,1,361,13,1062,0,2, 380,1,0,0,0,0,1,2043, 3,2400000,4,2,128,8,500 +"2",37,5098,10,0,0,160,4888,28137,28657,32,32,1,1129,17,290,2,2, 383,1,0,0,0,0,1,2036, 3,2400000,4,2,128,8,500 +"2",44,5099,10,0,0,164,4886,28137,28658,33,32,1,1127,17,291,2,2, 383,1,0,0,0,0,1,2036, 3,2400000,4,2,128,8,500 +"2",55,5096,9,0,0,164,4886,28137,28658,33,32,1,368,16,1055,0,2, 380,1,0,0,0,0,1,2042, 3,2400000,4,2,128,8,500 +"2",32,5099,10,0,0,164,4890,28137,28657,33,32,1,318,14,1104,1,1, 384,1,0,0,0,0,1,2039, 3,2400000,4,2,128,8,500 +"2",13,5105,10,0,0,161,4893,28137,28657,33,32,1,1124,17,294,2,2, 387,1,0,0,0,0,1,2031, 3,2400000,4,2,128,8,500 +"2",52,5101,11,0,0,163,4894,28137,28657,35,32,1,325,13,1098,0,2, 381,1,0,0,0,0,1,2043, 3,2400000,4,2,128,8,500 +"2",23,5104,10,0,0,166,4893,28137,28657,35,32,1,374,14,1049,1,2, 385,1,0,0,0,0,1,2038, 3,2400000,4,2,128,8,500 +"2",26,5160,12,0,0,162,4953,28137,28657,34,32,1,328,12,1095,1,2, 384,1,0,0,0,0,1,2038, 3,2400000,4,2,128,8,500 +"2",34,5306,12,0,0,166,5095,28137,28657,33,32,1,255,12,1169,0,2, 383,1,0,0,0,0,1,2040, 3,2400000,4,2,128,8,500 +"2",25,5309,11,0,0,162,5103,28137,28657,33,32,1,290,13,1133,0,2, 385,1,0,0,0,0,1,2038, 3,2400000,4,2,128,8,500 +"2",39,5315,10,0,0,163,5104,28137,28657,33,32,1,1066,17,354,1,2, 383,1,0,0,0,0,1,2037, 3,2400000,4,2,128,8,500 +"2",46,5316,10,0,0,163,5104,28137,28657,33,32,1,1069,16,351,1,2, 382,1,0,0,0,0,1,2038, 3,2400000,4,2,128,8,500 +"2",5,5317,10,0,0,160,5108,28137,28657,33,32,1,1065,16,354,1,2, 388,1,0,0,0,0,1,2031, 3,2400000,4,2,128,8,500 +"2",51,5314,11,0,0,163,5106,28137,28658,33,32,1,302,12,1122,0,2, 382,1,0,0,0,0,1,2042, 3,2400000,4,2,128,8,500 +"2",12,5320,10,0,0,162,5108,28136,28657,33,32,1,1065,16,355,1,2, 387,1,0,0,0,0,1,2032, 3,2400000,4,2,128,8,500 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3,2400000,4,2,128,8,500 +"2",53,5326,11,0,0,164,5116,28137,28658,34,32,1,260,12,1163,0,2, 381,1,0,0,0,0,1,2042, 3,2400000,4,2,128,8,500 +"2",24,5326,13,0,0,162,5119,28137,28657,34,32,1,242,10,1182,0,2, 385,1,0,0,0,0,1,2039, 3,2400000,4,2,128,8,500 +"2",10,5331,13,0,0,162,5120,28137,28657,33,32,1,1062,13,357,1,2, 387,1,0,0,0,0,1,2032, 3,2400000,4,2,128,8,500 +"2",31,5331,13,0,0,166,5121,28137,28657,34,32,1,309,11,1114,1,2, 384,1,0,0,0,0,1,2039, 3,2400000,4,2,128,8,500 +"2",29,5337,10,0,0,161,5126,28137,28657,34,32,1,1070,17,349,1,2, 384,1,0,0,0,0,1,2036, 3,2400000,4,2,128,8,500 +"2",60,5372,12,0,0,166,5161,28136,28658,34,32,1,256,11,1168,0,2, 380,1,0,0,0,0,1,2044, 3,2400000,4,2,128,8,500 +"2",58,5538,9,0,0,165,5328,28136,28657,34,32,1,259,14,1165,0,2, 380,1,0,0,0,0,1,2043, 3,2400000,4,2,128,8,500 +"2",21,5551,11,0,0,162,5342,28137,28657,33,32,1,994,14,427,1,2, 384,1,0,0,0,0,1,2036, 3,2400000,4,2,128,8,500 +"2",15,5549,11,0,0,162,5341,28137,28657,34,32,1,244,12,1179,0,2, 386,1,0,0,0,0,1,2037, 3,2400000,4,2,128,8,500 +"2",49,5558,10,0,0,164,5346,28137,28657,33,32,1,989,16,431,1,2, 381,1,0,0,0,0,1,2039, 3,2400000,4,2,128,8,500 +"2",28,5563,11,0,0,159,5355,28137,28657,35,32,1,996,14,424,1,2, 384,1,0,0,0,0,1,2036, 3,2400000,4,2,128,8,500 +"2",56,5565,9,0,0,165,5356,28136,28658,33,32,1,180,13,1245,0,2, 380,1,0,0,0,0,1,2044, 3,2400000,4,2,128,8,500 +"2",42,5591,11,0,0,163,5380,28137,28657,32,32,1,1007,14,414,1,2, 383,1,0,0,0,0,1,2038, 3,2400000,4,2,128,8,500 +"2",1,9914,9,0,1,320,9537,28136,28658,34,32,1,308,14,1115,0,2, 388,1,0,0,0,0,2,2036, 3,2400000,4,2,128,8,500 +"2",27,9948,11,0,1,322,9570,28137,28658,33,32,1,279,10,1146,0,2, 384,1,0,0,0,0,2,2041, 3,2400000,4,2,128,8,500 +"2",19,10105,10,0,1,322,9726,28137,28658,34,32,0,293,12,1131,0,2, 384,1,0,0,0,0,2,2040, 3,2400000,4,2,128,8,500 +"2",16,10357,9,0,1,322,9980,28137,28658,34,32,0,80,10,1347,0,1, 385,1,0,0,0,0,2,2043, 3,2400000,4,2,128,8,500 +"2",36,10385,11,0,1,326,10004,28137,28658,33,32,0,44,8,1384,0,2, 383,1,0,0,0,0,2,2045, 3,2400000,4,2,128,8,500 +"2",8,10613,11,0,1,322,10236,28137,28658,34,32,1,16,9,1411,0,2, 387,1,0,0,0,0,2,2040, 3,2400000,4,2,128,8,500 +"2",63,10625,19,1,1,323,10245,28137,28657,34,32,1,1085,2,335,1,2, 379,1,0,0,0,0,4,2043, 3,2400000,4,2,128,8,500 +"3",120,842,9,1,1,479,310,9210,9145,18,17,1,155,23,109,2,6, 384,1,0,0,0,0,4,579, 3,1200000,4,3,128,8,500 +"3",8,844,10,1,1,481,309,9210,9145,17,17,1,179,20,82,6,6, 397,1,0,0,0,0,4,563, 3,1200000,4,3,128,8,500 +"3",81,842,10,1,1,479,310,9210,9145,18,17,1,164,16,99,7,6, 388,1,0,0,0,0,5,574, 3,1200000,4,3,128,8,500 +"3",5,846,10,1,1,476,316,9210,9144,18,17,1,173,17,89,8,6, 397,1,0,0,0,0,5,564, 3,1200000,4,3,128,8,500 +"3",46,850,9,1,1,481,316,9210,9145,17,17,1,151,17,113,7,6, 393,1,1,0,0,0,5,570, 3,1200000,4,3,128,8,500 +"3",42,851,12,1,1,477,322,9210,9145,18,17,1,152,14,113,7,6, 394,1,1,0,0,0,5,570, 3,1200000,4,3,128,8,500 +"3",49,850,9,1,1,485,315,9210,9145,18,17,1,153,17,114,5,6, 392,1,0,0,0,0,4,573, 3,1200000,4,3,128,8,500 +"3",0,1058,8,1,2,620,380,9210,9145,18,17,1,195,18,66,11,0, 376,1,1,0,0,0,5,580, 3,1200000,4,3,128,8,500 +"3",36,1081,10,1,2,646,381,9210,9145,18,17,1,107,14,158,8,6, 394,1,0,0,0,0,5,569, 3,1200000,4,3,128,8,500 +"3",18,1081,10,1,2,647,379,9210,9145,18,17,1,109,13,156,9,6, 396,1,0,0,0,0,5,567, 3,1200000,4,3,128,8,500 +"3",2,1084,9,1,2,636,391,9210,9145,18,17,1,178,17,84,9,6, 397,1,0,0,0,0,5,563, 3,1200000,4,3,128,8,500 +"3",109,1084,9,1,2,641,388,9210,9145,18,17,1,162,20,103,3,6, 385,1,0,0,0,0,5,578, 3,1200000,4,3,128,8,500 +"3",16,1085,10,1,2,639,393,9210,9145,17,17,1,90,15,177,5,6, 396,1,0,0,0,0,5,569, 3,1200000,4,3,128,8,500 +"3",91,1085,9,1,2,647,386,9210,9145,18,17,1,89,15,178,6,6, 387,1,0,0,0,0,5,578, 3,1200000,4,3,128,8,500 +"3",38,1088,9,1,2,645,392,9210,9145,17,17,1,96,15,170,7,6, 394,1,0,0,0,0,5,570, 3,1200000,4,3,128,8,500 +"3",32,1090,9,1,2,644,394,9210,9145,19,17,1,92,14,174,8,6, 395,1,0,0,0,0,5,570, 3,1200000,4,3,128,8,500 +"3",22,1092,10,1,2,649,391,9210,9145,18,17,1,97,12,170,8,6, 396,1,0,0,0,0,5,569, 3,1200000,4,3,128,8,500 +"3",64,1090,17,2,2,650,392,9210,9145,18,17,0,87,6,183,3,6, 389,1,0,0,0,0,5,579, 3,1200000,4,3,128,8,500 +"3",83,1095,9,1,2,640,405,9210,9145,18,17,1,148,14,119,6,6, 388,1,0,0,0,0,6,578, 3,1200000,4,3,128,8,500 +"3",125,1097,14,1,2,647,398,9210,9145,18,17,1,153,14,113,3,6, 384,1,0,0,0,0,6,580, 3,1200000,4,3,128,8,500 +"3",123,1097,11,2,2,648,398,9210,9145,18,17,1,154,11,112,7,6, 384,1,0,0,0,0,6,581, 3,1200000,4,3,128,8,500 +"3",111,1099,9,1,2,647,398,9210,9145,18,17,1,151,20,114,2,6, 385,1,0,0,0,0,5,579, 3,1200000,4,3,128,8,500 +"3",73,1098,8,1,2,654,392,9210,9145,18,17,1,86,19,180,5,6, 389,1,0,0,0,0,5,576, 3,1200000,4,3,128,8,500 +"3",95,1095,9,1,2,647,398,9210,9145,18,17,1,75,13,195,5,6, 387,1,0,0,0,0,5,582, 3,1200000,4,3,128,8,500 +"3",55,1101,11,2,2,646,402,9210,9145,19,17,1,170,16,94,5,6, 391,1,1,0,0,0,5,571, 3,1200000,4,3,128,8,500 +"3",77,1102,11,1,2,643,405,9210,9145,18,17,1,186,19,77,4,6, 388,1,0,0,0,0,6,573, 3,1200000,4,3,128,8,500 +"3",48,1099,9,1,2,642,406,9210,9145,17,17,1,140,17,127,5,6, 392,1,0,0,0,0,5,574, 3,1200000,4,3,128,8,500 +"3",26,1098,9,1,2,643,405,9210,9145,17,17,1,78,13,190,7,6, 396,1,0,0,0,0,5,571, 3,1200000,4,3,128,8,500 +"3",45,1099,9,1,2,640,407,9210,9145,18,17,1,147,15,120,7,6, 393,1,0,0,0,0,5,572, 3,1200000,4,3,128,8,500 +"3",90,1095,9,1,2,644,405,9210,9146,18,17,1,59,11,213,6,6, 387,1,0,0,0,0,5,583, 3,1200000,4,3,128,8,500 +"3",20,1098,10,1,2,645,404,9210,9145,18,17,1,78,10,192,8,6, 396,1,0,0,0,0,5,572, 3,1200000,4,3,128,8,500 +"3",118,1100,10,1,2,638,411,9210,9145,18,17,0,140,16,127,3,6, 384,1,0,0,0,0,5,582, 3,1200000,4,3,128,8,500 +"3",37,1102,9,1,2,644,405,9210,9145,18,17,1,88,14,178,8,6, 394,1,1,0,0,0,6,571, 3,1200000,4,3,128,8,500 +"3",119,1102,9,1,2,645,405,9210,9145,18,17,1,147,17,119,4,6, 384,1,0,0,0,0,5,580, 3,1200000,4,3,128,8,500 +"3",126,1103,10,1,2,646,405,9210,9145,18,17,1,150,17,117,3,6, 384,1,0,0,0,0,5,582, 3,1200000,4,3,128,8,500 +"3",65,1100,9,1,2,647,405,9210,9145,18,16,0,66,15,204,3,6, 389,1,0,0,0,0,5,579, 3,1200000,4,3,128,8,500 +"3",108,1104,8,1,2,648,404,9210,9145,18,17,1,142,20,125,2,6, 385,1,0,0,0,0,5,580, 3,1200000,4,3,128,8,500 +"3",72,1099,9,1,2,647,405,9210,9145,17,17,1,59,15,212,2,6, 389,1,0,0,0,0,5,581, 3,1200000,4,3,128,8,500 +"3",9,1108,9,1,2,645,409,9210,9145,18,17,1,170,20,94,5,6, 397,1,0,0,0,0,5,566, 3,1200000,4,3,128,8,500 +"3",104,1101,9,1,2,642,411,9210,9145,18,17,1,52,16,220,1,6, 386,1,0,0,0,0,5,584, 3,1200000,4,3,128,8,500 +"3",63,1102,10,1,2,649,405,9210,9145,18,17,1,75,14,194,3,6, 390,1,0,0,0,0,5,578, 3,1200000,4,3,128,8,500 +"3",101,1101,10,2,2,649,406,9210,9145,18,17,1,71,13,200,2,6, 386,1,0,0,0,0,6,583, 3,1200000,4,3,128,8,500 +"3",1,1109,10,2,2,650,405,9210,9145,18,17,1,171,16,93,8,6, 397,1,0,0,0,0,5,565, 3,1200000,4,3,128,8,500 +"3",19,1105,12,1,2,648,406,9210,9145,17,17,1,88,9,179,8,6, 396,1,0,0,0,0,7,569, 3,1200000,4,3,128,8,500 +"3",82,1106,10,1,2,643,411,9210,9145,18,17,1,139,13,129,7,6, 388,1,0,0,0,0,6,578, 3,1200000,4,3,128,8,500 +"3",89,1104,9,1,2,655,400,9210,9145,18,17,1,73,13,197,5,6, 387,1,0,0,0,0,5,582, 3,1200000,4,3,128,8,500 +"3",44,1103,9,1,2,642,412,9210,9145,18,17,1,136,12,135,6,6, 394,1,0,0,0,0,5,575, 3,1200000,4,3,128,8,500 +"3",105,1104,9,1,2,649,406,9210,9145,18,17,1,74,17,196,1,6, 386,1,0,0,0,0,5,583, 3,1200000,4,3,128,8,500 +"3",124,1104,9,1,2,644,412,9210,9145,17,17,1,140,16,131,2,6, 384,1,0,0,0,0,5,585, 3,1200000,4,3,128,8,500 +"3",11,1109,9,1,2,638,418,9210,9145,18,17,1,156,18,110,5,6, 397,1,1,0,0,0,5,568, 3,1200000,4,3,128,8,500 +"3",107,1107,21,2,2,645,410,9210,9144,18,17,1,150,6,116,2,6, 385,1,0,0,0,0,6,579, 3,1200000,4,3,128,8,500 +"3",34,1105,9,1,2,650,405,9210,9145,18,17,1,78,11,191,8,6, 395,1,0,0,0,0,5,574, 3,1200000,4,3,128,8,500 +"3",115,1105,9,1,2,640,416,9210,9145,18,17,1,135,14,135,4,6, 385,1,0,0,0,0,5,584, 3,1200000,4,3,128,8,500 +"3",74,1103,11,1,2,646,411,9210,9145,18,17,1,48,11,225,3,6, 388,1,0,0,0,0,5,583, 3,1200000,4,3,128,8,500 +"3",12,1111,10,1,2,641,417,9210,9145,17,17,1,154,17,112,5,6, 397,1,0,0,0,0,5,568, 3,1200000,4,3,128,8,500 +"3",79,1110,10,1,2,653,405,9210,9145,18,17,1,85,18,182,2,6, 388,1,0,0,0,0,5,577, 3,1200000,4,3,128,8,500 +"3",51,1106,9,1,2,652,407,9210,9145,17,17,1,69,12,202,4,6, 392,1,0,0,0,0,5,578, 3,1200000,4,3,128,8,500 +"3",3,1114,9,1,2,644,417,9210,9145,18,17,1,158,15,107,7,6, 397,1,0,0,0,0,5,567, 3,1200000,4,3,128,8,500 +"3",27,1113,9,1,2,657,404,9210,9145,18,17,1,86,14,181,8,6, 395,1,0,0,0,0,5,570, 3,1200000,4,3,128,8,500 +"3",56,1107,9,1,2,642,420,9210,9145,18,17,1,51,11,222,3,6, 391,1,0,0,0,0,5,581, 3,1200000,4,3,128,8,500 +"3",25,1111,8,1,2,645,417,9210,9145,17,17,1,66,14,205,6,6, 396,1,0,0,0,0,4,574, 3,1200000,4,3,128,8,500 +"3",92,1110,11,2,2,645,417,9210,9145,18,17,1,139,11,133,2,6, 387,1,0,0,0,0,6,583, 3,1200000,4,3,128,8,500 +"3",23,1113,9,1,2,643,421,9210,9145,17,17,1,71,11,199,8,6, 396,1,1,0,0,0,5,573, 3,1200000,4,3,128,8,500 +"3",112,1112,9,1,2,646,418,9210,9146,18,17,1,131,15,140,3,6, 385,1,0,0,0,0,5,585, 3,1200000,4,3,128,8,500 +"3",24,1119,11,2,2,648,416,9210,9145,18,17,1,156,13,109,8,6, 396,1,1,0,0,0,5,568, 3,1200000,4,3,128,8,500 +"3",88,1112,11,1,2,653,412,9210,9145,18,17,1,52,9,221,4,6, 388,1,0,0,0,0,5,583, 3,1200000,4,3,128,8,500 +"3",66,1112,10,1,2,648,417,9210,9145,18,16,1,47,11,226,3,6, 389,1,0,0,0,0,5,582, 3,1200000,4,3,128,8,500 +"3",50,1113,11,1,2,654,412,9210,9145,18,17,1,48,11,224,3,6, 392,1,1,0,0,0,5,579, 3,1200000,4,3,128,8,500 +"3",52,1113,9,2,2,656,411,9210,9145,18,17,1,50,12,222,4,6, 392,1,0,0,0,0,5,579, 3,1200000,4,3,128,8,500 +"3",17,1116,9,1,2,650,417,9210,9145,17,17,1,68,11,202,7,6, 396,1,0,0,0,0,5,573, 3,1200000,4,3,128,8,500 +"3",41,1115,9,1,2,649,418,9210,9145,18,17,1,68,11,202,7,6, 394,1,0,0,0,0,5,575, 3,1200000,4,3,128,8,500 +"3",68,1114,11,1,2,650,418,9210,9145,19,17,0,42,12,232,2,6, 389,1,0,0,0,0,4,583, 3,1200000,4,3,128,8,500 +"3",102,1115,10,1,2,651,418,9210,9145,18,17,1,49,13,223,2,6, 386,1,0,0,0,0,5,585, 3,1200000,4,3,128,8,500 +"3",100,1115,9,1,2,657,413,9210,9145,19,17,1,57,16,215,0,6, 386,1,0,0,0,0,5,585, 3,1200000,4,3,128,8,500 +"3",7,1116,12,1,2,649,420,9210,9145,18,17,1,54,10,218,3,6, 397,1,0,0,0,0,5,574, 3,1200000,4,3,128,8,500 +"3",33,1118,9,1,2,650,419,9210,9145,18,17,1,68,11,203,7,6, 395,1,0,0,0,0,5,574, 3,1200000,4,3,128,8,500 +"3",110,1121,11,1,2,639,430,9210,9145,18,17,1,136,17,131,2,6, 385,1,0,0,0,0,5,581, 3,1200000,4,3,128,8,500 +"3",103,1118,10,1,2,654,417,9210,9145,19,17,1,55,15,216,1,6, 386,1,0,0,0,0,5,584, 3,1200000,4,3,128,8,500 +"3",122,1120,14,1,2,652,419,9210,9145,18,17,1,135,11,135,2,6, 384,1,0,0,0,0,5,585, 3,1200000,4,3,128,8,500 +"3",13,1124,10,1,2,642,429,9210,9145,18,17,1,154,17,112,5,6, 397,1,0,0,0,0,5,568, 3,1200000,4,3,128,8,500 +"3",87,1119,9,1,2,654,418,9210,9145,19,17,1,52,13,220,4,6, 388,1,0,0,0,0,5,583, 3,1200000,4,3,128,8,500 +"3",10,1126,9,1,2,655,417,9210,9145,17,17,1,156,18,109,5,6, 397,1,1,0,0,0,4,568, 3,1200000,4,3,128,8,500 +"3",93,1123,9,1,2,645,426,9210,9145,18,17,1,140,18,128,3,6, 387,1,0,0,0,0,5,579, 3,1200000,4,3,128,8,500 +"3",53,1122,9,1,2,668,406,9210,9145,18,17,1,69,14,202,4,6, 391,1,0,0,0,0,5,578, 3,1200000,4,3,128,8,500 +"3",94,1118,13,1,2,654,418,9210,9145,18,17,1,42,9,232,2,6, 387,1,0,0,0,0,5,586, 3,1200000,4,3,128,8,500 +"3",39,1122,10,1,2,656,418,9210,9145,19,17,1,72,11,198,6,6, 394,1,0,0,0,0,5,575, 3,1200000,4,3,128,8,500 +"3",116,1119,6,1,2,657,417,9210,9146,18,17,1,44,17,230,1,6, 385,1,0,0,0,0,4,588, 3,1200000,4,3,128,8,500 +"3",97,1121,10,1,2,656,418,9210,9145,18,17,1,53,13,220,2,6, 386,1,0,0,0,0,5,585, 3,1200000,4,3,128,8,500 +"3",114,1123,9,1,2,650,424,9210,9146,18,17,1,124,15,147,2,6, 385,1,0,0,0,0,5,584, 3,1200000,4,3,128,8,500 +"3",60,1121,9,1,2,654,421,9210,9145,18,17,1,50,12,223,3,6, 391,1,0,0,0,0,5,580, 3,1200000,4,3,128,8,500 +"3",106,1120,10,1,2,652,425,9210,9145,18,17,1,40,13,234,0,6, 386,1,0,0,0,0,5,587, 3,1200000,4,3,128,8,500 +"3",80,1125,12,1,2,646,431,9210,9145,17,17,1,58,13,213,1,6, 388,1,0,0,0,0,5,581, 3,1200000,4,3,128,8,500 +"3",76,1125,9,1,2,659,418,9210,9145,18,17,1,65,15,207,2,6, 388,1,0,0,0,0,5,581, 3,1200000,4,3,128,8,500 +"3",40,1125,9,1,2,660,419,9210,9145,19,17,1,63,12,208,5,6, 394,1,0,0,0,0,5,575, 3,1200000,4,3,128,8,500 +"3",14,1129,10,1,2,644,433,9210,9145,17,17,1,144,15,124,4,6, 397,1,0,0,0,0,5,569, 3,1200000,4,3,128,8,500 +"3",61,1125,10,1,2,660,417,9210,9145,18,17,1,56,12,216,3,6, 391,1,0,0,0,0,5,580, 3,1200000,4,3,128,8,500 +"3",84,1124,11,1,2,654,425,9210,9145,18,17,1,125,12,147,2,6, 388,1,0,0,0,0,6,583, 3,1200000,4,3,128,8,500 +"3",35,1126,9,1,2,646,432,9210,9145,18,17,1,53,11,219,6,6, 394,1,1,0,0,0,5,576, 3,1200000,4,3,128,8,500 +"3",58,1125,9,1,2,654,424,9210,9145,18,17,1,41,11,233,4,6, 391,1,0,0,0,0,5,581, 3,1200000,4,3,128,8,500 +"3",86,1124,7,1,2,650,428,9210,9146,18,17,1,31,15,244,2,6, 388,1,0,0,0,0,4,585, 3,1200000,4,3,128,8,500 +"3",69,1127,9,1,2,654,427,9210,9146,18,17,0,55,14,217,2,6, 389,1,0,0,0,0,5,582, 3,1200000,4,3,128,8,500 +"3",30,1128,9,1,2,653,428,9210,9146,18,17,1,66,10,207,6,6, 395,1,0,0,0,0,5,576, 3,1200000,4,3,128,8,500 +"3",85,1128,10,1,2,651,430,9210,9145,18,17,1,119,14,153,2,6, 388,1,0,0,0,0,5,582, 3,1200000,4,3,128,8,500 +"3",96,1128,9,1,2,649,431,9210,9145,20,17,1,60,14,213,2,6, 386,1,0,0,0,0,5,585, 3,1200000,4,3,128,8,500 +"3",117,1134,11,1,2,654,427,9210,9145,18,17,1,144,16,123,3,6, 384,1,1,0,0,0,5,581, 3,1200000,4,3,128,8,500 +"3",43,1131,10,1,2,651,432,9210,9145,18,17,1,119,10,153,5,6, 394,1,0,0,0,0,5,576, 3,1200000,4,3,128,8,500 +"3",31,1132,9,1,2,656,428,9210,9145,18,17,1,72,10,198,8,6, 395,1,0,0,0,0,5,574, 3,1200000,4,3,128,8,500 +"3",62,1132,9,1,2,650,433,9210,9145,17,17,1,61,14,211,3,6, 390,1,0,0,0,0,4,580, 3,1200000,4,3,128,8,500 +"3",113,1132,9,1,2,654,431,9210,9145,18,17,0,118,15,154,1,6, 385,1,0,0,0,0,5,585, 3,1200000,4,3,128,8,500 +"3",59,1133,11,1,2,655,431,9210,9145,18,17,1,58,12,213,3,6, 391,1,0,0,0,0,5,579, 3,1200000,4,3,128,8,500 +"3",70,1130,10,1,2,655,431,9210,9146,18,17,0,33,11,241,2,6, 389,1,0,0,0,0,5,584, 3,1200000,4,3,128,8,500 +"3",99,1131,9,1,2,656,431,9210,9145,17,17,1,33,14,242,0,6, 386,1,0,0,0,0,5,587, 3,1200000,4,3,128,8,500 +"3",67,1134,9,1,2,659,427,9210,9146,19,16,0,57,14,215,3,6, 389,1,0,0,0,0,5,581, 3,1200000,4,3,128,8,500 +"3",29,1134,9,1,2,655,431,9210,9145,18,17,1,53,11,219,5,6, 395,1,0,0,0,0,5,576, 3,1200000,4,3,128,8,500 +"3",98,1130,9,1,2,657,430,9210,9145,18,17,1,28,11,248,1,6, 386,1,0,0,0,0,5,588, 3,1200000,4,3,128,8,500 +"3",71,1131,10,1,2,659,429,9210,9146,19,16,0,41,10,234,2,6, 389,1,1,0,0,0,5,585, 3,1200000,4,3,128,8,500 +"3",78,1133,8,1,2,656,431,9210,9145,18,17,1,49,16,224,1,6, 388,1,0,0,0,0,5,584, 3,1200000,4,3,128,8,500 +"3",28,1136,9,1,2,659,431,9210,9145,17,17,1,48,10,224,6,6, 395,1,0,0,0,0,5,576, 3,1200000,4,3,128,8,500 +"3",57,1136,9,1,2,660,430,9210,9145,18,17,1,38,13,236,3,6, 391,1,0,0,0,0,4,581, 3,1200000,4,3,128,8,500 +"3",15,1137,8,1,2,659,433,9210,9145,18,17,1,52,14,220,3,6, 396,1,0,0,0,0,5,575, 3,1200000,4,3,128,8,500 +"3",21,1141,10,2,2,661,434,9210,9145,18,17,1,55,8,218,6,6, 396,1,1,0,0,0,5,575, 3,1200000,4,3,128,8,500 +"3",127,1326,11,1,3,796,475,9210,9145,18,17,1,185,12,80,9,6, 384,1,0,0,0,0,6,580, 3,1200000,4,3,128,8,500 +"3",4,1338,9,1,3,796,493,9210,9145,18,17,1,81,11,190,6,6, 397,1,0,0,0,0,6,572, 3,1200000,4,3,128,8,500 +"3",54,1336,9,1,3,796,495,9210,9145,18,17,1,57,9,216,4,6, 391,1,0,0,0,0,7,581, 3,1200000,4,3,128,8,500 +"3",75,1348,9,1,3,796,502,9210,9145,17,17,1,67,13,204,4,6, 389,1,0,0,0,0,6,581, 3,1200000,4,3,128,8,500 +"3",121,1358,9,1,3,794,519,9210,9145,18,17,1,38,12,237,0,6, 384,1,1,0,0,0,6,590, 3,1200000,4,3,128,8,500 +"3",6,1381,8,1,3,806,527,9210,9146,18,17,1,53,10,219,5,6, 397,1,0,0,0,0,6,574, 3,1200000,4,3,128,8,500 +"3",47,1382,8,1,3,795,541,9210,9145,18,17,1,42,8,233,4,6, 393,1,1,0,0,0,7,580, 3,1200000,4,3,128,8,500 +"4",105,604,3,0,0,26,555,5495,5540,10,9,2,259,6,150,3,7, 397,1,0,0,0,0,0,636, 3,600000,3,3,128,8,500 +"4",209,665,4,0,0,27,612,5495,5540,10,9,2,329,6,79,2,7, 387,1,0,0,0,0,0,646, 3,600000,3,3,128,8,500 +"4",48,692,5,0,0,28,639,5495,5540,10,9,1,328,6,81,1,7, 402,1,0,0,0,0,0,630, 3,600000,3,3,128,8,500 +"4",66,717,3,0,0,24,669,5495,5541,10,9,1,303,8,106,0,7, 401,1,0,0,0,0,0,632, 3,600000,3,3,128,8,500 +"4",206,721,4,0,0,28,668,5495,5540,9,9,2,309,4,101,3,7, 388,1,0,0,0,0,0,646, 3,600000,3,3,128,8,500 +"4",113,725,3,0,0,29,673,5495,5540,10,9,1,213,7,197,0,7, 397,1,0,0,0,0,0,637, 3,600000,3,3,128,8,500 +"4",213,725,5,0,0,27,675,5495,5541,10,9,1,311,4,98,2,7, 386,1,0,0,0,0,0,648, 3,600000,3,3,128,8,500 +"4",226,725,3,0,0,27,673,5495,5541,9,9,1,309,5,100,2,7, 385,1,0,0,0,0,0,649, 3,600000,3,3,128,8,500 +"4",29,726,5,0,0,25,676,5495,5540,10,9,2,303,5,106,1,7, 405,1,0,0,0,0,0,628, 3,600000,3,3,128,8,500 +"4",21,726,4,0,0,28,674,5495,5541,9,9,1,307,6,103,1,7, 406,1,0,0,0,0,0,628, 3,600000,3,3,128,8,500 +"4",42,726,5,0,0,26,676,5495,5541,10,9,2,307,6,103,0,7, 404,1,0,0,0,0,0,630, 3,600000,3,3,128,8,500 +"4",240,726,3,0,0,29,675,5495,5541,10,9,1,298,4,113,2,7, 384,1,0,0,0,0,0,651, 3,600000,3,3,128,8,500 +"4",0,1120,3,0,0,39,1055,5495,5541,10,9,1,323,7,85,1,0, 375,1,1,0,0,0,0,654, 3,600000,3,3,128,8,500 +"4",73,1131,3,0,0,45,1060,5495,5541,10,9,1,239,4,170,4,7, 401,1,0,0,0,0,0,633, 3,600000,3,3,128,8,500 +"4",146,1136,5,0,0,43,1066,5495,5541,10,9,1,324,5,84,2,7, 393,1,0,0,0,0,0,639, 3,600000,3,3,128,8,500 +"4",219,1137,4,0,0,47,1065,5494,5541,9,9,1,328,4,80,4,7, 386,1,0,0,0,0,1,647, 3,600000,3,3,128,8,500 +"4",182,1150,3,0,0,44,1079,5495,5541,10,9,1,329,6,79,3,7, 390,1,0,0,0,0,0,643, 3,600000,3,3,128,8,500 +"4",4,1173,3,0,0,44,1104,5495,5541,10,9,1,312,6,97,1,7, 407,1,0,0,0,0,0,627, 3,600000,3,3,128,8,500 +"4",150,1180,4,0,0,44,1109,5495,5541,10,9,1,312,5,97,2,7, 393,1,0,0,0,0,0,640, 3,600000,3,3,128,8,500 +"4",77,1183,3,0,0,45,1113,5495,5541,10,9,1,225,4,185,3,7, 400,1,0,0,0,0,0,634, 3,600000,3,3,128,8,500 +"4",108,1183,4,0,0,45,1114,5495,5541,10,9,1,118,4,292,2,7, 397,1,0,0,0,0,0,638, 3,600000,3,3,128,8,500 +"4",36,1191,5,0,0,45,1121,5495,5541,10,9,1,207,3,203,3,7, 404,1,0,0,0,0,0,629, 3,600000,3,3,128,8,500 +"4",178,1194,4,0,0,46,1122,5495,5541,11,9,1,310,6,98,2,7, 390,1,1,0,0,0,0,642, 3,600000,3,3,128,8,500 +"4",166,1225,3,0,0,46,1154,5495,5541,10,9,1,209,4,201,3,7, 392,1,0,0,0,0,1,642, 3,600000,3,3,128,8,500 +"4",75,1231,3,0,0,45,1162,5495,5541,10,9,1,207,4,202,3,7, 400,1,0,0,0,0,1,634, 3,600000,3,3,128,8,500 +"4",183,1233,4,0,0,45,1162,5495,5541,10,9,1,302,3,107,3,7, 390,1,0,0,0,0,1,643, 3,600000,3,3,128,8,500 +"4",190,1232,3,0,0,44,1163,5495,5541,10,9,1,301,4,109,3,7, 390,1,0,0,0,0,0,645, 3,600000,3,3,128,8,500 +"4",100,1233,3,0,0,44,1164,5495,5541,10,9,1,195,7,214,0,7, 398,1,0,0,0,0,0,636, 3,600000,3,3,128,8,500 +"4",1,1234,4,0,0,45,1164,5495,5541,9,9,1,296,6,112,1,7, 407,1,0,0,0,0,0,626, 3,600000,3,3,128,8,500 +"4",52,1234,3,0,0,45,1165,5494,5541,11,9,1,296,4,113,4,7, 402,1,0,0,0,0,0,631, 3,600000,3,3,128,8,500 +"4",154,1235,3,0,0,44,1166,5495,5541,10,9,1,295,5,114,2,7, 393,1,0,0,0,0,0,641, 3,600000,3,3,128,8,500 +"4",2,1236,4,0,0,45,1165,5495,5541,10,9,1,298,6,111,1,7, 407,1,0,0,0,0,0,626, 3,600000,3,3,128,8,500 +"4",8,1236,3,0,0,45,1165,5495,5541,10,9,1,294,7,115,1,7, 407,1,0,0,0,0,0,627, 3,600000,3,3,128,8,500 +"4",9,1236,4,0,0,45,1165,5495,5541,10,9,1,295,7,114,0,7, 406,1,0,0,0,0,0,627, 3,600000,3,3,128,8,500 +"4",155,1236,3,0,0,44,1165,5495,5541,10,9,1,295,6,114,2,7, 393,1,0,0,0,0,0,641, 3,600000,3,3,128,8,500 +"4",54,1236,3,0,0,46,1165,5495,5541,11,9,1,296,5,113,4,7, 402,1,0,0,0,0,0,631, 3,600000,3,3,128,8,500 +"4",217,1236,3,0,0,46,1164,5495,5541,9,9,1,298,5,111,3,7, 386,1,0,0,0,0,0,647, 3,600000,3,3,128,8,500 +"4",130,1236,4,0,0,46,1165,5495,5541,11,9,1,191,6,218,2,7, 395,1,0,0,0,0,0,639, 3,600000,3,3,128,8,500 +"4",218,1237,3,0,0,45,1165,5495,5541,9,9,1,301,5,108,3,7, 386,1,0,0,0,0,0,647, 3,600000,3,3,128,8,500 +"4",101,1236,3,0,0,44,1167,5495,5541,11,9,1,209,4,200,3,7, 398,1,0,0,0,0,0,636, 3,600000,3,3,128,8,500 +"4",254,1236,4,0,0,48,1163,5495,5541,9,9,1,266,4,143,3,7, 382,1,0,0,0,0,0,652, 3,600000,3,3,128,8,500 +"4",251,1237,4,0,0,46,1165,5495,5541,9,9,1,270,3,139,3,7, 383,1,0,0,0,0,0,652, 3,600000,3,3,128,8,500 +"4",147,1237,3,0,0,45,1167,5495,5541,10,9,1,296,6,113,2,7, 393,1,0,0,0,0,0,640, 3,600000,3,3,128,8,500 +"4",203,1238,3,0,0,47,1165,5495,5541,9,9,1,297,4,112,3,7, 388,1,0,0,0,0,0,645, 3,600000,3,3,128,8,500 +"4",72,1237,4,0,0,47,1166,5495,5541,11,9,1,193,6,217,0,7, 401,1,0,0,0,0,0,633, 3,600000,3,3,128,8,500 +"4",89,1238,3,0,0,45,1167,5495,5541,11,9,1,207,4,202,3,7, 398,1,0,0,0,0,0,636, 3,600000,3,3,128,8,500 +"4",247,1238,3,0,0,47,1166,5495,5541,11,9,1,270,4,140,3,7, 383,1,0,0,0,0,0,651, 3,600000,3,3,128,8,500 +"4",191,1238,3,0,0,46,1167,5495,5541,10,9,1,299,4,111,3,7, 389,1,0,0,0,0,1,645, 3,600000,3,3,128,8,500 +"4",180,1240,4,0,0,44,1169,5495,5541,10,9,1,298,5,111,3,7, 390,1,0,0,0,0,0,643, 3,600000,3,3,128,8,500 +"4",128,1239,4,0,0,46,1167,5495,5541,11,9,1,195,4,215,3,7, 395,1,0,0,0,0,0,640, 3,600000,3,3,128,8,500 +"4",144,1238,7,0,0,45,1168,5495,5541,11,9,1,293,1,116,2,7, 394,1,0,0,0,0,1,640, 3,600000,3,3,128,8,500 +"4",16,1239,3,0,0,49,1166,5495,5541,11,9,1,297,6,113,1,7, 406,1,0,0,0,0,0,628, 3,600000,3,3,128,8,500 +"4",127,1240,4,0,0,46,1169,5495,5541,10,9,1,209,3,201,3,7, 395,1,0,0,0,0,1,639, 3,600000,3,3,128,8,500 +"4",37,1242,3,0,0,45,1172,5495,5541,10,9,1,177,4,233,3,7, 404,1,0,0,0,0,0,630, 3,600000,3,3,128,8,500 +"4",164,1243,4,0,0,47,1170,5495,5541,10,9,1,298,4,110,3,7, 392,1,0,0,0,0,0,642, 3,600000,3,3,128,8,500 +"4",91,1243,3,0,0,47,1171,5495,5541,9,9,1,203,4,206,4,7, 398,1,0,0,0,0,0,636, 3,600000,3,3,128,8,500 +"4",223,1243,3,0,0,46,1172,5495,5541,9,9,1,312,4,97,3,7, 385,1,0,0,0,0,1,648, 3,600000,3,3,128,8,500 +"4",109,1243,3,0,0,46,1173,5495,5541,10,9,1,91,3,320,3,7, 397,1,0,0,0,0,0,638, 3,600000,3,3,128,8,500 +"4",64,1244,5,0,0,45,1174,5495,5541,10,9,1,293,4,116,0,7, 401,1,0,0,0,0,1,633, 3,600000,3,3,128,8,500 +"4",201,1259,5,0,0,49,1184,5495,5541,9,9,1,297,2,112,3,7, 388,1,0,0,0,0,0,645, 3,600000,3,3,128,8,500 +"4",132,1281,5,0,0,45,1212,5495,5541,10,9,1,180,3,231,2,7, 395,1,0,0,0,0,0,641, 3,600000,3,3,128,8,500 +"4",148,1282,3,0,0,46,1212,5495,5541,10,9,1,275,6,135,2,7, 393,1,0,0,0,0,0,641, 3,600000,3,3,128,8,500 +"4",5,1287,3,0,0,46,1216,5495,5541,9,9,1,280,6,130,1,7, 407,1,0,0,0,0,0,627, 3,600000,3,3,128,8,500 +"4",110,1286,3,0,0,45,1216,5495,5541,10,9,1,73,4,337,2,7, 397,1,0,0,0,0,0,638, 3,600000,3,3,128,8,500 +"4",50,1287,3,0,0,44,1217,5495,5541,10,9,1,180,7,229,0,7, 403,1,0,0,0,0,0,632, 3,600000,3,3,128,8,500 +"4",179,1287,4,0,0,44,1218,5495,5541,10,9,1,280,4,130,2,7, 390,1,0,0,0,0,0,644, 3,600000,3,3,128,8,500 +"4",6,1288,5,0,0,48,1216,5495,5541,10,9,1,280,4,131,1,7, 407,1,0,0,0,0,0,628, 3,600000,3,3,128,8,500 +"4",125,1290,3,0,0,46,1219,5495,5541,10,9,1,209,4,201,3,7, 395,1,0,0,0,0,1,639, 3,600000,3,3,128,8,500 +"4",39,1289,6,0,0,46,1219,5495,5541,10,9,1,159,4,252,0,7, 404,1,0,0,0,0,0,631, 3,600000,3,3,128,8,500 +"4",160,1290,4,0,0,45,1221,5495,5541,10,9,1,275,4,134,2,7, 392,1,0,0,0,0,1,642, 3,600000,3,3,128,8,500 +"4",158,1290,3,0,0,48,1218,5495,5541,9,9,1,278,4,132,2,7, 392,1,0,0,0,0,0,642, 3,600000,3,3,128,8,500 +"4",40,1291,3,0,0,48,1219,5495,5541,9,9,1,174,7,236,0,7, 404,1,0,0,0,0,0,630, 3,600000,3,3,128,8,500 +"4",176,1291,4,0,0,45,1220,5495,5541,10,9,1,280,4,130,2,7, 391,1,0,0,0,0,0,644, 3,600000,3,3,128,8,500 +"4",162,1291,4,0,0,46,1221,5495,5541,10,9,1,276,4,134,2,7, 392,1,0,0,0,0,0,642, 3,600000,3,3,128,8,500 +"4",33,1291,3,0,0,47,1220,5495,5541,10,9,1,162,7,248,0,7, 404,1,0,0,0,0,0,630, 3,600000,3,3,128,8,500 +"4",215,1291,3,0,0,48,1219,5495,5541,9,9,1,283,5,127,2,7, 386,1,0,0,0,0,1,648, 3,600000,3,3,128,8,500 +"4",12,1290,2,0,0,45,1222,5495,5541,10,9,1,181,6,231,0,7, 406,1,0,0,0,0,0,630, 3,600000,3,3,128,8,500 +"4",93,1292,2,0,0,47,1221,5495,5541,10,9,1,188,5,222,3,7, 398,1,0,0,0,0,0,636, 3,600000,3,3,128,8,500 +"4",134,1292,3,0,0,46,1221,5495,5541,10,9,1,179,5,231,2,7, 394,1,0,0,0,0,0,640, 3,600000,3,3,128,8,500 +"4",186,1292,3,0,0,46,1221,5495,5541,10,9,1,278,5,131,2,7, 390,1,0,0,0,0,0,644, 3,600000,3,3,128,8,500 +"4",107,1291,3,0,0,46,1221,5495,5541,10,9,1,73,5,338,2,7, 397,1,0,0,0,0,0,638, 3,600000,3,3,128,8,500 +"4",243,1292,3,0,0,47,1220,5495,5541,9,9,1,256,5,155,2,7, 383,1,0,0,0,0,0,652, 3,600000,3,3,128,8,500 +"4",187,1292,4,0,0,45,1223,5495,5541,10,9,1,279,3,132,2,7, 390,1,0,0,0,0,0,646, 3,600000,3,3,128,8,500 +"4",45,1291,4,0,0,45,1222,5495,5541,10,9,1,153,2,257,4,7, 404,1,0,0,0,0,1,631, 3,600000,3,3,128,8,500 +"4",151,1293,4,0,0,44,1223,5495,5541,10,9,1,279,4,131,2,7, 393,1,0,0,0,0,0,641, 3,600000,3,3,128,8,500 +"4",18,1293,2,0,0,45,1223,5495,5541,10,9,1,177,8,233,1,7, 406,1,0,0,0,0,0,628, 3,600000,3,3,128,8,500 +"4",205,1292,3,0,0,48,1221,5495,5541,9,9,1,279,2,131,3,7, 388,1,0,0,0,0,2,647, 3,600000,3,3,128,8,500 +"4",47,1292,4,0,0,46,1223,5495,5541,10,9,1,158,5,253,0,7, 403,1,0,0,0,0,0,632, 3,600000,3,3,128,8,500 +"4",96,1293,3,0,0,45,1224,5495,5541,10,9,1,177,7,233,0,7, 398,1,0,0,0,0,0,637, 3,600000,3,3,128,8,500 +"4",95,1293,4,0,0,45,1223,5495,5541,10,9,1,189,2,222,3,7, 398,1,0,0,0,0,1,637, 3,600000,3,3,128,8,500 +"4",35,1293,4,0,0,45,1224,5495,5541,10,9,1,160,4,250,1,7, 404,1,0,0,0,0,1,630, 3,600000,3,3,128,8,500 +"4",69,1295,3,0,0,45,1224,5495,5541,11,9,1,189,7,221,0,7, 401,1,0,0,0,0,0,633, 3,600000,3,3,128,8,500 +"4",237,1295,2,0,0,48,1223,5495,5541,10,9,1,248,5,162,3,7, 384,1,0,0,0,0,0,650, 3,600000,3,3,128,8,500 +"4",121,1295,4,0,0,46,1224,5495,5541,10,9,1,187,3,223,2,7, 396,1,0,0,0,0,0,639, 3,600000,3,3,128,8,500 +"4",222,1297,3,0,0,47,1224,5495,5541,9,9,1,282,4,128,3,7, 385,1,0,0,0,0,0,649, 3,600000,3,3,128,8,500 +"4",253,1296,4,0,0,48,1224,5495,5541,9,9,1,255,3,155,3,7, 382,1,0,0,0,0,0,652, 3,600000,3,3,128,8,500 +"4",221,1298,4,0,0,48,1224,5495,5541,10,9,1,277,3,132,3,7, 385,1,0,0,0,0,0,649, 3,600000,3,3,128,8,500 +"4",250,1297,5,0,0,47,1226,5495,5541,10,9,1,248,2,162,3,7, 383,1,0,0,0,0,0,652, 3,600000,3,3,128,8,500 +"4",76,1299,4,0,0,49,1224,5495,5541,10,9,1,176,4,234,3,7, 400,1,0,0,0,0,0,634, 3,600000,3,3,128,8,500 +"4",248,1297,3,0,0,47,1226,5495,5541,9,9,1,245,4,166,2,7, 383,1,0,0,0,0,1,652, 3,600000,3,3,128,8,500 +"4",97,1299,3,0,0,44,1230,5495,5541,11,9,1,187,7,223,0,7, 398,1,0,0,0,0,0,636, 3,600000,3,3,128,8,500 +"4",79,1298,3,0,0,53,1222,5494,5541,10,9,1,176,3,236,3,7, 400,1,0,0,0,0,0,636, 3,600000,3,3,128,8,500 +"4",246,1298,2,0,0,48,1226,5495,5541,9,9,1,249,3,162,3,7, 383,1,0,0,0,0,0,653, 3,600000,3,3,128,8,500 +"4",123,1300,5,0,0,55,1222,5495,5541,10,9,1,189,1,222,3,7, 395,1,0,0,0,0,0,640, 3,600000,3,3,128,8,500 +"4",38,1300,4,0,0,47,1230,5495,5541,10,9,1,167,2,244,3,7, 404,1,0,0,0,0,1,631, 3,600000,3,3,128,8,500 +"4",252,1303,4,0,0,47,1231,5495,5541,10,9,1,242,2,168,3,7, 382,1,0,0,0,0,0,653, 3,600000,3,3,128,8,500 +"4",238,1309,3,0,0,48,1236,5495,5541,10,9,1,244,4,167,3,7, 384,1,0,0,0,0,0,650, 3,600000,3,3,128,8,500 +"4",68,1310,4,0,0,45,1241,5495,5541,10,9,1,177,5,234,0,7, 401,1,0,0,0,0,0,634, 3,600000,3,3,128,8,500 +"4",210,1310,3,0,0,47,1240,5495,5541,9,9,1,158,4,253,2,7, 387,1,0,0,0,0,0,649, 3,600000,3,3,128,8,500 +"4",111,1310,4,0,0,46,1242,5495,5541,10,9,1,69,4,343,0,7, 397,1,0,0,0,0,0,640, 3,600000,3,3,128,8,500 +"4",13,1312,7,0,0,54,1235,5495,5541,10,9,1,287,1,123,0,7, 406,1,0,0,0,0,2,628, 3,600000,3,3,128,8,500 +"4",136,1343,3,0,0,46,1274,5495,5541,11,9,1,156,4,254,2,7, 394,1,0,0,0,0,0,640, 3,600000,3,3,128,8,500 +"4",3,1345,4,0,0,46,1274,5495,5541,10,9,1,261,5,149,1,7, 407,1,0,0,0,0,0,627, 3,600000,3,3,128,8,500 +"4",10,1346,3,0,0,45,1277,5495,5541,9,9,1,257,6,152,1,7, 406,1,0,0,0,0,1,627, 3,600000,3,3,128,8,500 +"4",167,1346,2,0,0,45,1277,5495,5541,10,9,1,164,6,246,3,7, 393,1,0,0,0,0,0,641, 3,600000,3,3,128,8,500 +"4",119,1346,3,0,0,44,1278,5495,5541,10,9,1,176,4,234,3,7, 396,1,0,0,0,0,1,639, 3,600000,3,3,128,8,500 +"4",117,1346,2,0,0,46,1276,5495,5541,10,9,1,172,5,239,2,7, 396,1,0,0,0,0,0,639, 3,600000,3,3,128,8,500 +"4",63,1347,4,0,0,47,1277,5495,5541,10,9,1,175,2,237,3,7, 401,1,0,0,0,0,0,635, 3,600000,3,3,128,8,500 +"4",41,1347,3,0,0,48,1275,5495,5541,10,9,1,140,6,271,0,7, 404,1,0,0,0,0,0,632, 3,600000,3,3,128,8,500 +"4",249,1348,4,0,0,48,1276,5495,5541,9,9,1,234,3,176,3,7, 383,1,0,0,0,0,0,652, 3,600000,3,3,128,8,500 +"4",200,1347,3,0,0,47,1276,5495,5541,9,9,1,261,3,149,3,7, 389,1,0,0,0,0,1,645, 3,600000,3,3,128,8,500 +"4",152,1349,3,0,0,46,1277,5495,5541,10,9,1,261,5,148,2,7, 393,1,0,0,0,0,0,641, 3,600000,3,3,128,8,500 +"4",34,1348,4,0,0,46,1278,5495,5541,10,9,1,151,5,259,0,7, 405,1,0,0,0,0,0,630, 3,600000,3,3,128,8,500 +"4",23,1347,3,0,0,46,1279,5495,5541,10,9,1,139,5,272,0,7, 406,1,0,0,0,0,0,631, 3,600000,3,3,128,8,500 +"4",202,1347,4,0,0,46,1276,5495,5541,9,9,1,263,1,148,3,7, 388,1,0,0,0,0,0,647, 3,600000,3,3,128,8,500 +"4",233,1347,3,0,0,48,1277,5495,5541,9,9,1,151,2,259,3,7, 385,1,0,0,0,0,1,650, 3,600000,3,3,128,8,500 +"4",53,1349,3,0,0,45,1279,5495,5541,10,9,1,264,4,146,3,7, 402,1,0,0,0,0,0,632, 3,600000,3,3,128,8,500 +"4",19,1348,3,0,0,46,1278,5495,5541,10,9,1,142,5,270,1,7, 406,1,0,0,0,0,0,630, 3,600000,3,3,128,8,500 +"4",55,1350,3,0,0,47,1278,5495,5541,10,9,1,262,4,147,3,7, 402,1,0,0,0,0,0,632, 3,600000,3,3,128,8,500 +"4",11,1350,4,0,0,46,1280,5495,5541,9,9,1,257,6,153,1,7, 406,1,0,0,0,0,0,628, 3,600000,3,3,128,8,500 +"4",231,1350,4,0,0,49,1276,5495,5541,11,9,1,153,4,257,3,7, 385,1,0,0,0,0,0,650, 3,600000,3,3,128,8,500 +"4",129,1350,3,0,0,46,1280,5495,5541,10,9,1,174,5,236,2,7, 395,1,0,0,0,0,0,640, 3,600000,3,3,128,8,500 +"4",229,1349,4,0,0,48,1277,5495,5541,9,9,1,156,3,254,3,7, 385,1,0,0,0,0,1,650, 3,600000,3,3,128,8,500 +"4",145,1350,2,0,0,46,1280,5494,5541,10,9,1,163,6,248,2,7, 394,1,0,0,0,0,0,641, 3,600000,3,3,128,8,500 +"4",126,1350,2,0,0,45,1280,5495,5541,10,9,1,162,4,248,3,7, 395,1,0,0,0,0,1,640, 3,600000,3,3,128,8,500 +"4",82,1349,4,0,0,47,1279,5495,5541,10,9,1,157,4,254,0,7, 399,1,0,0,0,0,0,637, 3,600000,3,3,128,8,500 +"4",124,1351,3,0,0,45,1281,5495,5541,10,9,1,163,4,248,3,7, 396,1,0,0,0,0,0,639, 3,600000,3,3,128,8,500 +"4",116,1351,3,0,0,48,1280,5495,5541,10,9,1,155,6,255,0,7, 396,1,0,0,0,0,0,639, 3,600000,3,3,128,8,500 +"4",46,1352,4,0,0,46,1281,5495,5541,10,9,1,148,3,262,3,7, 404,1,0,0,0,0,0,631, 3,600000,3,3,128,8,500 +"4",194,1352,5,0,0,49,1278,5495,5541,10,9,1,262,4,147,2,7, 389,1,0,0,0,0,0,645, 3,600000,3,3,128,8,500 +"4",102,1352,3,0,0,46,1281,5495,5541,10,9,1,162,3,248,3,7, 397,1,0,0,0,0,1,637, 3,600000,3,3,128,8,500 +"4",193,1352,4,0,0,48,1279,5495,5541,10,9,1,257,4,153,2,7, 389,1,0,0,0,0,0,645, 3,600000,3,3,128,8,500 +"4",139,1352,3,0,0,47,1281,5495,5541,10,9,1,171,5,239,2,7, 394,1,0,0,0,0,0,641, 3,600000,3,3,128,8,500 +"4",189,1352,3,0,0,44,1283,5495,5541,10,9,1,262,4,148,3,7, 390,1,0,0,0,0,0,645, 3,600000,3,3,128,8,500 +"4",62,1353,3,0,0,46,1281,5495,5541,10,9,1,163,4,247,3,7, 401,1,0,0,0,0,0,633, 3,600000,3,3,128,8,500 +"4",25,1352,4,0,0,47,1281,5495,5541,10,9,1,140,4,271,1,7, 405,1,0,0,0,0,0,630, 3,600000,3,3,128,8,500 +"4",26,1353,3,0,0,47,1281,5495,5541,10,9,1,155,7,255,1,7, 405,1,0,0,0,0,0,629, 3,600000,3,3,128,8,500 +"4",235,1353,5,0,0,48,1280,5495,5541,10,9,1,254,2,157,3,7, 384,1,0,0,0,0,0,650, 3,600000,3,3,128,8,500 +"4",230,1352,4,0,0,49,1279,5495,5541,9,9,1,140,3,272,3,7, 385,1,0,0,0,0,0,651, 3,600000,3,3,128,8,500 +"4",245,1353,4,0,0,47,1282,5495,5541,9,9,1,234,3,177,3,7, 383,1,0,0,0,0,0,652, 3,600000,3,3,128,8,500 +"4",242,1353,3,0,0,49,1280,5495,5541,10,9,1,229,5,182,2,7, 383,1,0,0,0,0,0,652, 3,600000,3,3,128,8,500 +"4",174,1353,5,0,0,44,1284,5495,5541,10,9,1,261,1,149,3,7, 391,1,0,0,0,0,0,644, 3,600000,3,3,128,8,500 +"4",232,1352,4,0,0,50,1279,5495,5541,9,9,1,136,2,276,3,7, 385,1,0,0,0,0,0,651, 3,600000,3,3,128,8,500 +"4",24,1353,4,0,0,48,1281,5495,5541,10,9,1,154,5,256,1,7, 406,1,0,0,0,0,1,629, 3,600000,3,3,128,8,500 +"4",175,1354,3,0,0,44,1284,5495,5541,10,9,1,263,4,147,3,7, 391,1,0,0,0,0,0,644, 3,600000,3,3,128,8,500 +"4",20,1354,3,0,0,46,1283,5495,5541,10,9,1,156,7,254,1,7, 406,1,0,0,0,0,0,629, 3,600000,3,3,128,8,500 +"4",208,1354,3,0,0,48,1282,5495,5541,10,9,1,265,5,145,2,7, 386,1,0,0,0,0,0,648, 3,600000,3,3,128,8,500 +"4",234,1352,3,0,0,49,1281,5495,5541,9,9,1,130,2,281,3,7, 385,1,0,0,0,0,1,651, 3,600000,3,3,128,8,500 +"4",83,1353,3,0,0,46,1283,5495,5541,10,9,1,168,6,243,0,7, 399,1,0,0,0,0,0,636, 3,600000,3,3,128,8,500 +"4",192,1354,4,0,0,48,1282,5495,5541,9,9,1,259,4,151,2,7, 389,1,0,0,0,0,0,645, 3,600000,3,3,128,8,500 +"4",195,1354,3,0,0,49,1281,5495,5541,9,9,1,262,5,148,2,7, 389,1,0,0,0,0,1,645, 3,600000,3,3,128,8,500 +"4",131,1354,3,0,0,47,1282,5495,5541,10,9,1,174,5,236,2,7, 394,1,0,0,0,0,0,640, 3,600000,3,3,128,8,500 +"4",90,1355,4,0,0,47,1283,5495,5541,9,9,1,159,3,251,3,7, 398,1,0,0,0,0,0,636, 3,600000,3,3,128,8,500 +"4",118,1354,3,0,0,48,1283,5495,5541,10,9,1,160,2,251,3,7, 396,1,0,0,0,0,1,639, 3,600000,3,3,128,8,500 +"4",67,1356,2,0,0,46,1285,5494,5541,10,9,1,162,8,248,0,7, 401,1,0,0,0,0,0,633, 3,600000,3,3,128,8,500 +"4",181,1357,4,0,0,45,1286,5495,5541,10,9,1,264,4,146,3,7, 390,1,0,0,0,0,0,643, 3,600000,3,3,128,8,500 +"4",81,1355,3,0,0,45,1286,5495,5541,10,9,1,168,6,242,0,7, 399,1,0,0,0,0,1,636, 3,600000,3,3,128,8,500 +"4",60,1356,2,0,0,47,1285,5495,5541,11,9,1,162,4,249,3,7, 402,1,0,0,0,0,0,634, 3,600000,3,3,128,8,500 +"4",165,1358,6,0,0,46,1287,5495,5541,11,9,1,265,2,145,3,7, 392,1,0,0,0,0,0,642, 3,600000,3,3,128,8,500 +"4",173,1358,4,0,0,46,1288,5495,5541,10,9,1,261,2,150,3,7, 391,1,0,0,0,0,0,644, 3,600000,3,3,128,8,500 +"4",216,1362,4,0,0,50,1287,5495,5541,9,9,1,265,3,145,3,7, 386,1,0,0,0,0,0,648, 3,600000,3,3,128,8,500 +"4",88,1361,5,0,0,52,1284,5495,5541,10,9,1,156,4,254,0,7, 399,1,0,0,0,0,0,637, 3,600000,3,3,128,8,500 +"4",153,1366,3,0,0,44,1297,5495,5541,10,9,1,260,5,149,2,7, 393,1,0,0,0,0,1,641, 3,600000,3,3,128,8,500 +"4",172,1369,3,0,0,46,1299,5495,5541,10,9,1,263,3,149,3,7, 391,1,0,0,0,0,0,645, 3,600000,3,3,128,8,500 +"4",138,1392,3,0,0,46,1321,5495,5541,10,9,1,158,4,253,2,7, 394,1,0,0,0,0,0,641, 3,600000,3,3,128,8,500 +"4",137,1392,3,0,0,47,1320,5495,5541,10,9,1,167,5,243,2,7, 394,1,0,0,0,0,0,640, 3,600000,3,3,128,8,500 +"4",244,1395,3,0,0,48,1324,5495,5541,9,9,1,227,5,184,2,7, 383,1,0,0,0,0,0,652, 3,600000,3,3,128,8,500 +"4",159,1400,3,0,0,46,1328,5495,5541,10,9,1,279,4,131,2,7, 392,1,0,0,0,0,0,642, 3,600000,3,3,128,8,500 +"4",61,1401,3,0,0,45,1332,5495,5541,10,9,1,177,2,233,3,7, 401,1,0,0,0,0,1,634, 3,600000,3,3,128,8,500 +"4",103,1401,3,0,0,44,1332,5495,5541,10,9,1,173,4,237,3,7, 397,1,0,0,0,0,0,637, 3,600000,3,3,128,8,500 +"4",149,1405,3,0,0,47,1334,5495,5541,10,9,1,241,4,169,2,7, 393,1,0,0,0,0,0,642, 3,600000,3,3,128,8,500 +"4",92,1404,3,0,0,45,1336,5495,5541,10,9,1,139,3,272,3,7, 398,1,0,0,0,0,0,638, 3,600000,3,3,128,8,500 +"4",236,1405,2,0,0,50,1331,5495,5541,10,9,1,130,3,282,3,7, 384,1,0,0,0,0,0,652, 3,600000,3,3,128,8,500 +"4",71,1406,3,0,0,45,1337,5495,5541,10,9,1,148,6,263,0,7, 401,1,0,0,0,0,0,634, 3,600000,3,3,128,8,500 +"4",122,1406,3,0,0,46,1336,5495,5541,10,9,1,142,4,269,2,7, 396,1,0,0,0,0,0,640, 3,600000,3,3,128,8,500 +"4",98,1406,3,0,0,46,1336,5495,5541,10,9,1,135,6,276,0,7, 398,1,0,0,0,0,0,638, 3,600000,3,3,128,8,500 +"4",188,1406,4,0,0,47,1336,5495,5541,10,9,1,264,2,147,3,7, 390,1,0,0,0,0,1,645, 3,600000,3,3,128,8,500 +"4",168,1407,4,0,0,45,1337,5495,5541,10,9,1,241,3,170,2,7, 392,1,0,0,0,0,0,643, 3,600000,3,3,128,8,500 +"4",80,1406,3,0,0,47,1335,5495,5541,10,9,1,150,6,261,0,7, 399,1,0,0,0,0,0,637, 3,600000,3,3,128,8,500 +"4",27,1406,4,0,0,46,1337,5495,5541,10,9,1,116,4,295,0,7, 405,1,0,0,0,0,0,631, 3,600000,3,3,128,8,500 +"4",84,1405,4,0,0,46,1337,5495,5541,10,9,1,134,4,277,0,7, 399,1,0,0,0,0,1,637, 3,600000,3,3,128,8,500 +"4",49,1408,2,0,0,44,1338,5495,5541,10,9,1,143,8,267,0,7, 403,1,0,0,0,0,0,632, 3,600000,3,3,128,8,500 +"4",196,1407,4,0,0,49,1335,5495,5541,10,9,1,239,2,171,2,7, 389,1,0,0,0,0,1,646, 3,600000,3,3,128,8,500 +"4",114,1408,3,0,0,45,1339,5495,5541,11,9,1,137,6,274,0,7, 396,1,0,0,0,0,0,639, 3,600000,3,3,128,8,500 +"4",184,1408,3,0,0,47,1338,5495,5541,10,9,1,243,4,168,2,7, 390,1,0,0,0,0,0,645, 3,600000,3,3,128,8,500 +"4",56,1409,3,0,0,46,1339,5495,5541,10,9,1,239,6,171,0,7, 402,1,0,0,0,0,0,632, 3,600000,3,3,128,8,500 +"4",141,1408,3,0,0,45,1339,5495,5541,10,9,1,151,4,260,2,7, 394,1,0,0,0,0,0,641, 3,600000,3,3,128,8,500 +"4",7,1408,4,0,0,45,1338,5495,5541,10,9,1,243,4,168,1,7, 407,1,0,0,0,0,0,629, 3,600000,3,3,128,8,500 +"4",78,1409,5,0,0,46,1339,5495,5541,10,9,1,241,2,170,2,7, 400,1,0,0,0,0,0,635, 3,600000,3,3,128,8,500 +"4",31,1408,4,0,0,45,1339,5495,5541,10,9,1,237,4,173,1,7, 405,1,0,0,0,0,1,630, 3,600000,3,3,128,8,500 +"4",169,1409,3,0,0,46,1339,5495,5541,11,9,1,239,4,171,2,7, 391,1,0,0,0,0,0,643, 3,600000,3,3,128,8,500 +"4",227,1408,4,0,0,48,1337,5495,5541,10,9,1,137,3,275,2,7, 385,1,0,0,0,0,0,651, 3,600000,3,3,128,8,500 +"4",74,1410,3,0,0,46,1339,5495,5541,10,9,1,158,4,252,3,7, 401,1,0,0,0,0,0,634, 3,600000,3,3,128,8,500 +"4",163,1408,5,0,0,45,1340,5495,5541,10,9,1,241,2,169,2,7, 392,1,0,0,0,0,1,643, 3,600000,3,3,128,8,500 +"4",177,1410,4,0,0,47,1338,5495,5541,11,9,1,244,4,166,2,7, 390,1,0,0,0,0,0,644, 3,600000,3,3,128,8,500 +"4",185,1410,4,0,0,45,1340,5495,5541,10,9,1,242,4,169,2,7, 390,1,0,0,0,0,0,645, 3,600000,3,3,128,8,500 +"4",70,1410,3,0,0,46,1339,5495,5541,10,9,1,136,7,275,0,7, 401,1,0,0,0,0,0,634, 3,600000,3,3,128,8,500 +"4",228,1408,3,0,0,48,1338,5495,5541,10,9,1,120,3,292,2,7, 385,1,0,0,0,0,0,652, 3,600000,3,3,128,8,500 +"4",156,1408,4,0,0,45,1340,5495,5541,10,9,1,243,2,169,2,7, 393,1,0,0,0,0,0,644, 3,600000,3,3,128,8,500 +"4",51,1411,4,0,0,45,1341,5495,5541,10,9,1,254,6,156,0,7, 402,1,0,0,0,0,0,632, 3,600000,3,3,128,8,500 +"4",43,1410,3,0,0,47,1339,5495,5541,9,9,1,239,7,171,0,7, 404,1,0,0,0,0,0,631, 3,600000,3,3,128,8,500 +"4",197,1410,4,0,0,48,1339,5495,5541,10,9,1,241,4,169,2,7, 389,1,0,0,0,0,0,646, 3,600000,3,3,128,8,500 +"4",198,1410,4,0,0,49,1337,5495,5541,9,9,1,241,3,169,2,7, 389,1,0,0,0,0,0,646, 3,600000,3,3,128,8,500 +"4",135,1410,4,0,0,47,1339,5495,5541,11,9,1,156,4,254,2,7, 394,1,0,0,0,0,0,641, 3,600000,3,3,128,8,500 +"4",224,1410,4,0,0,48,1338,5495,5541,9,9,1,240,4,170,2,7, 385,1,0,0,0,0,0,650, 3,600000,3,3,128,8,500 +"4",14,1411,3,0,0,48,1339,5495,5541,10,9,1,240,6,170,0,7, 406,1,0,0,0,0,0,628, 3,600000,3,3,128,8,500 +"4",171,1410,3,0,0,45,1342,5495,5541,10,9,1,243,4,168,2,7, 391,1,0,0,0,0,1,644, 3,600000,3,3,128,8,500 +"4",225,1411,4,0,0,46,1340,5495,5541,9,9,1,240,4,170,2,7, 385,1,0,0,0,0,0,649, 3,600000,3,3,128,8,500 +"4",86,1411,5,0,0,45,1342,5495,5541,10,9,1,138,4,273,0,7, 398,1,0,0,0,0,0,637, 3,600000,3,3,128,8,500 +"4",112,1411,3,0,0,45,1343,5495,5541,10,9,1,24,5,388,0,7, 397,1,0,0,0,0,0,640, 3,600000,3,3,128,8,500 +"4",58,1412,4,0,0,45,1343,5495,5541,10,9,1,239,5,172,0,7, 402,1,0,0,0,0,0,634, 3,600000,3,3,128,8,500 +"4",115,1412,3,0,0,47,1342,5495,5541,10,9,1,144,6,267,0,7, 396,1,0,0,0,0,0,639, 3,600000,3,3,128,8,500 +"4",57,1413,4,0,0,46,1343,5495,5541,10,9,1,236,5,174,0,7, 402,1,0,0,0,0,0,633, 3,600000,3,3,128,8,500 +"4",120,1413,3,0,0,46,1343,5495,5541,10,9,1,138,5,273,2,7, 396,1,0,0,0,0,0,639, 3,600000,3,3,128,8,500 +"4",142,1413,3,0,0,49,1340,5495,5541,10,9,1,135,4,275,2,7, 394,1,0,0,0,0,0,641, 3,600000,3,3,128,8,500 +"4",161,1414,4,0,0,45,1345,5495,5541,11,9,1,241,4,169,2,7, 392,1,0,0,0,0,0,643, 3,600000,3,3,128,8,500 +"4",170,1414,3,0,0,46,1344,5495,5541,10,9,1,241,4,169,2,7, 391,1,0,0,0,0,1,644, 3,600000,3,3,128,8,500 +"4",204,1414,4,0,0,47,1343,5495,5541,10,9,1,243,2,168,3,7, 388,1,0,0,0,0,0,647, 3,600000,3,3,128,8,500 +"4",85,1414,5,0,0,54,1335,5495,5541,10,9,1,153,4,258,0,7, 399,1,0,0,0,0,0,637, 3,600000,3,3,128,8,500 +"4",140,1415,4,0,0,47,1344,5495,5541,10,9,1,139,4,271,2,7, 394,1,0,0,0,0,0,641, 3,600000,3,3,128,8,500 +"4",30,1415,5,0,0,47,1344,5495,5541,10,9,1,237,3,174,1,7, 405,1,0,0,0,0,0,630, 3,600000,3,3,128,8,500 +"4",94,1415,4,0,0,47,1345,5495,5541,10,9,1,145,3,266,2,7, 398,1,0,0,0,0,0,637, 3,600000,3,3,128,8,500 +"4",199,1415,5,0,0,48,1343,5495,5541,10,9,1,240,3,171,2,7, 389,1,0,0,0,0,0,647, 3,600000,3,3,128,8,500 +"4",220,1416,3,0,0,48,1344,5495,5541,9,9,1,243,4,168,3,7, 386,1,0,0,0,0,0,650, 3,600000,3,3,128,8,500 +"4",99,1416,3,0,0,45,1346,5495,5541,11,9,1,148,6,263,0,7, 398,1,0,0,0,0,0,638, 3,600000,3,3,128,8,500 +"4",15,1416,4,0,0,45,1347,5495,5541,9,9,1,239,5,172,0,7, 406,1,0,0,0,0,0,629, 3,600000,3,3,128,8,500 +"4",133,1417,3,0,0,46,1347,5495,5541,10,9,1,154,4,256,2,7, 394,1,0,0,0,0,0,640, 3,600000,3,3,128,8,500 +"4",241,1416,4,0,0,49,1344,5495,5541,9,9,1,213,2,199,2,7, 383,1,0,0,0,0,0,653, 3,600000,3,3,128,8,500 +"4",87,1418,4,0,0,48,1346,5495,5541,10,9,1,151,5,260,0,7, 398,1,0,0,0,0,0,637, 3,600000,3,3,128,8,500 +"4",212,1419,4,0,0,49,1346,5495,5541,10,9,1,247,3,164,2,7, 386,1,0,0,0,0,1,649, 3,600000,3,3,128,8,500 +"4",28,1419,3,0,0,50,1346,5495,5541,10,9,1,138,5,273,0,7, 405,1,0,0,0,0,0,631, 3,600000,3,3,128,8,500 +"4",157,1420,3,0,0,48,1348,5495,5541,10,9,1,243,4,167,2,7, 392,1,0,0,0,0,0,643, 3,600000,3,3,128,8,500 +"4",143,1420,4,0,0,53,1345,5495,5541,10,9,1,151,3,260,1,7, 394,1,0,0,0,0,1,642, 3,600000,3,3,128,8,500 +"4",59,1423,3,0,0,48,1350,5494,5541,10,9,1,239,6,171,0,7, 401,1,0,0,0,0,0,633, 3,600000,3,3,128,8,500 +"4",255,1704,6,0,0,65,1613,5495,5541,10,9,1,300,1,110,3,7, 382,1,0,0,0,0,1,652, 3,600000,3,3,128,8,500 +"4",32,1817,3,0,0,63,1727,5495,5541,10,9,1,168,7,242,1,7, 405,1,0,0,0,0,0,630, 3,600000,3,3,128,8,500 +"4",65,1865,3,0,0,67,1773,5495,5541,10,9,1,139,6,272,0,7, 401,1,0,0,0,0,0,635, 3,600000,3,3,128,8,500 +"4",211,1866,3,0,0,68,1773,5495,5541,10,9,1,161,5,250,2,7, 386,1,0,0,0,0,1,648, 3,600000,3,3,128,8,500 +"4",239,1868,3,0,0,65,1776,5495,5541,9,9,1,159,4,251,3,7, 384,1,0,0,0,0,0,651, 3,600000,3,3,128,8,500 +"4",104,1869,3,0,0,65,1780,5495,5541,10,9,1,64,4,347,2,7, 397,1,0,0,0,0,1,638, 3,600000,3,3,128,8,500 +"4",17,1920,4,0,0,65,1830,5495,5541,10,9,1,126,4,285,0,7, 406,1,0,0,0,0,0,629, 3,600000,3,3,128,8,500 +"4",44,1967,4,0,0,63,1877,5495,5541,10,9,1,155,3,255,3,7, 404,1,0,0,0,0,1,631, 3,600000,3,3,128,8,500 +"4",207,1983,3,0,0,68,1890,5495,5541,10,9,1,125,3,286,3,7, 388,1,0,0,0,0,0,648, 3,600000,3,3,128,8,500 +"4",22,1982,4,0,0,65,1892,5495,5541,10,9,1,126,3,286,1,7, 406,1,0,0,0,0,1,630, 3,600000,3,3,128,8,500 +"4",214,1984,3,0,0,66,1893,5495,5541,9,9,1,99,3,313,2,7, 386,1,0,0,0,0,0,650, 3,600000,3,3,128,8,500 +"4",106,2042,4,0,0,65,1953,5495,5541,10,9,1,5,2,406,2,7, 397,1,0,0,0,0,0,639, 3,600000,3,3,128,8,500 +"5",224,17,2,0,0,0,0,3844,3819,148,5,1,238,4,12,2,7, 412,2,1,0,0,0,0,691, 3,300000,3,3,128,8,500 +"5",429,17,3,0,0,0,0,3844,3819,147,4,2,233,4,18,1,7, 391,2,1,0,0,0,0,713, 3,300000,3,3,128,8,500 +"5",434,17,3,0,0,0,0,3844,3819,147,5,1,233,4,17,2,7, 390,2,1,0,0,0,0,713, 3,300000,3,3,128,8,500 +"5",103,18,3,0,0,0,0,3844,3818,147,5,1,234,5,16,2,7, 425,2,1,0,0,0,0,679, 3,300000,3,3,128,8,500 +"5",214,17,2,0,0,0,0,3844,3818,147,5,1,234,6,16,1,7, 413,2,1,0,0,0,0,690, 3,300000,3,3,128,8,500 +"5",169,17,2,0,0,0,0,3844,3818,147,5,2,235,5,16,1,7, 417,2,1,0,0,0,0,686, 3,300000,3,3,128,8,500 +"5",110,17,3,0,0,0,0,3844,3819,57,5,2,233,3,17,2,7, 424,2,1,0,0,0,0,680, 3,300000,3,3,128,8,500 +"5",112,17,3,0,0,0,0,3844,3818,53,4,1,234,5,16,1,7, 423,2,1,0,0,0,0,680, 3,300000,3,3,128,8,500 +"5",0,545,4,0,0,19,507,3844,3818,12,5,1,212,1,40,2,0, 376,2,1,0,0,0,0,726, 3,300000,3,3,128,8,500 +"5",366,553,3,0,0,28,506,3844,3818,58,4,2,123,3,130,1,7, 398,2,1,0,0,0,0,708, 3,300000,3,3,128,8,500 +"5",146,555,2,0,0,27,508,3844,3819,148,5,1,206,4,46,1,7, 419,2,1,0,0,0,0,686, 3,300000,3,3,128,8,500 +"5",439,556,3,0,0,27,509,3844,3819,147,5,1,204,3,48,2,7, 390,2,1,0,0,0,0,715, 3,300000,3,3,128,8,500 +"5",73,558,3,0,0,31,509,3844,3819,147,5,2,206,2,46,2,7, 427,2,1,0,0,0,0,678, 3,300000,3,3,128,8,500 +"5",293,564,3,0,0,28,517,3844,3818,147,5,1,211,3,40,1,7, 405,2,1,0,0,0,0,700, 3,300000,3,3,128,8,500 +"5",8,598,2,0,0,25,554,3844,3818,31,5,2,196,3,56,2,7, 434,2,1,0,0,0,0,671, 3,300000,3,3,128,8,500 +"5",334,599,2,0,0,27,553,3844,3819,58,5,2,192,4,61,1,7, 400,2,1,0,0,0,0,705, 3,300000,3,3,128,8,500 +"5",4,601,2,0,0,25,556,3844,3819,149,5,1,196,3,56,2,7, 434,2,1,0,0,0,0,671, 3,300000,3,3,128,8,500 +"5",289,602,3,0,0,27,555,3844,3818,147,5,1,195,3,56,1,7, 405,2,1,0,0,0,0,700, 3,300000,3,3,128,8,500 +"5",423,602,3,0,0,27,556,3844,3818,147,5,1,101,2,151,1,7, 391,2,1,0,0,0,0,714, 3,300000,3,3,128,8,500 +"5",421,602,3,0,0,26,556,3844,3818,148,4,1,97,3,155,1,7, 391,2,1,0,0,0,0,714, 3,300000,3,3,128,8,500 +"5",75,602,2,0,0,27,555,3844,3819,147,5,2,191,2,61,2,7, 427,2,1,0,0,0,0,678, 3,300000,3,3,128,8,500 +"5",1,602,3,0,0,25,558,3844,3818,60,5,1,196,2,56,2,7, 434,2,1,0,0,0,0,671, 3,300000,3,3,128,8,500 +"5",365,602,2,0,0,28,555,3844,3819,148,5,2,98,4,155,1,7, 398,2,1,0,0,0,0,708, 3,300000,3,3,128,8,500 +"5",183,602,2,0,0,27,556,3844,3819,148,5,1,191,4,62,1,7, 416,2,1,0,0,0,0,690, 3,300000,3,3,128,8,500 +"5",32,603,2,0,0,28,556,3844,3818,147,5,1,197,3,55,2,7, 432,2,1,0,0,0,0,673, 3,300000,3,3,128,8,500 +"5",9,604,1,0,0,25,559,3844,3819,148,5,2,195,4,56,2,7, 434,2,1,0,0,0,0,671, 3,300000,3,3,128,8,500 +"5",508,603,2,0,0,27,557,3844,3819,5,5,2,194,3,58,2,7, 383,2,1,0,0,0,0,722, 3,300000,3,3,128,8,500 +"5",18,603,3,0,0,28,556,3844,3819,147,5,1,193,3,59,2,7, 433,2,1,0,0,0,0,672, 3,300000,3,3,128,8,500 +"5",510,603,2,0,0,29,555,3844,3818,58,5,2,195,3,57,2,7, 383,2,1,0,0,0,0,722, 3,300000,3,3,128,8,500 +"5",36,604,3,0,0,29,555,3844,3819,147,5,1,195,3,57,2,7, 432,2,1,0,0,0,0,673, 3,300000,3,3,128,8,500 +"5",447,604,2,0,0,28,557,3844,3819,147,5,2,189,3,63,2,7, 389,2,1,0,0,0,0,716, 3,300000,3,3,128,8,500 +"5",65,603,2,0,0,27,557,3844,3819,148,5,1,192,3,60,1,7, 428,2,1,0,0,0,0,678, 3,300000,3,3,128,8,500 +"5",89,604,1,0,0,27,557,3844,3818,148,4,2,191,4,61,2,7, 426,2,1,0,0,0,0,679, 3,300000,3,3,128,8,500 +"5",72,604,1,0,0,26,558,3844,3819,148,5,2,192,4,60,2,7, 427,2,1,0,0,0,0,678, 3,300000,3,3,128,8,500 +"5",252,604,1,0,0,26,558,3844,3819,5,5,2,193,4,59,2,7, 410,2,1,0,0,0,0,695, 3,300000,3,3,128,8,500 +"5",2,604,2,0,0,24,561,3844,3819,62,5,1,195,4,57,2,7, 435,2,1,0,0,0,0,671, 3,300000,3,3,128,8,500 +"5",257,604,2,0,0,29,556,3844,3819,61,5,1,195,4,57,1,7, 409,2,1,0,0,0,0,696, 3,300000,3,3,128,8,500 +"5",218,604,2,0,0,26,558,3844,3819,148,5,2,193,3,60,2,7, 413,2,1,0,0,0,0,693, 3,300000,3,3,128,8,500 +"5",436,604,2,0,0,27,558,3844,3819,148,5,1,189,3,63,2,7, 390,2,1,0,0,0,0,715, 3,300000,3,3,128,8,500 +"5",330,604,3,0,0,28,558,3844,3818,147,5,2,191,3,61,1,7, 401,2,1,0,0,0,0,705, 3,300000,3,3,128,8,500 +"5",109,605,2,0,0,28,558,3844,3819,148,5,2,189,4,63,2,7, 424,2,1,0,0,0,0,681, 3,300000,3,3,128,8,500 +"5",440,605,1,0,0,27,559,3844,3819,148,5,2,190,3,62,2,7, 390,2,1,0,0,0,1,715, 3,300000,3,3,128,8,500 +"5",144,605,3,0,0,28,558,3844,3819,53,5,1,190,3,63,1,7, 419,2,1,0,0,0,0,686, 3,300000,3,3,128,8,500 +"5",357,606,2,0,0,27,559,3844,3819,148,5,1,97,5,155,1,7, 398,2,1,0,0,0,0,707, 3,300000,3,3,128,8,500 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3,300000,3,3,128,8,500 +"5",16,606,3,0,0,28,559,3844,3818,53,5,1,193,2,60,2,7, 433,2,1,0,0,0,0,673, 3,300000,3,3,128,8,500 +"5",223,607,3,0,0,27,560,3844,3818,147,5,2,108,2,144,2,7, 412,2,1,0,0,0,0,693, 3,300000,3,3,128,8,500 +"5",384,607,2,1,0,27,560,3844,3819,17,5,1,109,4,143,1,6, 395,2,1,0,0,0,0,710, 3,300000,3,3,128,8,500 +"5",77,608,1,0,0,26,561,3844,3819,147,5,2,191,4,61,2,7, 427,2,1,0,0,0,0,679, 3,300000,3,3,128,8,500 +"5",179,608,3,0,0,29,559,3844,3818,147,5,1,191,3,61,1,7, 416,2,1,0,0,0,0,689, 3,300000,3,3,128,8,500 +"5",358,607,1,0,0,27,561,3844,3819,147,5,1,108,4,145,1,7, 398,2,1,0,0,0,0,707, 3,300000,3,3,128,8,500 +"5",382,607,2,0,0,28,560,3844,3819,58,5,2,107,4,145,1,7, 396,2,1,0,0,0,1,709, 3,300000,3,3,128,8,500 +"5",301,609,3,0,0,29,560,3844,3818,147,5,2,192,4,60,1,7, 404,2,1,0,0,0,0,701, 3,300000,3,3,128,8,500 +"5",261,609,2,0,0,27,562,3844,3818,148,5,1,197,3,55,1,7, 409,2,1,0,0,0,0,696, 3,300000,3,3,128,8,500 +"5",309,608,2,0,0,27,561,3844,3819,147,5,1,191,4,60,1,7, 403,2,1,0,0,0,0,702, 3,300000,3,3,128,8,500 +"5",147,609,2,0,0,26,562,3844,3819,148,5,1,191,4,61,1,7, 419,2,1,0,0,0,0,686, 3,300000,3,3,128,8,500 +"5",311,608,2,0,0,29,561,3844,3819,147,5,1,190,4,62,1,7, 403,2,1,0,0,0,0,702, 3,300000,3,3,128,8,500 +"5",295,608,2,0,0,28,561,3844,3819,148,5,1,195,3,57,1,7, 405,2,1,0,0,0,1,700, 3,300000,3,3,128,8,500 +"5",504,609,3,0,0,29,561,3844,3818,147,5,2,193,2,59,2,7, 384,2,1,0,0,0,0,721, 3,300000,3,3,128,8,500 +"5",368,610,2,0,0,30,561,3844,3818,53,5,1,108,4,144,1,7, 397,2,1,0,0,0,0,708, 3,300000,3,3,128,8,500 +"5",256,610,3,0,0,29,561,3844,3818,13,5,1,193,3,59,2,6, 409,2,1,0,0,0,0,696, 3,300000,3,3,128,8,500 +"5",494,609,2,0,0,28,562,3844,3819,58,5,2,190,3,62,2,7, 384,2,1,0,0,0,0,721, 3,300000,3,3,128,8,500 +"5",129,612,2,0,0,28,564,3844,3819,73,5,1,192,3,60,1,7, 421,2,1,0,0,0,0,684, 3,300000,3,3,128,8,500 +"5",403,612,3,0,0,28,565,3844,3818,148,5,1,98,3,154,1,7, 394,2,1,0,0,0,0,712, 3,300000,3,3,128,8,500 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3,300000,3,3,128,8,500 +"5",319,653,2,0,0,28,606,3844,3819,148,5,2,174,4,79,1,7, 402,2,1,0,0,0,0,704, 3,300000,3,3,128,8,500 +"5",151,653,0,0,0,20,614,3843,3820,148,5,1,79,6,174,1,7, 419,2,1,0,0,0,0,687, 3,300000,3,3,128,8,500 +"5",10,654,3,0,0,28,607,3844,3819,148,5,2,176,2,77,2,7, 434,2,1,0,0,0,0,671, 3,300000,3,3,128,8,500 +"5",131,653,3,0,0,27,608,3844,3819,148,5,1,181,1,72,1,7, 421,2,1,0,0,0,0,684, 3,300000,3,3,128,8,500 +"5",12,655,2,0,0,24,611,3843,3818,148,5,2,177,4,75,1,7, 434,2,1,0,0,0,0,672, 3,300000,3,3,128,8,500 +"5",291,655,3,0,0,27,609,3844,3818,147,5,1,178,3,74,1,7, 405,2,1,0,0,0,1,700, 3,300000,3,3,128,8,500 +"5",6,655,2,0,0,26,611,3844,3819,61,5,1,179,3,73,2,7, 434,2,1,0,0,0,0,671, 3,300000,3,3,128,8,500 +"5",437,655,3,0,0,26,609,3844,3819,147,5,1,173,2,80,2,7, 390,2,1,0,0,0,0,715, 3,300000,3,3,128,8,500 +"5",251,656,2,0,0,27,609,3844,3818,148,5,2,172,3,80,2,7, 410,2,1,0,0,0,0,696, 3,300000,3,3,128,8,500 +"5",13,656,3,0,0,24,612,3844,3818,12,5,2,174,3,79,1,7, 434,2,1,0,0,0,0,672, 3,300000,3,3,128,8,500 +"5",312,655,3,0,0,28,609,3844,3819,147,5,2,174,3,79,1,7, 403,2,1,0,0,0,0,702, 3,300000,3,3,128,8,500 +"5",3,656,2,0,0,24,611,3844,3819,148,5,1,179,4,73,2,7, 435,2,1,0,0,0,0,671, 3,300000,3,3,128,8,500 +"5",296,656,2,0,0,27,610,3844,3818,147,5,2,177,4,75,1,7, 405,2,1,0,0,0,0,700, 3,300000,3,3,128,8,500 +"5",38,656,3,0,0,29,608,3844,3818,147,5,1,179,3,73,2,7, 432,2,1,0,0,0,0,674, 3,300000,3,3,128,8,500 +"5",193,654,3,0,0,27,610,3844,3819,148,5,1,175,1,79,1,7, 415,2,1,0,0,0,0,693, 3,300000,3,3,128,8,500 +"5",305,656,2,0,0,29,608,3844,3819,148,5,1,174,4,78,1,7, 403,2,1,0,0,0,0,702, 3,300000,3,3,128,8,500 +"5",11,656,2,0,0,27,610,3844,3818,148,5,2,177,3,76,2,7, 434,2,1,0,0,0,0,672, 3,300000,3,3,128,8,500 +"5",97,657,2,0,0,27,610,3844,3818,148,5,1,174,3,78,1,7, 425,2,1,0,0,0,0,680, 3,300000,3,3,128,8,500 +"5",164,657,2,0,0,28,610,3844,3818,147,5,1,173,4,80,1,7, 418,2,1,0,0,0,0,688, 3,300000,3,3,128,8,500 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3,300000,3,3,128,8,500 +"5",136,658,2,0,0,28,611,3844,3819,31,4,2,173,4,79,1,7, 421,2,1,0,0,0,0,684, 3,300000,3,3,128,8,500 +"5",472,657,3,0,0,27,611,3844,3818,148,5,2,173,3,79,1,7, 387,2,1,0,0,0,0,719, 3,300000,3,3,128,8,500 +"5",417,657,4,0,0,28,611,3844,3818,147,4,1,75,2,178,1,7, 392,2,1,0,0,0,0,715, 3,300000,3,3,128,8,500 +"5",360,656,2,0,0,28,610,3844,3819,148,4,2,88,4,166,0,7, 398,2,1,0,0,0,0,708, 3,300000,3,3,128,8,500 +"5",275,656,2,0,0,28,611,3844,3819,148,5,1,174,3,79,1,7, 408,2,1,0,0,0,0,699, 3,300000,3,3,128,8,500 +"5",20,657,1,0,0,29,610,3844,3819,147,5,1,176,4,76,2,7, 433,2,1,0,0,0,0,672, 3,300000,3,3,128,8,500 +"5",82,657,2,0,0,27,612,3844,3819,147,5,1,172,3,81,1,7, 426,2,1,0,0,0,0,680, 3,300000,3,3,128,8,500 +"5",322,658,2,0,0,29,610,3844,3819,147,5,1,173,4,79,1,7, 401,2,1,0,0,0,0,704, 3,300000,3,3,128,8,500 +"5",243,657,2,0,0,29,611,3844,3818,147,5,1,174,3,78,1,7, 410,2,1,0,0,0,0,696, 3,300000,3,3,128,8,500 +"5",96,658,3,0,0,26,612,3844,3818,147,5,1,174,3,78,1,7, 425,2,1,0,0,0,0,680, 3,300000,3,3,128,8,500 +"5",52,656,2,0,0,27,611,3844,3818,148,5,1,176,2,77,2,7, 430,2,1,0,0,0,1,676, 3,300000,3,3,128,8,500 +"5",54,658,3,0,0,28,610,3844,3819,148,5,1,174,3,79,2,7, 430,2,1,0,0,0,0,676, 3,300000,3,3,128,8,500 +"5",321,658,2,0,0,29,610,3844,3819,148,5,1,171,3,81,1,7, 402,2,1,0,0,0,0,704, 3,300000,3,3,128,8,500 +"5",273,657,2,0,0,28,611,3844,3819,147,5,1,174,2,79,1,7, 408,2,1,0,0,0,0,699, 3,300000,3,3,128,8,500 +"5",105,658,2,0,0,27,611,3844,3819,147,5,2,90,3,163,1,7, 425,2,1,0,0,0,0,681, 3,300000,3,3,128,8,500 +"5",50,658,2,0,0,28,611,3844,3818,148,5,1,175,3,77,2,7, 430,2,1,0,0,0,0,675, 3,300000,3,3,128,8,500 +"5",381,657,2,0,0,26,612,3844,3819,148,5,2,76,4,177,1,7, 396,2,1,0,0,0,0,710, 3,300000,3,3,128,8,500 +"5",458,657,3,0,0,27,611,3844,3819,148,4,2,175,2,78,1,7, 388,2,1,0,0,0,0,718, 3,300000,3,3,128,8,500 +"5",234,657,3,0,0,28,611,3844,3819,147,5,2,174,2,79,2,7, 411,2,1,0,0,0,0,695, 3,300000,3,3,128,8,500 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3,300000,3,3,128,8,500 +"5",430,658,3,0,0,27,611,3844,3818,58,4,2,171,3,81,1,7, 390,2,1,0,0,0,0,715, 3,300000,3,3,128,8,500 +"5",318,658,3,0,0,29,611,3844,3819,57,5,2,173,3,80,1,7, 402,2,1,0,0,0,0,704, 3,300000,3,3,128,8,500 +"5",244,658,1,0,0,28,611,3844,3818,148,5,1,174,4,79,1,7, 410,2,1,0,0,0,0,696, 3,300000,3,3,128,8,500 +"5",24,658,2,0,0,27,612,3844,3819,147,5,2,175,2,78,2,7, 433,2,1,0,0,0,0,673, 3,300000,3,3,128,8,500 +"5",34,659,2,0,0,28,611,3844,3819,148,5,1,178,4,74,2,7, 432,2,1,0,0,0,0,673, 3,300000,3,3,128,8,500 +"5",95,659,3,0,0,27,612,3844,3818,148,4,2,173,2,79,2,7, 425,2,1,0,0,0,0,680, 3,300000,3,3,128,8,500 +"5",468,658,2,0,0,28,612,3844,3819,148,5,1,173,3,80,1,7, 387,2,1,0,0,0,0,719, 3,300000,3,3,128,8,500 +"5",416,659,2,0,0,27,612,3844,3819,148,5,1,86,4,167,1,6, 392,2,1,0,0,0,0,714, 3,300000,3,3,128,8,500 +"5",33,659,2,0,0,28,612,3844,3818,147,5,1,180,3,72,2,7, 432,2,1,0,0,0,0,674, 3,300000,3,3,128,8,500 +"5",160,659,2,0,0,27,612,3844,3818,148,5,1,174,4,79,1,7, 418,2,1,0,0,0,0,688, 3,300000,3,3,128,8,500 +"5",266,659,2,0,0,27,612,3844,3818,148,5,2,175,4,78,1,7, 408,2,1,0,0,0,0,697, 3,300000,3,3,128,8,500 +"5",101,659,3,0,0,27,612,3844,3818,147,5,1,173,2,79,1,7, 425,2,1,0,0,0,0,681, 3,300000,3,3,128,8,500 +"5",177,659,2,0,0,27,612,3844,3818,148,5,1,174,4,78,1,7, 416,2,1,0,0,0,0,690, 3,300000,3,3,128,8,500 +"5",478,659,2,0,0,29,611,3844,3819,58,5,2,179,2,74,2,7, 386,2,1,0,0,0,0,720, 3,300000,3,3,128,8,500 +"5",138,659,2,0,0,27,613,3844,3819,148,4,2,173,3,79,1,7, 420,2,1,0,0,0,0,685, 3,300000,3,3,128,8,500 +"5",375,659,3,0,0,27,612,3844,3818,148,5,1,78,3,175,0,7, 397,2,1,0,0,0,0,709, 3,300000,3,3,128,8,500 +"5",153,658,2,0,0,27,613,3844,3818,148,5,2,90,2,163,1,7, 419,2,1,0,0,0,1,687, 3,300000,3,3,128,8,500 +"5",378,659,2,0,0,27,612,3844,3818,147,5,2,87,5,165,1,7, 397,2,1,0,0,0,0,709, 3,300000,3,3,128,8,500 +"5",166,659,5,0,0,26,613,3844,3818,147,5,1,172,1,81,0,7, 418,2,1,0,0,0,0,688, 3,300000,3,3,128,8,500 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3,300000,3,3,128,8,500 +"5",68,660,2,0,0,29,613,3844,3819,147,4,1,175,4,77,1,7, 428,2,1,0,0,0,0,678, 3,300000,3,3,128,8,500 +"5",259,660,3,0,0,28,613,3844,3818,148,5,1,178,3,74,1,7, 409,2,1,0,0,0,0,696, 3,300000,3,3,128,8,500 +"5",79,660,2,0,0,28,613,3844,3819,147,5,2,174,3,78,2,7, 427,2,1,0,0,0,0,679, 3,300000,3,3,128,8,500 +"5",424,659,2,0,0,27,612,3844,3818,148,5,2,88,3,165,1,7, 391,2,1,0,0,0,0,715, 3,300000,3,3,128,8,500 +"5",444,660,1,0,0,26,613,3844,3819,6,5,2,171,4,81,2,7, 389,2,1,0,0,0,0,716, 3,300000,3,3,128,8,500 +"5",48,660,3,0,0,29,611,3844,3819,5,5,1,176,3,76,2,7, 430,2,1,0,0,0,0,675, 3,300000,3,3,128,8,500 +"5",194,659,2,0,0,28,612,3844,3819,147,5,1,174,3,79,1,7, 415,2,1,0,0,0,0,691, 3,300000,3,3,128,8,500 +"5",208,659,3,0,0,28,613,3844,3819,54,5,1,174,2,79,2,7, 413,2,1,0,0,0,0,693, 3,300000,3,3,128,8,500 +"5",196,659,2,0,0,28,612,3844,3819,147,5,1,174,3,79,1,7, 414,2,1,0,0,0,0,691, 3,300000,3,3,128,8,500 +"5",488,660,2,0,0,30,611,3844,3818,147,5,2,173,3,79,1,7, 385,2,1,0,0,0,0,720, 3,300000,3,3,128,8,500 +"5",44,660,2,0,0,28,613,3844,3818,148,5,2,175,3,77,1,7, 431,2,1,0,0,0,0,675, 3,300000,3,3,128,8,500 +"5",145,660,2,0,0,28,613,3844,3818,148,5,1,174,4,78,1,7, 419,2,1,0,0,0,0,686, 3,300000,3,3,128,8,500 +"5",162,660,2,0,0,28,613,3844,3819,147,5,1,175,4,78,1,7, 418,2,1,0,0,0,0,688, 3,300000,3,3,128,8,500 +"5",352,660,2,0,0,28,613,3844,3819,147,5,1,87,4,166,1,7, 399,2,1,0,0,0,0,707, 3,300000,3,3,128,8,500 +"5",201,660,3,0,0,29,613,3844,3819,148,5,2,175,2,78,2,7, 414,2,1,0,0,0,0,692, 3,300000,3,3,128,8,500 +"5",254,660,1,0,0,27,613,3844,3818,58,5,2,175,3,77,2,7, 409,2,1,0,0,0,0,696, 3,300000,3,3,128,8,500 +"5",128,660,2,0,0,29,613,3844,3819,17,5,1,175,3,78,2,6, 421,2,1,0,0,0,0,685, 3,300000,3,3,128,8,500 +"5",486,660,3,0,0,28,613,3844,3819,147,5,1,172,3,80,1,7, 385,2,1,0,0,0,0,720, 3,300000,3,3,128,8,500 +"5",210,660,1,0,0,26,614,3844,3818,148,5,1,174,4,79,1,7, 413,2,1,0,0,0,0,692, 3,300000,3,3,128,8,500 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3,300000,3,3,128,8,500 +"5",474,660,1,0,0,28,613,3844,3819,148,5,2,178,4,75,2,7, 386,2,1,0,0,0,0,719, 3,300000,3,3,128,8,500 +"5",431,661,3,0,0,28,613,3844,3819,147,4,2,171,3,81,1,7, 390,2,1,0,0,0,0,715, 3,300000,3,3,128,8,500 +"5",27,660,2,0,0,29,613,3844,3819,147,5,2,176,3,77,1,7, 433,2,1,0,0,0,0,673, 3,300000,3,3,128,8,500 +"5",460,660,3,0,0,30,612,3844,3819,147,5,2,175,2,78,2,7, 388,2,1,0,0,0,0,718, 3,300000,3,3,128,8,500 +"5",432,661,2,0,0,29,613,3844,3819,27,5,1,171,4,81,1,7, 390,2,1,0,0,0,0,715, 3,300000,3,3,128,8,500 +"5",74,661,2,0,0,28,614,3844,3819,148,5,2,174,2,78,2,7, 427,2,1,0,0,0,0,679, 3,300000,3,3,128,8,500 +"5",215,661,2,0,0,29,613,3844,3819,147,5,1,173,3,80,1,7, 413,2,1,0,0,0,0,693, 3,300000,3,3,128,8,500 +"5",408,661,1,0,0,29,614,3844,3818,149,4,2,87,4,166,1,7, 393,2,1,0,0,0,0,712, 3,300000,3,3,128,8,500 +"5",422,660,2,0,0,27,615,3844,3819,147,4,1,88,2,165,1,7, 391,2,1,0,0,0,0,715, 3,300000,3,3,128,8,500 +"5",350,660,2,0,0,29,613,3844,3818,58,5,2,81,4,172,0,7, 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3,300000,3,3,128,8,500 +"5",238,661,2,0,0,28,614,3844,3818,58,5,2,175,3,78,1,7, 411,2,1,0,0,0,0,695, 3,300000,3,3,128,8,500 +"5",501,661,3,0,0,28,614,3844,3818,148,5,1,175,1,78,2,7, 384,2,1,0,0,0,0,722, 3,300000,3,3,128,8,500 +"5",509,662,2,0,0,30,614,3844,3819,148,5,2,177,2,75,2,7, 383,2,1,0,0,0,1,722, 3,300000,3,3,128,8,500 +"5",336,662,2,0,0,27,615,3844,3818,54,4,1,172,3,81,1,7, 400,2,1,0,0,0,0,706, 3,300000,3,3,128,8,500 +"5",139,661,2,0,0,27,615,3844,3819,148,5,2,175,2,79,1,7, 420,2,1,0,0,0,0,686, 3,300000,3,3,128,8,500 +"5",184,662,2,0,0,29,615,3844,3818,147,5,2,173,3,80,1,7, 416,2,1,0,0,0,0,690, 3,300000,3,3,128,8,500 +"5",25,662,2,0,0,27,616,3844,3819,147,5,2,175,3,78,2,7, 433,2,1,0,0,0,0,673, 3,300000,3,3,128,8,500 +"5",485,661,2,0,0,28,615,3844,3819,147,5,1,173,2,80,1,7, 385,2,1,0,0,0,0,721, 3,300000,3,3,128,8,500 +"5",502,662,3,0,0,27,616,3844,3819,147,5,1,175,2,77,2,7, 384,2,1,0,0,0,1,722, 3,300000,3,3,128,8,500 +"5",122,662,3,0,0,27,616,3844,3818,148,5,2,171,2,82,2,7, 422,2,1,0,0,0,0,683, 3,300000,3,3,128,8,500 +"5",507,662,3,0,0,28,615,3844,3819,147,5,2,177,1,75,2,7, 383,2,1,0,0,0,0,722, 3,300000,3,3,128,8,500 +"5",37,663,3,0,0,28,615,3844,3818,147,5,1,179,2,73,2,7, 432,2,1,0,0,0,0,673, 3,300000,3,3,128,8,500 +"5",467,662,3,0,0,27,615,3844,3818,148,5,1,172,3,81,1,7, 387,2,1,0,0,0,0,719, 3,300000,3,3,128,8,500 +"5",269,662,2,0,0,28,615,3844,3818,13,5,2,176,3,77,1,7, 408,2,1,0,0,0,0,698, 3,300000,3,3,128,8,500 +"5",492,662,2,0,0,28,616,3844,3818,147,4,2,173,2,80,2,7, 385,2,1,0,0,0,0,721, 3,300000,3,3,128,8,500 +"5",445,662,2,0,0,26,616,3844,3819,147,5,2,172,2,80,2,7, 389,2,1,0,0,0,0,716, 3,300000,3,3,128,8,500 +"5",405,662,2,0,0,29,614,3844,3818,148,5,1,73,3,180,1,7, 393,2,1,0,0,0,0,713, 3,300000,3,3,128,8,500 +"5",391,662,2,0,0,29,615,3844,3818,148,5,1,80,3,173,1,7, 395,2,1,0,0,0,0,711, 3,300000,3,3,128,8,500 +"5",304,663,2,0,0,29,615,3844,3819,5,5,1,173,4,79,1,7, 403,2,1,0,0,0,0,702, 3,300000,3,3,128,8,500 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3,300000,3,3,128,8,500 +"5",126,663,2,0,0,26,617,3844,3818,57,5,2,174,3,79,2,7, 422,2,1,0,0,0,0,684, 3,300000,3,3,128,8,500 +"5",419,662,3,0,0,28,616,3844,3818,147,4,1,79,2,174,1,7, 391,2,1,0,0,0,0,715, 3,300000,3,3,128,8,500 +"5",45,662,3,0,0,29,616,3844,3818,147,5,2,176,1,78,1,7, 431,2,1,0,0,0,0,676, 3,300000,3,3,128,8,500 +"5",255,664,2,0,0,28,616,3844,3819,148,5,2,174,3,79,2,7, 409,2,1,0,0,0,0,696, 3,300000,3,3,128,8,500 +"5",262,664,3,0,0,30,615,3844,3819,62,5,1,178,3,74,1,7, 409,2,1,0,0,0,0,696, 3,300000,3,3,128,8,500 +"5",76,664,3,0,0,29,616,3844,3819,147,5,2,174,2,78,2,7, 427,2,1,0,0,0,0,679, 3,300000,3,3,128,8,500 +"5",332,664,2,0,0,27,617,3844,3819,148,5,2,173,4,80,1,7, 401,2,1,0,0,0,0,705, 3,300000,3,3,128,8,500 +"5",361,664,2,0,0,29,616,3844,3819,147,4,2,76,4,177,0,7, 398,2,1,0,0,0,1,708, 3,300000,3,3,128,8,500 +"5",19,665,2,0,0,27,618,3844,3819,148,5,1,175,4,77,1,7, 433,2,1,0,0,0,0,672, 3,300000,3,3,128,8,500 +"5",270,664,3,0,0,26,619,3844,3818,58,5,2,174,3,78,1,7, 408,2,1,0,0,0,0,698, 3,300000,3,3,128,8,500 +"5",181,665,2,0,0,28,618,3844,3819,148,5,1,173,4,80,1,7, 416,2,1,0,0,0,0,690, 3,300000,3,3,128,8,500 +"5",404,665,3,0,0,29,618,3844,3818,148,5,1,88,3,165,1,7, 394,2,1,0,0,0,0,712, 3,300000,3,3,128,8,500 +"5",320,666,2,0,0,29,618,3844,3819,148,5,1,172,4,80,1,7, 402,2,1,0,0,0,0,704, 3,300000,3,3,128,8,500 +"5",307,667,3,0,0,29,618,3844,3819,148,5,1,171,3,81,1,7, 403,2,1,0,0,0,0,702, 3,300000,3,3,128,8,500 +"5",479,667,1,0,0,29,619,3844,3819,148,5,2,176,4,77,2,7, 386,2,1,0,0,0,0,720, 3,300000,3,3,128,8,500 +"5",411,667,3,0,0,29,621,3844,3818,148,4,2,77,2,177,1,7, 393,2,1,0,0,0,0,714, 3,300000,3,3,128,8,500 +"5",93,668,3,0,0,29,619,3844,3818,148,4,2,174,2,78,2,7, 425,2,1,0,0,0,0,680, 3,300000,3,3,128,8,500 +"5",383,668,3,0,0,29,621,3844,3819,147,5,2,79,3,174,1,7, 396,2,1,0,0,0,0,710, 3,300000,3,3,128,8,500 +"5",88,670,2,0,0,27,624,3844,3818,148,4,2,174,3,78,2,7, 426,2,1,0,0,0,0,679, 3,300000,3,3,128,8,500 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3,300000,3,3,128,8,500 +"5",279,676,2,0,0,26,630,3844,3818,148,5,1,173,3,80,1,7, 407,2,1,0,0,0,0,699, 3,300000,3,3,128,8,500 +"5",148,691,3,0,0,31,641,3844,3819,148,5,1,174,2,79,1,7, 419,2,1,0,0,0,0,686, 3,300000,3,3,128,8,500 +"5",344,705,0,0,0,18,668,3844,3819,148,5,1,47,7,206,0,7, 400,2,1,0,0,0,0,706, 3,300000,3,3,128,8,500 +"5",118,707,0,0,0,19,670,3843,3819,148,4,1,49,5,204,1,7, 423,2,1,0,0,0,0,684, 3,300000,3,3,128,8,500 +"5",165,707,0,0,0,18,672,3843,3819,147,5,1,53,5,201,1,7, 418,2,1,0,0,0,0,689, 3,300000,3,3,128,8,500 +"5",142,708,0,0,0,19,671,3844,3819,58,5,1,49,5,205,1,7, 420,2,1,0,0,0,0,687, 3,300000,3,3,128,8,500 +"5",167,711,0,0,0,18,674,3843,3819,147,5,1,49,5,205,1,7, 418,2,1,0,0,0,0,689, 3,300000,3,3,128,8,500 +"5",92,714,2,0,0,26,667,3844,3819,6,4,2,152,3,100,2,7, 425,2,1,0,0,0,0,680, 3,300000,3,3,128,8,500 +"5",456,713,2,0,0,31,664,3844,3819,148,4,2,152,3,101,1,7, 388,2,1,0,0,0,0,718, 3,300000,3,3,128,8,500 +"5",192,713,2,0,0,27,667,3844,3819,148,5,1,173,3,80,1,7, 415,2,1,0,0,0,0,692, 3,300000,3,3,128,8,500 +"5",284,713,2,0,0,27,667,3844,3819,6,5,2,149,3,104,1,7, 406,2,1,0,0,0,0,700, 3,300000,3,3,128,8,500 +"5",220,713,2,0,0,27,668,3844,3818,6,5,2,52,2,202,1,7, 412,2,1,0,0,0,0,694, 3,300000,3,3,128,8,500 +"5",149,714,0,0,0,20,675,3843,3820,148,5,1,52,6,201,1,7, 419,2,1,0,0,0,0,687, 3,300000,3,3,128,8,500 +"5",342,713,0,0,0,19,675,3844,3819,148,4,1,48,5,206,1,7, 400,2,1,0,0,0,0,708, 3,300000,3,3,128,8,500 +"5",402,714,2,0,0,27,668,3844,3819,148,5,1,62,3,191,1,7, 394,2,1,0,0,0,0,713, 3,300000,3,3,128,8,500 +"5",482,714,2,0,0,28,667,3844,3818,147,5,1,149,2,104,1,7, 386,2,1,0,0,0,0,721, 3,300000,3,3,128,8,500 +"5",299,715,2,0,0,27,669,3844,3818,147,5,2,154,3,99,1,7, 404,2,1,0,0,0,0,702, 3,300000,3,3,128,8,500 +"5",15,714,3,0,0,24,671,3844,3818,148,5,2,154,2,99,1,7, 434,2,1,0,0,0,0,673, 3,300000,3,3,128,8,500 +"5",228,715,2,0,0,26,670,3844,3818,148,5,1,150,3,103,1,7, 412,2,1,0,0,0,0,695, 3,300000,3,3,128,8,500 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3,300000,3,3,128,8,500 +"5",7,717,2,0,0,26,671,3844,3819,148,5,1,157,4,95,1,7, 434,2,1,0,0,0,0,671, 3,300000,3,3,128,8,500 +"5",127,716,2,0,0,27,670,3844,3818,147,5,2,150,2,103,1,7, 422,2,1,0,0,0,0,684, 3,300000,3,3,128,8,500 +"5",58,716,2,0,0,28,669,3844,3819,148,5,2,150,4,103,1,7, 429,2,1,0,0,0,0,677, 3,300000,3,3,128,8,500 +"5",292,715,1,0,0,29,669,3844,3819,147,5,1,158,3,96,1,7, 405,2,1,0,0,0,0,702, 3,300000,3,3,128,8,500 +"5",233,716,3,0,0,28,670,3844,3818,147,5,2,151,2,102,2,7, 411,2,1,0,0,0,0,695, 3,300000,3,3,128,8,500 +"5",133,716,2,0,0,29,670,3844,3819,149,5,1,153,2,100,1,7, 421,2,1,0,0,0,0,685, 3,300000,3,3,128,8,500 +"5",331,716,3,0,0,28,669,3844,3818,147,5,2,152,3,101,1,7, 401,2,1,0,0,0,0,705, 3,300000,3,3,128,8,500 +"5",71,717,2,0,0,26,670,3844,3819,147,5,1,152,3,101,1,7, 427,2,1,0,0,0,0,679, 3,300000,3,3,128,8,500 +"5",455,716,2,0,0,28,670,3844,3819,147,5,1,149,3,103,1,7, 388,2,1,0,0,0,0,718, 3,300000,3,3,128,8,500 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387,2,1,0,0,0,0,719, 3,300000,3,3,128,8,500 +"5",198,717,3,0,0,29,669,3844,3819,147,5,1,152,2,102,1,7, 414,2,1,0,0,0,0,692, 3,300000,3,3,128,8,500 +"5",230,717,2,0,0,28,670,3844,3819,147,5,1,151,3,103,1,7, 412,2,1,0,0,0,0,695, 3,300000,3,3,128,8,500 +"5",53,717,2,0,0,28,671,3844,3819,147,5,1,155,3,98,1,7, 430,2,1,0,0,0,0,676, 3,300000,3,3,128,8,500 +"5",425,717,3,0,0,27,671,3844,3819,147,4,2,48,3,205,1,7, 391,2,1,0,0,0,0,715, 3,300000,3,3,128,8,500 +"5",377,717,2,0,0,29,670,3844,3819,148,5,2,49,4,205,1,7, 397,2,1,0,0,0,0,710, 3,300000,3,3,128,8,500 +"5",453,717,3,0,0,29,670,3844,3818,147,5,1,150,2,103,1,7, 388,2,1,0,0,0,0,718, 3,300000,3,3,128,8,500 +"5",395,717,3,0,0,27,671,3844,3818,148,5,2,50,2,204,1,7, 394,2,1,0,0,0,0,712, 3,300000,3,3,128,8,500 +"5",306,716,4,0,0,29,670,3844,3819,147,5,1,155,1,98,1,7, 403,2,1,0,0,0,1,703, 3,300000,3,3,128,8,500 +"5",209,717,2,0,0,27,672,3844,3819,148,5,1,174,2,79,1,7, 413,2,1,0,0,0,1,693, 3,300000,3,3,128,8,500 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3,300000,3,3,128,8,500 +"5",343,718,3,0,0,26,672,3844,3818,148,4,1,149,3,104,1,7, 399,2,1,0,0,0,0,706, 3,300000,3,3,128,8,500 +"5",186,716,2,0,0,28,671,3844,3819,147,5,2,153,1,100,1,7, 415,2,1,0,0,0,1,691, 3,300000,3,3,128,8,500 +"5",370,718,2,0,0,27,671,3844,3818,148,5,1,58,3,195,0,7, 397,2,1,0,0,0,0,709, 3,300000,3,3,128,8,500 +"5",398,718,2,0,0,28,672,3844,3819,57,5,2,60,3,194,1,7, 394,2,1,0,0,0,0,713, 3,300000,3,3,128,8,500 +"5",390,718,2,0,0,28,671,3844,3818,61,5,1,61,3,192,1,7, 395,2,1,0,0,0,0,712, 3,300000,3,3,128,8,500 +"5",406,718,2,0,0,28,672,3844,3819,148,5,1,60,4,193,1,7, 393,2,1,0,0,0,0,713, 3,300000,3,3,128,8,500 +"5",450,718,2,0,0,28,672,3844,3819,147,5,1,151,3,102,1,7, 388,2,1,0,0,0,0,718, 3,300000,3,3,128,8,500 +"5",157,718,2,0,0,27,671,3844,3818,148,5,2,150,3,103,1,7, 419,2,1,0,0,0,0,687, 3,300000,3,3,128,8,500 +"5",116,718,2,0,0,27,672,3844,3818,148,5,1,48,3,206,1,7, 423,2,1,0,0,0,0,683, 3,300000,3,3,128,8,500 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425,2,1,0,0,0,0,682, 3,300000,3,3,128,8,500 +"5",454,718,3,0,0,30,670,3844,3819,148,5,1,151,2,102,1,7, 388,2,1,0,0,0,0,718, 3,300000,3,3,128,8,500 +"5",245,718,2,0,0,28,672,3844,3818,148,5,1,154,3,99,1,7, 410,2,1,0,0,0,0,696, 3,300000,3,3,128,8,500 +"5",363,718,3,0,0,29,670,3844,3818,147,4,2,52,2,202,1,7, 398,2,1,0,0,0,0,709, 3,300000,3,3,128,8,500 +"5",117,718,3,0,0,26,673,3844,3819,148,5,1,63,2,190,1,7, 423,2,1,0,0,0,0,684, 3,300000,3,3,128,8,500 +"5",249,718,2,0,0,29,672,3844,3819,148,5,2,152,2,101,1,7, 410,2,1,0,0,0,0,696, 3,300000,3,3,128,8,500 +"5",465,719,3,0,0,28,671,3844,3819,148,5,1,151,3,102,1,7, 387,2,1,0,0,0,0,719, 3,300000,3,3,128,8,500 +"5",278,718,2,0,0,28,672,3844,3818,147,5,1,154,2,99,1,7, 407,2,1,0,0,0,0,699, 3,300000,3,3,128,8,500 +"5",47,719,3,0,0,29,671,3844,3818,147,4,2,151,3,102,1,7, 430,2,1,0,0,0,0,675, 3,300000,3,3,128,8,500 +"5",70,719,3,0,0,28,672,3844,3818,147,5,1,152,3,101,1,7, 427,2,1,0,0,0,0,679, 3,300000,3,3,128,8,500 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3,300000,3,3,128,8,500 +"5",400,719,3,0,0,28,673,3844,3818,53,5,1,61,2,192,1,7, 394,2,1,0,0,0,0,712, 3,300000,3,3,128,8,500 +"5",469,719,1,0,0,26,674,3844,3819,148,5,1,151,4,102,1,7, 387,2,1,0,0,0,0,719, 3,300000,3,3,128,8,500 +"5",246,719,2,0,0,27,673,3844,3819,148,5,1,153,3,100,1,7, 410,2,1,0,0,0,0,696, 3,300000,3,3,128,8,500 +"5",141,720,2,0,0,28,672,3844,3819,62,5,2,152,4,101,1,7, 420,2,1,0,0,0,0,686, 3,300000,3,3,128,8,500 +"5",477,719,3,0,0,29,672,3844,3818,148,5,2,157,2,96,2,7, 386,2,1,0,0,0,0,720, 3,300000,3,3,128,8,500 +"5",498,719,3,0,0,30,671,3844,3818,147,5,1,155,2,98,2,7, 384,2,1,0,0,0,0,722, 3,300000,3,3,128,8,500 +"5",31,719,2,0,0,29,673,3844,3819,148,5,2,151,4,102,1,7, 432,2,1,0,0,0,0,674, 3,300000,3,3,128,8,500 +"5",372,719,2,0,0,28,673,3844,3818,147,5,1,59,4,194,1,7, 397,2,1,0,0,0,0,709, 3,300000,3,3,128,8,500 +"5",459,719,2,0,0,28,673,3844,3818,147,4,2,153,2,100,2,7, 388,2,1,0,0,0,0,718, 3,300000,3,3,128,8,500 +"5",355,719,2,0,0,30,671,3844,3818,147,5,1,52,4,201,1,7, 398,2,1,0,0,0,0,708, 3,300000,3,3,128,8,500 +"5",435,720,2,0,0,28,673,3844,3819,148,5,1,151,3,102,2,7, 390,2,1,0,0,0,0,716, 3,300000,3,3,128,8,500 +"5",324,719,2,0,0,28,673,3844,3819,148,5,1,154,3,100,1,7, 401,2,1,0,0,0,0,705, 3,300000,3,3,128,8,500 +"5",483,720,2,0,0,28,673,3844,3818,147,5,1,151,3,102,1,7, 385,2,1,0,0,0,0,721, 3,300000,3,3,128,8,500 +"5",288,720,2,0,0,30,672,3844,3819,147,5,1,151,4,101,1,7, 405,2,1,0,0,0,0,701, 3,300000,3,3,128,8,500 +"5",189,720,2,0,0,27,674,3844,3818,147,5,2,152,3,101,1,7, 415,2,1,0,0,0,0,691, 3,300000,3,3,128,8,500 +"5",175,719,3,0,0,28,673,3844,3819,147,5,2,149,1,104,1,7, 417,2,1,0,0,0,1,690, 3,300000,3,3,128,8,500 +"5",451,720,2,0,0,28,673,3844,3818,147,5,1,150,3,103,1,7, 388,2,1,0,0,0,0,718, 3,300000,3,3,128,8,500 +"5",242,719,3,0,0,28,674,3844,3819,148,5,1,153,1,101,1,7, 410,2,1,0,0,0,0,697, 3,300000,3,3,128,8,500 +"5",489,718,1,0,0,28,674,3844,3818,147,4,2,152,2,102,2,7, 385,2,1,0,0,0,0,722, 3,300000,3,3,128,8,500 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3,300000,3,3,128,8,500 +"5",229,719,2,0,0,27,674,3844,3819,147,5,1,150,3,103,1,7, 412,2,1,0,0,0,0,695, 3,300000,3,3,128,8,500 +"5",449,720,2,0,0,28,674,3844,3819,147,5,1,152,3,101,1,7, 389,2,1,0,0,0,0,717, 3,300000,3,3,128,8,500 +"5",120,720,2,0,0,27,674,3844,3819,147,5,2,149,3,104,1,7, 423,2,1,0,0,0,0,683, 3,300000,3,3,128,8,500 +"5",61,720,2,0,0,27,674,3844,3818,147,5,2,151,3,102,1,7, 429,2,1,0,0,0,0,677, 3,300000,3,3,128,8,500 +"5",172,719,2,0,0,27,674,3844,3819,147,5,2,152,2,103,1,7, 417,2,1,0,0,0,0,691, 3,300000,3,3,128,8,500 +"5",285,719,2,0,0,28,673,3844,3819,148,5,2,151,3,103,1,7, 406,2,1,0,0,0,0,700, 3,300000,3,3,128,8,500 +"5",457,720,2,0,0,27,674,3844,3818,148,4,2,150,3,103,1,7, 388,2,1,0,0,0,0,718, 3,300000,3,3,128,8,500 +"5",49,720,2,0,0,27,674,3844,3819,147,5,1,155,3,98,1,7, 430,2,1,0,0,0,0,676, 3,300000,3,3,128,8,500 +"5",114,720,2,0,0,26,674,3844,3819,148,4,1,46,4,207,1,7, 423,2,1,0,0,0,0,683, 3,300000,3,3,128,8,500 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3,300000,3,3,128,8,500 +"5",176,720,2,0,0,29,672,3844,3819,28,5,1,152,3,101,1,7, 416,2,1,0,0,0,0,690, 3,300000,3,3,128,8,500 +"5",392,721,2,0,0,29,674,3844,3819,31,5,2,59,3,194,1,7, 395,2,1,0,0,0,0,711, 3,300000,3,3,128,8,500 +"5",39,721,2,0,0,28,674,3844,3819,148,5,1,158,3,95,1,7, 432,2,1,0,0,0,0,675, 3,300000,3,3,128,8,500 +"5",470,721,2,0,0,28,674,3844,3819,148,5,1,153,3,100,1,7, 387,2,1,0,0,0,0,719, 3,300000,3,3,128,8,500 +"5",491,721,2,0,0,28,674,3844,3819,147,4,2,152,3,101,2,7, 385,2,1,0,0,0,0,721, 3,300000,3,3,128,8,500 +"5",121,721,2,0,0,27,675,3844,3819,148,5,2,149,3,104,2,7, 423,2,1,0,0,0,0,684, 3,300000,3,3,128,8,500 +"5",414,721,2,0,0,27,674,3844,3819,58,4,2,58,3,196,1,7, 392,2,1,0,0,0,0,714, 3,300000,3,3,128,8,500 +"5",283,721,2,0,0,28,673,3844,3819,148,5,2,147,3,106,1,7, 407,2,1,0,0,0,0,700, 3,300000,3,3,128,8,500 +"5",14,721,2,0,0,25,677,3844,3819,57,5,2,153,3,100,1,7, 434,2,1,0,0,0,0,672, 3,300000,3,3,128,8,500 +"5",23,722,2,0,0,29,674,3844,3818,147,5,1,155,3,98,1,7, 433,2,1,0,0,0,0,673, 3,300000,3,3,128,8,500 +"5",281,721,2,0,0,29,674,3844,3819,148,5,2,150,3,103,1,7, 407,2,1,0,0,0,0,700, 3,300000,3,3,128,8,500 +"5",497,722,2,0,0,28,675,3844,3818,147,5,1,153,3,100,2,7, 384,2,1,0,0,0,0,722, 3,300000,3,3,128,8,500 +"5",418,721,2,0,0,27,675,3844,3819,148,4,1,61,3,192,1,7, 391,2,1,0,0,0,0,715, 3,300000,3,3,128,8,500 +"5",156,720,2,0,0,29,674,3844,3819,6,5,2,152,2,102,1,7, 419,2,1,0,0,0,1,688, 3,300000,3,3,128,8,500 +"5",328,722,2,0,0,28,675,3844,3818,147,5,2,152,3,101,1,7, 401,2,1,0,0,0,0,705, 3,300000,3,3,128,8,500 +"5",99,722,2,0,0,27,675,3844,3818,147,5,1,151,3,102,1,7, 425,2,1,0,0,0,0,681, 3,300000,3,3,128,8,500 +"5",271,722,3,0,0,28,675,3844,3819,148,5,2,154,3,99,1,7, 408,2,1,0,0,0,0,698, 3,300000,3,3,128,8,500 +"5",28,722,2,0,0,30,673,3844,3819,6,5,2,154,3,99,1,7, 433,2,1,0,0,0,0,673, 3,300000,3,3,128,8,500 +"5",442,721,3,0,0,29,675,3844,3819,148,5,2,153,2,100,2,7, 390,2,1,0,0,0,0,717, 3,300000,3,3,128,8,500 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400,2,1,0,0,0,0,706, 3,300000,3,3,128,8,500 +"5",410,722,1,0,0,29,675,3844,3819,148,5,2,58,4,195,1,7, 393,2,1,0,0,0,0,713, 3,300000,3,3,128,8,500 +"5",168,722,2,0,0,27,676,3844,3818,147,5,2,53,2,200,0,7, 418,2,1,0,0,0,0,689, 3,300000,3,3,128,8,500 +"5",409,722,2,0,0,29,676,3844,3818,148,4,2,49,3,204,1,7, 393,2,1,0,0,0,0,713, 3,300000,3,3,128,8,500 +"5",200,722,2,0,0,27,677,3844,3818,147,5,2,151,2,102,1,7, 414,2,1,0,0,0,0,692, 3,300000,3,3,128,8,500 +"5",428,722,2,0,0,31,674,3844,3819,148,4,2,61,2,192,1,7, 391,2,1,0,0,0,0,716, 3,300000,3,3,128,8,500 +"5",356,722,2,0,0,28,676,3844,3818,148,5,1,58,3,195,0,7, 398,2,1,0,0,0,1,708, 3,300000,3,3,128,8,500 +"5",394,722,2,0,0,30,674,3844,3818,148,5,2,59,3,194,1,7, 394,2,1,0,0,0,0,712, 3,300000,3,3,128,8,500 +"5",135,723,3,0,0,30,675,3844,3819,149,5,1,153,2,100,1,7, 421,2,1,0,0,0,0,685, 3,300000,3,3,128,8,500 +"5",323,723,3,0,0,29,675,3844,3819,147,5,1,153,3,100,1,7, 401,2,1,0,0,0,0,705, 3,300000,3,3,128,8,500 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3,300000,3,3,128,8,500 +"5",315,724,3,0,0,29,677,3844,3818,148,5,2,151,2,103,1,7, 402,2,1,0,0,0,0,704, 3,300000,3,3,128,8,500 +"5",351,725,2,0,0,30,676,3844,3818,148,5,2,38,4,215,1,7, 399,2,1,0,0,0,0,707, 3,300000,3,3,128,8,500 +"5",264,725,2,0,0,27,679,3844,3818,31,5,2,157,3,96,1,7, 409,2,1,0,0,0,0,697, 3,300000,3,3,128,8,500 +"5",327,725,2,0,0,30,676,3844,3819,148,5,1,151,3,102,1,7, 401,2,1,0,0,0,0,705, 3,300000,3,3,128,8,500 +"5",314,725,4,0,0,29,677,3844,3818,148,5,2,149,2,104,0,7, 403,2,1,0,0,0,0,704, 3,300000,3,3,128,8,500 +"5",226,725,3,0,0,32,675,3844,3819,147,5,1,150,2,103,1,7, 412,2,1,0,0,0,0,694, 3,300000,3,3,128,8,500 +"5",268,725,3,0,0,31,676,3844,3818,149,5,2,151,1,103,1,7, 408,2,1,0,0,0,0,699, 3,300000,3,3,128,8,500 +"5",280,726,3,0,0,30,677,3844,3819,148,5,2,151,2,102,1,7, 407,2,1,0,0,0,0,699, 3,300000,3,3,128,8,500 +"5",347,725,3,0,0,28,679,3844,3819,148,5,2,48,2,207,1,7, 399,2,1,0,0,0,0,708, 3,300000,3,3,128,8,500 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+"6",510,277,2,0,0,109,61,1592,1574,82,2,5,20,3,81,17,81, 400,2,2,0,0,0,1,286, 3,150000,3,4,128,8,500 +"6",47,277,2,0,0,108,62,1592,1574,71,2,5,48,4,53,2,94, 398,3,2,0,0,0,1,288, 3,150000,3,4,128,8,500 +"6",744,277,2,0,0,106,64,1592,1574,82,2,5,50,2,51,3,94, 399,2,2,0,0,0,1,287, 3,150000,3,4,128,8,500 +"6",955,277,2,0,0,106,64,1592,1574,82,2,5,27,3,74,3,94, 400,2,2,0,0,0,1,287, 3,150000,3,4,128,8,500 +"6",99,277,2,0,0,104,66,1592,1574,82,2,5,43,5,58,0,94, 397,2,2,0,0,0,0,289, 3,150000,3,4,128,8,500 +"6",634,277,2,0,0,104,66,1592,1574,82,2,5,16,3,85,4,94, 400,3,2,0,0,0,0,287, 3,150000,3,4,128,8,500 +"6",97,278,1,0,0,104,66,1592,1574,78,2,5,42,6,59,1,94, 397,2,2,0,0,0,0,290, 3,150000,3,4,128,8,500 +"6",942,278,1,0,0,108,63,1592,1574,73,2,5,32,3,68,4,94, 399,2,2,0,0,0,1,286, 3,150000,3,4,128,8,500 +"6",535,278,3,0,0,106,64,1592,1574,82,2,5,26,3,75,1,94, 397,2,2,0,0,0,1,289, 3,150000,3,4,128,8,500 +"6",198,276,2,0,0,103,67,1592,1574,83,2,5,12,3,91,0,94, 398,2,2,0,0,0,1,291, 3,150000,3,4,128,8,500 +"6",734,281,2,0,0,113,59,1592,1574,82,2,5,49,4,50,4,94, 399,2,2,0,0,0,0,286, 3,150000,3,4,128,8,500 +"6",195,279,2,0,0,105,67,1592,1574,60,2,5,21,6,80,1,94, 397,2,2,0,0,0,1,289, 3,150000,3,4,128,8,500 +"6",85,279,3,0,0,104,67,1592,1574,82,2,5,39,5,63,0,94, 397,2,2,0,0,0,1,290, 3,150000,3,4,128,8,500 +"6",866,279,2,0,0,106,66,1592,1574,82,2,5,34,2,68,3,94, 398,2,2,0,0,0,1,289, 3,150000,3,4,128,8,500 +"6",468,280,2,0,0,105,67,1593,1574,82,2,5,30,4,71,2,94, 398,2,2,0,0,0,1,289, 3,150000,3,4,128,8,500 +"6",855,280,1,0,0,104,68,1592,1574,82,2,5,47,5,54,2,94, 398,2,2,0,0,0,1,288, 3,150000,3,4,128,8,500 +"6",565,279,2,0,0,106,67,1592,1574,82,2,5,35,3,67,2,94, 399,2,2,0,0,0,1,289, 3,150000,3,4,128,8,500 +"6",28,281,2,0,0,105,67,1592,1574,82,2,4,30,5,71,2,94, 397,2,2,0,0,0,1,289, 3,150000,3,4,128,8,500 +"6",330,281,1,0,0,106,67,1592,1574,58,2,5,37,3,63,3,94, 398,2,2,0,0,0,1,289, 3,150000,3,4,128,8,500 +"6",113,279,2,1,0,106,67,1592,1574,66,2,5,35,3,68,0,94, 398,2,2,0,0,0,0,291, 3,150000,3,4,128,8,500 +"6",426,280,1,0,0,106,67,1592,1574,82,2,5,20,3,83,2,94, 398,3,2,0,0,0,1,291, 3,150000,3,4,128,8,500 +"6",368,281,1,0,0,109,67,1592,1574,70,2,5,16,3,86,2,94, 400,2,2,0,0,0,1,289, 3,150000,3,4,128,8,500 +"6",284,284,2,0,0,110,67,1592,1574,82,2,4,22,4,79,2,94, 397,2,2,0,0,0,1,289, 3,150000,3,4,128,8,500 +"6",962,285,1,0,0,111,67,1592,1574,78,2,5,34,3,68,2,94, 397,2,2,0,0,0,1,290, 3,150000,3,4,128,8,500 diff --git a/hpc/archer2/slurm/strong_1_nodes_fft.slurm b/hpc/archer2/slurm/strong_1_nodes_fft.slurm index 76083463..71201b92 100644 --- a/hpc/archer2/slurm/strong_1_nodes_fft.slurm +++ b/hpc/archer2/slurm/strong_1_nodes_fft.slurm @@ -35,11 +35,12 @@ n_samples=500 block_size=128 # Set parameters for strong scaling run -n_points=(9600000 4800000 2400000 1200000 600000 300000 150000 75000) -local_depth=(5 4 4 4 3 3 3 3) -n_tasks=(16 32 64 128 256 512 1024 2048) # Equal to the number of local trees +n_points=(9600000 4800000 2400000 1200000 600000 300000 150000 75000) +# local_depth=(5 4 4 4 3 3 3 3) slightly unbalanced +local_depth=(5 5 5 4 4 4 3 2) +n_tasks=(16 32 64 128 256 512 1024 2048) # Equal to the number of local trees n_nodes=(1 2 4 8 16 32 64 128) -n_threads=8 # See if bandwidth saturates with different threading parameters for Rayon thread pool +n_threads=8 cpus_per_task=8 # Create a scratch directory for this run From a22bf23ebc1a42ec9b40fc43e892e9ccb444e8e2 Mon Sep 17 00:00:00 2001 From: srinath kailasa Date: Fri, 3 Oct 2025 10:53:01 +0100 Subject: [PATCH 15/52] Update weak scaling scripts --- hpc/archer2/slurm/weak_1_fft.slurm | 7 ++-- hpc/archer2/slurm/weak_2_fft.slurm | 66 ++++++++++++++---------------- hpc/archer2/slurm/weak_3_fft.slurm | 63 ++++++++++++++-------------- 3 files changed, 65 insertions(+), 71 deletions(-) diff --git a/hpc/archer2/slurm/weak_1_fft.slurm b/hpc/archer2/slurm/weak_1_fft.slurm index 8594c607..0fc9ec63 100644 --- a/hpc/archer2/slurm/weak_1_fft.slurm +++ b/hpc/archer2/slurm/weak_1_fft.slurm @@ -7,7 +7,7 @@ #SBATCH --nodes=16 #SBATCH --ntasks-per-node=32 #SBATCH --cpus-per-task=4 -#SBATCH --contiguous +##SBATCH --contiguous #SBATCH --account=e738 #SBATCH --partition=standard @@ -38,10 +38,11 @@ n_tasks=(8 16 32 64 128 256 512) n_nodes=(1 1 1 2 4 8 16) n_threads=4 # See if bandwidth saturates with different threading parameters for Rayon thread pool cpus_per_task=4 +max_points=$(echo {n_tasks[@]: -1} * ${n_points} | bc -l) # Create a scratch directory for this run last_nodes=${n_nodes[@]: -1} -export SCRATCH=${WORK}/weak_fft_n=${n_points}_p=${last_nodes}_${SLURM_JOBID} +export SCRATCH=${WORK}/weak_fft_n=${max_points}_p=${last_nodes}_${SLURM_JOBID} ## Load Spack #source $HOME/spack/share/spack/setup-env.sh @@ -86,5 +87,5 @@ for i in ${!n_tasks[@]}; do --local-depth $local_depth \ --n-samples $n_samples \ --block-size $block_size \ - --n-threads $n_threads >> ${OUTPUT} + --n-threads $n_threads >> ${OUTPUT} 2> ${SCRATCH}/err_run_${i}.log done diff --git a/hpc/archer2/slurm/weak_2_fft.slurm b/hpc/archer2/slurm/weak_2_fft.slurm index e331a1a2..67414493 100644 --- a/hpc/archer2/slurm/weak_2_fft.slurm +++ b/hpc/archer2/slurm/weak_2_fft.slurm @@ -1,23 +1,17 @@ #!/bin/bash -# Here, we perform a weak scaling run where the number of MPI processes is set equal to the number of CCX units, each of which -# share an L3 cache, conist of 4 processors. There are 16 CCX regions per processor, and 32 per node - -# Running on just a single node, we try and examine the scaling performance as the number of tasks is increased -# just to see what a single node is capable of (in the best parameter settings found) - -# This should give a good baseline of what to expect when communication costs are minimal +# Here, we perform a weak scaling run where the number of MPI processes are tied to CCX units, each of which +# share an L3 cache, consist of 4 processors. There are 16 CCX regions per processor, and 32 per node (i.e. two AMD EPYC Rome units per node) #SBATCH --job-name=weak_scaling_fft #SBATCH --time=00:30:00 #SBATCH --nodes=128 #SBATCH --ntasks-per-node=32 #SBATCH --cpus-per-task=4 -#SBATCH --contiguous +##SBATCH --contiguous #SBATCH --account=e738 #SBATCH --partition=standard #SBATCH --qos=standard -# Development environment for KiFMM # Restore AMD compiler env module load PrgEnv-aocc @@ -28,28 +22,6 @@ module load cray-mpich-ucx export HOME="/home/e738/e738/skailasa" export WORK="/work/e738/e738/skailasa" -# Create a scratch directory for this run -export SCRATCH=${WORK}/weak_${SLURM_JOBID} - -# Load Spack -source $HOME/spack/share/spack/setup-env.sh -. "$HOME/.cargo/env" - -# Load BLAS -spack load openblas - -# Ensure Rust can find the Cray libraries -# export RUSTFLAGS="-L $(echo $CRAY_LD_LIBRARY_PATH)" -# export LD_LIBRARY_PATH=$CRAY_LD_LIBRARY_PATH:$LD_LIBRARY_PATH -export RUSTFLAGS="-L $(spack location -i openblas)/lib" -export LD_LIBRARY_PATH=$(spack location -i openblas)/lib:$LD_LIBRARY_PATH - -mkdir -p ${SCRATCH} -cd ${SCRATCH} - -# Pass variable to SRUN from SBATCH -export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK - # Script being run, expected in the work directory script_name="fmm_m2l_fft_mpi_f32" @@ -58,7 +30,7 @@ expansion_order=3 n_points=250000 # points per MPI process (n_tasks) global_depth=(4 4 4) # Number of local roots local_depth=4 -n_samples=5000 +n_samples=500 block_size=128 # Set parameters for weak scaling run @@ -66,6 +38,31 @@ n_tasks=(1024 2048 4096) n_nodes=(32 64 128) n_threads=4 # See if bandwidth saturates with different threading parameters for Rayon thread pool cpus_per_task=4 +max_points=$(echo {n_tasks[@]: -1} * ${n_points} | bc -l) + +# Create a scratch directory for this run +last_nodes=${n_nodes[@]: -1} +export SCRATCH=${WORK}/weak_fft_n=${max_points}_p=${last_nodes}_${SLURM_JOBID} + +## Load Spack +#source $HOME/spack/share/spack/setup-env.sh +#. "$HOME/.cargo/env" +# +## Load BLAS +#spack load openblas +# +## Ensure Rust can find the Cray libraries +## export RUSTFLAGS="-L $(echo $CRAY_LD_LIBRARY_PATH)" +## export LD_LIBRARY_PATH=$CRAY_LD_LIBRARY_PATH:$LD_LIBRARY_PATH +#export RUSTFLAGS="-L $(spack location -i openblas)/lib" +#export LD_LIBRARY_PATH=$(spack location -i openblas)/lib:$LD_LIBRARY_PATH + +mkdir -p ${SCRATCH} +cd ${SCRATCH} + +# Pass variable to SRUN from SBATCH +export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK + export OMP_NUM_THREADS=1 # Need to set to 1 to avoid oversubsciption between Rayon and OpenMP # Create a CSV output file for analysis @@ -90,6 +87,5 @@ for i in ${!n_tasks[@]}; do --local-depth $local_depth \ --n-samples $n_samples \ --block-size $block_size \ - --n-threads $n_threads >> ${OUTPUT} -done - + --n-threads $n_threads >> ${OUTPUT} 2> ${SCRATCH}/err_run_${i}.log +done \ No newline at end of file diff --git a/hpc/archer2/slurm/weak_3_fft.slurm b/hpc/archer2/slurm/weak_3_fft.slurm index 60bb2f35..a68e0c1f 100644 --- a/hpc/archer2/slurm/weak_3_fft.slurm +++ b/hpc/archer2/slurm/weak_3_fft.slurm @@ -1,23 +1,18 @@ #!/bin/bash -# Here, we perform a weak scaling run where the number of MPI processes is set equal to the number of CCX units, each of which -# share an L3 cache, conist of 4 processors. There are 16 CCX regions per processor, and 32 per node +# Here, we perform a weak scaling run where the number of MPI processes are tied to CCX units, each of which +# share an L3 cache, consist of 4 processors. There are 16 CCX regions per processor, and 32 per node (i.e. two AMD EPYC Rome units per node) -# Running on just a single node, we try and examine the scaling performance as the number of tasks is increased -# just to see what a single node is capable of (in the best parameter settings found) - -# This should give a good baseline of what to expect when communication costs are minimal #SBATCH --job-name=weak_scaling_fft #SBATCH --time=00:30:00 #SBATCH --nodes=1024 #SBATCH --ntasks-per-node=32 #SBATCH --cpus-per-task=4 -#SBATCH --contiguous +# #SBATCH --contiguous #SBATCH --account=e738 #SBATCH --partition=standard #SBATCH --qos=standard -# Development environment for KiFMM # Restore AMD compiler env module load PrgEnv-aocc @@ -28,37 +23,15 @@ module load cray-mpich-ucx export HOME="/home/e738/e738/skailasa" export WORK="/work/e738/e738/skailasa" -# Create a scratch directory for this run -export SCRATCH=${WORK}/weak_${SLURM_JOBID} - -# Load Spack -source $HOME/spack/share/spack/setup-env.sh -. "$HOME/.cargo/env" - -# Load BLAS -spack load openblas - -# Ensure Rust can find the Cray libraries -# export RUSTFLAGS="-L $(echo $CRAY_LD_LIBRARY_PATH)" -# export LD_LIBRARY_PATH=$CRAY_LD_LIBRARY_PATH:$LD_LIBRARY_PATH -export RUSTFLAGS="-L $(spack location -i openblas)/lib" -export LD_LIBRARY_PATH=$(spack location -i openblas)/lib:$LD_LIBRARY_PATH - -mkdir -p ${SCRATCH} -cd ${SCRATCH} - -# Pass variable to SRUN from SBATCH -export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK - # Script being run, expected in the work directory script_name="fmm_m2l_fft_mpi_f32" # Set simulation parameters for FMM expansion_order=3 n_points=250000 # points per MPI process (n_tasks) -global_depth=(4 4 4) # Number of local roots +global_depth=(5 5 5) # Number of local roots local_depth=4 -n_samples=5000 +n_samples=500 block_size=128 # Set parameters for weak scaling run @@ -66,6 +39,30 @@ n_tasks=(8192 16384 32768) n_nodes=(256 512 1024) n_threads=4 # See if bandwidth saturates with different threading parameters for Rayon thread pool cpus_per_task=4 + +# # Create a scratch directory for this run +# export SCRATCH=${WORK}/weak_${SLURM_JOBID} +# +# # Load Spack +# source $HOME/spack/share/spack/setup-env.sh +# . "$HOME/.cargo/env" +# +# # Load BLAS +# spack load openblas +# +# # Ensure Rust can find the Cray libraries +# # export RUSTFLAGS="-L $(echo $CRAY_LD_LIBRARY_PATH)" +# # export LD_LIBRARY_PATH=$CRAY_LD_LIBRARY_PATH:$LD_LIBRARY_PATH +# export RUSTFLAGS="-L $(spack location -i openblas)/lib" +# export LD_LIBRARY_PATH=$(spack location -i openblas)/lib:$LD_LIBRARY_PATH + + +mkdir -p ${SCRATCH} +cd ${SCRATCH} + +# Pass variable to SRUN from SBATCH +export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK + export OMP_NUM_THREADS=1 # Need to set to 1 to avoid oversubsciption between Rayon and OpenMP # Create a CSV output file for analysis @@ -90,5 +87,5 @@ for i in ${!n_tasks[@]}; do --local-depth $local_depth \ --n-samples $n_samples \ --block-size $block_size \ - --n-threads $n_threads >> ${OUTPUT} + --n-threads $n_threads >> ${OUTPUT} 2> ${SCRATCH}/err_run_${i}.log done From 05b6eeff6fb90eb479898a8dfb0f4fe9b83ca989 Mon Sep 17 00:00:00 2001 From: srinath kailasa Date: Fri, 3 Oct 2025 11:05:21 +0100 Subject: [PATCH 16/52] Fix scripts --- hpc/archer2/slurm/weak_1_fft.slurm | 6 ++++- hpc/archer2/slurm/weak_2_fft.slurm | 7 ++++-- hpc/archer2/slurm/weak_3_fft.slurm | 37 +++++++++++++++++------------- 3 files changed, 31 insertions(+), 19 deletions(-) diff --git a/hpc/archer2/slurm/weak_1_fft.slurm b/hpc/archer2/slurm/weak_1_fft.slurm index 0fc9ec63..e891a464 100644 --- a/hpc/archer2/slurm/weak_1_fft.slurm +++ b/hpc/archer2/slurm/weak_1_fft.slurm @@ -42,6 +42,10 @@ max_points=$(echo {n_tasks[@]: -1} * ${n_points} | bc -l) # Create a scratch directory for this run last_nodes=${n_nodes[@]: -1} +last_tasks=${n_tasks[@]: -1} + +# Multiply +max_points=$(( last_tasks * n_points )) export SCRATCH=${WORK}/weak_fft_n=${max_points}_p=${last_nodes}_${SLURM_JOBID} ## Load Spack @@ -66,7 +70,7 @@ export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK export OMP_NUM_THREADS=1 # Need to set to 1 to avoid oversubsciption between Rayon and OpenMP # Create a CSV output file for analysis -export OUTPUT=${SCRATCH}/weak_fft_${SLURM_JOBID}.csv +export OUTPUT=${SCRATCH}/weak_fft_n=${max_points}_p=${last_nodes}_${SLURM_JOBID}.csv touch ${OUTPUT} echo " experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ diff --git a/hpc/archer2/slurm/weak_2_fft.slurm b/hpc/archer2/slurm/weak_2_fft.slurm index 67414493..260f62c6 100644 --- a/hpc/archer2/slurm/weak_2_fft.slurm +++ b/hpc/archer2/slurm/weak_2_fft.slurm @@ -38,10 +38,13 @@ n_tasks=(1024 2048 4096) n_nodes=(32 64 128) n_threads=4 # See if bandwidth saturates with different threading parameters for Rayon thread pool cpus_per_task=4 -max_points=$(echo {n_tasks[@]: -1} * ${n_points} | bc -l) # Create a scratch directory for this run last_nodes=${n_nodes[@]: -1} +last_tasks=${n_tasks[@]: -1} + +# Multiply +max_points=$(( last_tasks * n_points )) export SCRATCH=${WORK}/weak_fft_n=${max_points}_p=${last_nodes}_${SLURM_JOBID} ## Load Spack @@ -66,7 +69,7 @@ export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK export OMP_NUM_THREADS=1 # Need to set to 1 to avoid oversubsciption between Rayon and OpenMP # Create a CSV output file for analysis -export OUTPUT=${SCRATCH}/weak_fft_${SLURM_JOBID}.csv +export OUTPUT=${SCRATCH}/weak_fft_n=${max_points}_p=${last_nodes}_${SLURM_JOBID}.csv touch ${OUTPUT} echo " experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ diff --git a/hpc/archer2/slurm/weak_3_fft.slurm b/hpc/archer2/slurm/weak_3_fft.slurm index a68e0c1f..79d641e9 100644 --- a/hpc/archer2/slurm/weak_3_fft.slurm +++ b/hpc/archer2/slurm/weak_3_fft.slurm @@ -40,22 +40,27 @@ n_nodes=(256 512 1024) n_threads=4 # See if bandwidth saturates with different threading parameters for Rayon thread pool cpus_per_task=4 -# # Create a scratch directory for this run -# export SCRATCH=${WORK}/weak_${SLURM_JOBID} -# -# # Load Spack -# source $HOME/spack/share/spack/setup-env.sh -# . "$HOME/.cargo/env" + +# Create a scratch directory for this run +last_nodes=${n_nodes[@]: -1} +last_tasks=${n_tasks[@]: -1} + +# Multiply +max_points=$(( last_tasks * n_points )) +export SCRATCH=${WORK}/weak_fft_n=${max_points}_p=${last_nodes}_${SLURM_JOBID} + +## Load Spack +#source $HOME/spack/share/spack/setup-env.sh +#. "$HOME/.cargo/env" # -# # Load BLAS -# spack load openblas +## Load BLAS +#spack load openblas # -# # Ensure Rust can find the Cray libraries -# # export RUSTFLAGS="-L $(echo $CRAY_LD_LIBRARY_PATH)" -# # export LD_LIBRARY_PATH=$CRAY_LD_LIBRARY_PATH:$LD_LIBRARY_PATH -# export RUSTFLAGS="-L $(spack location -i openblas)/lib" -# export LD_LIBRARY_PATH=$(spack location -i openblas)/lib:$LD_LIBRARY_PATH - +## Ensure Rust can find the Cray libraries +## export RUSTFLAGS="-L $(echo $CRAY_LD_LIBRARY_PATH)" +## export LD_LIBRARY_PATH=$CRAY_LD_LIBRARY_PATH:$LD_LIBRARY_PATH +#export RUSTFLAGS="-L $(spack location -i openblas)/lib" +#export LD_LIBRARY_PATH=$(spack location -i openblas)/lib:$LD_LIBRARY_PATH mkdir -p ${SCRATCH} cd ${SCRATCH} @@ -66,7 +71,7 @@ export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK export OMP_NUM_THREADS=1 # Need to set to 1 to avoid oversubsciption between Rayon and OpenMP # Create a CSV output file for analysis -export OUTPUT=${SCRATCH}/weak_fft_${SLURM_JOBID}.csv +export OUTPUT=${SCRATCH}/weak_fft_n=${max_points}_p=${last_nodes}_${SLURM_JOBID}.csv touch ${OUTPUT} echo " experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ @@ -88,4 +93,4 @@ for i in ${!n_tasks[@]}; do --n-samples $n_samples \ --block-size $block_size \ --n-threads $n_threads >> ${OUTPUT} 2> ${SCRATCH}/err_run_${i}.log -done +done \ No newline at end of file From 86381d02c18c1334031092e08ef51455485acf5e Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Fri, 3 Oct 2025 11:45:54 +0100 Subject: [PATCH 17/52] Update notebooks --- ...aplace_blas_uniform_f32_m1_1000000_4_0.csv | 46 ++ ...aplace_blas_uniform_f32_m1_1000000_4_1.csv | 46 ++ ...aplace_blas_uniform_f32_m1_1000000_4_2.csv | 46 ++ ...aplace_blas_uniform_f32_m1_1000000_5_0.csv | 46 ++ ...aplace_blas_uniform_f32_m1_1000000_5_1.csv | 46 ++ ...aplace_blas_uniform_f32_m1_1000000_5_2.csv | 46 ++ ...ce_blas_uniform_f32_m1_8000000_3_0_5_0.csv | 6 + ...e_blas_uniform_f32_m1_8000000_3_0_5_12.csv | 6 + 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a/data/blas_f32_1/grid_search_laplace_blas_uniform_f32_m1_1000000_4_2.csv b/data/blas_f32_1/grid_search_laplace_blas_uniform_f32_m1_1000000_4_2.csv new file mode 100644 index 00000000..0df2ea3e --- /dev/null +++ b/data/blas_f32_1/grid_search_laplace_blas_uniform_f32_m1_1000000_4_2.csv @@ -0,0 +1,46 @@ +depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd +4,0,0,2,562,0.03487918,0.040229473,0.053911474,0.03865559,0,20,449,4,1 +4,0,0,3,585,0.0000050574286,0.00043314937,0.0006942123,0.00046776718,0,20,467,13,1 +4,0,0,4,571,0.00003577735,0.00008145509,0.00013965143,0.00008302265,0,20,480,28,1 +4,0,0.0000001,2,580,0.034879345,0.040229622,0.053911656,0.038655724,0,20,458,4,1 +4,0,0.0000001,3,723,0.0000050574286,0.00043317646,0.0006942877,0.0004677927,0,20,461,13,1 +4,0,0.0000001,4,567,0.000035928304,0.00008150327,0.00013965143,0.00008306787,0,20,496,28,1 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b/data/fft_f32_1/grid_search_laplace_fft_0_f32_m1_3_4_16_1000000.csv new file mode 100644 index 00000000..65323b9a --- /dev/null +++ b/data/fft_f32_1/grid_search_laplace_fft_0_f32_m1_3_4_16_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,3,16,493,0.00029853775,0.00034218995,0.00038464146,0.0003419967,855 diff --git a/data/fft_f32_1/grid_search_laplace_fft_10_f32_m1_4_5_32_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_10_f32_m1_4_5_32_1000000.csv new file mode 100644 index 00000000..a4b17bf8 --- /dev/null +++ b/data/fft_f32_1/grid_search_laplace_fft_10_f32_m1_4_5_32_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,4,32,252,0.000015539365,0.000062159874,0.00008493679,0.00006458634,1243 diff --git a/data/fft_f32_1/grid_search_laplace_fft_11_f32_m1_5_5_32_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_11_f32_m1_5_5_32_1000000.csv new file mode 100644 index 00000000..8ecbfb73 --- /dev/null +++ b/data/fft_f32_1/grid_search_laplace_fft_11_f32_m1_5_5_32_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,5,32,404,0.0000011857528,0.000005537652,0.000010227907,0.000006343344,1292 diff --git a/data/fft_f32_1/grid_search_laplace_fft_12_f32_m1_3_4_64_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_12_f32_m1_3_4_64_1000000.csv new file mode 100644 index 00000000..88a45217 --- /dev/null +++ b/data/fft_f32_1/grid_search_laplace_fft_12_f32_m1_3_4_64_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,3,64,533,0.00029853775,0.00034218995,0.00038464146,0.0003419967,926 diff --git a/data/fft_f32_1/grid_search_laplace_fft_13_f32_m1_4_4_64_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_13_f32_m1_4_4_64_1000000.csv new file mode 100644 index 00000000..59e17665 --- /dev/null +++ b/data/fft_f32_1/grid_search_laplace_fft_13_f32_m1_4_4_64_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,4,64,525,0.0000006038583,0.00004300728,0.00010778607,0.00004613934,930 diff --git a/data/fft_f32_1/grid_search_laplace_fft_14_f32_m1_5_4_64_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_14_f32_m1_5_4_64_1000000.csv new file mode 100644 index 00000000..eeff91c1 --- /dev/null +++ b/data/fft_f32_1/grid_search_laplace_fft_14_f32_m1_5_4_64_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,5,64,523,0,0.000004249616,0.000013534111,0.0000051436073,949 diff --git a/data/fft_f32_1/grid_search_laplace_fft_15_f32_m1_3_5_64_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_15_f32_m1_3_5_64_1000000.csv new file mode 100644 index 00000000..0af97f5d --- /dev/null +++ b/data/fft_f32_1/grid_search_laplace_fft_15_f32_m1_3_5_64_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,3,64,167,0.00043466355,0.0004631569,0.00048573408,0.00046296025,1157 diff --git a/data/fft_f32_1/grid_search_laplace_fft_16_f32_m1_4_5_64_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_16_f32_m1_4_5_64_1000000.csv new file mode 100644 index 00000000..8e881f05 --- /dev/null +++ b/data/fft_f32_1/grid_search_laplace_fft_16_f32_m1_4_5_64_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,4,64,233,0.000015539365,0.000062159874,0.00008493679,0.00006458634,1207 diff --git a/data/fft_f32_1/grid_search_laplace_fft_17_f32_m1_5_5_64_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_17_f32_m1_5_5_64_1000000.csv new file mode 100644 index 00000000..eddf2df5 --- /dev/null +++ b/data/fft_f32_1/grid_search_laplace_fft_17_f32_m1_5_5_64_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,5,64,381,0.0000011857528,0.000005537652,0.000010227907,0.000006343344,1275 diff --git a/data/fft_f32_1/grid_search_laplace_fft_18_f32_m1_3_4_128_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_18_f32_m1_3_4_128_1000000.csv new file mode 100644 index 00000000..39f96601 --- /dev/null +++ b/data/fft_f32_1/grid_search_laplace_fft_18_f32_m1_3_4_128_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,3,128,481,0.00029853775,0.00034218995,0.00038464146,0.0003419967,863 diff --git a/data/fft_f32_1/grid_search_laplace_fft_19_f32_m1_4_4_128_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_19_f32_m1_4_4_128_1000000.csv new file mode 100644 index 00000000..5b911e07 --- /dev/null +++ b/data/fft_f32_1/grid_search_laplace_fft_19_f32_m1_4_4_128_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,4,128,516,0.0000006038583,0.00004300728,0.00010778607,0.00004613934,913 diff --git a/data/fft_f32_1/grid_search_laplace_fft_1_f32_m1_4_4_16_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_1_f32_m1_4_4_16_1000000.csv new file mode 100644 index 00000000..2d7e77ae --- /dev/null +++ b/data/fft_f32_1/grid_search_laplace_fft_1_f32_m1_4_4_16_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,4,16,497,0.0000006038583,0.00004300728,0.00010778607,0.00004613934,891 diff --git a/data/fft_f32_1/grid_search_laplace_fft_20_f32_m1_5_4_128_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_20_f32_m1_5_4_128_1000000.csv new file mode 100644 index 00000000..bbc364f1 --- /dev/null +++ b/data/fft_f32_1/grid_search_laplace_fft_20_f32_m1_5_4_128_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,5,128,521,0,0.000004249616,0.000013534111,0.0000051436073,989 diff --git a/data/fft_f32_1/grid_search_laplace_fft_21_f32_m1_3_5_128_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_21_f32_m1_3_5_128_1000000.csv new file mode 100644 index 00000000..2571e8b6 --- /dev/null +++ b/data/fft_f32_1/grid_search_laplace_fft_21_f32_m1_3_5_128_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,3,128,156,0.00043466355,0.0004631569,0.00048573408,0.00046296025,1156 diff --git a/data/fft_f32_1/grid_search_laplace_fft_22_f32_m1_4_5_128_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_22_f32_m1_4_5_128_1000000.csv new file mode 100644 index 00000000..9ba420cd --- /dev/null +++ b/data/fft_f32_1/grid_search_laplace_fft_22_f32_m1_4_5_128_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,4,128,255,0.000015539365,0.000062159874,0.00008493679,0.00006458634,1205 diff --git a/data/fft_f32_1/grid_search_laplace_fft_23_f32_m1_5_5_128_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_23_f32_m1_5_5_128_1000000.csv new file mode 100644 index 00000000..06dbd8c6 --- /dev/null +++ b/data/fft_f32_1/grid_search_laplace_fft_23_f32_m1_5_5_128_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,5,128,360,0.0000011857528,0.000005537652,0.000010227907,0.000006343344,1273 diff --git a/data/fft_f32_1/grid_search_laplace_fft_2_f32_m1_5_4_16_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_2_f32_m1_5_4_16_1000000.csv new file mode 100644 index 00000000..dc89b85f --- /dev/null +++ b/data/fft_f32_1/grid_search_laplace_fft_2_f32_m1_5_4_16_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,5,16,531,0,0.000004249616,0.000013534111,0.0000051436073,933 diff --git a/data/fft_f32_1/grid_search_laplace_fft_3_f32_m1_3_5_16_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_3_f32_m1_3_5_16_1000000.csv new file mode 100644 index 00000000..6ab3db7e --- /dev/null +++ b/data/fft_f32_1/grid_search_laplace_fft_3_f32_m1_3_5_16_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,3,16,172,0.00043466355,0.0004631569,0.00048573408,0.00046296025,1154 diff --git a/data/fft_f32_1/grid_search_laplace_fft_4_f32_m1_4_5_16_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_4_f32_m1_4_5_16_1000000.csv new file mode 100644 index 00000000..446eabb3 --- /dev/null +++ b/data/fft_f32_1/grid_search_laplace_fft_4_f32_m1_4_5_16_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,4,16,278,0.000015539365,0.000062159874,0.00008493679,0.00006458634,1204 diff --git a/data/fft_f32_1/grid_search_laplace_fft_5_f32_m1_5_5_16_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_5_f32_m1_5_5_16_1000000.csv new file mode 100644 index 00000000..fb29e1ea --- /dev/null +++ b/data/fft_f32_1/grid_search_laplace_fft_5_f32_m1_5_5_16_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,5,16,431,0.0000011857528,0.000005537652,0.000010227907,0.000006343344,1251 diff --git a/data/fft_f32_1/grid_search_laplace_fft_6_f32_m1_3_4_32_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_6_f32_m1_3_4_32_1000000.csv new file mode 100644 index 00000000..661a3510 --- /dev/null +++ b/data/fft_f32_1/grid_search_laplace_fft_6_f32_m1_3_4_32_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,3,32,557,0.00029853775,0.00034218995,0.00038464146,0.0003419967,853 diff --git a/data/fft_f32_1/grid_search_laplace_fft_7_f32_m1_4_4_32_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_7_f32_m1_4_4_32_1000000.csv new file mode 100644 index 00000000..0bde5c04 --- /dev/null +++ b/data/fft_f32_1/grid_search_laplace_fft_7_f32_m1_4_4_32_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,4,32,563,0.0000006038583,0.00004300728,0.00010778607,0.00004613934,917 diff --git a/data/fft_f32_1/grid_search_laplace_fft_8_f32_m1_5_4_32_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_8_f32_m1_5_4_32_1000000.csv new file mode 100644 index 00000000..85b1c660 --- /dev/null +++ b/data/fft_f32_1/grid_search_laplace_fft_8_f32_m1_5_4_32_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,5,32,647,0,0.000004249616,0.000013534111,0.0000051436073,953 diff --git a/data/fft_f32_1/grid_search_laplace_fft_9_f32_m1_3_5_32_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_9_f32_m1_3_5_32_1000000.csv new file mode 100644 index 00000000..416c9e2e --- /dev/null +++ b/data/fft_f32_1/grid_search_laplace_fft_9_f32_m1_3_5_32_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,3,32,161,0.00043466355,0.0004631569,0.00048573408,0.00046296025,1197 diff --git a/data/fft_f32_8/grid_search_laplace_fft_0_f32_m1_3_5_16_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_0_f32_m1_3_5_16_8000000.csv new file mode 100644 index 00000000..de2f38f5 --- /dev/null +++ b/data/fft_f32_8/grid_search_laplace_fft_0_f32_m1_3_5_16_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,3,16,4122,0.00014249298,0.00045863597,0.000723327,0.0004806604,3885 diff --git a/data/fft_f32_8/grid_search_laplace_fft_10_f32_m1_4_6_32_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_10_f32_m1_4_6_32_8000000.csv new file mode 100644 index 00000000..293075cf --- /dev/null +++ b/data/fft_f32_8/grid_search_laplace_fft_10_f32_m1_4_6_32_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +6,4,32,1974,0.00014757123,0.00022088861,0.0002971805,0.0002260225,5345 diff --git a/data/fft_f32_8/grid_search_laplace_fft_11_f32_m1_5_6_32_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_11_f32_m1_5_6_32_8000000.csv new file mode 100644 index 00000000..3e608599 --- /dev/null +++ b/data/fft_f32_8/grid_search_laplace_fft_11_f32_m1_5_6_32_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +6,5,32,3814,0.00004457746,0.00011925035,0.00019870921,0.00012959247,5275 diff --git a/data/fft_f32_8/grid_search_laplace_fft_12_f32_m1_3_5_64_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_12_f32_m1_3_5_64_8000000.csv new file mode 100644 index 00000000..3379ee35 --- /dev/null +++ b/data/fft_f32_8/grid_search_laplace_fft_12_f32_m1_3_5_64_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,3,64,4061,0.00014249298,0.00045863597,0.000723327,0.0004806604,3798 diff --git a/data/fft_f32_8/grid_search_laplace_fft_13_f32_m1_4_5_64_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_13_f32_m1_4_5_64_8000000.csv new file mode 100644 index 00000000..7ed78b8b --- /dev/null +++ b/data/fft_f32_8/grid_search_laplace_fft_13_f32_m1_4_5_64_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,4,64,4156,0.00000055630085,0.00014264838,0.0003826771,0.00016835216,3873 diff --git a/data/fft_f32_8/grid_search_laplace_fft_14_f32_m1_5_5_64_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_14_f32_m1_5_5_64_8000000.csv new file mode 100644 index 00000000..ca58f704 --- /dev/null +++ b/data/fft_f32_8/grid_search_laplace_fft_14_f32_m1_5_5_64_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,5,64,4426,0.000001288938,0.00012569428,0.00030825654,0.00014677555,3944 diff --git a/data/fft_f32_8/grid_search_laplace_fft_15_f32_m1_3_6_64_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_15_f32_m1_3_6_64_8000000.csv new file mode 100644 index 00000000..08ad129c --- /dev/null +++ b/data/fft_f32_8/grid_search_laplace_fft_15_f32_m1_3_6_64_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +6,3,64,1260,0.0003791737,0.0004618011,0.000542133,0.00046473174,5030 diff --git a/data/fft_f32_8/grid_search_laplace_fft_16_f32_m1_4_6_64_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_16_f32_m1_4_6_64_8000000.csv new file mode 100644 index 00000000..6c11c03a --- /dev/null +++ b/data/fft_f32_8/grid_search_laplace_fft_16_f32_m1_4_6_64_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +6,4,64,1941,0.00014757123,0.00022088861,0.0002971805,0.0002260225,5181 diff --git a/data/fft_f32_8/grid_search_laplace_fft_17_f32_m1_5_6_64_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_17_f32_m1_5_6_64_8000000.csv new file mode 100644 index 00000000..a99db93e --- /dev/null +++ b/data/fft_f32_8/grid_search_laplace_fft_17_f32_m1_5_6_64_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +6,5,64,3694,0.00004457746,0.00011925035,0.00019870921,0.00012959247,5275 diff --git a/data/fft_f32_8/grid_search_laplace_fft_18_f32_m1_3_5_128_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_18_f32_m1_3_5_128_8000000.csv new file mode 100644 index 00000000..4d795ade --- /dev/null +++ b/data/fft_f32_8/grid_search_laplace_fft_18_f32_m1_3_5_128_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,3,128,4033,0.00014249298,0.00045863597,0.000723327,0.0004806604,3821 diff --git a/data/fft_f32_8/grid_search_laplace_fft_19_f32_m1_4_5_128_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_19_f32_m1_4_5_128_8000000.csv new file mode 100644 index 00000000..aa1ac574 --- /dev/null +++ b/data/fft_f32_8/grid_search_laplace_fft_19_f32_m1_4_5_128_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,4,128,4203,0.00000055630085,0.00014264838,0.0003826771,0.00016835216,3890 diff --git a/data/fft_f32_8/grid_search_laplace_fft_1_f32_m1_4_5_16_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_1_f32_m1_4_5_16_8000000.csv new file mode 100644 index 00000000..fd7ba475 --- /dev/null +++ b/data/fft_f32_8/grid_search_laplace_fft_1_f32_m1_4_5_16_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,4,16,4388,0.00000055630085,0.00014264838,0.0003826771,0.00016835216,3877 diff --git a/data/fft_f32_8/grid_search_laplace_fft_20_f32_m1_5_5_128_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_20_f32_m1_5_5_128_8000000.csv new file mode 100644 index 00000000..7c64744e --- /dev/null +++ b/data/fft_f32_8/grid_search_laplace_fft_20_f32_m1_5_5_128_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,5,128,4396,0.000001288938,0.00012569428,0.00030825654,0.00014677555,3990 diff --git a/data/fft_f32_8/grid_search_laplace_fft_21_f32_m1_3_6_128_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_21_f32_m1_3_6_128_8000000.csv new file mode 100644 index 00000000..634ccb5b --- /dev/null +++ b/data/fft_f32_8/grid_search_laplace_fft_21_f32_m1_3_6_128_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +6,3,128,1292,0.0003791737,0.0004618011,0.000542133,0.00046473174,5092 diff --git a/data/fft_f32_8/grid_search_laplace_fft_22_f32_m1_4_6_128_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_22_f32_m1_4_6_128_8000000.csv new file mode 100644 index 00000000..25a7092a --- /dev/null +++ b/data/fft_f32_8/grid_search_laplace_fft_22_f32_m1_4_6_128_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +6,4,128,1970,0.00014757123,0.00022088861,0.0002971805,0.0002260225,5202 diff --git a/data/fft_f32_8/grid_search_laplace_fft_23_f32_m1_5_6_128_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_23_f32_m1_5_6_128_8000000.csv new file mode 100644 index 00000000..1807aed7 --- /dev/null +++ b/data/fft_f32_8/grid_search_laplace_fft_23_f32_m1_5_6_128_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +6,5,128,3759,0.00004457746,0.00011925035,0.00019870921,0.00012959247,5345 diff --git a/data/fft_f32_8/grid_search_laplace_fft_2_f32_m1_5_5_16_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_2_f32_m1_5_5_16_8000000.csv new file mode 100644 index 00000000..3b6da7d9 --- /dev/null +++ b/data/fft_f32_8/grid_search_laplace_fft_2_f32_m1_5_5_16_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,5,16,4436,0.000001288938,0.00012569428,0.00030825654,0.00014677555,3927 diff --git a/data/fft_f32_8/grid_search_laplace_fft_3_f32_m1_3_6_16_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_3_f32_m1_3_6_16_8000000.csv new file mode 100644 index 00000000..15a27503 --- /dev/null +++ b/data/fft_f32_8/grid_search_laplace_fft_3_f32_m1_3_6_16_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +6,3,16,1367,0.0003791737,0.0004618011,0.000542133,0.00046473174,5022 diff --git a/data/fft_f32_8/grid_search_laplace_fft_4_f32_m1_4_6_16_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_4_f32_m1_4_6_16_8000000.csv new file mode 100644 index 00000000..b9982fc2 --- /dev/null +++ b/data/fft_f32_8/grid_search_laplace_fft_4_f32_m1_4_6_16_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +6,4,16,2410,0.00014757123,0.00022088861,0.0002971805,0.0002260225,5228 diff --git a/data/fft_f32_8/grid_search_laplace_fft_5_f32_m1_5_6_16_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_5_f32_m1_5_6_16_8000000.csv new file mode 100644 index 00000000..9c166a97 --- /dev/null +++ b/data/fft_f32_8/grid_search_laplace_fft_5_f32_m1_5_6_16_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +6,5,16,4785,0.00004457746,0.00011925035,0.00019870921,0.00012959247,5564 diff --git a/data/fft_f32_8/grid_search_laplace_fft_6_f32_m1_3_5_32_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_6_f32_m1_3_5_32_8000000.csv new file mode 100644 index 00000000..05006482 --- /dev/null +++ b/data/fft_f32_8/grid_search_laplace_fft_6_f32_m1_3_5_32_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,3,32,4109,0.00014249298,0.00045863597,0.000723327,0.0004806604,3755 diff --git a/data/fft_f32_8/grid_search_laplace_fft_7_f32_m1_4_5_32_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_7_f32_m1_4_5_32_8000000.csv new file mode 100644 index 00000000..430cbb99 --- /dev/null +++ b/data/fft_f32_8/grid_search_laplace_fft_7_f32_m1_4_5_32_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,4,32,4196,0.00000055630085,0.00014264838,0.0003826771,0.00016835216,3856 diff --git a/data/fft_f32_8/grid_search_laplace_fft_8_f32_m1_5_5_32_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_8_f32_m1_5_5_32_8000000.csv new file mode 100644 index 00000000..8dd207b2 --- /dev/null +++ b/data/fft_f32_8/grid_search_laplace_fft_8_f32_m1_5_5_32_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,5,32,4355,0.000001288938,0.00012569428,0.00030825654,0.00014677555,3910 diff --git a/data/fft_f32_8/grid_search_laplace_fft_9_f32_m1_3_6_32_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_9_f32_m1_3_6_32_8000000.csv new file mode 100644 index 00000000..89d5fd17 --- /dev/null +++ b/data/fft_f32_8/grid_search_laplace_fft_9_f32_m1_3_6_32_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +6,3,32,1367,0.0003791737,0.0004618011,0.000542133,0.00046473174,5127 diff --git a/data/fft_f64_1/grid_search_laplace_fft_0_f64_m1_6_4_16_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_0_f64_m1_6_4_16_1000000.csv new file mode 100644 index 00000000..9c53a89a --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_0_f64_m1_6_4_16_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,6,16,1369,0.000000016686702299490217,0.0000001806345983488817,0.0000007584427734354027,0.000000211894886813628,1099 diff --git a/data/fft_f64_1/grid_search_laplace_fft_10_f64_m1_10_5_16_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_10_f64_m1_10_5_16_1000000.csv new file mode 100644 index 00000000..d6e783f6 --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_10_f64_m1_10_5_16_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,10,16,9988,0.000000000014970181371864145,0.000000000021195864797384797,0.00000000003488288867966985,0.000000000021794415120378065,3412 diff --git a/data/fft_f64_1/grid_search_laplace_fft_11_f64_m1_11_5_16_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_11_f64_m1_11_5_16_1000000.csv new file mode 100644 index 00000000..216ae86f --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_11_f64_m1_11_5_16_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,11,16,14059,0.0000000000013449553261033692,0.000000000001930021538710101,0.0000000000028292678699804097,0.0000000000019795309392416133,4899 diff --git a/data/fft_f64_1/grid_search_laplace_fft_12_f64_m1_6_4_32_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_12_f64_m1_6_4_32_1000000.csv new file mode 100644 index 00000000..3526d691 --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_12_f64_m1_6_4_32_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,6,32,1294,0.000000016686702299490217,0.0000001806345983488817,0.0000007584427734354027,0.000000211894886813628,1123 diff --git a/data/fft_f64_1/grid_search_laplace_fft_13_f64_m1_7_4_32_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_13_f64_m1_7_4_32_1000000.csv new file mode 100644 index 00000000..389fd9c8 --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_13_f64_m1_7_4_32_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,7,32,1371,0.0000000012761168495142224,0.00000001493895643652047,0.00000004286895467535286,0.000000016375904107358673,1250 diff --git a/data/fft_f64_1/grid_search_laplace_fft_14_f64_m1_8_4_32_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_14_f64_m1_8_4_32_1000000.csv new file mode 100644 index 00000000..44c797c1 --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_14_f64_m1_8_4_32_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,8,32,1512,0.00000000002776493525828481,0.0000000014979825499069186,0.000000006541376024334807,0.0000000016842635718787517,1532 diff --git a/data/fft_f64_1/grid_search_laplace_fft_15_f64_m1_9_4_32_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_15_f64_m1_9_4_32_1000000.csv new file mode 100644 index 00000000..953f9d07 --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_15_f64_m1_9_4_32_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,9,32,1737,0.00000000006671614691618004,0.000000000165335668400375,0.0000000005717558733701871,0.00000000019006236803274044,2168 diff --git a/data/fft_f64_1/grid_search_laplace_fft_16_f64_m1_10_4_32_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_16_f64_m1_10_4_32_1000000.csv new file mode 100644 index 00000000..7823b204 --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_16_f64_m1_10_4_32_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,10,32,1853,0.0000000000005858423417081946,0.000000000014318952674526936,0.00000000009358838086157819,0.000000000018006086872929633,2648 diff --git a/data/fft_f64_1/grid_search_laplace_fft_17_f64_m1_11_4_32_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_17_f64_m1_11_4_32_1000000.csv new file mode 100644 index 00000000..c60fd885 --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_17_f64_m1_11_4_32_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,11,32,2143,0.00000000000016981362434069548,0.0000000000011634261801119752,0.0000000000060448245956206634,0.0000000000013546754842041066,3625 diff --git a/data/fft_f64_1/grid_search_laplace_fft_18_f64_m1_6_5_32_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_18_f64_m1_6_5_32_1000000.csv new file mode 100644 index 00000000..61f8e780 --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_18_f64_m1_6_5_32_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,6,32,1173,0.00000007363947317807973,0.00000027384696535735265,0.0000004085263879221838,0.00000028466801173195193,1508 diff --git a/data/fft_f64_1/grid_search_laplace_fft_19_f64_m1_7_5_32_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_19_f64_m1_7_5_32_1000000.csv new file mode 100644 index 00000000..107137d8 --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_19_f64_m1_7_5_32_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,7,32,1752,0.000000020760057606409505,0.0000000313831060299183,0.000000046302488262898814,0.000000032194309086371234,1820 diff --git a/data/fft_f64_1/grid_search_laplace_fft_1_f64_m1_7_4_16_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_1_f64_m1_7_4_16_1000000.csv new file mode 100644 index 00000000..f76cb134 --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_1_f64_m1_7_4_16_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,7,16,1454,0.0000000012761168495142224,0.00000001493895643652047,0.00000004286895467535286,0.000000016375904107358673,1298 diff --git a/data/fft_f64_1/grid_search_laplace_fft_20_f64_m1_8_5_32_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_20_f64_m1_8_5_32_1000000.csv new file mode 100644 index 00000000..be7aca15 --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_20_f64_m1_8_5_32_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,8,32,2504,0.0000000006775990793078764,0.0000000026624143464007455,0.000000004205206987239361,0.0000000028020022441379728,2141 diff --git a/data/fft_f64_1/grid_search_laplace_fft_21_f64_m1_9_5_32_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_21_f64_m1_9_5_32_1000000.csv new file mode 100644 index 00000000..4db43b60 --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_21_f64_m1_9_5_32_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,9,32,3556,0.00000000021553372389520778,0.00000000037141593359165344,0.0000000006141323217507616,0.00000000038741810927020284,3762 diff --git a/data/fft_f64_1/grid_search_laplace_fft_22_f64_m1_10_5_32_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_22_f64_m1_10_5_32_1000000.csv new file mode 100644 index 00000000..3ba1b72e --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_22_f64_m1_10_5_32_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,10,32,7319,0.000000000014970181371864145,0.000000000021195864797384797,0.00000000003488288867966985,0.000000000021794415120378065,3442 diff --git a/data/fft_f64_1/grid_search_laplace_fft_23_f64_m1_11_5_32_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_23_f64_m1_11_5_32_1000000.csv new file mode 100644 index 00000000..96ac66dc --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_23_f64_m1_11_5_32_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,11,32,13356,0.0000000000013449553261033692,0.000000000001930021538710101,0.0000000000028292678699804097,0.0000000000019795309392416133,4929 diff --git a/data/fft_f64_1/grid_search_laplace_fft_24_f64_m1_6_4_64_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_24_f64_m1_6_4_64_1000000.csv new file mode 100644 index 00000000..007a1666 --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_24_f64_m1_6_4_64_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,6,64,1327,0.000000016686702299490217,0.0000001806345983488817,0.0000007584427734354027,0.000000211894886813628,1114 diff --git a/data/fft_f64_1/grid_search_laplace_fft_25_f64_m1_7_4_64_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_25_f64_m1_7_4_64_1000000.csv new file mode 100644 index 00000000..60216c6b --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_25_f64_m1_7_4_64_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,7,64,1388,0.0000000012761168495142224,0.00000001493895643652047,0.00000004286895467535286,0.000000016375904107358673,1279 diff --git a/data/fft_f64_1/grid_search_laplace_fft_26_f64_m1_8_4_64_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_26_f64_m1_8_4_64_1000000.csv new file mode 100644 index 00000000..144405e6 --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_26_f64_m1_8_4_64_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,8,64,1528,0.00000000002776493525828481,0.0000000014979825499069186,0.000000006541376024334807,0.0000000016842635718787517,1551 diff --git a/data/fft_f64_1/grid_search_laplace_fft_27_f64_m1_9_4_64_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_27_f64_m1_9_4_64_1000000.csv new file mode 100644 index 00000000..fd53d7e0 --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_27_f64_m1_9_4_64_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,9,64,1708,0.00000000006671614691618004,0.000000000165335668400375,0.0000000005717558733701871,0.00000000019006236803274044,2465 diff --git a/data/fft_f64_1/grid_search_laplace_fft_28_f64_m1_10_4_64_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_28_f64_m1_10_4_64_1000000.csv new file mode 100644 index 00000000..76f188f3 --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_28_f64_m1_10_4_64_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,10,64,1837,0.0000000000005858423417081946,0.000000000014318952674526936,0.00000000009358838086157819,0.000000000018006086872929633,2446 diff --git a/data/fft_f64_1/grid_search_laplace_fft_29_f64_m1_11_4_64_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_29_f64_m1_11_4_64_1000000.csv new file mode 100644 index 00000000..2e0f758a --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_29_f64_m1_11_4_64_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,11,64,2186,0.00000000000016981362434069548,0.0000000000011634261801119752,0.0000000000060448245956206634,0.0000000000013546754842041066,3715 diff --git a/data/fft_f64_1/grid_search_laplace_fft_2_f64_m1_8_4_16_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_2_f64_m1_8_4_16_1000000.csv new file mode 100644 index 00000000..4cbf7597 --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_2_f64_m1_8_4_16_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,8,16,1568,0.00000000002776493525828481,0.0000000014979825499069186,0.000000006541376024334807,0.0000000016842635718787517,1543 diff --git a/data/fft_f64_1/grid_search_laplace_fft_30_f64_m1_6_5_64_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_30_f64_m1_6_5_64_1000000.csv new file mode 100644 index 00000000..4aaea6af --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_30_f64_m1_6_5_64_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,6,64,1158,0.00000007363947317807973,0.00000027384696535735265,0.0000004085263879221838,0.00000028466801173195193,1513 diff --git a/data/fft_f64_1/grid_search_laplace_fft_31_f64_m1_7_5_64_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_31_f64_m1_7_5_64_1000000.csv new file mode 100644 index 00000000..2f0b63ed --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_31_f64_m1_7_5_64_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,7,64,1685,0.000000020760057606409505,0.0000000313831060299183,0.000000046302488262898814,0.000000032194309086371234,1767 diff --git a/data/fft_f64_1/grid_search_laplace_fft_32_f64_m1_8_5_64_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_32_f64_m1_8_5_64_1000000.csv new file mode 100644 index 00000000..e24f762e --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_32_f64_m1_8_5_64_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,8,64,2349,0.0000000006775990793078764,0.0000000026624143464007455,0.000000004205206987239361,0.0000000028020022441379728,2158 diff --git a/data/fft_f64_1/grid_search_laplace_fft_33_f64_m1_9_5_64_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_33_f64_m1_9_5_64_1000000.csv new file mode 100644 index 00000000..b11c60f9 --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_33_f64_m1_9_5_64_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,9,64,3498,0.00000000021553372389520778,0.00000000037141593359165344,0.0000000006141323217507616,0.00000000038741810927020284,3221 diff --git a/data/fft_f64_1/grid_search_laplace_fft_34_f64_m1_10_5_64_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_34_f64_m1_10_5_64_1000000.csv new file mode 100644 index 00000000..a35edfa6 --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_34_f64_m1_10_5_64_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,10,64,6187,0.000000000014970181371864145,0.000000000021195864797384797,0.00000000003488288867966985,0.000000000021794415120378065,3486 diff --git a/data/fft_f64_1/grid_search_laplace_fft_35_f64_m1_11_5_64_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_35_f64_m1_11_5_64_1000000.csv new file mode 100644 index 00000000..7229669a --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_35_f64_m1_11_5_64_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,11,64,13139,0.0000000000013449553261033692,0.000000000001930021538710101,0.0000000000028292678699804097,0.0000000000019795309392416133,4885 diff --git a/data/fft_f64_1/grid_search_laplace_fft_36_f64_m1_6_4_128_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_36_f64_m1_6_4_128_1000000.csv new file mode 100644 index 00000000..cd8b6f3e --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_36_f64_m1_6_4_128_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,6,128,1331,0.000000016686702299490217,0.0000001806345983488817,0.0000007584427734354027,0.000000211894886813628,1133 diff --git a/data/fft_f64_1/grid_search_laplace_fft_37_f64_m1_7_4_128_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_37_f64_m1_7_4_128_1000000.csv new file mode 100644 index 00000000..4b9af556 --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_37_f64_m1_7_4_128_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,7,128,1421,0.0000000012761168495142224,0.00000001493895643652047,0.00000004286895467535286,0.000000016375904107358673,1273 diff --git a/data/fft_f64_1/grid_search_laplace_fft_38_f64_m1_8_4_128_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_38_f64_m1_8_4_128_1000000.csv new file mode 100644 index 00000000..7a9620be --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_38_f64_m1_8_4_128_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,8,128,1536,0.00000000002776493525828481,0.0000000014979825499069186,0.000000006541376024334807,0.0000000016842635718787517,1545 diff --git a/data/fft_f64_1/grid_search_laplace_fft_39_f64_m1_9_4_128_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_39_f64_m1_9_4_128_1000000.csv new file mode 100644 index 00000000..f1fe4a8c --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_39_f64_m1_9_4_128_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,9,128,1727,0.00000000006671614691618004,0.000000000165335668400375,0.0000000005717558733701871,0.00000000019006236803274044,2361 diff --git a/data/fft_f64_1/grid_search_laplace_fft_3_f64_m1_9_4_16_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_3_f64_m1_9_4_16_1000000.csv new file mode 100644 index 00000000..7cbea65b --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_3_f64_m1_9_4_16_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,9,16,1783,0.00000000006671614691618004,0.000000000165335668400375,0.0000000005717558733701871,0.00000000019006236803274044,2389 diff --git a/data/fft_f64_1/grid_search_laplace_fft_40_f64_m1_10_4_128_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_40_f64_m1_10_4_128_1000000.csv new file mode 100644 index 00000000..22771c6e --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_40_f64_m1_10_4_128_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,10,128,1870,0.0000000000005858423417081946,0.000000000014318952674526936,0.00000000009358838086157819,0.000000000018006086872929633,2573 diff --git a/data/fft_f64_1/grid_search_laplace_fft_41_f64_m1_11_4_128_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_41_f64_m1_11_4_128_1000000.csv new file mode 100644 index 00000000..736be11b --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_41_f64_m1_11_4_128_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,11,128,2217,0.00000000000016981362434069548,0.0000000000011634261801119752,0.0000000000060448245956206634,0.0000000000013546754842041066,3847 diff --git a/data/fft_f64_1/grid_search_laplace_fft_42_f64_m1_6_5_128_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_42_f64_m1_6_5_128_1000000.csv new file mode 100644 index 00000000..54b7014a --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_42_f64_m1_6_5_128_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,6,128,1104,0.00000007363947317807973,0.00000027384696535735265,0.0000004085263879221838,0.00000028466801173195193,1516 diff --git a/data/fft_f64_1/grid_search_laplace_fft_43_f64_m1_7_5_128_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_43_f64_m1_7_5_128_1000000.csv new file mode 100644 index 00000000..5c0f206d --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_43_f64_m1_7_5_128_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,7,128,1647,0.000000020760057606409505,0.0000000313831060299183,0.000000046302488262898814,0.000000032194309086371234,1748 diff --git a/data/fft_f64_1/grid_search_laplace_fft_44_f64_m1_8_5_128_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_44_f64_m1_8_5_128_1000000.csv new file mode 100644 index 00000000..07206ab9 --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_44_f64_m1_8_5_128_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,8,128,2341,0.0000000006775990793078764,0.0000000026624143464007455,0.000000004205206987239361,0.0000000028020022441379728,2146 diff --git a/data/fft_f64_1/grid_search_laplace_fft_45_f64_m1_9_5_128_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_45_f64_m1_9_5_128_1000000.csv new file mode 100644 index 00000000..483142ce --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_45_f64_m1_9_5_128_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,9,128,3450,0.00000000021553372389520778,0.00000000037141593359165344,0.0000000006141323217507616,0.00000000038741810927020284,3365 diff --git a/data/fft_f64_1/grid_search_laplace_fft_46_f64_m1_10_5_128_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_46_f64_m1_10_5_128_1000000.csv new file mode 100644 index 00000000..7b1bdb4c --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_46_f64_m1_10_5_128_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,10,128,6333,0.000000000014970181371864145,0.000000000021195864797384797,0.00000000003488288867966985,0.000000000021794415120378065,3454 diff --git a/data/fft_f64_1/grid_search_laplace_fft_47_f64_m1_11_5_128_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_47_f64_m1_11_5_128_1000000.csv new file mode 100644 index 00000000..723d24f5 --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_47_f64_m1_11_5_128_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,11,128,13430,0.0000000000013449553261033692,0.000000000001930021538710101,0.0000000000028292678699804097,0.0000000000019795309392416133,4926 diff --git a/data/fft_f64_1/grid_search_laplace_fft_4_f64_m1_10_4_16_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_4_f64_m1_10_4_16_1000000.csv new file mode 100644 index 00000000..4f4c41fa --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_4_f64_m1_10_4_16_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,10,16,2083,0.0000000000005858423417081946,0.000000000014318952674526936,0.00000000009358838086157819,0.000000000018006086872929633,2495 diff --git a/data/fft_f64_1/grid_search_laplace_fft_5_f64_m1_11_4_16_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_5_f64_m1_11_4_16_1000000.csv new file mode 100644 index 00000000..5c5235ee --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_5_f64_m1_11_4_16_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +4,11,16,2665,0.00000000000016981362434069548,0.0000000000011634261801119752,0.0000000000060448245956206634,0.0000000000013546754842041066,3689 diff --git a/data/fft_f64_1/grid_search_laplace_fft_6_f64_m1_6_5_16_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_6_f64_m1_6_5_16_1000000.csv new file mode 100644 index 00000000..b7978af5 --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_6_f64_m1_6_5_16_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,6,16,1332,0.00000007363947317807973,0.00000027384696535735265,0.0000004085263879221838,0.00000028466801173195193,1502 diff --git a/data/fft_f64_1/grid_search_laplace_fft_7_f64_m1_7_5_16_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_7_f64_m1_7_5_16_1000000.csv new file mode 100644 index 00000000..bfb90120 --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_7_f64_m1_7_5_16_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,7,16,2597,0.000000020760057606409505,0.0000000313831060299183,0.000000046302488262898814,0.000000032194309086371234,1748 diff --git a/data/fft_f64_1/grid_search_laplace_fft_8_f64_m1_8_5_16_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_8_f64_m1_8_5_16_1000000.csv new file mode 100644 index 00000000..8684a0df --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_8_f64_m1_8_5_16_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,8,16,3871,0.0000000006775990793078764,0.0000000026624143464007455,0.000000004205206987239361,0.0000000028020022441379728,2167 diff --git a/data/fft_f64_1/grid_search_laplace_fft_9_f64_m1_9_5_16_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_9_f64_m1_9_5_16_1000000.csv new file mode 100644 index 00000000..a6da8ad5 --- /dev/null +++ b/data/fft_f64_1/grid_search_laplace_fft_9_f64_m1_9_5_16_1000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,9,16,6463,0.00000000021553372389520778,0.00000000037141593359165344,0.0000000006141323217507616,0.00000000038741810927020284,3275 diff --git a/data/fft_f64_8/grid_search_laplace_fft_0_f64_m1_3_5_16_8000000.csv b/data/fft_f64_8/grid_search_laplace_fft_0_f64_m1_3_5_16_8000000.csv new file mode 100644 index 00000000..8a28885c --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_0_f64_m1_3_5_16_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,3,16,9475,0.0004302094661582775,0.00046954050705483785,0.0005225590372846689,0.0004693975719874745,3866 diff --git a/data/fft_f64_8/grid_search_laplace_fft_0_f64_m1_6_5_16_16.csv b/data/fft_f64_8/grid_search_laplace_fft_0_f64_m1_6_5_16_16.csv new file mode 100644 index 00000000..bf93b741 --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_0_f64_m1_6_5_16_16.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,6,16,10684,0.0000000018085910958350794,0.00000027833851082400454,0.0000006058196768266803,0.0000002939845117603853,4157 diff --git a/data/fft_f64_8/grid_search_laplace_fft_0_f64_m1_6_6_16_16.csv b/data/fft_f64_8/grid_search_laplace_fft_0_f64_m1_6_6_16_16.csv new file mode 100644 index 00000000..ad8415cb --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_0_f64_m1_6_6_16_16.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +6,6,16,20248,0.0000002852841387803858,0.000000490332516469548,0.0000006666482311861262,0.0000005076318473697164,5995 diff --git a/data/fft_f64_8/grid_search_laplace_fft_10_f64_m1_10_5_32_32.csv b/data/fft_f64_8/grid_search_laplace_fft_10_f64_m1_10_5_32_32.csv new file mode 100644 index 00000000..6dc26112 --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_10_f64_m1_10_5_32_32.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,10,32,18611,0.000000000003209449173744516,0.00000000001977977698348399,0.00000000006610840164089346,0.000000000020958030444558576,6028 diff --git a/data/fft_f64_8/grid_search_laplace_fft_10_f64_m1_3_6_64_8000000.csv b/data/fft_f64_8/grid_search_laplace_fft_10_f64_m1_3_6_64_8000000.csv new file mode 100644 index 00000000..f823bbbb --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_10_f64_m1_3_6_64_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +6,3,64,2375,0.0005657645803314287,0.0005748734450373959,0.0005876071582981932,0.0005746622153030085,5097 diff --git a/data/fft_f64_8/grid_search_laplace_fft_11_f64_m1_11_5_32_32.csv b/data/fft_f64_8/grid_search_laplace_fft_11_f64_m1_11_5_32_32.csv new file mode 100644 index 00000000..00ef28f4 --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_11_f64_m1_11_5_32_32.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,11,32,25233,0.0000000000002474688982449729,0.0000000000018509659491341897,0.000000000004404429213456524,0.0000000000019383191959881632,7460 diff --git a/data/fft_f64_8/grid_search_laplace_fft_11_f64_m1_4_6_64_8000000.csv b/data/fft_f64_8/grid_search_laplace_fft_11_f64_m1_4_6_64_8000000.csv new file mode 100644 index 00000000..7d5a3ecc --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_11_f64_m1_4_6_64_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +6,4,64,4881,0.00008435830741809251,0.00010984211976832667,0.00012372103266019484,0.00011054320386726452,5239 diff --git a/data/fft_f64_8/grid_search_laplace_fft_12_f64_m1_3_5_128_8000000.csv b/data/fft_f64_8/grid_search_laplace_fft_12_f64_m1_3_5_128_8000000.csv new file mode 100644 index 00000000..b2699eca --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_12_f64_m1_3_5_128_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,3,128,9461,0.0004302094661582775,0.00046954050705483785,0.0005225590372846689,0.0004693975719874745,3894 diff --git a/data/fft_f64_8/grid_search_laplace_fft_12_f64_m1_6_5_64_64.csv b/data/fft_f64_8/grid_search_laplace_fft_12_f64_m1_6_5_64_64.csv new file mode 100644 index 00000000..7ac2c814 --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_12_f64_m1_6_5_64_64.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,6,64,11284,0.0000000018085910958350794,0.00000027833851082400454,0.0000006058196768266803,0.0000002939845117603853,4300 diff --git a/data/fft_f64_8/grid_search_laplace_fft_13_f64_m1_4_5_128_8000000.csv b/data/fft_f64_8/grid_search_laplace_fft_13_f64_m1_4_5_128_8000000.csv new file mode 100644 index 00000000..1505c239 --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_13_f64_m1_4_5_128_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,4,128,9659,0.000018623691843535644,0.00006629082280926517,0.00012655915349306347,0.00006863001374941591,3883 diff --git a/data/fft_f64_8/grid_search_laplace_fft_13_f64_m1_7_5_64_64.csv b/data/fft_f64_8/grid_search_laplace_fft_13_f64_m1_7_5_64_64.csv new file mode 100644 index 00000000..403156db --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_13_f64_m1_7_5_64_64.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,7,64,12626,0.000000008499656438237967,0.00000002967474956988892,0.00000006065169002641236,0.000000030592830030632814,4489 diff --git a/data/fft_f64_8/grid_search_laplace_fft_14_f64_m1_3_6_128_8000000.csv b/data/fft_f64_8/grid_search_laplace_fft_14_f64_m1_3_6_128_8000000.csv new file mode 100644 index 00000000..b2b18e14 --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_14_f64_m1_3_6_128_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +6,3,128,2367,0.0005657645803314287,0.0005748734450373959,0.0005876071582981932,0.0005746622153030085,5106 diff --git a/data/fft_f64_8/grid_search_laplace_fft_14_f64_m1_8_5_64_64.csv b/data/fft_f64_8/grid_search_laplace_fft_14_f64_m1_8_5_64_64.csv new file mode 100644 index 00000000..178fbc2d --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_14_f64_m1_8_5_64_64.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,8,64,12236,0.00000000030899035730904755,0.0000000024785245691022726,0.000000005816543064295436,0.0000000026090605207527885,5124 diff --git a/data/fft_f64_8/grid_search_laplace_fft_15_f64_m1_4_6_128_8000000.csv b/data/fft_f64_8/grid_search_laplace_fft_15_f64_m1_4_6_128_8000000.csv new file mode 100644 index 00000000..6eeee5ad --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_15_f64_m1_4_6_128_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +6,4,128,5090,0.00008435830741809251,0.00010984211976832667,0.00012372103266019484,0.00011054320386726452,5259 diff --git a/data/fft_f64_8/grid_search_laplace_fft_15_f64_m1_9_5_64_64.csv b/data/fft_f64_8/grid_search_laplace_fft_15_f64_m1_9_5_64_64.csv new file mode 100644 index 00000000..477125bb --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_15_f64_m1_9_5_64_64.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,9,64,13827,0.0000000001292219574041934,0.000000000336548279925672,0.000000000881821704938404,0.0000000003581873967442577,5856 diff --git a/data/fft_f64_8/grid_search_laplace_fft_16_f64_m1_10_5_64_64.csv b/data/fft_f64_8/grid_search_laplace_fft_16_f64_m1_10_5_64_64.csv new file mode 100644 index 00000000..312dee77 --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_16_f64_m1_10_5_64_64.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,10,64,18719,0.000000000003209449173744516,0.00000000001977977698348399,0.00000000006610840164089346,0.000000000020958030444558576,6122 diff --git a/data/fft_f64_8/grid_search_laplace_fft_17_f64_m1_11_5_64_64.csv b/data/fft_f64_8/grid_search_laplace_fft_17_f64_m1_11_5_64_64.csv new file mode 100644 index 00000000..75123944 --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_17_f64_m1_11_5_64_64.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,11,64,23998,0.0000000000002474688982449729,0.0000000000018509659491341897,0.000000000004404429213456524,0.0000000000019383191959881632,7480 diff --git a/data/fft_f64_8/grid_search_laplace_fft_18_f64_m1_6_5_128_128.csv b/data/fft_f64_8/grid_search_laplace_fft_18_f64_m1_6_5_128_128.csv new file mode 100644 index 00000000..bc0388e2 --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_18_f64_m1_6_5_128_128.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,6,128,13085,0.0000000018085910958350794,0.00000027833851082400454,0.0000006058196768266803,0.0000002939845117603853,4243 diff --git a/data/fft_f64_8/grid_search_laplace_fft_19_f64_m1_7_5_128_128.csv b/data/fft_f64_8/grid_search_laplace_fft_19_f64_m1_7_5_128_128.csv new file mode 100644 index 00000000..3f556c28 --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_19_f64_m1_7_5_128_128.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,7,128,12442,0.000000008499656438237967,0.00000002967474956988892,0.00000006065169002641236,0.000000030592830030632814,4416 diff --git a/data/fft_f64_8/grid_search_laplace_fft_1_f64_m1_4_5_16_8000000.csv b/data/fft_f64_8/grid_search_laplace_fft_1_f64_m1_4_5_16_8000000.csv new file mode 100644 index 00000000..292aef63 --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_1_f64_m1_4_5_16_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,4,16,9716,0.000018623691843535644,0.00006629082280926517,0.00012655915349306347,0.00006863001374941591,3908 diff --git a/data/fft_f64_8/grid_search_laplace_fft_1_f64_m1_7_5_16_16.csv b/data/fft_f64_8/grid_search_laplace_fft_1_f64_m1_7_5_16_16.csv new file mode 100644 index 00000000..61471ab0 --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_1_f64_m1_7_5_16_16.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,7,16,11903,0.000000008499656438237967,0.00000002967474956988892,0.00000006065169002641236,0.000000030592830030632814,4475 diff --git a/data/fft_f64_8/grid_search_laplace_fft_1_f64_m1_7_6_16_16.csv b/data/fft_f64_8/grid_search_laplace_fft_1_f64_m1_7_6_16_16.csv new file mode 100644 index 00000000..5a1b0b8f --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_1_f64_m1_7_6_16_16.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +6,7,16,43875,0.00000004086067052549173,0.00000005115037059510694,0.00000006422988427871442,0.000000051870925441346174,6504 diff --git a/data/fft_f64_8/grid_search_laplace_fft_20_f64_m1_8_5_128_128.csv b/data/fft_f64_8/grid_search_laplace_fft_20_f64_m1_8_5_128_128.csv new file mode 100644 index 00000000..ae89ce8c --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_20_f64_m1_8_5_128_128.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,8,128,12760,0.00000000030899035730904755,0.0000000024785245691022726,0.000000005816543064295436,0.0000000026090605207527885,4834 diff --git a/data/fft_f64_8/grid_search_laplace_fft_21_f64_m1_9_5_128_128.csv b/data/fft_f64_8/grid_search_laplace_fft_21_f64_m1_9_5_128_128.csv new file mode 100644 index 00000000..576a8e63 --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_21_f64_m1_9_5_128_128.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,9,128,16400,0.0000000001292219574041934,0.000000000336548279925672,0.000000000881821704938404,0.0000000003581873967442577,6289 diff --git a/data/fft_f64_8/grid_search_laplace_fft_22_f64_m1_10_5_128_128.csv b/data/fft_f64_8/grid_search_laplace_fft_22_f64_m1_10_5_128_128.csv new file mode 100644 index 00000000..c8c913cc --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_22_f64_m1_10_5_128_128.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,10,128,19029,0.000000000003209449173744516,0.00000000001977977698348399,0.00000000006610840164089346,0.000000000020958030444558576,6189 diff --git a/data/fft_f64_8/grid_search_laplace_fft_23_f64_m1_11_5_128_128.csv b/data/fft_f64_8/grid_search_laplace_fft_23_f64_m1_11_5_128_128.csv new file mode 100644 index 00000000..8c05d7a3 --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_23_f64_m1_11_5_128_128.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,11,128,24240,0.0000000000002474688982449729,0.0000000000018509659491341897,0.000000000004404429213456524,0.0000000000019383191959881632,7465 diff --git a/data/fft_f64_8/grid_search_laplace_fft_2_f64_m1_3_6_16_8000000.csv b/data/fft_f64_8/grid_search_laplace_fft_2_f64_m1_3_6_16_8000000.csv new file mode 100644 index 00000000..9e1299aa --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_2_f64_m1_3_6_16_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +6,3,16,3007,0.0005657645803314287,0.0005748734450373959,0.0005876071582981932,0.0005746622153030085,5152 diff --git a/data/fft_f64_8/grid_search_laplace_fft_2_f64_m1_8_5_16_16.csv b/data/fft_f64_8/grid_search_laplace_fft_2_f64_m1_8_5_16_16.csv new file mode 100644 index 00000000..d75f8f4d --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_2_f64_m1_8_5_16_16.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,8,16,15089,0.00000000030899035730904755,0.0000000024785245691022726,0.000000005816543064295436,0.0000000026090605207527885,5104 diff --git a/data/fft_f64_8/grid_search_laplace_fft_2_f64_m1_8_6_16_16.csv b/data/fft_f64_8/grid_search_laplace_fft_2_f64_m1_8_6_16_16.csv new file mode 100644 index 00000000..7163e935 --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_2_f64_m1_8_6_16_16.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +6,8,16,120908,0.0000000030530646268500647,0.000000004711174702371123,0.000000006279905852902143,0.000000004868062124009261,7352 diff --git a/data/fft_f64_8/grid_search_laplace_fft_3_f64_m1_4_6_16_8000000.csv b/data/fft_f64_8/grid_search_laplace_fft_3_f64_m1_4_6_16_8000000.csv new file mode 100644 index 00000000..cb78c331 --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_3_f64_m1_4_6_16_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time 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b/data/fft_f64_8/grid_search_laplace_fft_5_f64_m1_11_5_16_16.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,11,16,26403,0.0000000000002474688982449729,0.0000000000018509659491341897,0.000000000004404429213456524,0.0000000000019383191959881632,7532 diff --git a/data/fft_f64_8/grid_search_laplace_fft_5_f64_m1_4_5_32_8000000.csv b/data/fft_f64_8/grid_search_laplace_fft_5_f64_m1_4_5_32_8000000.csv new file mode 100644 index 00000000..2570c365 --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_5_f64_m1_4_5_32_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,4,32,9748,0.000018623691843535644,0.00006629082280926517,0.00012655915349306347,0.00006863001374941591,3924 diff --git a/data/fft_f64_8/grid_search_laplace_fft_6_f64_m1_3_6_32_8000000.csv b/data/fft_f64_8/grid_search_laplace_fft_6_f64_m1_3_6_32_8000000.csv new file mode 100644 index 00000000..054d09c1 --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_6_f64_m1_3_6_32_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +6,3,32,2427,0.0005657645803314287,0.0005748734450373959,0.0005876071582981932,0.0005746622153030085,5173 diff --git a/data/fft_f64_8/grid_search_laplace_fft_6_f64_m1_6_5_32_32.csv b/data/fft_f64_8/grid_search_laplace_fft_6_f64_m1_6_5_32_32.csv new file mode 100644 index 00000000..e506a293 --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_6_f64_m1_6_5_32_32.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,6,32,10778,0.0000000018085910958350794,0.00000027833851082400454,0.0000006058196768266803,0.0000002939845117603853,4102 diff --git a/data/fft_f64_8/grid_search_laplace_fft_6_f64_m1_6_6_16_16.csv b/data/fft_f64_8/grid_search_laplace_fft_6_f64_m1_6_6_16_16.csv new file mode 100644 index 00000000..3831bea5 --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_6_f64_m1_6_6_16_16.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +6,6,16,23075,0.0000002852841387803858,0.000000490332516469548,0.0000006666482311861262,0.0000005076318473697164,6616 diff --git a/data/fft_f64_8/grid_search_laplace_fft_7_f64_m1_4_6_32_8000000.csv b/data/fft_f64_8/grid_search_laplace_fft_7_f64_m1_4_6_32_8000000.csv new file mode 100644 index 00000000..1fa284be --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_7_f64_m1_4_6_32_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +6,4,32,5803,0.00008435830741809251,0.00010984211976832667,0.00012372103266019484,0.00011054320386726452,5345 diff --git a/data/fft_f64_8/grid_search_laplace_fft_7_f64_m1_7_5_32_32.csv b/data/fft_f64_8/grid_search_laplace_fft_7_f64_m1_7_5_32_32.csv new file mode 100644 index 00000000..9c8502eb --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_7_f64_m1_7_5_32_32.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,7,32,11952,0.000000008499656438237967,0.00000002967474956988892,0.00000006065169002641236,0.000000030592830030632814,4491 diff --git a/data/fft_f64_8/grid_search_laplace_fft_7_f64_m1_7_6_16_16.csv b/data/fft_f64_8/grid_search_laplace_fft_7_f64_m1_7_6_16_16.csv new file mode 100644 index 00000000..4f2349bb --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_7_f64_m1_7_6_16_16.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +6,7,16,57901,0.00000004086067052549173,0.00000005115037059510694,0.00000006422988427871442,0.000000051870925441346174,6947 diff --git a/data/fft_f64_8/grid_search_laplace_fft_8_f64_m1_3_5_64_8000000.csv b/data/fft_f64_8/grid_search_laplace_fft_8_f64_m1_3_5_64_8000000.csv new file mode 100644 index 00000000..284b7e7d --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_8_f64_m1_3_5_64_8000000.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,3,64,9478,0.0004302094661582775,0.00046954050705483785,0.0005225590372846689,0.0004693975719874745,3880 diff --git a/data/fft_f64_8/grid_search_laplace_fft_8_f64_m1_8_5_32_32.csv b/data/fft_f64_8/grid_search_laplace_fft_8_f64_m1_8_5_32_32.csv new file mode 100644 index 00000000..f089f6ce --- /dev/null +++ b/data/fft_f64_8/grid_search_laplace_fft_8_f64_m1_8_5_32_32.csv @@ -0,0 +1,2 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time 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b/data/grid_search_laplace_f32_blas_m1_8.csv new file mode 100644 index 00000000..dedffe01 --- /dev/null +++ b/data/grid_search_laplace_f32_blas_m1_8.csv @@ -0,0 +1,181 @@ +depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd +6,0,0.0,4,2793,7.6439355e-05,0.00015457728,0.00022920404,0.00016214211,0,5,10674,28,1 +6,0,1e-07,4,2596,7.595044e-05,0.00015424017,0.00022879746,0.00016183592,0,5,11245,28,1 +6,0,1e-05,4,2584,7.660232e-05,0.00015480968,0.00022936668,0.00016236695,0,5,10642,28,1 +6,0,0.001,4,2453,7.5461525e-05,0.00015384502,0.00022839087,0.0001614787,0,5,11209,28,1 +6,0,0.1,4,2307,0.00010186603,0.0001739418,0.00025548023,0.00018121242,0,5,11482,25,1 +6,2,0.0,3,2909,1.7944601e-05,0.00011256409,0.00019415516,0.00012566122,0,5,10936,25,1 +6,2,1e-07,3,2391,1.7863405e-05,0.000112552494,0.00019415516,0.00012565956,0,5,10921,25,1 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+5,7,128,1647,2.07600576e-08,3.1383106e-08,4.63024882e-08,3.2194309e-08,1748 +5,6,64,1158,7.36394731e-08,2.738469653e-07,4.085263879e-07,2.846680117e-07,1513 +5,10,64,6187,1.49701e-11,2.11958e-11,3.48828e-11,2.17944e-11,3486 +4,6,32,1294,1.66867022e-08,1.806345983e-07,7.584427734e-07,2.118948868e-07,1123 +5,9,32,3556,2.155337e-10,3.714159e-10,6.141323e-10,3.874181e-10,3762 diff --git a/data/grid_search_laplace_f64_fft_m1_8.csv b/data/grid_search_laplace_f64_fft_m1_8.csv new file mode 100644 index 00000000..8998ed9f --- /dev/null +++ b/data/grid_search_laplace_f64_fft_m1_8.csv @@ -0,0 +1,47 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time +5,3,64,9478,0.0004302094661582,0.0004695405070548,0.0005225590372846,0.0004693975719874,3880 +6,7,16,57901,4.08606705e-08,5.11503705e-08,6.42298842e-08,5.18709254e-08,6947 +5,11,64,23998,2.474e-13,1.8509e-12,4.4044e-12,1.9383e-12,7480 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+6,4,16,5335,8.4358307418e-05,0.0001098421197683,0.0001237210326601,0.0001105432038672,5338 +5,6,16,10684,1.808591e-09,2.783385108e-07,6.058196768e-07,2.939845117e-07,4157 +6,6,16,23075,2.852841387e-07,4.903325164e-07,6.666482311e-07,5.076318473e-07,6616 +6,3,128,2367,0.0005657645803314,0.0005748734450373,0.0005876071582981,0.000574662215303,5106 +5,8,128,12760,3.089903e-10,2.4785245e-09,5.816543e-09,2.6090605e-09,4834 +6,7,16,43875,4.08606705e-08,5.11503705e-08,6.42298842e-08,5.18709254e-08,6504 diff --git a/hpc/archer2/data/ijhpca/Strong Scaling.ipynb b/hpc/archer2/data/ijhpca/Strong Scaling.ipynb index 2aad2d3a..296bccf0 100644 --- a/hpc/archer2/data/ijhpca/Strong Scaling.ipynb +++ b/hpc/archer2/data/ijhpca/Strong Scaling.ipynb @@ -2,18 +2,20 @@ "cells": [ { "cell_type": "code", - "execution_count": 123, + "execution_count": 1, "id": "2137b1d9-7f65-410a-b79d-7ed81dcf9b46", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", - "import numpy as np" + "import numpy as np\n", + "\n", + "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 2, "id": "20e41bec-f758-4668-aef5-cada4ba8652b", "metadata": {}, "outputs": [], @@ -61,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 3, "id": "f0abc6a1-73c9-4608-80f7-43c6c64c0257", "metadata": {}, "outputs": [ @@ -69,18 +71,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "Total problem size n=1.024B\n", - "global depth [2 2 3 3 3 4 4 4 5]\n", - "local depth [5 5 4 4 4 3 3 3 3]\n", - "points per rank [32000000 16000000 8000000 4000000 2000000 1000000 500000 250000\n", - " 125000]\n" + "Total problem size n=0.256B\n", + "global depth [2 2 2 3 3 3 4 4]\n", + "local depth [5 4 4 4 3 3 3 3]\n", + "points per rank [16000000 8000000 4000000 2000000 1000000 500000 250000 125000]\n" ] } ], "source": [ - "max_nodes = 256\n", + "max_nodes = 128\n", "min_nodes = 1\n", - "ranks_per_node=32 # ranks on 1 node, such that 1 per CCX region\n", + "ranks_per_node=16 # ranks on 1 node, such that 1 per CCX region\n", "total_ranks = max_nodes*ranks_per_node # 16 nodes, 32 ranks per node (1 per CCX region)\n", "min_points_per_rank = 125e3\n", "n = min_points_per_rank*total_ranks # total problem size\n", @@ -95,7 +96,7 @@ " points_per_node.append(p)\n", "points_per_node = np.array(points_per_node)\n", " \n", - "total_ranks = n_nodes*32\n", + "total_ranks = n_nodes*ranks_per_node\n", "points_per_rank = np.int64(points_per_node/ranks_per_node)\n", "\n", "\n", @@ -116,39 +117,396 @@ }, { "cell_type": "code", - "execution_count": 137, - "id": "7e4cee87-4ec2-43b7-aae5-473898e26188", + "execution_count": 9, + "id": "56128a60-0d71-4b4d-a62e-10c8684b8798", "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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experiment_idrankruntimep2mm2ml2lm2lp2psource_treetarget_tree...ghost_fmm_udisplacement_mapmetadata_creationexpansion_ordern_pointslocal_depthglobal_depthblock_sizen_threadsn_samples
00146784771327392825567431173812...029320739600000521288500
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3008837711333525532717431173813...038320839600000521288500
40118845881632527032647431273812...040320539600000521288500
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5 rows × 33 columns

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" + ], "text/plain": [ - "array([1024000000, 1024000000, 1024000000, 1024000000, 1024000000,\n", - " 1024000000, 1024000000, 1024000000, 1024000000])" + " experiment_id rank runtime p2m m2m l2l m2l p2p source_tree \\\n", + "0 0 14 6784 77 13 27 3928 2556 74311 \n", + "1 0 4 8776 69 13 24 5233 3273 74311 \n", + "2 0 6 8795 69 13 27 5260 3267 74311 \n", + "3 0 0 8837 71 13 33 5255 3271 74311 \n", + "4 0 11 8845 88 16 32 5270 3264 74312 \n", + "\n", + " target_tree ... ghost_fmm_u displacement_map metadata_creation \\\n", + "0 73812 ... 0 29 3207 \n", + "1 73813 ... 0 31 3247 \n", + "2 73813 ... 0 36 3248 \n", + "3 73813 ... 0 38 3208 \n", + "4 73812 ... 0 40 3205 \n", + "\n", + " expansion_order n_points local_depth global_depth block_size \\\n", + "0 3 9600000 5 2 128 \n", + "1 3 9600000 5 2 128 \n", + "2 3 9600000 5 2 128 \n", + "3 3 9600000 5 2 128 \n", + "4 3 9600000 5 2 128 \n", + "\n", + " n_threads n_samples \n", + "0 8 500 \n", + "1 8 500 \n", + "2 8 500 \n", + "3 8 500 \n", + "4 8 500 \n", + "\n", + "[5 rows x 33 columns]" ] }, - "execution_count": 137, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "total_ranks * points_per_rank" + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "480e7a52-3277-4f55-9286-f4baa1054f19", + "metadata": {}, + "outputs": [], + "source": [ + "def runtime_vs_nodes(df, n_nodes, parameter='runtime'):\n", + "\n", + " # --- compute average runtime per experiment_id ---\n", + " runtime = []\n", + " for (id, experiment) in df.groupby('experiment_id'):\n", + " r = experiment[parameter].mean()\n", + " runtime.append(r)\n", + " runtime = np.array(runtime)\n", + "\n", + "\n", + " # --- figure with 2 subplots ---\n", + " fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6/1.6))\n", + "\n", + " # ==============================\n", + " # Top plot: Runtime vs Nodes\n", + " # ==============================\n", + " # ax1.plot(n_nodes, runtime, marker=\"o\", label=\"Measured runtime\")\n", + " ax1.set_xscale('log')\n", + " ax1.set_yscale('log')\n", + " ax1.set_ylim(100, runtime.max() + 0.5*runtime.max())\n", + "\n", + " # Perfect scaling line\n", + " T1 = runtime[0]\n", + " ideal_runtime = T1 / n_nodes\n", + " ax1.plot(n_nodes, ideal_runtime, '--', color='black', label=\"Perfect scaling\")\n", + "\n", + " # Shaded regions (using geometric means for edges)\n", + " region_edges = np.sqrt(n_nodes[:-1] * n_nodes[1:])\n", + " region_edges = np.concatenate([[n_nodes.min() / np.sqrt(2)], \n", + " region_edges, \n", + " [n_nodes.max() * np.sqrt(2)]])\n", + " y0, y1 = ax1.get_ylim()\n", + " shrink = 0.1 \n", + "\n", + " for i in range(len(region_edges) - 1):\n", + " left, right = region_edges[i], region_edges[i+1]\n", + "\n", + " # shrink inward in log space\n", + " log_left, log_right = np.log10(left), np.log10(right)\n", + " log_pad = (log_right - log_left) * shrink\n", + " left_adj, right_adj = 10**(log_left + log_pad), 10**(log_right - log_pad)\n", + "\n", + " ax1.axvspan(left_adj, right_adj, alpha=0.15,\n", + " color='orange' if i % 2 == 0 else 'lightblue')\n", + "\n", + " # label at log-midpoint\n", + " xmid = np.sqrt(left * right)\n", + " ax1.text(xmid, (y0 + y1) / 20, f\"{n_nodes[i]} nodes\",\n", + " ha='center', va='center', rotation=90,\n", + " bbox=dict(facecolor='white', alpha=0.6, edgecolor='none'))\n", + "\n", + " \n", + " # group by node count (assuming df has 'nodes' column)\n", + " stats = df.groupby('experiment_id')[parameter].agg(['mean','std']).reset_index()\n", + " \n", + " # n_nodes = stats['experiment_id'].to_numpy()\n", + " # runtime = stats['mean'].to_numpy()\n", + " runtime_err = stats['std'].to_numpy()\n", + " \n", + " # Runtime with error bars\n", + " ax1.errorbar(n_nodes, runtime, yerr=runtime_err,\n", + " fmt='o-', capsize=4, label=\"Measured runtime\")\n", + "\n", + " \n", + " ax1.set_ylabel(\"Runtime (ms)\")\n", + " ax1.legend()\n", + " ax1.set_title(f\"Strong Scaling: {parameter}\")\n", + "\n", + " # ==============================\n", + " # Bottom plot: Parallel Efficiency\n", + " # ==============================\n", + " efficiency = T1 / (n_nodes * runtime)\n", + " efficiency_err = efficiency * (runtime_err / runtime) # propagate efficiency error\n", + " \n", + " ax2.plot(n_nodes, efficiency, 's--', color=\"red\", label=\"Efficiency\")\n", + "\n", + " ax2.set_xscale('log')\n", + " ax2.set_ylim(0, 1.05)\n", + " ax2.set_xlabel(\"Number of Nodes\")\n", + " ax2.set_ylabel(\"Parallel Efficiency\")\n", + " ax2.legend()\n", + " ax2.set_title(\"Parallel Efficiency\")\n", + "\n", + " # Efficiency with error bars\n", + " ax2.errorbar(n_nodes, efficiency, yerr=efficiency_err,\n", + " fmt='s--', color=\"red\", capsize=4, label=\"Efficiency\")\n", + "\n", + "\n", + " plt.tight_layout()\n", + " return fig, (ax1, ax2), n_nodes, runtime, efficiency\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "343e0505-8c95-42d5-ad49-0773686a9cb5", + "metadata": {}, + "source": [ + "# First Experiment" ] }, { "cell_type": "code", - "execution_count": 139, - "id": "1e9f65ac-7c18-42c7-ba6c-9c405be10761", + "execution_count": 33, + "id": "dd481ef6-025e-41e6-af21-04d939989779", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total problem size n=0.1536B\n", + "global depth [2 2 2 3 3 3 4 4]\n", + "local depth [5 4 4 4 3 3 3 3]\n", + "points per rank [9600000 4800000 2400000 1200000 600000 300000 150000 75000]\n" + ] + } + ], + "source": [ + "max_nodes = 128\n", + "min_nodes = 1\n", + "ranks_per_node=16 # ranks on 1 node, such that 1 per CCX region\n", + "total_ranks = max_nodes*ranks_per_node # 16 nodes, 32 ranks per node (1 per CCX region)\n", + "min_points_per_rank = 75e3\n", + "n = min_points_per_rank*total_ranks # total problem size\n", + "print(f\"Total problem size n={n/1e9}B\")\n", + "\n", + "# Double number of nodes used until we reach max_nodes\n", + "n_nodes = np.array(list(filter(pow2, [i for i in range(min_nodes, max_nodes+1)])))\n", + "\n", + "points_per_node = [n]\n", + "for i in range(len(n_nodes)-1):\n", + " p = points_per_node[i]/2\n", + " points_per_node.append(p)\n", + "points_per_node = np.array(points_per_node)\n", + " \n", + "total_ranks = n_nodes*ranks_per_node\n", + "points_per_rank = np.int64(points_per_node/ranks_per_node)\n", + "\n", + "\n", + "# The global depth is a function of the ranks per node (as will need enough global leaves to cover this many ranks (local trees))\n", + "global_depth = v_global_depth(total_ranks)\n", + "print(f\"global depth {global_depth}\")\n", + "\n", + "# Calculate max threshold for points per leaf from largest scale problem\n", + "min_local_depth = 3\n", + "max_points_per_leaf = points_per_rank[-1] / 8**min_local_depth\n", + "\n", + "\n", + "local_depth = v_local_depth(points_per_rank, max_points_per_leaf = 2000, min_local_depth=min_local_depth)\n", + "print(f\"local depth {local_depth}\")\n", + "\n", + "print(f\"points per rank {points_per_rank}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "6d60d6e9-1da3-4292-af2f-3c04a2f49291", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192])" + "array([ 16, 32, 64, 128, 256, 512, 1024, 2048])" ] }, - "execution_count": 139, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -159,29 +517,314 @@ }, { "cell_type": "code", - "execution_count": 140, - "id": "f9d7f392-ae99-4f18-a1a1-595a3b422e07", + "execution_count": 35, + "id": "e0b74e0c-970a-4b7e-8b42-bdd6e2d66057", + "metadata": {}, + "outputs": [], + "source": [ + "# n_tasks=total_ranks\n", + "\n", + "df = pd.read_csv('strong_fft_p=153600000_n=128_11018635.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "92255628-33e5-4b3a-9a58-29b909cf383d", "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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experiment_idrankruntimep2mm2ml2lm2lp2psource_treetarget_tree...ghost_fmm_udisplacement_mapmetadata_creationexpansion_ordern_pointslocal_depthglobal_depthblock_sizen_threadsn_samples
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" + ], "text/plain": [ - "array([ 1, 2, 4, 8, 16, 32, 64, 128, 256])" + " experiment_id rank runtime p2m m2m l2l m2l p2p source_tree \\\n", + "0 0 14 6784 77 13 27 3928 2556 74311 \n", + "1 0 4 8776 69 13 24 5233 3273 74311 \n", + "2 0 6 8795 69 13 27 5260 3267 74311 \n", + "3 0 0 8837 71 13 33 5255 3271 74311 \n", + "4 0 11 8845 88 16 32 5270 3264 74312 \n", + "\n", + " target_tree ... ghost_fmm_u displacement_map metadata_creation \\\n", + "0 73812 ... 0 29 3207 \n", + "1 73813 ... 0 31 3247 \n", + "2 73813 ... 0 36 3248 \n", + "3 73813 ... 0 38 3208 \n", + "4 73812 ... 0 40 3205 \n", + "\n", + " expansion_order n_points local_depth global_depth block_size \\\n", + "0 3 9600000 5 2 128 \n", + "1 3 9600000 5 2 128 \n", + "2 3 9600000 5 2 128 \n", + "3 3 9600000 5 2 128 \n", + "4 3 9600000 5 2 128 \n", + "\n", + " n_threads n_samples \n", + "0 8 500 \n", + "1 8 500 \n", + "2 8 500 \n", + "3 8 500 \n", + "4 8 500 \n", + "\n", + "[5 rows x 33 columns]" ] }, - "execution_count": 140, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "n_nodes" + "df.head()" ] }, + { + "cell_type": "code", + "execution_count": 43, + "id": "7d1e07e3-9e86-4daf-af6d-0b60d859b911", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(
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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n_nodes = np.array([1, 2, 4, 8, 16, 32, 64])\n", + "runtime_vs_nodes(df, n_nodes, 'source_tree')" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "56e3de2d-905e-4e1f-b0f6-7019b9ee59de", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1.536e+08, 7.680e+07, 3.840e+07, 1.920e+07, 9.600e+06, 4.800e+06,\n", + " 2.400e+06, 1.200e+06])" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "points_per_node" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da498450-e577-4b33-a4e1-29f02554c400", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "099fc685-5267-4756-ad2d-7479fde532e2", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, - "id": "23d20266-53e5-40a5-9561-fcc5347a011c", + "id": "022354c9-5234-47ad-88d1-aab701839b8d", "metadata": {}, "outputs": [], "source": [] diff --git a/hpc/archer2/data/ijhpca/Weak Scaling (16 Nodes).ipynb b/hpc/archer2/data/ijhpca/Weak Scaling (16 Nodes).ipynb index 6d08f88c..de5dc120 100644 --- a/hpc/archer2/data/ijhpca/Weak Scaling (16 Nodes).ipynb +++ b/hpc/archer2/data/ijhpca/Weak Scaling (16 Nodes).ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "e74144a3-a310-4e35-8b6f-ceadad982fc0", "metadata": {}, "outputs": [], @@ -14,19 +14,20 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 2, "id": "a15c010f-3256-4039-97b3-4ab6c4bb403e", "metadata": {}, "outputs": [], "source": [ - "df_5e6 = pd.read_csv('weak_fft_10984742.csv')\n", - "df_1e6 = pd.read_csv('weak_fft_10984754.csv')\n", - "df_250k = pd.read_csv('weak_fft_11004445.csv')" + "# df_5e6 = pd.read_csv('weak_fft_10984742.csv')\n", + "# df_1e6 = pd.read_csv('weak_fft_10984754.csv')\n", + "df_250k = pd.read_csv('weak_fft_11004445.csv')\n", + "df_250k_nc = pd.read_csv('weak_fft_11018099_non_contiguous.csv')" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 3, "id": "6cbe52e4-fe73-477f-87aa-4ffd218c4542", "metadata": {}, "outputs": [ @@ -232,7 +233,7 @@ "[5 rows x 33 columns]" ] }, - "execution_count": 10, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -243,28 +244,7 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "b5623876-8efb-48c1-9e93-a2f8459f2a67", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1016, 33)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_1e6.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "id": "3bff612b-6a2b-449e-a301-cb45048cf545", "metadata": {}, "outputs": [], @@ -275,7 +255,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 5, "id": "caf1a774-3904-4e65-95bf-f37f1180a3d6", "metadata": {}, "outputs": [], @@ -398,13 +378,13 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 6, "id": "022c161a-007f-4f90-8f6d-3b73ed57dc00", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -419,13 +399,13 @@ }, { "cell_type": "code", - "execution_count": 18, - "id": "eb5e74a3-f5b1-4188-8cc9-0887efacbb2e", + "execution_count": 7, + "id": "9125de69-3963-4cb2-9d59-80ed2dc441b2", "metadata": {}, "outputs": [ { "data": { - "image/png": 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yThgxmlWCfBUSwxu/TURD7HU7UqQsXJC9ODkhxGHn9NErDEmSrSOEOBdJdoSmbM0EU2L90DVhn6C6LmgbgE6BA4U1HDlZ44jwhHCYdHu9TqhDzzuiva1uR4qUhTgTSXaEptKauE1EQ0L99fRu5Q/IVJZwPRnHHD+yA6fqdrYdqaK0WorzhWiIJDtCU+nN2CaiIfZ+O7IEXbiQsioT2YXWBLxrgmNHdhLDfUiO9MGswnp53gvRIEl2hKZObRPRvOJkG9sS9HVZFaiqrE4RrmFPnnVUJy7Un4ggxzzX67J1U5a6HSEaJsmO0MzxchMF5WYUICXGMSM7fVv7E+CjcLzCbF/lJYTWTtXrOHYKy8a+T1ZWuST5QjRAkh2hGdsUVnKkD4G+jnkq+hl0DEq0ju6slbod4SKcVa9jc0FSID46yCk2cahIivOF+DNJdoRm0h1YnFyXvd+OLEEXLsLWOdnR9To2Qb46+reRJehCnIkkO0Iz9uLkZmwT0RBbv51N2ZUYzTKkL7RVY7Kwr6AMgBQnJTtQdypLkh0h/kySHaEZe3FyrGMLNrvE+BIdqKeiRuX3I1UOPbcQ52t/QRkms0qIv4FW4f5Oux9bsrPhUIUk+UL8iSQ7QhPVJgv7Tti2iXDsyI5OURhSZ1WWEFpKt9XrxIegNLNx5tl0i/MjKlBPuVFl22FZgi5EXZLsCE3sPW7EZIEwfx0JIQaHn39Y8qkNEoXQkn2ncydOYYE1ybfVq8lUlhD1SbIjNJGef6o42RnfdofWNhfccbSKkirpKiu04+zi5LrsdTtSpCxEPZLsCE2caibo2Cksm9Zhp7rKbsqWIX2hDYtFJTPPucvO67KNaO7KreZEucnp9yeEu5BkR2ji1DYRju8mazPUvnWEfMsV2jhcVEF5tRlfg4520UFOv7/YYIP9NSVTuEKcIsmOaHGqqtqTnW5OGtmBU99y10j9gtCIrTi5U2wwPvqWebu17YK+SqayhLCTZEe0uNxSE8WVFgw66BTtvJGd1LYB6BTYf6KGYyXSVVa0vJYqTq7rwtq6nTUHZH84IWw0TXbatWuHoiin/UyePBmAqqoqJk+eTFRUFMHBwYwfP568vLx658jOzmbs2LEEBgYSGxvL448/jskkc9WuzFac3CHKFz+D856CYQF6eiZYR45kF3ShhYxc68hO1xao17Hp38a6P1xBuZkM2R9OCEDjZGfLli0cO3bM/rNs2TIAbrjhBgAeeeQRFi9ezMKFC1m1ahVHjx7luuuus9/ebDYzduxYjEYj69evZ8GCBfz3v//lmWee0eTxiMY51UzQeVNYNsOkbkdoyDay0yW+5UZ2/Aw6LmgrW0cIUZemyU5MTAzx8fH2nx9++IEOHTpw4YUXcvLkSd5//33mzp3LyJEj6d+/Px9++CHr169n48aNAPz888+kpaXxySef0KdPH8aMGcPzzz/P/PnzMRrlG42rSnPySqy6bEXKa7NkSF+0rOOl1RwvM6Io0Dk+uEXv21a3s/pAeYverxCuymVqdoxGI5988gmTJk1CURS2bt1KTU0No0aNsh+TkpJC27Zt2bBhAwAbNmygZ8+exMXF2Y8ZPXo0JSUl7N69+4z3VV1dTUlJSb0f0XJaYiWWTb82/vgbrEP6e49LAixaTnrtqE67qCACfR3fOPNsbP12tuRUUWG0tOh9C+GKXCbZWbRoEcXFxdx+++0A5Obm4uvrS3h4eL3j4uLiyM3NtR9TN9GxXW+77kxmzpxJWFiY/ScxMdFxD0ScVYXRQlahtVi4Jaax/A06BiZah/TXyqos0YIyjrVcf50/ax/pQ+swA0azyqYcqVcTwmWSnffff58xY8bQqlUrp9/X1KlTOXnypP0nJyfH6fcprDILjKhAdJCemOCW+bZra6EvRcqiJWXktlzn5D9TFIURydJNWQgbl0h2Dh06xPLly/nb3/5mvyw+Ph6j0UhxcXG9Y/Py8oiPj7cf8+fVWbbfbcc0xM/Pj9DQ0Ho/omW0RH+dPxta+6a/MbuCGtkNWrQQ28hOl/iWH9mBultHSN2OEC6R7Hz44YfExsYyduxY+2X9+/fHx8eHFStW2C/LzMwkOzub1NRUAFJTU/njjz/Iz8+3H7Ns2TJCQ0Pp1q1byz0A0WgtuRLLpmusL5EB1t2gtx+tarH7Fd6rrMpEdqF1REWLkR2AIe0C0df2mTpyUvpMCe+mebJjsVj48MMPmThxIgbDqWmNsLAw7rzzTqZMmcLKlSvZunUrd9xxB6mpqVxwwQUAXHbZZXTr1o1bb72VHTt2sHTpUqZNm8bkyZPx82u5D1PReKdGdpxfnGyjUxSGtJO6HdFyMmv768SF+hMR1HLP9brC/PX0ae0PSBdxITRPdpYvX052djaTJk067brXXnuNK6+8kvHjxzNixAji4+P55ptv7Nfr9Xp++OEH9Ho9qamp3HLLLdx2223MmDGjJR+CaCSLqpJRZ7fzljQsWfrtiJZzql5Hmyksm+FStyMEAC27HrIBl1122Rn7n/j7+zN//nzmz59/xtsnJSXx448/Ois84UCHi02UGS346hXaR7Xst11bv53fj1RRWm0mxE/fovcvvIvW9To2I9oHMm9NIWsPVmCyqBh0iqbxCKEVzUd2hPewNRPsHOPb4m+6ieE+JEX4YFZhc7asyhLOZeucrFW9jk3vBH/C/HWUVFnYIfVqwotJsiNaTFp+yxcn1zXUVrcjS9CFE9WYLOwrKANadgPQhuh1in3LFKnbEd5Mkh3RYlqyc3JD7P125E1fONG+gjJMZpXQAAOtwv21Dofh7aVuRwhJdkSLSW/BPbEakpoUiALsOW4kr9SkSQzC89k7J8eHoija18jY+u1sP1rFyUqzxtEIoQ1JdkSLKKkyc/ikNcHoptE0VniAnp4J1vuWVVnCWU7tdK5tcbJNq1AfOkb5YlHleS+8lyQ7okXYlpy3DjUQFqDdSij7Lujypi+cxFWKk+uyje5I3Y7wVpLsiBZhr9fRaArLxlasuS6r4owtD4RoKotFJSNXuw1Az6Ru3Y4874U3kmRHtIh0jVdi2fRP9MfPoJBXZmb/CWmhLxzrcFEFFUYzvgYdydFBWodjd0HbAHz1CkdKTPK8F15Jkh3RItLytF2JZeNv0DGwjXWFjGwdIRwtvbY4uVNsMAa967y9BvjoGJRofd7LxqDCG7nOq1F4LJNFJbOgdpsIjaex4NQu6FK3IxzNVq+jdX+dhoxobx1pkrod4Y0k2RFOd7CwhmqTSqCPQlKEj9bh2PvtbDxUicki9QvCcWwjO1rvidUQW93OxuxKqk0WjaMRomVJsiOczjaF1SXWD50L9B3pFudHeICOMqO00BeOZdsA1BVHdlJifIkN1lNZo/JbjjzvhXeRZEc4ndadk/9MpygMTaqdypIhfeEgx0urOVFmRFGgU1yw1uGcRlGUU7ugy/NeeBlJdoTT2ZKdbi5Qr2Njq9uRJmvCUdJr63XaRQUR6GvQOJqG2ep2pEhZeBtJdoTT2beJ0HjZeV22fjvbjlRRbpT6BdF89m0iXLBex2ZYuwAUID3fSH6ZbJkivIckO8KpCivM5JWZUYAUF0p22kb4kBhuwGSBzdmyC7povvRc1+uc/GdRQQZ6xFtfh7IqS3gTSXaEU9lGdZIifAjyda2n2zDZOkI4UKZ9ZMd1kx04tXWE7IIuvIlrffoIj5PmIp2TGzK0ztYRQjRHWZWJ7ELr8yjFRTYAPRNb3c7arAossnWE8BKS7AinstfrxLnGSqy6hrQLRAEyCqR+QTRPZu1+WHGh/kQEud5zva6+rf0J8lU4UWG2t4UQwtNJsiOcypVHdiID9XSvrV9YL1NZohky7PU6rj2qA+CrV0itbb2wSqayhJeQZEc4jdGssv+462wT0ZChUrcjHCDDTep1bC6Uuh3hZSTZEU6z77iRGguE+utoHeqafUeGJQcAsC6rElXqF0QT2ffEcvF6HRtbkfK2w5WUVUvrBeH5JNkRTpNeZwpLcYFtIhoysE0AvnqFY6UmDhTWaB2OcEM1Jgv7CsoA9xnZSYrwpW24DzUW2JgtozvC80myI5zGFZsJ/pm/j44BbfwBWZUlmmZfQRkms0pogIFW4f5ah9NosgRdeBNJdoTTnNomwrVXp9i2jpC6HdEUp6awQl12BLMhtmRHmgsKbyDJjnAKVVVJz6stTnbhkR041Vxww6FKTBap2xHnxx22iWhIalIABh1kFdaQUyxTuMKzSbIjnCKvzExhpRm9Ap1iXHtkp0e8H2H+OkqrLew8VqV1OMLN1B3ZcSchfnr6tbZOu62SjUGFh5NkRziFrV6nfZQv/gbXfprpdQpDkmzdlGWfLNF4FotKRq57juxA3V3QZSpLeDbX/hQSbutUM0HXHtWxGVq7BF3qdsT5yCmqoMJoxs+gIzk6SOtwzputbmfDoUpqzDKFKzyXJDvCKU5tE+Ha9To2trqdbYcrqTBK3xHROLZ6nU5xwRj07vd22j3Oj4gA6xTu9qMyhSs8l/u9OoVbSHfhbSIakhThQ+swAzUW2JwjU1micWz1Ol3crF7HRq9TGJYsS9CF55NkRzhcVY2FrNoGfd3cZGRHURT76M46mcoSjZReO7LjDntincmpuh0pUhaeS5Id4XCZBUYsKkQH6okNds1tIhpi3ydL+o6IRrJtAOounZMbMqJ2ZGfnsWqKKswaRyOEc0iyIxzOPoXlJqM6NkPbWYuU0/ONHC83aRyNcHUFpdWcKDOiU6BznPuO7MSFGOgS44uKFOgLzyXJjnC4U9tEuMdKLJuoIIN92m3dQanbEWdnq9dpFx1EgK9e42iaR7aOEJ5Okh3hcGluthKrrmG1oztStyPOxbYSq4ub7HR+NqeSnXJUVZagC88jyY5wKFVVyShwj20iGmLfJyurQt70xVml19brdHXjeh2bgYkB+BkU8srM7D1u1DocIRxOkh3hUIdPmiittuCrV+gQ5V7TWAAD2wTgq1c4WmLiYJHsFyTOzL5NhAckO/4GHYPbWkc1ZSpLeCJJdoRD2aawOkb74qN3nx2gbQJ9dfb9gmRVljiTsioTOYXWuq4UD5jGglNTWask2REeSJId4VC2lVjd4txvVMfG1mRN6nbEmWTW7ocVF+pPRJD7PtfrurD2eb85p5KqGukiLjyLJDvCoU6txHK/eh0bW7+d9YcqMVukbkecLsNer+MZozpgHY1NCDFQbVKli7jwOJLsCIdKz3ff4mSbXgl+hPjpKKmy8EdutdbhCBeU7kH1OjaKojBclqALDyXJjnCY0moz2cXutU1EQ/Q6hSFJtUvQpW5HNMC27DzFg0Z24FQ3ZUl2hKeRZEc4TGbtqE5CiIHwAPdusmar25GOsuLPakwW9heUAZ6x7LyuYcmB6BTYc9zIsRJZjSg8hyQ7wmFONRN0/4JNW93O1sNVVEqxpqhjX0EZJrNKaICBhDB/rcNxqPAAPb0SrI9pjYxqCg8iyY5wGPueWG5cr2OTHOlDq1ADRrPKFinWFHXY++vEh6Io7tde4VxkKkt4Ikl2hMOkeVCyoyiKfXRH9skSdaV7aL2Oja3fztqDFbIaUXgMSXaEQ5gtqr1mxx33xGrIsGRrkbI0FxR1ZXrgSqy6+rT2J8RPR3GlrEYUnkOSHeEQB4tqqDKpBPgotIvw0TochxiSZP2GuzuvmsIKs8bRCFdgsahk1DYU9JTOyX9m0CkMrd0Qd41MZQkPYWjKjbKyslizZg2HDh2ioqKCmJgY+vbtS2pqKv7+nlWwJxrH1kywS4wfep1n1DHEBBtIifUlI9/IuoMVXNXNMz/cROPlFFVQYTTjZ9CRHB2kdThOMzw5iCWZ5aw+UM4DwyK1DkeIZjuvZOfTTz/l9ddf57fffiMuLo5WrVoREBBAYWEh+/fvx9/fnwkTJvDkk0+SlJTkrJiFCzpVnOz+K7HqGtYuUJIdYWer1+kUF4xB77kD47a6nW1HqiipMhPq796tJIRo9Ku1b9++vPHGG9x+++0cOnSIY8eOsXXrVtauXUtaWholJSV89913WCwWBgwYwMKFC50Zt3Axp/bE8ox6HZuhtStT1mRVoKpSrOntPL1exyYx3If2kT6YVeu2KUK4u0YnO7NmzWLTpk38/e9/JzEx8bTr/fz8uOiii3jnnXfIyMigffv2Dg1UuLa0PPffJqIhgxID8NHBkZMme3do4b1sIzue1kywIbbRHanbEZ6g0cnO6NGjG33SqKgo+vfv36SAhPspqjCTW2oCIMXDkp0gXx19W9tWZck3XG9n2wC0i4cWJ9c13NZvJ6tcRjWF22vSpPO2bdv4448/7L9/9913XHPNNfzjH//AaDQ6LDjhHmxTWG3DfQj287w6BtsS9HWydYRXKyit5kSZEZ0CneM8P9m5ICkQHx3kFJs4WCSjmsK9NemT6Z577mHPnj0AHDhwgJtuuonAwEAWLlzIE0884dAAhetL89DiZBtbc8H1h6TJmjez7XTeLjqIAF/PL9gN8tUxINGa6Es3ZeHumrT0fM+ePfTp0weAhQsXMmLECD777DPWrVvHTTfdxLx58xwYonB16fY9sTxrCsumd6tTTdbS8qrpmSDtFbxRpr1zsufX69iMaB/IhkOVrMmqYOKAcK3DEY3lH+v8+6jKd/59OFCTRnZUVcVisW6OuHz5cq644goAEhMTOX78uOOiE24hvbZzsqetxLIx6BQuSKptsibdlL1Weq5tTyzPn8KysdXtbDhUgdEso5rCfTUp2RkwYAAvvPACH3/8MatWrWLs2LGAtdlgXFycQwMUrq3GrLLvuGeuxKprmH2fLEl2vFWGlyw7r6tbnB/RgXrKjSpbD0uBvnBfTUp25s2bx7Zt27j//vv55z//SceOHQH46quvGDJkiEMDFK5t/wkjRrNKiJ+ONmFNmhV1C7a6nS05VVTVWDSORrS00qoacgqtH/ZdPXQD0IboFIXh7WUXdOH+mvTp1KtXr3qrsWxefvll9HrPL9wTp9TtnKwobrBNRBPnsju0htduDKOyxkIRQST4n+Wl42Zz2d7Ar5ndjkst8NK1PQjwNRAX0nDNVrXZBZNgB9RuPDgqgoGdKogI1IN/8OkHuOjzvbl/88Zwyb+5aFCznw1lZWWUlJRQUlKC0WiksvL8hjqPHDnCLbfcQlRUFAEBAfTs2ZPffvvNfr2qqjzzzDMkJCQQEBDAqFGj2Lt3b71zFBYWMmHCBEJDQwkPD+fOO++krKysuQ9NNEKarTjZg6ewABQF4kOsCY6tp5DwHsWV1qna8EDP2OT2fNie90UVZiprpG5HuKcmJTtZWVmMHTuWoKAgwsLCiIiIICIigvDwcCIiIhp9nqKiIoYOHYqPjw8//fQTaWlpvPrqq/XOMWfOHN544w3eeecdNm3aRFBQEKNHj6aqqsp+zIQJE9i9ezfLli3jhx9+YPXq1dx9991NeWjiPHn6Sqy6bG/6eZLseJ2iCmufGW9MdgJ8FMIDrCP2kugLd9WkaaxbbrkFVVX54IMPiIuLa/L0xezZs0lMTOTDDz+0X5acnGz/f1VVmTdvHtOmTePqq68G4KOPPiIuLo5FixZx0003kZ6ezpIlS9iyZQsDBgwA4M033+SKK67glVdeoVWrVk2KTZybqqr2lViePrIDp5Kdwgoz1SYVP4MbTNsJhyi2Jzue2UvqXBJCDRRXWjulJ0d6X8LnycxmM3/sSiOpbSIREeFah+M0TRrZ2bFjBx9++CF/+ctfuOiii7jwwgvr/TTW999/z4ABA7jhhhuIjY2lb9++vPfee/brs7KyyM3NZdSoUfbLwsLCGDx4MBs2bABgw4YNhIeH2xMdgFGjRqHT6di0aVOD91tdXW2ferP9iPNXUG7mRIUZnQJdYjz/QyDARyGsdvdnGd3xHhaLSkmlNdmJ8MKRHYCE2kT/WIkJ2TnCvT386FTe//BjwJroXDhqLP0GX0hihx78umqtxtE5T5OSnYEDB5KTk9PsOz9w4ABvv/02nTp1YunSpdx33308+OCDLFiwAIDc3FyA05azx8XF2a/Lzc0lNrZ+EZ7BYCAyMtJ+zJ/NnDmTsLAw+09DG5uKc7PV6yRH+uDv43nbRDTENrpzTJIdr1FSVYOqqvgYdAR6QefkhsQE6dHrFKpMFoqrzFqHI5rhq2+/p3evHgAs/mEJWQezydi5mUcevI9/Tn9e4+icp0nTWP/5z3+49957OXLkCD169MDHp/63nV69ejXqPBaLhQEDBvDSSy8B0LdvX3bt2sU777zDxIkTmxJao0ydOpUpU6bYfy8pKZGEpwlsK7E8tZlgQ+JDDGQWVMvIjhexT2EF+ALeOXWp1ynEBes5WmLiWImJiADvTPo8wfHjJ4iPsw4Q/LhkGTdcdzWdO3dk0sRbeP2tdzWOznma9HW8oKCA/fv3c8cddzBw4ED69OlD37597f9trISEBLp161bvsq5du5KdnQ1AfHw8AHl5efWOycvLs18XHx9Pfn79pY8mk4nCwkL7MX/m5+dHaGhovR9x/tK9YCVWSUkJi777H+npmQDEButRFCgzWiitlmWnnshsNrNj+3aKiooAKKrwnpVYZrOZ7Tv+oKio+LTrEkKtj98TRzVLSkr47rtFpKenax2K08XFxZCWnonZbGbJsuVceslFAFRUVqBvgeX6WmnSI5s0aRJ9+/Zlw4YNHDhwgKysrHr/bayhQ4eSmZlZ77I9e/aQlJQEWIuV4+PjWbFihf36kpISNm3aRGpqKgCpqakUFxezdetW+zG//PILFouFwYMHN+XhiUayFyd70MjOjX+9g7f+9W8AKisrGZA6khsn3EGvAcP4+tvv8dErRAfKqixP8uiUR/jwg/cB64f9qIsvZvDAAXRol8SqX3+tU5zsecnO+dRv2KZwC8rM1Lj51hF/vekv/Gv+fMD6Ok8dPIgJN93EgL59+PabrzWOzrnuuG0CN064gx59h6AoCqNqk51Nm7eS0qWztsE5UZOSnUOHDjF79mwGDx5Mu3btSEpKqvfTWI888ggbN27kpZdeYt++fXz22Wf8+9//ZvLkyQAoisLDDz/MCy+8wPfff88ff/zBbbfdRqtWrbjmmmsA60jQ5Zdfzl133cXmzZtZt24d999/PzfddJOsxHKiqhoLB07U7onlQSM7q9euZ/gwayL97Xc/oKoqxfkHeWPuLF6Y+Qog/XY8zbdff02vXr0B+OGHxRw8mMXO3Wk8+NDDPPP0NE5W2IqTPa8I/3zqN0L9dAT66rCoKvll7l23s3bNGoYNGwbAd4u+RVVV8k8UMnfePGbWllV4qmeffor/vPMGd985kXUrl+DnZ33/1uv1PPXYw9oG50RNSnZGjhzJjh07mn3nAwcO5Ntvv+Xzzz+nR48ePP/888ybN48JEybYj3niiSd44IEHuPvuuxk4cCBlZWUsWbIEf/9TXUw//fRTUlJSuOSSS7jiiisYNmwY//73v5sdnzizPceNmFWIDNATG+w58/cnT5YQWdvnacnPKxh/7VUEBgYydsxl7N1nHbW0JztlsjLFExw/fpy42invJT/9xHXXX0/nzp2ZeMcd7Nq1C5PFgk6nEHK2rtlu6mz1G3/sqj+loyh1VmW5eaJ/8uRJIiIjAfh56VKuvfY6AgMDGXPFWPb9qWmtJ7r+uqt55KG/Ex0dZb9s4q03c/W4KzSMyrma9Oq96qqreOSRR/jjjz/o2bPnaQXK48aNa/S5rrzySq688sozXq8oCjNmzGDGjBlnPCYyMpLPPvus0fcpmu9UM0E32SaikRLbtGbDpi1ERkaw5OcVfPGxdXqjqLgYf3/rN6DIID0GnYLRpFJUaSYy0HOSPW8UFxdHeloaCQkJLFu6lDfeqp3eqKhAp7P+bcMCfDzqeW5jq99ISIhnybLlvP3Gq8CZ6zdahRrYf8JIbmkN0PC2Ge6gTWIimzZuIDIykp+XLuXjT62fH8VFRfW+SHsis9nMS7Pn8s57H5KXl8+eXb/Rvn07nn72RdolteXOO27VOkSnaFKyc++99wI0mIAoioLZ7N5DnOLcTu2J5TlTWAAPP3AvEybeTXBwEG0T23DRhdah7tVrNtCzh7WYXq9AXIiBIydryC01SbLj5m6beDsTbr6J+IQEFEXhktq+Xps3byKpvXWTY09tJmir30iIj29U/UZsiAEFKKmyUG60EOTrngWtDzz4EBNvvZXg4GAS27blwosuAmDNmjX06NFT2+Cc7MVZr7Lg48+Z89Kz3HXfw/bLe3Tvyrw335Fkpy6LRVaheLu0PM8rTgb4+71/Y9DA/uQcPsKll1yETmd9M2+fnMQLz/7Tflx8nWTHm5bee6Knp0+ne4/u5OQcZvz119erYbh+0v2A5zYTfPbpp+jRvSs5OUe4YfzV56zf8NMrRAUZOF5uXYLeMdo9k8B777uPgQMHcvhwDpeMutT+Ok9OTubZs8wieIKPPvmCf/9rHpeMvJB773/Ufnnvnj3IyNyjYWTO5XmT0MLprNtEeObIDsCA/n3p1bM7WVmH6NAhGYPBwNgrRtc7Jr62Tim/3IzJomLQed4Uhze5bvz1APX23Lv1toks3nGU6hoz4QGemeyAtX4D6j/2ibfefMbjE0Jqk51S9012APoPGEDPXr3IysqiQ4cOGAwGrhg7VuuwnO7I0WN07JB82uUW1UJNjXvXYp1No8cgv/jii0afNCcnh3Xr1jUpIOH6jpSYKK224KPDrd/sGlJRUcGd9zxAYHgruvdNJTv7MAAPPPwEs15+zX5cqL8efx8dFovK8XKZtnVnZrOZl154geS2iUSGhdrbZ0ybNo2lX1trOcI8dBrLbDbz/Esv0zq5G8GRbThw4CAATz/7on1J+p8lhJ5ajWhx0wL9iooK7rnrb4SHBNO3V097b7eHH3qQl2fP1jg65+rWtQtr1m047fKvvvmevn08dwqv0cnO22+/TdeuXZkzZ06DjZdOnjzJjz/+yF//+lf69evHiRMnHBqocB224uSO0b746j1rRGPqtBns2LmLX5ctrleoOGrkRXy58Fv773VXpsgSdPc266WX+OijBbw0axa+vqeSmrYdu7Dy+y8I8fdB76Ejdy/OepX/fvQZc156tt5j79G9K/85Q7ITGajHV69QY1Y5UeGeif60f/6DnTt3smzFL/Ve5yMvuYSFC/9Pw8ic75l/PMH9Dz/B7FfmYbFY+GbRYu667yFenPUqz/zjCa3Dc5pGJzurVq1i9uzZLFu2jB49ehAaGkqnTp3o2bMnbdq0ISoqikmTJtG2bVt27dp1XiuyhHuxT2F5YK3KosX/4615cxg2NLXe6pvu3VLYX/ut1yZOkh2P8MknH/Ovt9/h5r9OQK8/VWzepkNXjh7a75HNBG1s9RsTbr6x3mM/W/2GTqmzR1yJez73F3/3HfNef4Ohw4bVe51369adA/v3axiZ81097goWf/M5y1esIigokGdmzCQ9I5PF33zOpaMu1jo8pzmvmp1x48Yxbtw4jh8/ztq1azl06BCVlZVER0fTt29f+vbtay/0Ep4rPc/zmgnaFBScIDYm5rTLyyvK+fPKY9sbfmGFmWqTip/BM7/9e7qjR47QoWPH0y4/WWHEbDJ5ZDNBm6bWbySEGsguruFYqYleEc6M0DkKCgqI+dMG0gAV5eUe2WLgz4YPG8Kyn74994EepEkFytHR0fYOxsL7pHl0cXIf/vfTzzww+W4A+xvffz74mNTBg+odG+ijEOqvp6TKTH6ZicRwzx0B8GRdu3Vj3do1p3V/X7p4Ee06d/fokR1b/UZSUtt6l5+rfsOW6J8oN3Gy0kyYm20M2r//AH768Ucm329dbWd7nX/wwfsMvuACLUMTTiKrscR5Kau2cKjI2j7fE6exXprxNGPG3UhaegYmk4nX33qHtPRM1m/czKrlP5x2fHyIgZIqM8dKJdlxV/+YNo2/3XEHR48cxWKxsOjbb8nMzGDhx5/w2Cvve3Sy88w/nmDi3+7jyNFj9vqNzL37+OiTL/jh2zMvSgny1dkT/XUHK7iia0gLRt18M154gXFXjiU9PQ2TycRbb75BeloaGzdsYPkvK7UOz+Ei4to1esSqMDfLydFoQ+acxHnJLLCO6sQF6z2ymd6woals37wak9lMzx7d+Hn5SmJjo9mwein9+/U57fj4EOu/gdTtuK9x467mm0XfsWLFcoKCgpjx7HR2707n0Vf+w6DhF+Nr8LznuU1z6jdsBfqrsypaIlSHGjpsGJu3bsNsMtGjR0+WL1tGbGwsq9euo1///lqH53DzXpnJay+/xGsvv8S0px4DYPSlI3l22lM8O+0pRl86EoCnpz6uZZhOJSM74ryk5XlucbJNhw7JvPf26406NjbY2lG2rNpCWbWFYD/5/uCOhg0fzk9Lf7b/vi+/lO3ZxR49qmPT1PqNhFADmQXVrD5Qgaqqblfr0qFDB95+1zv2UKzbN2n8X25jxjNTuf/vd9sve/D+e3jrX/9m+S+reOShv2sRotNJsiPOiyc2EywpKWn0saGhofV+963TUTavzESwn+cWs3qTIvtO556f7DRVbLAenaJwtMTE/hM1Lt9zqzmvc0+ydNkvzH7x2dMuv/yyUTw1zXO7Rzcr2TEajfW6TwrPl55fuxLLg0Z2wmMbP59trjy9f1R8nY6yHaJc+w1fWMVFR531b15jVlFRMeh05B8/3oKROZ+j6jcMOoXY2k7iqw+Uu3yyExsV2ejHXWmscXI02omKiuC7xT/y6CP317v8u8U/EhXlhkvrGqlJGUpFRQUPPPAACxYsAGDPnj20b9+eBx54gNatW/PUU085NEjhGiyqSqYHjuys/Hmx/f8PHsrmqWnPcfutN5M6eCAAGzZtYcEnXzDz+WcavH18iIFduZBXakZVwb0G873TK3Pn2v//xIlCZr30IpdedhmDL0hFVVW+/mklOzet4ompUzWM0jnmvTLT/v8nThTywqxXGH3pSPtqww2bNrN02S+Nqt+oW7czaZBrf1D+vHyF/f8PHTrItH/8g1tvm8jgVOvqq00bNvLJxx/x/IsvahVii3ju6an87d4H+XX1WgYPGgDAps2/seTnFY2evndHiqqq593w+6GHHmLdunXMmzePyy+/nJ07d9K+fXu+++47nn32WX7//XdnxOo0JSUlhIWFcfLkSccOX5qroWg76INA79rfehojq9DIxe8cwt+gsPuxDu7ZVdb/9N4adV0y+mr+NulWbv7L9fUu/+yLhfz7/QX8uuz0FVlmFb7eWYLJojImJZgIxUu7h5uNYC6HiD6gb1wybLaoFFfXYNAp6JxY8+GnP3Mt1V9uuJ4LL7qYv0+eDEBRhZEVaXms+PojCjJ/46tvGlfPUm1u/AbJFlMVauF2QgLC0Ps48YvDOZ7v4/9yGxdfOKxe/QZgr99Y9NWnZ719UaWZvjM34m9Q2D6lPf6Gc9esmU1G6988rDM6g/Me+9n+5qMvvZRJd07iLzfV3wPsi88/4/33/sOyX35p1H245N8czvl337T5N96Y/y7pGdbGkV1TOvPg5HvsyU+jVOU3/tgmvDc0VmM/v5tUTblo0SLeeusthv2p+2T37t3Z7+HdJ72ZbZuILjG+7pnoNMKGTVsY0K/vaZcP6NeXzVu2NXgbvYJ9OF9WZbmfZT//zGWjT230erK2XmfExZfyy4oVZ7qZR1i67Bcuv2zUaZdfftkolv+y6py3D/fXExusp8qksjWn6pzHu4pNGzfQr//pH+z9+g9gy5bNGkTUsgYPGsCnC95j26ZVbNu0ik8XvHd+iY4balKyU1BQQGwD3SfLvaT7pLfyhpVYiW1a894HC067/D8ffkRim9ZnvJ29fb4kO24nKiqKxd9/b/+9qMJal/b72mVERUVpFVaLsNVv/Flj6zcUBYYnBwLutQS9TWIiH/znP6dd/uH779MmMVGDiFqW2Wzm62+/54WZr/DCzFf49rsfMJvdc5+zxmpSzc6AAQP43//+xwMPPADU6TL7n/+QmprquOiES7EVJ3tSvc6fvfbyi4y/aSI/LV1u/6azectW9u47wNdfnJ4E2diSnYIyM9UmC36NGM4XruHp6dO59+67Wb3qVwYNGsyB42X8sW0rf2xaxdvvvqt1eE7liPqNEe2D+PqPUlYdKGfqyGhnhuswL7/yKjfdeANLly5h0CBrrdKWLVvYt3cvX/zfQo2jc659+w4w9pq/cPjIUbp0tm6TMnPOayS2ac3/Fn1Jhwa2D/EETUp2XnrpJcaMGUNamrX75Ouvv05aWhrr169n1apzD30K9+SJy87/7Ioxl7F392/86933ycjcC8BVYy/n3rvuIDGxzRlvF+avx9+go8pkYduRKlKTAlsqZNFMt028nZSUrsx/600WffstxZU1tErqwA8//8LFI4ZqHZ5T3X7bX+ma0pk35r/LN4ushfpdUzqzduVPjZ7WGJ4ciAJk5BvJLzMRG+z6K3PHXHEFuzMyefftt8nMzABg7Ngrueuee0j08JGdB6c8SfvkJDas/pnISOvo3YkThdxy+908OOVJ/vedZ+763qQCZYD9+/cza9YsduzYQVlZGf369ePJJ5+kZ88z76fiqqRA+dyKK830ee0AADuntCfU3027yp6jcK851h+s5GCRkaN5R3jsIvf4hutQblqgXFdZVQ1LduWi0ylc27f1eU3Lu2SxqhOf73ZV+Yz7MJudx6p59ao4xvc8+3uoKxQoO4pL/s3hrH/3oIjWbFzzMz17dK93+Y6dfzD0ojGUFR5u3H24WYFyk1PwDh068N577zX15sLN2EZ1EsMN7pvoNFJx8Une//Bj0jOtKxW6d01h0u0TCAsLO+vt4kIMHCwysvZgJY+1RKDCYcxmM99/t4hN2/4gp7CClG7duLr3rej1nv1cB+tjX/T9/+wrc7p3S2HclWPO67EPTw5k5zFrN+VzJTuuori4mA8/+IDMjHQAunbrzu133HHO17m78/PzpbS07LTLy8rK8fX13CaazUp98/Pz2bVrFzt37qz3IzyPbSWWJ09hAfy29Xc6dO3La2++TWFhEYWFRcx9Yz4duvZj2+87znpb2z5ZO49VcbLKs4v9PMm+ffvo3bMHk26/nR8Xf8dvvy7hlakP0KdXT49fXbpv3wG69b6A2ybdxzeLFvPNosXccvs9dO+Tyv79jd8QckT7IADWHKjA0rTJgha19bff6Nq5E2++Po/CwkIKCwt5Y95rdO3cid+3Nbzq0lNcecVo7v77w2za/BuqqqKqKhs3beHe+6cw7soxWofnNE0a2dm6dSsTJ04kPT2dP8+CKYri8VXd3sgbipMBHnn8H4y7cgzvvf26vSu4yWTib/c+yMOPTWX1itNXrtgE+eoI8dNhUWHjoUpGdwluqbBFM0x5+CGSk5NZvXYdu4+bySupIjlE5dkp9zLl4Yf4bvHpvZU8haPqN/q19ifYV0dhpZndudX0TPB3ZtjN9vijj3LlVVfx9rv/rvc6v/fuu3hsyhRW/PqrtgE60RtzZzPxzvtIHXEZPj7WkRyTycS4K8fw+qszz3Fr99WkZGfSpEl07tyZ999/n7i4OFlu7gXsxckevOwc4Let23nvX6/X2/7EYDDwxKMPMiB15Dlvb1uVte5ghSQ7bmLN6tWsWbeeyMgIirOPAZDcOo4XX5rJRSOGaxydc61as56Na04lOgBRUZHMenE6Qy9q/Ld8H71CarsAlu0pZ3VWhcsnO1u3/sa/3n33tNf5o489bu8k7anCw8P47uvP2Lt3PxmZtqaCXejYsb3GkTlXk5KdAwcO8PXXX9OxY0dHxyNcUI1ZZU9B7Z5YHj6yExoaQnbOYVJSOte7PCfnCCEh505e4kOtL6m1btRzxNv5+flRWlpKVY2FapN1VDos0JeMsjJ8fd17YcG5OLJ+Y0RyoDXZOVDB5CGRjgrRKUJDQ8nJziYlJaXe5Tk5OYSEhGgUVcvq1KkDnTp10DqMFtOkZOeSSy5hx44dkux4iQMnjBjNKsG+OtqEu/6y0ub4y/XXcue9D/DKrOcZcoH1G966DZt4fOoz3Hzj+HPePi7YgE6BA4U1HDlZQ+swzy348xRXjB3L3++7lxfnvoUa2o5Qfx9+27yJ+yf/nSuvukrr8JzKVr/x/rtvMmhgf8DaZ6cp9Rsj2lvbLWw9XElZtYVgP9ftNXX9jTdy7913MWvOHC5IHQLAhvXrmPrkk9x4000aR+dcZrOZ/370GStWriI//zgWtf6Ksl+Wfn+GW7q3Jn1y/ec//2HixIns2rWLHj162Of9bMaNG+eQ4IRrsE1hpcT6OnV5sCt4ZfbzKIrCbZPuw2SydkP28fHhvrsnMevF6ee8va9eoXcrf34/UsW6gxXc2NuzV3Z4grnzXufOO27nustHojf4oABms4krr7qKV1+bp3V4TuXI+o2kCF+SInw4VFTDhkMVXNrZdadxZ895GUVRmHT77fVe53ffcy8vzvTcuhWAh6Y8xX8//pyxYy6jR/euXlOG0qRkZ8OGDaxbt46ffvrptOukQNnzeEtxMoCvry+vz53FzBeeYf8B62qUDu2TCQxsfJPAYe0Ca5OdSkl23EB4eDhff7uIhSu28PvOXbSLDmJkaj+vGLl2dP3GiORAPi46yZos1052fH19mfvaPF548SUO1K64a9+hw3m9zt3VFwu/4f8+/YArxlymdSgtqknJzgMPPMAtt9zC008/TVxcnKNjEi7GG/bE+rPAwMDTmm411tB2Aby5DtZlVaCqqtd8c3J3wbFt6Dc8nhGdY4gNde0CW0dzVP3G8PaBfLztJKsPuEfNWmBgID3csBFuc/j6+tKxg2cXIzekScnOiRMneOSRRyTR8RK2aaxuXpDslJeXM+vleafmsy3157MPZG4/5zn6tvYnwEfheIWZzAIjKV4wIubOzGYzH374IZ988z9KCk/wbpBPvenapcuXaxidczm6fiM1KQCDDg4W1ZBdVEPbCNesWSsvL+fl2bNZ+csv5Bfkn/Y6z9y7T6PInO/Rhybz+lvv8NbrL3vVF7EmJTvXXXcdK1eupEMH76nk9lYFZSaOl5vRKdAlxrNXpgD87d4HWbVmPbf+9UYS4uOb9GbgZ9AxKDGAVQcqWHuwQpIdFzflkYf5aMECel5wEe06pdA+xnWnXxzN0fUbIX56+rUOYHNOJauzyrklItwxgTrYvXffxZrVq/nrhFuIT0jwqg/9tes3snLVGn5aupzu3VJOq7n95v8+1igy52pSstO5c2emTp3K2rVr6dmz52n/WA8++KBDghPas43qtIvwIcDHdVdXOMpPS5fzv0VfMnTIBc06z/DkQGuyk1XB3wZFnPsGQjMLv/ySV97+kLDOg0gID2BoR+/Z18wZ9RsXtg+0JjsHKrilX7jDzutIS5csYdH3ixky1LM3em1IeHgY1159pdZhtLgmr8YKDg5m1apVp+1yriiKJDsexF6c7AVTWAAR4eFERjQ/ORmabC103JRdidGs4qv3nm+O7sbX15fw+LaoQESga067OIsz6jeGtw/k5VUnWH+wkhqzio8LPvfDIyKIiHTtXkDO8uF787UOQRNN+qqelZV1xp8DBw44OkahIVtxsqc3E7R5/tl/8MyMmVRUNK/AskuML9GBeiprVH4/UuWg6IQzPPTIFD59/21UVSU80POnauuy1W/8eduf5ugR70dkgJ4yo8Vln/vPPvccM56d3uzXuXAfnt0hTjRbupetxHp13nz2HzhIXGIX2iUlnjZFu23TqjPcsj6dojCkXQDfp5WxLquCwW0DnBGucIB1a9ey8peVbF7zC/1698Tfr37C839ffa1RZM7njPoNnaIwLNn63F+TVcEgF3zuz3vtNQ7s309iqwSS2rU77XFv2vKbRpEJZ2l0sjNlyhSef/55goKCmDJlylmPnTt3brMDE9qrMlnYf8LWY8c7vvFeM26sw841LDmQ79PKWHuwgikXRjnsvMKxAkNCGXDhZeh0CnGRnt9npS5n1W8Mbx/E92llrD5QzqMu+NwfN+5qrUMQLazRyc7vv/9OTU2N/f+F59t33IhZhfAAnX2DS083fdqTDjvX0HbWD84dR6soqTIT6q932LmF4zz36ny2HiwkNsSfEV1itA6nRTmrfmNEbc3azmPVFFaYiQx0ref+tGee0ToE0cIa/Qm2cuXKBv9feC57M8FYP69amukorcN8SI70Iauwhk3ZlS7dUdabFVdYRy/Dvaw42ZniQgykxPiSUWBkbVYF47p7x+aawnU1qUB50qRJlJaWnnZ5eXk5kyZNanZQwjXYVmJ5QzNBZ7GN7qw7KIWQrqq4wjpiLcmOY9k2Bl2TJc99V3D48BGOHz9h/33N2vVMmHgXw0eO4Zbb72bDxs0aRud8TUp2FixYQGVl5WmXV1ZW8tFHHzU7KOEa0uuM7IimGZYsb/iuTFVVTtqTHe+oS2spw2uTndUHyh262ks0zfibJrJx0xYAvvv+Ry669CrKysoZmjqYiopKLhx1JT/8b4nGUTrPeRVilJSUoKoqqqpSWlqKv/+p/WPMZjM//vgjsbGxDg9StDxVVe0NBb1lJZYzpLYNQKfA/hM1HCupISFURg9cSVm1CZPFgk6nEOLvHXVpLWVgYgD+BoW8MjN7Cox0kS9NmtqdlkH3bl0BmPnya7z0/NM8+djD9uvf+te/eWbGTK4ce7lGETrXeY3shIeHExkZiaIodO7cmYiICPtPdHQ0kyZNYvLkyc6KVbSgY6UmTlZZMOigY5R8QDdVWICengnWN/l1B08fDRXask9hBfhIXRo4dATG36Czt1xYLSObmjMY9JSWWctPsg4eYszoUfWuHzP6UjL3eO6eYOeV7KxcuZIVK1agqipfffUVv/zyi/1n7dq1ZGdn889//tNZsYoWZCtO7hjli5/B87eJOJecnMNMuvv+Jt12mNTtuKzcwhIyt2+h6EjWaddVVVXxycfeNS3vFxJHenqmw843wj6V5TrP/X/Nn8+k2yfyf19+AcCnn3xM75496Nm9G0//85+YTCaNI3SOC4cP5fMvrT2j+vbuxa+r1tW7fuWqNbRulaBFaC3ivMZtL7zwQsDaQTkxMRGdTj4EPVV6nndtE3EuhUVFLPj4cz7491vnfduh7QKZv76ItVkVqKoqIwguYs+ePYwfdSn5x46gKApvDx3Gx599RkKC9Q3/5MmT3HXnndxy620aR+p4Ux5v+Eup2Wxm1ivziKrdSmHuyy82635sS9A351RSVWPBX+P99Wa++CKvvvIyoy69lMcffZTsQ9nMffUVHnzoYXQ6HW+8Pg8fHx+eefZZTeN0hlkvTGf4JVdw9Fguw4ZewD+nv8CWrdvomtKZzD37+HLht7zzluf2yGvSJHVSUhLFxcVs3ryZ/Px8LBZLvetvu83z3hy8jb1ex0vm2b9f/ONZrz+QdajJ5+7Xxh9/g0JBuZm9x410jvGOf1NX98+pT9G6fRee++B7esX68Py0J7loxHCWrfiFtm3bah2eU81782169+pBeHhYvctVVSU9Yw9BQYEOSco7RvuSEGLgWKmJTTmVXNg+qNnnbI6PPlrAfz74gGuuvY6dO3ZwwaCBvP/hh9z81wkAdEnpwtSnnvLIZKdr1y5sWrOcac++wJxX36C8vJxPP1+IwWBg4IC+fPHx+1xzteOaqrqaJiU7ixcvZsKECZSVlREaGlrvRaEoiiQ7HuBUcbJ3rFC55oZbUBTlrDULTX3z9zfoGJgYwJos6y7okuy4hg0bNjBl7keEhEfSp0cbvv3uex64fzKXXHQhS5evIChI2w9mZ3rp+af5938W8OrsFxh58Qj75T5BMfz3P/Pp1jXFIfejKAoj2gfy5Y4SVh+o0DzZOXb0KP36DwCgV+/e6HQ6evfuY7++T99+HDt6VKPonK9Dh2Q+//h9VFUlP78Ai8VCdHTUadtleKImjSk++uijTJo0ibKyMoqLiykqKrL/FBYWOjpG0cIqjBYOFloLN71lZCchIZ5vvvwYS1Vhgz+N3RPrTGxL0KVI2XVUVlSi1xsI9fdBr1NQFIW35v+LK668kktHXszePXu0DtFpnnr8Eb789APue+BRHnvyaXt3fGew99txgbqduPh40tPSANi7dy9ms5n09DT79Wm7dxPjBSuKFUUhLi6WhIR4r0h0oInJzpEjR3jwwQcJDPSufWS8RUZBNSoQG6wnOsg7luP279ubrb9vP+P1itK8lSq2ZGdjdgU1Zuk54gqSOnTiQMbO05oJvv7Gm1w5bhzjr71Gm8BayMAB/di6cSUFx48zIPVidu1Oc0o92dB2gegU2HPcyLES5yVVjXHzzX/lzjtu57577ubKK8bw6GOP89QTT/Dvd9/hvX+/ywOT/87VV1+jaYxaac4iDHfQpGRn9OjR/Pab7ArrqezFyV4yqgPw+JQHGHLBoDNe37FDe1b+vLjJ5+8a60tkgJ5yo8r2o1VNPo9wnCGXjGHDz9832Ezw9Tfe5MabbvL4ZnjBwcEseP9tpj7xCKPGXIvZbHb4fYQH6OmVYO3JpnVzzWeefZYHHnyI3Nxc7rzzbzz/4ou8OHMmc2bNYvrTT3PxyJE8O2OGpjFqxbYIw1M16Wv72LFjefzxx0lLS6Nnz56nDYONGzfOIcEJbXhjM8Hhw4ac9fqgoCAuHDG0yefXKQpD2gXwQ3oZa7MqGJgY0ORzCccYe+t9XHTjXWfcJuLNt+bz5lvO2SjT1dx043iGDbmArdt2kNQ20eHnH9E+kO1Hq1h1oILx3bV77ut0Op6cOrXeZTf+5SZu/MtNGkXUcpy5CMMdNCnZueuuuwCY0UAGrCiKU74diJbjbSuxWsqw5EB+SC9j3cEKHhkRpXU4Xq3GbKG82tpPRfbEsmrTpjVt2rR2yrkvbB/IG2sLWXewArMl0in3Ic7OmYsw3EGTprEsFssZfyTRcW8WVbXvidUt1jtWYrUU26agvx+porRaXidasnVODvQ14GvQaxyN5+vdyp8QPx3FlRZ21U6Ti5bl7EUYrk66Aop6sotqqKhR8dUrJEdJsuNIieE+JEX4YFZhc7asytJScYX1A1dGdVqGQacwtJ1t6wh57mvB2YswXF2TprEamr6q65lnnmlSMEJ7timsLjG+GHSeO6SplaHtAjhUVMPag5Vc0ilY63C8ln1PLEl2WsyI9kEsySxnTVYVt/b33B5GrurxKQ9QXn7mAvHmLsJwdU1Kdr799tt6v9fU1JCVlYXBYKBDhw6S7Lgxj98moipf07sf1i6Qz34vYa1sjNiiqs31u7w/t3g3e/LKeP3mvnSIC9Eoqhag8fO9ruG17Re2H62mtNpChJPv789/c2/n7EUYrq5Jyc7vv/9+2mUlJSXcfvvtXHvttc0OSmgnTYqTnWpIu0AUYO9xI3mlJuJCvKOPkSsxmiwcKCgHoGuCByc6LiYx3If2kT4cKKxhS041l3l+7z7hQhxWsxMaGspzzz3H008/7ahTCg3YprG6eerIjsbCA/T0TLD+28ou6NrYl1+GyaISFuBDfJi/1uF4FVs35Y2HqjWORHgbhxYonzx5kpMnTzrylKIFnawyc+SkdTluiqzEchrbqqy1kuxoIuNYCQAp8SEevdTWFY2o3RtrQ3a1RxfDCtfTpDH0N954o97vqqpy7NgxPv74Y8aMGeOQwETLy8i31uu0DjMQ5i/LcZ1lWLtA3t5QxLqsClRVlQ/cFpZuS3YSQjWOxPtc0DYAHx0cLTGTXVhJcpyMrImW0aRk57XXXqv3u06nIyYmhokTJzL1T90phfuw9deReh3n6p/oj59BIa/MzP4TNXSMllG0lpSZWwpAitTrtLhAXx392/ixMbua9fsLSY5zdpmyl3KhwnRX0aRkJysr64zXVVZKDwV3ZStO7ibJjlP5G3QMbOPP2oOVrM2qkGSnBVksqj3Z6SojO5oYkRxQm+wUMeHsC4SEcBiH1exUV1czd+5ckpOTHXVK0cLsIztx8uHrbEOTpW5HC9mFFVQYzfj76GgXLb1etDC8trngloNF1JhkebhoGeeV7FRXVzN16lQGDBjAkCFDWLRoEQAffPABycnJvPbaazzyyCONPt+zzz6Loij1flJSUuzXV1VVMXnyZKKioggODmb8+PHk5eXVO0d2djZjx44lMDCQ2NhYHn/8cUwm0/k8LAGYLCqZBd6327lWhrWzrUqppMYshZotxVac3Ck2BL00zdRESqwPUYE6KmssbM8p1joc4SXOK9l55plnePvtt2nXrh0HDx7khhtu4O6772bevHnMnTuXgwcP8uSTT55XAN27d+fYsWP2n7Vr19qve+SRR1i8eDELFy5k1apVHD16lOuuu85+vdlsZuzYsRiNRtavX8+CBQv473//K00NmyDrhBGjWSXIV6FthHSVdbbu8X6EB+goM1rYeaxK63C8RvoxqdfRmk5RGNy2tv3CvuMaRyO8xXklOwsXLuSjjz7iq6++4ueff8ZsNmMymdixYwc33XQTev35r+AxGAzEx8fbf6KjowHrMvb333+fuXPnMnLkSPr378+HH37I+vXr2bhxIwA///wzaWlpfPLJJ/Tp04cxY8bw/PPPM3/+fIxG2WzufKTVrsRKifVDJ6uDnE6nKAxNqp3Kkm7KLSYzV1ZiuYLUJGuys16SHdFCzivZOXz4MP379wegR48e+Pn58cgjjzRr6ezevXtp1aoV7du3Z8KECWRnZwOwdetWampqGDVqlP3YlJQU2rZty4YNGwDYsGEDPXv2JC4uzn7M6NGjKSkpYffu3We8z+rqakpKSur9eLt06Zzc4mx1O9JcsGWoqmof2ZHOydq6oHZkJ/1YKcfLpMGgcL7zSnbMZjO+vqeKVw0GA8HBTd/McPDgwfz3v/9lyZIlvP3222RlZTF8+HBKS0vJzc3F19eX8PDwereJi4sjNzcXgNzc3HqJju1623VnMnPmTMLCwuw/iYmJTX4MnkKWnbc8W93OtiNVlBulUNPZCkqrKSw3olOgY6wkO1qKDNSTEm/97Ni4/4TG0QhvcF5Lz1VV5fbbb8fPz/qBWFVVxb333ktQUP1VDd98802jzle3AWGvXr0YPHgwSUlJ/N///R8BAQHnE9p5mTp1KlOmTLH/XlJS4vUJj31kR1ZitZi2ET4khhvIKTaxObuSizvK6iBnyqgd1UmODiLAV5pmai21fQQZuWWs33eCK3u30joc4eHOa2Rn4sSJxMbG2kdEbrnlFlq1alVvlCQsLKzJwYSHh9O5c2f27dtHfHw8RqOR4uLiesfk5eURHx8PQHx8/Gmrs2y/245piJ+fH6GhofV+vNnxchP5ZWYUICVGRnZa0jDZOqLFZEi9jksZ0iESgPX7j2OxyIpE4VznNbLz4YcfOisOAMrKyti/fz+33nor/fv3x8fHhxUrVjB+/HgAMjMzyc7OJjU1FYDU1FRefPFF8vPziY21bqG7bNkyQkND6datm1Nj9SS2UZ12kT4E+jp0uzRxDkPbBfL59hLWSZGy02Uck2aCrqRv2zACfPWcKDOyN7+ULvHydxHOo+kn22OPPcaqVas4ePAg69ev59prr0Wv13PzzTcTFhbGnXfeyZQpU1i5ciVbt27ljjvuIDU1lQsuuACAyy67jG7dunHrrbeyY8cOli5dyrRp05g8ebJ9qk2cW3qe9NfRypB2gShARoGR/DLpD+VMp/bEknodV+Cj1zGonXV0Z91eqdsRzqVpsnP48GFuvvlmunTpwo033khUVBQbN24kJiYGsO7BdeWVVzJ+/HhGjBhBfHx8vXogvV7PDz/8gF6vJzU1lVtuuYXbbruNGTNmaPWQ3JKsxNJOZKCe7vG1y3BlKstpSqtqOFxk3comJV6SHVeR2jEKkCXowvmatDeWo3zxxRdnvd7f35/58+czf/78Mx6TlJTEjz/+6OjQvIpsE6Gtoe0C2ZVbzdqDFVzTQ4byncE2hZUQ5k9YoDzPXcXQjta+atuyi6gwmgj01fQjSXgwKdDwctUmC/tOWKexusXJyI4WhiVbVx6uy6pEVaVQ0xlO7XQuyaQrSYoKpHV4ADVmld8OFmkdjvBgkux4uX0najBZIMxfR0KIfKvSwsA2AfjqFY6VmjhQWKN1OB7JVq8jzQRdi6IoDJGpLNECJNnxcnWbCTanE7ZoOn8fHQPa+APIqiwnsW0AKit+XM+Q2qms5Wn5/LjzGFuyCjHLUnThYJLseLlTzQRlCktLtq0jpN+O4xlNFg4UlAMysuOKKo3WVYh5JVU8+dVOJn24hdFzV7E8Le8ctxSi8STZ8XJp9pEdKdrUkq254IZDlZjkW61D7csvw2RRCQvwIT7MX+twRB3L0/L45ze7Trs8v6SaKV9sl4RHOIwkO15MVVX7yI4UJ2urR7wfYf46Sqst7DxWpXU4HiWjTn8dmap1HWaLyqwf02kotbddNvvHDJnSEg4hyY4Xyy01UVxpQa9Ax2gZ2dGSXqcwJKl2F/SsSo2j8SynipOlXseVbMsuJq/kzDueq0BuSRXbDskqLdF8kux4sfR865LzDlG++BvkqaC1obVL0KVux7FsPXa6SDNBl3K8zNio4wpKz5wQCdFY8gnnxU41E5QpLFdgq9vZdriSCqNF42g8g8Wikpkne2K5oujgxo0mx4TI+5NoPkl2vFia1Ou4lKQIH1qHGaixwOYcmcpyhOzCCiqNZvx9dLSLDtI6HFFHv7bhxIX6cbYqqvhQf/olRbRYTMJzSbLjxdJlJZZLURTFPrqzTqayHMJWnNw5LgS9ToqTXYlep/DUFV0BzpjwPHlFivzdhENIsuOlKmssHCyyduuVDUBdx9DaZGetNBd0iHSp13Fpo7rFMfemPsSGNvweVFljbuGIhKeS/QG8VGaBEYsK0UF6YoLlaeAqhrSzFimn5xspKDPJ36aZTi07l3odVzWqWxwXp8Sy7VARBaXVxIT4seVgIW+v3M8Li9PokxhOYmSg1mEKNycjO14qrc42EcJ1RAcZ7DVU6w9J3U5zqKpKRq6tOFlGdlyZXqcwMDmSK3olMDA5knsu7ED/pAgqjGaeXLiTGrMU7IvmkWTHS9nqdaQ42fUMqx3dkbqd5ikoraaw3IhOgU5xkuy4E71OYdb1PQnxN/DHkZP865d9Wock3JwkO17KvieWFCe7HPs+WVkVqKp0j20qW3+d5Ogg/H30Gkcjzld8WADPXd0dgPfXZrHpwAmNIxLuTJIdL2RRVTJqGwrKNJbrGdgmAB8dHC0x8f7mYjYcqpCW+U2QLvU6bu/S7vGM798GVYV/fP0HReWNa0QoxJ9JsuOFDhebKDNa8NUrtI+SkR1Xs/pABbYtnF5YcZybPz3CsPkHWZJRpm1gbiYzV5oJeoInxnQhOTqI/NJqpn+3W0Y7RZNIsuOFbM0EO8f44qOXHhauZElGGfd9cwzjn1bc5paauO+bY5LwnIf0OhuACvcV6Gtgzg298NErrMzI5/+25GgdknBDkux4oXRZieWSzBaV55YVnHUX6OeWF8iUViOUVtVwuMi6mi1Feuy4vZSEUB65tDMALy/JZG/tFiBCNJYkO15IipNd0+acSo6Vms54vQocKzHJVhKNYCtOTgjzJyxQnuee4JbUJIZ1iqbaZOGJhTupkoaD4jxIsuOF7MmOLDt3KflljXvzbuxx3kyaCXoeRVF4/toeRAX7si+/jLk/79E6JOFGJNnxMiVVZnKKraMH3WQay6XEBjdueXRjj/Nm0kzQM0UH+/HitT0B+HxTNr9m5GsckXAXkux4GduS81ahBsIC5EPTlQxKDCAhxHDWXaATQg0MSgxosZjclYzseK6hnaK5bUgSAE8v2kV+SZXGEQl3IMmOlzlVryOjOq5Gr1OYfmkMcOZdoCenRsgu0OdgNFk4UFAOSHGyp3poVGe6JoRQXFHDP7/ZhUWK9sU5SLLjZaQ42bVdnhLM29clEB9SfwNQWwPg79NKZTXWOezNL8VkUQkL8CE+zF/rcIQT+Bp0zL6hNwE+ejYeOMF/1x3UOiTh4iTZ8TL2ZedSnOyyLk8JZu3kdnw+oTWvXx3P5xNa8/Pfkgj21bE5p4p3NhRpHaJLy6xdiZWSEIKiyCiYp0qODuKpK1IAeHPFXnYfOalxRMKVSbLjRcwWlcwCa82ObADq2vQ6hdSkQK7uHkJqUiDJUb48e5l1iuu1NSfYeUzqFM7E1kxQOid7vmv7tebS7nGYLCpPLNxJefWZWzcI7ybJjhfJKqyhyqQS6KOQFOGjdTjiPI3vGcLYlGBMFnj4u1wqjBatQ3JJGXVGdoRnUxSF6eO6Ex/mT3ZhBS/9L13rkISLkmTHi9jqdbrE+qGT4X23oygKL46JJT7EwIHCGp5fXqB1SC7HbFHJrO2umxIvIzveICzAh1nje6FT4PvtR/lx5zGtQxIuSJIdLyLFye4vPEDP3KviUIDPt5fw8x7ZK6uu7MIKKo1m/H10tIsO0joc0UL6t4vg7gs7APD84jQOF1VoHJFwNZLseJE02RPLIwxpF8hdg8MBeOp/+eSXSZ2CTWZtvU7nuBBZou9l7rmwPX0SwymrNvHUV39gMss0rzhFkh0vYluJJcXJ7u/RC6PoFudHYaWZx3/IQ1VlOTpAur1eR6awvI1Br2PW9b0I8TewI6eYd37dr3VIwoVIsuMlCivM5NXuqdQlRpIdd+dn0PH6uDj8DAqrDlSw4DdZdgunOid3kWaCXql1RADPXNUNgPdWH+C3g4UaRyRchSQ7XsI2qpMU4UOwn/zZPUGnGD/+MTIagJd+Oc6egmqNI9KWqqqyJ5bg8p4JXNO3NRYVnvrqD05WGLUOSbgA+dTzElKc7Jlu6x/GRR0CMZpVHvwul2qT99YpFJRWU1huRK9T6BQnyY43m3pFCklRgeSVVPHs97tlmldIsuMt0vKlXscTKYrCnLFxRAboycg38sqqE1qHpBlbf53k6CD8fWSTW28W6Gdg9g29MOgVlqfl8/XWw1qHJDQmyY6XSJeVWB4rNtjAnCtjAXhvUzHrsrxz2W26fadzGdUR0L1VGA9d0gmA2T9lcKBA2jR4M0l2vIDRrLLvuHXeWvbE8kyjOgXz177WFUiP/pBHcaVZ44haXkZubbIjzQRFrduGtCO1QxRVNRaeWLgToxdP83o7SXa8wP4TRmosEOqvo3Wo4dw3EG5p2iUxtI/0IbfUxD9+yve6OoV02SZC/IlOp/DidT2JCPQhM7eU15bt0TokoRFJdryArZlgSoyf7ALtwQJ9dbx+dTwGHfyYUcZXf5RqHVKLKams4UhRJQApsuxc1BET4scL1/YE4JMNh1izR7ZZ8UaS7HiBU80EZSWWp+uZ4M8jI6IAePbnfA4Vecey28zaJeetwv0JC5TnuahvRJcY/jq4LQDTvt3F8TLvbtPgjSTZ8QKnlp1LvY43uPeCCAYlBlBuVHnk+zxMFs+fzjrVTFDqdUTDplzWmc5xwRSWG5n2zR9YvOB1IU6RZMfDqapKep4UJ3sTvU5h7rg4Qvx0bDtSxVvrPL+LrDQTFOfi56Nnzg298TPoWLfvBB9vOKR1SKIFSbLj4fLLzBRWmtEr0Dlahve9RZswH164PAaAN9cWsu1IpcYROVeGfdm5jOyIM+sQG8wTY1IAmLd8j71dgfB8kux4OFszwfZRvvj7yJ/bm1zdPZSru4dgVuHh7/Ioq/bMZbdGk4UDBeWAjOyIc7thQBtGdo3FZFZ5YuFOKowmrUMSLUA+/TzcqWaCMqrjjWaMjqF1qIHs4hqeW+aZq1D25pdisqiEB/oQF+qvdTjCxSmKwnNXdyc21I+Dx8uZ/WOG1iGJFiDJjoezFydLvY5XCvPXM3dcPAqwcGcJP2V43nJ02zYRXeJDpLWCaJTwQF9mXtcTRYFvth1h6a5crUMSTibJjodLk20ivN7gtgHcNyQCgKd+zCe31LOG7W31Ol2lXkech0Hto/jb8PYAPPf9bo4Ve3Zdm7eTZMeDVdVYyCqsAWQDUG/38PAoesb7cbLKwqOLc7F4UHflDOmcLJrovos70KtNGKVVJp76aicms2fWtQlJdjxaZoERiwpRgXpigmQXaG/mq1eYd3U8AT4K6w5W8sHmYq1DcgizRSUzz7bsXEZ2xPnx0euYdX0vgvz0bMsu5r3VB7QOSTiJJDse7FQzQV+pZRB0iPJl2iXW5ehzfj1hn+J0Z9mFFVQazfj76EiKCtI6HOGGEiMDmXZlNwDe+XU/vx8q0jgi4QyS7Hgw+0osmcIStf7aN5RRnYIwmlUe/i6Xqhr3Hra31et0jgtBr5OEXjTNlb1bcWXvBCwqPPnVTkoqa7QOSTiYJDsezDayI/U6wkZRFGZfEUt0kJ49x43MWnlC65Ca5VS9jkxhieb559hutIkI4NjJKmYsTkP1oLo2IcmOx1JVlfT82m0iZCWWqCMqyMDLV8YB8N/fill1oFzjiJruVOdkKU4WzRPsb2DODb0w6BSW7spl0e9HtQ5JOJAkOx7q8EkTpdUWfPUKHaKkoaCo7+IOQUzsHwbAY4vzOFHufsvRVVWtsyeWjOyI5uvZJpzJIzsCMPPHdA4ed98vAqI+SXY8lG0Kq2O0Lz56qWUQp5s6MppO0b4UlJt56qd8txu2zy+tprDciF6n0DE2WOtwhIe4Y1gyg5IjqTSaeWLhTmpM7l3XJqwk2fFQabJNhDgHfx8d866Ox0cHy/aU88V299oUMbO2Xic5Ogh/H2mtIBxDr1N4aXxPwgJ8SD9Wwhsr9modknAASXY8lKzEEo3RPc6Pxy+KBmDG8gIOnDBqHFHjpUu9jnCSuFB/ZlzTA4D/rjvI+n3HNY5INJckOx7KVpzcTYqTxTn8bXA4Q5ICqKxReeT7XGrM7jGdlZFbm+zES72OcLyRXWP5y8BEAP7xzR+cKHP/vlTeTJIdD1RabSa72NonQlZiiXPRKQqvXhVHmL+OHceqeX2NeyxHT5dtIoSTPXZ5FzrEBHGizMgzi3a7XV2bOMVlkp1Zs2ahKAoPP/yw/bKqqiomT55MVFQUwcHBjB8/nry8vHq3y87OZuzYsQQGBhIbG8vjjz+OyeR+K0scKbN2VCc+xEBEoNQyiHNLCPXhpTGxAPxrQxFbclx7U8SSyhqOFFljTImXZEc4h7+Pnjk39sbXoGP1ngI+25StdUiiiVwi2dmyZQvvvvsuvXr1qnf5I488wuLFi1m4cCGrVq3i6NGjXHfddfbrzWYzY8eOxWg0sn79ehYsWMB///tfnnnmmZZ+CC7lVDNBKU4WjTe2awjje4ZgUeHh73MpqTJrHdIZZdYuOW8V7k9YoDzPhfN0jgvh0dGdAXh1aab9uSfci+bJTllZGRMmTOC9994jIiLCfvnJkyd5//33mTt3LiNHjqR///58+OGHrF+/no0bNwLw888/k5aWxieffEKfPn0YM2YMzz//PPPnz8dodJ9CS0c7tRJLprDE+Xn2shgSww0cOWli+s8FWodzRqeaCUq9jnC+mwe15cLOMdSYVZ5YuINKo+t+ERAN0zzZmTx5MmPHjmXUqFH1Lt+6dSs1NTX1Lk9JSaFt27Zs2LABgA0bNtCzZ0/i4uLsx4wePZqSkhJ27959xvusrq6mpKSk3o8nkc7JoqlC/PTMGxePToFvd5Xy/W7X/BZrayYoU1iiJSiKwoxrexAT4seBgnJeXpKhdUjiPGma7HzxxRds27aNmTNnnnZdbm4uvr6+hIeH17s8Li6O3Nxc+zF1Ex3b9bbrzmTmzJmEhYXZfxITE5v5SFyH2aKSWSDLzkXT9W8TwP1DIwGYtjSfoyWutyliuozsiBYWGeTLi9f1RFFg4W+HWZGWd+4bCZehWbKTk5PDQw89xKeffoq/v3+L3vfUqVM5efKk/ScnJ6dF79+ZDhbVUFmj4m9QaBfho3U4wk09OCySPq38KamyMOX7PMwW11mFUl1jJqvA2sa/q6zEEi0otUMUtw9tB8D073aTe7JK24BEo2mW7GzdupX8/Hz69euHwWDAYDCwatUq3njjDQwGA3FxcRiNRoqLi+vdLi8vj/j4eADi4+NPW51l+912TEP8/PwIDQ2t9+MpbM0Eu8T6otfJNhGiaQw6hXnj4gj0UdiYXcl7m4q0DsluX0EZJotKeKAPcaEt+0VJiAdGdqJ7q1BOVtYw9eudLvVFQJyZZsnOJZdcwh9//MH27dvtPwMGDGDChAn2//fx8WHFihX222RmZpKdnU1qaioAqamp/PHHH+Tn59uPWbZsGaGhoXTr1q3FH5MrsK/Eknod0UztIn2ZfmkMAK+uOsGuXNf4Fpth668TH4qiSEIvWpaPQcfsG3oR4Kvnt4NFfLAmS+uQRCNoluyEhITQo0ePej9BQUFERUXRo0cPwsLCuPPOO5kyZQorV65k69at3HHHHaSmpnLBBRcAcNlll9GtWzduvfVWduzYwdKlS5k2bRqTJ0/Gz887P+xtyY4UJwtHuLF3KKM7B1FjgYe+y6WyRvtNEWWbCKG1pKgg/jm2KwDzV+5jR06xtgGJc9J8NdbZvPbaa1x55ZWMHz+eESNGEB8fzzfffGO/Xq/X88MPP6DX60lNTeWWW27htttuY8aMGRpGra30vNqVWFKcLBxAURRmXRFHbLCe/SdqeGmF9nsEZUjnZOECxvVpxZie8ZgtKk9+tZPSKtcr5BenGLQOoK5ff/213u/+/v7Mnz+f+fPnn/E2SUlJ/Pjjj06OzD0UV5o5VmrtHp0iu50LB4kI1PPqVXHc+vlRPt52kos7BjGyY5AmsZgtKnvyrMlOV1mJJTSkKApPX9WNnTknOVJUyYs/pDPr+l7nvqHQhEuP7IjzY2sm2DbchxA/2SZCOM7w5CAmDQwH4Ikf8jhers2WLNmFFVQazQT46EmK0ibhEsImxN+HWdf3RK9T+N/OYyzeflTrkMQZSLLjQU7V68iojnC8Jy6OIiXGl+MVZp74X54mmyLaOid3iguW1YbCJfRpG8G9F3UA4IUf0sg+Ua5xRKIhkux4EHuyI/U6wgn8DTrmXR2Pr17hl30VfLLtZIvHIM0EhSu6a0R7+iVFUGE08+RXO6kxa1/IL+qTZMeDpOXJNhHCuVJi/Xjy4igAXlxxnH3HW3YPukwpThYuSK9TmDW+JyH+BnYdKWH+L/u0Dkn8iSQ7HqLGrNo/eLrJyI5wojsGhjM8OZAqk8rD3+diNLfMdJaqqvY9saQ4WbiahPAAnru6OwAfrM1i04ETGkck6pJkx0PsP2HEaFYJ8dPRJsylFtkJD6NTFF65Mo6IAB27cquZu7pl3tTzS6spLDei1yl0jA1ukfsU4nxc2j2e8f3boKrwj6//oKi8ZUc+xZlJsuMhbPU6KTG+0lVWOF1ciIGZV1g33X13QxEbDlU4/T5txcnJ0UH4+8hqQ+GanhjTheToIPJLq3nmu12aFPKL00my4yFse2JJcbJoKZd3CeYvvUNRgUcX53GyyuzU+5NmgsIdBPoamHNDL3z0Cr9mFPDlFs/ZaNqdSbLjIdLzpV5HtLxnLo2hXYQPR0tMTPu50KnfYjNya1dixUu9jnBtKQmhPHJpZwBeWZLJ3tpGmEI7kux4CPvIjqzEEi0oyFfHa+Pi0SuwOL2CRenVTruv9GO24mQZ2RGub8IFSQztGE21ycITC3dSVePckU9xdpLseID8MhPHK8zoFOgSIw0FRcvq29qfh4ZHAvDM8nJyiiodfh8llTUcqT1vF1mJJdyATqfwwnU9iAr2ZV9+GXOXH9A6JK8myY4HsBUnJ0f64O8jf1LR8v4+JJL+rf0oNapMWbgbs8Wx01mZtUvOW4X7Exbg49BzC+Es0cF+vHBtDwC+3HKUVVmyWahW5JPRA6TJFJbQmEGnMO/KKIJ9FbYcKubtXx3bVC1DOicLNzWsUwy3piYBMH1FJXll2uwr5+0k2fEAshJLuILEcB+eu8S6Oee85XvZkVPssHPbmgmmxEu9jnA/D1/amZT4YIqrVB773wksshy9xUmy4wHsK7FkZEdo7LpufoztGYfJovLwl9spr3bMt1jbnljSOVm4I1+DjtnXdcXfAOuzq/n3xiKtQ/I6kuy4uSqThQMnavfEkpEdoTFFUXjp6hQSwvzJOl7OC/9La/Y5q2vMHCiw7iQtPXaEu2oXHcgTwwMAeGXVCXYcrdI4Iu8iyY6b21tgxKxCZICeuGDpKiu0Fxbgw6s39kZR4PPNOSzdndus8+0rKMNsUQkP9CEu1N9BUQrR8q7t5sOYzgGYLPDgd7mUVcvu6C1Fkh03Z1uJ1TVOtokQrmNIh2juHt4egKe+3kl+SdO/xdo7J8eHynNcuDVFUXhxdBStQg0cKqph+s/5mC0qGw5V8N3uUjYcqnD4SkZhJTtGurm0vNopLKnXES5mymWdWbP3OGnHSnjsq5389/aB6HTnn6ycqteRKSzh/sL8dcwbF89Nnx7m6z9KWbG3nOKqUyM8CSEGpl8aw+UpstmtI8nIjps7NbIjyY5wLX4GPW/c3Ac/g47VewpYsOFgk85zak8sKU4WnmFQ2wAu72JNZuomOgC5pSbu++YYSzLKtAjNY0my48ZUVZUeO8KldYwN4Z9juwIw86cMe3PAxjJbVPbkygagwrOYLSrbjjTcadw2ifXc8gKZ0nIgSXbc2JESE6XVFnx00DFatokQrunWC5K4uEsMRpOFh774nWpT4/cIyi6soLLGTICPnqSoICdGKUTL2ZxTSW7pmV8HKnCsxMTmHMdvveKtJNlxY7Zmgh2iffHVS+GmcE2KojDn+t5EBfmSkVvKy0syG31bW+fkTnHB6JtQ7yOEK8ova1zC39jjxLlJsuPGbPU60kxQuLqYED/mXN8LgP+szWLt3uONup00ExSeKLaRbUIae5w4N0l23Fh6njQTFO7jkq5xTBjcFoBHF26nqNx4ztvYipO7SL2O8CCDEgNICDFwprFKBUgINTAoMaAlw/Jokuy4MRnZEe5m2thutI8JIq+kmn98+wfqWfYIUlXVXtAsIzvCk+h1CtMvjQE4LeGx/T59VIxM3TqQJDtuqtxo4VBRDSAjO8J9BPjqef0vfTHoFH7alcvX2w6f8dj80moKy43odQqdYqXniPAsl6cE8/Z1CcSH1G93Fx9q4O3rEqTPjoNJU0E3lZFfjQrEBeuJDJR5XeE+erYJY8plnZmzJJMZi9Po0iqU5OjTV1rZipOTo4Pw85HnuPA8l6cEc2nnIDbnVJJfZiY2WM+gxAAZ0XECGdlxU9JMULize0Z0YFByJOVGM09/uwuT+fQ9gtKPyRSW8Hx6nUJqUiBXdw8hNSlQEh0nkWTHTUkzQeHO9DqF1/7ShxB/A38cPsl7qw+cdoxtZKdLvBQnCyGaR5IdN5WeL3tiCffWOjyAGVf3AODfqw6wPbu43vUZ9uJkSXaEEM0jyY4bsqgqmbaVWDKNJdzYuN6tGNMzHosKU7/eSXm1CYCSyhqOFFm7x3aRaSwhRDNJsuOGDhXVUFGj4mdQaBfpo3U4QjTLk1ek0Crcn8NFlcz6MQOzReW7348AEB3kS7CfrKMQQjSPJDtuyLZNRJcYXwxSzCbcXIi/Dy9e1xOdAot+P8KFs1cyp3ZLiePlRkbPXcXytDyNoxRCuDNJdtyQrV5HprCEp+iXFMHIrnEAnKysqXddfkk1U77YLgmPEKLJJNlxQ/Zl51KcLDyE2aKy83Bxg9fZeizPrp3iEkKI8yXJjhtKl2XnwsNsO1REfkn1Ga9XgdySKrYdKmq5oIQQHkOSHTdzstLMkRLripWUWF+NoxHCMY6XnTnRqaugtHHHCSFEXZLsuJm02imsNmEGQv2lhb7wDNHBjRuljAmR0UwhxPmTZMfN2KewpDhZeJB+SRHEhfqdtgO0jQLEh/rTLymiJcMSQngISXbcjHROFp5Ir1N46oquAKclPLbfn7wiRfYNEkI0iSQ7bsa2EqubJDvCw4zqFsfcm/oQG1r/uR0X6s/cm/owqlucRpEJIdydtCZ1IyaLyp6C2pGdOClOFp5nVLc4Lk6JZduhIgpKq4kJ8aNfUoSM6AghmkWSHTdy4IQRo1kl2FdHYrhsEyE8k16nMDA5UuswhBAeRKax3IhtCisl1hedIt90hRBCiMaQZMeNpOVJcbIQQghxviTZcSP2bSJk2bkQQgjRaJLsuJE02SZCCCGEOG+S7LiJgjITx8vNKECXGFmJJYQQQjSWJDtuwGxR+eaPEgDiQ/T4GaQ4WQghhGgsSXZc3JKMMobNP8jMlScAOFZqZtj8gyzJKNM4MiGEEMI9SLLjwpZklHHfN8c4Vmqqd3luqYn7vjkmCY8QQgjRCJLsuCizReW5ZQWoDVxnu+y55QWYLQ0dIYQQQggbSXZc1OacytNGdOpSgWMlJjbnVLZcUEIIIYQbkmTHReWXmR16nBBCCOGtJNlxUbHBeoceJ4QQQngrSXZc1KDEABJCDJxpkbkCJIQaGJQY0JJhCSGEEG5Hkh0XpdcpTL80BuC0hMf2+/RRMeh10nNHCCGEOBtJdlzY5SnBvH1dAvEhhnqXx4caePu6BC5PCdYoMiGEEMJ9GM59iNDS5SnBXNo5iM05leSXmYkN1jMoMUBGdIQQQohGkmQHUFVrr5qSkhLHnthcDaXloFSD0rz9rLqHWn+ghvKTVQ4JTwiHUo2gmkBfAvrGbVZrtqiUVtegKKBTvCeBt5iqUErLUY1m9I38t/IUZouR0hoFhRJ0Bu/Z58+b/+ZNeW9oLNvntu1z/Ewk2QFKS0sBSExM1DgSIYQQQpyv0tJSwsLCzni9op4rHfICFouFo0ePEhISgtKEb5cDBw5ky5YtTr/d+Rx/rmNLSkpITEwkJyeH0NDQRsfgKZr6N3OWlorHGffT3HPK68f9yOvHdc7p7a8fVVUpLS2lVatW6HRnLkOWkR1Ap9PRpk2bJt9er9c36Q92vrc7n+Mbe2xoaKhXvlk39W/mLC0VjzPup7nnlNeP+5HXj+ucU14/nHVEx0ZWYznA5MmTW+R253N8U2PyFq7279NS8Tjjfpp7Tnn9uB9X+/eR14/zb+furx+ZxvJSJSUlhIWFcfLkSZf6hiaEO5DXjxBNp8XrR0Z2vJSfnx/Tp0/Hz8/LVgUI4QDy+hGi6bR4/cjIjhBCCCE8mozsCCGEEMKjSbIjhBBCCI8myY4QQgghPJokO0IIIYTwaJLsCCGEEMKjSbIjGnTttdcSERHB9ddfr3UoQriVnJwcLrroIrp160avXr1YuHCh1iEJ4TaKi4sZMGAAffr0oUePHrz33nsOOa8sPRcN+vXXXyktLWXBggV89dVXWocjhNs4duwYeXl59OnTh9zcXPr378+ePXsICgrSOjQhXJ7ZbKa6uprAwEDKy8vp0aMHv/32G1FRUc06r4zsiAZddNFFhISEaB2GEG4nISGBPn36ABAfH090dDSFhYXaBiWEm9Dr9QQGBgJQXV2Nqqo4YkxGkh0PtHr1aq666ipatWqFoigsWrTotGPmz59Pu3bt8Pf3Z/DgwWzevLnlAxXCBTny9bN161bMZjOJiYlOjloI1+CI109xcTG9e/emTZs2PP7440RHRzc7Lkl2PFB5eTm9e/dm/vz5DV7/5ZdfMmXKFKZPn862bdvo3bs3o0ePJj8/v4UjFcL1OOr1U1hYyG233ca///3vlghbCJfgiNdPeHg4O3bsICsri88++4y8vLzmB6YKjwao3377bb3LBg0apE6ePNn+u9lsVlu1aqXOnDmz3nErV65Ux48f3xJhCuGSmvr6qaqqUocPH65+9NFHLRWqEC6nOZ8/Nvfdd5+6cOHCZsciIztexmg0snXrVkaNGmW/TKfTMWrUKDZs2KBhZEK4vsa8flRV5fbbb2fkyJHceuutWoUqhMtpzOsnLy+P0tJSAE6ePMnq1avp0qVLs+9bkh0vc/z4ccxmM3FxcfUuj4uLIzc31/77qFGjuOGGG/jxxx9p06aNJEJC0LjXz7p16/jyyy9ZtGgRffr0oU+fPvzxxx9ahCuES2nM6+fQoUMMHz6c3r17M3z4cB544AF69uzZ7Ps2NPsMwiMtX75c6xCEcEvDhg3DYrFoHYYQbmnQoEFs377d4eeVkR0vEx0djV6vP63gKy8vj/j4eI2iEsI9yOtHiKbT8vUjyY6X8fX1pX///qxYscJ+mcViYcWKFaSmpmoYmRCuT14/QjSdlq8fmcbyQGVlZezbt8/+e1ZWFtu3bycyMpK2bdsyZcoUJk6cyIABAxg0aBDz5s2jvLycO+64Q8OohXAN8voRoulc9vXT7PVcwuWsXLlSBU77mThxov2YN998U23btq3q6+urDho0SN24caN2AQvhQuT1I0TTuerrR/bGEkIIIYRHk5odIYQQQng0SXaEEEII4dEk2RFCCCGER5NkRwghhBAeTZIdIYQQQng0SXaEEEII4dEk2RFCCCGER5NkRwghhBAeTZIdIYQmDh48iKIoTtnhuKkyMjK44IIL8Pf3p0+fPlqHg6IoLFq0SOswhHB7kuwI4aVuv/12FEVh1qxZ9S5ftGgRiqJoFJW2pk+fTlBQEJmZmfU2K6zL9u+mKAo+Pj4kJyfzxBNPUFVV1cLRCiEaS5IdIbyYv78/s2fPpqioSOtQHMZoNDb5tvv372fYsGEkJSURFRV1xuMuv/xyjh07xoEDB3jttdd49913mT59epPvVwjhXJLsCOHFRo0aRXx8PDNnzjzjMc8+++xpUzrz5s2jXbt29t9vv/12rrnmGl566SXi4uIIDw9nxowZmEwmHn/8cSIjI2nTpg0ffvjhaefPyMhgyJAh+Pv706NHD1atWlXv+l27djFmzBiCg4OJi4vj1ltv5fjx4/brL7roIu6//34efvhhoqOjGT16dIOPw2KxMGPGDNq0aYOfnx99+vRhyZIl9usVRWHr1q3MmDEDRVF49tlnz/hv4ufnR3x8PImJiVxzzTWMGjWKZcuW2a8/ceIEN998M61btyYwMJCePXvy+eef1zvHRRddxIMPPsgTTzxBZGQk8fHxZ71PsI48JSQksHPnTgD+9a9/0alTJ/z9/YmLi+P6668/6+2F8FaS7AjhxfR6PS+99BJvvvkmhw8fbta5fvnlF44ePcrq1auZO3cu06dP58orryQiIoJNmzZx7733cs8995x2P48//jiPPvoov//+O6mpqVx11VWcOHECgOLiYkaOHEnfvn357bffWLJkCXl5edx44431zrFgwQJ8fX1Zt24d77zzToPxvf7667z66qu88sor7Ny5k9GjRzNu3Dj27t0LwLFjx+jevTuPPvoox44d47HHHmvU4961axfr16/H19fXfllVVRX9+/fnf//7H7t27eLuu+/m1ltvZfPmzafFHRQUxKZNm5gzZw4zZsyolzTZqKrKAw88wEcffcSaNWvo1asXv/32Gw8++CAzZswgMzOTJUuWMGLEiEbFLITXcfq+6kIIlzRx4kT16quvVlVVVS+44AJ10qRJqqqq6rfffqvWfWuYPn262rt373q3fe2119SkpKR650pKSlLNZrP9si5duqjDhw+3/24ymdSgoCD1888/V1VVVbOyslRAnTVrlv2YmpoatU2bNurs2bNVVVXV559/Xr3sssvq3XdOTo4KqJmZmaqqquqFF16o9u3b95yPt1WrVuqLL75Y77KBAweqf//73+2/9+7dW50+ffpZzzNx4kRVr9erQUFBqp+fnwqoOp1O/eqrr856u7Fjx6qPPvqo/fcLL7xQHTZs2GnxPPnkk/bfAXXhwoXqX//6V7Vr167q4cOH7dd9/fXXamhoqFpSUnLW+xVCqKpB00xLCOESZs+ezciRIxs9mtGQ7t27o9OdGiyOi4ujR48e9t/1ej1RUVHk5+fXu11qaqr9/w0GAwMGDCA9PR2AHTt2sHLlSoKDg0+7v/3799O5c2cA+vfvf9bYSkpKOHr0KEOHDq13+dChQ9mxY0cjH+EpF198MW+//Tbl5eW89tprGAwGxo8fb7/ebDbz0ksv8X//938cOXIEo9FIdXU1gYGB9c7Tq1ever8nJCSc9u/zyCOP4Ofnx8aNG4mOjrZffumll5KUlET79u25/PLLufzyy7n22mtPuw8hhExjCSGAESNGMHr0aKZOnXradTqdDlVV611WU1Nz2nE+Pj71fretVvrzZRaLpdFxlZWVcdVVV7F9+/Z6P3v37q03ZRMUFNToczpCUFAQHTt2pHfv3nzwwQds2rSJ999/3379yy+/zOuvv86TTz7JypUr2b59O6NHjz6teLox/z6XXnopR44cYenSpfUuDwkJYdu2bXz++eckJCTwzDPP0Lt3b4qLix37YIXwAJLsCCEAmDVrFosXL2bDhg31Lo+JiSE3N7dewuPI3jgbN260/7/JZGLr1q107doVgH79+rF7927atWtHx44d6/2cT4ITGhpKq1atWLduXb3L161bR7du3ZoVv06n4x//+AfTpk2jsrLSft6rr76aW265hd69e9O+fXv27NnTpPOPGzeOzz77jL/97W988cUX9a4zGAyMGjWKOXPmsHPnTg4ePMgvv/zSrMcjhCeSZEcIAUDPnj2ZMGECb7zxRr3LL7roIgoKCpgzZw779+9n/vz5/PTTTw673/nz5/Ptt9+SkZHB5MmTKSoqYtKkSQBMnjyZwsJCbr75ZrZs2cL+/ftZunQpd9xxB2az+bzu5/HHH2f27Nl8+eWXZGZm8tRTT7F9+3YeeuihZj+GG264Ab1ez/z58wHo1KkTy5YtY/369aSnp3PPPfeQl5fX5PNfe+21fPzxx9xxxx189dVXAPzwww+88cYbbN++nUOHDvHRRx9hsVjo0qVLsx+PEJ5Gkh0hhN2MGTNOm0bp2rUr//rXv5g/fz69e/dm8+bNzart+bNZs2Yxa9Ysevfuzdq1a/n+++/ttSm20Riz2cxll11Gz549efjhhwkPD69XH9QYDz74IFOmTOHRRx+lZ8+eLFmyhO+//55OnTo1+zEYDAbuv/9+5syZQ3l5OdOmTaNfv36MHj2aiy66iPj4eK655ppm3cf111/PggULuPXWW/nmm28IDw/nm2++YeTIkXTt2pV33nmHzz//nO7duzf78QjhaRT1z5PxQgghhBAeREZ2hBBCCOHRJNkRQgghhEeTZEcIIYQQHk2SHSGEEEJ4NEl2hBBCCOHRJNkRQgghhEeTZEcIIYQQHk2SHSGEEEJ4NEl2hBBCCOHRJNkRQgghhEeTZEcIIYQQHk2SHSGEEEJ4tP8HYoPIrMEtTlsAAAAASUVORK5CYII=", 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", "text/plain": [ "
" ] @@ -435,52 +415,18 @@ } ], "source": [ - "(fig, ax, nprocs_250k, runtime_250k) = runtime_vs_nprocesses(df_250k)" + "(fig, ax, nprocs_250k, runtime_250k) = runtime_vs_npoints(df_250k_nc)" ] }, { "cell_type": "code", - "execution_count": 212, - "id": "698e8d04-6885-4eff-86f3-e58a2b944579", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(
,\n", - " ,\n", - " array([ 8, 16, 32, 64, 128, 256, 512]),\n", - " array([4046, 1599, 2010, 4134, 1660, 2152, 4325]))" - ] - }, - "execution_count": 212, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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", 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" ] @@ -490,39 +436,18 @@ } ], "source": [ - "(fig, ax, nprocs_250k, runtime_1e6) = runtime_vs_nprocesses(df_1e6)" - ] - }, - { - "cell_type": "code", - "execution_count": 214, - "id": "81c4b69a-4ba2-4207-a093-ed34e25efe7d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 8, 16, 32, 64, 128, 256, 512])" - ] - }, - "execution_count": 214, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "n_tasks" + "(fig, ax, nprocs_250k, runtime_250k) = runtime_vs_nprocesses(df_250k)" ] }, { "cell_type": "code", - "execution_count": 215, - "id": "4490d705-be26-482c-beda-918422da1555", + "execution_count": 9, + "id": "fe11d91c-64e3-428e-85be-79c5d433cb4a", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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5lUWksGCnfv36cnVCydOYMWPk9Tk5OXI7ODgYPj4+GDp0KBISEordx+nTpzFgwAB4eXkhLCwM48ePh9HIGit6HtmJDnWTHdTZOoKU0um8uionXy3Y33SSrSOIFBHs7Ny5E+fPn7edVq9eLffffffd8nzs2LFYtmwZFi1ahPXr1+PcuXMYMmSI7edNJpMMdPLy8rBlyxYsWLAAX331FSZPnuywx0Tlk5ZjwunCnAJ7BzsiYO7JJeikADGFK7Gqs5hgSddFerB1BJGSgp3Q0FBERETYTsuXL0ejRo3Qq1cvpKamYv78+Zg9ezZ69+6NDh064Msvv5RBzbZt2+TP//nnnzh48CC++eYbtG3bFv369cO0adMwb948GQCRch1KtPx9avu5IMDTUG1D+cxbICVMYzWrpjYRpXFh6wgi5ebsiOBEBC2jRo2S38x37dqF/Px89OnTx3ab6Oho1K1bF1u3bpWXxXnr1q0RHh5uu03fvn2RlpaGAwcOlPm7cnNz5W2KnsgxxQSb23lUp2TriP1sHUEOkpqVh/OpOTUe7AgM9okUGuwsXboUKSkpGDFihLwcHx8PNzc3BAQEFLudCGzEddbbFA10rNdbryvLzJkz4e/vbztFRUVVwyMiRyQnF20dEc3WEeRA1iXntQM94etRtSa3FcXWEUQKDXbElJWYhqpVq1a1/64JEybIaTLrKS4urtp/J9VMcnJRzNshJQQ71dXp/FqtI+oHukIsRmTrCCKFBDunTp3CmjVr8Mgjj9j2iRweMbUlRnuKEquxxHXW25RcnWW9bL1Nadzd3eHn51fsRDUnz1SAI0n2bRNRGraOIGWsxKr5YEdgnzgihQU7IvFYLBsXK6usREKyq6sr1q5da9sXGxsrl5p37dpVXhbn+/btQ2Jiou02YkWXCF5atGhRw4+CyuvIhTzkmwF/D2eZoFxdiraOOHaR1WTJQSM7kY75MsV6O0QKCnbMZrMMdoYPHw4Xl8sffCKX5uGHH8a4ceOwbt06mbA8cuRIGeB06dJF3ubWW2+VQc2DDz6IvXv3YtWqVZg4caKszSNGb0jZbSLEFFZ1doEu2jpiw3GuSqGak28041hShsOmsQS2jiBSULAjpq/EaI1YhVXSnDlzMHDgQFlMsGfPnnJqavHixbbrDQaDXK4uzkUQ9MADD+Chhx7Ca6+9VsOPgpSWr2PFoXxyBBHoGE0F8PN0QYS/pX1DTROtI9qxdQSRVH1zCOUkRmfKyqfw8PCQNXPEqSz16tXDihUrqvEIyd4OFq7EqpFgRwzl/3W5dYS7nRqOEpUnX0d0Oq/O0cvyBPv/nMmRwf597fwddhxEjsZ3fqpR5oIC28hOdS07L6t1hFiGS1QTYs+nOTQ5uWTezma2jiCdY7BDNepMihEZeWa4GZzQKNit2n8fW0eQQ0d2arBNxNVaR6TmmLEvnq0jSL8Y7JBDigk2C3WDq8ierMlVKccZ7FD1E9Py1pVYjh7ZKdY6gs9/0jEGO6TZ5GQrto6gmnQuJQfpOUa4GJzQMMTH0Ydj64LOPlmkZwx2SFNtIkrD1hHkiE7njUN94KqAhPiehSObbB1Beub4VyLpiiNGdgRr3s6mkxzKpxrqdO7gfB0rto4gYrBDNUhMIYlqxiJTJzqsZoOdonk7bB1BNbXsXCms9aZYb4f0isEO1ZiDiZbVIPWDXOHjXrNPPdE6QqwAO8/WEVTNYgunsRydnFx63g6DHdInBjtUYw4ULn1tUcOjOtbWEZ3rsnUEVa/U7HyZoKy0YMfaOuI4W0eQTjHYoRpzMKEw2KnGTudXw9YRVN0OF05h1Q7whJ+nK5TCz4OtI0jfGOyQ5pOTS+btWFtHENlbjLVyskKSk4ti3g7pGYMdqhHZ+WYcT665NhGlYesIqm6xCdbkZAUGOw0vr0hk6wjSGwY7VCNiEvMg3l9FsCHq3jiCaB3BqSyqkWXnClqJVbR1hK87W0eQPjHYIc0WEyyNLdhh6Xyys3yjGUeTMhTRE6vM1hH1LUn6fP6T3jDYoZpNTnZwsGNdgitaRyRnsZos2c/xCxkwmgrg6+GCSH9LMrDS9GjgLc/ZOoL0hsEO1YgDhcGOo0d2ireO4Ldbsp+Y85fzdcSUqRIVbR2RkcskfdIPBjtU7YzmApmzo4SRnaKtI5i3Q9VROblZpPLydUptHXGaz3/SDwY7VO1OXMxDrrEAXq5OsnqyYqrJsnUE2VFs4bJzJa7EKuv5T6QXDHaoxtpENA93h7MChvevj2LrCLIvETTbRnYUHuxwRSLpEYMdqrligg5oE1Eato4gezufmoP0HCNcDE5oFOoDJSvaOuJMKoN90gcGO1TtDsQXLjt3UJuIq1aTZZIy2TE5WQQ6ri7Kfltl6wjSI2W/KkkTw/uObhNxtWqyW0+xdQRps9P51bDeFOkNgx2qVvHpRiRnm+SwedNQNygFW0eQPVnzdZSenFwySZmtI0gvGOxQjRQTbBziBg8FDe+zdQRVR5uIaAUvOy+qTS22jiB9Uc6nD2mSUooJloZdoMke0rLzcTYlW243VcnITtHWEXz+kx4w2CFdtIm42lD+vvNsHUGVF1s4hVUrwAP+no6vI1XR1hEbmLdDOsBgh2pkZEeJwQ5bR5A9gx0ldjovz8jm7rPZbB1Bmsdgh6pNao4JcSlGxQY7AvN2yF7BjlqSk63qBrqiHltHkE4w2KFqY+2HVdvPBQGeBih9CS5bR1CVlp1HqivYEbgEnfSCwQ5VezHBFgoqJlgSW0dQVeQbzTiamCG3o1U2jSVwZJP0gsEOVXtPLKW0ibhW6wi+4VNFHb+QiXxTAXw9XGSCstqwdQTpBYMdqjYHCut3KKlNxFW7QJ9gnyyqfOVkUbtJbUTriLZsHUE6wGCHqkWeqQBHLyivTcTVhvLZOoIq2xNLbcnJRTFvh/SAwQ5Vi8NJucg3A/4ezjJBWcnE8nO2jqCqrMRqqsJ8ndKa4rJ1BGkVgx2q9mKCSh/edy7SOoJD+VReYvVeTOE0VrQKV2KV1jpiP1tHkEYx2CHdVU4uDVelUEXFp+YgLdsoWy80CvWBWhVtHcHnP2kVgx2q1mBHiT2xSsPWEVTZTucNQ73hpqAmt1V5/rN1BGmVul+hpEjmggIcTFBHcnKx1hGhbB1B5Rdz3jqFpd58HauehX2y2DqCtIrBDtldXEo+MvLMslhfo2A3qEWPhpzKosr0xFJvvk5prSO2s3UEaRCDHaq2KaxmoW5wFRXLVKJokjJbR1B5p7HUvOy8KOatkZYx2CHdFhMsq3XEuTS2jqCrS8vOx9lL2ZoZ2RGYt0NaxmCHdNkmoqzWEddHWarJ8tstXc3hBMuoTqS/B/y91DNVezVsHUFaxmCH7E6tIztCj4aWRE22jqBy5euouL5OSf5sHUEaxmCH7OpCphEJGSaITJ1moSoMdgqH8redypYtL4iuFuyosdP51TBvh7SKwQ5VS3Jy/SBX+Lir7+llbR2RJVpHnLHkZBBpsSfW1YIdUX6BrSNIS9T3aUSKprbKyVdrHcFvt1SafJMZRxO1N41VtHVESjZbR5C2MNghu1J7sFN0VQqDHSrNiQuZyDcVwMfdBbUDLG0WtEK0jrihHltHkPYw2CG7OqCyNhGl6VGkdcQlto6gEmILp7DEknOlN7mtDBbXJC1isEN2k5VnxvHC+jRqHtlh6wi6Gmunc63U1ymzdcQZto4g7XCpzA+dOHECGzduxKlTp5CVlYXQ0FC0a9cOXbt2hYeHZeki6U9MUq4MEEK9DTJgUPu325ikPPntdmALbX6oUdVGdqI1lq9TsnXEqUv5snXEzU3U29GdyKpCn0jffvst3n33Xfzzzz8IDw9HrVq14OnpieTkZBw7dkwGOsOGDcOLL76IevXqVeSuSQO0kK9TdCrrs+0pMtgRrSO0OF1BFSeeC9Y2Ec00tuy8qO71vXDqUqp8/jPYIV1NY4mRm/feew8jRoyQIzrnz5/Hrl27sGnTJhw8eBBpaWn49ddfYTab0bFjRyxatKh6j5wUR83FBEti6wgqTUJaDlKz82Uib6NQy3SPFlnzdtg6gnQ3sjNr1iz07du3zOvd3d1x4403ytP06dNx8uRJex0jqYRa20RcrXXEppPZ8ttt4xBttAQg+9TXaRDqDXdXA7SqaOuIs6n5qO3v6uhDIqqZkZ2rBTolBQcHo0OHDpU9JlIho7kAMYl5mpnGKto6gqXz6cpO59qdwhLYOoK0plKrsXbv3o19+/bZLovpqzvuuAMvv/wy8vIsH3ikLycu5iHXWAAvVydZPVkLrEvQt57KYusIkmI1vhKrZN6OsIHBDuk12Hn88cdx+PBhuX38+HHce++98PLyknk6L7zwgr2PkVQ0hdU83F1WIdYC2TrCi60jqJQ2ERpdiVVUz8K8HbaOIC2o1PpgEei0bdtWbosAp2fPnvjuu++wefNmGfjMnTvX3sdJaklO1sgUliCCNlFNeemBdJm306We5c2f9Ck9Jx9nLmXrZmSnZOsIcZlUwiOs+n9HTiI0P7Ijll+KVVfCmjVr0L9/f7kdFRWFCxcu2PcISRW0tOy8KFaTJavD8RnyPMLfAwFe2k9YL9o6gnk7pHaVCnbE0vLXX38dX3/9NdavX48BAwbYig2K+jukLyL41UKbiNKwdQSVrJystU7n5VqCzmCH9BjsiGkqkaT85JNP4pVXXkHjxo3l/p9//hk33HCDvY+RFC4+3YhL2Wa5VLVJqLa+8bJ1BFkdthUT1FGwUxjss3UE6TJn57rrriu2GsvqrbfegsGg3doTVDrrqE6TEDd4uDhrbi57xhA/xCTmIjTYDfDw1Nxcth64G6r+vOzTPBwd6wWia6OQUu8v12TWXO5GvUjgvf/zR0aeGanwgo+Hq2qe7/b4m1+LIv/mVKoqPxsyMjJk9WRxEsvOs7O5akVvtJqvYxXha/lOcD7diAIuStElc0EB0nIslbQDvLRRWqG8Ivysz39O45LOgh2RmyPydLy9veHv74/AwEB5CggIkOekz5VYWg12wnwMcmWW6OqexqF8XUrPMcJsLoCLwRlebvoavY4sDPbj09k2hXQ2jfXAAw/IpNQvvvhCJiSzSaK+2dpEaDTYEatSRMAjcpPEyd9DW3lJdG0pWZZiqQGerrp7vwvzdYF4xGk5ZmTmmeHtpvCpaqoQk8mEffsPol7dKAQGBkCrKvWs3bt3L7788kv83//9n+yF1atXr2Knijh79qwMnkSLCdFBvXXr1rKrupUIqiZPnozIyEh5fZ8+fXDkyJFi9yG6rotu635+fnJ06eGHH5bTa1T9UnNMiEsxajrYKTaVlWZ5rKQvqVmWUQ1/nU1hCe4GJwR7W0d3+PxXu2efm4D5X35tC3R69RmA9p17IapRK/y9fhO0qlLBTqdOnRAXF1flX37p0iV069YNrq6u+OOPP2T39HfeeafYVNibb74pu61//PHH2L59u5w6E326cnJybLcRgc6BAwewevVqLF++HBs2bMBjjz1W5eOjaztUmK9T298FAZ4GzectJGYYwc4R+pOSbc3X0eeoHoN97fh5yW9oc10rub1s+UqcOHkaMf/twNinR+OVKdOgVZWaxvr888/xxBNPyFGZVq1ayWCl5Gqt8njjjTdkIUIxSmTVoEGDYqM6Ypn7xIkTMXjwYLlv4cKFcups6dKlslrzoUOHsHLlSuzcuVPW/xHef/99Wejw7bffRq1atSrzEKmctJ6cbBXoYZArzXKMZlzIMCK88M2f9KDg8jSWDkd2hEg/F+yPB+IzjBCdI5z1NZOnKRcuXEREuGWV3oqVq3H3kMFo2rQxRg1/AO9+8Am0qlIjO0lJSTh27BhGjhwpR3lE64h27drZzsvrt99+kwHK3XffjbCwMPmzn332WbFE6Pj4eDl1ZSUSojt37oytW7fKy+JcTF1ZAx1B3N7Z2VmOBJUmNzfXtoLMeqLK0WoxQSvx3Fj66++IiYm1fbvlUL62iaH9vXv2yJFnITvPjDyjWebq+JW29Fpjj33P3n24dCml2P5gLwNcDU7IMxbgUrZJk6/zX39dKr88a114eCgOHoqVf+uVq9fglptvlPuzsrNgqIHl+o5SqUc2atQoGZiIQEM0AhVBSdHz8hK3/eijj9CkSROsWrUKo0ePxtNPP40FCxbI60WgI5SsyiwuW68T5yJQKsrFxQVBQUG225Q0c+ZMGTRZT2J0iSpHayM799w/Eh98+KncFmUUOnbtjXuGjcR1Hbtjz4Y/bEvQSTueGzcWX34xX26LD4A+N92Ezp06olH9elj/99+2UR1fDxcYNDakUd78DfGww30Kg30NTGXdf+//4cN582yv866dr8ewe+9Fx3ZtsWTxL9CykQ8Nk+9prdrdIAP4PoXBzvYduxDdrCm0qlLBzqlTp+QUlBhhqV+/PurVq1fsVF6iv1b79u0xY8YMGTyJPJtHH31U5udUpwkTJiA1NdV2skf+kR7lGs04ciFPUyM7GzZtQY/uXeX2kl+Xy6nUlMSTeG/2LHzyvqXBbXKWCblGJu5oxZJffsF117WR28uXL8PJkyfw34GDePqZZzFl0iSkFCYna3EKqyL5G0XrTandpo0b0b17d7n969Il8nWeeDEZs+fOxcwZM6BlUye9hM8/fg+PPTwcm9ethLu75b1bFAR+6flnoVWVCnZ69+4tV2RVlVhh1aJFi2L7mjdvjtOnT8vtiIgIeZ6QkFDsNuKy9TpxnphYvIKn0WiUK7SstylJ/HHFyq2iJ6o4EegYzYC/hzNqFSbwql1qahqCChPkV/65FkPvvB1eXl4Y0O9WHDt2HP4eliRsTmVph2heHF74XrHyjz8w5K670LRpUwwfORL79++7nJzs6aar/I19+w9dkbcjfybTiHyVZ+mLL7mBQUFy+89Vq3DnnUPk67xf/wE4WmK1rxbdNWQwxj7zP4SEBNv2DX/wPgweZGnqrUWVCnZuv/12jB07FlOnTsUvv/wic2+KnspLrMSKjY0ttu/w4cO20SGRrCwClrVr1xabWxW5OF27Wr59i/OUlBTs2rXLdpu//vpLjhqJkSeqmSksrdQeiapTG1u370RmZqYMdm7t01vuv5SSAg8Pd9sbPoMd7RDT4ocOHpTTOKtXrcLNN1tyBLOzRA6DQdPJyRXJ3/B1d4aPm7NMUE7MUHfeTp2oKGzftlW+zkWw0+eWW+T+lEuX4OHhAS0zmUyYNuMt1G7QAj5BdXD8+Em5f9LU6bYpTS2q1NdxsRJLeO211664Tnzoif/M8hABk2gcKqax7rnnHuzYsQOffvqpPFnv69lnn5Ud1kVejwh+Jk2aJFdY3XHHHbaRoNtuu802/ZWfny8blIqVWlyJVTPBjlamsIRnn3oCw4Y/Bh8fb9SNqoMbe1mGujds3IrWrVrIoXzRJ8vaOkIjMZ6uPTR8BIbddy8iIiPle87NhQsiduzYjqbNmiEz16jZGjvW/I3IiIhy5W+IEgxHL+TJ578oN6FWTz39DIY/+CB8fHwQVbcuet1oedwbN25Eq1atoWXTZ72DBV9/jzdnTMWjoy9PW7Vq2Rxz3/8YD498EFpUqWerGDWxB7GSa8mSJTKHRgROIpgRS81F3RyrF154QUbfIp9HjOCIeVax1Lxo9P3tt9/KAOfmm2+Wq7CGDh0qa/NQ9dJacrLwvycewfWdOiDuzFn5LVc8n4SGDerh9amvXNE6QkzhkbpNmjIFLVu1RFzcGQy9665iOQxPPD1Obnu6ucDdxaDJ/A3xIRcXdxZ3Dx18zfwN0TpCBDuW1hHqHQF5YvRo+flz5kwcbu5zi+11Lj6DppbyJV5LFn7zAz79cC5u7t0LTzz5nG1/m9atEBN7GFrlVCAys3ROTI2JVVliHteu+TumXODSHsDgDRjcNNcY8bp3jstuyKseqYtmYSoJeMrZBVo0tT1x4hQaNWogV/cV9dfRTDmN1aGOJ5qFuqmmC3S1M+UBpkwgsC1gKN/zwWQuQEpuvmzJIYJIR3fAFsVKi36ROpqYjj2nUxDp74luTULs1gHbbMxBQfIe+Hr6w+Dqroiu5yUfe2lyTQX45T9LqY7BLX0trSMq+Hw3GfMsf3P/pnB2cXfo39zyOj+BRo0aXfE6Lw9F/s2v8Xf39I9EzH/bUa9eXfgGR2Hvzo1o2LA+Dh6KwfXd+iAj+Uz5fkdF/u6VeG+w9+d3ub+W/vDDD+X+5WJ10+bNm8t9e1KfuJR8Gei4GZzQMFg7gVxWVhYefvwpeAXUQst2XXH6tOWF/9SzL2DWW3PkNqvJaouYdp/x+utoUDcKQf5+tvIZUydPxjdffanZfJ3K5G+I1hEhGmgdIV7njz/6CAJ8fdDuuta2RTHPPvM03nrjDWhZi+bNsHGzpU5dUT8v/g3t2mp3Cq/cwY6ohyPyY0T7htIKL4moasWKFbj//vvlcvKLFy/a+1hJgZ3Oo8PcZLExrZgw8TXs/W8//l69rNi33D69b8SPi5bIbbaO0JZZM2Zg4cIFmDFrFtzcLgfuYmpryQ/faDrYEfkbXy38TuZvFH3sYmrr8zKSVbUQ7E985WX8999/WL32r2Kv894334xFi36Clk1++QU8+ewLeOPtuTIlZfHSZXh09DPyuSCug96DnfXr18vaOqL/lGgRIYaLRNKwaNxZp04d2chTFBusW7cu9u/fj0GDBlXvkZNDaTFfR1i67Hd8MPdNdO/WtdgKs5YtonGs8FuvtXWE0VyAC5nqXpVCwDfffI0PP/oY990/TOaqWLVufR3iThzRdE8sa/7GsPvuKfbYr5a/YVuRWNg6Qo2W/for5r77Hrp1717sdd6iRUscP3YMWjZ4UH8sW/w91qxdD29vL0x+bSYOxcTKfbf0uQlaVaFJShHAiJOoS7Fp0yZZXFBUnwwJCZFFAcXJmuhF2qbVNhFJSRcRFhp6xf7MrEzbyitxLr7dnryUh/i0fIT7aC9xVU/OnT2LRo0bX7E/PSdf1uxyMTjDy02bf+Oz586jcaPL/QitzAVm5OeXPnJTsnVEsArf8kXLo9ASlfeFrEzxOtfOSHVZenS/Aav/sIxU60WlVmOJ4Ma69Jv0SasjOx07tMXvf/yJp8Y8Ji9b3/g+/+JrWVLeyhrsiCW4ltq7pFbNW7TA5k0br6j+/tOiRajftCX8PV01+wFozd8Qyarlzd+wto44k5ovW0cEB0B1OnToiD9WrMCYJ5+Ul61/3y++mI/OXbo4+OioOqi3UAI5jKigmpBhgnh7iFbLKqxymvHaJPQbdI9cmSC+1b/7wcey6NqWbTuwfs1y2+2seTvW1hHuLtr8MNSDlydOxCMjR+Lc2XMyh2HpkiU4cjgWX3/9NZ57a75m83UEkaMx/JHRcoTHmr8Re+SonN5avqTsRSki2BfBjgj2W6ow2Hnt9dcxaOAAHDp0UL7OP3j/PVlYctvWrVjz1zpoTWB4/XIH7MnxJ6BFKhyAJKWM6jQIcrUsPdUQkauzZ8cGGE0mWUTwzzXrEBYWgq0bVqFD+7a223m5OrF1hEYMGjQYi5f+irVr18Db2xuvTZ2CmEMxeG3eQrTu3EOz+TpVyd8o2joiM88+dddqksjV2bFrN0xGoywiuGb1atlQesOmzWjfoQO0Zu7bMzHnrRnyNPGl5+W+vrf0xtSJL8mT2BYmTRgPrWKdHdbZqbCPtibjjXUXMaC5D+bdGQlVqUDdkWvZfTZHVlNuFOyGznU9L1/BOjuqrbNzWQF+23MOeUYzbm4ejkBvN3XWXLHj872k3w6ky/ITEa7J6N3YW5V1dqpKkX/za/zdh/7fQ7ipV3c8+T/LVL3VBx9+ijV/rcfSn7/VZJ0dTmMR9N4mQrxYyqvoi4mtI7QrO88sAx0x9O/nqd1prKqwto7YcDyrQsGO2l7nWrNq9V94Y/rUK/bfdmsfvDRRu9WjqxTsVLX6JKmT1pKTA8LKP59tyr5cP4qtI9QrPCT4qn9zsaTaaDbDCU4YmqytmmH2yt+wto7YdCILahAWHFTux52dZ+l0r0XBwYH4ddkKPDfWkpxtJfaJ67TKpbLVJ5966iksWLDA1qm8YcOGcl/t2rXx0ksv2fs4SSHEB/vxi/maGtlZ9+cy2/bJU6fx0sRXMeLB+9C1cye5T3RBX/DND5g5bXKxnxNTL6E+BiSkG2Xejr+HdqYqte7t2bNt2xcvJmPWjOm45dZb0blLV7lv9boN2Pj3WowYY+mNpbX8jaKP/fVZb8ucDetqw63bd8hv/9fK3wgrLC549GIezqXlo5afskfA/lyz1rZ96tRJTHz5ZTz40HB07mpZfbV96zZ88/VCTJs+HVr26qQJeOSJp/H3hk3ofH1HuW/7jn+w8s+1+Oyjd6FVlQp2ROPOvXv34u+//5Ydx6369OmDqVOnMtjRsJikXIgkr1BvA0J9tDGa16tnN9v2a33fxOw3X8d9/3eXbd+g2/vLZOVP5y/A8Afvu+LbrTXYKbVPFimS+JCz+r+778Lkqa/if2PG2PZ16H8/wj/9CAd2b4PWFH0Oi/yN1yZPKJa/8fSTj9vyN8Y+878y76do6wgxunNPG38oWc9evWzbfW95HW++/Tb+797L/xe33z4IrVq3wvzPPi/2/NCaEQ/dj+bRTfHevE/k6jtBXN607g9b8KNFlRp3X7p0KT744APZgbxYldmWLXFM49Un9c7aJqJlhDZGdUoSozgd27e7Yr/Yt2Pn7iv2W5egi4CHrSPUafWff+LWvn2L7UvNysN1XXph+6b10DIxgiNyNUoS+0Swcy3W1hEib0dNtm/bivYdrvxgF/t27twBret8fUd8u+Az7N6+Xp7EtpYDnUoHO6L6pFimV1KmTqpP6pnW8nVKiqpTG599YZmeLerzLxfK60pi6wj1E61ulv32m+2y0WRGRq4RuzasRnBQMLTMmr9RUnnzN8TIprD5ZBbMKlrYWycqCl98/vkV+7+cP19ep3Umkwm/LPkNr898W56W/Lpc7tOySs1DdOzYEb///rvM0SlWZfbzz9G1q2XOmzQe7GismKDVnLemY+i9w/HHqjW2bzo7du7CkaPH8csPVwZB4qkf7uuCU2wdoVqTpkzBE489hg3r/8b113dGVp4Rq//ejP+2r8fHn3wCLatq/kaQtwG+7s64lG2Wo76tIy831VSyt95+B/feczdWrVqJ66+35Crt3LkTR48cwQ8/LYKWHT16HAPu+D+cOXsOzZpa2qTMfHOO/DL3+9If0aiU9iG6DXZmzJiBfv364eBBS/XJd999V25v2bJFNgwlbRKjFzFJeZqexurf71YcOfAPPvxkPmJiLU0gbx9wG554dCSiouqU+e1WBDtsHaFODw0fgejo5pj3wftYunQJcvNNCKzVAO9+8xseuqv49JbWVDV/w+AEdK3niT8PZ2LDiSzVBDv9+vfHgZhYfPLRR4iNjZH7BgwYiEcffxxRGh/ZeXrci2jYoB62bvgTQUGBtkT1B0Y8Jq/7/Vdtdn2vVLAjcnX27NmDWbNmya7nf/75J9q3b4+tW7fKy6RNxy/mydYI3m5OqBeo7JUXVVGnTm3MKLHy6mquaB1RjcdG1eP6zp3lSdh18hJOXMhAdKR2a61ckb9RhXyNHg28ZLCz8XgWxtwQBLWoU6eO5ldelWb9xi3YtvFyoCMEBwdh1vQp6HZjP2hVpZfTiNo6n332mX2PhlQxhdU8zL1aq906WkpKKuZ/+TUOxR6Wl1s2j8aoEcNklc7SWFtHpOaY5KqsekWKKZM6iHyF335dKttEHEvKQHCdhug04l7o5bEv/e13HIopfL63iMaggf1gMBjKHewIu85ky9YRamkhk5KSgi+/+AKxMYfk5eYtWmLEyJFlvs61wt3dDenpGVfsz8jIhJubdr/EVulZmZiYiP379+O///4rdiJt0npysvDPrn/RqHk7zHn/IyQnX5Kn2e/NQ6Pm7bH7373X7BXEPlnqc/ToUbRp3QqjRoyQTUA3rv4dH00di/49rtf86lKRv9GiTRc8NGq0nMYSpwdGPI6Wbbvi2LHyNYQUo7xRAS7INwPbT2dDDXb98w+aN22C99+di+TkZHl6b+4cue/f3VeuutSSgf374rH/PStzs0S3KHHatn0nnnhynAxytapSIzu7du3C8OHDcejQIfkfVZRIVtZ6VrdeHdBYm4jSjB3/snzBi+RMa1VwkZcmkjiffX4CNqy9cuXKFa0jQgu4KlFFxj37DBo0aCCbQLp4+uLPA/HITkvFT++8IK/7ddnlbvdaY4/8DfFcF6M73/2bho0n1NE6Yvxzz2Hg7bfjo08+LfY6f+KxR/H8uHFY+/ff0Kr3Zr+B4Q+PRteet8LV1dX22MX73rvvXC44qTWVCnZGjRqFpk2bYv78+QgPD+cbuw6IoFYfIzt78NmHlwMdQWy/8NzT6NjV0hm4NEVbRxxPzpfNQUkdNm7YgI2btyAoKAinkzPlvjq1wjB9xkzc2LMHtMxe+Rs9G3hbgh2V1NvZtUssQvjkitf5c8+Pt1WS1qqAAH/8+st3OHLkGGIKp+qbRzdD48YNoWWVCnaOHz+OX375BY0bW5atkfaJEQuxvNTFGWii4UrBfn6+OB13BtHRTYvtj4s7C19fnzJ/rmjrCFFNlsGOeri7uyM9PV1up2ZZWqEEeLki42IG3Ny0/Xe0V/5G1/qecHZST+sI0egz7vRpREdHF9sfFxcHX19f6EGTJo3kSS8qlbNz8803y3YRpB/WUZ3GwW6yiJ5W/d9dd+LhJ57Cj4sWIy7ujDz98NMveGT007jvnqHlKrAmhvJJPfoPGID/jX4CO7ZvR3JmnhzFPHlwD54c8z851aFl9srfEAn6bWpZlp2roTHoXffcI6esFv30owxwxOmnH3/A6Mcfwz33ajsx3WQyyQUY9z/0CPrcdgd69x1U7KRVlRrZEcUDRc6OSE5u1aqVbd7PatAg7f6H6b1NhJansIS335gmp2VFwqaYxxbE83v0Y6Pk0P41l6CfA7aeykKeqQBuoggJKd7sue/i4ZEj0LN7NxhcLO9lZpNRBjrvzJkLLbNn/obI2/n3bI4M9pXeJ+uNN9+Sr3ORlF70df7Y409g+kzt5q0Iz4x7CV99/T0G9LsVrVo2100aSqWCHVFPZ/Pmzfjjjz+uuI4Jytpky9fRaDFBKzFt8e7sWZj5+mQcO25ZjdKoYQN4eVmW116NaB3h7uKMzLwC+abfuS7XoKtBQEAAflmyFPsPxuC7VVvlvhEDuqNp0ybQOnvmb/Rs4IX3NiXLkR3ROkLJ5SnE63z2nLl4ffoMHC9ccdewUaNyvc7V7odFi/HTt1/IAqp6UqlgR7SJeOCBBzBp0iSZoEz6WYml1TYRJYk3vdatWlboZ8R7u7Ux4sbjmQx2VCa4dj207+ENPw9XNG0aAT2xR/6GmMZSW+sI8TpvpbNCuG5ubmjcSNvJyHYLdi5evIixY8cy0NEJUSzvTKpR88vOrc1sZ701F2vXrUdi4gWYzeZi1x+P3VO+vJ2TWXi+Wo+U7EWMRC9c8BV+/X0VTp+Nh5uLE97xuDw1v2rNGmj5sX+18LvLz/eC4s/3v1ZdbpB6La4GJ9W0jhCv87feeAPr/voLiUmJV7zOY48chVY998wYvPvBx/jgXctUnl5UKtgZMmQI1q1bJ6sok/YdKhzVqe3vAn9PbTe6FPV0xHLcB++/B5ERERV+M7C2jvjvXC5Ssk0I0Pj/lxaMG/ssvl6wANf37IOoRk0R4uuBQC9tr8KqrvwNtbSOEMnJouTA/cMeQERkpK4+9Ddt2YZ16zfKZseiWnbJnNvFP30NLapUsCNq7EyYMAGbNm2SvbBK/mc9/fTT9jo+UgA9FBO0Em8AovNvtxu6VOrnReuIpiFuOHwhD5tPZmFAc30sY1WzRT/+iG+//wFOddshI8eIHk1DEe6n3FEJJedvFG0dIWpOeSm0dcSqlSux9LdluKFbN+hNQIA/7hw8EHpT6dVYPj4+ssN5yS7nIkJmsKMteigmaBUYEICgwMsF1iqjR0MvGeyIVSkMdtSRw1C/QUMcyLRM1QZ4KrtGjJLzN6ytI+JSjLJ1xE0KraYcEBiIwCDljjxVpy8/mwc9qlTYfeLEiTJPouAgaYueRnamTX0Zk1+biaysrCp/uxVD+SXbqZDyPDN2HOa8+678W3m4GuDuqp+pR2v+hr2ep9bWEYLI21Gqqa++itemTqnS65x00vWc9CHXaMbRC3m6Gdl5Z+48HDt+EuFRzVC/XtQVU7S7txcfySyNWIUlauycTTPiRHI+GrKasqJt2bwJ69b9jRUr/kCjptFYGFB8NOKnn3+BVlVH/oatT5aCW0fMnTNHLjmPqhWJevXrX/G4t+/8x2HHRg4OdsaNG4dp06bB29tbbl/N7Nmz7XFspABHLuTBaBYVUp1RqzD5VsvuGDSgyvfh6eqMTlEe2HwyW05lMdhRfp2dHrf0R0ZOPvy9XOGvk+Tk6srfuKGel611xPm0fEQqsHXEoEGDHX0IVMPK/en177//Ij8/37ZN+srXEVNYelixMGXii3a5n+4NvGzBzvCOAXa5T6oen83/An8dSpCtIro0DEadIO0XlqvO/A2xYlPU3FFyNeWJkyc7+hBIqcGOWGpe2jZpm17aRNibGMp/Y91F2Toi31Qga5CQMol8lZRsyxc5MbJDVaem1hGkD5VKUB41apStS3DJQk3iOtLgyI7G20TYmwgOg70MttYRpFwZuUaYzQUwODvDx137U7U1QbSOEKytI8jxzpw5iwsXLtoub9y0BcOGP4oevfvhgRGPYeu2HdCySgU7CxYsQHZ29hX7xb6FCxfa47hIAcSb1KHEPF21ibAX0RdITGUJ7IKubClZl0d19DBVWxPENJaP2+XWEeR4Q+8dLjvaC7/+tgI33nI7MjIy0a1rZ2RlZaNXn4FY/vtKaFWFvsakpaXJIV9xEiM7Hh4excqOr1ixAmFhYdVxnOQApy/lIyPPLFcWNQrRT9KmPYfyfz2Qjg0nMvFcr2BHHw6VISXLEtAH6qi+TnWTrSPqe2K1qKas8NYRenHgYAxatmgut2e+NQczpk3Ci88/a7v+gw8/lWU3Bg64DdD7yI5YtRAUFCS//YgqyoGiMFPhKSQkRE5hjRkzpvqOlhwyhRUd5gYXsbyCKsQ6smNtHUHKH9nRO3vWhbJOZXFkUxlcXAxIz7Ckn5w4eQr9+vYpdn2/vrcg9rB2e4JVKNgRiclr166VL4iff/4Zf/31l+0kWkecPn0ar7zySvUdLdUoPRUTvJa4uDMY9diTFfoZ0QFdtI4QHx+idQQpT3Z2FrZu2YwzJ44goESwk5OTg2++1te0vLtvOA4dirXLfVmLC/4TZ2kdoRQfzpuHUSOG46cff5CXv/3ma7Rp3QqtW7bApFdegdFoqaStNb16dMP3P1pqRrVrcx3+Xr+52PWi3lLtWpHQqgpNY/Xq1Uuei0rJUVFRcHZWZt8Tsg89tYm4luRLl7Dg6+/xxacfVKp1hEjUZOsIZTl8+DAG3HYb4uJOi9K/WNatG7757ntERlre8FNTU/Howw/jgQcfgtaMG1/6l1KRjjDr7bkILmylMPut6ZpqHTFz+nS88/Zb6HPLLRj/3HM4feo0Zr/zNp5+5ln5efbeu3NlgcHJU6dCa2a9PgU9bu6Pc+fj0b1bF7wy5XXs3LUbzaObyhGdHxctwccfaLdGXqWWHtSrVw8pKSnYsWMHEhMTYTYXj9ofekh7bw56HtnRQ7Dz27IVV73++IlTlf52O39HiiydL0ZEmQCrHK9MeAmNo5vj5U+XAHlZ+PXjGbixZw+sXvsX6tatCy2b+/5HaHNdK1lUsCjxHD0Ucxje3l5Vfq6Kn+9e3wvf70mTz38lBDsLFy7A5198gTvuHIL/9u5Fl+s7Yf6XX+K++4fJ65tFN8OEl17SZLDTvHkzbN+4BhOnvo4333lPrp7+9vtFcHFxQaeO7fDD1/Nxx+CqF1XVVLCzbNkyDBs2DBkZGfDz8yv2ohDbDHbULynDiMQME8RfNloHK7HuuPsB+dy9Ws5CZd78ba0jUtk6Qmm2bd2KD79ZjFy/IEQF1cGIX3/DU0+Owc039sKqNWtltXitEsmpn36+AO+88Tp639TTtt/VOxRffT4PLZpH2+X39GxoCXbEyKYSnD93Du07dJTb17VpI0dz2rRpa7u+bbv28jZa1ahRA3z/9Xz5PpeYmCQHKkJCgq9ol6FFlZqHeu6552Qysgh2xAjPpUuXbKfk5GT7HyXVuIOJllGdBkGu8HbT/nRlZGQEFv/4Ncw5yaWeytMTq6zWER3rWFaiMFFTWUSpjMJG5wjwcpPB7AfzPkT/gQNxS++bcOTwYWjVS+PH4sdvv8Dop57D8y9OslXHtzdr6wjRdka0jnC08IgIHDp4UG4fOXJETtsdOmS5LBw8cAChOlhR7OTkhPDwMPm+p4dAR6jUp9jZs2fx9NNPw8tLP2XV9UZv+Tod2rXBrn/3lHm9GNSp7EoVkbcjMNhRlmbNovHvbkvDx6LJye++9z4GDhqEoXfeAS3r1LE9dm1bh6QLF9Cx603Yf+Cg3adZra0jlPL8v++++/HwyBEY/fhjGNi/H557fjxeeuEFfPrJx/js00/w1Jj/YfBgbf/d7bkIQ/PBTt++ffHPP+wKq2V6C3bGj3sKN3S5vszrGzdqiHV/LqvSqhRr6whShoGDB2Pd70vltn+JGjsi4Lnn3nvtuhRbiXx8fLBg/keY8MJY9Ol3pxzpsLce9ZUT7ItcnKeefgbx8fF4+OFHMG36dEyfORNvzpqFKZMm4abevTH1tdeg50UYWlWpnJ0BAwZg/PjxOHjwIFq3bn3FMNigQYPsdXzkINaqp3ppE9Gj+w1XvV7kb/Tq2a1KrSMuZplk64jr63pW8ijJnh57ahya3PIAPFwN8lTS+x/Mkyc9uPeeoeh+Qxfs2r0X9epG2fW+xcjme5uTsflEtsNbR4gcnRcnTCi2757/u1eetO63alqEoelg59FHH5Xnr5USAYth0Or4dkA1R9TEEMm0AttE2K91hKimLL7dMthRVjHBkvV19KpOndryZG9tC1tHJGeb5Ihx8xDt5wDqaRGGWlTqWScyuMs6MdBRv0OJubIQXpiPAaE+bIxoz2rKonUEKUNqYafzAE+ukKuJ1hHChuOOn8rSq8hqWoShFgyxCXrP16kJ1rwdto5QXk8stomofmwdoe1FGGpQqa/tpU1fFTV58uTKHg8pAIMd+7O2jhDVlLeczEJ/VlN2KPGmbhvZYbBTYyObSmsdoSfjxz2FzMysalmEodlgZ8mSJcUuixoNooWEqMTYqFEjBjsaCXY02RMrJ9Fhv9raOkJ8u2WwU7NyTcU/YI8nZeDFX/bB09WA/m1qXXG9Zjjw+V5U/UBX1PF3wZlUI3acyUXb2tU/Pa7Zv6kCF2GoQaWecf/+++8V+9LS0jBixAjceeed9jguchCjuQAxSZbhfY7s2BdbRyhHbLyl+3OTcB8YRNU7qlbiuS6e/5ZqytloW5vBPqk0Z0e0jXj11VcxadIke90lOcDxi3nINRbA281JNvIj+ynZOoIcJ+a8JdiJjvRz9KHohmgdIWw6mePoQyEdsmuCsugSLE6k/ims5mHucsk02Q9bRyhHbHyaPG8WwRGGmmJrHXExHwnpTNInFUxjvffee8UuiyH58+fP4+uvv0a/fv3sdWzkwE7nmszXUQCRt7PlVLYMdoZ3DHD04ehWTOE0VjSDnRojWkdcF+mBPedysP10LobUcfQRkZ5UKtiZM2fOFVUpQ0NDMXz4cEwoUZ2S1IUrsaqXyFt4Y91FW+sIUYOEataF9FxczMiTowxNwhns1PQSdBHsbBPBztXzZUkDiemqD3bEyqurdRImdRIjdLaRHZ20iahpbB3heDGFU1j1gr3h6XZlmwiqPtbWEdvjcmXrCBZ6o5pit+dabm4uZs+ejQYNGtjrLqmGnU83IiXbDBdnoHEIq8pWB5EH1a2wmizzdhydnMxRnZpmaR3hJN9nYuMzHH04pCPOFQ1oxDRVx44dccMNN2DpUkvH4C+++EIGOWJ6a+zYsdV1rFRDzT8bB7vBQ0Q8VC16NPSW5wx2HLvsnMnJNU9M23apa0nS33Is2dGHQzpSoU80USzwo48+Qv369XHy5EncfffdeOyxxzB37lw5qiP2vfjii9V3tFQz+TqcwqqZ1hHnc9g6wqHJyVx27gjdC0c2tx2/5OhDIR2pUM7OokWLsHDhQgwaNAj79+/HddddB6PRiL1797JAmgZY83WYnFy92DrCcbLyjDh10dKMtRmnsRyie33LyM6/cany7+HlxmbDpLCRnTNnzqBDhw5yu1WrVnB3d5fTVgx0tEHTbSIU2iuIU1k160hCBkSvwxAfN4T48HnuCPUDXVDLzyBXI+46ydEdUmCwYzKZ4OZ2OXFV9MLy8fGp9C+fOnWqDJSKnqKjo23X5+TkYMyYMQgODpa/Z+jQoUhISCh2H6dPn8aAAQPg5eWFsLAwjB8/Xo42UcWk5phk3xqhRRg/BGpiVYpgbR1BNZ2vwyksRxHv813qWt5jthy76OjDIZ2o0PiheFMW/a/EiI41GHniiSdkA7GiFi9eXO77bNmyJdasWXP5gFwuH5IYNfr999/l9Jm/vz+efPJJDBkyBJs3b7YFXyLQiYiIwJYtW2Rhw4ceegiurq6YMWNGRR6a7llHdUSzPlH8i6pX56jLrSNOXspHgyCufqvJYIcrsRyrc113LN6fha1HLzj6UEgnKhTsiKKBRT3wwANVPwAXFxmslCTaTsyfPx/fffcdevfuLfd9+eWXaN68ObZt24YuXbrgzz//xMGDB2WwFB4ejrZt22LatGkySVqMGhUdhaKrYzHBmuXlZmkdIaspH89isFNDYs6zTYQSdIoS7WiAY0mZSEjLQbifJY+HSBHBjgg27O3IkSOoVasWPDw80LVrV8ycORN169bFrl27kJ+fjz59+thuK6a4xHVbt26VwY44b926tQx0rPr27YvRo0fjwIEDaNeuXZlL6MWpaMd2vWObCMe1jhBTWQ+xdUS1M5kLZM6OwGDHsfw9nNGyli/2nU3H1mMXcUe72o4+JNI4hxZT6dy5M7766iusXLlSLmkXlZl79OiB9PR0xMfHy5GZgIDiHwIisBHXCeK8aKBjvd56XVlEQCWmxaynqKgo6B1Hdhy3BN3aOoKq1+nkLGTnm+Dh6iyrJ5NjdW0YJM+3cCqLtB7siKaholaPWMIuRmRWrFiBlJQU/PTTT9X6e0VhRGuHdnGKi4uDnuUazTh6IU9uM9ipOeL/OsjTgMy8Atk6gqpXbOEUVtNwXxjEHAo5VNdGgfJ827GLMJsZ7FP1UlSZXDGK07RpUxw9elTm8eTl5cngpyixGsua4yPOS67Osl4uLQ/ISiRY+/n5FTvp2ZELeTCagQBPZ9TyY82Lmmwd0b2BpcDaJi5Br7FigpzCUobWtf3g7W7Apax829+GSBfBTkZGBo4dO4bIyEhZz0esqlq7dq3t+tjYWLnUXOT2COJ83759SEy83OF19erVMnhp0aKFQx6DmttEiCXnrJnkmNYRIm+Haqonlr6/3CiFq8EZnRpYprK2HuNUFmk42Hn++eexfv162WZCLB2/8847YTAYcN9998lcmocffhjjxo3DunXrZMLyyJEjZYAjkpOFW2+9VQY1Dz74oKzivGrVKkycOFHW5rEuj6cKFBNkm4gax9YRNSe2sNs5R3aU44ZGIfJ861HW26Hq5dA5C1GRWQQ2Fy9eRGhoKLp37y6XlYttQTQWdXZ2lsUExeopkdfz4Ycf2n5eBEbLly+Xq69EECTq/Yjl8a+99poDH5X6MDnZsa0jmoS4yalEto6oPhfSc3EhIw9i4LJJeOULoZJ93dA4WJ7vPn2JrSOoWjn0mfXDDz9c9XqxHH3evHnyVJZ69erJxGaqHHNBAQ4mctm5o0d3RLAjWkcw2KneYoJiFRY/UJWjbpAXagd44mxKtmwd0aOp5YsukaZzdqjmnb6UL1cDubs4oWEwC9s5AltHVL+YwimsaE5hKa91RCPL6I6ot0NUXRjs6Jy1mGB0qBtcuBxXEa0jqDp7YjHYUepUFoMdqk4MdnSO+TrKaR0hiNYRVJ0rsRjsKE3nBkEQX7OOJmbg++2nsfNEsqx2TWRPDHZ0zjqyw2DHsboXrsriEnT7E4mvJy9myu1odjtXnJ0nL8HFYBlVnvH7IYz6cif6zl6PNQeL11AjqgoGOzrHkR1l6FmYt7PtVDZbR9jZ0YQMiFSoYB83hPjyea4kIqAZ98OeK57ziWm5cj8DHrIXBjs6lpRhRGKGSQ4hR4fxQ0AJrSMy8szYc46tI+zJWp2XycnKIqaqZq04hNJCe+u+N1bEcEqL7ILBjo5Zl5w3CHKFtxufCkppHcG8Hfs6XBjsNGWwoyi7T6cgIc3yHlQaEeLEp+Vg96lLNXpcpE38hNMxTmEpC/N2qntkh/k6SiKKPJZHUnrZARFReTHY0TFrTyy2iVBe64hUto6wCzEFcjiBK7GUKMSnfHW9QplnRXbAYEfHOLKjLJF+rrJ1hEhR2HKKozv2EJechew8EzxcnWX1ZFKO9nUDEO7nLnMGyxLh54H29QJr8KhIqxjs6FRmnhknkvNt3c5JWaM7G5i3Y9cprCZhvjCwaKaiiL/HS/2by+2y/jIjutXn343sgsGOTsUk5soEwDAfA0J92CtIKdg6wr5izxd2OucUliL1aRGO2fe2RZhf8S9cbi6Wj6bl/52D0WR20NGRlvBTTqc4haWO1hENgtivrCq47FwdAc9N0WFy1ZVIRhY5OrUDPXHXh1uw/2waPt94Ak/c2MjRh0kqx5EdnQc77HSuvNYRHdg6ohp6YnEllpKJqapODYLQ/7pIeV4rwBOvDLRMcX3y9zEcPGcZoSOqLAY7OsU2EcrP29l4ksFOVVzIyJUjBU5OQNNwH0cfDlVQ/9aRuKVlOIzmArz8y3/IzecKRao8Bjs6JN48YhItNS44sqPc1hFiZGfxvjRsPZXFKrJVKCZYL8gLXu6csVcbJycnTBrYQrb5OJaUiffXHnX0IZGKMdjRoeMX85BnKoCPmzPqBro6+nCohNMp+XJ1So6xAOOWJeC+b8+i+7yTWBmT4ehDU2Wn82aRnMJSq0BvN7w6uKXcXrj1JP45mezoQyKVYrCj4yms5mFusk0BKYcIaMYsjr+iX1B8uhGjF59nwFMBMfGWPA8mJ6tbr2ZhGNK+tmzmOnHxfmTmGh19SKRCDHZ0iCuxlElMVb26OumqjRFfXZPEKa0KJycz2FG78bdFo3aAJ86mZOOtlbGOPhxSIQY7OsQ2Ecq0Iy4b59PL/tYqQpzzaUZ5O7o6UTX55IVMuR3NaSzV8/FwwbQ7W8lk8192ncGG2CRHHxKpDIMdnRGF6qzdzjmyoyyJGSa73k7Pjiamy7YbQd5u5e7BRMomlqQ/2LWe3J7y636kZJWvkSiRwGBHZ86lGZGSbYYoUCr6MJFyiGrW9rydnsUUmcISq3pIG56+uQkahnrLjunTlh1klXEqNwY7Os3XaRziBvfCkuykDNdHeSLS16XMPkFif6Sfi7wdXV1s4UosJidri7urATOGtoaLsxP+PJCAP/bFO/qQSCX4aaczLCao7CqyU24JldulBTziO+yUPqFsjFiR5GT2xNKclrX88VivhnJ7+u8HkZCW4+hDIhVgsKMzbBOhbLdF++CjIZGI8C29CJ67CwOdazGbCxCbYB3ZYXKyFj3SsyFa1vJDWrYRU5Ye4HQWXRODHZ3hsnN1BDybxtTH98Nq493BEfJ8ZCd/ed0LvycgOYsJylcTdylLrsYS07T1gi3VqElbXA3OcjpL/I03H72ARf+ccfQhkcIx2NGR1GwTzqRaljYz2FE2MVXVtZ4XBrf0lecv3hQiE8qTMk2YuDKR32TLUTm5SbgPXAx8i9OqhqE+eKZPE7n99qpYxCWzlxyVje8EOmJdcl7H3wX+HlzRoyYeLs6YMyhcrqJbEZOBXw9YPtDpSux0rh/DutRDp/qBciTvlcX7WHCTysRgR4fJyczXUadWER54pnuw3J60Kgnn0vIdfUiKFHO+sE0Ek5M1z9nZCa8PaQ1vdwP+PZ2CBZtPOvqQSKEY7OgI83XUb/QNgWhX2wPpuWaMX54AM6ezyhzZ4bJzfagV4IkX+zWX2x/8dQSHC5PTiYpisKMjBwvbRDDYUS9RX2T27eHwdHXC5pPZWPhPqqMPSVEuZuQiMT1XthVoEs5gRy/uaFcLNzYLRb6pAC//sg/5RrOjD4kUhsGOTuQYzTh60VJenT2x1K1BkBte7h0it2euu4CjF1g2v+SoTt0gL3i7l758n7RHVMmeMrglAr1c5XPgo7+POfqQSGEY7OjEkaQ8iC87AZ7OskovqdsD7f3Rs6EXco0FGLcsXn6jJXY617MQH3dMur2F3J6/8Tj2xqU4+pBIQRjs6LCYIHsFqZ/4G745IBz+Hs7473wu5m1JdvQhKaonFosJ6tMtLSMwsE2kbAIrVmdl5VlKbRAx2NEJJidrj6iyPK1vmNx+f1My9p5j2fzYwpVYbBOhXxP6N0eYnztOXczC3NVHHH04pBAMdnSCy861aVBLX9zewgdiFmvsb/HIztdvYmZOvgknLmTKba7E0i8/T1dMu6OV3P5++2lsPXbR0YdECsBgRwfE8uRDhQUFObKjPWJ0J9zHgOPJ+XhjnX7f2I8mZsjpiyBvN4T68nmuZzc0DsG910fJ7UlL9iMtmzWp9I7Bjg6cupSPzLwC2USyYbCbow+H7CzA04A3B4bL7a/+ScGmE1m6LibYNNyXeWmEsbc2lavyRFf0WStiHH045GAMdnSUrxMd6ibrtJD29GrojQfbW5qFimKDqTkm/SYnM1+HAHi5uWD6kNYQb3nL9p7DmoMJjj4kciAGOzrK1+EUlrZN6B2CBkGuOJ9uxNQ/k6A3h7nsnEpoWzcAo7o3kNuv/XYAFzIs74WkPwx2dIArsfTBy80Z79weLr/JLtmfjhWH9FM232wuuNwmIpLLzumy/93UWAbAl7Ly8epvB1DAFiu6xGBHBw6wTYRutK/tiTE3BMntV1YmIjFDH3VGzlzKQlaeCW4uzqgf7OXowyEFcXVxltNZLgYn/B2ThF/3nHP0IZEDMNjROPFhl5RpgsjUaR7GYEcPnuoeJEsMXMo248XfE3TxTdaar9MkzAcuBr6tUXFiZOfJ3o3l9qwVh3AuJdvRh0Q1jO8KGmddci5yOcQ0B2mfm8EJcweFy/N1x7Lw/R7LKiUtiznP5GS6uhHdGqBtVAAyc01yObqY+iT94KefTqaw2PxTX5qEuuOFm4Ll9utrknDqUp5OemIxX4dKZ3B2wvQhreDpasCOE8n4bvtpRx8S1SAGOxrH5GT9GtUpAF3qeiIrvwDPLUuAScPfZGPiLaNXrJxMV1M32BvP39ZMbs9dfRjHkzIcfUhUQxjs6KgBKOmLs5MT3r49HL7uzvjnTA4+2XYJWpScmYfENMvzvCmDHbqGuzvWQbfGwcg1mvHyL/uQb9JvixU9YbCjYZl5ZpxItpRJZ3KyPtXxd8WUW0Ll9pwNF23BrxansES1XG93F0cfDimcqK796h2t4OvhggPn0vD5huOOPiSqAQx2NCwmMRdi4iLMx4BQH34I6NXQ1r64tak3RI/Qcb/Fy2+0WsJO51RR4X4eeGVgc7n96frjOHAu1dGHRNWMwY6GcQqLrN9kZ/YLQ4iXATFJeZi9IRmabBPBKSyqgP6tI3Fry3AYzQV45Zd9yM3XX4sVPWGwo2FsE0FWwd4umNk/TG5/uu0SdpzWTp0RW+VkrsSiCn4JmDiwBYJ93HAsKRPvrz3q6EOiasRgR8M4skNF3dLUB/e08ZNTm88tj0dGrvqns3LyTThxIVNucxqLKirQ2w2vDm4ptxduPYmdJ7Q16kmXMdjRKDE0G5Noqa3CkR2ymtQnBHX8XRCXYsS0NepvFnosMUMuqQ/wckWYL5/nVHG9moVhSPvaEIXGRbHBzFx9tFjRGwY7GnXsQh7yTAXwcXNG3UBXRx8OKYSvuwHv3B4h24f8uDcNa45kaCJfR7QDENMSRJUx/rZo1A7wxNmUbLy1MsbRh0PVgMGORh0sbBPRPMxN1lshsupc1xOPdg6Q2y/9noiLmer9JhtTuBKL+TpUFT4eLph2ZyuIt8pfdp3Fhlj1j3pScQx2NIptIuhqxvUKRrNQN1zIMuHlPxJV2yz0sHVkh/k6VEWdGgThwa715PbkX/fjUqa2W6zoDYMdjWKbCLoaDxdnzB4UAVdnYNXhTCzebwka1EQ0coxN4LJzsp+nb26CRqHeuJiRh9eXH1TtlwC6EoMdDRIvUC47p2sRq/Se7WlpFjr1zyScTbVU21YLkV8hOli7uTijfoi3ow+HNMDd1YDpQ1vDxdkJfx5IwB/74h19SGQnDHY06FyaEak5Zrg4A01C3Bx9OKRgT3QJRIc6HkjPNeP55Qkwq+ibrDVfp3GYD1wNfCsj+2hZyx+P9Woot8XoTkJajqMPieyA7xAaZB3VaRziBncR8RCVweDshHcGhsPL1QlbT2Xjy50pUAtWTqbq8kjPhmhZyw/pOUZMWbqf01kawE9CDWIxQaqI+kFueOVmS7PQN9ZdxJEkdTQLjT3P5GSqHmKkcMbQ1vLL4uajF7HonzOOPiSqIgY7GsTkZKqo+9v54cZGXrI209hlCfJcPSM7XHZO9tcw1AfP9Gkit99eGYvTFy2VukmdGOxoeBqLIztUXqIg35sDwhHg6Yz98bn4YJOyy+aLZcHWXIqm4RzZoeoxrEs9dKofiOx8E15ZvF9W6yZ1UkywM2vWLPmG++yzz9r25eTkYMyYMQgODoaPjw+GDh2KhISEYj93+vRpDBgwAF5eXggLC8P48eNhNKq3SFpVpWabcDbV8vibM9ihCgjzccH02yzNQudtSca/Z3MU3/wzKshTFoQjqg7Ozk54fUhreLsbsCcuBV9tPuHoQyI1Bzs7d+7EJ598guuuu67Y/rFjx2LZsmVYtGgR1q9fj3PnzmHIkCG2600mkwx08vLysGXLFixYsABfffUVJk+eDL2P6kQFuMDfw+DowyGVGdDcF3e09IWYxRq3LB5ZecpsFspO51RTagV44sV+zeX2vL+O2p57pC4OD3YyMjIwbNgwfPbZZwgMDLTtT01Nxfz58zF79mz07t0bHTp0wJdffimDmm3btsnb/Pnnnzh48CC++eYbtG3bFv369cO0adMwb948GQDpuU1EizCO6lDlvHprKCJ8XXAiOR+z1l2AEsXEp9l6YhFVtzva1cKN0aHINxXglcX7kG9U5pcAUnCwI6apxOhMnz59iu3ftWsX8vPzi+2Pjo5G3bp1sXXrVnlZnLdu3Rrh4eG22/Tt2xdpaWk4cOBAmb8zNzdX3qboSSsOFraJYHIyVZa/pwFvD7S8phbuSsWG48pLzIwpXIkVzZVYVANEisWUQS0R6OUqR3Y++vuYow+J1BTs/PDDD9i9ezdmzpx5xXXx8fFwc3NDQIClYaGVCGzEddbbFA10rNdbryuL+H3+/v62U1RUFDS37Jw9sagKujfwwoiO/nJ7/PIEpGSboBS5+SacuGAJwDiyQzUlxMcdk25vIbfnbzyOPafVU5OKHBjsxMXF4ZlnnsG3334LDw+PGv3dEyZMkNNk1pM4Fi3IMZpx5IJl+o4jO1RVL94UgoZBrkjIMGHyqkQoxdGkDLkqxt/TFeF+NfveQfp2S8sIDGwTCbEoa+KSfcjK0+9iGLVxWLAjpqkSExPRvn17uLi4yJNIQn7vvffkthihEXk3KSnFo2exGisiIkJui/OSq7Osl623KY27uzv8/PyKnbTgSFKeTCwN9HRGpC9XqFDVeLpamoUanIDfDmZg2cF0RRUTFJWTxfQCUU2a0L85wvzccepiFub8edjRh0NKD3Zuvvlm7Nu3D3v27LGdOnbsKJOVrduurq5Yu3at7WdiY2PlUvOuXbvKy+Jc3IcImqxWr14tg5cWLSzDjXotJsgPAbKHtrU8MKZbkNyeuDIRCelGxRQTZOVkcgQ/T1dMu6OV3P5hRxy2HFVmEj8pJNjx9fVFq1atip28vb1lTR2xLXJpHn74YYwbNw7r1q2TI0EjR46UAU6XLl3kfdx6660yqHnwwQexd+9erFq1ChMnTpRJz2L0Rm9YTJCqw1PdgnBdpLtsLjv+9wSH9wnisnNytBsah+De6y25npOW7kdqdr6jD4mUvhrraubMmYOBAwfKYoI9e/aUU1OLFy+2XW8wGLB8+XJ5LoKgBx54AA899BBee+016BHbRFB1cDU4YfbtEXB3ccKG41n4Zneqw47FbC5ArHXZOUd2yIHG3toUdYO8kJiWi1krDjn6cOgaFJXY8ffffxe7LBKXRc0ccSpLvXr1sGLFCuiduaAAhwpr7HBkh+ytcYgbXrwxGK+tuYAZf12Qq7UaBLnV+HGcTclGZq5JBmANQrxr/PcTWXm5uWD6kNYYPn87lu89j97RYTKBmZRJ0SM7VH6nLuUjM69AfvtuEFzzH0KkfSM6BaBbfU9k5xdg3LIEGB3QJ8g6hdU4zFd2piZypLZ1A/Bwj4Zy+7VlB3Ehw/KFk5SH7xYacaCwmGB0mBtcnJmcTPbn7OSEtwaGw9fdWfbN+njrpRo/hpjzliksFhMkpRh9YyNZ7yklKx+v/nrA4TltVDoGOxrBNhFUE2r5ucp2EsLcjRexPz7HISM7LCZISuHq4iyns8TU6t+xSVj67zlHHxKVgsGOxkZ2mJxM1e3OVr7o18wHoj3Q2N8SZDHLml52zpVYpCQi+B7Tu7HcfuOPQzh7KdvRh0QlMNjRCLaJoJoiajhN7xeGEG+DrNj99t8Xa+T3pmTlIT7VMpLUNMKnRn4nUXmN6NYAbaMCZAL9pCX75MpBUg4GOxqQmGFEUqYJIlMnOpTBDlW/IC8D3hxg6UM3f0cKtp7KqrEprDqBnvD1cK3230dUEQZnJ0wf0gqergbsPHkJ32475ehDoiIY7GhoVKdhsCu83PgnpZrRu7E37mvrB/H99fnlCUjPrd7pLHY6J6WrG+yN529rJrffXXMEx5MyHH1IVIifjBrAYoLkKBP7hKJugCvOphrx6prkGkpOZr4OKdfdHeugW+Ng5BrNePmXfcg31VxOG5WNwY4GsE0EOYq3mzPeuT1cTqH+vD8Tq45UX52RGGvlZK7EIoXntL16Ryv4erjgwLk0fL7huKMPiRjsaMMhjuyQA3WK8sTjXQPl9st/ZuBCRp7df0duvgknkjJt3c6JlCzczwMTB1qaUX+6/jgOnHNcixWyYLCjcpl5ZpxItjShY7BDjjK2RxCiQ11xMbsALy05aPfCaseSMmXFZj9PF0T4e9j1vomqQ7/WEbi1Zbh83orprJx8k6MPSdcY7KhcTGKuTBAN9zEgxFtRrc5IR9xdnDFnYAjcDMCamAtYtOuMXe/f2vxT1NcR0wRESieep2J0J8THDceTMvHBupOOPiRdY7CjciwmSErRPMwN47p52foExSVn2X0lFvN1SE0Cvd3w6uBWcvubbWfwz1mjow9JtxjsaKRNBJOTSQke7eiJTvUCkJFrxHOL9sJkp8Jq1pVYXHZOatOzWSiGdqgtR+Anr8mq9hINVDoGOyrHZeektMJq79zVAt5uBuw4kYwvNp2o8n2K/B8uOyc1G39bNGoFeOBcegFmrKv5BrrEYEfV8k0FiEm0rHxhmwhSirpBXphUuBLlrVWxtkClss6mZMuRItFosWGIt52OkqjmeLu7YNqgZrJEw0/7MrH2CIsN1jQGOyp2/GIe8kwF8HFzRlQAy+eTcvxfpyjcHB2GPJMZz/64B3lVaBYaW5iv0zjMR3aYJlKjjvUD8EBbN7n94opEJGdxdVZN4juHBooJtgh3gzNXqJDCVqLMHNoagV6uOHQ+De+uPVzlTuecwiK1e7KLB5oEu+JCpgkTVybavUQDlY3BjooxX4eULMzXAzPubC23P/r7GHadSq5S5WQmJ5Paubs44a3+wRADlCtiMvDbwapN8VL5MdjRxMgOgx1Spn6tIzGkXW2IRVnjftqLzFxjpaexuOyctKB1hBue6hYktyetSsLZ1HxsPZWFXw+ky3N7rWCk4liFTqXE8CdHdkgNpg5uiW3HL+LUxSzMWHEI0wtHe8ojNSsP51Nz5DaDHdKK/90QhL+OZmLv+Vzc9PEpmXtpFenrgim3hOK2aB+HHqPWcGRHpc6mGZGaY5bDoU1CLElvRErk5+GKt+9uI7e/3X4a62ITy/2z1pVctQM94evBJHzSBrGy8I5Wlhy0ooGOEJ9uxOjF57Eyhiu27InBjkpZR3VEoCNK9RMp2Q2NQzCyW325/cLP/+FSZl6FkpPZ/JO0RExVfbqt9Ho71tDn1TVJnNKyI35KqhSnsEhtXrwtWi4fT0rPxcSl+8u1EuVyMUEGO6QdO+KycT697Pw18co4n2aUtyP7YLCj8uRktokgtfBwNWDOPW3h4uyE3/edx297z13zZ2LOW1Zicdk5aUlihsmut6NrY7CjUhzZITVqXccfT/VuIrcnLd2P86llf3MVhQhFt2iBy85JS8J8DHa9HV0bgx0VSsk24WyqZQiUwQ6pzZibGqFNVADScox48Zf/YC5jOutYUgaM5gL4ergg0t+jxo+TqLpcH+UpV12VVQpW7I/0c5G3I/tgsKPiUZ2oABf4eTDyJ3VxMThj9j1t4OHqjM1HL2LRzrhSbxdz/nJysqjITKSlhrlieblQ8pltvTylT6i8HdkHgx0VBzvM1yG1ahTqgwn9msvtd1cfwckLlumqomILKyc3i2S+DmmPqKPz0ZBIRPgWL3cX4eci97POjn2xqKAKMV+HtODBLvWw+mACNh29gFcW78PXj3SWoz4lV2Jx2TlplQhobmnqLVddiWRkkaMjpq44omN/HNlRIbaJIC1wdnbCrKGtZU7O/rNp+GzDcdt1Ylm6LdhhcjJpmAhsutbzwuCWvvKcgU71YLCjMjlGM45esBRk4zQWqV2kvyde7B8ttz9dfxwHzqbK7XMpOUjPMcLF4ISGIRzOJ6KqYbCjMkeS8iCqiwd6Ol8x10ukRre1isCtLcPlyqsJv+yTzUKX7T0rr4v085AjQEREVcFgR8XFBLlChbRAPI9fGdgcob7uOHEhE73f+hvz/jomr4u7lI2+s9djzcEERx8mEakYgx2VYXIyaVGAlxvuaFdLbmflFa8am5iWi3E/7GHAQ0SVxmBHrSM7EQx2SDtEw8Pf9pTePsJacvCNFTFsjEhElcJgR0VEpdlD1pGdMAY7pB27T11CQprluV0aEeLEp+XI2xERVRSDHRU5mZyPrPwCeLg4oWGwm6MPh8huLmSUHegUJTqmExFVFIMdFebrNAtzYy0G0pQQn/KNVIokZiKiimKwo8ZigpzCIo1pXy8Q4X7uV22MGOHnIW9HRFRRDHbU2BOLycmkMWKk8qX+za/aGFEUH+SIJhFVBoMdFeGyc9KyPi3CMfvetgjzK/78DvfzkPvF9URElcESvCqRmGFEUqYJ4ottc05jkUaJgOam6DC56kokI4scHTF1xREdIqoKBjsqIGqL/LIvTW5H+BrgZuAbP2mXCGw6NQhy9GEQkYZwGkvhVsZkoPu8k3hj3UV5+VyaSV4W+4mIiOjaGOwomAhoRi8+j/PpxmL749ONcj8DHiIiomtjsKPgqatXVyfZSuUXZd336pokls8nIiK6BgY7CrUjLvuKEZ2iRIhzPs0ob0dERERlY7CjUIkZJrvejoiISK8Y7ChUmI/BrrcjIiLSKwY7CnV9lCcifV2uWj4/0s9F3o6IiIjKxmBHwbVGptwSetXy+VP6hLLYGhER0TUw2FGw26J98NGQSET4Fq/9GOHnIveL64mIiOjqWEFZ4URAc0tTb7nqSiQjixwdMXXFER0iIqLyYbAjlnEXWGrVpKVZWjLYjSkXSM8EnHIBJ7cq3VVLP8sJyEdmao69jpDIfgrygAIjYEgDDOXr3ybqRKXn5sPJCXAW/+iE2ZgDp/RMFOSZYCjn/5VWmMx5SM93ghPS4OxStfdFNdHz3xyVeG8oL+vntvVzvCwMdgCkp6fL86ioKEcfChEREVXic9zf37/M650KrhUO6YDZbMa5c+fg6+sLp0p8u+zUqRN27txZ7T9Xkdtf67YiGhbBXVxcHPz85JCRrlT2b6b246mO31PV++TrR334+lHOfer99VNQUCADnVq1asHZuew0ZI7siCxtZ2fUqVOn0j9vMBgq9Qer6M9V5Pblva24jR7frCv7N1P78VTH76nqffL1oz58/SjnPvn6wVVHdKy4GssOxowZUyM/V5HbV/aY9EJp/z81dTzV8Xuqep98/aiP0v5/+Pqp/p8bo/LXD6exdEoMI4poODU1VVHf0IjUgK8fInW9fjiyo1Pu7u6YMmWKPCeiiuHrh0hdrx+O7BAREZGmcWSHiIiINI3BDhEREWkagx0iIiLSNAY7REREpGkMdoiIiEjTGOxQqe68804EBgbirrvucvShEKmKKIF/4403okWLFrjuuuuwaNEiRx8SkWqkpKSgY8eOaNu2LVq1aoXPPvvMLvfLpedUqr///lv2G1mwYAF+/vlnRx8OkWqcP38eCQkJ8s06Pj4eHTp0wOHDh+Ht7e3oQyNSPJPJhNzcXHh5eSEzM1MGPP/88w+Cg4OrdL8c2aFSiW+mojEqEVVMZGSkDHSEiIgIhISEIDk52dGHRaQKoq+WCHQEEfSI8Rh7jMkw2NGgDRs24Pbbb5ddYEUX96VLl15xm3nz5qF+/frw8PBA586dsWPHDoccK5GWXz+7du2S31RFh2ciPdhgh9ePmMpq06aNbNA9fvx4+YWhqhjsaJAY+hNPFPGEKs2PP/6IcePGyXLdu3fvlrft27cvEhMTa/xYibT6+hGjOQ899BA+/fTTGjpyIm28fgICArB3716cOHEC3333nZwWrjKRs0PaJf7ES5YsKbbv+uuvLxgzZoztsslkKqhVq1bBzJkzi91u3bp1BUOHDq2xYyXSyusnJyenoEePHgULFy6s0eMl0srnj9Xo0aMLFi1aVFBVHNnRmby8PDm03qdPH9s+Z2dneXnr1q0OPTYiLbx+xHv8iBEj0Lt3bzz44IMOPFoi9b1+EhIS5OIYQXRFF9NizZo1q/LvZrCjMxcuXJA5BOHh4cX2i8ti5YiVePLdfffdWLFihZw3ZSBEVL7Xz+bNm+VQvchVEInK4rRv3z4HHTGRcpTn9XPq1Cn06NFDTm+J86eeegqtW7eu8u92qfI9kCatWbPG0YdApErdu3eH2Wx29GEQqdL111+PPXv22P1+ObKjMyKrXSztK5nwJS6LZbJEVDa+fojU+fphsKMzbm5ussjZ2rVrbfvEt1BxuWvXrg49NiKl4+uHSJ2vH05jaVBGRgaOHj1quyyW74lhwaCgINStW1cu+xs+fLgsyS2GDOfOnSuXC44cOdKhx02kBHz9EGnw9VPl9VykOGLJuPjTljwNHz7cdpv333+/oG7dugVubm5yKeC2bdscesxESsHXD5H2Xj/sjUVERESaxpwdIiIi0jQGO0RERKRpDHaIiIhI0xjsEBERkaYx2CEiIiJNY7BDREREmsZgh4iIiDSNwQ4RERFpGoMdInKIkydPwsnJqVo6HFdWTEwMunTpAg8PD7Rt29bRhyP/f5YuXerowyBSPQY7RDo1YsQI+WE6a9asYvvFh6vYr0dTpkyBt7c3YmNjizUrLO3/TZxcXV3RoEEDvPDCC8jJyanx4yWi8mGwQ6RjYgTjjTfewKVLl6AVeXl5lf7ZY8eOoXv37qhXrx6Cg4PLvN1tt92G8+fP4/jx45gzZw4++eQTGSgRkTIx2CHSsT59+iAiIgIzZ84s8zZTp069YkpHdCquX79+sdGOO+64AzNmzEB4eDgCAgLw2muvwWg0Yvz48bLjcZ06dfDll1+WOnV0ww03yMCrVatWWL9+fbHr9+/fj379+sHHx0fe94MPPogLFy7Yrr/xxhvx5JNP4tlnn0VISAj69u1b6uMwm83ymMRxuLu7y8e0cuVK2/VipGbXrl3yNmJbPO6yiJ8X/29RUVHycYv/x9WrV9uuv3jxIu677z7Url0bXl5eaN26Nb7//vti9yGO++mnn5ajQuL/R9zf1X6nIAKqyMhI/Pfff/Lyhx9+iCZNmsj/O/F/c9ddd13154n0isEOkY4ZDAYZoLz//vs4c+ZMle7rr7/+wrlz57BhwwbMnj1bfjAPHDgQgYGB2L59O5544gk8/vjjV/weEQw999xz+Pfff9G1a1fcfvvtMlgQUlJS0Lt3b7Rr1w7//POPDE4SEhJwzz33FLuPBQsWwM3NDZs3b8bHH39c6vG9++67eOedd/D222/LYEEERYMGDcKRI0fk9WKkpmXLlvJYxPbzzz9frsctgrEtW7bI328lprQ6dOiA33//XV7/2GOPySBtx44dVxy3mDYT/z9vvvmmDLSKBk1Wol/zU089hYULF2Ljxo247rrr5P+HCJbEz4hpN/F/07Nnz3IdM5HuVHtfdSJSpOHDhxcMHjxYbnfp0qVg1KhRcnvJkiUFRd8apkyZUtCmTZtiPztnzpyCevXqFbsvcdlkMtn2NWvWrKBHjx62y0ajscDb27vg+++/l5dPnDghf8+sWbNst8nPzy+oU6dOwRtvvCEvT5s2reDWW28t9rvj4uLkz8XGxsrLvXr1KmjXrt01H2+tWrUKpk+fXmxfp06dCv73v//ZLovHKR7v1YjHajAY5GNxd3eXx+Ls7Fzw888/X/XnBgwYUPDcc8/ZLovj7t69+xXH8+KLL9oui/tetGhRwf3331/QvHnzgjNnztiu++WXXwr8/PwK0tLSrvnYifTOxdHBFhE5nsjbESMo5R3NKI0YFXF2vjxYLKZVxLRU0VEkkQeTmJhY7OfEaI6Vi4sLOnbsiEOHDsnLe/fuxbp16+QUVmn5NU2bNpXbYhTlatLS0uSoU7du3YrtF5fF76iom266CR999BEyMzNlzo447qFDh9quN5lMcsTsp59+wtmzZ2UeUW5urpzSKkqM0BQlpqhK/v+MHTtWTptt27ZNTtNZ3XLLLTK3qGHDhjKHSJzuvPPOK34HEXEai4gAOf0hpnUmTJhwxXUigLEMMlyWn59/xe3EyqSirKuVSu4TuTPllZGRIae1xPL0oicx9VR0ykZMBdUk8fsaN26MNm3a4IsvvpDTUPPnz7dd/9Zbb8lpsxdffFEGa+KYxf9vyeTp8vz/iKBGBEyrVq0qtt/X1xe7d++WuUAiSJo8ebI8HjH1R0TFMdghIkksQV+2bBm2bt1abH9oaCji4+OLBTz2rI0jRiysREKzSBJu3ry5vNy+fXscOHBAJkOL4KLoqSIBjp+fH2rVqiVzeooSl1u0aFGl4xfB4Msvv4yJEyciOzvbdr+DBw/GAw88IAMQMfpy+PDhSt2/yCv67rvv8Mgjj+CHH34odp0YURLJ0SLfR+QhidpFIneKiIpjsENEklgxNGzYMLz33ntXrBpKSkqSH6hi6mjevHn4448/7PZ7xf0tWbJErsoaM2aMXAY/atQoeZ24nJycLFc27dy5U/5+McIxcuRIOVVUESIRWkzX/fjjjzKh96WXXpJB2zPPPFPlx3D33XfLaTrxWASxQkokGovEZTElJxKzRWJ1ZYnpqa+//lo+7p9//lnuW758ufxbicdw6tQpmbwsRoWaNWtW5cdDpDUMdojIRqzsKTmNIkZZxBJn8UEuRinEiqKq5PaUNqIkTuK+N23ahN9++82Wm2IdjRGBza233ioDMrHEXCxtL5ofVB5i5dK4cePkaitxP2L1kvhdIjCpKjHCIpa/i4BQ5PGIUR4xKiWmrkSwKJaViyXqVSGWlYvVW2JV1+LFi+X/gTgXuVbibyRWoYkpLZE7RUTFOYks5RL7iIiIiDSDIztERESkaQx2iIiISNMY7BAREZGmMdghIiIiTWOwQ0RERJrGYIeIiIg0jcEOERERaRqDHSIiItI0BjtERESkaQx2iIiISNMY7BAREZGmMdghIiIiaNn/AxQd6MRgWT8UAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -532,42 +457,16 @@ } ], "source": [ - "(fig, ax, nprocs_5e6, runtime_5e6) = runtime_vs_nprocesses(df_5e6)" + "(fig, ax, nprocs_250k, runtime_250k) = runtime_vs_nprocesses(df_250k_nc)" ] }, { "cell_type": "code", - "execution_count": 216, - "id": "ef7e67ce-b959-4e4e-bbfb-e511f9350a46", + "execution_count": null, + "id": "1fb95e60-148c-407d-a14b-b4edadc29727", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(
,\n", - " ,\n", - " array([ 8, 16, 32, 64, 128, 256, 512]),\n", - " array([15229, 9749, 9664, 15484, 10043, 9867, 15925]))" - ] - }, - "execution_count": 216, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "runtime_vs_npoints(df_5e6)" - ] + "outputs": [], + "source": [] }, { "cell_type": "code", @@ -594,7 +493,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.15" + "version": "3.10.17" } }, "nbformat": 4, diff --git a/m1/f32/sphere/blas/grid_search_laplace_blas_high_sphere_f32_m1_0.csv b/m1/f32/sphere/blas/grid_search_laplace_blas_high_sphere_f32_m1_0.csv new file mode 100644 index 00000000..5cf0769a --- /dev/null +++ b/m1/f32/sphere/blas/grid_search_laplace_blas_high_sphere_f32_m1_0.csv @@ -0,0 +1,61 @@ +depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd +5,0,0,2,316,0.04092792,0.041248366,0.041423026,0,0,634,7,0 +5,0,0,3,317,0.00056633825,0.0005905715,0.00063787855,0,0,630,25,0 +5,0,0,4,355,0.00018511455,0.0001955784,0.000203598,0,0,676,55,0 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a/m1/f32/spheroid/blas/grid_search_laplace_blas_high_spheroid_f32_m1_2.csv b/m1/f32/spheroid/blas/grid_search_laplace_blas_high_spheroid_f32_m1_2.csv new file mode 100644 index 00000000..2d1f1c58 --- /dev/null +++ b/m1/f32/spheroid/blas/grid_search_laplace_blas_high_spheroid_f32_m1_2.csv @@ -0,0 +1,61 @@ +depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd +5,0,0,2,433,0.035972074,0.036229625,0.036808416,0,10,550,4,1 +5,0,0,3,443,0.00071892305,0.0007955282,0.0009205429,0,10,541,13,1 +5,0,0,4,444,0.000277132,0.00030399757,0.00032446327,0,10,565,28,1 +5,0,0,5,465,0.000088297034,0.000095245865,0.00010283427,0,10,648,49,1 +5,0,0.0000001,2,418,0.035972074,0.036229614,0.036808416,0,10,545,4,1 +5,0,0.0000001,3,428,0.00071924826,0.0007958079,0.0009209499,0,10,562,13,1 +5,0,0.0000001,4,449,0.0002770508,0.00030394807,0.00032430093,0,10,599,28,1 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a/m1/f32/uniform/fft/grid_search_laplace_fft_f32_m1_uniform.csv b/m1/f32/uniform/fft/grid_search_laplace_fft_f32_m1_uniform.csv new file mode 100644 index 00000000..2aa9aeac --- /dev/null +++ b/m1/f32/uniform/fft/grid_search_laplace_fft_f32_m1_uniform.csv @@ -0,0 +1,25 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,setup_time +4,3,16,561,0.00029853775,0.00034218995,0.00038464146,943 +4,3,32,546,0.00029853775,0.00034218995,0.00038464146,948 +4,3,64,556,0.00029853775,0.00034218995,0.00038464146,965 +4,3,128,551,0.00029853775,0.00034218995,0.00038464146,967 +4,4,16,559,0.0000006038583,0.00004300728,0.00010778607,1021 +4,4,32,559,0.0000006038583,0.00004300728,0.00010778607,1007 +4,4,64,573,0.0000006038583,0.00004300728,0.00010778607,1019 +4,4,128,557,0.0000006038583,0.00004300728,0.00010778607,989 +4,5,16,597,0,0.000004249616,0.000013534111,1057 +4,5,32,597,0,0.000004249616,0.000013534111,1042 +4,5,64,588,0,0.000004249616,0.000013534111,1089 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diff --git a/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_high_5_1_m1_0.csv b/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_high_5_1_m1_0.csv new file mode 100644 index 00000000..912ffc3d --- /dev/null +++ b/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_high_5_1_m1_0.csv @@ -0,0 +1,16 @@ +depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd +5,0,0,8,1513,0.000000026975762998253564,0.000000027728030412903238,0.000000028426510519812952,0,0,4728,295,0 +5,0,0.00000000001,8,1334,0.00000002697574374210684,0.000000027728012012775006,0.00000002842649309833891,0,0,4998,295,0 +5,0,0.000000001,8,1581,0.000000026976730940562114,0.00000002772901191491341,0.00000002842751711424462,0,0,4771,295,0 +5,0,0.0000001,8,1291,0.000000026956816783797622,0.000000027725301404386878,0.000000028443156829950386,0,0,4594,284,0 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b/m1/f64/uniform/blas/grid_search_laplace_blas_uniform_f64_max_5_1_m1_3.csv new file mode 100644 index 00000000..46533295 --- /dev/null +++ b/m1/f64/uniform/blas/grid_search_laplace_blas_uniform_f64_max_5_1_m1_3.csv @@ -0,0 +1,16 @@ +depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd +5,0,0,10,4396,0.000000000026396071154366918,0.00000000039828576099619637,0.0000000015938074140186408,0,20,6915,244,1 +5,0,0.00000000001,10,3625,0.000000000026396362757607677,0.0000000003982854856188178,0.0000000015938063535166314,0,20,6880,244,1 +5,0,0.000000001,10,3481,0.00000000002639184290737592,0.00000000039831501056677865,0.0000000015938734681438017,0,20,6875,244,1 +5,0,0.0000001,10,3229,0.00000000002491560150103911,0.0000000004003869893938374,0.0000000015967300060564187,0,20,6864,244,1 +5,0,0.00001,10,3079,0.0000000019848306969466277,0.000000003767071667552059,0.000000006107812835370922,0,20,6834,244,1 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+5,9,128,5620,0.00000000021553372389520778,0.00000000037141593359165344,0.0000000006141323217507616,3029 +5,10,16,9947,0.000000000014970181371864145,0.000000000021195864797384797,0.00000000003488288867966985,3714 +5,10,32,8079,0.000000000014970181371864145,0.000000000021195864797384797,0.00000000003488288867966985,3734 +5,10,64,7248,0.000000000014970181371864145,0.000000000021195864797384797,0.00000000003488288867966985,3686 +5,10,128,7324,0.000000000014970181371864145,0.000000000021195864797384797,0.00000000003488288867966985,3692 diff --git a/m1/f64/uniform/fft/grid_search_laplace_fft_low_f64_m1_uniform.csv b/m1/f64/uniform/fft/grid_search_laplace_fft_low_f64_m1_uniform.csv new file mode 100644 index 00000000..bf4a3259 --- /dev/null +++ b/m1/f64/uniform/fft/grid_search_laplace_fft_low_f64_m1_uniform.csv @@ -0,0 +1,25 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,setup_time +4,6,16,1598,0.000000016686702299490217,0.0000001806345983488817,0.0000007584427734354027,1167 +4,6,32,1357,0.000000016686702299490217,0.0000001806345983488817,0.0000007584427734354027,1135 +4,6,64,1394,0.000000016686702299490217,0.0000001806345983488817,0.0000007584427734354027,1125 +4,6,128,1400,0.000000016686702299490217,0.0000001806345983488817,0.0000007584427734354027,1131 +4,7,16,1517,0.0000000012761168495142224,0.00000001493895643652047,0.00000004286895467535286,1313 +4,7,32,1476,0.0000000012761168495142224,0.00000001493895643652047,0.00000004286895467535286,1313 +4,7,64,1499,0.0000000012761168495142224,0.00000001493895643652047,0.00000004286895467535286,1304 +4,7,128,1507,0.0000000012761168495142224,0.00000001493895643652047,0.00000004286895467535286,1296 +4,8,16,1951,0.00000000002776493525828481,0.0000000014979825499069186,0.000000006541376024334807,1571 +4,8,32,1810,0.00000000002776493525828481,0.0000000014979825499069186,0.000000006541376024334807,1562 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+5,8,16,3995,0.0000000006775990793078764,0.0000000026624143464007455,0.000000004205206987239361,2367 +5,8,32,3515,0.0000000006775990793078764,0.0000000026624143464007455,0.000000004205206987239361,2345 +5,8,64,3516,0.0000000006775990793078764,0.0000000026624143464007455,0.000000004205206987239361,2385 +5,8,128,3850,0.0000000006775990793078764,0.0000000026624143464007455,0.000000004205206987239361,2322 diff --git a/m1/f64/uniform/fft/grid_search_laplace_fft_max_f64_m1_uniform.csv b/m1/f64/uniform/fft/grid_search_laplace_fft_max_f64_m1_uniform.csv new file mode 100644 index 00000000..dbe29157 --- /dev/null +++ b/m1/f64/uniform/fft/grid_search_laplace_fft_max_f64_m1_uniform.csv @@ -0,0 +1,9 @@ +depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,setup_time +4,11,16,2838,0.00000000000016981362434069548,0.0000000000011634261801119752,0.0000000000060448245956206634,3623 +4,11,32,2772,0.00000000000016981362434069548,0.0000000000011634261801119752,0.0000000000060448245956206634,3664 +4,11,64,2790,0.00000000000016981362434069548,0.0000000000011634261801119752,0.0000000000060448245956206634,3475 +4,11,128,2804,0.00000000000016981362434069548,0.0000000000011634261801119752,0.0000000000060448245956206634,3430 +5,11,16,15399,0.0000000000013449553261033692,0.000000000001930021538710101,0.0000000000028292678699804097,5028 +5,11,32,10807,0.0000000000013449553261033692,0.000000000001930021538710101,0.0000000000028292678699804097,5413 +5,11,64,11245,0.0000000000013449553261033692,0.000000000001930021538710101,0.0000000000028292678699804097,5157 +5,11,128,11925,0.0000000000013449553261033692,0.000000000001930021538710101,0.0000000000028292678699804097,5096 From 695df0e77e7e2c0c46dede5188f9429155f194ed Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Fri, 3 Oct 2025 11:48:51 +0100 Subject: [PATCH 18/52] Remove local data --- ...aplace_blas_uniform_f32_m1_1000000_4_0.csv | 46 -- ...aplace_blas_uniform_f32_m1_1000000_4_1.csv | 46 -- ...aplace_blas_uniform_f32_m1_1000000_4_2.csv | 46 -- ...aplace_blas_uniform_f32_m1_1000000_5_0.csv | 46 -- ...aplace_blas_uniform_f32_m1_1000000_5_1.csv | 46 -- ...aplace_blas_uniform_f32_m1_1000000_5_2.csv | 46 -- ...ce_blas_uniform_f32_m1_8000000_3_0_5_0.csv | 6 - ...e_blas_uniform_f32_m1_8000000_3_0_5_12.csv | 6 - ...ce_blas_uniform_f32_m1_8000000_3_0_5_6.csv | 6 - ...ce_blas_uniform_f32_m1_8000000_3_0_6_0.csv | 6 - ...e_blas_uniform_f32_m1_8000000_3_0_6_12.csv | 6 - ...ce_blas_uniform_f32_m1_8000000_3_0_6_6.csv | 6 - ...ce_blas_uniform_f32_m1_8000000_3_1_5_1.csv | 6 - ...e_blas_uniform_f32_m1_8000000_3_1_5_13.csv | 6 - ...ce_blas_uniform_f32_m1_8000000_3_1_5_7.csv | 6 - ...ce_blas_uniform_f32_m1_8000000_3_1_6_1.csv | 6 - ...e_blas_uniform_f32_m1_8000000_3_1_6_13.csv | 6 - ...ce_blas_uniform_f32_m1_8000000_3_1_6_7.csv | 6 - ...e_blas_uniform_f32_m1_8000000_3_2_5_14.csv | 6 - 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100644 m1/f64/uniform/blas/grid_search_laplace_blas_uniform_f64_max_4_m1_1.csv delete mode 100644 m1/f64/uniform/blas/grid_search_laplace_blas_uniform_f64_max_4_m1_2.csv delete mode 100644 m1/f64/uniform/blas/grid_search_laplace_blas_uniform_f64_max_4_m1_3.csv delete mode 100644 m1/f64/uniform/blas/grid_search_laplace_blas_uniform_f64_max_5_1_m1_0.csv delete mode 100644 m1/f64/uniform/blas/grid_search_laplace_blas_uniform_f64_max_5_1_m1_1.csv delete mode 100644 m1/f64/uniform/blas/grid_search_laplace_blas_uniform_f64_max_5_1_m1_2.csv delete mode 100644 m1/f64/uniform/blas/grid_search_laplace_blas_uniform_f64_max_5_1_m1_3.csv delete mode 100644 m1/f64/uniform/fft/grid_search_laplace_fft_high_f64_m1_uniform.csv delete mode 100644 m1/f64/uniform/fft/grid_search_laplace_fft_low_f64_m1_uniform.csv delete mode 100644 m1/f64/uniform/fft/grid_search_laplace_fft_max_f64_m1_uniform.csv diff --git a/data/blas_f32_1/grid_search_laplace_blas_uniform_f32_m1_1000000_4_0.csv b/data/blas_f32_1/grid_search_laplace_blas_uniform_f32_m1_1000000_4_0.csv deleted file mode 100644 index 7ad7a7fc..00000000 --- a/data/blas_f32_1/grid_search_laplace_blas_uniform_f32_m1_1000000_4_0.csv +++ /dev/null @@ -1,46 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,2,579,0.034881216,0.04023055,0.053912647,0.038656566,0,5,434,4,1 -4,0,0,3,619,0.00047892344,0.0007744486,0.0010716659,0.0007886685,0,5,468,13,1 -4,0,0,4,747,0,0.000026743848,0.00015032286,0.000037734393,0,5,487,28,1 -4,0,0.0000001,2,593,0.03488097,0.040230483,0.053912647,0.0386565,0,5,459,4,1 -4,0,0.0000001,3,580,0.00047892344,0.0007745534,0.0010718914,0.0007887745,0,5,486,13,1 -4,0,0.0000001,4,562,0,0.000026742164,0.00015032286,0.00003773225,0,5,515,28,1 -4,0,0.00001,2,585,0.034881134,0.040230524,0.053912647,0.038656536,0,5,462,4,1 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a/data/blas_f32_1/grid_search_laplace_blas_uniform_f32_m1_1000000_4_2.csv +++ /dev/null @@ -1,46 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,2,562,0.03487918,0.040229473,0.053911474,0.03865559,0,20,449,4,1 -4,0,0,3,585,0.0000050574286,0.00043314937,0.0006942123,0.00046776718,0,20,467,13,1 -4,0,0,4,571,0.00003577735,0.00008145509,0.00013965143,0.00008302265,0,20,480,28,1 -4,0,0.0000001,2,580,0.034879345,0.040229622,0.053911656,0.038655724,0,20,458,4,1 -4,0,0.0000001,3,723,0.0000050574286,0.00043317646,0.0006942877,0.0004677927,0,20,461,13,1 -4,0,0.0000001,4,567,0.000035928304,0.00008150327,0.00013965143,0.00008306787,0,20,496,28,1 -4,0,0.00001,2,588,0.03487918,0.04022943,0.053911474,0.038655546,0,20,455,4,1 -4,0,0.00001,3,602,0.0000050574286,0.0004331772,0.0006942123,0.00046779498,0,20,467,13,1 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b/data/blas_f32_1/grid_search_laplace_blas_uniform_f32_m1_1000000_5_1.csv deleted file mode 100644 index c876b0c2..00000000 --- a/data/blas_f32_1/grid_search_laplace_blas_uniform_f32_m1_1000000_5_1.csv +++ /dev/null @@ -1,46 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,2,220,0.054142825,0.05789544,0.06266272,0.054715563,0,10,1250,4,1 -5,0,0,3,244,0.00011893576,0.00043025773,0.0006364307,0.00045200041,0,10,1313,13,1 -5,0,0,4,297,0.0000076112938,0.000049855083,0.00007494653,0.000052876243,0,10,1322,28,1 -5,0,0.0000001,2,199,0.054142565,0.057895314,0.06266272,0.05471545,0,10,1281,4,1 -5,0,0.0000001,3,248,0.00011893576,0.00043032807,0.0006364307,0.000452066,0,10,1301,13,1 -5,0,0.0000001,4,302,0.0000076112938,0.000049981685,0.00007510632,0.000052998694,0,10,1302,28,1 -5,0,0.00001,2,215,0.05414265,0.057895355,0.06266272,0.054715488,0,10,1250,4,1 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-5,2,0.001,3,263,0.000012762086,0.000056718654,0.0000890172,0.00006057676,0,10,1384,25,1 -5,2,0.001,4,339,0.0000005593441,0.000012687886,0.00004646241,0.00001688151,0,10,1514,55,1 -5,2,0.1,2,223,0.004956861,0.005750384,0.006277967,0.005792724,0,10,1299,7,1 -5,2,0.1,3,275,0.00040244742,0.0004361767,0.00047028583,0.0004361674,0,10,1378,25,1 -5,2,0.1,4,314,0.000021471997,0.000060144175,0.000095308096,0.000062595886,0,10,1539,33,1 diff --git a/data/blas_f32_1/grid_search_laplace_blas_uniform_f32_m1_1000000_5_2.csv b/data/blas_f32_1/grid_search_laplace_blas_uniform_f32_m1_1000000_5_2.csv deleted file mode 100644 index 79af6acd..00000000 --- a/data/blas_f32_1/grid_search_laplace_blas_uniform_f32_m1_1000000_5_2.csv +++ /dev/null @@ -1,46 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,2,219,0.054141697,0.057894386,0.062661245,0.05471462,0,20,1353,4,1 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a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_0_5_0.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_0_5_0.csv deleted file mode 100644 index 2a8cb4bf..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_0_5_0.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,3,4380,0.000337002,0.000716691,0.0010274249,0.0007328532,0,5,3891,13,1 -5,0,0.0000001,3,4220,0.00033708345,0.0007167352,0.0010275053,0.0007328975,0,5,3844,13,1 -5,0,0.00001,3,4260,0.000337002,0.0007166764,0.0010274249,0.00073284225,0,5,3893,13,1 -5,0,0.001,3,4308,0.00033651356,0.0007163989,0.0010272645,0.00073257304,0,5,3971,13,1 -5,0,0.1,3,4125,0.0015282771,0.0019104568,0.0022524148,0.0019126992,0,5,4180,13,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_0_5_12.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_0_5_12.csv deleted file mode 100644 index 2ae0cb04..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_0_5_12.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,3,4153,0.0000018622756,0.00022684959,0.00041515203,0.00025277748,0,20,3802,13,1 -5,0,0.0000001,3,4087,0.0000017813069,0.00022681261,0.00041515203,0.00025274098,0,20,3786,13,1 -5,0,0.00001,3,4110,0.0000020242128,0.00022692625,0.00041531218,0.0002528533,0,20,3879,13,1 -5,0,0.001,3,4107,0.0000017813069,0.00022678543,0.00041515203,0.00025270964,0,20,3881,13,1 -5,0,0.1,3,4104,0.00079065707,0.0011883754,0.0014427983,0.0011972679,0,20,4173,13,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_0_5_6.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_0_5_6.csv deleted file mode 100644 index d7728fa1..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_0_5_6.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,3,4178,0.00000016299649,0.0004232514,0.00067899586,0.0004558648,0,10,3842,13,1 -5,0,0.0000001,3,4107,0.00000008149825,0.0004233083,0.00067899586,0.00045592396,0,10,3761,13,1 -5,0,0.00001,3,4099,0.00000016299649,0.0004233325,0.000679076,0.00045594727,0,10,3890,13,1 -5,0,0.001,3,4093,0.00000016299649,0.00042330878,0.000679076,0.00045592614,0,10,3888,13,1 -5,0,0.1,3,4087,0.0010937029,0.0015171285,0.001804885,0.0015237799,0,10,4240,13,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_0_6_0.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_0_6_0.csv deleted file mode 100644 index 24299452..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_0_6_0.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,0,0,3,2170,0.00043885523,0.00053692335,0.00059342757,0.0005387737,0,5,12606,13,1 -6,0,0.0000001,3,1929,0.0004387738,0.0005367837,0.000593265,0.00053863187,0,5,11421,13,1 -6,0,0.00001,3,1832,0.00043885523,0.0005368536,0.000593265,0.0005387015,0,5,11723,13,1 -6,0,0.001,3,1857,0.00043852953,0.0005366442,0.0005931024,0.00053849583,0,5,11450,13,1 -6,0,0.1,3,1820,0.0017517754,0.0018524723,0.0018964951,0.0018496307,0,5,11547,13,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_0_6_12.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_0_6_12.csv deleted file mode 100644 index 5edef331..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_0_6_12.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,0,0,3,2497,0.000004303515,0.0001318825,0.00020742383,0.00014696842,0,20,10761,13,1 -6,0,0.0000001,3,1906,0.000004222317,0.00013184761,0.00020734222,0.00014694128,0,20,10756,13,1 -6,0,0.00001,3,1871,0.000004222317,0.00013184766,0.00020734222,0.00014693713,0,20,10624,13,1 -6,0,0.001,3,1982,0.0000045471093,0.00013218481,0.00020775029,0.0001472464,0,20,11224,13,1 -6,0,0.1,3,1881,0.0008897501,0.0009680628,0.0010846914,0.00096913625,0,20,11681,13,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_0_6_6.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_0_6_6.csv deleted file mode 100644 index 5cf4bf36..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_0_6_6.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,0,0,3,2186,0.0000013880132,0.000076359785,0.00018638586,0.00009540433,0,10,10616,13,1 -6,0,0.0000001,3,1931,0.0000013063652,0.00007629002,0.0001862234,0.00009530187,0,10,10900,13,1 -6,0,0.00001,3,1955,0.0000013063652,0.00007635973,0.00018646709,0.000095426934,0,10,10535,13,1 -6,0,0.001,3,2008,0.0000013063652,0.00007625514,0.0001862234,0.00009527327,0,10,10727,13,1 -6,0,0.1,3,1985,0.0011936714,0.0012820308,0.001382531,0.0012817241,0,10,10927,13,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_1_5_1.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_1_5_1.csv deleted file mode 100644 index 6661d853..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_1_5_1.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,3,4417,0.0000005636312,0.00013269814,0.00033047434,0.00015390843,0,5,3852,25,1 -5,1,0.0000001,3,4225,0.0000005636312,0.0001326794,0.00033031788,0.00015388711,0,5,3838,25,1 -5,1,0.00001,3,4320,0.00000048311244,0.00013268762,0.00033047434,0.00015389493,0,5,3897,25,1 -5,1,0.001,3,4258,0.0000009508514,0.00013225693,0.00032757982,0.0001533508,0,5,3948,25,1 -5,1,0.1,3,4296,0.00020850761,0.0005651768,0.00084469235,0.0005870571,0,5,4298,25,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_1_5_13.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_1_5_13.csv deleted file mode 100644 index a94b4190..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_1_5_13.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,3,4344,0.00000079559595,0.00015935331,0.0004245761,0.00019511998,0,20,3804,25,1 -5,1,0.0000001,3,4147,0.0000007160363,0.00015939762,0.00042488897,0.00019519794,0,20,3768,25,1 -5,1,0.00001,3,4142,0.0000007956177,0.00015937052,0.00042465434,0.00019514852,0,20,3880,25,1 -5,1,0.001,3,4145,0.0000008000555,0.00015788339,0.0004214475,0.00019284179,0,20,3892,25,1 -5,1,0.1,3,4157,0.00012636262,0.00048047132,0.0007697185,0.00050519244,0,20,4318,25,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_1_5_7.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_1_5_7.csv deleted file mode 100644 index 48ae2a9c..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_1_5_7.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,3,4217,0.0000037000455,0.00014068658,0.00036215645,0.00016534285,0,10,3792,25,1 -5,1,0.0000001,3,4156,0.0000037000455,0.0001406896,0.00036215645,0.00016535247,0,10,3829,25,1 -5,1,0.00001,3,4138,0.0000038609164,0.00014072491,0.00036223468,0.00016540741,0,10,3866,25,1 -5,1,0.001,3,4148,0.00000064348814,0.00013980735,0.00035902744,0.00016399183,0,10,3895,25,1 -5,1,0.1,3,4118,0.00017530343,0.0005221208,0.00080158503,0.00054499594,0,10,4194,25,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_1_6_1.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_1_6_1.csv deleted file mode 100644 index 059f73d3..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_1_6_1.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,1,0,3,2582,0.00011196054,0.000208009,0.0002877489,0.00021594744,0,5,10467,25,1 -6,1,0.0000001,3,2374,0.000111798174,0.00020792766,0.0002875863,0.00021587845,0,5,10524,25,1 -6,1,0.00001,3,2381,0.000112041715,0.00020807872,0.0002877489,0.00021601252,0,5,10634,25,1 -6,1,0.001,3,2238,0.000108307395,0.00020463855,0.00028425266,0.00021272233,0,5,11191,25,1 -6,1,0.1,3,2292,0.00036777556,0.0004500601,0.0005378248,0.0004534786,0,5,11190,25,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_1_6_13.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_1_6_13.csv deleted file mode 100644 index 56eb5711..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_1_6_13.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,1,0,3,2696,0.00023801807,0.0003259965,0.0004099392,0.0003306741,0,20,11132,25,1 -6,1,0.0000001,3,2396,0.00023809922,0.00032612443,0.0004100205,0.00033079437,0,20,10875,25,1 -6,1,0.00001,3,2451,0.00023801807,0.00032605464,0.0004099392,0.0003307277,0,20,10630,25,1 -6,1,0.001,3,2287,0.00023379772,0.0003216275,0.00040546837,0.00032634672,0,20,12427,25,1 -6,1,0.1,3,2328,0.00025633193,0.00034136357,0.0004237934,0.0003456304,0,20,11858,25,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_1_6_7.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_1_6_7.csv deleted file mode 100644 index 334cab94..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_1_6_7.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,1,0,3,2736,0.0001597736,0.0002594152,0.00033677495,0.00026603232,0,10,10637,25,1 -6,1,0.0000001,3,2347,0.00015969243,0.00025931053,0.00033677495,0.00026593806,0,10,10668,25,1 -6,1,0.00001,3,2374,0.00015985477,0.00025948495,0.00033685626,0.00026610174,0,10,11025,25,1 -6,1,0.001,3,2159,0.00015636433,0.0002559636,0.00033344165,0.00026267802,0,10,11157,25,1 -6,1,0.1,3,2316,0.0003144521,0.00039425687,0.00048637335,0.0003983548,0,10,11260,25,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_2_5_14.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_2_5_14.csv deleted file mode 100644 index be0e6032..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_2_5_14.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,3,4266,0.00000064820364,0.00012959325,0.0003173844,0.00015312582,0,20,3870,25,1 -5,2,0.0000001,3,4222,0.00000047317712,0.00012963936,0.00031778537,0.00015320588,0,20,3839,25,1 -5,2,0.00001,3,4240,0.0000006309027,0.00012961928,0.00031770518,0.00015318072,0,20,3931,25,1 -5,2,0.001,3,4185,0.00000072530827,0.00013016297,0.00032027136,0.0001540625,0,20,3951,25,1 -5,2,0.1,3,4215,0.00011876743,0.00043573737,0.00071634457,0.00046051442,0,20,4337,25,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_2_5_2.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_2_5_2.csv deleted file mode 100644 index cec27c46..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_2_5_2.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,3,4296,0.0000017349867,0.00013162608,0.00033719253,0.00015696861,0,5,3857,25,1 -5,2,0.0000001,3,4244,0.0000013406709,0.00013155512,0.00033695193,0.00015685915,0,5,3917,25,1 -5,2,0.00001,3,4174,0.0000018927129,0.00013166273,0.00033727274,0.00015702008,0,5,3987,25,1 -5,2,0.001,3,4168,0.0000018509735,0.00013224251,0.00033975882,0.00015792144,0,5,3936,25,1 -5,2,0.1,3,4196,0.00012432678,0.00044473005,0.0007307911,0.0004691754,0,5,4392,25,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_2_5_8.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_2_5_8.csv deleted file mode 100644 index 63c26a96..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_2_5_8.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,3,4304,0.0000004855544,0.00013017192,0.0003267671,0.00015424423,0,10,3918,25,1 -5,2,0.0000001,3,4190,0.0000004861522,0.00013020335,0.0003267671,0.0001542868,0,10,3830,25,1 -5,2,0.00001,3,4251,0.00000056648014,0.00013016979,0.00032668692,0.00015423141,0,10,3967,25,1 -5,2,0.001,3,4149,0.0000004029489,0.00013066384,0.00032901255,0.00015504948,0,10,3935,25,1 -5,2,0.1,3,4193,0.000117671225,0.0004367955,0.0007222836,0.00046165637,0,10,4326,25,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_2_6_14.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_2_6_14.csv deleted file mode 100644 index b3fa2071..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_2_6_14.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,2,0,3,2772,0.000025170977,0.00012041093,0.00020171813,0.0001328315,0,20,11647,25,1 -6,2,0.0000001,3,2498,0.000025170977,0.000120457385,0.00020188076,0.00013288482,0,20,11026,25,1 -6,2,0.00001,3,2626,0.000025170977,0.00012042253,0.00020188076,0.00013285647,0,20,11014,25,1 -6,2,0.001,3,2288,0.000022653938,0.000117632546,0.00019895317,0.00013028354,0,20,11315,25,1 -6,2,0.1,3,2330,0.00021929809,0.00030157366,0.00039364403,0.00030662928,0,20,11427,25,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_2_6_2.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_2_6_2.csv deleted file mode 100644 index 0222d563..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_2_6_2.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,2,0,3,2909,0.000017944601,0.00011256409,0.00019415516,0.00012566122,0,5,10936,25,1 -6,2,0.0000001,3,2391,0.000017863405,0.000112552494,0.00019415516,0.00012565956,0,5,10921,25,1 -6,2,0.00001,3,2406,0.000017944601,0.00011259897,0.0001943178,0.00012570909,0,5,10607,25,1 -6,2,0.001,3,2365,0.000015265132,0.000109739194,0.00019139018,0.00012312157,0,5,11244,25,1 -6,2,0.1,3,2320,0.00022914638,0.00031159117,0.0004030706,0.00031645648,0,5,11321,25,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_2_6_8.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_2_6_8.csv deleted file mode 100644 index ba39dcd9..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_3_2_6_8.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,2,0,3,2841,0.000022410351,0.000118190655,0.00019911582,0.00013089647,0,10,11043,25,1 -6,2,0.0000001,3,2453,0.000022491547,0.00011829529,0.00019935978,0.00013100368,0,10,10723,25,1 -6,2,0.00001,3,2471,0.000022329157,0.00011813252,0.00019911582,0.00013084304,0,10,10692,25,1 -6,2,0.001,3,2299,0.000019730905,0.000115517,0.00019643219,0.00012847866,0,10,11064,25,1 -6,2,0.1,3,2441,0.00022117006,0.00030337714,0.0003954318,0.00030840645,0,10,11373,25,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_0_5_15.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_0_5_15.csv deleted file mode 100644 index eb382bf8..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_0_5_15.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,4,4234,0.00000031805442,0.00015675595,0.00042614285,0.00019219668,0,20,3853,28,1 -5,0,0.0000001,4,4218,0.00000031805442,0.00015674834,0.00042606538,0.00019218527,0,20,3796,28,1 -5,0,0.00001,4,4188,0.000000397568,0.00015678129,0.0004262978,0.00019223598,0,20,3927,28,1 -5,0,0.001,4,4219,0.00000047708204,0.00015641302,0.00042536808,0.0001916483,0,20,3928,28,1 -5,0,0.1,4,4251,0.000001028777,0.00020544314,0.0004519668,0.0002433274,0,20,4254,28,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_0_5_3.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_0_5_3.csv deleted file mode 100644 index ec005622..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_0_5_3.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,4,4388,0.0000011259978,0.00013245243,0.00036671548,0.00015563284,0,5,3887,28,1 -5,0,0.0000001,4,4185,0.0000014300884,0.00013251824,0.0003671029,0.00015570906,0,5,3778,28,1 -5,0,0.00001,4,4195,0.0000012064261,0.00013244951,0.00036671548,0.00015562594,0,5,3861,28,1 -5,0,0.001,4,4234,0.0000000804285,0.00013234436,0.0003660956,0.00015549164,0,5,3927,28,1 -5,0,0.1,4,4192,0.00000031312666,0.00027856353,0.0005296208,0.00031608203,0,5,4262,25,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_0_5_9.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_0_5_9.csv deleted file mode 100644 index d0ed0258..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_0_5_9.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,4,4229,0.0000021707817,0.00013911394,0.00038422676,0.00016348285,0,10,3867,28,1 -5,0,0.0000001,4,4232,0.0000024923797,0.00013902309,0.00038383933,0.00016333835,0,10,3844,28,1 -5,0,0.00001,4,4199,0.0000021707817,0.00013911091,0.00038422676,0.00016347488,0,10,3939,28,1 -5,0,0.001,4,4144,0.000002813978,0.00013891123,0.00038345193,0.0001631384,0,10,3912,28,1 -5,0,0.1,4,4178,0.00000039258282,0.0002433904,0.0005026652,0.00028145194,0,10,4331,26,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_0_6_15.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_0_6_15.csv deleted file mode 100644 index 5e76d227..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_0_6_15.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,0,0,4,3061,0.00018089301,0.00025534313,0.00033230346,0.00025977867,0,20,10899,28,1 -6,0,0.0000001,4,2801,0.0001806486,0.000255134,0.00033205957,0.0002595748,0,20,10684,28,1 -6,0,0.00001,4,2543,0.00018097447,0.00025535477,0.00033230346,0.0002597936,0,20,10582,28,1 -6,0,0.001,4,2464,0.00018015978,0.00025455287,0.00033149045,0.0002589965,0,20,11179,28,1 -6,0,0.1,4,2385,0.000011308384,0.000089246205,0.0001662022,0.000101163554,0,20,11396,28,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_0_6_3.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_0_6_3.csv deleted file mode 100644 index e21c25ef..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_0_6_3.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,0,0,4,2793,0.000076439355,0.00015457728,0.00022920404,0.00016214211,0,5,10674,28,1 -6,0,0.0000001,4,2596,0.00007595044,0.00015424017,0.00022879746,0.00016183592,0,5,11245,28,1 -6,0,0.00001,4,2584,0.00007660232,0.00015480968,0.00022936668,0.00016236695,0,5,10642,28,1 -6,0,0.001,4,2453,0.000075461525,0.00015384502,0.00022839087,0.0001614787,0,5,11209,28,1 -6,0,0.1,4,2307,0.00010186603,0.0001739418,0.00025548023,0.00018121242,0,5,11482,25,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_0_6_9.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_0_6_9.csv deleted file mode 100644 index c28dd51c..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_0_6_9.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,0,0,4,2992,0.00011766938,0.0001977733,0.00026912914,0.00020409192,0,10,10940,28,1 -6,0,0.0000001,4,2697,0.0001175879,0.0001976339,0.00026904783,0.00020395465,0,10,10750,28,1 -6,0,0.00001,4,2601,0.00011742494,0.00019752933,0.00026880388,0.00020385104,0,10,10638,28,1 -6,0,0.001,4,2465,0.000116854586,0.00019707602,0.00026839733,0.0002034171,0,10,11095,28,1 -6,0,0.1,4,2222,0.00005890403,0.00013216597,0.0002129183,0.0001412586,0,10,11270,26,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_1_5_10.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_1_5_10.csv deleted file mode 100644 index 871320a5..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_1_5_10.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,4,4360,0.00000055578533,0.00013043829,0.00031402297,0.00015145914,0,10,3891,49,1 -5,1,0.0000001,4,4303,0.000000714581,0.00013045284,0.00031410047,0.00015147172,0,10,3880,49,1 -5,1,0.00001,4,4276,0.000000714581,0.00013045887,0.00031410047,0.00015147876,0,10,4049,49,1 -5,1,0.001,4,4296,0.00000007939796,0.00013034043,0.00031340303,0.00015134213,0,10,4001,49,1 -5,1,0.1,4,4246,0.0000014191307,0.00016564057,0.00040191525,0.00020025442,0,10,4389,30,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_1_5_16.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_1_5_16.csv deleted file mode 100644 index 18ead24e..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_1_5_16.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,4,4395,0.0000002431704,0.00012859756,0.00031317055,0.00014943816,0,20,3884,49,1 -5,1,0.0000001,4,4314,0.00000024131137,0.00012858615,0.00031309307,0.00014942398,0,20,3873,49,1 -5,1,0.00001,4,4299,0.00000024131137,0.00012858388,0.00031301557,0.00014941614,0,20,3985,49,1 -5,1,0.001,4,4283,0.0000009652466,0.00012850265,0.00031200814,0.00014931297,0,20,4001,49,1 -5,1,0.1,4,4209,0.0000000787657,0.00016657599,0.00039429564,0.00020087454,0,20,4390,30,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_1_5_4.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_1_5_4.csv deleted file mode 100644 index aa3b1ade..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_1_5_4.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,4,4461,0.0000024167498,0.00012994284,0.00030317382,0.00015072546,0,5,3935,49,1 -5,1,0.0000001,4,4323,0.0000020945172,0.0001299171,0.00030286386,0.00015070081,0,5,3930,49,1 -5,1,0.00001,4,4274,0.0000020945172,0.00012991749,0.00030294133,0.00015069862,0,5,4031,49,1 -5,1,0.001,4,4257,0.0000009667014,0.0001298145,0.0003019339,0.00015058639,0,5,4009,49,1 -5,1,0.1,4,4184,0.0000021348465,0.00016950956,0.0004090537,0.00020477592,0,5,4311,30,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_1_6_10.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_1_6_10.csv deleted file mode 100644 index d40dbe36..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_1_6_10.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,1,0,4,3862,0.00008760276,0.00015870678,0.00023635989,0.00016571219,0,10,10796,49,1 -6,1,0.0000001,4,3799,0.00008752127,0.00015869511,0.00023635989,0.00016571001,0,10,11002,49,1 -6,1,0.00001,4,3834,0.00008752127,0.00015864866,0.00023619726,0.00016565395,0,10,10918,49,1 -6,1,0.001,4,3296,0.00008670644,0.00015783498,0.0002353841,0.0001648746,0,10,11346,49,1 -6,1,0.1,4,3000,0.000006259396,0.000054959335,0.00012584886,0.00006766388,0,10,11715,30,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_1_6_16.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_1_6_16.csv deleted file mode 100644 index 11f89580..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_1_6_16.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,1,0,4,3970,0.00007040938,0.00014460782,0.00022009645,0.00015247254,0,20,11103,49,1 -6,1,0.0000001,4,3591,0.00007040938,0.00014464265,0.00022025908,0.00015251251,0,20,10790,49,1 -6,1,0.00001,4,3475,0.00007040938,0.0001447704,0.00022025908,0.00015263598,0,20,11175,49,1 -6,1,0.001,4,3393,0.00006959451,0.00014385227,0.0002194459,0.00015177122,0,20,11264,49,1 -6,1,0.1,4,2826,0.000014388598,0.000059206013,0.00013163673,0.000071394905,0,20,11443,30,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_1_6_4.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_1_6_4.csv deleted file mode 100644 index 5ce01827..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_1_6_4.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,1,0,4,3842,0.00008418043,0.00015426721,0.00023221277,0.00016145351,0,5,10752,49,1 -6,1,0.0000001,4,3666,0.00008418043,0.00015426721,0.00023229409,0.0001614566,0,5,10955,49,1 -6,1,0.00001,4,3388,0.00008369152,0.00015383714,0.00023180619,0.00016104497,0,5,11151,49,1 -6,1,0.001,4,3056,0.00008263222,0.00015280266,0.00023074906,0.00016006385,0,5,11300,49,1 -6,1,0.1,4,2516,0.000013331794,0.000059623602,0.00013147369,0.00007189233,0,5,11352,30,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_2_5_11.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_2_5_11.csv deleted file mode 100644 index d9f9be10..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_2_5_11.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,4,4473,0.000000483112,0.00012776205,0.00029666422,0.00014845653,0,10,4031,55,1 -5,2,0.0000001,4,4397,0.000000483112,0.00012775286,0.00029650924,0.00014845235,0,10,4008,55,1 -5,2,0.00001,4,4319,0.00000040259334,0.0001277501,0.00029643174,0.00014844499,0,10,4042,55,1 -5,2,0.001,4,4334,0.0000006441492,0.00012775186,0.00029643174,0.00014843981,0,10,4194,55,1 -5,2,0.1,4,4255,0.0000042088177,0.00013584664,0.00031024724,0.00015944772,0,10,4522,33,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_2_5_17.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_2_5_17.csv deleted file mode 100644 index bc90e2ab..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_2_5_17.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,4,4452,0,0.00012784451,0.00029798166,0.00014855246,0,20,3975,55,1 -5,2,0.0000001,4,4410,0,0.00012784693,0.00029798166,0.00014855794,0,20,3986,55,1 -5,2,0.00001,4,4362,0.00000016111707,0.00012784259,0.00029798166,0.00014855324,0,20,4105,55,1 -5,2,0.001,4,4302,0,0.00012782207,0.00029774918,0.00014851519,0,20,4103,55,1 -5,2,0.1,4,4295,0.00000558477,0.00013557929,0.0003088038,0.0001590831,0,20,4430,33,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_2_5_5.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_2_5_5.csv deleted file mode 100644 index f490770a..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_2_5_5.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,4,4533,0.00000080558584,0.00012777801,0.00029674172,0.00014846891,0,5,3967,55,1 -5,2,0.0000001,4,4356,0.00000080558584,0.0001277873,0.00029674172,0.00014847408,0,5,3938,55,1 -5,2,0.00001,4,4329,0.0000006441492,0.00012776238,0.00029666422,0.00014845256,0,5,4072,55,1 -5,2,0.001,4,4398,0.00000080558584,0.0001277534,0.00029666422,0.00014843613,0,5,4056,55,1 -5,2,0.1,4,4272,0.0000042088177,0.00013587714,0.00031040763,0.000159494,0,5,4470,33,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_2_6_11.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_2_6_11.csv deleted file mode 100644 index 267a56bc..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_2_6_11.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,2,0,4,4379,0.000060223454,0.0001343428,0.0002106634,0.00014282159,0,10,11693,55,1 -6,2,0.0000001,4,4612,0.000060223454,0.00013431959,0.00021058209,0.00014279844,0,10,11147,55,1 -6,2,0.00001,4,4489,0.00006046792,0.00013448232,0.0002106634,0.00014292786,0,10,11710,55,1 -6,2,0.001,4,3823,0.000060630893,0.00013462188,0.00021082605,0.0001430671,0,10,11519,55,1 -6,2,0.1,4,3010,0.000017945245,0.000052127314,0.00011217508,0.000059572354,0,10,12131,33,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_2_6_17.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_2_6_17.csv deleted file mode 100644 index c70edf97..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_2_6_17.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,2,0,4,4308,0.000061038336,0.0001352727,0.00021147661,0.00014370949,0,20,10985,55,1 -6,2,0.0000001,4,4276,0.000061038336,0.0001352495,0.0002113953,0.00014368635,0,20,11266,55,1 -6,2,0.00001,4,4015,0.000061038336,0.00013534249,0.00021155793,0.00014377933,0,20,11108,55,1 -6,2,0.001,4,3590,0.00006160875,0.00013583068,0.00021196452,0.00014422697,0,20,11232,55,1 -6,2,0.1,4,2839,0.000017458035,0.000052487885,0.000112663096,0.000059975966,0,20,11613,33,1 diff --git a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_2_6_5.csv b/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_2_6_5.csv deleted file mode 100644 index ee01f0df..00000000 --- a/data/blas_f32_8/grid_search_laplace_blas_uniform_f32_m1_8000000_4_2_6_5.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,2,0,4,4201,0.000060956845,0.0001351683,0.00021123265,0.00014360028,0,5,11227,55,1 -6,2,0.0000001,4,4057,0.000060956845,0.00013507527,0.00021115133,0.00014351122,0,5,10797,55,1 -6,2,0.00001,4,3764,0.000061038336,0.00013511018,0.00021115133,0.0001435272,0,5,10800,55,1 -6,2,0.001,4,3788,0.000061038336,0.00013509853,0.00021115133,0.00014352791,0,5,11294,55,1 -6,2,0.1,4,2587,0.000018107648,0.000052162155,0.00011217508,0.000059587943,0,5,11536,33,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_0_4_15.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_0_4_15.csv deleted file mode 100644 index 79aa655d..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_0_4_15.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,10,1662,0.000000000023146942284607055,0.0000000007328881232805002,0.0000000017112364208572434,0.0000000007918559781975208,0,5,5687,244,1 -4,0,0.0000001,10,1469,0.000000000019251658152170758,0.0000000007317506131507065,0.0000000017094069085265848,0.0000000007909179356481119,0,5,5607,244,1 -4,0,0.00001,10,1450,0.000000000007822610506189796,0.0000000009823207727564374,0.000000005571085955427502,0.0000000013746709581953902,0,5,5629,244,1 -4,0,0.001,10,1388,0.0000000001277329242531626,0.0000000164407160588001,0.000000053110562308971504,0.000000020300749884641273,0,5,5126,130,1 -4,0,0.1,10,1356,0.00000003617014132307663,0.000002956469197325761,0.0000045045900865310014,0.0000031017889863241965,0,5,5077,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_0_4_57.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_0_4_57.csv deleted file mode 100644 index ca71df29..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_0_4_57.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,10,1729,0.0000000000005182314852136318,0.0000000005527359582718859,0.0000000016320964789577806,0.0000000006315401168859861,0,10,5625,244,1 -4,0,0.0000001,10,1465,0.0000000000006629960122732125,0.0000000005521686549160937,0.0000000016335531687437338,0.0000000006311092731284161,0,10,5600,244,1 -4,0,0.00001,10,1451,0.000000000005346618649921524,0.000000001035343215616781,0.000000005855436841878558,0.000000001472388197528985,0,10,5610,244,1 -4,0,0.001,10,1382,0.00000000011558589673034807,0.000000016451211736063843,0.00000005300384939745425,0.000000020300427464708877,0,10,5075,130,1 -4,0,0.1,10,1355,0.000000035924528161157674,0.0000029563130391651417,0.000004504529813131626,0.0000031016326977557376,0,10,4983,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_0_4_99.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_0_4_99.csv deleted file mode 100644 index e373663d..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_0_4_99.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,10,1655,0.000000000002484653666779622,0.00000000035998495646457583,0.0000000015968562057955444,0.000000000428421716410225,0,20,5836,244,1 -4,0,0.0000001,10,1465,0.000000000002248417955441537,0.00000000035970325060127925,0.0000000015997704653175158,0.0000000004282647115390853,0,20,5774,244,1 -4,0,0.00001,10,1482,0.0000000000062632642796720374,0.0000000011960785000200798,0.000000006123123908744193,0.0000000016720236846149606,0,20,5767,244,1 -4,0,0.001,10,1399,0.00000000006581327441045456,0.000000016457155413278856,0.00000005274880596776378,0.00000002028936659261518,0,20,5274,130,1 -4,0,0.1,10,1356,0.00000003573600367701763,0.0000029560687115476437,0.000004504357808619445,0.000003101383657526916,0,20,5164,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_0_5_120.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_0_5_120.csv deleted file mode 100644 index c4502180..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_0_5_120.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,10,4147,0.00000000002639592535274654,0.0000000003982860720634831,0.0000000015938083230203632,0.0000000005330844838963911,0,20,6831,244,1 -5,0,0.0000001,10,2489,0.000000000024916038905900244,0.0000000004003869515962031,0.0000000015967303090569929,0.0000000005350643821858401,0,20,6770,244,1 -5,0,0.00001,10,2425,0.0000000019848306969466277,0.000000003767071524343392,0.000000006107812835370922,0.000000003882425692150822,0,20,6822,244,1 -5,0,0.001,10,1596,0.000000015591797005685933,0.00000002499963050463021,0.0000000333184913754253,0.000000025364194496833303,0,20,6422,130,1 -5,0,0.1,10,1133,0.00000008080082377255026,0.0000013793461754433478,0.000002888554617650768,0.000001630861929290565,0,20,6866,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_0_5_36.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_0_5_36.csv deleted file mode 100644 index 764b6a03..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_0_5_36.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,10,4102,0.000000000022255384329996148,0.00000000043631807737861974,0.0000000010140829297089194,0.0000000004955330246466426,0,5,6651,244,1 -5,0,0.0000001,10,2509,0.000000000018397560615827435,0.0000000004343993942844562,0.0000000010184344724593368,0.0000000004943588496110236,0,5,6794,244,1 -5,0,0.00001,10,2375,0.0000000014067925932601879,0.000000003172244382028526,0.0000000055481694161865445,0.0000000033243558262345135,0,5,6691,244,1 -5,0,0.001,10,1634,0.00000001508757696368765,0.00000002453120434055313,0.00000003270137687689772,0.00000002490110380801346,0,5,6229,130,1 -5,0,0.1,10,1133,0.00000008043294074740218,0.000001379542748836151,0.0000028891608513895346,0.0000016311308244912422,0,5,6895,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_0_5_78.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_0_5_78.csv deleted file mode 100644 index cd0d77d0..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_0_5_78.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,10,4016,0.000000000011678809838044296,0.000000000280444635380277,0.000000001370439481226685,0.00000000041786665348856324,0,10,6619,244,1 -5,0,0.0000001,10,2450,0.000000000015680032033043168,0.00000000028137045261512,0.000000001374992973856748,0.000000000419129624482652,0,10,6698,244,1 -5,0,0.00001,10,2477,0.0000000016176624143316417,0.000000003420016657201664,0.000000005834090148611898,0.0000000035607010612374933,0,10,6725,244,1 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-depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,1,0,10,1855,0.00000000000110730924144539,0.00000000006055657818162293,0.0000000002991963748205055,0.00000000008423497755512976,0,5,12287,301,1 -4,1,0.0000001,10,1538,0.0000000000004989010413471807,0.00000000006105668844893405,0.0000000003044822908161541,0.00000000008554402796263302,0,5,12494,301,1 -4,1,0.00001,10,1501,0.0000000000016316749919030758,0.0000000015021198716377244,0.000000005451336015795496,0.0000000019381134475344913,0,5,12275,267,1 -4,1,0.001,10,1428,0.000000000033206276560611485,0.00000001561892350907784,0.00000005259572288847533,0.000000019531983266947417,0,5,11239,130,1 -4,1,0.1,10,1384,0.00000007187274162645077,0.0000029306590304474155,0.000004509568631552781,0.0000030788649708801634,0,5,11630,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_1_4_58.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_1_4_58.csv deleted file mode 100644 index 551ddee0..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_1_4_58.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,1,0,10,1844,0.000000000002772056009776822,0.00000000008723228366139246,0.00000000035817695483091795,0.00000000010200635838350443,0,10,12340,301,1 -4,1,0.0000001,10,1529,0.000000000002873564259327166,0.00000000008763784903057999,0.00000000036866064236367796,0.00000000010326796300647924,0,10,12095,301,1 -4,1,0.00001,10,1511,0.000000000001261565284013908,0.0000000015344796563201998,0.000000005500395013038196,0.000000001970661460586796,0,10,11873,267,1 -4,1,0.001,10,1425,0.000000000016815484279767383,0.00000001561452753954223,0.00000005257438566210486,0.00000001952554844376304,0,10,11123,130,1 -4,1,0.1,10,1372,0.00000007192811874172961,0.0000029306373761212566,0.000004509547936984387,0.0000030788427717554677,0,10,11168,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_1_5_121.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_1_5_121.csv deleted file mode 100644 index 33c6d79e..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_1_5_121.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,10,5387,0.0000000000017917277717139883,0.00000000005239685081729917,0.00000000017191821224730954,0.00000000007147085589205532,0,20,13843,301,1 -5,1,0.0000001,10,2923,0.0000000000053742898338343136,0.00000000005285387145921567,0.00000000017728876324872143,0.0000000000723679373313712,0,20,13911,301,1 -5,1,0.00001,10,2744,0.0000000023983866394025324,0.0000000040287243051395526,0.000000005350190054577824,0.0000000041007658780781475,0,20,13538,268,1 -5,1,0.001,10,1733,0.000000012831128842168073,0.000000021322279446179042,0.000000029752669338552153,0.00000002174359988750812,0,20,13939,130,1 -5,1,0.1,10,1242,0.00000004442740788798207,0.0000013502968792816393,0.000002795750601455774,0.0000015773795530343099,0,20,13887,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_1_5_37.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_1_5_37.csv deleted file mode 100644 index 1d1ed4e3..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_1_5_37.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,10,5735,0.000000000012017621326998727,0.00000000011550498915387964,0.0000000002959538504024787,0.00000000014101835171490062,0,5,13217,301,1 -5,1,0.0000001,10,3200,0.000000000003547076232390837,0.00000000012088427135046088,0.0000000003057635077396554,0.00000000014728745006313945,0,5,13188,301,1 -5,1,0.00001,10,2792,0.0000000024543627972239064,0.00000000410101945073172,0.00000000543462681192319,0.000000004174559211734078,0,5,12932,267,1 -5,1,0.001,10,1736,0.000000012908826377862474,0.000000021410484156582924,0.000000029779647855211036,0.000000021834828589852262,0,5,13445,130,1 -5,1,0.1,10,1340,0.00000004441641040162698,0.0000013503011699580638,0.000002795762441385373,0.000001577383764897174,0,5,14375,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_1_5_79.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_1_5_79.csv deleted file mode 100644 index c444a776..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_1_5_79.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,10,5370,0.000000000008205558574146612,0.00000000016061314596585972,0.00000000035945983527955976,0.0000000001821389580528385,0,10,13355,301,1 -5,1,0.0000001,10,2951,0.00000000002353935717732876,0.00000000016889186595636527,0.0000000003698301006342356,0.00000000018945485985544852,0,10,13360,301,1 -5,1,0.00001,10,2618,0.000000002483370466858438,0.000000004161984961762733,0.000000005484602168736736,0.000000004235712420928297,0,10,13047,267,1 -5,1,0.001,10,1682,0.000000012916888040847854,0.000000021434950959945775,0.000000029782420140303927,0.000000021856194196580544,0,10,12953,130,1 -5,1,0.1,10,1255,0.00000004447156179330068,0.0000013502946410919939,0.0000027957283272657144,0.0000015773677311712286,0,10,13371,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_2_4_101.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_2_4_101.csv deleted file mode 100644 index a919bbe0..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_2_4_101.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,2,0,10,2059,0.00000000000006986887300252291,0.000000000005702497045197445,0.00000000003210877447498676,0.000000000007565825387268193,0,20,19461,364,1 -4,2,0.0000001,10,1611,0.00000000000008416891847828904,0.000000000007417677820960084,0.0000000000436968922441906,0.00000000001040292203643898,0,20,19265,364,1 -4,2,0.00001,10,1521,0.00000000000607178009445046,0.0000000010828169259559332,0.000000004305470442779322,0.0000000014377977573644256,0,20,18194,268,1 -4,2,0.001,10,1466,0.000000020235268628820543,0.0000000492963623121929,0.0000000672566998512588,0.000000051090769353975916,0,20,17464,136,1 -4,2,0.1,10,1401,0.000000013377288227562653,0.0000030736057838727984,0.000004670728081010301,0.000003229255753676953,0,20,17374,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_2_4_17.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_2_4_17.csv deleted file mode 100644 index a6a7214f..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_2_4_17.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,2,0,10,2136,0.000000000000007268545610736564,0.000000000010880612253463922,0.000000000035803913424037654,0.000000000012710562513302648,0,5,19010,364,1 -4,2,0.0000001,10,1627,0.000000000000012428445306749648,0.000000000012410736355049712,0.000000000046205106857171254,0.000000000015049917387920277,0,5,18907,364,1 -4,2,0.00001,10,1544,0.0000000000022221614099746458,0.0000000010884168126513642,0.0000000043184273532599,0.000000001444122213661616,0,5,17891,268,1 -4,2,0.001,10,1461,0.000000020226293111046455,0.00000004929120403330667,0.00000006725184923349523,0.00000005108581230083694,0,5,18154,136,1 -4,2,0.1,10,1446,0.000000013382586191575684,0.0000030736037744589343,0.0000046707239319448455,0.0000032292536731392117,0,5,18266,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_2_4_59.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_2_4_59.csv deleted file mode 100644 index b4d6124c..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_2_4_59.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,2,0,10,2059,0.00000000000022017166247441204,0.000000000011002759106498225,0.00000000003271033390146558,0.000000000013050722310889798,0,10,19194,364,1 -4,2,0.0000001,10,1594,0.000000000000035487681910715596,0.000000000012657145710608569,0.0000000000436243437046164,0.000000000016001407277090788,0,10,18946,364,1 -4,2,0.00001,10,1513,0.000000000005120978216691865,0.0000000010894568283811745,0.000000004306890606462573,0.000000001445192286546681,0,10,17895,268,1 -4,2,0.001,10,1434,0.000000020229905492240044,0.000000049291493814682724,0.00000006725379873172309,0.00000005108643911406232,0,10,17064,136,1 -4,2,0.1,10,1400,0.00000001338212211439439,0.0000030736040670119065,0.000004670726109272319,0.000003229254025308374,0,10,16906,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_2_5_122.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_2_5_122.csv deleted file mode 100644 index f7412497..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_2_5_122.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,10,7072,0.0000000000038393854826870085,0.00000000001472103533396726,0.000000000031924682555812916,0.00000000001639656674842269,0,20,20574,364,1 -5,2,0.0000001,10,3392,0.000000000010059075701276165,0.00000000002274826942034504,0.00000000004350962632641464,0.000000000024445792753914816,0,20,20497,364,1 -5,2,0.00001,10,2681,0.0000000017690784171859426,0.000000003121300006680518,0.000000004288626186453551,0.000000003185410487873839,0,20,19310,268,1 -5,2,0.001,10,1745,0.00000002072331971647442,0.000000032745930063967755,0.00000004561084147399257,0.00000003366799055312429,0,20,20024,136,1 -5,2,0.1,10,1513,0.0000000024447239303458816,0.000001399868225073932,0.0000029432632987096153,0.000001650865437647791,0,20,20541,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_2_5_38.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_2_5_38.csv deleted file mode 100644 index 6fe1f35a..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_2_5_38.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,10,7189,0.000000000004657207190226176,0.000000000019249775517797558,0.00000000003568027862117796,0.00000000002018694049873759,0,5,20147,364,1 -5,2,0.0000001,10,3421,0.000000000012848926076387013,0.00000000002699643288108366,0.00000000004608645976640983,0.000000000027872239935211295,0,5,19987,364,1 -5,2,0.00001,10,2917,0.000000001777419436254572,0.0000000031318584136581154,0.00000000430155022137245,0.0000000031956093800416655,0,5,18797,268,1 -5,2,0.001,10,1828,0.000000020726269298146634,0.000000032739702217681537,0.00000004560277911382115,0.00000003366177742940822,0,5,19578,136,1 -5,2,0.1,10,1409,0.00000000244982695961506,0.0000013998671772509123,0.000002943259269338566,0.0000016508637441370537,0,5,20197,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_2_5_80.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_2_5_80.csv deleted file mode 100644 index 0520ccfa..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_10_2_5_80.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,10,7095,0.000000000016429218186427358,0.000000000024877041821497064,0.00000000003261374604868338,0.000000000025332886399104264,0,10,20223,364,1 -5,2,0.0000001,10,3397,0.000000000020486731479595033,0.000000000033092341593966764,0.00000000004350705689897138,0.000000000033699260816654885,0,10,20225,364,1 -5,2,0.00001,10,2737,0.0000000017777951670289333,0.0000000031338096772997162,0.000000004290050592144865,0.000000003197413047070222,0,10,19085,268,1 -5,2,0.001,10,1839,0.000000020713203062683307,0.00000003273790587589969,0.000000045601751217504466,0.00000003366010122608783,0,10,19678,136,1 -5,2,0.1,10,1355,0.000000002449525958507656,0.0000013998672180328602,0.000002943259395554176,0.00000165086365845615,0,10,19417,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_0_4_102.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_0_4_102.csv deleted file mode 100644 index fb91194e..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_0_4_102.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,11,1891,0.00000000000019136232915192763,0.00000000004188562017835142,0.00000000016437220650674363,0.000000000053112541090154736,0,20,9860,301,1 -4,0,0.0000001,11,1588,0.0000000000006011318932144102,0.00000000004149148121036251,0.00000000016702161200051994,0.00000000005294471485450918,0,20,9622,301,1 -4,0,0.00001,11,1562,0.000000000009976032952911753,0.0000000010565571829758345,0.000000004072569982259021,0.0000000013993025810049738,0,20,9311,268,1 -4,0,0.001,11,1475,0.000000019071093639936263,0.00000004832489381419111,0.00000006635823395187641,0.00000005012707977363511,0,20,8647,136,1 -4,0,0.1,11,1504,0.000000007455265016799002,0.0000030548009607619418,0.0000046170317346616015,0.0000032072343682171993,0,20,8570,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_0_4_18.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_0_4_18.csv deleted file mode 100644 index db8b4c97..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_0_4_18.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,11,1960,0.00000000000027997071905382053,0.000000000057511081450630744,0.00000000028879776140995456,0.00000000008066728561507985,0,5,9620,301,1 -4,0,0.0000001,11,1687,0.00000000000026376312475978816,0.00000000005798546595375642,0.00000000028971745112942504,0.00000000008165804795821634,0,5,9827,301,1 -4,0,0.00001,11,1608,0.000000000011494538788468004,0.000000001080583792603793,0.000000004202289262800466,0.0000000014334950739897754,0,5,9155,268,1 -4,0,0.001,11,1495,0.000000018961106486084373,0.00000004829511541466562,0.00000006635099319032935,0.00000005010443450594206,0,5,9516,136,1 -4,0,0.1,11,1470,0.00000000746917770644147,0.0000030548094891345656,0.000004617047015521802,0.000003207242786412027,0,5,9140,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_0_4_60.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_0_4_60.csv deleted file mode 100644 index 703fba1f..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_0_4_60.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,11,1907,0.000000000000367335685430481,0.00000000008181102163355619,0.0000000003399808616487793,0.00000000009660568137659428,0,10,9425,301,1 -4,0,0.0000001,11,1576,0.00000000000014800278206216998,0.00000000008218820991245125,0.00000000034730264131669116,0.00000000009767238282403812,0,10,9424,301,1 -4,0,0.00001,11,1554,0.0000000000019269341072905604,0.0000000011084866998982042,0.000000004259546441366941,0.0000000014642599845618978,0,10,9143,268,1 -4,0,0.001,11,1470,0.000000018941283313185355,0.00000004826562564689392,0.00000006633039551216405,0.00000005007550325110177,0,10,8376,136,1 -4,0,0.1,11,1434,0.000000007412215088448911,0.000003054786654354249,0.000004617026422883486,0.000003207220300894919,0,10,8266,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_0_5_123.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_0_5_123.csv deleted file mode 100644 index c3fd86ec..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_0_5_123.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,11,5803,0.00000000000041006066605324217,0.00000000005184369644801952,0.00000000016510652840073416,0.00000000007092038391078126,0,20,10981,301,1 -5,0,0.0000001,11,3524,0.000000000003951163174922416,0.00000000005123856611834597,0.00000000016696429840675226,0.00000000007028492183064634,0,20,10886,301,1 -5,0,0.00001,11,3444,0.0000000017278056236436166,0.000000003022270738567884,0.000000004055723915580128,0.000000003083105855952525,0,20,10577,268,1 -5,0,0.001,11,2166,0.000000019345459026038223,0.00000003166655476895471,0.000000044705727021318574,0.00000003263577953519865,0,20,11284,136,1 -5,0,0.1,11,1635,0.00000008290837480174232,0.0000014085729696979478,0.0000029662151372167653,0.00000166776291127791,0,20,11035,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_0_5_39.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_0_5_39.csv deleted file mode 100644 index 2f11cd19..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_0_5_39.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,11,6142,0.000000000017714755295789426,0.00000000010797594751041205,0.0000000002807136904083564,0.00000000013230218203684576,0,5,10612,301,1 -5,0,0.0000001,11,3579,0.00000000001423006795449385,0.00000000011201642416229812,0.00000000028745762188723175,0.0000000001366980639326324,0,5,10698,301,1 -5,0,0.00001,11,3609,0.0000000017922769129206282,0.000000003107643180692669,0.000000004185558735517109,0.0000000031711579582491954,0,5,10438,268,1 -5,0,0.001,11,2137,0.000000019235259697029907,0.000000031574755225948296,0.00000004467103669342203,0.00000003255316652584842,0,5,11166,136,1 -5,0,0.1,11,1693,0.00000008294386056432458,0.0000014085794214976009,0.000002966227685922181,0.0000016677656243149607,0,5,10802,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_0_5_81.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_0_5_81.csv deleted file mode 100644 index 94c50115..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_0_5_81.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,11,5979,0.000000000013893182369561128,0.00000000014960327499399595,0.0000000003399481960614965,0.00000000017083187897198595,0,10,10723,301,1 -5,0,0.0000001,11,3715,0.0000000000017866428910652844,0.0000000001550089997068887,0.0000000003471621490454641,0.00000000017624403107118187,0,10,10732,301,1 -5,0,0.00001,11,3307,0.0000000018224403520340442,0.0000000031676019734549113,0.0000000042427138341260385,0.0000000032307454212548157,0,10,10309,268,1 -5,0,0.001,11,2142,0.000000019215417922882092,0.00000003154123488394861,0.00000004461908673744876,0.000000032519692844190083,0,10,10827,136,1 -5,0,0.1,11,1644,0.00000008296490240047878,0.0000014085636269374564,0.000002966193336364319,0.000001667746867192801,0,10,10885,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_1_4_103.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_1_4_103.csv deleted file mode 100644 index 5047bdd6..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_1_4_103.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,1,0,11,2109,0.000000000000012396803248342475,0.000000000005301093580458936,0.00000000002478832454614587,0.000000000006649309057995807,0,20,22072,364,1 -4,1,0.0000001,11,1691,0.000000000000020981316636335204,0.000000000007236974936406969,0.0000000000380365947294637,0.000000000009734101742925836,0,20,21686,364,1 -4,1,0.00001,11,1611,0.0000000000047662491507491475,0.0000000010794298087386092,0.0000000038572238186626385,0.0000000013752479374122826,0,20,20935,271,1 -4,1,0.001,11,1514,0.00000005654865050360528,0.00000007740957469233812,0.00000009160616900984979,0.00000007749639862894378,0,20,20084,136,1 -4,1,0.1,11,1470,0.0000006642414820876115,0.000004550812028865774,0.000006271275416100946,0.00000465275846286311,0,20,19854,53,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_1_4_19.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_1_4_19.csv deleted file mode 100644 index bf830b61..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_1_4_19.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,1,0,11,2179,0.0000000000004630828481745181,0.000000000010594083062041846,0.00000000003106021026836271,0.000000000012203845578912147,0,5,21605,364,1 -4,1,0.0000001,11,1717,0.00000000000024674939361992175,0.000000000012233819394499106,0.000000000041815769070144545,0.000000000014685054450192222,0,5,21604,364,1 -4,1,0.00001,11,1697,0.000000000008205712289301605,0.0000000010859828417999246,0.0000000038664923031827075,0.000000001381556576736894,0,5,22656,271,1 -4,1,0.001,11,1581,0.00000005654079718162707,0.00000007740526633049495,0.00000009159880285391805,0.00000007749206277983895,0,5,22447,136,1 -4,1,0.1,11,1524,0.0000006642378763759777,0.000004550809384486217,0.000006271270822437095,0.000004652755936368268,0,5,21097,53,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_1_4_61.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_1_4_61.csv deleted file mode 100644 index 588f7b33..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_1_4_61.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,1,0,11,2120,0.0000000000002068692483457621,0.000000000010761329882860534,0.000000000028211890515273713,0.000000000012350569588400486,0,10,21640,364,1 -4,1,0.0000001,11,1695,0.00000000000006545235883691837,0.000000000012527817392591518,0.00000000003894804638765728,0.00000000001551166542986008,0,10,21402,364,1 -4,1,0.00001,11,1602,0.00000000000864588527840957,0.0000000010866962184923667,0.000000003857658775984835,0.0000000013828136077496502,0,10,20259,271,1 -4,1,0.001,11,1538,0.00000005654350808091309,0.0000000774053026816969,0.0000000915935315015618,0.00000007749214604293727,0,10,20804,136,1 -4,1,0.1,11,1482,0.0000006642363874291737,0.000004550809801487058,0.000006271273251382913,0.000004652756426947154,0,10,20689,53,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_1_5_124.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_1_5_124.csv deleted file mode 100644 index 478b2504..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_1_5_124.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,11,7513,0.000000000004123966036925874,0.00000000001094398712955343,0.000000000024762630271712346,0.000000000012195024673407661,0,20,23228,364,1 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-depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,11,7451,0.0000000000019283282579681657,0.000000000015870472994560687,0.000000000030340788090511374,0.000000000016897213674582137,0,5,22647,364,1 -5,1,0.0000001,11,4358,0.000000000012869316913353642,0.000000000024870046470197947,0.00000000004183511534736436,0.00000000002556036440083886,0,5,22565,364,1 -5,1,0.00001,11,3654,0.0000000017414860438339895,0.00000000289446138106745,0.000000003854732547953398,0.000000002941368457758258,0,5,22826,271,1 -5,1,0.001,11,2280,0.00000007462118788869645,0.00000008232808310679863,0.00000008596861644954272,0.00000008237635030417187,0,5,22981,136,1 -5,1,0.1,11,1758,0.0000006871995576286121,0.000003057717329024712,0.0000047709407396281164,0.0000033158344894617483,0,5,22794,53,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_1_5_82.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_1_5_82.csv deleted file mode 100644 index ff66f336..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_1_5_82.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,11,7721,0.000000000010226117909146691,0.000000000021172800366445632,0.00000000002828741071848014,0.000000000021529741264419813,0,10,22651,364,1 -5,1,0.0000001,11,4378,0.000000000020519245240936536,0.000000000030550202250560724,0.000000000038972470580360445,0.000000000030914969466298874,0,10,22743,364,1 -5,1,0.00001,11,3640,0.0000000017422589382208858,0.0000000028970179378204195,0.000000003845934471822332,0.0000000029437832697694894,0,10,23088,271,1 -5,1,0.001,11,2261,0.00000007461123281072473,0.00000008232611147538602,0.00000008596193659358264,0.00000008237440485680169,0,10,22828,136,1 -5,1,0.1,11,1721,0.0000006871980150505642,0.00000305771676994112,0.0000047709409055707325,0.000003315834118917986,0,10,22309,53,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_2_4_104.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_2_4_104.csv deleted file mode 100644 index a643f636..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_2_4_104.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,2,0,11,2668,0.000000000000022915771734553893,0.0000000000016899465458041228,0.000000000004629671415883535,0.0000000000019129132401963802,0,20,28906,433,1 -4,2,0.0000001,11,1792,0.000000000000027393920818479084,0.0000000000034625973536294665,0.0000000000154978551914076,0.000000000004380590548562574,0,20,28839,433,1 -4,2,0.00001,11,1672,0.000000000000007926771918781899,0.0000000008826254051792233,0.0000000029925283583868733,0.000000001107064415504079,0,20,28145,274,1 -4,2,0.001,11,1556,0.000000056248395933038474,0.00000007714411000959991,0.00000009149150054026629,0.00000007723656124038016,0,20,27219,144,1 -4,2,0.1,11,1535,0.000002163419871082259,0.000005451766613232121,0.00000714325623149228,0.000005519578349895149,0,20,26711,53,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_2_4_20.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_2_4_20.csv deleted file mode 100644 index 25446f9d..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_2_4_20.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,2,0,11,2779,0.0000000000000903066266556347,0.0000000000015189718128823909,0.000000000005177928853441475,0.0000000000017689691077406263,0,5,27731,433,1 -4,2,0.0000001,11,1851,0.00000000000003100563145885249,0.000000000003681589941924323,0.000000000016056000474155894,0.000000000004740272765809592,0,5,27516,433,1 -4,2,0.00001,11,1828,0.00000000000026869023435050904,0.0000000008829206980446533,0.000000002995750617983451,0.0000000011076143905424398,0,5,26793,274,1 -4,2,0.001,11,1585,0.00000005624741789747841,0.0000000771435526211953,0.00000009149053240366666,0.00000007723602002917498,0,5,28404,144,1 -4,2,0.1,11,1696,0.0000021634188410273473,0.000005451766371137343,0.000007143256356796398,0.000005519578125021783,0,5,26077,53,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_2_4_62.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_2_4_62.csv deleted file mode 100644 index 98ffd2ca..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_2_4_62.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,2,0,11,2629,0.00000000000005919545787081072,0.00000000000152538815868731,0.000000000004586054244950908,0.0000000000017434403767875998,0,10,28015,433,1 -4,2,0.0000001,11,1785,0.00000000000006303537550930319,0.000000000003586148086749553,0.000000000016325519485696343,0.000000000004634028382526015,0,10,27900,433,1 -4,2,0.00001,11,1716,0.00000000000013653181287697,0.0000000008827631309417848,0.000000002995645628284809,0.0000000011074090728958834,0,10,25350,274,1 -4,2,0.001,11,1580,0.00000005624782163595447,0.00000007714358140031887,0.00000009149092481706463,0.00000007723604071792385,0,10,26775,144,1 -4,2,0.1,11,1528,0.000002163419322497344,0.000005451766508946094,0.000007143256319520573,0.000005519578256625117,0,10,26761,53,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_2_5_125.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_2_5_125.csv deleted file mode 100644 index 401ea6c9..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_2_5_125.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,11,10555,0.00000000000007535763397325196,0.000000000001527235168722806,0.000000000003652058873853686,0.0000000000018553492511445233,0,20,30276,433,1 -5,2,0.0000001,11,5031,0.000000000002364806313235728,0.000000000008668691467718159,0.000000000015346020702273865,0.000000000009368027168881513,0,20,32163,433,1 -5,2,0.00001,11,3907,0.000000001400933992566871,0.0000000022774083913614354,0.000000002985230135078414,0.0000000023101616215817447,0,20,31159,274,1 -5,2,0.001,11,2491,0.00000007387625727758428,0.00000008193166744728518,0.00000008551900385005131,0.00000008198233598963764,0,20,29657,144,1 -5,2,0.1,11,1861,0.0000022115183529558475,0.000004369196038770943,0.0000059309929439466304,0.000004527543316737087,0,20,29458,53,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_2_5_41.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_2_5_41.csv deleted file mode 100644 index effb99be..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_2_5_41.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,11,10858,0.00000000000002144404847006858,0.0000000000015895444030893724,0.000000000005134624609934218,0.0000000000020773185104363623,0,5,29081,433,1 -5,2,0.0000001,11,5356,0.000000000003783807373986735,0.000000000009875447205087992,0.000000000015946617266549206,0.000000000010499831322696891,0,5,30880,433,1 -5,2,0.00001,11,3614,0.000000001401652065545188,0.0000000022790106974256905,0.000000002988449061668733,0.0000000023118025524524803,0,5,29117,274,1 -5,2,0.001,11,2399,0.00000007387519605430944,0.00000008193022634077722,0.00000008551853197794715,0.00000008198090097732859,0,5,28425,144,1 -5,2,0.1,11,1898,0.0000022115173194163143,0.000004369195498863653,0.000005930992382936147,0.000004527542829788474,0,5,27992,53,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_2_5_83.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_2_5_83.csv deleted file mode 100644 index bf3743d5..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_11_2_5_83.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,11,10643,0.000000000000038491627778067905,0.0000000000015228952058347056,0.000000000004534358867753601,0.0000000000019289903178744554,0,10,29563,433,1 -5,2,0.0000001,11,5517,0.0000000000032178194508922134,0.00000000000973963731992425,0.00000000001620310725336095,0.000000000010417527345813525,0,10,30422,433,1 -5,2,0.00001,11,3930,0.0000000014012454248271117,0.000000002278625412746063,0.0000000029883413449649298,0.000000002311440813738572,0,10,30340,274,1 -5,2,0.001,11,2492,0.00000007387527566854494,0.00000008193055551253407,0.00000008551810539249867,0.00000008198122943220378,0,10,28763,144,1 -5,2,0.1,11,2069,0.0000022115178054313867,0.000004369195766365444,0.000005930992755281752,0.000004527543077295227,0,10,28713,53,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_0_4_0.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_0_4_0.csv deleted file mode 100644 index 79a2c343..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_0_4_0.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,5,1190,0.00000005074126813211148,0.000009714047089212755,0.00003153464785724936,0.000012765883278511553,0,5,583,49,1 -4,0,0.0000001,5,1179,0.00000005071067658197815,0.000009714047060263119,0.00003153462364397733,0.000012765877832183578,0,5,609,49,1 -4,0,0.00001,5,1170,0.000000050301841400025816,0.000009713937357976432,0.000031536990550571205,0.000012765905630836406,0,5,577,49,1 -4,0,0.001,5,1191,0.00000006128650486675001,0.000009681634657608752,0.00003129043134522257,0.000012683850788877881,0,5,578,49,1 -4,0,0.1,5,1279,0.00000024637080837992506,0.000023032361031586123,0.00008240861719725798,0.00002767698847561788,0,5,572,33,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_0_4_42.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_0_4_42.csv deleted file mode 100644 index 907546b2..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_0_4_42.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,5,1205,0.000000021645715825862453,0.000006987195299197176,0.000020415713761985043,0.000008282088903731695,0,10,585,49,1 -4,0,0.0000001,5,1173,0.000000021642306302397278,0.000006987194560620113,0.000020415771915926747,0.000008282091145460443,0,10,582,49,1 -4,0,0.00001,5,1208,0.000000022972381869052036,0.000006987405240784396,0.000020414095022772614,0.000008282564328559175,0,10,575,49,1 -4,0,0.001,5,1195,0.000000012271979141624263,0.000006901157047049557,0.0000207107913468478,0.000008205776424015679,0,10,569,49,1 -4,0,0.1,5,1195,0.000000006305637854646725,0.00001830560190596099,0.00007743332346373243,0.000023178519357281747,0,10,575,33,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_0_4_84.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_0_4_84.csv deleted file mode 100644 index 84f61e3c..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_0_4_84.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,5,1356,0.00000008247598614526569,0.000003140577912717874,0.000005945679632744197,0.0000034055421113246857,0,20,618,49,1 -4,0,0.0000001,5,1289,0.00000008245112149003225,0.0000031405879099178916,0.000005945733121436051,0.000003405556391331042,0,20,598,49,1 -4,0,0.00001,5,1312,0.00000008223896373659683,0.0000031406934432053978,0.0000059445812120659,0.0000034055111993023026,0,20,607,49,1 -4,0,0.001,5,1268,0.00000014102883710939902,0.0000031340684221388547,0.000005975404919542964,0.0000034138679787230114,0,20,634,49,1 -4,0,0.1,5,1170,0.0000000777006396935828,0.00001745295538264058,0.00007842141097227281,0.00002301791929176809,0,20,632,33,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_0_5_105.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_0_5_105.csv deleted file mode 100644 index 181616d2..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_0_5_105.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,5,647,0.0000003101850193809976,0.000002177382015994411,0.0000036948707176375502,0.000002419461571613946,0,20,1342,49,1 -5,0,0.0000001,5,639,0.00000031021405955981665,0.000002177380286056386,0.00000369492291226881,0.000002419464233133424,0,20,1352,49,1 -5,0,0.00001,5,577,0.0000003113737141877569,0.0000021781984322614295,0.0000036944764703697506,0.0000024200040798516524,0,20,1350,49,1 -5,0,0.001,5,565,0.00000003977659990689749,0.0000019368290738290396,0.00000360353901992447,0.0000022415332990364354,0,20,1359,49,1 -5,0,0.1,5,456,0.000025179624702423496,0.00005143882906776455,0.00007748916702229903,0.00005309532195091624,0,20,1361,33,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_0_5_21.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_0_5_21.csv deleted file mode 100644 index 7dec1f07..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_0_5_21.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,5,649,0.0000001251623211441401,0.000008440190501447115,0.000019538309714316054,0.000010423253475206067,0,5,1346,49,1 -5,0,0.0000001,5,635,0.0000001252415054106911,0.00000844018272324244,0.00001953825289243018,0.00001042323927627419,0,5,1340,49,1 -5,0,0.00001,5,587,0.00000012785806922913577,0.000008439293025870909,0.000019536702481423414,0.000010422498687759132,0,5,1366,49,1 -5,0,0.001,5,565,0.00000011916893155299058,0.000008368287903429882,0.000019055839880077794,0.000010309313722743295,0,5,1349,49,1 -5,0,0.1,5,460,0.000027341434746667307,0.00005329863598682658,0.00008160816828922883,0.00005470333692526039,0,5,1375,33,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_0_5_63.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_0_5_63.csv deleted file mode 100644 index 2527cd8f..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_0_5_63.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,5,641,0.000001311809749101357,0.000009674213242143376,0.00002094642414483519,0.000011153422813662149,0,10,1341,49,1 -5,0,0.0000001,5,615,0.0000013118343453511545,0.000009674228045811478,0.000020946481587314666,0.000011153444153674891,0,10,1329,49,1 -5,0,0.00001,5,574,0.0000013096018034133544,0.000009673304523459738,0.00002094469088550322,0.000011152240794079733,0,10,1335,49,1 -5,0,0.001,5,551,0.0000016214128907135626,0.00000977521543619442,0.00002124121055670636,0.000011316602292750314,0,10,1364,49,1 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-4,1,0.00001,5,1242,0.000000009362986337979417,0.0000009018776400266682,0.0000025106405523597123,0.000001058927471967184,0,5,791,76,1 -4,1,0.001,5,1191,0.0000000029962593911413435,0.000000831984019581816,0.0000023632141291789344,0.000000986020577502081,0,5,794,76,1 -4,1,0.1,5,1193,0.000000016550812587169807,0.000017992165633373794,0.00008034061024589047,0.000024327852831158486,0,5,780,36,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_1_4_43.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_1_4_43.csv deleted file mode 100644 index a851186e..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_1_4_43.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,1,0,5,1219,0.000000020431298125267704,0.0000008294251945313265,0.0000017417570419905573,0.0000009108413052697704,0,10,827,76,1 -4,1,0.0000001,5,1200,0.000000020273778758919457,0.0000008294132101241473,0.0000017417662117420616,0.0000009108353893373217,0,10,812,76,1 -4,1,0.00001,5,1200,0.000000011346954150577114,0.0000008277791766530229,0.0000017397404235988583,0.0000009095312541438856,0,10,805,76,1 -4,1,0.001,5,1195,0.00000001626619006295389,0.0000007206419466454342,0.0000017184951411749555,0.0000008297319157979509,0,10,808,76,1 -4,1,0.1,5,1207,0.000000021043438773121712,0.00001794821529261117,0.00008028459569867411,0.000024302481949050173,0,10,784,36,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_1_4_85.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_1_4_85.csv deleted file mode 100644 index 5d1fc552..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_1_4_85.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,1,0,5,1217,0.0000000031433821987073465,0.00000040502044909877786,0.0000015328970194150396,0.00000046828513651378605,0,20,876,76,1 -4,1,0.0000001,5,1233,0.000000003164151906801131,0.00000040501230526136456,0.0000015328646940007462,0.0000004682738284536617,0,20,878,76,1 -4,1,0.00001,5,1195,0.000000003334371982268508,0.00000040321947424069514,0.0000015311066227931563,0.00000046643750020487363,0,20,847,76,1 -4,1,0.001,5,1194,0.00000000022813634461363043,0.0000002619266527618114,0.0000014743727796878586,0.00000035614836074038594,0,20,848,76,1 -4,1,0.1,5,1182,0.000000042137606912524486,0.000017375855338715517,0.00007977892543224731,0.00002385641535440384,0,20,869,36,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_1_5_106.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_1_5_106.csv deleted file mode 100644 index 4d984fac..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_1_5_106.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,5,984,0.000000023366245282834744,0.0000006014008792705987,0.0000009355470776199391,0.000000646716450026413,0,20,1640,76,1 -5,1,0.0000001,5,792,0.000000023512113085854937,0.0000006013244572258667,0.0000009354622200826636,0.0000006466347900108129,0,20,1632,76,1 -5,1,0.00001,5,756,0.0000000339076393832372,0.0000005953941361680243,0.0000009298700957276634,0.0000006402859106013327,0,20,1656,76,1 -5,1,0.001,5,709,0.000000040659511050042575,0.00000025995819840135087,0.0000008400215253884669,0.0000003219931230358857,0,20,1648,76,1 -5,1,0.1,5,482,0.000027561241801875424,0.000053891242033285994,0.00007985333137447789,0.00005557271997811337,0,20,1622,36,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_1_5_22.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_1_5_22.csv deleted file mode 100644 index 012df8ba..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_1_5_22.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,5,1183,0.000000001885427040376251,0.0000005231990126214036,0.0000017450922776095682,0.0000007146680497736454,0,5,1600,76,1 -5,1,0.0000001,5,807,0.0000000017308305517905773,0.0000005231724217529541,0.0000017450006575764861,0.0000007146167794668082,0,5,1607,76,1 -5,1,0.00001,5,741,0.00000000770765844449705,0.0000005214810247758239,0.0000017381453168768114,0.0000007107492794082454,0,5,1573,76,1 -5,1,0.001,5,698,0.00000010651881419089735,0.0000005722106170264501,0.0000013653560699630556,0.000000661809544432154,0,5,1604,76,1 -5,1,0.1,5,473,0.000028170473418434947,0.000054520992502654735,0.00008042806032280126,0.00005616261308359097,0,5,1608,36,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_1_5_64.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_1_5_64.csv deleted file mode 100644 index 2127f7c6..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_1_5_64.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,5,924,0.0000000018360577539331089,0.0000005942197266760003,0.0000013190830669730447,0.0000006824645055283801,0,10,1623,76,1 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-depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,2,0,5,1280,0.0000000015387721238730426,0.0000004697558611255234,0.0000016195293739153634,0.0000005643084132119607,0,5,1101,97,1 -4,2,0.0000001,5,1284,0.0000000015295580364149467,0.0000004697808439690108,0.000001619499247622706,0.0000005643414436750632,0,5,1080,97,1 -4,2,0.00001,5,1380,0.0000000029219413520481724,0.00000047418061316817096,0.0000016166545197865722,0.000000569367404008816,0,5,1153,97,1 -4,2,0.001,5,1299,0.00000004274227924101973,0.0000009035292715073614,0.0000024904020653448543,0.0000010137680925827026,0,5,1075,97,1 -4,2,0.1,5,1268,0.00000006249792812558405,0.000016210456254858397,0.0000790820055865458,0.00002300895912453421,0,5,1052,36,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_2_4_44.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_2_4_44.csv deleted file mode 100644 index 2520c470..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_2_4_44.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,2,0,5,1270,0.0000000022758532138945365,0.0000003151291580399236,0.0000014668985920675476,0.00000042136076249095165,0,10,1104,97,1 -4,2,0.0000001,5,1230,0.0000000022951285783961675,0.0000003151586867183078,0.0000014668725643863473,0.0000004213978947086932,0,10,1109,97,1 -4,2,0.00001,5,1230,0.0000000007779903971944739,0.00000031881152225709604,0.0000014641766411238368,0.00000042570053189418185,0,10,1138,97,1 -4,2,0.001,5,1225,0.000000031196458275162455,0.000000737245693521866,0.0000024103213363697086,0.0000008626695337504017,0,10,1115,97,1 -4,2,0.1,5,1193,0.00000001159469886814149,0.000016289180134042925,0.0000791975025641452,0.000023083356520537585,0,10,1077,36,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_2_4_86.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_2_4_86.csv deleted file mode 100644 index 2533483f..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_2_4_86.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,2,0,5,1264,0.0000000026350663108339072,0.0000003847494228733604,0.0000015163000765215337,0.0000004962937238193293,0,20,1185,97,1 -4,2,0.0000001,5,1220,0.000000002661172903245234,0.0000003847797532634,0.000001516273061554123,0.0000004963350118032089,0,20,1196,97,1 -4,2,0.00001,5,1227,0.0000000034401781594001564,0.00000038916000256034,0.0000015138675529739465,0.0000005015660494797205,0,20,1166,97,1 -4,2,0.001,5,1237,0.000000016052144516415166,0.0000008038911231049653,0.000002543480453995674,0.0000009337518409564624,0,20,1163,97,1 -4,2,0.1,5,1218,0.000000001788134188370785,0.000016256105015512475,0.00007903740059202114,0.00002304669327385497,0,20,1134,36,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_2_5_107.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_2_5_107.csv deleted file mode 100644 index 61d57314..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_2_5_107.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,5,1157,0.0000004515117717633351,0.000000968042871331079,0.0000015569101302862,0.000001004999237041599,0,20,2005,97,1 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-depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,5,1222,0.0000004089456805289366,0.0000009884924767648186,0.0000014840939086009545,0.0000010261840798897253,0,5,1881,97,1 -5,2,0.0000001,5,969,0.0000004090346970396566,0.0000009886091093235148,0.000001484267641948998,0.0000010263023364649074,0,5,1900,97,1 -5,2,0.00001,5,923,0.00000042043259427894526,0.0000010023120911807258,0.0000015026493761494388,0.0000010400719150646774,0,5,1908,97,1 -5,2,0.001,5,869,0.000001164402533844223,0.000001856366464333253,0.0000025540355255175312,0.0000018903054849076823,0,5,1907,97,1 -5,2,0.1,5,511,0.00002595888386639527,0.00005273674514492962,0.00007897455854282358,0.00005450870251172642,0,5,1837,36,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_2_5_65.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_2_5_65.csv deleted file mode 100644 index 0b7b951f..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_5_2_5_65.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,5,1144,0.00000031114795912967554,0.0000007960568305388931,0.0000013689715554320232,0.0000008376229055328244,0,10,1924,97,1 -5,2,0.0000001,5,962,0.00000031124455950104947,0.0000007961785751623403,0.0000013691543159399827,0.000000837746058086064,0,10,1889,97,1 -5,2,0.00001,5,919,0.00000032060184421423,0.000000808650818137764,0.0000013853870645065803,0.0000008502372468042682,0,10,1858,97,1 -5,2,0.001,5,876,0.0000009598237074731183,0.0000016814517182936965,0.0000024730811187454457,0.0000017169262046048133,0,10,1898,97,1 -5,2,0.1,5,512,0.000026095439729901695,0.00005285689144003016,0.00007909180158836633,0.00005462565427979524,0,10,1861,36,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_0_4_3.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_0_4_3.csv deleted file mode 100644 index 169d1321..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_0_4_3.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,6,1255,0.000000034066587499195295,0.0000010239269510734262,0.000002639737977269477,0.0000011333756161796357,0,5,821,76,1 -4,0,0.0000001,6,1432,0.00000003402596831955882,0.0000010239068553568326,0.0000026397686973287872,0.0000011333552399011081,0,5,784,76,1 -4,0,0.00001,6,1258,0.00000002882876805627348,0.0000010198714901336538,0.0000026387968162327354,0.000001129264815558884,0,5,846,76,1 -4,0,0.001,6,1495,0.000000013604727739719427,0.0000007479884998427314,0.0000025150292759545566,0.0000008751522611165504,0,5,810,76,1 -4,0,0.1,6,1238,0.000023878832714409224,0.00004384953413647572,0.00005644935199459533,0.00004419613105482603,0,5,780,39,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_0_4_45.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_0_4_45.csv deleted file mode 100644 index 6a4f33f8..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_0_4_45.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,6,1234,0.00000013580871511551342,0.0000010005279192436428,0.00000211125714106216,0.000001060793446402205,0,10,819,76,1 -4,0,0.0000001,6,1241,0.00000013583460735714292,0.0000010005129723922864,0.000002111302373107827,0.0000010607720750748863,0,10,817,76,1 -4,0,0.00001,6,1215,0.00000013598111435142557,0.000000996185663001541,0.0000021105026744416643,0.000001055973073245576,0,10,803,76,1 -4,0,0.001,6,1221,0.000000004090450836541857,0.000000708164612614514,0.000002007154060256033,0.0000007679100561121999,0,10,796,76,1 -4,0,0.1,6,1207,0.00002380232839534451,0.000043812167217820774,0.000056454125181322893,0.000044160306353069395,0,10,791,39,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_0_4_87.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_0_4_87.csv deleted file mode 100644 index 5091a759..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_0_4_87.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,6,1246,0.000000008278862031065913,0.0000005818811054639834,0.000002358022223832609,0.0000007431384528135897,0,20,844,76,1 -4,0,0.0000001,6,1212,0.000000008304430381032725,0.0000005818718099906163,0.00000235789742996329,0.000000743112538878671,0,20,824,76,1 -4,0,0.00001,6,1226,0.000000008630849960371663,0.0000005777454097977293,0.000002342501633935123,0.0000007375860271118256,0,20,813,76,1 -4,0,0.001,6,1205,0.0000000033309813723082377,0.0000003229340043399889,0.0000016024064884740993,0.000000435907742938353,0,20,814,76,1 -4,0,0.1,6,1195,0.000023058571883419738,0.00004305278421031346,0.000055568884232501707,0.00004341322655987823,0,20,781,39,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_0_5_108.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_0_5_108.csv deleted file mode 100644 index af5a9154..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_0_5_108.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,6,942,0.0000008570283353597726,0.000001568197563187005,0.0000023977788704771095,0.0000016084799805687845,0,20,1653,76,1 -5,0,0.0000001,6,826,0.0000008569641622805946,0.0000015681136132913592,0.0000023976546305544422,0.000001608393429641919,0,20,1619,76,1 -5,0,0.00001,6,787,0.0000008464693044242027,0.0000015558234678959477,0.000002382335779425587,0.0000015960394174256777,0,20,1616,76,1 -5,0,0.001,6,740,0.00000032985989941127666,0.0000009395560619588075,0.000001644807842118113,0.0000009851062997316181,0,20,1632,76,1 -5,0,0.1,6,552,0.000047013216293097526,0.00005071749441090464,0.00005341805495695367,0.00005075449763909273,0,20,1597,39,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_0_5_24.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_0_5_24.csv deleted file mode 100644 index 7112224a..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_0_5_24.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,6,937,0.0000006324176372021341,0.000001281813691575329,0.0000018513380207105453,0.00000130928977290087,0,5,1600,76,1 -5,0,0.0000001,6,804,0.0000006323108250185794,0.0000012817014279589827,0.0000018512804864545095,0.0000013091806878852462,0,5,1571,76,1 -5,0,0.00001,6,762,0.0000006225734093131267,0.0000012702440316351937,0.0000018428435710443135,0.000001297895897955219,0,5,1572,76,1 -5,0,0.001,6,737,0.00000007256116500139444,0.0000006459356830640391,0.000001379025439573256,0.0000007045800495041599,0,5,1570,76,1 -5,0,0.1,6,528,0.000047339082671569455,0.00005128781473693812,0.00005390204887346535,0.00005132993281755856,0,5,1555,39,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_0_5_66.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_0_5_66.csv deleted file mode 100644 index ce445352..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_0_5_66.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,6,947,0.0000009491930426518826,0.000001480414849863989,0.0000020042301277572575,0.0000015020056159681817,0,10,1604,76,1 -5,0,0.0000001,6,832,0.0000009490493689139408,0.0000014803069600245247,0.0000020040742585945473,0.0000015018974294615028,0,10,1589,76,1 -5,0,0.00001,6,768,0.0000009335104365489974,0.0000014680367284476537,0.0000019892490525008578,0.0000014897120200450068,0,10,1590,76,1 -5,0,0.001,6,731,0.00000017822118422530663,0.0000008421449643694348,0.0000012533524421957965,0.0000008768127794840691,0,10,1615,76,1 -5,0,0.1,6,536,0.00004739315826547138,0.0000512847112418284,0.00005397676053459859,0.00005132591384579672,0,10,1606,39,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_1_4_4.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_1_4_4.csv deleted file mode 100644 index 2111a1b2..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_1_4_4.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,1,0,6,1290,0.0000000011342120567118738,0.0000001632524018409511,0.0000005965286922821182,0.00000020733188160451229,0,5,1350,109,1 -4,1,0.0000001,6,1260,0.0000000011326857374277305,0.00000016324066957969396,0.0000005964466933142489,0.00000020731616443341473,0,5,1315,109,1 -4,1,0.00001,6,1242,0.0000000022271255870661967,0.00000016204853372464613,0.0000005822635584740166,0.00000020534394337469674,0,5,1336,109,1 -4,1,0.001,6,1227,0.0000000004080873816900056,0.00000030538290286360323,0.000000745312780357936,0.0000003315865245841313,0,5,1314,107,1 -4,1,0.1,6,1203,0.000010917074657608247,0.000022598532007462697,0.00003135274702020653,0.000022841825622483724,0,5,1232,39,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_1_4_46.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_1_4_46.csv deleted file mode 100644 index 06db5b90..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_1_4_46.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,1,0,6,1281,0.0000000006674958800784289,0.00000016298494345861866,0.0000007951909805982587,0.00000021398770797910563,0,10,1336,109,1 -4,1,0.0000001,6,1250,0.0000000006551525163036075,0.00000016297329556324043,0.0000007951146506780324,0.00000021397151643802313,0,10,1326,109,1 -4,1,0.00001,6,1239,0.0000000003940408108059654,0.00000015944898827376449,0.0000007782110277238317,0.0000002096919215708741,0,10,1324,109,1 -4,1,0.001,6,1245,0.00000000017878549412420513,0.00000013646523057465718,0.0000005193250097562752,0.00000016833721493940715,0,10,1340,107,1 -4,1,0.1,6,1211,0.00001099281132565451,0.000022707404602139805,0.00003146460021010121,0.000022949204938438724,0,10,1263,39,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_1_4_88.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_1_4_88.csv deleted file mode 100644 index ee2749d8..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_1_4_88.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,1,0,6,1297,0.00000000007244763931867639,0.00000011126164203374784,0.0000006029587127992478,0.00000015963634960797673,0,20,1438,109,1 -4,1,0.0000001,6,1243,0.00000000007405935562422475,0.00000011124651706810036,0.0000006028717254452001,0.00000015961820000614803,0,20,1426,109,1 -4,1,0.00001,6,1227,0.0000000005551175610412415,0.00000010803738120497601,0.0000005861713824901424,0.00000015593339350957658,0,20,1398,109,1 -4,1,0.001,6,1227,0.0000000068503087018537995,0.00000025640445953199653,0.000000506979017320789,0.00000027353745767673103,0,20,1401,108,1 -4,1,0.1,6,1204,0.000010976807068225042,0.000022670987996957912,0.000031424953681977596,0.000022912386781562667,0,20,1358,39,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_1_5_109.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_1_5_109.csv deleted file mode 100644 index 69543a73..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_1_5_109.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,6,1289,0.000000022390077355814423,0.0000002735632809525473,0.0000005719060931798721,0.0000002973552235540814,0,20,2236,109,1 -5,1,0.0000001,6,1068,0.000000022344565084403845,0.0000002735005812031814,0.0000005718195746434325,0.0000002972922004755172,0,20,2227,109,1 -5,1,0.00001,6,984,0.000000013585606294887835,0.00000026140421983290746,0.0000005551895589397262,0.0000002853826204571636,0,20,2235,109,1 -5,1,0.001,6,926,0.0000001664912722560884,0.0000003450763834241215,0.0000004955828957499669,0.00000035185745250472136,0,20,2227,108,1 -5,1,0.1,6,555,0.000013770765466856129,0.000021520594176390324,0.000025871974954109853,0.000021867999632856694,0,20,2170,39,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_1_5_25.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_1_5_25.csv deleted file mode 100644 index 5fbe745a..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_1_5_25.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,6,1292,0.0000000022509179125324485,0.0000003012983300268793,0.0000005837188069101092,0.0000003477405132355009,0,5,2106,109,1 -5,1,0.0000001,6,1059,0.0000000022914766750979967,0.00000030124762199705823,0.0000005836373929749093,0.0000003476858536109295,0,5,2225,109,1 -5,1,0.00001,6,981,0.000000012509975297677764,0.0000002915888435943064,0.0000005695158226191329,0.00000033750513512459234,0,5,2246,109,1 -5,1,0.001,6,951,0.0000001010341648475699,0.0000003054931601040471,0.0000005638149636797058,0.0000003308388711608614,0,5,2166,107,1 -5,1,0.1,6,548,0.000013772896515718941,0.000021463389604579743,0.000025898248229942425,0.000021812667847943838,0,5,2206,39,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_1_5_67.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_1_5_67.csv deleted file mode 100644 index 3e7ebc63..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_1_5_67.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,6,1313,0.00000009483725129968219,0.00000038296318111199987,0.0000007632856283090802,0.0000004084343462965205,0,10,2208,109,1 -5,1,0.0000001,6,1059,0.00000009479558699853455,0.00000038290766016693025,0.0000007632098035012623,0.0000004083780647359727,0,10,2140,109,1 -5,1,0.00001,6,996,0.0000000856268791654887,0.00000037061579450102206,0.0000007463695883474119,0.0000003961710410079634,0,10,2123,109,1 -5,1,0.001,6,952,0.000000014943364626575077,0.00000014318222035350208,0.00000030787388303435885,0.00000015955536766428377,0,10,2118,107,1 -5,1,0.1,6,545,0.000013916073341441129,0.000021596268453808145,0.000026004394773698557,0.000021942103405271848,0,10,2060,39,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_2_4_47.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_2_4_47.csv deleted file mode 100644 index f5b3aeee..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_2_4_47.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,2,0,6,1353,0.0000000005741734071947063,0.00000007845154740977006,0.0000004446610873528417,0.00000011525429594526167,0,10,1920,148,1 -4,2,0.0000001,6,1291,0.0000000005777722886000693,0.00000007844892153617266,0.00000044466680374164725,0.00000011525098685467687,0,10,1878,148,1 -4,2,0.00001,6,1267,0.00000000035212327642833276,0.00000007797116249150461,0.00000044626904062203156,0.00000011395715007813194,0,10,1837,148,1 -4,2,0.001,6,1250,0.0000000015789967172165367,0.00000032706223807082104,0.0000005766205466503218,0.0000003392141646713532,0,10,1803,115,1 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/dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,6,1820,0.000000013612637462425957,0.00000016380434407507317,0.00000031438654502978677,0.00000018349412077479321,0,20,2784,148,1 -5,2,0.0000001,6,1297,0.00000001361703411016854,0.00000016379536295603744,0.0000003143717437453874,0.0000001834844040025113,0,20,2754,148,1 -5,2,0.00001,6,1243,0.00000001816021712222533,0.0000001567162972818629,0.00000030339037678681103,0.00000017591909437555614,0,20,2737,148,1 -5,2,0.001,6,1005,0.0000002379895634278881,0.00000039468150430589577,0.0000005344548376239666,0.0000003991801398106294,0,20,2694,115,1 -5,2,0.1,6,578,0.00000006439269948042463,0.000006628549517837201,0.000012881787247437333,0.000007771296958158237,0,20,2622,39,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_2_5_26.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_2_5_26.csv deleted file mode 100644 index 2bb171e2..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_6_2_5_26.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,6,1947,0.000000003518623037167668,0.00000014979909084457977,0.00000028921834337673057,0.00000016965858861678082,0,5,2699,148,1 -5,2,0.0000001,6,1376,0.0000000035092696367729015,0.00000014978793871263902,0.00000028920241205177784,0.00000016964688014934895,0,5,2687,148,1 -5,2,0.00001,6,1239,0.0000000035100145403761417,0.0000001432749796814146,0.00000027848886436740764,0.00000016248460780820164,0,5,2670,148,1 -5,2,0.001,6,1088,0.000000212481910895823,0.0000003910343821277712,0.0000005260206637074877,0.00000039679780258494663,0,5,2594,115,1 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-4,0,0,7,1306,0.00000000031493913842653445,0.0000001482512193918206,0.000000502389467216545,0.00000016743456401982057,0,10,1230,109,1 -4,0,0.0000001,7,1252,0.00000000031625485411069976,0.0000001482503642543049,0.0000005023654002159985,0.00000016743265293045952,0,10,1219,109,1 -4,0,0.00001,7,1256,0.0000000003771086161884487,0.0000001472188491030671,0.000000497378505008041,0.00000016613627038653795,0,10,1204,109,1 -4,0,0.001,7,1252,0.000000001747756111732091,0.00000016623027223643898,0.000000524969680930247,0.00000019436996560446083,0,10,1207,109,1 -4,0,0.1,7,1217,0.00000042475616685965936,0.00000825572615330955,0.000014174009555943334,0.000008651318437093083,0,10,1158,39,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_0_4_6.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_0_4_6.csv deleted file mode 100644 index 92cc7cbf..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_0_4_6.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,7,1308,0.0000000005071201093977738,0.00000009913477214998047,0.00000037009815997111507,0.00000013078512663459375,0,5,1206,109,1 -4,0,0.0000001,7,1257,0.0000000005075036649488847,0.00000009913166198229116,0.0000003700922017466667,0.00000013078161134157615,0,5,1260,109,1 -4,0,0.00001,7,1274,0.000000000464582147216491,0.0000000985340682870656,0.00000037060265764872856,0.0000001300917606814242,0,5,1198,109,1 -4,0,0.001,7,1253,0.00000004103401532752771,0.0000002921699868798993,0.0000006457350043503767,0.0000003112771343376426,0,5,1180,108,1 -4,0,0.1,7,1211,0.00000049083717542959,0.000008164074377189995,0.000013975714273937093,0.000008559276207589206,0,5,1135,39,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_0_4_90.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_0_4_90.csv deleted file mode 100644 index 93803fbe..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_0_4_90.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,7,1329,0.0000000004568024284240184,0.00000008665962319974263,0.0000002996713508204722,0.00000009763791415979975,0,20,1290,109,1 -4,0,0.0000001,7,1270,0.0000000004534424042918708,0.00000008665904325064073,0.00000029965203092875746,0.00000009763638032066054,0,20,1267,109,1 -4,0,0.00001,7,1259,0.0000000014469131706939847,0.00000008553183382470441,0.0000002938030178301407,0.00000009639209252085503,0,20,1232,109,1 -4,0,0.001,7,1253,0.0000000024716989099611667,0.00000020634001704552618,0.0000005627444358099356,0.00000023118838375273005,0,20,1272,109,1 -4,0,0.1,7,1222,0.0000005536206988174315,0.000008229925641058435,0.000014144068382362417,0.000008623272683881008,0,20,1181,39,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_0_5_111.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_0_5_111.csv deleted file mode 100644 index 48c99ece..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_0_5_111.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,7,1345,0.00000007740700664024572,0.00000014217173619977826,0.00000030317017502567006,0.00000015266088797707676,0,20,2121,109,1 -5,0,0.0000001,7,1091,0.00000007739466186890477,0.0000001421568139747866,0.00000030315087101293466,0.00000015264680914155945,0,20,2115,109,1 -5,0,0.00001,7,1034,0.00000007233360124314098,0.0000001380793235781754,0.0000002973393428669472,0.00000014868100900665062,0,20,2080,109,1 -5,0,0.001,7,985,0.00000028244236438717895,0.00000039392716836962483,0.0000005583905645245262,0.0000003993722919185832,0,20,2114,109,1 -5,0,0.1,7,574,0.0000004769225755206405,0.000004480362220396939,0.000008462280889500967,0.000005054559907040297,0,20,2039,39,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_0_5_27.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_0_5_27.csv deleted file mode 100644 index bc065d75..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_0_5_27.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd 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+0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,7,1335,0.00000014717036883538712,0.000000247552247648906,0.0000005049615989839365,0.00000026350444095564667,0,10,2059,109,1 -5,0,0.0000001,7,1094,0.00000014715784928498694,0.00000024753497445102655,0.0000005049376163694521,0.0000002634879985271777,0,10,2048,109,1 -5,0,0.00001,7,1048,0.00000014424888821946637,0.0000002436121073025855,0.0000004999751501802247,0.000000259724499765441,0,10,2027,109,1 -5,0,0.001,7,1075,0.0000001201628221808982,0.0000003060647187094973,0.0000005199746475856605,0.00000031872323324625287,0,10,2087,109,1 -5,0,0.1,7,633,0.00000032924887147976046,0.000004487141540336467,0.000008435063220866403,0.000005074379578079263,0,10,1994,39,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_1_4_49.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_1_4_49.csv deleted file mode 100644 index a6e89ac7..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_1_4_49.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,1,0,7,1366,0.000000000038185309120639994,0.0000000061163873429790905,0.000000024528866178896754,0.00000000781785680003163,0,10,2194,148,1 -4,1,0.0000001,7,1284,0.00000000004033101309646248,0.000000006113957944490845,0.00000002451271762890813,0.000000007815091302407698,0,10,2180,148,1 -4,1,0.00001,7,1274,0.000000000007882764605321042,0.000000006122826869217684,0.000000021017839262815075,0.000000007742302181907482,0,10,2200,148,1 -4,1,0.001,7,1255,0.00000009830608563030387,0.0000003434646473934005,0.0000006636285578029567,0.0000003609763481785786,0,10,2134,116,1 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a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_1_5_112.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,7,1868,0.0000000005798381940115087,0.000000005442411140987414,0.000000018602196670132475,0.000000006715135090384627,0,20,3167,148,1 -5,1,0.0000001,7,1344,0.0000000005699673590503036,0.000000005439244198570866,0.000000018612327209645584,0.000000006714446367456726,0,20,3203,148,1 -5,1,0.00001,7,1255,0.000000000024208820579035557,0.000000005347656444814189,0.000000021671846429983627,0.00000000761498666544604,0,20,3187,148,1 -5,1,0.001,7,1015,0.00000044541850035433033,0.0000005826076244768397,0.0000006818293304666976,0.0000005846639219917176,0,20,3118,116,1 -5,1,0.1,7,607,0.00000013067825341315122,0.0000044155833355891935,0.000011232805174821082,0.000005140510226526018,0,20,3036,39,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_1_5_28.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_1_5_28.csv deleted file mode 100644 index e8746544..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_1_5_28.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,7,1930,0.0000000004204922323396726,0.00000001271381763737612,0.000000026021344531280206,0.000000014633984016072636,0,5,3008,148,1 -5,1,0.0000001,7,1338,0.00000000042456335810683344,0.000000012725045042523445,0.000000026034924381717294,0.000000014644375563570312,0,5,3043,148,1 -5,1,0.00001,7,1246,0.0000000025595507159055757,0.000000015918824865802772,0.00000002944699799842011,0.000000017495469413401404,0,5,3020,148,1 -5,1,0.001,7,1118,0.0000004510134677647432,0.0000005856944397969281,0.0000006836393680256279,0.0000005881259191358963,0,5,3000,116,1 -5,1,0.1,7,611,0.00000013801519693768708,0.000004414961409025767,0.000011229739762923889,0.00000513992768200863,0,5,2899,39,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_1_5_70.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_1_5_70.csv deleted file mode 100644 index 5e322833..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_1_5_70.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,7,1928,0.00000000012145448160352015,0.000000008759288047302456,0.000000024179611446632744,0.000000010773207287765974,0,10,3034,148,1 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-depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,2,0,7,1457,0.00000000004349621255918464,0.000000002733172566928626,0.000000013837795550953168,0.0000000033547978386555126,0,10,3482,193,1 -4,2,0.0000001,7,1330,0.000000000051777470119790326,0.000000002736180982610981,0.000000013839867536439188,0.0000000033570308348938835,0,10,3492,193,1 -4,2,0.00001,7,1333,0.000000000015467799226961838,0.0000000022661577300052125,0.000000012958576808584535,0.0000000029368833300257493,0,10,3505,193,1 -4,2,0.001,7,1322,0.00000009101941637920849,0.0000002931379343637833,0.0000005960082046023871,0.0000003084133732484118,0,10,3328,124,1 -4,2,0.1,7,1250,0.0000003002237512106953,0.000005644246267708346,0.000012989504170961802,0.000006007695641086339,0,10,3232,43,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_2_4_8.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_2_4_8.csv deleted file mode 100644 index 7bce2b76..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_2_4_8.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,2,0,7,1471,0.0000000000432870896907166,0.000000003734107256959428,0.000000015502810406018274,0.0000000043185587314021935,0,5,3418,193,1 -4,2,0.0000001,7,1342,0.000000000050331540823971087,0.000000003737461835561185,0.000000015505623225291258,0.0000000043210640376507675,0,5,3407,193,1 -4,2,0.00001,7,1334,0.000000000009427906570822501,0.0000000031823595435546277,0.000000014622334316878479,0.000000003762883294001479,0,5,3415,193,1 -4,2,0.001,7,1286,0.0000000905039580660338,0.0000002921404385358128,0.0000005957554613673883,0.00000030749705956316065,0,5,3178,124,1 -4,2,0.1,7,1245,0.0000002985675275871007,0.000005644939161431977,0.000012988533977719528,0.000006008373359427924,0,5,3115,43,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_2_4_92.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_2_4_92.csv deleted file mode 100644 index 1796ee9a..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_2_4_92.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,2,0,7,1463,0.000000000003886524370226795,0.0000000029733037389698385,0.000000012904213471946096,0.0000000036406875525183954,0,20,3787,193,1 -4,2,0.0000001,7,1352,0.000000000004975801217864789,0.000000002975611321758935,0.000000012906674217528862,0.0000000036425598061354995,0,20,3754,193,1 -4,2,0.00001,7,1365,0.000000000004756953923611108,0.000000002547506064470184,0.000000012001543550346506,0.0000000031705275290148856,0,20,3774,193,1 -4,2,0.001,7,1285,0.00000009067982036026204,0.00000029310315910969735,0.0000005949830171640613,0.00000030835344745433793,0,20,3569,124,1 -4,2,0.1,7,1248,0.00000030196198012828676,0.0000056439780510875084,0.00001298901397770063,0.000006007388044148451,0,20,3578,43,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_2_5_113.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_2_5_113.csv deleted file mode 100644 index 7402cd76..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_2_5_113.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,7,2639,0.0000000001436858059976222,0.0000000034407617988349754,0.000000007616040464698221,0.000000004015947733421727,0,20,4679,193,1 -5,2,0.0000001,7,1733,0.00000000013422504546097045,0.0000000034416206635798813,0.000000007612568489571525,0.000000004016600893653508,0,20,4638,193,1 -5,2,0.00001,7,1619,0.0000000000549361558574325,0.0000000037150082454975408,0.000000007918102609778454,0.000000004476486318333273,0,20,4659,193,1 -5,2,0.001,7,1108,0.0000003814405457710652,0.0000005026632576710851,0.0000005944671624800477,0.0000005048195992145747,0,20,4453,124,1 -5,2,0.1,7,663,0.00000004363771688380251,0.000004424068657552509,0.000013044036766887494,0.000005774273296007626,0,20,4350,43,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_2_5_29.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_2_5_29.csv deleted file mode 100644 index 30c3a6b8..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_2_5_29.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,7,2678,0.0000000007251433191007698,0.0000000034519431477686614,0.000000007168420755040935,0.000000003904886172954595,0,5,4424,193,1 -5,2,0.0000001,7,1749,0.0000000007194912484680226,0.0000000034523763329778837,0.0000000071651251553650845,0.000000003904701767494517,0,5,4268,193,1 -5,2,0.00001,7,1626,0.00000000005166854766934487,0.000000003413298999275506,0.000000007203989932898986,0.000000004102353490441222,0,5,4470,193,1 -5,2,0.001,7,1079,0.00000037967518211586565,0.0000005021187595110243,0.0000005952657169701447,0.0000005042814854154538,0,5,4059,124,1 -5,2,0.1,7,640,0.00000004286779998691879,0.000004423887070680172,0.000013043584427463118,0.0000057741147444889155,0,5,3988,43,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_2_5_71.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_2_5_71.csv deleted file mode 100644 index 18c9dc18..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_7_2_5_71.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,7,2659,0.00000000006573079364274118,0.0000000027892138318757185,0.000000005029952514884514,0.0000000031849281256021402,0,10,4376,193,1 -5,2,0.0000001,7,1713,0.000000000055369158189717806,0.0000000027896544607536796,0.000000005045097853793908,0.0000000031872034943524858,0,10,4353,193,1 -5,2,0.00001,7,1613,0.00000000011730591901840506,0.000000003336055937628864,0.00000000859376683143572,0.000000004223144955958649,0,10,4355,193,1 -5,2,0.001,7,1107,0.00000038081285511045713,0.0000005029695428748287,0.0000005955180073715709,0.000000505109780283316,0,10,4173,124,1 -5,2,0.1,7,665,0.00000004238625330171625,0.000004424228723560477,0.000013044547860680603,0.000005774683487645937,0,10,4087,43,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_0_4_51.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_0_4_51.csv deleted file mode 100644 index d4eb09e5..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_0_4_51.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,8,1384,0.00000000008333329255878122,0.000000005553766479756864,0.00000002150123105636467,0.000000007108513805503695,0,10,1928,148,1 -4,0,0.0000001,8,1324,0.0000000000775331240246069,0.000000005550680148392705,0.0000000214982935261512,0.000000007105446711917493,0,10,1893,148,1 -4,0,0.00001,8,1315,0.00000000004741967575284766,0.000000005556974774369151,0.00000002182031305605201,0.000000007156972287376315,0,10,1882,148,1 -4,0,0.001,8,1288,0.00000010161848069133508,0.00000030953121099887693,0.0000006106817505794884,0.0000003249226156840466,0,10,1886,124,1 -4,0,0.1,8,1250,0.00000004414755397500682,0.000006121445100031704,0.000011252982560970212,0.000006444204074105529,0,10,1772,43,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_0_4_9.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_0_4_9.csv deleted file mode 100644 index dfbcb908..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_0_4_9.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,8,1386,0.000000000276453653731162,0.000000011055057915069203,0.00000003595470018465963,0.000000013453972479497293,0,5,1897,148,1 -4,0,0.0000001,8,1345,0.0000000002821590701713717,0.000000011053457667330682,0.00000003594628527922887,0.000000013452384987848704,0,5,1883,148,1 -4,0,0.00001,8,1334,0.000000000024906229136850908,0.00000001177821844755186,0.00000003614176740416132,0.000000014114509090699658,0,5,1907,148,1 -4,0,0.001,8,1297,0.0000001021020764610727,0.0000003104459460446884,0.000000623462861522811,0.0000003268020092051675,0,5,1832,124,1 -4,0,0.1,8,1263,0.00000006640161665308196,0.000006114800567977493,0.000011279829035805895,0.000006437923431595508,0,5,1762,43,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_0_4_93.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_0_4_93.csv deleted file mode 100644 index 11d17e1b..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_0_4_93.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,8,1381,0.000000000026759409104019327,0.000000005025048112389837,0.000000018017352548519956,0.000000006425278208164184,0,20,1979,148,1 -4,0,0.0000001,8,1304,0.000000000021866406698249674,0.000000005025736727747427,0.000000018025672051996494,0.000000006426104917613103,0,20,1947,148,1 -4,0,0.00001,8,1300,0.000000000006756317802572684,0.0000000050838301824135155,0.000000021233073240634274,0.00000000656542717278622,0,20,1977,148,1 -4,0,0.001,8,1299,0.0000000934227277868684,0.0000003071335277364603,0.0000006324676127101696,0.0000003243787919766185,0,20,1902,124,1 -4,0,0.1,8,1263,0.00000004898866214083482,0.000006126218911307416,0.00001127775035376578,0.000006449383182596384,0,20,1835,43,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_0_5_114.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_0_5_114.csv deleted file mode 100644 index a4388385..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_0_5_114.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,8,1904,0.0000000003040830006059633,0.000000004690257305243963,0.000000018245414920132802,0.0000000066014545644134096,0,20,2871,148,1 -5,0,0.0000001,8,1420,0.00000000030907632386822233,0.000000004689632826467438,0.000000018253725657323998,0.0000000066026226174126524,0,20,2893,148,1 -5,0,0.00001,8,1315,0.000000000017838947212627626,0.000000005944551556522961,0.000000021461884372133794,0.00000000808168982866407,0,20,2890,148,1 -5,0,0.001,8,1116,0.000000403697480260712,0.0000005334399944770466,0.0000006323784132411915,0.000000535590527615071,0,20,2847,124,1 -5,0,0.1,8,674,0.0000001397629202830791,0.000004090169820288717,0.000011333146163211173,0.000005001387798932884,0,20,2749,43,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_0_5_30.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_0_5_30.csv deleted file mode 100644 index 95a59b41..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_0_5_30.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,8,1982,0.0000000036722253779578407,0.000000013313460898346472,0.000000024014967776380943,0.000000014635406297509694,0,5,2778,148,1 -5,0,0.0000001,8,1529,0.0000000036769461457908114,0.000000013320586386005113,0.000000024023561795283466,0.00000001464240328021766,0,5,2791,148,1 -5,0,0.00001,8,1305,0.0000000060521017934617506,0.000000015782675566888495,0.00000002665111901049106,0.000000016955498999882966,0,5,2790,148,1 -5,0,0.001,8,1242,0.00000040547252228060855,0.0000005316677828845714,0.0000006240779150496822,0.0000005341534272853446,0,5,2747,124,1 -5,0,0.1,8,675,0.0000001225078003667981,0.000004089801938691978,0.000011335335920241131,0.000005004195508603843,0,5,2755,43,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_0_5_72.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_0_5_72.csv deleted file mode 100644 index 22f8aee0..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_0_5_72.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,8,1890,0.00000000006445010365254836,0.000000008414484408424572,0.000000018894900213785523,0.000000009874746358115054,0,10,2772,148,1 -5,0,0.0000001,8,1372,0.00000000007034808502328884,0.000000008410710535412372,0.00000001888392739790222,0.000000009870862111403865,0,10,2767,148,1 -5,0,0.00001,8,1307,0.0000000001365417594430284,0.000000007533518176371593,0.00000001581199057472328,0.000000009006713639209195,0,10,2755,148,1 -5,0,0.001,8,1113,0.0000003989153331073227,0.0000005241713365948545,0.0000006106985713328696,0.0000005260151485026712,0,10,2740,124,1 -5,0,0.1,8,669,0.00000014467206850500527,0.000004085604406676569,0.000011308446812927185,0.00000499277893009834,0,10,2671,43,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_1_4_10.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_1_4_10.csv deleted file mode 100644 index f3e2041f..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_1_4_10.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,1,0,8,1488,0.00000000008352198260083512,0.000000004033592329414345,0.000000011148420726576233,0.000000004632694958994342,0,5,4025,193,1 -4,1,0.0000001,8,1372,0.00000000007549483802121056,0.0000000040350009099417544,0.000000011133946843282178,0.000000004632192496636318,0,5,3979,193,1 -4,1,0.00001,8,1370,0.000000000028048455246426393,0.0000000029926621157382123,0.000000008296088779758934,0.000000003355628725798686,0,5,3981,193,1 -4,1,0.001,8,1317,0.0000000009601955734270202,0.000000041602850847900525,0.00000008983882290712539,0.000000047192618300410805,0,5,3868,125,1 -4,1,0.1,8,1366,0.0000000023616077619792455,0.000010134040822469542,0.00001506708249775188,0.000010543540115187017,0,5,3703,48,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_1_4_52.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_1_4_52.csv deleted file mode 100644 index 1d084d04..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_1_4_52.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,1,0,8,1483,0.00000000005583889587564556,0.000000002981146900946247,0.000000010069713810294016,0.000000003648582818834299,0,10,4144,193,1 -4,1,0.0000001,8,1354,0.00000000005852458113713884,0.000000002982078197055923,0.000000010055699578912854,0.000000003647335668053455,0,10,4114,193,1 -4,1,0.00001,8,1349,0.00000000014201517397073325,0.0000000019902830918010284,0.000000007027513737954341,0.000000002351129660800495,0,10,4108,193,1 -4,1,0.001,8,1342,0.00000000007238246605277939,0.00000004052179670141871,0.00000008930464657331248,0.000000046227863505149997,0,10,4011,125,1 -4,1,0.1,8,1288,0.0000000030070899700336115,0.000010133163974278049,0.000015066021916506436,0.00001054264634506438,0,10,3812,48,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_1_4_94.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_1_4_94.csv deleted file mode 100644 index 0a07ea55..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_1_4_94.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,1,0,8,1474,0.00000000008286002363959894,0.000000003349514554074146,0.000000012229581495403104,0.000000004140110333970766,0,20,4430,193,1 -4,1,0.0000001,8,1368,0.000000000084526256772358,0.000000003350088352977836,0.000000012214503883417123,0.000000004138731866263199,0,20,4413,193,1 -4,1,0.00001,8,1347,0.00000000009496603489634079,0.000000002422145159447854,0.000000007873843605613654,0.0000000029470093324400077,0,20,4406,193,1 -4,1,0.001,8,1311,0.0000000010829776244586527,0.00000004045512460039454,0.00000008956553161882712,0.00000004614299723129711,0,20,4197,125,1 -4,1,0.1,8,1284,0.000000001126055375970832,0.000010132908656906258,0.000015066715213522141,0.000010542373671369073,0,20,4187,48,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_1_5_115.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_1_5_115.csv deleted file mode 100644 index 41743f87..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_1_5_115.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,8,2725,0.0000000026063814638131865,0.000000007537481588638068,0.000000012238908004833092,0.000000007934834758656735,0,20,5387,193,1 -5,1,0.0000001,8,1757,0.000000002600289276689515,0.000000007528802080221558,0.000000012223884629951097,0.000000007926103723323731,0,20,5430,193,1 -5,1,0.00001,8,1659,0.000000000312097546684399,0.000000004312388593070669,0.000000007886221027801163,0.00000000483728790103773,0,20,5438,193,1 -5,1,0.001,8,1190,0.000000003745706772960452,0.000000015906648920463224,0.000000022359444296614032,0.00000001674278660473576,0,20,5176,125,1 -5,1,0.1,8,722,0.000000031371172314697905,0.000004751834857534268,0.000009750948908424008,0.00000550348904066175,0,20,5188,48,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_1_5_31.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_1_5_31.csv deleted file mode 100644 index 894089b1..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_1_5_31.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,8,2865,0.000000003947312982927407,0.000000007996167720026319,0.000000011134403162158431,0.000000008130243964130746,0,5,5032,193,1 -5,1,0.0000001,8,1799,0.0000000039411525139507924,0.000000007987821320538151,0.000000011119970789944493,0.00000000812174229043085,0,5,4958,193,1 -5,1,0.00001,8,1738,0.0000000011257001573350663,0.000000004706514442123404,0.000000006636793744733053,0.000000004869060779495168,0,5,4948,193,1 -5,1,0.001,8,1208,0.0000000036126038198942584,0.000000016651279001361487,0.00000002433622450753685,0.000000017338080510483208,0,5,4721,125,1 -5,1,0.1,8,715,0.00000003012296245766495,0.000004752138432358068,0.000009751298337946367,0.000005504015786897931,0,5,4644,48,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_1_5_73.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_1_5_73.csv deleted file mode 100644 index eb395810..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_1_5_73.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,8,2724,0.0000000033170505325183228,0.0000000067757094006313206,0.000000010056669483773245,0.0000000069390468399037116,0,10,5013,193,1 -5,1,0.0000001,8,1791,0.0000000033105605846234246,0.000000006767102806119346,0.000000010042700096478464,0.000000006930217196777604,0,10,5000,193,1 -5,1,0.00001,8,1677,0.0000000004797559094109416,0.000000003488347012350228,0.000000005856429065228538,0.000000003685511208527898,0,10,5117,193,1 -5,1,0.001,8,1206,0.000000002717153448675459,0.000000015676573190359218,0.000000023536401051732397,0.00000001641170331476717,0,10,4845,125,1 -5,1,0.1,8,752,0.000000029474533074204488,0.000004751702038320565,0.000009750567376455278,0.000005503483801185598,0,10,4777,48,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_2_4_11.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_2_4_11.csv deleted file mode 100644 index f4ec1187..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_2_4_11.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,2,0,8,1628,0.000000000006791727629995313,0.0000000008209758425595186,0.0000000025583105808624207,0.0000000009294363162015899,0,5,6370,244,1 -4,2,0.0000001,8,1412,0.000000000006706558237481858,0.0000000008188194587442309,0.000000002547741902249256,0.0000000009265701961809027,0,5,6253,244,1 -4,2,0.00001,8,1442,0.00000000000856545652525361,0.0000000009746616636065862,0.000000003982602999532751,0.0000000012119934412409648,0,5,6567,240,1 -4,2,0.001,8,1369,0.00000000010198752588509538,0.000000032331748631012335,0.00000008739796973609097,0.000000039885284478945913,0,5,5864,127,1 -4,2,0.1,8,1344,0.0000001424434180326409,0.000003832762735258984,0.0000053707169999654325,0.0000039604813028241425,0,5,5728,49,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_2_4_53.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_2_4_53.csv deleted file mode 100644 index 5a39253b..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_2_4_53.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,2,0,8,1630,0.00000000001139993075143522,0.0000000006355486048408477,0.0000000022293889848103306,0.0000000007428955726456201,0,10,6318,244,1 -4,2,0.0000001,8,1408,0.000000000013483254154365625,0.0000000006333666496167951,0.000000002218831648421983,0.0000000007401056495973656,0,10,6295,244,1 -4,2,0.00001,8,1401,0.000000000001483416819573201,0.000000001089813917467854,0.000000004149095850804574,0.0000000013476669244879245,0,10,6257,242,1 -4,2,0.001,8,1327,0.0000000003038357008556203,0.00000003217516063920974,0.00000008725384995629014,0.00000003974803622237964,0,10,5797,127,1 -4,2,0.1,8,1290,0.00000014237795451351166,0.000003832591713370132,0.0000053705704913365004,0.000003960313497010418,0,10,5695,49,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_2_4_95.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_2_4_95.csv deleted file mode 100644 index cbd3903e..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_2_4_95.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,2,0,8,1597,0.0000000000011357066531118784,0.0000000004167554044779442,0.0000000020224810707443524,0.00000000056333530537008,0,20,6591,244,1 -4,2,0.0000001,8,1413,0.0000000000005307891945166242,0.00000000041515887071070425,0.0000000020102788103115676,0.0000000005607863196210969,0,20,6570,244,1 -4,2,0.00001,8,1401,0.00000000000014134030843983557,0.000000001336745712724212,0.000000004331856338632054,0.000000001599438126831213,0,20,6545,243,1 -4,2,0.001,8,1327,0.00000000015469238588799106,0.000000031882691342546264,0.00000008695935398689363,0.00000003948422092652624,0,20,6094,127,1 -4,2,0.1,8,1292,0.00000014220743571359653,0.0000038323261203180766,0.0000053704012869111805,0.000003960048463404489,0,20,5962,49,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_2_5_116.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_2_5_116.csv deleted file mode 100644 index d311d89d..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_2_5_116.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,8,3888,0.00000000021912153751892898,0.0000000009832463592623712,0.0000000019934407585955865,0.0000000010974502701520678,0,20,7579,244,1 -5,2,0.0000001,8,2116,0.0000000002140344905659531,0.0000000009753358727823658,0.000000001981332260497812,0.0000000010895001758362344,0,20,7534,244,1 -5,2,0.00001,8,2064,0.0000000019223832799790437,0.0000000030124396355718946,0.000000004353259693069895,0.0000000030507496658554107,0,20,7527,243,1 -5,2,0.001,8,1262,0.00000000016357065451269136,0.000000004704386899454569,0.000000012085743300799107,0.0000000055297097692035545,0,20,7214,127,1 -5,2,0.1,8,788,0.00000012045016895642995,0.0000021512700243768506,0.000003957233451320264,0.000002494069608951511,0,20,7024,49,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_2_5_32.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_2_5_32.csv deleted file mode 100644 index d790f50f..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_2_5_32.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,8,4297,0.0000000010375042215597338,0.00000000158800669916426,0.0000000025337395006594398,0.0000000016533731987038208,0,5,7453,244,1 -5,2,0.0000001,8,2202,0.0000000010321879094022867,0.0000000015798026496841275,0.0000000025234119723289125,0.0000000016451033380255383,0,5,7312,244,1 -5,2,0.00001,8,2049,0.0000000013594083789851197,0.0000000024505504183195973,0.000000003978472720521634,0.000000002508119129779514,0,5,7314,240,1 -5,2,0.001,8,1310,0.00000000024908666795629905,0.00000000468769385779175,0.000000012710107991379655,0.0000000055202498433741354,0,5,6746,127,1 -5,2,0.1,8,843,0.0000001206840225572525,0.00000215165062511785,0.0000039578552730613876,0.0000024944543678827593,0,5,6789,49,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_2_5_74.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_2_5_74.csv deleted file mode 100644 index 706a17fb..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_8_2_5_74.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,8,3742,0.0000000007737297971641945,0.0000000012762953520016147,0.000000002201901497889922,0.0000000013401623927322795,0,10,7209,244,1 -5,2,0.0000001,8,2112,0.0000000007662329199723067,0.0000000012681583213227793,0.000000002191460003353937,0.0000000013320037948141447,0,10,7218,244,1 -5,2,0.00001,8,2120,0.000000001554922081863785,0.0000000027075432617252237,0.000000004145746677731173,0.000000002759737758099046,0,10,7261,242,1 -5,2,0.001,8,1230,0.00000000002914960104114361,0.000000004655697821577879,0.000000012522566424197695,0.00000000550189316851453,0,10,6791,127,1 -5,2,0.1,8,797,0.00000012062128676939966,0.0000021514849312470928,0.000003957584313690223,0.000002494287878050836,0,10,6715,49,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_0_4_12.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_0_4_12.csv deleted file mode 100644 index 47cf87d8..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_0_4_12.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,9,1507,0.00000000014327952338748173,0.0000000034486976384771242,0.000000009480603965377618,0.000000003877611953738563,0,5,3412,193,1 -4,0,0.0000001,9,1389,0.00000000014745399058951434,0.000000003446933744659642,0.00000000947843946631679,0.000000003875348076294472,0,5,3364,193,1 -4,0,0.00001,9,1379,0.000000000008412725497795067,0.000000002217070726778363,0.000000007849410388742975,0.000000002533465543291374,0,5,3396,193,1 -4,0,0.001,9,1372,0.00000000016570315355012586,0.00000002985843829826968,0.00000008506446828263092,0.00000003755043586250795,0,5,3179,127,1 -4,0,0.1,9,1321,0.00000004197509926697018,0.000003835431672123138,0.000005299711443869909,0.000003948023239933074,0,5,3062,49,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_0_4_54.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_0_4_54.csv deleted file mode 100644 index d35c8507..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_0_4_54.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,9,1551,0.000000000025994568986191438,0.0000000023838834035682666,0.00000000793564944254229,0.0000000028490255366545454,0,10,3451,193,1 -4,0,0.0000001,9,1391,0.000000000023839173106503998,0.0000000023820919092611292,0.000000007929190317650528,0.000000002846557004189854,0,10,3433,193,1 -4,0,0.00001,9,1378,0.0000000000027021088629063325,0.0000000013174905519431899,0.00000000593595900754161,0.0000000016049581422361604,0,10,3443,193,1 -4,0,0.001,9,1341,0.0000000003818523060501431,0.000000029163340855967124,0.00000008462707587473991,0.0000000367813326293014,0,10,3300,127,1 -4,0,0.1,9,1288,0.000000041005310546330343,0.0000038344550654420015,0.000005298918159244821,0.000003947077529284433,0,10,3121,49,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_0_4_96.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_0_4_96.csv deleted file mode 100644 index 1014e795..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_0_4_96.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,9,1513,0.00000000014223586226614262,0.000000002777797805884634,0.000000008284756866241758,0.00000000336159309566095,0,20,3735,193,1 -4,0,0.0000001,9,1395,0.00000000014110944629648887,0.000000002776055302637837,0.000000008278384936629822,0.000000003359281672760546,0,20,3633,193,1 -4,0,0.00001,9,1386,0.000000000004077348722374852,0.0000000017465325624108487,0.000000006242444456071544,0.000000002223531977720387,0,20,3623,193,1 -4,0,0.001,9,1349,0.000000000010670560612422034,0.000000029170691385670563,0.00000008493586442167993,0.00000003675578119084846,0,20,3466,127,1 -4,0,0.1,9,1321,0.000000041430399481194884,0.000003834323875417392,0.000005299726916413285,0.000003946905730969469,0,20,3335,49,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_0_5_117.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_0_5_117.csv deleted file mode 100644 index 5359aa64..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_0_5_117.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,9,2840,0.000000000541566349949476,0.0000000048031199937395525,0.000000008292590336217941,0.000000005291254503914109,0,20,4696,193,1 -5,0,0.0000001,9,1954,0.0000000005363137037680063,0.000000004795990487452284,0.000000008286420659420914,0.000000005284666649109324,0,20,4656,193,1 -5,0,0.00001,9,1823,0.0000000000300419891592077,0.00000000190214501618024,0.000000004694012878480056,0.00000000239050445099293,0,20,4701,193,1 -5,0,0.001,9,1360,0.0000000010995148788218534,0.000000010324376623714209,0.000000020617809661811775,0.000000012057643313822224,0,20,4447,127,1 -5,0,0.1,9,869,0.00000007234876227034613,0.0000023257697517805268,0.0000040193972611116475,0.000002627944601757998,0,20,4376,49,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_0_5_33.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_0_5_33.csv deleted file mode 100644 index 1f00d64e..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_0_5_33.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,9,2937,0.000000001969092855524912,0.000000005304146416420885,0.000000007364203699702661,0.000000005466348559185813,0,5,4283,193,1 -5,0,0.0000001,9,1853,0.000000001962530003568081,0.000000005297030050239618,0.000000007358367940679381,0.000000005459514958954283,0,5,4373,193,1 -5,0,0.00001,9,1854,0.00000000007650092356561685,0.0000000017305718269372904,0.000000004021292045434459,0.0000000020553554923574984,0,5,4323,193,1 -5,0,0.001,9,1331,0.0000000001323255137088218,0.000000009809616658530142,0.000000018057398515468758,0.000000011259405564021689,0,5,4144,127,1 -5,0,0.1,9,844,0.00000007286767461610025,0.000002326556000526531,0.000004019707570113205,0.0000026287066717278813,0,5,4127,49,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_0_5_75.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_0_5_75.csv deleted file mode 100644 index f630980a..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_0_5_75.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,9,2984,0.0000000013117023690939313,0.0000000040800472939368465,0.000000006390962418675379,0.0000000042503020797836326,0,10,4520,193,1 -5,0,0.0000001,9,1889,0.0000000013048894754561203,0.000000004072757034784079,0.000000006383541359881746,0.00000000424338937627536,0,10,4442,193,1 -5,0,0.00001,9,1784,0.000000000012479642021616233,0.0000000011139577861123002,0.000000003283982181000431,0.0000000014083504227062796,0,10,4467,193,1 -5,0,0.001,9,1339,0.0000000006574857853826695,0.000000010624906526486357,0.000000019038657025823896,0.000000012044426308676625,0,10,4233,127,1 -5,0,0.1,9,842,0.00000007190452200359168,0.0000023254019346467772,0.0000040189604146054004,0.000002627686699957452,0,10,4183,49,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_1_4_13.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_1_4_13.csv deleted file mode 100644 index 82ea75e2..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_1_4_13.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,1,0,9,1687,0.000000000003918659433280277,0.0000000007430499813197571,0.0000000017859149743192926,0.0000000008055441701531513,0,5,7379,244,1 -4,1,0.0000001,9,1448,0.0000000000026824656307020664,0.0000000007413817428532353,0.00000000178450354198559,0.0000000008042701393332768,0,5,7385,244,1 -4,1,0.00001,9,1429,0.000000000011091002859824025,0.0000000011098070940563084,0.000000006282000591822491,0.0000000015830252141959587,0,5,7444,243,1 -4,1,0.001,9,1389,0.000000000025403818007035253,0.000000017146198404014748,0.000000047743329994886315,0.00000002058458741175224,0,5,6883,130,1 -4,1,0.1,9,1321,0.000000017103441085476043,0.0000037584824672502916,0.000005310271550973425,0.000003892182645843513,0,5,6824,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_1_4_55.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_1_4_55.csv deleted file mode 100644 index 51ce5b88..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_1_4_55.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,1,0,9,1622,0.000000000007103954063881696,0.0000000005629309875582948,0.0000000015469217883487173,0.0000000006438300846797935,0,10,7443,244,1 -4,1,0.0000001,9,1440,0.000000000006525888464650978,0.000000000562106064159189,0.000000001549062631946192,0.0000000006432385966499486,0,10,7314,244,1 -4,1,0.00001,9,1423,0.00000000000039771599954228807,0.0000000011885744284691075,0.000000006557316455339278,0.000000001696822368658755,0,10,7255,244,1 -4,1,0.001,9,1354,0.000000000010572954314593513,0.00000001719021005921349,0.00000004763346034198278,0.000000020617469483524224,0,10,6831,130,1 -4,1,0.1,9,1389,0.00000001704493777276343,0.000003758314143625887,0.000005310203213168656,0.000003892017652868967,0,10,6781,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_1_4_97.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_1_4_97.csv deleted file mode 100644 index bd61551b..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_1_4_97.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,1,0,9,1653,0.000000000018114183075973858,0.0000000003672916616073726,0.0000000016234179936244882,0.00000000043626839673338625,0,20,7536,244,1 -4,1,0.0000001,9,1493,0.00000000001592091326116387,0.0000000003672374394783745,0.0000000016305033590495116,0.0000000004363682262569822,0,20,7490,244,1 -4,1,0.00001,9,1443,0.000000000013129127353572591,0.0000000013726700380576283,0.000000006818382201852164,0.0000000019058481629151525,0,20,7531,244,1 -4,1,0.001,9,1388,0.000000000015600741388343563,0.00000001725356304948612,0.00000004737474595077489,0.000000020668451931725216,0,20,7051,130,1 -4,1,0.1,9,1326,0.000000016886851599031305,0.000003758057442649565,0.000005310029487819601,0.0000038917614933697355,0,20,6933,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_1_5_118.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_1_5_118.csv deleted file mode 100644 index aea3cd5c..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_1_5_118.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd 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a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_1_5_34.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,9,3912,0.00000000000190883307282262,0.0000000004456531798973381,0.0000000010843313403935387,0.0000000005094517649353587,0,5,8328,244,1 -5,1,0.0000001,9,2202,0.00000000000464763917256857,0.00000000044235626992749745,0.0000000010931435060999095,0.0000000005075049010042402,0,5,8538,244,1 -5,1,0.00001,9,2280,0.0000000016999999425195076,0.000000003688466694461395,0.000000006258165960883062,0.000000003845738822985314,0,5,8382,243,1 -5,1,0.001,9,1448,0.000000020633170848350823,0.000000030828932280846535,0.00000003884831655575931,0.00000003113566439209673,0,5,7935,130,1 -5,1,0.1,9,918,0.000000009923879880855685,0.0000020573330282508117,0.0000038564264499454655,0.000002407738945391909,0,5,8010,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_1_5_76.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_1_5_76.csv deleted file mode 100644 index 52c19df5..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_1_5_76.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,9,4013,0.00000000000017737029508931176,0.00000000028633615923197585,0.0000000014028816012121086,0.000000000431255693199578,0,10,8344,244,1 -5,1,0.0000001,9,2215,0.0000000000057990940603748094,0.000000000288525845257373,0.0000000014111583648981794,0.00000000043361820034014364,0,10,8331,244,1 -5,1,0.00001,9,2160,0.0000000019231471346652322,0.000000003939288596170726,0.000000006534903048591895,0.000000004085051364951172,0,10,8339,244,1 -5,1,0.001,9,1385,0.000000020781861084267075,0.000000030995278606034686,0.000000039074602526664456,0.00000003129826317448951,0,10,7880,130,1 -5,1,0.1,9,917,0.000000009979603439719529,0.000002057175741323147,0.000003856156734317061,0.0000024075771614101563,0,10,7913,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_2_4_14.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_2_4_14.csv deleted file mode 100644 index a54c7b19..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_2_4_14.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,2,0,9,1908,0.000000000000006441978531978428,0.00000000006020917983622453,0.0000000003184279237320921,0.0000000000831854800721562,0,5,10715,301,1 -4,2,0.0000001,9,1540,0.0000000000006653191893908584,0.00000000006041032129994134,0.0000000003184232500666925,0.00000000008362274124078283,0,5,10610,301,1 -4,2,0.00001,9,1486,0.000000000002126698238412134,0.0000000013082805765142256,0.0000000043454678237679865,0.000000001636765714759279,0,5,10247,259,1 -4,2,0.001,9,1385,0.000000000024147479152673957,0.00000001584292047005266,0.00000005433134039252732,0.00000001985679504906231,0,5,9756,130,1 -4,2,0.1,9,1412,0.00000004765397359588544,0.0000037035482826203794,0.000005337487266836189,0.000003846160959449314,0,5,9739,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_2_4_56.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_2_4_56.csv deleted file mode 100644 index 8220f729..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_2_4_56.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,2,0,9,1807,0.0000000000000030356565802362725,0.00000000008455324516910391,0.0000000002971406834050259,0.00000000009876882948380751,0,10,10735,301,1 -4,2,0.0000001,9,1484,0.00000000000007511839599561416,0.00000000008471414696648143,0.0000000003013827673176662,0.00000000009927584639150186,0,10,10640,301,1 -4,2,0.00001,9,1456,0.0000000000019590759129080055,0.00000000134409301966806,0.000000004332786770673133,0.0000000016675187098190707,0,10,10233,259,1 -4,2,0.001,9,1388,0.00000000006051845538092648,0.00000001583919615972231,0.00000005430942875795461,0.000000019849784751431048,0,10,9658,130,1 -4,2,0.1,9,1340,0.000000047643128041612975,0.0000037035249877182648,0.000005337466629724111,0.000003846138148355847,0,10,9584,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_2_4_98.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_2_4_98.csv deleted file mode 100644 index 6ece0ffb..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_2_4_98.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,2,0,9,1890,0.00000000000008421209991260766,0.000000000047565250944760674,0.00000000023090083080488652,0.00000000006048172650832765,0,20,11120,301,1 -4,2,0.0000001,9,1505,0.00000000000012555134227901857,0.0000000000470562998729977,0.0000000002212307189465669,0.0000000000600877563150194,0,20,11006,301,1 -4,2,0.00001,9,1490,0.00000000000014135262116718305,0.0000000012830028499672626,0.000000004141364313475881,0.0000000015983419717396024,0,20,10739,259,1 -4,2,0.001,9,1407,0.0000000000025576190235004,0.000000015829253062440956,0.00000005433895742030312,0.00000001984540487329792,0,20,10111,130,1 -4,2,0.1,9,1348,0.00000004770875545677522,0.000003703543208243699,0.000005337473929737816,0.000003846154960844043,0,20,10057,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_2_5_119.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_2_5_119.csv deleted file mode 100644 index dd9029c1..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_2_5_119.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,9,5114,0.000000000003367710222734564,0.00000000006176731441855394,0.00000000022910676439890916,0.00000000008301973814025358,0,20,12339,301,1 -5,2,0.0000001,9,2703,0.0000000000006517432254604126,0.00000000005947092132987289,0.00000000021942907752624348,0.00000000008105960723610262,0,20,12205,301,1 -5,2,0.00001,9,2433,0.0000000020413944353419984,0.0000000032315589479394497,0.000000004129421563134664,0.0000000032730592809486988,0,20,11811,259,1 -5,2,0.001,9,1456,0.000000012560429769817755,0.000000021173660192859015,0.00000002985801349942243,0.000000021615085849884543,0,20,11314,130,1 -5,2,0.1,9,978,0.00000005655479795834808,0.0000019072561737252564,0.000003824076968828122,0.0000022612768465335722,0,20,11862,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_2_5_35.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_2_5_35.csv deleted file mode 100644 index aa160171..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_2_5_35.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,9,5210,0.000000000006491455198720684,0.00000000009505371571887445,0.00000000025302496538507714,0.00000000012226624375681302,0,5,11680,301,1 -5,2,0.0000001,9,2811,0.0000000000000040614842277499525,0.00000000009769874710833674,0.0000000002554913801672237,0.0000000001249021682721826,0,5,11734,301,1 -5,2,0.00001,9,2560,0.0000000021109148346707476,0.000000003336570025531713,0.000000004333639856674929,0.000000003382021384916272,0,5,11259,259,1 -5,2,0.001,9,1456,0.00000001263975474319967,0.00000002126346308281867,0.000000029885801087976865,0.000000021707776054313923,0,5,10819,130,1 -5,2,0.1,9,996,0.00000005649998873964087,0.0000019072498304987923,0.000003824086020013204,0.0000022612722663229337,0,5,11479,50,1 diff --git a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_2_5_77.csv b/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_2_5_77.csv deleted file mode 100644 index 5436f7b3..00000000 --- a/data/blas_f64_1/grid_search_laplace_blas_uniform_f64_m1_1000000_9_2_5_77.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,9,5155,0.00000000000033417671044294325,0.0000000001406911591447355,0.000000000299612861498842,0.0000000001598441826939297,0,10,11816,301,1 -5,2,0.0000001,9,2683,0.000000000008419258018250008,0.00000000014472909358895463,0.00000000030377214017306846,0.00000000016347263660151085,0,10,11804,301,1 -5,2,0.00001,9,2391,0.000000002136592832735021,0.0000000033809244994952512,0.0000000043209104101175205,0.000000003424805847248561,0,10,11429,259,1 -5,2,0.001,9,1433,0.000000012647168609606465,0.000000021287619863358312,0.00000002988801374891873,0.000000021728672668656868,0,10,10805,130,1 -5,2,0.1,9,971,0.00000005648899174008304,0.000001907226168842116,0.0000038240526615058655,0.000002261246324612785,0,10,11378,50,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_10_0_5_15.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_10_0_5_15.csv deleted file mode 100644 index 8c9e6a19..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_10_0_5_15.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,10,15559,0.000000000005052621475989264,0.00000000036263899485474447,0.0000000010617247908754691,0.00000000041802607296140055,0,5,9564,244,1 -5,0,0.0000001,10,13926,0.0000000000025169671947773823,0.0000000003613242651751895,0.0000000010657585959001453,0.0000000004167708823228172,0,5,9505,244,1 -5,0,0.00001,10,13759,0.0000000011404804672842128,0.00000000296726293289059,0.0000000060874494129368636,0.0000000031227457633316307,0,5,10852,244,1 -5,0,0.001,10,12994,0.000000005610570912004042,0.000000020567672370799496,0.00000003228324296718124,0.000000021046772495524303,0,5,10235,130,1 -5,0,0.1,10,12470,0.0000000667731835416529,0.0000014403986866365682,0.00000402709951915523,0.0000016547449729270097,0,5,10229,50,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_10_0_5_36.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_10_0_5_36.csv deleted file mode 100644 index b2006341..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_10_0_5_36.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,10,15581,0.0000000000007172571819864374,0.00000000026944762222283297,0.0000000013966988156183385,0.0000000003500590032963557,0,10,9500,244,1 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-depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,10,15644,0.000000000005141950444777131,0.0000000003896187751885536,0.0000000015718132941892785,0.0000000005113606537495359,0,20,9854,244,1 -5,0,0.0000001,10,14371,0.000000000004189618494420787,0.0000000003911118636200161,0.0000000015744180905564796,0.0000000005128351112953545,0,20,9917,244,1 -5,0,0.00001,10,14392,0.0000000016412617317455503,0.0000000035606762538573523,0.0000000064591276824639574,0.0000000036869706453525746,0,20,10969,244,1 -5,0,0.001,10,12966,0.000000006061699157733324,0.00000002105159166781925,0.00000003289193400358517,0.00000002151834054141564,0,20,10563,130,1 -5,0,0.1,10,12009,0.00000006730222131263572,0.0000014401615354796372,0.000004027316146285889,0.0000016544853544872705,0,20,10212,50,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_10_1_5_16.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_10_1_5_16.csv deleted file mode 100644 index dff85b95..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_10_1_5_16.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,10,17559,0.0000000000006304711071382324,0.00000000011487438353923908,0.00000000030293916047499805,0.00000000013420515444446899,0,5,16097,301,1 -5,1,0.0000001,10,15188,0.0000000000002675449020573537,0.00000000012042373126888772,0.0000000003086276409144943,0.00000000013984078598372706,0,5,16141,301,1 -5,1,0.00001,10,14947,0.0000000020937273575272732,0.00000000387405183998343,0.000000006363025438191203,0.00000000396242799295502,0,5,17218,267,1 -5,1,0.001,10,13039,0.000000003889636209861568,0.00000001764953357975058,0.000000029347720075238832,0.00000001816876606829884,0,5,16375,130,1 -5,1,0.1,10,12078,0.00000001052514454203805,0.0000014024665631121839,0.000004330832103097693,0.000001621447276906386,0,5,16335,50,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_10_1_5_37.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_10_1_5_37.csv deleted file mode 100644 index c662b005..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_10_1_5_37.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,10,17167,0.000000000002271108457616759,0.0000000001531447212198928,0.0000000003591069274555107,0.00000000016987296366794484,0,10,16196,301,1 -5,1,0.0000001,10,14987,0.0000000000014467762528623295,0.00000000015968482553775032,0.00000000036840179680216436,0.0000000001763713207441459,0,10,16362,301,1 -5,1,0.00001,10,14912,0.000000002128085006356693,0.000000003925791542557711,0.000000006484577798010182,0.000000004015192260693565,0,10,17443,267,1 -5,1,0.001,10,13183,0.00000000390474122493423,0.000000017666100488257802,0.000000029323805711735652,0.000000018184197522136145,0,10,16816,130,1 -5,1,0.1,10,12196,0.000000010561829710228686,0.0000014024534558755144,0.000004330870680608795,0.0000016214346478779097,0,10,16307,50,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_10_1_5_58.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_10_1_5_58.csv deleted file mode 100644 index 00d5abba..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_10_1_5_58.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,10,17285,0.0000000000003584785999476013,0.000000000046668463714260956,0.0000000001728783583040642,0.0000000000615229985135534,0,20,16655,301,1 -5,1,0.0000001,10,15423,0.00000000000007125492764267447,0.00000000004773996987456719,0.00000000017964944066405094,0.0000000000625588548575195,0,20,17968,301,1 -5,1,0.00001,10,14473,0.00000000205885322863821,0.000000003801151917785459,0.000000006274227521208772,0.000000003888216692895767,0,20,17926,268,1 -5,1,0.001,10,13145,0.000000003838928522268257,0.00000001755584969621227,0.000000029137370631510026,0.00000001807366239608657,0,20,16954,130,1 -5,1,0.1,10,12150,0.00000001051567290498324,0.0000014024638321024904,0.000004330794649832844,0.000001621443847718908,0,20,16512,50,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_10_2_5_17.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_10_2_5_17.csv deleted file mode 100644 index fe7ba3bd..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_10_2_5_17.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,10,19433,0.00000000000025088986798676,0.0000000000191372120159678,0.000000000035246484296666906,0.000000000019760088868323058,0,5,22901,364,1 -5,2,0.0000001,10,15950,0.000000000008479476604067177,0.000000000025974834840936294,0.00000000004512080337347786,0.000000000026578733087390338,0,5,24427,364,1 -5,2,0.00001,10,14517,0.000000001500714451267081,0.0000000029679965211511326,0.000000005028415061602789,0.0000000030462786503939873,0,5,23619,268,1 -5,2,0.001,10,13580,0.000000023636607394979678,0.000000038164373574614505,0.00000005139648583339164,0.0000000389602311284038,0,5,23020,136,1 -5,2,0.1,10,12602,0.000000003700877926715444,0.000001447332681190023,0.000004310401899905207,0.0000016699558390848493,0,5,22343,50,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_10_2_5_38.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_10_2_5_38.csv deleted file mode 100644 index 0524973b..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_10_2_5_38.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,10,19506,0.000000000014261681999016475,0.000000000024118091580998408,0.00000000003660175732111079,0.000000000024582683888766014,0,10,23112,364,1 -5,2,0.0000001,10,15829,0.000000000017185845025864558,0.00000000003137357026217633,0.000000000048114345158077036,0.00000000003204087087350893,0,10,24850,364,1 -5,2,0.00001,10,14375,0.0000000015007997110075738,0.0000000029694957778655836,0.000000005030956307248498,0.0000000030480169020325584,0,10,23522,268,1 -5,2,0.001,10,13269,0.000000023622943811936618,0.0000000381628381048233,0.000000051396341803763856,0.00000003895898587136836,0,10,22692,136,1 -5,2,0.1,10,12294,0.0000000037028418995496554,0.0000014473326965908249,0.000004310403681156776,0.000001669955864144934,0,10,23086,50,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_10_2_5_59.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_10_2_5_59.csv deleted file mode 100644 index b3e39a8f..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_10_2_5_59.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,10,19786,0.000000000004042231990624809,0.000000000014349339435539436,0.00000000003306603161412886,0.00000000001559790005632287,0,20,23725,364,1 -5,2,0.0000001,10,15872,0.00000000000899019816407764,0.000000000021433469087343015,0.000000000043928397129508214,0.00000000002285102894876001,0,20,25790,364,1 -5,2,0.00001,10,15043,0.0000000014914222975802979,0.0000000029579690299974336,0.0000000050253339595226935,0.00000000303688743507975,0,20,24096,268,1 -5,2,0.001,10,13433,0.000000023633299704869154,0.00000003817058982839531,0.00000005140213382134837,0.00000003896658834244748,0,20,23473,136,1 -5,2,0.1,10,12238,0.0000000036986125068880648,0.0000014473338895929056,0.000004310396799762622,0.0000016699570028668014,0,20,23559,50,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_11_0_5_18.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_11_0_5_18.csv deleted file mode 100644 index b84c428b..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_11_0_5_18.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,11,17845,0.000000000001992631179505303,0.00000000010615774323459533,0.0000000002923806902912641,0.00000000012535220207525418,0,5,13495,301,1 -5,0,0.0000001,11,15259,0.000000000001383254790828092,0.00000000011014234078099219,0.00000000029634161129736255,0.00000000012934924474129334,0,5,15038,301,1 -5,0,0.00001,11,14553,0.0000000015050881746269848,0.0000000029503727848656716,0.0000000049369134384086225,0.0000000030267538207200592,0,5,14329,268,1 -5,0,0.001,11,13345,0.00000002263845267557966,0.00000003696843830345188,0.0000000503810005287458,0.000000037810805868833975,0,5,13627,136,1 -5,0,0.1,11,12631,0.00000009076768136801028,0.0000014537148189880613,0.000004092706849920174,0.0000016717393384560328,0,5,13535,50,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_11_0_5_39.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_11_0_5_39.csv deleted file mode 100644 index e14eb539..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_11_0_5_39.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,11,18016,0.000000000000026545703771569095,0.00000000014243410124924604,0.0000000003402956453577573,0.00000000015889215020532726,0,10,13668,301,1 -5,0,0.0000001,11,15590,0.0000000000058339912789456654,0.00000000014725244894820187,0.0000000003468003346394084,0.0000000001636680657819357,0,10,15023,301,1 -5,0,0.00001,11,14812,0.0000000015406516191475333,0.0000000030025878004447868,0.00000000505925277776185,0.0000000030795922065922473,0,10,14262,268,1 -5,0,0.001,11,13537,0.000000022638372901612038,0.00000003694213892280723,0.00000005035778440032849,0.00000003778533899003069,0,10,13755,136,1 -5,0,0.1,11,12522,0.00000009080874244916977,0.0000014537006539708244,0.00000409274542546658,0.0000016717242554473766,0,10,13461,50,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_11_0_5_60.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_11_0_5_60.csv deleted file mode 100644 index fdf2583e..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_11_0_5_60.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,11,18136,0.000000000000048261199951011554,0.00000000004536682966172991,0.00000000016551572340169814,0.00000000006041150624778815,0,20,13724,301,1 -5,0,0.0000001,11,15335,0.0000000000005716158763182425,0.00000000004504950340618503,0.00000000016774306158556277,0.00000000005993979239565956,0,20,15425,301,1 -5,0,0.00001,11,14821,0.0000000014649830928235241,0.000000002866842593263657,0.00000000482457067203252,0.0000000029410464817951444,0,20,14619,268,1 -5,0,0.001,11,13487,0.000000022738355673444208,0.000000037063722269048645,0.000000050427074810439115,0.00000003790033428490754,0,20,14001,136,1 -5,0,0.1,11,12655,0.00000009076277985892167,0.0000014537127701030667,0.000004092669291839991,0.0000016717370559467432,0,20,13815,50,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_11_1_5_19.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_11_1_5_19.csv deleted file mode 100644 index 0bf130c8..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_11_1_5_19.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,11,20222,0.0000000000005515070092096617,0.00000000001606361435696503,0.00000000003215541392060362,0.00000000001691145911557443,0,5,25401,364,1 -5,1,0.0000001,11,16573,0.000000000009259338463166875,0.000000000024083884053607962,0.000000000040360729468553,0.000000000024575087610360526,0,5,28246,364,1 -5,1,0.00001,11,15692,0.0000000014968081365693696,0.0000000027404607245463077,0.000000004412029439068654,0.0000000027977572639898662,0,5,25993,271,1 -5,1,0.001,11,14200,0.00000007403243088262627,0.00000008319854170355108,0.00000008795459675054627,0.00000008323033935068261,0,5,25283,136,1 -5,1,0.1,11,13618,0.000000016245392216377286,0.0000029776273210902327,0.000004786681251540175,0.000003218882806353479,0,5,25661,53,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_11_1_5_40.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_11_1_5_40.csv deleted file mode 100644 index a5ccd5fb..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_11_1_5_40.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,11,20066,0.000000000008530959915605354,0.000000000020791760726580688,0.00000000003121439873996175,0.000000000021070293488977488,0,10,25465,364,1 -5,1,0.0000001,11,16646,0.00000000001782261514393094,0.000000000029146123773648535,0.0000000000396287831046673,0.000000000029487614039938185,0,10,27715,364,1 -5,1,0.00001,11,15518,0.000000001497316076177845,0.0000000027426329271319234,0.00000000441356037978129,0.000000002800089115713345,0,10,25997,271,1 -5,1,0.001,11,14397,0.00000007401938250429077,0.00000008319683540684669,0.00000008795567498696325,0.00000008322868066140752,0,10,25339,136,1 -5,1,0.1,11,13225,0.000000016243410298431585,0.000002977626843040101,0.000004786681283161933,0.0000032188824781029932,0,10,25402,53,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_11_1_5_61.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_11_1_5_61.csv deleted file mode 100644 index 3464631e..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_11_1_5_61.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,11,20308,0.000000000002994007876212484,0.000000000011022145709179621,0.000000000024876287359280084,0.000000000011816542793102216,0,20,25906,364,1 -5,1,0.0000001,11,16757,0.000000000009635942991706795,0.000000000019158352715496674,0.00000000003760870733577537,0.000000000019970065120052715,0,20,28145,364,1 -5,1,0.00001,11,15524,0.000000001488212246637661,0.0000000027315502785900067,0.000000004407164171722189,0.000000002789319858385165,0,20,26583,271,1 -5,1,0.001,11,14509,0.00000007402787054545842,0.00000008320368028644653,0.00000008796156118973308,0.00000008323551938278842,0,20,26289,136,1 -5,1,0.1,11,13535,0.000000016249892107528188,0.0000029776307301533604,0.000004786686306903191,0.000003218885973555232,0,20,25584,53,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_11_2_5_20.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_11_2_5_20.csv deleted file mode 100644 index ce57be76..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_11_2_5_20.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,11,22382,0.0000000000000341437326541559,0.000000000001470866168475165,0.0000000000052426699799815154,0.0000000000018657015592874047,0,5,32298,433,1 -5,2,0.0000001,11,17517,0.0000000000024102885283630152,0.000000000008820666204802832,0.000000000015954644013772818,0.00000000000947460776527774,0,5,36644,433,1 -5,2,0.00001,11,16235,0.0000000012075138755549452,0.0000000021569951603914515,0.0000000033910126457198998,0.0000000021976766492392316,0,5,31368,274,1 -5,2,0.001,11,14305,0.00000007345592869749956,0.00000008287980751725467,0.00000008756435638440749,0.00000008291120793922235,0,5,31760,144,1 -5,2,0.1,11,13643,0.00000012733776752761452,0.0000041020845297478574,0.000005857290576983804,0.000004288637241001825,0,5,30677,53,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_11_2_5_41.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_11_2_5_41.csv deleted file mode 100644 index 8634bcba..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_11_2_5_41.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,11,22616,0.0000000000000506305744960456,0.0000000000013353834945583418,0.0000000000046177645835256025,0.0000000000016525420325431375,0,10,32265,433,1 -5,2,0.0000001,11,17347,0.0000000000021786831205573324,0.000000000008545349897033126,0.000000000016779616296469526,0.000000000009276423302434054,0,10,37622,433,1 -5,2,0.00001,11,16056,0.0000000012072717031509922,0.000000002156558039435667,0.000000003390911966079365,0.0000000021972672624687764,0,10,32357,274,1 -5,2,0.001,11,14551,0.00000007345582563733506,0.00000008288009997082913,0.00000008756497998574978,0.00000008291150103024148,0,10,31825,144,1 -5,2,0.1,11,13787,0.00000012733820675119657,0.0000041020847809073895,0.000005857291088669059,0.000004288637475636045,0,10,31739,53,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_11_2_5_62.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_11_2_5_62.csv deleted file mode 100644 index 07162067..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_11_2_5_62.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,11,22492,0.00000000000001714172909318095,0.0000000000014042272821489323,0.000000000003947088401167053,0.000000000001665227355068694,0,20,33467,433,1 -5,2,0.0000001,11,17897,0.0000000000015690124624453477,0.000000000007538157524518315,0.000000000016149763883532353,0.00000000000833792496102904,0,20,37678,433,1 -5,2,0.00001,11,15971,0.0000000012071050920111534,0.0000000021554784801884966,0.000000003388120546773082,0.0000000021961272220856937,0,20,34078,274,1 -5,2,0.001,11,16192,0.00000007345678097200866,0.00000008288112324565059,0.00000008756577329574308,0.00000008291251934308711,0,20,33296,144,1 -5,2,0.1,11,14173,0.00000012733880563704502,0.000004102085039021703,0.000005857291303991627,0.000004288637703870463,0,20,32933,53,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_0_5_0.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_0_5_0.csv deleted file mode 100644 index 7049d74b..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_0_5_0.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,3,9593,0.0004889529570960641,0.00071951673327022,0.0009500462488017901,0.0007271641991065809,0,5,3917,13,1 -5,0,0.0000001,3,9550,0.0004889529570959115,0.0007195167332703058,0.0009500462488019375,0.0007271641991066633,0,5,3876,13,1 -5,0,0.00001,3,9521,0.0004889511671760218,0.0007195154061633514,0.0009500449686317469,0.000727162900917243,0,5,3989,13,1 -5,0,0.001,3,9533,0.0004885381542343867,0.0007191842103705465,0.0009497346817924625,0.0007268378731431413,0,5,4800,13,1 -5,0,0.1,3,9547,0.0018114607475011788,0.0019167697872451111,0.002077483882648804,0.0019141466926808955,0,5,4823,13,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_0_5_12.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_0_5_12.csv deleted file mode 100644 index 769eff21..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_0_5_12.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,3,9584,0.000010807191540496935,0.0004112952790574006,0.0007289049200362533,0.0004438478901275457,0,10,4026,13,1 -5,0,0.0000001,3,9632,0.000010807191540496935,0.0004112952790573522,0.0007289049200361083,0.0004438478901275032,0,10,3974,13,1 -5,0,0.00001,3,9518,0.000010807926541271742,0.0004112946988726684,0.0007289044357608094,0.0004438473410349914,0,10,3982,13,1 -5,0,0.001,3,9520,0.000010915027182788387,0.00041119648615228066,0.0007287981734767204,0.0004437535994760199,0,10,4042,13,1 -5,0,0.1,3,9523,0.0011907884145291798,0.0015127845088883062,0.0017258652187467365,0.001516177100701901,0,10,4626,13,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_0_5_24.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_0_5_24.csv deleted file mode 100644 index 7747bccf..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_0_5_24.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,3,9742,0.0000010476875079794818,0.0002015474684677915,0.00048721480794388023,0.00023782882186908894,0,20,4114,13,1 -5,0,0.0000001,3,9674,0.0000010476875082807018,0.00020154746846784142,0.00048721480794402513,0.00023782882186913873,0,20,3894,13,1 -5,0,0.00001,3,9723,0.0000010465102413670652,0.0002015467628883506,0.0004872140165617485,0.00023782807930901027,0,20,3969,13,1 -5,0,0.001,3,9613,0.0000007328026114209008,0.00020134914622440175,0.00048697134324266275,0.00023761812065676573,0,20,4023,13,1 -5,0,0.1,3,9543,0.0008812274156978961,0.0011874633196459487,0.001361106529495731,0.0011919752512940512,0,20,4699,13,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_0_6_18.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_0_6_18.csv deleted file mode 100644 index 937613a8..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_0_6_18.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,0,0,3,3496,0.000054029923952024794,0.00022807873853404416,0.00032476197849708784,0.00025395533966990976,0,10,10659,13,1 -6,0,0.0000001,3,3586,0.000054029923952024794,0.00022807873853419522,0.00032476197849723855,0.0002539553396700738,0,10,10512,13,1 -6,0,0.00001,3,3455,0.00005402916117434036,0.00022807803601219097,0.00032476130159669566,0.0002539547201722918,0,10,10680,13,1 -6,0,0.001,3,3263,0.0000539287420666999,0.00022797566064531764,0.0003246564457419271,0.0002538618095914464,0,10,11489,13,1 -6,0,0.1,3,3089,0.0012885803865335954,0.0014193401505260192,0.0014933205933960996,0.0014205787867439064,0,10,11807,13,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_0_6_30.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_0_6_30.csv deleted file mode 100644 index a9d8cebf..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_0_6_30.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,0,0,3,3449,0.00007245567410193929,0.00010178026744973767,0.000145646787001455,0.00010388262427931598,0,20,10430,13,1 -6,0,0.0000001,3,3526,0.00007245567410193929,0.00010178026744980398,0.0001456467870013025,0.0001038826242793537,0,20,10363,13,1 -6,0,0.00001,3,3467,0.00007245447872206529,0.00010177991357342736,0.000145648110576899,0.00010388244847071998,0,20,10788,13,1 -6,0,0.001,3,3320,0.00007214225277352995,0.0001016833823545028,0.00014599009871900587,0.00010383250213214728,0,20,11308,13,1 -6,0,0.1,3,2997,0.000978372444343304,0.0011040274234789434,0.001174848649700635,0.0011063384563901348,0,20,11519,13,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_0_6_6.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_0_6_6.csv deleted file mode 100644 index 8c911602..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_0_6_6.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,0,0,3,3428,0.000586535599870233,0.0006759145418751575,0.0007338499299008763,0.0006789340145179981,0,5,10355,13,1 -6,0,0.0000001,3,3551,0.0005865355998703858,0.0006759145418752586,0.0007338499299010272,0.0006789340145180948,0,5,10258,13,1 -6,0,0.00001,3,3443,0.0005865338094861657,0.0006759129324932121,0.0007338484070887585,0.0006789324237530005,0,5,10586,13,1 -6,0,0.001,3,3227,0.0005861206053863209,0.0006755309417039912,0.0007334808391164611,0.0006785542999676755,0,5,11434,13,1 -6,0,0.1,3,3007,0.0019226985038545647,0.0019714397407154197,0.002004269248240792,0.001968172356164417,0,5,11921,13,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_1_5_1.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_1_5_1.csv deleted file mode 100644 index f5b40d19..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_1_5_1.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,3,9788,0.00000020235393634093864,0.000027791392363644056,0.00008635979134213802,0.00003153925539801765,0,5,3960,25,1 -5,1,0.0000001,3,9727,0.00000020240169513457794,0.000027791368297062947,0.00008635976263055637,0.000031539230626319126,0,5,3929,25,1 -5,1,0.00001,3,9734,0.00000016706485844584056,0.000027818437206720138,0.00008638298537337799,0.00003156388939165308,0,5,3950,25,1 -5,1,0.001,3,9899,0.00000002888813664425655,0.000025782384771653285,0.00008379243956527197,0.000029701113771056418,0,5,3956,25,1 -5,1,0.1,3,9788,0.00047721845770564943,0.0005792911539021672,0.000662984110266303,0.0005796583474727832,0,5,4884,25,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_1_5_13.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_1_5_13.csv deleted file mode 100644 index 65369491..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_1_5_13.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,3,9826,0.0000026286750736732853,0.00006357103770531793,0.00012420991953189203,0.00006744619117592863,0,10,3979,25,1 -5,1,0.0000001,3,9736,0.000002628690845522247,0.00006357102746160737,0.00012420991206641286,0.00006744618132794504,0,10,3982,25,1 -5,1,0.00001,3,9632,0.0000026167693412009185,0.0000635795938424488,0.00012421286686709946,0.0000674542961158568,0,10,3941,25,1 -5,1,0.001,3,9627,0.0000008792729744252484,0.00006058949008579772,0.00012126040536689842,0.00006450984322796806,0,10,3965,25,1 -5,1,0.1,3,9632,0.0004381395238617921,0.0005359870075031538,0.0006283575454228992,0.0005363779041279173,0,10,4602,25,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_1_5_25.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_1_5_25.csv deleted file mode 100644 index 7895ede8..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_1_5_25.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,3,9934,0.00003853008668729738,0.00011694030004546813,0.00017185785618325668,0.00011846953450193724,0,20,4006,25,1 -5,1,0.0000001,3,9680,0.00003853008045217408,0.00011694030184526813,0.00017185787373056672,0.00011846953669083581,0,20,3920,25,1 -5,1,0.00001,3,9752,0.00003852822623015408,0.00011694421573376687,0.0001718659582678815,0.0001184734851019238,0,20,4052,25,1 -5,1,0.001,3,9716,0.000035126816004933094,0.00011314116603278112,0.00016735029179306953,0.00011470613011263252,0,20,4017,25,1 -5,1,0.1,3,9705,0.00041547773169313905,0.00049341137670704,0.000562826203306972,0.0004935140142305374,0,20,4553,25,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_1_6_19.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_1_6_19.csv deleted file mode 100644 index f720e2f5..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_1_6_19.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,1,0,3,4664,0.00011479845902304295,0.0001402279145561195,0.00016992318176310198,0.00014143138999416603,0,10,10623,25,1 -6,1,0.0000001,3,4741,0.00011479844666739804,0.00014022790217794567,0.0001699231698283841,0.00014143137775005335,0,10,10494,25,1 -6,1,0.00001,3,4660,0.0001148125184586955,0.00014024332387186817,0.00016994136562273462,0.00014144683387999418,0,10,10896,25,1 -6,1,0.001,3,4056,0.00011142139197422336,0.00013681794053587825,0.00016643825456914585,0.00013805065180377954,0,10,11545,25,1 -6,1,0.1,3,3761,0.0004938693631463821,0.0005047318803357443,0.0005177993691334243,0.0005045743505919469,0,10,11764,25,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_1_6_31.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_1_6_31.csv deleted file mode 100644 index 9fe97220..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_1_6_31.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,1,0,3,4983,0.0001775083697757559,0.00020424778346468832,0.00023472528122758934,0.00020516435319328736,0,20,10459,25,1 -6,1,0.0000001,3,4711,0.00017750837807330497,0.0002042477947151976,0.00023472529865376358,0.00020516436472139417,0,20,10349,25,1 -6,1,0.00001,3,4872,0.0001775146149822901,0.00020425475489134213,0.00023473337249252515,0.00020517137565935638,0,20,10721,25,1 -6,1,0.001,3,4190,0.0001733688794390057,0.00019998924779366585,0.00023021527673626696,0.0002009141103208489,0,20,11552,25,1 -6,1,0.1,3,3988,0.00044481512419087513,0.00045392271310511754,0.0004681364139845441,0.00045384499111413343,0,20,11620,25,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_1_6_7.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_1_6_7.csv deleted file mode 100644 index de9798d9..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_1_6_7.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,1,0,3,5262,0.00006756633466623544,0.0000898681702411931,0.00011311543057191505,0.0000911306092819222,0,5,10560,25,1 -6,1,0.0000001,3,5185,0.0000675662948699051,0.00008986812278069421,0.00011311536916314395,0.00009113056104003315,0,5,10403,25,1 -6,1,0.00001,3,5047,0.00006761830384902088,0.00008992624079744216,0.00011318677861579178,0.00009118893399246061,0,5,10585,25,1 -6,1,0.001,3,4611,0.00006443040362920798,0.00008670907466730393,0.00010994071944527317,0.00008801920132474116,0,5,11872,25,1 -6,1,0.1,3,4109,0.0005489662723202726,0.0005593458015259599,0.0005688855806054486,0.0005590598606519294,0,5,11958,25,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_2_5_14.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_2_5_14.csv deleted file mode 100644 index 25632f5a..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_2_5_14.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,3,9906,0.0000034008829479336713,0.000060843308879626005,0.00015327029036480536,0.00006641597090175509,0,10,4033,25,1 -5,2,0.0000001,3,9735,0.0000034008947065211963,0.000060843317161749405,0.0001532702969693892,0.00006641597837287459,0,10,3992,25,1 -5,2,0.00001,3,9727,0.0000033677548460805554,0.0000608227713527035,0.00015325215041477134,0.00006639796058947685,0,10,3987,25,1 -5,2,0.001,3,9761,0.000006191714489349249,0.00006318871745039437,0.00015538530428476716,0.00006860117568303536,0,10,4009,25,1 -5,2,0.1,3,9902,0.00039867696527084084,0.000450660434582729,0.0005201273788537745,0.0004508960569925683,0,10,4477,25,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_2_5_2.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_2_5_2.csv deleted file mode 100644 index 1dfb6d6e..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_2_5_2.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,3,9778,0.00000809406175525066,0.00006738431602439227,0.000165020605841035,0.00007268838920552601,0,5,3985,25,1 -5,2,0.0000001,3,9751,0.000008094073062886791,0.00006738432407636983,0.0001650206124191444,0.00007268839664120963,0,5,3980,25,1 -5,2,0.00001,3,9836,0.000008060933676419418,0.00006736320580468215,0.00016500036516577053,0.00007266944157769171,0,5,4017,25,1 -5,2,0.001,3,9814,0.000011216247236739229,0.00006994860445355262,0.00016737814720015596,0.00007510591865424648,0,5,4074,25,1 -5,2,0.1,3,9772,0.0004069465457201946,0.0004585682474393989,0.000528567925628884,0.0004587789272555811,0,5,4870,25,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_2_5_26.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_2_5_26.csv deleted file mode 100644 index 87138749..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_2_5_26.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,3,9837,0.000002896047797584494,0.00005971057708944588,0.00014271965774379696,0.00006543573339580394,0,20,4092,25,1 -5,2,0.0000001,3,9704,0.0000028960621928875357,0.00005971058698356037,0.00014271966607423594,0.00006543574217758019,0,20,4070,25,1 -5,2,0.00001,3,9864,0.000002865578059060623,0.00005969139999252531,0.00014270268766390849,0.0000654191596258939,0,20,4014,25,1 -5,2,0.001,3,10033,0.000005847042054781063,0.00006214489592118592,0.00014496445475388833,0.00006768128811002023,0,20,4059,25,1 -5,2,0.1,3,9969,0.0003966193465252685,0.0004499804968588044,0.0005134154576911069,0.0004502336610240115,0,20,4736,25,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_2_6_20.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_2_6_20.csv deleted file mode 100644 index 60d1756e..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_2_6_20.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,2,0,3,4876,0.00001868313029831827,0.000029033971108306075,0.00003920586117440329,0.000029812046323038486,0,10,11070,25,1 -6,2,0.0000001,3,5068,0.00001868314084627707,0.00002903397412641616,0.00003920587171590274,0.000029812048316747706,0,10,10545,25,1 -6,2,0.00001,3,4913,0.00001865217922989571,0.000029026668459155944,0.00003917638325218373,0.0000298078078063944,0,10,11266,25,1 -6,2,0.001,3,4218,0.00002131907235637329,0.000029867042379703307,0.00004186327206843481,0.000030513666743583146,0,10,11722,25,1 -6,2,0.1,3,3988,0.00039240702140491766,0.0004251822870656419,0.00045052831696418787,0.0004257371397185136,0,10,11555,25,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_2_6_32.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_2_6_32.csv deleted file mode 100644 index 19386272..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_2_6_32.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,2,0,3,5253,0.000014670431690816317,0.000028775779101081604,0.000041387560897183715,0.000029939205305958224,0,20,10749,25,1 -6,2,0.0000001,3,5188,0.00001467044490319422,0.000028775782675520282,0.000041387545229982665,0.00002993920648259974,0,20,10647,25,1 -6,2,0.00001,3,5014,0.000014641193061631134,0.00002876898343591641,0.00004142341305638714,0.000029937766292966992,0,20,11552,25,1 -6,2,0.001,3,4404,0.000017478459907626994,0.000029638473703151893,0.000040286462181152176,0.000030443078791835337,0,20,11440,25,1 -6,2,0.1,3,4141,0.00039033259994474516,0.00042354054262720215,0.0004493745064474636,0.0004241292297829564,0,20,11763,25,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_2_6_8.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_2_6_8.csv deleted file mode 100644 index b5647f55..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_3_2_6_8.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,2,0,3,5013,0.000024048939713358733,0.00003123573545020126,0.00004525879405574952,0.000031949334780916275,0,5,10915,25,1 -6,2,0.0000001,3,5263,0.000024048928144699078,0.00003123573839640219,0.00004525880418521413,0.00003194933860435441,0,5,10727,25,1 -6,2,0.00001,3,4903,0.000024088057621280164,0.00003122832535048952,0.000045228097752215286,0.000031939093428348297,0,5,11387,25,1 -6,2,0.001,3,4298,0.00002083046996218176,0.00003214411360139428,0.00004822580470593531,0.00003322733284687334,0,5,11950,25,1 -6,2,0.1,3,3956,0.00040073722152205056,0.00043332136288031696,0.0004586153818133709,0.0004338559778752115,0,5,11842,25,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_0_5_15.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_0_5_15.csv deleted file mode 100644 index 703c2702..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_0_5_15.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,4,9946,0.000004635200975677945,0.00005521543660126191,0.0001269266477421791,0.00005800324662753191,0,10,4008,28,1 -5,0,0.0000001,4,9905,0.00000463518567231264,0.000055215432158611786,0.0001269266189621964,0.00005800324215961497,0,10,3989,28,1 -5,0,0.00001,4,9885,0.000004648138261248255,0.000055230049232956977,0.00012694370325411338,0.00005801753050417238,0,10,3978,28,1 -5,0,0.001,4,9894,0.000003927374935987972,0.000054641530171685356,0.00012636225235627762,0.000057473205122686005,0,10,3990,28,1 -5,0,0.1,4,9829,0.00013834155207410323,0.0002493971827221713,0.0002904588327925898,0.0002499852118773792,0,10,4633,26,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_0_5_27.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_0_5_27.csv deleted file mode 100644 index 4482962f..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_0_5_27.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,4,9799,0.00006584293992047821,0.00011000938945474425,0.00017116753723209758,0.00011121511230750513,0,20,3929,28,1 -5,0,0.0000001,4,9861,0.00006584294546951655,0.0001100094332040252,0.0001711675375876349,0.00011121515654003296,0,20,3977,28,1 -5,0,0.00001,4,9849,0.00006585089821519,0.00011001538371624283,0.00017117124873294919,0.00011122110942388308,0,20,3961,28,1 -5,0,0.001,4,9819,0.00006502795160421823,0.00010915965817381417,0.00017036796750517395,0.00011037649255095612,0,20,4036,28,1 -5,0,0.1,4,9840,0.00009496090769439367,0.00020278952857346976,0.0002317690741151845,0.0002035791780167357,0,20,4815,28,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_0_5_3.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_0_5_3.csv deleted file mode 100644 index 0464f974..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_0_5_3.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,4,9974,0.0000006006662457924971,0.000025900609388575814,0.0001079919101611001,0.00003064201601685568,0,5,4025,28,1 -5,0,0.0000001,4,9814,0.0000006006677049114904,0.000025900601361942613,0.00010799184105799891,0.000030642006696579715,0,5,4035,28,1 -5,0,0.00001,4,9792,0.000000608801381595472,0.00002591370976264723,0.0001080068864449205,0.000030653294633927796,0,5,4036,28,1 -5,0,0.001,4,9777,0.0000000640841892231418,0.00002528380193768022,0.00010732964236284273,0.000030097756086928506,0,5,4093,28,1 -5,0,0.1,4,9723,0.0001736127399529597,0.0002893545763433415,0.00033702489163268734,0.0002899743277666022,0,5,4924,25,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_0_6_21.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_0_6_21.csv deleted file mode 100644 index cd0434e1..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_0_6_21.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,0,0,4,5232,0.00005961842849278199,0.00008917433469164287,0.00010473196524556074,0.00008997529570547641,0,10,10612,28,1 -6,0,0.0000001,4,5168,0.00005961842231460315,0.00008917433094736405,0.00010473196241550463,0.00008997529218407265,0,10,10805,28,1 -6,0,0.00001,4,4853,0.000059635730393907974,0.00008919410291682273,0.0001047499229630281,0.00008999494291036191,0,10,11473,28,1 -6,0,0.001,4,4761,0.00005910641113573302,0.00008877698333947845,0.00010427200547411868,0.00008958534646191815,0,10,11851,28,1 -6,0,0.1,4,4052,0.00021628251908975386,0.00023882213963932945,0.00025971383462997116,0.00023904989319444144,0,10,11562,26,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_0_6_33.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_0_6_33.csv deleted file mode 100644 index 832f673c..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_0_6_33.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,0,0,4,5690,0.00012409803217835872,0.0001451088235912412,0.00016212846948003353,0.00014552602699723622,0,20,10702,28,1 -6,0,0.0000001,4,5367,0.00012409811191124728,0.0001451089233303654,0.0001621285536076175,0.00014552612679833934,0,20,10506,28,1 -6,0,0.00001,4,5260,0.00012410575564651376,0.00014511489561956102,0.00016213470772426716,0.00014553206307095203,0,20,11497,28,1 -6,0,0.001,4,4574,0.00012326386400630262,0.0001442464118716292,0.00016126990645908695,0.0001446657704234065,0,20,12062,28,1 -6,0,0.1,4,4316,0.00017135758753082563,0.00019606919680947648,0.00020887592136276022,0.00019637690709095783,0,20,12105,28,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_0_6_9.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_0_6_9.csv deleted file mode 100644 index ac3e5749..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_0_6_9.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,0,0,4,5217,0.000025446888009192685,0.00004799628705708171,0.00006719957643891764,0.00004924268489937551,0,5,10755,28,1 -6,0,0.0000001,4,5399,0.00002544687202202845,0.0000479962766479764,0.00006719956939803716,0.00004924267576224704,0,5,10577,28,1 -6,0,0.00001,4,4969,0.00002546548544756023,0.00004801505108326622,0.00006721683908981356,0.000049260834284955233,0,5,10989,28,1 -6,0,0.001,4,4437,0.000024701540908533384,0.00004738882016920123,0.00006656580959161781,0.00004865546764671286,0,5,11627,28,1 -6,0,0.1,4,4575,0.00025711469734625536,0.0002787219193606246,0.0003030819321570628,0.00027889188834056243,0,5,12204,25,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_1_5_16.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_1_5_16.csv deleted file mode 100644 index 6904649e..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_1_5_16.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,4,10115,0.0000001276913943943977,0.00002108781068513609,0.00006848821890150169,0.000026835191460268704,0,10,4095,49,1 -5,1,0.0000001,4,9896,0.00000012764211362667421,0.000021087785733024986,0.00006848820944425004,0.00002683516733459995,0,10,4045,49,1 -5,1,0.00001,4,9935,0.00000012994624406960546,0.000021090224018943556,0.00006849466225261203,0.000026837878218368908,0,10,4035,49,1 -5,1,0.001,4,9955,0.000000417278458748007,0.000020504204009188566,0.00006744936638806013,0.0000261788715872729,0,10,4106,49,1 -5,1,0.1,4,10071,0.000043731226801314104,0.00013838433243961218,0.00017050982299371832,0.0001395157077725237,0,10,4753,30,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_1_5_28.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_1_5_28.csv deleted file mode 100644 index 6fbba93c..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_1_5_28.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,4,10059,0.0000000091477016394654,0.000014771713158732285,0.0000613498073830344,0.000019362982971951735,0,20,4083,49,1 -5,1,0.0000001,4,9989,0.000000009085769050925581,0.000014771678210210278,0.00006134974797456266,0.00001936293703452747,0,20,4079,49,1 -5,1,0.00001,4,10049,0.000000013846787617469317,0.00001477420310633065,0.00006135154434405323,0.000019366155444697543,0,20,4083,49,1 -5,1,0.001,4,10185,0.00000020470763633746604,0.000014457967450366709,0.00006046365672954329,0.00001888566617356234,0,20,4120,49,1 -5,1,0.1,4,9924,0.000043945550210307484,0.00013954005754578341,0.00016707112129432048,0.0001406992827722341,0,20,4938,30,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_1_5_4.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_1_5_4.csv deleted file mode 100644 index d276fc93..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_1_5_4.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,4,10025,0.000000013584695956060127,0.000019123596788478844,0.00006527335977283614,0.000024174588256884833,0,5,4102,49,1 -5,1,0.0000001,4,10010,0.000000013533289551547367,0.00001912360296927064,0.00006527340737024439,0.000024174594043228863,0,5,4015,49,1 -5,1,0.00001,4,9949,0.00000001516454026844421,0.000019124612060501814,0.00006528234121805356,0.000024176349638163877,0,5,4010,49,1 -5,1,0.001,4,10011,0.00000000943976111453952,0.00001895612346737988,0.00006431460825967312,0.000023815865190413936,0,5,4040,49,1 -5,1,0.1,4,9878,0.00005078960344879557,0.00014532471855960486,0.00017632616143167497,0.00014638449690992247,0,5,4759,30,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_1_6_10.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_1_6_10.csv deleted file mode 100644 index 44e1c498..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_1_6_10.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,1,0,4,7874,0.000019303971883436703,0.00004421773938270707,0.00006149151774048147,0.000046451703760570354,0,5,10847,49,1 -6,1,0.0000001,4,7403,0.000019303975160885545,0.000044217757544395066,0.00006149156537866919,0.000046451724447602845,0,5,11061,49,1 -6,1,0.00001,4,6514,0.00001931162243896947,0.000044224752865395537,0.0000615004870102098,0.000046458524614024105,0,5,11487,49,1 -6,1,0.001,4,6170,0.000018402157468206932,0.00004324199849449862,0.000060531591866428406,0.00004552396669136285,0,5,12276,49,1 -6,1,0.1,4,4695,0.00013102456343179518,0.000155654594225807,0.00016995155957983483,0.00015616046650604906,0,5,12850,30,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_1_6_22.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_1_6_22.csv deleted file mode 100644 index 6cb5f0a9..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_1_6_22.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,1,0,4,7461,0.000026759317675183176,0.00004970027419463935,0.00006471598962110705,0.00005139101063390553,0,10,10752,49,1 -6,1,0.0000001,4,6994,0.000026759276331706373,0.0000497002430638405,0.0000647159802411143,0.00005139098295911028,0,10,11047,49,1 -6,1,0.00001,4,6477,0.000026763264206276954,0.00004970573682047623,0.00006472241824297418,0.00005139632445374066,0,10,11451,49,1 -6,1,0.001,4,6345,0.00002568419920388289,0.00004865347984403201,0.0000636760909057743,0.00005038048801362413,0,10,12180,49,1 -6,1,0.1,4,4631,0.0001244659689426137,0.00014930690635608977,0.00016435010964123057,0.00014986391090182413,0,10,12086,30,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_1_6_34.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_1_6_34.csv deleted file mode 100644 index 50f76e32..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_1_6_34.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,1,0,4,8432,0.000012701766548510127,0.00003754628599588771,0.00005088734271589442,0.00003939878413241024,0,20,10924,49,1 -6,1,0.0000001,4,7944,0.000012701657390240698,0.00003754619413767571,0.000050887252445648054,0.000039398696632456464,0,20,14571,49,1 -6,1,0.00001,4,7971,0.000012708888194267183,0.000037554131404450316,0.000050896270008043134,0.00003940633592156602,0,20,14974,49,1 -6,1,0.001,4,7189,0.000011647537802622538,0.000036491476267309275,0.00004979776395868647,0.0000383930658142311,0,20,13697,49,1 -6,1,0.1,4,5363,0.00012835418610920613,0.00015385406840779265,0.00016867808962956125,0.00015440150617545865,0,20,16021,30,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_2_5_17.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_2_5_17.csv deleted file mode 100644 index 4217b0a9..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_2_5_17.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,4,10691,0.00000007921149372803878,0.000012449554253989188,0.00004565999103634495,0.00001567938269329917,0,10,4261,55,1 -5,2,0.0000001,4,10334,0.00000007925926668830029,0.000012449561252987949,0.00004565998161665526,0.000015679390365093823,0,10,4177,55,1 -5,2,0.00001,4,10289,0.00000008788188615199165,0.000012450986939462395,0.00004566299882156082,0.00001568086252445325,0,10,4160,55,1 -5,2,0.001,4,10207,0.00000019838208827775495,0.000012485024667782738,0.00004550718418766378,0.00001571180494162477,0,10,4269,55,1 -5,2,0.1,4,10245,0.0000027923921785774786,0.000059934676542278626,0.00008574719397258096,0.00006188461838236283,0,10,5148,33,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_2_5_29.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_2_5_29.csv deleted file mode 100644 index 06d646c3..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_2_5_29.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,4,10391,0.00000015355496464896187,0.000012348404429065245,0.00004689228613391754,0.000015613073572636584,0,20,4266,55,1 -5,2,0.0000001,4,10335,0.00000015358356957403786,0.00001234841221454511,0.00004689227387098234,0.000015613080413591284,0,20,4218,55,1 -5,2,0.00001,4,10775,0.000000153537479946731,0.000012348657737966905,0.00004689115498930406,0.000015613294823278747,0,20,4172,55,1 -5,2,0.001,4,10116,0.00000017854860957060581,0.000012388660059303314,0.00004671335377340881,0.000015646340824067023,0,20,4402,55,1 -5,2,0.1,4,10095,0.000001960424698325035,0.00005914972462680116,0.00008495573686574284,0.00006111918275926342,0,20,5126,33,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_2_5_5.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_2_5_5.csv deleted file mode 100644 index 3724103e..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_2_5_5.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,4,10247,0.000000017798106965503815,0.000012481744298137645,0.00004570610622154019,0.000015703618837081217,0,5,4222,55,1 -5,2,0.0000001,4,10307,0.000000017762397833338997,0.00001248175331694502,0.00004570610062082016,0.000015703628958733387,0,5,4181,55,1 -5,2,0.00001,4,10377,0.000000007611233963960122,0.000012483054275813548,0.00004570950760993965,0.000015705177925632265,0,5,4137,55,1 -5,2,0.001,4,10296,0.00000011527348809418433,0.000012512712037050825,0.000045559101826404075,0.000015731816499921107,0,5,4190,55,1 -5,2,0.1,4,10087,0.0000029676698825219872,0.000060073906298042966,0.00008591534540846422,0.0000620206691570107,0,5,5028,33,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_2_6_11.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_2_6_11.csv deleted file mode 100644 index 38497118..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_2_6_11.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,2,0,4,9162,0.0000020043854539746904,0.000025764368317926023,0.000037611790367666805,0.00002811814708978171,0,5,10881,55,1 -6,2,0.0000001,4,8076,0.000002004430637471331,0.0000257644057477247,0.00003761183064305289,0.000028118181767669032,0,5,11141,55,1 -6,2,0.00001,4,7784,0.000002015202133394557,0.000025776398699641294,0.00003762591926281204,0.000028129401110338382,0,5,12015,55,1 -6,2,0.001,4,6922,0.000002096442550968394,0.000025890121395987512,0.00003779478841662874,0.000028238696348513953,0,5,12139,55,1 -6,2,0.1,4,5441,0.00004816933173029093,0.00007130034450305796,0.00008439140158366205,0.00007243373814565621,0,5,13015,33,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_2_6_23.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_2_6_23.csv deleted file mode 100644 index 997a4c09..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_4_2_6_23.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,2,0,4,9341,0.0000017976395851298305,0.00002543790001093359,0.00003709231689636444,0.00002780189390594586,0,10,11048,55,1 -6,2,0.0000001,4,8038,0.0000017976759682479363,0.000025437929701788262,0.00003709234692550069,0.000027801921023733463,0,10,11050,55,1 -6,2,0.00001,4,7686,0.000001807763401166865,0.000025448849420685232,0.000037105079131606335,0.000027812114116000446,0,10,11713,55,1 -6,2,0.001,4,6947,0.0000018962590746263416,0.000025564496774645008,0.000037276581849315216,0.000027923328736296272,0,10,12502,55,1 -6,2,0.1,4,5424,0.0000480603746034554,0.00007119003193384683,0.00008431473162324856,0.00007232473773661772,0,10,12396,33,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_5_0_5_0.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_5_0_5_0.csv deleted file mode 100644 index 9b1288d4..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_5_0_5_0.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,5,10399,0.000000045283955841948644,0.000007687845955379363,0.000021266681083564664,0.000009013798963111231,0,5,4187,49,1 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-depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,5,10463,0.00000001221515702242215,0.000008171517733511917,0.000022333533671030515,0.000010109700115074995,0,10,4162,49,1 -5,0,0.0000001,5,10887,0.000000012192802261617668,0.000008171534188212233,0.00002233360118367199,0.000010109722025131933,0,10,4120,49,1 -5,0,0.00001,5,11214,0.000000014314895009126888,0.000008170682928366708,0.00002233160214094289,0.000010108501003182771,0,10,4091,49,1 -5,0,0.001,5,10327,0.00000011039513788186887,0.00000829195733153374,0.000022832175694896936,0.000010281620863949716,0,10,4083,49,1 -5,0,0.1,5,10309,0.000014150532455381445,0.00004303550542684961,0.00008298438013816811,0.00004493851607887825,0,10,4933,33,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_5_0_5_42.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_5_0_5_42.csv deleted file mode 100644 index 0858c72d..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_5_0_5_42.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,5,11093,0.00000019368637270619596,0.0000024840497449759195,0.0000043585225055792524,0.0000026759029115578285,0,20,4220,49,1 -5,0,0.0000001,5,10432,0.00000019365498345303934,0.000002484051140158971,0.000004358533963095995,0.000002675906900201176,0,20,4121,49,1 -5,0,0.00001,5,10503,0.00000019239098509595442,0.0000024846807272224205,0.000004359065761623955,0.0000026764122621890693,0,20,4129,49,1 -5,0,0.001,5,10230,0.000000049861478866622694,0.0000022996028279380646,0.000004222610179278824,0.000002520805157189571,0,20,4263,49,1 -5,0,0.1,5,10150,0.000016124145793508925,0.00004630328762585346,0.00009212467733849482,0.000048405082263664225,0,20,4889,33,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_5_0_6_0.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_5_0_6_0.csv deleted file mode 100644 index b1c9f50e..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_5_0_6_0.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,0,0,5,8721,0.0000010203078840352743,0.000007468354310784356,0.00001316779846252887,0.000008317269410542373,0,5,11426,49,1 -6,0,0.0000001,5,8283,0.000001020221116371597,0.000007468422127054315,0.00001316790326605917,0.000008317349423871216,0,5,12100,49,1 -6,0,0.00001,5,7754,0.0000010193156490213449,0.000007469252351091817,0.000013169100323245754,0.000008318244155332891,0,5,12340,49,1 -6,0,0.001,5,7701,0.00000044158942130113185,0.000007905181091097702,0.000013809104185882504,0.00000882920859049783,0,5,12675,49,1 -6,0,0.1,5,5878,0.00006072448973732088,0.00007116662153651404,0.00008668387293879125,0.00007178851509263911,0,5,12339,33,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_5_1_5_1.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_5_1_5_1.csv deleted file mode 100644 index 0da95c1f..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_5_1_5_1.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,5,10823,0.000000011745629710142618,0.0000005859941211910652,0.0000017180945573769845,0.0000007050722572950427,0,5,4358,76,1 -5,1,0.0000001,5,10565,0.000000011778320088918634,0.0000005859565054525637,0.0000017180021163211597,0.0000007050244872766426,0,5,4414,76,1 -5,1,0.00001,5,10612,0.000000015026911686187096,0.0000005829412296708159,0.0000017108309974952855,0.0000007013371542729295,0,5,4358,76,1 -5,1,0.001,5,10486,0.000000004923640281744568,0.000000518586107470123,0.0000018522555924089705,0.0000006390327367274093,0,5,4851,76,1 -5,1,0.1,5,10706,0.00002174108163876011,0.00004954635000484218,0.00009587139436062726,0.0000515501868944184,0,5,5245,36,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_5_1_5_22.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_5_1_5_22.csv deleted file mode 100644 index 73979d72..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_5_1_5_22.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,5,11145,0.0000000003731209025152415,0.0000005455909664443533,0.0000013233169520983665,0.0000006254081808279658,0,10,4421,76,1 -5,1,0.0000001,5,10877,0.0000000004673769993859183,0.0000005455219848496174,0.0000013232379409571045,0.0000006253439776558781,0,10,4353,76,1 -5,1,0.00001,5,10698,0.000000002684055096384118,0.0000005404347538618789,0.0000013158507074660545,0.0000006203644505897708,0,10,4329,76,1 -5,1,0.001,5,11095,0.000000008557448169572532,0.00000038281882913521306,0.0000016804052094904734,0.0000004774370200832916,0,10,4663,76,1 -5,1,0.1,5,10842,0.00002169538615025701,0.00004951652797327193,0.00009587261755332855,0.00005152705739407686,0,10,5049,36,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_5_1_5_43.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_5_1_5_43.csv deleted file mode 100644 index 4a9b961e..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_5_1_5_43.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,5,11572,0.00000002223979369045636,0.0000005635162158086813,0.0000014025702483100263,0.0000006073748577514794,0,20,4448,76,1 -5,1,0.0000001,5,10796,0.00000002242100981620452,0.0000005634448263204168,0.0000014024791282527119,0.0000006073022191191694,0,20,4393,76,1 -5,1,0.00001,5,10629,0.000000033276365633048404,0.0000005573502366155744,0.0000013949044641639086,0.0000006011781269331628,0,20,4397,76,1 -5,1,0.001,5,10434,0.0000000026484308370399575,0.0000002463883845934103,0.000001134117158545372,0.000000314128893190063,0,20,4725,76,1 -5,1,0.1,5,10323,0.000021043073404310464,0.00004892040914095198,0.00009539826315744227,0.00005097303044834842,0,20,5106,36,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_5_1_6_1.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_5_1_6_1.csv deleted file mode 100644 index 87da2ab1..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_5_1_6_1.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,1,0,5,13081,0.00000004376570358001504,0.00000043061853175986896,0.0000008649034085209529,0.0000005290565171233364,0,5,11715,76,1 -6,1,0.0000001,5,11195,0.00000004362525023462155,0.000000430649592470507,0.0000008650995484450268,0.0000005291568197525471,0,5,11670,76,1 -6,1,0.00001,5,10450,0.00000003487668744926798,0.0000004322092854757357,0.000000876284723474224,0.0000005349165241425003,0,5,12312,76,1 -6,1,0.001,5,9938,0.0000004588496102718881,0.0000010308445521572042,0.0000017373994374404235,0.0000011441863671723235,0,5,13437,76,1 -6,1,0.1,5,6768,0.00006745460563632784,0.00007792436225354465,0.0000959784122727784,0.00007858739584550263,0,5,13256,36,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_5_2_5_2.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_5_2_5_2.csv deleted file mode 100644 index 144ecf9f..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_5_2_5_2.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,5,11240,0.00000040058842179828626,0.0000009720222017563256,0.0000017840041483178464,0.0000010091670178835169,0,5,4658,97,1 -5,2,0.0000001,5,11047,0.00000040067776352948336,0.0000009721293545737032,0.0000017842043591593257,0.0000010092756180616895,0,5,4703,97,1 -5,2,0.00001,5,11202,0.00000041214882678362927,0.0000009849806153219608,0.0000018050972885935848,0.0000010221882514658236,0,5,4597,97,1 -5,2,0.001,5,10970,0.0000010202808089572205,0.0000017955225131994714,0.0000029462321164050786,0.0000018293953872553342,0,5,5046,97,1 -5,2,0.1,5,10602,0.000019912543046478585,0.00004772778212036598,0.00009476021958903881,0.00004987874287436984,0,5,5467,36,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_5_2_5_23.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_5_2_5_23.csv deleted file mode 100644 index dae3b888..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_5_2_5_23.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,5,11483,0.00000006824821304575649,0.0000007743838667092269,0.000001627932731580993,0.0000008166904307331606,0,10,4946,97,1 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-depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,5,11611,0.00000015232761601918603,0.0000009421764354501272,0.0000018163384987621045,0.0000009788240950440263,0,20,4761,97,1 -5,2,0.0000001,5,11041,0.00000015245177033589275,0.0000009422946362258768,0.000001816557169554322,0.0000009789442934094275,0,20,4735,97,1 -5,2,0.00001,5,10888,0.00000016710281319137756,0.0000009551789876892666,0.0000018369077575185525,0.000000991872997258723,0,20,4777,97,1 -5,2,0.001,5,10708,0.0000009286511211468486,0.0000017460277650322748,0.0000029591577945909007,0.000001781065604617427,0,20,5307,97,1 -5,2,0.1,5,10721,0.000019992546124300057,0.0000477754162162203,0.00009475274012263527,0.00004992171773722448,0,20,5625,36,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_5_2_6_2.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_5_2_6_2.csv deleted file mode 100644 index 7511850d..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_5_2_6_2.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,2,0,5,16437,0.0000010361517568633487,0.0000015082335870858203,0.0000019653117311609684,0.0000015443520564573088,0,5,11765,97,1 -6,2,0.0000001,5,13746,0.0000010363060759800957,0.0000015084029751623963,0.000001965460302576143,0.0000015445201541480534,0,5,12207,97,1 -6,2,0.00001,5,12829,0.0000010533447059538773,0.0000015267049613052207,0.0000019824858376340565,0.0000015626343271103917,0,5,13098,97,1 -6,2,0.001,5,11906,0.000002035439407542599,0.000002559721181940283,0.0000030166930602694506,0.000002586889169738273,0,5,13383,97,1 -6,2,0.1,5,7381,0.00006593827122901061,0.0000764149291422121,0.00009469382270602528,0.00007711181098285283,0,5,13376,36,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_0_5_24.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_0_5_24.csv deleted file mode 100644 index 231b5016..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_0_5_24.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,6,11129,0.0000009654909485140394,0.0000014208179082551447,0.000001965190771431077,0.0000014366866960974437,0,10,4448,76,1 -5,0,0.0000001,6,10902,0.0000009653475113328596,0.0000014207204409665229,0.000001965035528848016,0.0000014365878353871889,0,10,4331,76,1 -5,0,0.00001,6,10965,0.0000009495486886289867,0.0000014090969228589048,0.0000019499909119300004,0.0000014249672896615591,0,10,4426,76,1 -5,0,0.001,6,10914,0.00000019426041708038827,0.0000008190523339109302,0.000001211164159068338,0.0000008421528478568671,0,10,4955,76,1 -5,0,0.1,6,10535,0.000040597901973039035,0.00005024409974336286,0.00005331887827872891,0.00005029955215740121,0,10,5191,39,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_0_5_3.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_0_5_3.csv deleted file mode 100644 index 5c80481e..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_0_5_3.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,6,11087,0.0000006113203866727646,0.0000012574626631483648,0.0000019965180353544314,0.0000012787168299235343,0,5,4402,76,1 -5,0,0.0000001,6,10970,0.0000006112137555777236,0.000001257360647275853,0.0000019964590524282636,0.0000012786176009017236,0,5,4391,76,1 -5,0,0.00001,6,11193,0.0000006014120793554331,0.0000012465040686589764,0.0000019869995681852405,0.0000012678991091407087,0,5,4339,76,1 -5,0,0.001,6,10856,0.0000000015898268926478904,0.0000006578294712523184,0.0000015167497304962542,0.0000007062835126808882,0,5,4858,76,1 -5,0,0.1,6,10984,0.000040578823735703024,0.00005025603445315125,0.000053254006033756775,0.000050312072092608476,0,5,5308,39,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_0_5_45.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_0_5_45.csv deleted file mode 100644 index f7858406..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_0_5_45.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,6,11136,0.0000008266886354307012,0.0000014759473793442443,0.0000023899077878171176,0.0000015187143106340338,0,20,4487,76,1 -5,0,0.0000001,6,10942,0.0000008266476811667742,0.0000014758725168668734,0.0000023897742971373123,0.0000015186358386027086,0,20,4369,76,1 -5,0,0.00001,6,10729,0.0000008176618874547375,0.0000014642616460921973,0.000002373464061283038,0.0000015069094484779257,0,20,4356,76,1 -5,0,0.001,6,10739,0.0000003277315499129776,0.0000008809979493177136,0.0000016355696427357303,0.0000009276815293355239,0,20,4918,76,1 -5,0,0.1,6,10448,0.000040388964755186126,0.000049667416792855475,0.00005276536046473598,0.00004971812227410305,0,20,5202,39,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_0_6_3.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_0_6_3.csv deleted file mode 100644 index 03376634..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_0_6_3.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,0,0,6,13781,0.0000011550584193294342,0.0000013670718529451854,0.0000016048467773735892,0.0000013740680761833538,0,5,11712,76,1 -6,0,0.0000001,6,11609,0.0000011549091682745903,0.0000013669090973447368,0.0000016047101547652367,0.0000013739062503559973,0,5,12316,76,1 -6,0,0.00001,6,10750,0.0000011411994900452883,0.0000013521142254683613,0.0000015914783241445285,0.000001359174369119541,0,5,13111,76,1 -6,0,0.001,6,10866,0.00000040784001455390955,0.0000005688689059535524,0.0000008825347764602878,0.0000005883690576685885,0,5,13157,76,1 -6,0,0.1,6,8048,0.0000406359757261518,0.00004549681403918947,0.00004833460546650883,0.00004561950925416347,0,5,12759,39,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_1_5_25.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_1_5_25.csv deleted file mode 100644 index ce3baadd..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_1_5_25.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,6,11698,0.000000015678996181132883,0.0000003578358484710909,0.0000008077437438092214,0.00000039445110614392136,0,10,5013,109,1 -5,1,0.0000001,6,11356,0.00000001570883342013471,0.0000003577870456826869,0.0000008076433968589007,0.0000003944014660032909,0,10,4913,109,1 -5,1,0.00001,6,11137,0.00000002213428557396061,0.00000034644632782345547,0.0000007889392568635973,0.00000038315685652951945,0,10,4943,109,1 -5,1,0.001,6,11014,0.0000000008451478804746236,0.000000149823448615748,0.0000006042840013814588,0.00000017641221789688896,0,10,5628,107,1 -5,1,0.1,6,10614,0.000005577627838498569,0.000021372626570966963,0.000026150320526411103,0.000021776143197899057,0,10,5602,39,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_1_5_4.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_1_5_4.csv deleted file mode 100644 index d7cc17e1..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_1_5_4.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,6,11485,0.0000000024843652732060065,0.00000028554345840092356,0.0000007843567147991723,0.00000033019856418979934,0,5,4941,109,1 -5,1,0.0000001,6,11193,0.0000000024623925401014772,0.00000028549875759421945,0.0000007842559646767132,0.0000003301514407890834,0,5,4879,109,1 -5,1,0.00001,6,11207,0.0000000017350413813571964,0.00000027682788438558774,0.0000007672141101511907,0.00000032112042049881113,0,5,4966,109,1 -5,1,0.001,6,11016,0.0000000298034134057493,0.0000003089595362563329,0.0000009472304898407179,0.00000034000174197558856,0,5,5640,107,1 -5,1,0.1,6,10871,0.00000544377952859732,0.000021238981487808208,0.000026049423882006648,0.000021645380863978566,0,5,5717,39,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_1_5_46.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_1_5_46.csv deleted file mode 100644 index a3695083..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_1_5_46.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,6,11763,0.0000000037209866909202917,0.00000025049325512901055,0.0000006546014823989789,0.0000002872015442245075,0,20,5079,109,1 -5,1,0.0000001,6,11266,0.0000000036742703695557996,0.0000002504383591247799,0.0000006544901896049807,0.0000002871463035114163,0,20,5018,109,1 -5,1,0.00001,6,11491,0.00000000784918489205755,0.00000023952148462750366,0.0000006363414078446569,0.00000027636454265490304,0,20,4939,109,1 -5,1,0.001,6,10917,0.00000003767881147321643,0.00000034377772847169824,0.0000007182279051712902,0.0000003587847111732533,0,20,5535,108,1 -5,1,0.1,6,11008,0.000005480514492920591,0.000021300960144280186,0.000025978065708575724,0.00002170646387893691,0,20,5843,39,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_1_6_4.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_1_6_4.csv deleted file mode 100644 index 919564e1..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_1_6_4.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,1,0,6,19815,0.00000030992879437004497,0.0000005344806827074024,0.0000007696675422336763,0.0000005568244536598991,0,5,12414,109,1 -6,1,0.0000001,6,15333,0.00000030985679997418856,0.0000005344000611803557,0.0000007695668433693461,0.000000556744940489722,0,5,13118,109,1 -6,1,0.00001,6,14119,0.00000029629802706397657,0.0000005198954575672438,0.0000007525368538478775,0.0000005425955387115035,0,5,13330,109,1 -6,1,0.001,6,14453,0.00000007154889150771059,0.0000002195775401749086,0.000000407683810797586,0.00000025464754348069,0,5,13583,107,1 -6,1,0.1,6,8091,0.000005440927765787601,0.000012406057449758077,0.000016455337196547368,0.00001314852132081349,0,5,13776,39,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_2_5_26.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_2_5_26.csv deleted file mode 100644 index adb5c710..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_2_5_26.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,6,12192,0.0000000026097027162951076,0.00000015680447529957997,0.00000044593227644347916,0.0000001853184726389404,0,10,5561,148,1 -5,2,0.0000001,6,12179,0.000000002601554292425431,0.00000015679312412878304,0.0000004459041713914784,0.0000001853064486688708,0,10,5571,148,1 -5,2,0.00001,6,11815,0.0000000005992913367578588,0.00000015065345566531402,0.0000004357130321021917,0.00000017907474397606452,0,10,5496,148,1 -5,2,0.001,6,11313,0.00000000030499430136956105,0.0000003836649546398378,0.0000007499518637696581,0.00000039418870272049894,0,10,6247,115,1 -5,2,0.1,6,10622,0.0000000346764214591517,0.0000073576271675358555,0.000012767945570470295,0.0000082577553275057,0,10,6263,39,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_2_5_47.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_2_5_47.csv deleted file mode 100644 index f4fea34c..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_2_5_47.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,6,12364,0.00000000017050444867444012,0.00000015301655854972834,0.00000044758786151360316,0.00000018078719169252945,0,20,5762,148,1 -5,2,0.0000001,6,11899,0.00000000016532970089877523,0.0000001530092911215208,0.00000044758288595096397,0.00000018077969368216252,0,20,5551,148,1 -5,2,0.00001,6,11701,0.0000000042753122818217834,0.0000001467657984129964,0.0000004427298914849281,0.00000017433247518008318,0,20,5705,148,1 -5,2,0.001,6,11054,0.00000000039793481505723514,0.0000003939834216522781,0.0000007682754569106004,0.00000040415339387090806,0,20,6043,115,1 -5,2,0.1,6,10656,0.000000021091112132517143,0.000007357625029627253,0.000012770932170189752,0.000008258014025598741,0,20,6747,39,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_2_5_5.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_2_5_5.csv deleted file mode 100644 index 6d2c9df2..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_2_5_5.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,6,12285,0.0000000014586451892317525,0.0000001411747769619704,0.00000042684073601635663,0.00000016846694440363564,0,5,5461,148,1 -5,2,0.0000001,6,11595,0.0000000014540361712777177,0.0000001411663644808026,0.0000004268366656467836,0.00000016845788580487645,0,5,5415,148,1 -5,2,0.00001,6,11587,0.0000000018518935602132255,0.00000013531629093041517,0.0000004221004422837077,0.00000016239640545627185,0,5,5459,148,1 -5,2,0.001,6,11522,0.000000013690821857041296,0.00000039018510466397494,0.0000007707090754914159,0.0000004004885038268718,0,5,6069,115,1 -5,2,0.1,6,10682,0.00000003993046990701216,0.00000736409935930019,0.000012782973098045052,0.000008265286267015148,0,5,6197,39,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_2_6_5.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_2_6_5.csv deleted file mode 100644 index cbca5b54..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_6_2_6_5.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,2,0,6,26156,0.0000000949375380828853,0.00000029175183490192125,0.00000047144122491250196,0.00000032246675063662437,0,5,12949,148,1 -6,2,0.0000001,6,20874,0.00000009492151096813913,0.00000029173430763117754,0.00000047142533948484945,0.0000003224504335885052,0,5,14061,148,1 -6,2,0.00001,6,18571,0.00000008535036210298954,0.00000028130885509699083,0.000000462372471498118,0.0000003128312939476733,0,5,14752,148,1 -6,2,0.001,6,14800,0.00000011648154302759175,0.00000033546488012443865,0.000000507787807093948,0.0000003587607148022195,0,5,14328,115,1 -6,2,0.1,6,8435,0.0000001871753756524323,0.000004412735908062283,0.00001241494835866344,0.000006521527447309675,0,5,14428,39,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_0_5_27.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_0_5_27.csv deleted file mode 100644 index 54f48bb7..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_0_5_27.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,7,11692,0.00000009328414838948899,0.00000024041024109881555,0.0000005087900877494333,0.0000002559486083424112,0,10,4916,109,1 -5,0,0.0000001,7,11377,0.00000009327192582667154,0.00000024039518966706134,0.0000005087669200352766,0.0000002559339833518755,0,10,4857,109,1 -5,0,0.00001,7,11520,0.00000008954376196302853,0.0000002367432319384186,0.0000005038004947205498,0.0000002523978418863154,0,10,4903,109,1 -5,0,0.001,7,11101,0.00000010284223840185006,0.00000028920622194678324,0.0000005229061893031266,0.00000030052291394399485,0,10,5469,109,1 -5,0,0.1,7,10664,0.00000001586453480569451,0.0000047447000197633485,0.00001713795825050761,0.00000548865819066413,0,10,5902,39,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_0_5_48.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_0_5_48.csv deleted file mode 100644 index 3df8a37f..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_0_5_48.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,7,12223,0.00000004078133134567093,0.0000001348187207985935,0.00000030590855271809834,0.00000014636431823033858,0,20,4936,109,1 -5,0,0.0000001,7,11429,0.0000000407632250404686,0.00000013480578231181612,0.0000003058897681785052,0.00000014635177766951885,0,20,4936,109,1 -5,0,0.00001,7,11391,0.00000003668761072532575,0.00000013094799876931003,0.000000300060085457582,0.00000014260238881738653,0,20,4939,109,1 -5,0,0.001,7,11354,0.0000002260192116086416,0.0000003767180072665184,0.0000005633371458360031,0.00000038207754919187037,0,20,5514,109,1 -5,0,0.1,7,10936,0.000000020514253847564006,0.000004716789178293114,0.000017229184578324813,0.0000054673290102268195,0,20,5807,39,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_0_5_6.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_0_5_6.csv deleted file mode 100644 index 1ae861ff..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_0_5_6.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,7,12185,0.00000000014675704238091955,0.00000016525175331471115,0.0000003843541995461421,0.00000019047040898967058,0,5,4838,109,1 -5,0,0.0000001,7,11429,0.00000000013440994524338323,0.0000001652405262352718,0.00000038434297728127684,0.0000001904586448003462,0,5,4912,109,1 -5,0,0.00001,7,11615,0.000000002736870177512839,0.0000001628886745229065,0.00000038056906918762355,0.0000001878356193214723,0,5,4791,109,1 -5,0,0.001,7,11089,0.000000166268463137901,0.0000003834812992571313,0.0000006210088610795098,0.0000003950268395798917,0,5,5678,108,1 -5,0,0.1,7,10798,0.0000000028404540693271318,0.000004695932496324654,0.000017249304305747078,0.000005445762259936457,0,5,5763,39,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_0_6_6.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_0_6_6.csv deleted file mode 100644 index a1aa6c8d..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_0_6_6.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,0,0,7,20390,0.00000015379914261692358,0.00000024681599293191994,0.00000034439418918790984,0.0000002512378890488958,0,5,12723,109,1 -6,0,0.0000001,7,16123,0.00000015377636273475865,0.0000002467902247132977,0.00000034436234114455905,0.00000025121219413621894,0,5,13407,109,1 -6,0,0.00001,7,16125,0.00000014945527615257745,0.0000002422064455825272,0.00000033893779082800697,0.0000002466616024715785,0,5,13920,109,1 -6,0,0.001,7,15374,0.0000003649137909815174,0.00000042161225556753516,0.0000004978071187979697,0.0000004231897833391053,0,5,13655,108,1 -6,0,0.1,7,9259,0.0000048178510400104265,0.000009146980041180891,0.000017228361947373747,0.000010337338534426642,0,5,13810,39,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_1_5_28.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_1_5_28.csv deleted file mode 100644 index c7cecd08..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_1_5_28.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,7,12865,0.000000000023569405346833953,0.00000000780438151239458,0.00000002634428184821985,0.000000010025509591827182,0,10,5828,148,1 -5,1,0.0000001,7,11864,0.000000000015761140598756785,0.000000007797487806200255,0.000000026329573475139917,0.000000010017222862770901,0,10,5817,148,1 -5,1,0.00001,7,12226,0.000000000043879987600414696,0.0000000064081038601250714,0.000000022339249429629877,0.000000008101218753191222,0,10,5968,148,1 -5,1,0.001,7,11448,0.00000040003526620409053,0.0000005599225270958471,0.0000007319363806815702,0.0000005623656846047703,0,10,6517,116,1 -5,1,0.1,7,10839,0.00000002245655187265704,0.000004297713485578157,0.000020228758094822635,0.00000537407243473095,0,10,6770,39,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_1_5_49.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_1_5_49.csv deleted file mode 100644 index 4d405b5b..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_1_5_49.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,7,12501,0.000000000023629708839359073,0.000000004451127869812599,0.000000019663664298624502,0.000000005739888208166491,0,20,5962,148,1 -5,1,0.0000001,7,12196,0.00000000004208990499109154,0.000000004447113179546406,0.00000001967284563429115,0.000000005738170231415534,0,20,5995,148,1 -5,1,0.00001,7,11864,0.000000000015051562801766085,0.000000004380851646902944,0.000000022752224131726715,0.0000000064708928502499345,0,20,6187,148,1 -5,1,0.001,7,11254,0.00000039993130426507406,0.00000056654546696848,0.0000007400644047147024,0.0000005691499503913183,0,20,6590,116,1 -5,1,0.1,7,10916,0.000000008237825335581569,0.000004300090875249544,0.00002025317634644817,0.000005379543103793388,0,20,6968,39,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_1_5_7.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_1_5_7.csv deleted file mode 100644 index 7812fedc..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_1_5_7.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,7,12431,0.00000000024153454467409236,0.00000001146124319274789,0.00000002694063667526526,0.000000012583545865107013,0,5,5849,148,1 -5,1,0.0000001,7,12171,0.00000000024702502032982805,0.00000001147069886506874,0.000000026945942820669047,0.000000012592343789986161,0,5,5823,148,1 -5,1,0.00001,7,11928,0.0000000016287473069471516,0.000000014377697007859586,0.000000029377220825581672,0.00000001529750062365185,0,5,5831,148,1 -5,1,0.001,7,11261,0.00000040638130426672637,0.0000005688577327223334,0.0000007556714661442125,0.0000005717882539784452,0,5,6434,116,1 -5,1,0.1,7,10926,0.00000001331187631158464,0.0000042988122899301805,0.00002025976389933707,0.000005379265442876955,0,5,6943,39,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_1_6_7.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_1_6_7.csv deleted file mode 100644 index be7d91ef..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_1_6_7.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,1,0,7,27570,0.000000007937107520635602,0.000000012155029862401723,0.000000014834841804680301,0.00000001236898375012593,0,5,13833,148,1 -6,1,0.0000001,7,20170,0.000000007958290364266111,0.000000012172235735187705,0.00000001485573317971752,0.000000012385756041171184,0,5,14610,148,1 -6,1,0.00001,7,18572,0.000000012877149576717316,0.000000016365369297927203,0.000000019687725474449702,0.000000016515467168352466,0,5,15025,148,1 -6,1,0.001,7,16757,0.0000006406403966368705,0.0000006852632471021457,0.0000007564271409135336,0.0000006859935082351751,0,5,14555,116,1 -6,1,0.1,7,10503,0.000007314370450795648,0.000011846293001875232,0.000020242889905037186,0.000012850106630148486,0,5,14535,39,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_2_5_29.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_2_5_29.csv deleted file mode 100644 index 8b9f003b..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_2_5_29.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,7,13381,0.000000000037241205423746846,0.0000000025692908265124176,0.00000001235783850880625,0.000000003138568003849676,0,10,7225,193,1 -5,2,0.0000001,7,12528,0.000000000030021156495591194,0.000000002570047824107976,0.000000012375511599025552,0.00000000314081760539354,0,10,7237,193,1 -5,2,0.00001,7,12539,0.000000000007796000915431883,0.0000000027507440200343086,0.0000000165469040857213,0.0000000040557297903066095,0,10,7543,193,1 -5,2,0.001,7,11880,0.00000033987992962569345,0.0000004846018720367353,0.0000006483157463361732,0.0000004872191104185721,0,10,7871,124,1 -5,2,0.1,7,11182,0.00000004034182639781349,0.000004137322321701314,0.000021424799441969773,0.0000057281812889957825,0,10,8017,43,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_2_5_50.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_2_5_50.csv deleted file mode 100644 index 0c33eb86..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_2_5_50.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,7,13992,0.000000000010772133441994639,0.0000000030908547453760014,0.000000010581354060067713,0.0000000037236180417632264,0,20,7479,193,1 -5,2,0.0000001,7,12611,0.000000000018641254200534436,0.0000000030917140226694814,0.000000010599401800643123,0.0000000037246837065822223,0,20,7471,193,1 -5,2,0.00001,7,12381,0.000000000011761852996721767,0.000000003177687850953851,0.000000014666645182550795,0.000000004226562578401111,0,20,7837,193,1 -5,2,0.001,7,11681,0.0000003399098546163265,0.0000004844788111293284,0.0000006473669032701545,0.0000004870942434262972,0,20,8285,124,1 -5,2,0.1,7,10989,0.00000004011502128504194,0.000004136930049124844,0.000021422932411159485,0.000005727706013217272,0,20,8267,43,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_2_5_8.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_2_5_8.csv deleted file mode 100644 index 025b3b1d..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_2_5_8.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,7,13711,0.00000000001180309709804592,0.0000000031428837901270524,0.00000001145570333405347,0.0000000036514071624839775,0,5,7076,193,1 -5,2,0.0000001,7,12655,0.000000000015768298650288433,0.00000000314252403804284,0.000000011473316016521317,0.0000000036517715193680045,0,5,7031,193,1 -5,2,0.00001,7,12633,0.00000000002966073666629178,0.0000000028149342667728484,0.000000015597443691926504,0.000000003886186549495518,0,5,7199,193,1 -5,2,0.001,7,11672,0.00000033894900201943155,0.0000004838012694078146,0.0000006479515356129208,0.0000004864380922782466,0,5,7688,124,1 -5,2,0.1,7,11025,0.000000039629943786926526,0.000004137170253833248,0.000021424043401837514,0.000005727745620357134,0,5,8151,43,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_2_6_8.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_2_6_8.csv deleted file mode 100644 index 97b7a3b6..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_7_2_6_8.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,2,0,7,37963,0.0000000023606659683752518,0.000000005455958369526363,0.000000011309784519736305,0.000000006398658216708464,0,5,15525,193,1 -6,2,0.0000001,7,25091,0.0000000023719452693492054,0.000000005469093026361234,0.000000011327380727359283,0.000000006411142215895706,0,5,16298,193,1 -6,2,0.00001,7,24299,0.0000000056130216924010795,0.000000009001646180555502,0.00000001545232711247987,0.00000000972181020179444,0,5,16343,193,1 -6,2,0.001,7,16283,0.0000005575976476802613,0.0000005895420058037377,0.0000006481230224133668,0.0000005902600304589886,0,5,15828,124,1 -6,2,0.1,7,10118,0.000009343227287483036,0.00001356587919839745,0.000021411778175217768,0.000014334663128060625,0,5,16436,43,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_8_0_5_30.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_8_0_5_30.csv deleted file mode 100644 index f9079e7e..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_8_0_5_30.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd 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a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_8_0_5_51.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,8,12606,0.000000000027570167180731145,0.000000004002477289997378,0.000000017048281910747135,0.000000005395836288427531,0,20,5697,148,1 -5,0,0.0000001,8,12653,0.00000000001995613503643649,0.00000000400215702139843,0.00000001705555770864226,0.000000005396209356656831,0,20,5691,148,1 -5,0,0.00001,8,12221,0.0000000001298532342476011,0.0000000050479194710121345,0.000000020277125621065667,0.000000006755970606528319,0,20,5825,148,1 -5,0,0.001,8,11649,0.0000003586170761537489,0.0000005135981018073231,0.0000006782893818196538,0.0000005162656587105552,0,20,6467,124,1 -5,0,0.1,8,11012,0.00000002198783562802662,0.000003846898811131159,0.000019501527526749173,0.00000510209822620726,0,20,6506,43,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_8_0_5_9.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_8_0_5_9.csv deleted file mode 100644 index 2c4099af..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_8_0_5_9.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,8,12696,0.0000000024709335755079987,0.000000011433069312497792,0.000000024417059486852524,0.000000012336608968674208,0,5,5623,148,1 -5,0,0.0000001,8,12205,0.0000000024750967950244587,0.000000011438486436562462,0.000000024430776878493478,0.000000012342057391932285,0,5,5636,148,1 -5,0,0.00001,8,11982,0.000000004735699205440014,0.00000001370403445924654,0.000000028249698909987382,0.000000014536367955572118,0,5,5770,148,1 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-depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,8,13662,0.0000000033919143773859676,0.000000007272002075737052,0.000000011115682171906643,0.000000007419659819835034,0,5,7651,193,1 -5,1,0.0000001,8,12762,0.0000000033863366977984297,0.000000007265363514817928,0.000000011102368902615324,0.000000007412737188696378,0,5,7691,193,1 -5,1,0.00001,8,12449,0.00000000028124078695553317,0.000000004199280420565545,0.0000000070732567388311805,0.0000000043535743436603766,0,5,7844,193,1 -5,1,0.001,8,11777,0.000000006578494757036214,0.000000018608758385305375,0.000000031770992200413234,0.000000019116593406144097,0,5,8600,125,1 -5,1,0.1,8,11237,0.000000014605522110497415,0.000005426566750226041,0.000014702665741585289,0.000006219294223426123,0,5,8585,48,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_8_1_5_31.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_8_1_5_31.csv deleted file mode 100644 index 5c40a6a0..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_8_1_5_31.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,8,13754,0.0000000026998232551846586,0.00000000624192589553944,0.000000010070278515707008,0.000000006401906760851734,0,10,7861,193,1 -5,1,0.0000001,8,12792,0.000000002691530730704188,0.000000006235185888933646,0.000000010057316361303214,0.0000000063948068828051054,0,10,7800,193,1 -5,1,0.00001,8,13458,0.00000000009062322189240475,0.000000003174269652102811,0.000000005697920602980144,0.0000000033281120370655726,0,10,8317,193,1 -5,1,0.001,8,12140,0.000000005762866741439815,0.00000001769606637382622,0.00000003075510698201299,0.000000018223273780158768,0,10,8773,125,1 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-6,1,0,8,41725,0.000000008538239816782901,0.000000009250690903361715,0.00000001048112259294955,0.000000009270488837813829,0,5,16375,193,1 -6,1,0.0000001,8,27004,0.000000008526444029760288,0.000000009237760076016493,0.00000001046522309910412,0.00000000925748950014736,0,5,17201,193,1 -6,1,0.00001,8,25668,0.000000004368059706482041,0.000000004822321661792167,0.000000005753713006468499,0.000000004840579093943798,0,5,17028,193,1 -6,1,0.001,8,16909,0.000000017529748685255452,0.00000001918483334358127,0.00000002123291509042799,0.00000001921453699261691,0,5,16787,125,1 -6,1,0.1,8,12082,0.0000029799340336800588,0.0000070800220521356175,0.000014699201083875795,0.000008421639484035207,0,5,17765,48,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_8_2_5_11.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_8_2_5_11.csv deleted file mode 100644 index 0ac3a31e..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_8_2_5_11.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,8,15025,0.00000000007939058925613091,0.0000000014766948407949603,0.0000000031267133543085697,0.0000000015449322964855462,0,5,10066,244,1 -5,2,0.0000001,8,13177,0.00000000007417466706108085,0.0000000014696451249274597,0.000000003114226180366379,0.0000000015377956700142924,0,5,9985,244,1 -5,2,0.00001,8,13101,0.0000000009457020031391343,0.0000000023217571712420097,0.000000004066509420729541,0.0000000023779005764289433,0,5,10535,240,1 -5,2,0.001,8,12533,0.00000000007668041047822327,0.000000006233332913900396,0.00000002367654603056289,0.000000008077766953737514,0,5,10730,127,1 -5,2,0.1,8,11380,0.00000003798998824851902,0.0000023549394744611984,0.000004012220257665776,0.0000026261888020090986,0,5,10627,49,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_8_2_5_32.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_8_2_5_32.csv deleted file mode 100644 index 94ca296d..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_8_2_5_32.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,8,15136,0.0000000002363390470060987,0.0000000011860555949404534,0.000000002631868643618192,0.0000000012588247034759118,0,10,9989,244,1 -5,2,0.0000001,8,13497,0.00000000024125713921707875,0.0000000011790596653967978,0.0000000026192374672916823,0.0000000012517730907853871,0,10,10008,244,1 -5,2,0.00001,8,13206,0.0000000010792628806253592,0.0000000025598887451706147,0.000000004211269924570672,0.000000002614662861529497,0,10,10544,242,1 -5,2,0.001,8,12374,0.000000000022275723724077692,0.000000006086284884911527,0.000000023498389062831443,0.000000007935613733598902,0,10,10707,127,1 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-5,0,0,9,14070,0.000000000972923120809369,0.000000004815526704021282,0.000000007873123451471565,0.000000004955166044330182,0,5,7221,193,1 -5,0,0.0000001,9,13606,0.0000000009654570088970814,0.000000004809290676959911,0.00000000786714241276691,0.000000004949127675501093,0,5,7066,193,1 -5,0,0.00001,9,12980,0.000000000006494595430432146,0.0000000014978168026313946,0.000000004296125419687853,0.000000001780705057469324,0,5,7624,193,1 -5,0,0.001,9,12209,0.00000000007238863765831721,0.000000006085434293555981,0.000000017701841777881976,0.000000007096789245483749,0,5,8088,127,1 -5,0,0.1,9,11500,0.0000002228155150785714,0.0000024961779170052403,0.000004070186822084817,0.000002736905490066935,0,5,7955,49,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_9_0_5_33.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_9_0_5_33.csv deleted file mode 100644 index 39a63da9..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_9_0_5_33.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,9,14582,0.00000000010333295568476062,0.000000003777714173582019,0.000000006170699349945819,0.0000000039119325243074895,0,10,7233,193,1 -5,0,0.0000001,9,13218,0.00000000009553337907709963,0.00000000377142281583757,0.000000006163875454881093,0.000000003905916511421489,0,10,7189,193,1 -5,0,0.00001,9,13000,0.000000000013328887789693024,0.0000000010105628794353422,0.000000003916983916293885,0.000000001270660359591362,0,10,7679,193,1 -5,0,0.001,9,12072,0.00000000009196103567186823,0.000000006516108838468386,0.000000016658404084068697,0.000000007509148960175994,0,10,7853,127,1 -5,0,0.1,9,11793,0.000000223771765269181,0.000002495118418677897,0.000004068809510683477,0.000002735887813219008,0,10,8069,49,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_9_0_5_54.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_9_0_5_54.csv deleted file mode 100644 index e2e9781d..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_9_0_5_54.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,9,14201,0.00000000011707303454083237,0.000000004279656865644358,0.0000000087946582714682,0.000000004740498272532605,0,20,7405,193,1 -5,0,0.0000001,9,13756,0.00000000011240615888782869,0.000000004273592191780892,0.000000008786625729088421,0.000000004734737218762181,0,20,7399,193,1 -5,0,0.00001,9,12895,0.0000000000006911305047803673,0.0000000017748392450635437,0.0000000051922558420037384,0.000000002139049336451103,0,20,7927,193,1 -5,0,0.001,9,12141,0.000000000010109356636225686,0.000000006506862599727599,0.000000017051382120198926,0.000000007650495869348357,0,20,8129,127,1 -5,0,0.1,9,11721,0.00000022327905436653231,0.0000024952558140778047,0.0000040676146593493,0.000002735924999736102,0,20,8144,49,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_9_1_5_13.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_9_1_5_13.csv deleted file mode 100644 index a51adb82..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_9_1_5_13.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,9,15234,0.000000000005170278175875506,0.0000000003738758756981853,0.0000000011502477097236888,0.00000000043153497291505626,0,5,11231,244,1 -5,1,0.0000001,9,13453,0.000000000011105275586146776,0.00000000037157551535698044,0.0000000011585994545782912,0.0000000004294601095841094,0,5,11141,244,1 -5,1,0.00001,9,13445,0.0000000013543632839986022,0.0000000034471795041068305,0.000000006968888180352427,0.000000003614231783849437,0,5,11759,243,1 -5,1,0.001,9,12776,0.000000011158495828969078,0.00000002776164591248558,0.00000003887062858616955,0.000000028152988609393136,0,5,11777,130,1 -5,1,0.1,9,11522,0.000000016878828994455852,0.0000022491652319261934,0.000003884242924335371,0.0000025225189088208215,0,5,11671,50,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_9_1_5_34.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_9_1_5_34.csv deleted file mode 100644 index 767c978f..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_9_1_5_34.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,9,15529,0.0000000000007137099747222429,0.00000000028265031413912947,0.0000000014480115916819017,0.00000000036906708120664596,0,10,11154,244,1 -5,1,0.0000001,9,14085,0.0000000000062789786291146775,0.0000000002838890580727718,0.000000001455867350813231,0.00000000037116929845099517,0,10,11075,244,1 -5,1,0.00001,9,13351,0.0000000015347094929217657,0.0000000036915281142855955,0.0000000071713244924130765,0.000000003849766324487968,0,10,11919,244,1 -5,1,0.001,9,12295,0.000000011313592847628969,0.00000002792027446998953,0.00000003892941933022685,0.00000002830671563621961,0,10,11728,130,1 -5,1,0.1,9,11790,0.000000016765368628080962,0.0000022490029900764456,0.000003883935651418963,0.0000025223587563072338,0,10,12036,50,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_9_1_5_55.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_9_1_5_55.csv deleted file mode 100644 index e3dfab79..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_9_1_5_55.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,1,0,9,15474,0.000000000009833180687574803,0.0000000003903540372023752,0.0000000016160545231719737,0.0000000005158566453420694,0,20,11503,244,1 -5,1,0.0000001,9,13929,0.000000000005667439195179151,0.0000000003942605884418928,0.0000000016227491938275993,0.0000000005197327674497161,0,20,11356,244,1 -5,1,0.00001,9,13960,0.000000001864184541538937,0.0000000040334760648044336,0.000000007271106526324024,0.000000004167976040148356,0,20,12120,244,1 -5,1,0.001,9,12446,0.000000011634236486301485,0.00000002828171508258456,0.00000003950786451331431,0.000000028665003359348265,0,20,11996,130,1 -5,1,0.1,9,11675,0.00000001657513064520013,0.000002248783261773014,0.000003883536326850489,0.000002522137821993472,0,20,12251,50,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_9_2_5_14.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_9_2_5_14.csv deleted file mode 100644 index a46dac01..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_9_2_5_14.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,9,17033,0.000000000003502315917875473,0.00000000009736113877877559,0.00000000029830009989897015,0.00000000011880287468331665,0,5,14485,301,1 -5,2,0.0000001,9,14357,0.0000000000018372589051013395,0.0000000000997332657543964,0.00000000030075631932887745,0.00000000012109830900977862,0,5,14489,301,1 -5,2,0.00001,9,14431,0.0000000018432731788523534,0.00000000317635327485562,0.000000004834246595639693,0.0000000032308304417601336,0,5,15619,259,1 -5,2,0.001,9,12508,0.0000000032632935507260616,0.000000017389000738510578,0.000000029567508613842655,0.00000001793411366907705,0,5,14690,130,1 -5,2,0.1,9,11883,0.00000001286284920179852,0.00000211206370244146,0.0000037898842358747275,0.000002394575746603274,0,5,15039,50,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_9_2_5_35.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_9_2_5_35.csv deleted file mode 100644 index b32b94e1..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_9_2_5_35.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,9,16923,0.00000000000028799730841305054,0.00000000013335542677469942,0.0000000003217781975916275,0.00000000014956867453554853,0,10,14529,301,1 -5,2,0.0000001,9,14974,0.0000000000029274840732440485,0.00000000013656623952806205,0.00000000032446011478439494,0.0000000001526542031377888,0,10,14570,301,1 -5,2,0.00001,9,13926,0.000000001877753433811281,0.000000003213250053521423,0.000000004911694103801518,0.000000003266565962599429,0,10,15796,259,1 -5,2,0.001,9,12554,0.000000003278167394671494,0.00000001740508176420802,0.00000002954197080856912,0.000000017948861677573527,0,10,15037,130,1 -5,2,0.1,9,11833,0.000000012832813739010236,0.000002112040401555004,0.0000037898495124597497,0.0000023945519990929703,0,10,14803,50,1 diff --git a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_9_2_5_56.csv b/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_9_2_5_56.csv deleted file mode 100644 index fff449e0..00000000 --- a/data/blas_f64_8/grid_search_laplace_blas_uniform_f64_m1_8000000_9_2_5_56.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0,9,17412,0.0000000000001161086105404901,0.00000000005530424877346039,0.00000000022539857673283792,0.00000000007150577845086167,0,20,15017,301,1 -5,2,0.0000001,9,14509,0.000000000000005492567714925847,0.00000000005400820065911612,0.00000000021564927824500364,0.00000000006987779141766245,0,20,15022,301,1 -5,2,0.00001,9,13904,0.0000000018016292239000699,0.0000000030748485748132205,0.0000000046702264953381276,0.000000003125122128583416,0,20,15687,259,1 -5,2,0.001,9,12695,0.000000003211411044645677,0.00000001729353345212324,0.00000002935384205727052,0.00000001783692021617976,0,20,15590,130,1 -5,2,0.1,9,11881,0.000000012892292373997526,0.000002112070923902686,0.000003789874006183035,0.00000239458215318108,0,20,15279,50,1 diff --git a/data/fft_f32_1/grid_search_laplace_fft_0_f32_m1_3_4_16_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_0_f32_m1_3_4_16_1000000.csv deleted file mode 100644 index 65323b9a..00000000 --- a/data/fft_f32_1/grid_search_laplace_fft_0_f32_m1_3_4_16_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,3,16,493,0.00029853775,0.00034218995,0.00038464146,0.0003419967,855 diff --git a/data/fft_f32_1/grid_search_laplace_fft_10_f32_m1_4_5_32_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_10_f32_m1_4_5_32_1000000.csv deleted file mode 100644 index a4b17bf8..00000000 --- a/data/fft_f32_1/grid_search_laplace_fft_10_f32_m1_4_5_32_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,4,32,252,0.000015539365,0.000062159874,0.00008493679,0.00006458634,1243 diff --git a/data/fft_f32_1/grid_search_laplace_fft_11_f32_m1_5_5_32_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_11_f32_m1_5_5_32_1000000.csv deleted file mode 100644 index 8ecbfb73..00000000 --- a/data/fft_f32_1/grid_search_laplace_fft_11_f32_m1_5_5_32_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,5,32,404,0.0000011857528,0.000005537652,0.000010227907,0.000006343344,1292 diff --git a/data/fft_f32_1/grid_search_laplace_fft_12_f32_m1_3_4_64_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_12_f32_m1_3_4_64_1000000.csv deleted file mode 100644 index 88a45217..00000000 --- a/data/fft_f32_1/grid_search_laplace_fft_12_f32_m1_3_4_64_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,3,64,533,0.00029853775,0.00034218995,0.00038464146,0.0003419967,926 diff --git a/data/fft_f32_1/grid_search_laplace_fft_13_f32_m1_4_4_64_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_13_f32_m1_4_4_64_1000000.csv deleted file mode 100644 index 59e17665..00000000 --- a/data/fft_f32_1/grid_search_laplace_fft_13_f32_m1_4_4_64_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,4,64,525,0.0000006038583,0.00004300728,0.00010778607,0.00004613934,930 diff --git a/data/fft_f32_1/grid_search_laplace_fft_14_f32_m1_5_4_64_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_14_f32_m1_5_4_64_1000000.csv deleted file mode 100644 index eeff91c1..00000000 --- a/data/fft_f32_1/grid_search_laplace_fft_14_f32_m1_5_4_64_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,5,64,523,0,0.000004249616,0.000013534111,0.0000051436073,949 diff --git a/data/fft_f32_1/grid_search_laplace_fft_15_f32_m1_3_5_64_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_15_f32_m1_3_5_64_1000000.csv deleted file mode 100644 index 0af97f5d..00000000 --- a/data/fft_f32_1/grid_search_laplace_fft_15_f32_m1_3_5_64_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,3,64,167,0.00043466355,0.0004631569,0.00048573408,0.00046296025,1157 diff --git a/data/fft_f32_1/grid_search_laplace_fft_16_f32_m1_4_5_64_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_16_f32_m1_4_5_64_1000000.csv deleted file mode 100644 index 8e881f05..00000000 --- a/data/fft_f32_1/grid_search_laplace_fft_16_f32_m1_4_5_64_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,4,64,233,0.000015539365,0.000062159874,0.00008493679,0.00006458634,1207 diff --git a/data/fft_f32_1/grid_search_laplace_fft_17_f32_m1_5_5_64_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_17_f32_m1_5_5_64_1000000.csv deleted file mode 100644 index eddf2df5..00000000 --- a/data/fft_f32_1/grid_search_laplace_fft_17_f32_m1_5_5_64_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,5,64,381,0.0000011857528,0.000005537652,0.000010227907,0.000006343344,1275 diff --git a/data/fft_f32_1/grid_search_laplace_fft_18_f32_m1_3_4_128_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_18_f32_m1_3_4_128_1000000.csv deleted file mode 100644 index 39f96601..00000000 --- a/data/fft_f32_1/grid_search_laplace_fft_18_f32_m1_3_4_128_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,3,128,481,0.00029853775,0.00034218995,0.00038464146,0.0003419967,863 diff --git a/data/fft_f32_1/grid_search_laplace_fft_19_f32_m1_4_4_128_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_19_f32_m1_4_4_128_1000000.csv deleted file mode 100644 index 5b911e07..00000000 --- a/data/fft_f32_1/grid_search_laplace_fft_19_f32_m1_4_4_128_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,4,128,516,0.0000006038583,0.00004300728,0.00010778607,0.00004613934,913 diff --git a/data/fft_f32_1/grid_search_laplace_fft_1_f32_m1_4_4_16_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_1_f32_m1_4_4_16_1000000.csv deleted file mode 100644 index 2d7e77ae..00000000 --- a/data/fft_f32_1/grid_search_laplace_fft_1_f32_m1_4_4_16_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,4,16,497,0.0000006038583,0.00004300728,0.00010778607,0.00004613934,891 diff --git a/data/fft_f32_1/grid_search_laplace_fft_20_f32_m1_5_4_128_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_20_f32_m1_5_4_128_1000000.csv deleted file mode 100644 index bbc364f1..00000000 --- a/data/fft_f32_1/grid_search_laplace_fft_20_f32_m1_5_4_128_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,5,128,521,0,0.000004249616,0.000013534111,0.0000051436073,989 diff --git a/data/fft_f32_1/grid_search_laplace_fft_21_f32_m1_3_5_128_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_21_f32_m1_3_5_128_1000000.csv deleted file mode 100644 index 2571e8b6..00000000 --- a/data/fft_f32_1/grid_search_laplace_fft_21_f32_m1_3_5_128_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,3,128,156,0.00043466355,0.0004631569,0.00048573408,0.00046296025,1156 diff --git a/data/fft_f32_1/grid_search_laplace_fft_22_f32_m1_4_5_128_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_22_f32_m1_4_5_128_1000000.csv deleted file mode 100644 index 9ba420cd..00000000 --- a/data/fft_f32_1/grid_search_laplace_fft_22_f32_m1_4_5_128_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,4,128,255,0.000015539365,0.000062159874,0.00008493679,0.00006458634,1205 diff --git a/data/fft_f32_1/grid_search_laplace_fft_23_f32_m1_5_5_128_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_23_f32_m1_5_5_128_1000000.csv deleted file mode 100644 index 06dbd8c6..00000000 --- a/data/fft_f32_1/grid_search_laplace_fft_23_f32_m1_5_5_128_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,5,128,360,0.0000011857528,0.000005537652,0.000010227907,0.000006343344,1273 diff --git a/data/fft_f32_1/grid_search_laplace_fft_2_f32_m1_5_4_16_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_2_f32_m1_5_4_16_1000000.csv deleted file mode 100644 index dc89b85f..00000000 --- a/data/fft_f32_1/grid_search_laplace_fft_2_f32_m1_5_4_16_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,5,16,531,0,0.000004249616,0.000013534111,0.0000051436073,933 diff --git a/data/fft_f32_1/grid_search_laplace_fft_3_f32_m1_3_5_16_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_3_f32_m1_3_5_16_1000000.csv deleted file mode 100644 index 6ab3db7e..00000000 --- a/data/fft_f32_1/grid_search_laplace_fft_3_f32_m1_3_5_16_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,3,16,172,0.00043466355,0.0004631569,0.00048573408,0.00046296025,1154 diff --git a/data/fft_f32_1/grid_search_laplace_fft_4_f32_m1_4_5_16_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_4_f32_m1_4_5_16_1000000.csv deleted file mode 100644 index 446eabb3..00000000 --- a/data/fft_f32_1/grid_search_laplace_fft_4_f32_m1_4_5_16_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,4,16,278,0.000015539365,0.000062159874,0.00008493679,0.00006458634,1204 diff --git a/data/fft_f32_1/grid_search_laplace_fft_5_f32_m1_5_5_16_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_5_f32_m1_5_5_16_1000000.csv deleted file mode 100644 index fb29e1ea..00000000 --- a/data/fft_f32_1/grid_search_laplace_fft_5_f32_m1_5_5_16_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,5,16,431,0.0000011857528,0.000005537652,0.000010227907,0.000006343344,1251 diff --git a/data/fft_f32_1/grid_search_laplace_fft_6_f32_m1_3_4_32_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_6_f32_m1_3_4_32_1000000.csv deleted file mode 100644 index 661a3510..00000000 --- a/data/fft_f32_1/grid_search_laplace_fft_6_f32_m1_3_4_32_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,3,32,557,0.00029853775,0.00034218995,0.00038464146,0.0003419967,853 diff --git a/data/fft_f32_1/grid_search_laplace_fft_7_f32_m1_4_4_32_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_7_f32_m1_4_4_32_1000000.csv deleted file mode 100644 index 0bde5c04..00000000 --- a/data/fft_f32_1/grid_search_laplace_fft_7_f32_m1_4_4_32_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,4,32,563,0.0000006038583,0.00004300728,0.00010778607,0.00004613934,917 diff --git a/data/fft_f32_1/grid_search_laplace_fft_8_f32_m1_5_4_32_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_8_f32_m1_5_4_32_1000000.csv deleted file mode 100644 index 85b1c660..00000000 --- a/data/fft_f32_1/grid_search_laplace_fft_8_f32_m1_5_4_32_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,5,32,647,0,0.000004249616,0.000013534111,0.0000051436073,953 diff --git a/data/fft_f32_1/grid_search_laplace_fft_9_f32_m1_3_5_32_1000000.csv b/data/fft_f32_1/grid_search_laplace_fft_9_f32_m1_3_5_32_1000000.csv deleted file mode 100644 index 416c9e2e..00000000 --- a/data/fft_f32_1/grid_search_laplace_fft_9_f32_m1_3_5_32_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,3,32,161,0.00043466355,0.0004631569,0.00048573408,0.00046296025,1197 diff --git a/data/fft_f32_8/grid_search_laplace_fft_0_f32_m1_3_5_16_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_0_f32_m1_3_5_16_8000000.csv deleted file mode 100644 index de2f38f5..00000000 --- a/data/fft_f32_8/grid_search_laplace_fft_0_f32_m1_3_5_16_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,3,16,4122,0.00014249298,0.00045863597,0.000723327,0.0004806604,3885 diff --git a/data/fft_f32_8/grid_search_laplace_fft_10_f32_m1_4_6_32_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_10_f32_m1_4_6_32_8000000.csv deleted file mode 100644 index 293075cf..00000000 --- a/data/fft_f32_8/grid_search_laplace_fft_10_f32_m1_4_6_32_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -6,4,32,1974,0.00014757123,0.00022088861,0.0002971805,0.0002260225,5345 diff --git a/data/fft_f32_8/grid_search_laplace_fft_11_f32_m1_5_6_32_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_11_f32_m1_5_6_32_8000000.csv deleted file mode 100644 index 3e608599..00000000 --- a/data/fft_f32_8/grid_search_laplace_fft_11_f32_m1_5_6_32_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -6,5,32,3814,0.00004457746,0.00011925035,0.00019870921,0.00012959247,5275 diff --git a/data/fft_f32_8/grid_search_laplace_fft_12_f32_m1_3_5_64_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_12_f32_m1_3_5_64_8000000.csv deleted file mode 100644 index 3379ee35..00000000 --- a/data/fft_f32_8/grid_search_laplace_fft_12_f32_m1_3_5_64_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,3,64,4061,0.00014249298,0.00045863597,0.000723327,0.0004806604,3798 diff --git a/data/fft_f32_8/grid_search_laplace_fft_13_f32_m1_4_5_64_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_13_f32_m1_4_5_64_8000000.csv deleted file mode 100644 index 7ed78b8b..00000000 --- a/data/fft_f32_8/grid_search_laplace_fft_13_f32_m1_4_5_64_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,4,64,4156,0.00000055630085,0.00014264838,0.0003826771,0.00016835216,3873 diff --git a/data/fft_f32_8/grid_search_laplace_fft_14_f32_m1_5_5_64_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_14_f32_m1_5_5_64_8000000.csv deleted file mode 100644 index ca58f704..00000000 --- a/data/fft_f32_8/grid_search_laplace_fft_14_f32_m1_5_5_64_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,5,64,4426,0.000001288938,0.00012569428,0.00030825654,0.00014677555,3944 diff --git a/data/fft_f32_8/grid_search_laplace_fft_15_f32_m1_3_6_64_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_15_f32_m1_3_6_64_8000000.csv deleted file mode 100644 index 08ad129c..00000000 --- a/data/fft_f32_8/grid_search_laplace_fft_15_f32_m1_3_6_64_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -6,3,64,1260,0.0003791737,0.0004618011,0.000542133,0.00046473174,5030 diff --git a/data/fft_f32_8/grid_search_laplace_fft_16_f32_m1_4_6_64_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_16_f32_m1_4_6_64_8000000.csv deleted file mode 100644 index 6c11c03a..00000000 --- a/data/fft_f32_8/grid_search_laplace_fft_16_f32_m1_4_6_64_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -6,4,64,1941,0.00014757123,0.00022088861,0.0002971805,0.0002260225,5181 diff --git a/data/fft_f32_8/grid_search_laplace_fft_17_f32_m1_5_6_64_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_17_f32_m1_5_6_64_8000000.csv deleted file mode 100644 index a99db93e..00000000 --- a/data/fft_f32_8/grid_search_laplace_fft_17_f32_m1_5_6_64_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -6,5,64,3694,0.00004457746,0.00011925035,0.00019870921,0.00012959247,5275 diff --git a/data/fft_f32_8/grid_search_laplace_fft_18_f32_m1_3_5_128_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_18_f32_m1_3_5_128_8000000.csv deleted file mode 100644 index 4d795ade..00000000 --- a/data/fft_f32_8/grid_search_laplace_fft_18_f32_m1_3_5_128_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,3,128,4033,0.00014249298,0.00045863597,0.000723327,0.0004806604,3821 diff --git a/data/fft_f32_8/grid_search_laplace_fft_19_f32_m1_4_5_128_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_19_f32_m1_4_5_128_8000000.csv deleted file mode 100644 index aa1ac574..00000000 --- a/data/fft_f32_8/grid_search_laplace_fft_19_f32_m1_4_5_128_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,4,128,4203,0.00000055630085,0.00014264838,0.0003826771,0.00016835216,3890 diff --git a/data/fft_f32_8/grid_search_laplace_fft_1_f32_m1_4_5_16_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_1_f32_m1_4_5_16_8000000.csv deleted file mode 100644 index fd7ba475..00000000 --- a/data/fft_f32_8/grid_search_laplace_fft_1_f32_m1_4_5_16_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,4,16,4388,0.00000055630085,0.00014264838,0.0003826771,0.00016835216,3877 diff --git a/data/fft_f32_8/grid_search_laplace_fft_20_f32_m1_5_5_128_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_20_f32_m1_5_5_128_8000000.csv deleted file mode 100644 index 7c64744e..00000000 --- a/data/fft_f32_8/grid_search_laplace_fft_20_f32_m1_5_5_128_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,5,128,4396,0.000001288938,0.00012569428,0.00030825654,0.00014677555,3990 diff --git a/data/fft_f32_8/grid_search_laplace_fft_21_f32_m1_3_6_128_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_21_f32_m1_3_6_128_8000000.csv deleted file mode 100644 index 634ccb5b..00000000 --- a/data/fft_f32_8/grid_search_laplace_fft_21_f32_m1_3_6_128_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -6,3,128,1292,0.0003791737,0.0004618011,0.000542133,0.00046473174,5092 diff --git a/data/fft_f32_8/grid_search_laplace_fft_22_f32_m1_4_6_128_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_22_f32_m1_4_6_128_8000000.csv deleted file mode 100644 index 25a7092a..00000000 --- a/data/fft_f32_8/grid_search_laplace_fft_22_f32_m1_4_6_128_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -6,4,128,1970,0.00014757123,0.00022088861,0.0002971805,0.0002260225,5202 diff --git a/data/fft_f32_8/grid_search_laplace_fft_23_f32_m1_5_6_128_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_23_f32_m1_5_6_128_8000000.csv deleted file mode 100644 index 1807aed7..00000000 --- a/data/fft_f32_8/grid_search_laplace_fft_23_f32_m1_5_6_128_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -6,5,128,3759,0.00004457746,0.00011925035,0.00019870921,0.00012959247,5345 diff --git a/data/fft_f32_8/grid_search_laplace_fft_2_f32_m1_5_5_16_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_2_f32_m1_5_5_16_8000000.csv deleted file mode 100644 index 3b6da7d9..00000000 --- a/data/fft_f32_8/grid_search_laplace_fft_2_f32_m1_5_5_16_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,5,16,4436,0.000001288938,0.00012569428,0.00030825654,0.00014677555,3927 diff --git a/data/fft_f32_8/grid_search_laplace_fft_3_f32_m1_3_6_16_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_3_f32_m1_3_6_16_8000000.csv deleted file mode 100644 index 15a27503..00000000 --- a/data/fft_f32_8/grid_search_laplace_fft_3_f32_m1_3_6_16_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -6,3,16,1367,0.0003791737,0.0004618011,0.000542133,0.00046473174,5022 diff --git a/data/fft_f32_8/grid_search_laplace_fft_4_f32_m1_4_6_16_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_4_f32_m1_4_6_16_8000000.csv deleted file mode 100644 index b9982fc2..00000000 --- a/data/fft_f32_8/grid_search_laplace_fft_4_f32_m1_4_6_16_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -6,4,16,2410,0.00014757123,0.00022088861,0.0002971805,0.0002260225,5228 diff --git a/data/fft_f32_8/grid_search_laplace_fft_5_f32_m1_5_6_16_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_5_f32_m1_5_6_16_8000000.csv deleted file mode 100644 index 9c166a97..00000000 --- a/data/fft_f32_8/grid_search_laplace_fft_5_f32_m1_5_6_16_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -6,5,16,4785,0.00004457746,0.00011925035,0.00019870921,0.00012959247,5564 diff --git a/data/fft_f32_8/grid_search_laplace_fft_6_f32_m1_3_5_32_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_6_f32_m1_3_5_32_8000000.csv deleted file mode 100644 index 05006482..00000000 --- a/data/fft_f32_8/grid_search_laplace_fft_6_f32_m1_3_5_32_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,3,32,4109,0.00014249298,0.00045863597,0.000723327,0.0004806604,3755 diff --git a/data/fft_f32_8/grid_search_laplace_fft_7_f32_m1_4_5_32_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_7_f32_m1_4_5_32_8000000.csv deleted file mode 100644 index 430cbb99..00000000 --- a/data/fft_f32_8/grid_search_laplace_fft_7_f32_m1_4_5_32_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,4,32,4196,0.00000055630085,0.00014264838,0.0003826771,0.00016835216,3856 diff --git a/data/fft_f32_8/grid_search_laplace_fft_8_f32_m1_5_5_32_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_8_f32_m1_5_5_32_8000000.csv deleted file mode 100644 index 8dd207b2..00000000 --- a/data/fft_f32_8/grid_search_laplace_fft_8_f32_m1_5_5_32_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,5,32,4355,0.000001288938,0.00012569428,0.00030825654,0.00014677555,3910 diff --git a/data/fft_f32_8/grid_search_laplace_fft_9_f32_m1_3_6_32_8000000.csv b/data/fft_f32_8/grid_search_laplace_fft_9_f32_m1_3_6_32_8000000.csv deleted file mode 100644 index 89d5fd17..00000000 --- a/data/fft_f32_8/grid_search_laplace_fft_9_f32_m1_3_6_32_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -6,3,32,1367,0.0003791737,0.0004618011,0.000542133,0.00046473174,5127 diff --git a/data/fft_f64_1/grid_search_laplace_fft_0_f64_m1_6_4_16_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_0_f64_m1_6_4_16_1000000.csv deleted file mode 100644 index 9c53a89a..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_0_f64_m1_6_4_16_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,6,16,1369,0.000000016686702299490217,0.0000001806345983488817,0.0000007584427734354027,0.000000211894886813628,1099 diff --git a/data/fft_f64_1/grid_search_laplace_fft_10_f64_m1_10_5_16_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_10_f64_m1_10_5_16_1000000.csv deleted file mode 100644 index d6e783f6..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_10_f64_m1_10_5_16_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,10,16,9988,0.000000000014970181371864145,0.000000000021195864797384797,0.00000000003488288867966985,0.000000000021794415120378065,3412 diff --git a/data/fft_f64_1/grid_search_laplace_fft_11_f64_m1_11_5_16_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_11_f64_m1_11_5_16_1000000.csv deleted file mode 100644 index 216ae86f..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_11_f64_m1_11_5_16_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,11,16,14059,0.0000000000013449553261033692,0.000000000001930021538710101,0.0000000000028292678699804097,0.0000000000019795309392416133,4899 diff --git a/data/fft_f64_1/grid_search_laplace_fft_12_f64_m1_6_4_32_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_12_f64_m1_6_4_32_1000000.csv deleted file mode 100644 index 3526d691..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_12_f64_m1_6_4_32_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,6,32,1294,0.000000016686702299490217,0.0000001806345983488817,0.0000007584427734354027,0.000000211894886813628,1123 diff --git a/data/fft_f64_1/grid_search_laplace_fft_13_f64_m1_7_4_32_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_13_f64_m1_7_4_32_1000000.csv deleted file mode 100644 index 389fd9c8..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_13_f64_m1_7_4_32_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,7,32,1371,0.0000000012761168495142224,0.00000001493895643652047,0.00000004286895467535286,0.000000016375904107358673,1250 diff --git a/data/fft_f64_1/grid_search_laplace_fft_14_f64_m1_8_4_32_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_14_f64_m1_8_4_32_1000000.csv deleted file mode 100644 index 44c797c1..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_14_f64_m1_8_4_32_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,8,32,1512,0.00000000002776493525828481,0.0000000014979825499069186,0.000000006541376024334807,0.0000000016842635718787517,1532 diff --git a/data/fft_f64_1/grid_search_laplace_fft_15_f64_m1_9_4_32_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_15_f64_m1_9_4_32_1000000.csv deleted file mode 100644 index 953f9d07..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_15_f64_m1_9_4_32_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,9,32,1737,0.00000000006671614691618004,0.000000000165335668400375,0.0000000005717558733701871,0.00000000019006236803274044,2168 diff --git a/data/fft_f64_1/grid_search_laplace_fft_16_f64_m1_10_4_32_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_16_f64_m1_10_4_32_1000000.csv deleted file mode 100644 index 7823b204..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_16_f64_m1_10_4_32_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,10,32,1853,0.0000000000005858423417081946,0.000000000014318952674526936,0.00000000009358838086157819,0.000000000018006086872929633,2648 diff --git a/data/fft_f64_1/grid_search_laplace_fft_17_f64_m1_11_4_32_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_17_f64_m1_11_4_32_1000000.csv deleted file mode 100644 index c60fd885..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_17_f64_m1_11_4_32_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,11,32,2143,0.00000000000016981362434069548,0.0000000000011634261801119752,0.0000000000060448245956206634,0.0000000000013546754842041066,3625 diff --git a/data/fft_f64_1/grid_search_laplace_fft_18_f64_m1_6_5_32_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_18_f64_m1_6_5_32_1000000.csv deleted file mode 100644 index 61f8e780..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_18_f64_m1_6_5_32_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,6,32,1173,0.00000007363947317807973,0.00000027384696535735265,0.0000004085263879221838,0.00000028466801173195193,1508 diff --git a/data/fft_f64_1/grid_search_laplace_fft_19_f64_m1_7_5_32_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_19_f64_m1_7_5_32_1000000.csv deleted file mode 100644 index 107137d8..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_19_f64_m1_7_5_32_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,7,32,1752,0.000000020760057606409505,0.0000000313831060299183,0.000000046302488262898814,0.000000032194309086371234,1820 diff --git a/data/fft_f64_1/grid_search_laplace_fft_1_f64_m1_7_4_16_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_1_f64_m1_7_4_16_1000000.csv deleted file mode 100644 index f76cb134..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_1_f64_m1_7_4_16_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,7,16,1454,0.0000000012761168495142224,0.00000001493895643652047,0.00000004286895467535286,0.000000016375904107358673,1298 diff --git a/data/fft_f64_1/grid_search_laplace_fft_20_f64_m1_8_5_32_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_20_f64_m1_8_5_32_1000000.csv deleted file mode 100644 index be7aca15..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_20_f64_m1_8_5_32_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,8,32,2504,0.0000000006775990793078764,0.0000000026624143464007455,0.000000004205206987239361,0.0000000028020022441379728,2141 diff --git a/data/fft_f64_1/grid_search_laplace_fft_21_f64_m1_9_5_32_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_21_f64_m1_9_5_32_1000000.csv deleted file mode 100644 index 4db43b60..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_21_f64_m1_9_5_32_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,9,32,3556,0.00000000021553372389520778,0.00000000037141593359165344,0.0000000006141323217507616,0.00000000038741810927020284,3762 diff --git a/data/fft_f64_1/grid_search_laplace_fft_22_f64_m1_10_5_32_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_22_f64_m1_10_5_32_1000000.csv deleted file mode 100644 index 3ba1b72e..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_22_f64_m1_10_5_32_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,10,32,7319,0.000000000014970181371864145,0.000000000021195864797384797,0.00000000003488288867966985,0.000000000021794415120378065,3442 diff --git a/data/fft_f64_1/grid_search_laplace_fft_23_f64_m1_11_5_32_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_23_f64_m1_11_5_32_1000000.csv deleted file mode 100644 index 96ac66dc..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_23_f64_m1_11_5_32_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,11,32,13356,0.0000000000013449553261033692,0.000000000001930021538710101,0.0000000000028292678699804097,0.0000000000019795309392416133,4929 diff --git a/data/fft_f64_1/grid_search_laplace_fft_24_f64_m1_6_4_64_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_24_f64_m1_6_4_64_1000000.csv deleted file mode 100644 index 007a1666..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_24_f64_m1_6_4_64_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,6,64,1327,0.000000016686702299490217,0.0000001806345983488817,0.0000007584427734354027,0.000000211894886813628,1114 diff --git a/data/fft_f64_1/grid_search_laplace_fft_25_f64_m1_7_4_64_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_25_f64_m1_7_4_64_1000000.csv deleted file mode 100644 index 60216c6b..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_25_f64_m1_7_4_64_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,7,64,1388,0.0000000012761168495142224,0.00000001493895643652047,0.00000004286895467535286,0.000000016375904107358673,1279 diff --git a/data/fft_f64_1/grid_search_laplace_fft_26_f64_m1_8_4_64_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_26_f64_m1_8_4_64_1000000.csv deleted file mode 100644 index 144405e6..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_26_f64_m1_8_4_64_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,8,64,1528,0.00000000002776493525828481,0.0000000014979825499069186,0.000000006541376024334807,0.0000000016842635718787517,1551 diff --git a/data/fft_f64_1/grid_search_laplace_fft_27_f64_m1_9_4_64_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_27_f64_m1_9_4_64_1000000.csv deleted file mode 100644 index fd53d7e0..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_27_f64_m1_9_4_64_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,9,64,1708,0.00000000006671614691618004,0.000000000165335668400375,0.0000000005717558733701871,0.00000000019006236803274044,2465 diff --git a/data/fft_f64_1/grid_search_laplace_fft_28_f64_m1_10_4_64_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_28_f64_m1_10_4_64_1000000.csv deleted file mode 100644 index 76f188f3..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_28_f64_m1_10_4_64_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,10,64,1837,0.0000000000005858423417081946,0.000000000014318952674526936,0.00000000009358838086157819,0.000000000018006086872929633,2446 diff --git a/data/fft_f64_1/grid_search_laplace_fft_29_f64_m1_11_4_64_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_29_f64_m1_11_4_64_1000000.csv deleted file mode 100644 index 2e0f758a..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_29_f64_m1_11_4_64_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,11,64,2186,0.00000000000016981362434069548,0.0000000000011634261801119752,0.0000000000060448245956206634,0.0000000000013546754842041066,3715 diff --git a/data/fft_f64_1/grid_search_laplace_fft_2_f64_m1_8_4_16_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_2_f64_m1_8_4_16_1000000.csv deleted file mode 100644 index 4cbf7597..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_2_f64_m1_8_4_16_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,8,16,1568,0.00000000002776493525828481,0.0000000014979825499069186,0.000000006541376024334807,0.0000000016842635718787517,1543 diff --git a/data/fft_f64_1/grid_search_laplace_fft_30_f64_m1_6_5_64_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_30_f64_m1_6_5_64_1000000.csv deleted file mode 100644 index 4aaea6af..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_30_f64_m1_6_5_64_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,6,64,1158,0.00000007363947317807973,0.00000027384696535735265,0.0000004085263879221838,0.00000028466801173195193,1513 diff --git a/data/fft_f64_1/grid_search_laplace_fft_31_f64_m1_7_5_64_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_31_f64_m1_7_5_64_1000000.csv deleted file mode 100644 index 2f0b63ed..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_31_f64_m1_7_5_64_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,7,64,1685,0.000000020760057606409505,0.0000000313831060299183,0.000000046302488262898814,0.000000032194309086371234,1767 diff --git a/data/fft_f64_1/grid_search_laplace_fft_32_f64_m1_8_5_64_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_32_f64_m1_8_5_64_1000000.csv deleted file mode 100644 index e24f762e..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_32_f64_m1_8_5_64_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,8,64,2349,0.0000000006775990793078764,0.0000000026624143464007455,0.000000004205206987239361,0.0000000028020022441379728,2158 diff --git a/data/fft_f64_1/grid_search_laplace_fft_33_f64_m1_9_5_64_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_33_f64_m1_9_5_64_1000000.csv deleted file mode 100644 index b11c60f9..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_33_f64_m1_9_5_64_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,9,64,3498,0.00000000021553372389520778,0.00000000037141593359165344,0.0000000006141323217507616,0.00000000038741810927020284,3221 diff --git a/data/fft_f64_1/grid_search_laplace_fft_34_f64_m1_10_5_64_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_34_f64_m1_10_5_64_1000000.csv deleted file mode 100644 index a35edfa6..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_34_f64_m1_10_5_64_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,10,64,6187,0.000000000014970181371864145,0.000000000021195864797384797,0.00000000003488288867966985,0.000000000021794415120378065,3486 diff --git a/data/fft_f64_1/grid_search_laplace_fft_35_f64_m1_11_5_64_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_35_f64_m1_11_5_64_1000000.csv deleted file mode 100644 index 7229669a..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_35_f64_m1_11_5_64_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,11,64,13139,0.0000000000013449553261033692,0.000000000001930021538710101,0.0000000000028292678699804097,0.0000000000019795309392416133,4885 diff --git a/data/fft_f64_1/grid_search_laplace_fft_36_f64_m1_6_4_128_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_36_f64_m1_6_4_128_1000000.csv deleted file mode 100644 index cd8b6f3e..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_36_f64_m1_6_4_128_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,6,128,1331,0.000000016686702299490217,0.0000001806345983488817,0.0000007584427734354027,0.000000211894886813628,1133 diff --git a/data/fft_f64_1/grid_search_laplace_fft_37_f64_m1_7_4_128_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_37_f64_m1_7_4_128_1000000.csv deleted file mode 100644 index 4b9af556..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_37_f64_m1_7_4_128_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,7,128,1421,0.0000000012761168495142224,0.00000001493895643652047,0.00000004286895467535286,0.000000016375904107358673,1273 diff --git a/data/fft_f64_1/grid_search_laplace_fft_38_f64_m1_8_4_128_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_38_f64_m1_8_4_128_1000000.csv deleted file mode 100644 index 7a9620be..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_38_f64_m1_8_4_128_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,8,128,1536,0.00000000002776493525828481,0.0000000014979825499069186,0.000000006541376024334807,0.0000000016842635718787517,1545 diff --git a/data/fft_f64_1/grid_search_laplace_fft_39_f64_m1_9_4_128_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_39_f64_m1_9_4_128_1000000.csv deleted file mode 100644 index f1fe4a8c..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_39_f64_m1_9_4_128_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,9,128,1727,0.00000000006671614691618004,0.000000000165335668400375,0.0000000005717558733701871,0.00000000019006236803274044,2361 diff --git a/data/fft_f64_1/grid_search_laplace_fft_3_f64_m1_9_4_16_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_3_f64_m1_9_4_16_1000000.csv deleted file mode 100644 index 7cbea65b..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_3_f64_m1_9_4_16_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,9,16,1783,0.00000000006671614691618004,0.000000000165335668400375,0.0000000005717558733701871,0.00000000019006236803274044,2389 diff --git a/data/fft_f64_1/grid_search_laplace_fft_40_f64_m1_10_4_128_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_40_f64_m1_10_4_128_1000000.csv deleted file mode 100644 index 22771c6e..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_40_f64_m1_10_4_128_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,10,128,1870,0.0000000000005858423417081946,0.000000000014318952674526936,0.00000000009358838086157819,0.000000000018006086872929633,2573 diff --git a/data/fft_f64_1/grid_search_laplace_fft_41_f64_m1_11_4_128_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_41_f64_m1_11_4_128_1000000.csv deleted file mode 100644 index 736be11b..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_41_f64_m1_11_4_128_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,11,128,2217,0.00000000000016981362434069548,0.0000000000011634261801119752,0.0000000000060448245956206634,0.0000000000013546754842041066,3847 diff --git a/data/fft_f64_1/grid_search_laplace_fft_42_f64_m1_6_5_128_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_42_f64_m1_6_5_128_1000000.csv deleted file mode 100644 index 54b7014a..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_42_f64_m1_6_5_128_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,6,128,1104,0.00000007363947317807973,0.00000027384696535735265,0.0000004085263879221838,0.00000028466801173195193,1516 diff --git a/data/fft_f64_1/grid_search_laplace_fft_43_f64_m1_7_5_128_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_43_f64_m1_7_5_128_1000000.csv deleted file mode 100644 index 5c0f206d..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_43_f64_m1_7_5_128_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,7,128,1647,0.000000020760057606409505,0.0000000313831060299183,0.000000046302488262898814,0.000000032194309086371234,1748 diff --git a/data/fft_f64_1/grid_search_laplace_fft_44_f64_m1_8_5_128_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_44_f64_m1_8_5_128_1000000.csv deleted file mode 100644 index 07206ab9..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_44_f64_m1_8_5_128_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,8,128,2341,0.0000000006775990793078764,0.0000000026624143464007455,0.000000004205206987239361,0.0000000028020022441379728,2146 diff --git a/data/fft_f64_1/grid_search_laplace_fft_45_f64_m1_9_5_128_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_45_f64_m1_9_5_128_1000000.csv deleted file mode 100644 index 483142ce..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_45_f64_m1_9_5_128_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,9,128,3450,0.00000000021553372389520778,0.00000000037141593359165344,0.0000000006141323217507616,0.00000000038741810927020284,3365 diff --git a/data/fft_f64_1/grid_search_laplace_fft_46_f64_m1_10_5_128_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_46_f64_m1_10_5_128_1000000.csv deleted file mode 100644 index 7b1bdb4c..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_46_f64_m1_10_5_128_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,10,128,6333,0.000000000014970181371864145,0.000000000021195864797384797,0.00000000003488288867966985,0.000000000021794415120378065,3454 diff --git a/data/fft_f64_1/grid_search_laplace_fft_47_f64_m1_11_5_128_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_47_f64_m1_11_5_128_1000000.csv deleted file mode 100644 index 723d24f5..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_47_f64_m1_11_5_128_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,11,128,13430,0.0000000000013449553261033692,0.000000000001930021538710101,0.0000000000028292678699804097,0.0000000000019795309392416133,4926 diff --git a/data/fft_f64_1/grid_search_laplace_fft_4_f64_m1_10_4_16_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_4_f64_m1_10_4_16_1000000.csv deleted file mode 100644 index 4f4c41fa..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_4_f64_m1_10_4_16_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,10,16,2083,0.0000000000005858423417081946,0.000000000014318952674526936,0.00000000009358838086157819,0.000000000018006086872929633,2495 diff --git a/data/fft_f64_1/grid_search_laplace_fft_5_f64_m1_11_4_16_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_5_f64_m1_11_4_16_1000000.csv deleted file mode 100644 index 5c5235ee..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_5_f64_m1_11_4_16_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -4,11,16,2665,0.00000000000016981362434069548,0.0000000000011634261801119752,0.0000000000060448245956206634,0.0000000000013546754842041066,3689 diff --git a/data/fft_f64_1/grid_search_laplace_fft_6_f64_m1_6_5_16_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_6_f64_m1_6_5_16_1000000.csv deleted file mode 100644 index b7978af5..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_6_f64_m1_6_5_16_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,6,16,1332,0.00000007363947317807973,0.00000027384696535735265,0.0000004085263879221838,0.00000028466801173195193,1502 diff --git a/data/fft_f64_1/grid_search_laplace_fft_7_f64_m1_7_5_16_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_7_f64_m1_7_5_16_1000000.csv deleted file mode 100644 index bfb90120..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_7_f64_m1_7_5_16_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,7,16,2597,0.000000020760057606409505,0.0000000313831060299183,0.000000046302488262898814,0.000000032194309086371234,1748 diff --git a/data/fft_f64_1/grid_search_laplace_fft_8_f64_m1_8_5_16_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_8_f64_m1_8_5_16_1000000.csv deleted file mode 100644 index 8684a0df..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_8_f64_m1_8_5_16_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,8,16,3871,0.0000000006775990793078764,0.0000000026624143464007455,0.000000004205206987239361,0.0000000028020022441379728,2167 diff --git a/data/fft_f64_1/grid_search_laplace_fft_9_f64_m1_9_5_16_1000000.csv b/data/fft_f64_1/grid_search_laplace_fft_9_f64_m1_9_5_16_1000000.csv deleted file mode 100644 index a6da8ad5..00000000 --- a/data/fft_f64_1/grid_search_laplace_fft_9_f64_m1_9_5_16_1000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,9,16,6463,0.00000000021553372389520778,0.00000000037141593359165344,0.0000000006141323217507616,0.00000000038741810927020284,3275 diff --git a/data/fft_f64_8/grid_search_laplace_fft_0_f64_m1_3_5_16_8000000.csv b/data/fft_f64_8/grid_search_laplace_fft_0_f64_m1_3_5_16_8000000.csv deleted file mode 100644 index 8a28885c..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_0_f64_m1_3_5_16_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,3,16,9475,0.0004302094661582775,0.00046954050705483785,0.0005225590372846689,0.0004693975719874745,3866 diff --git a/data/fft_f64_8/grid_search_laplace_fft_0_f64_m1_6_5_16_16.csv b/data/fft_f64_8/grid_search_laplace_fft_0_f64_m1_6_5_16_16.csv deleted file mode 100644 index bf93b741..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_0_f64_m1_6_5_16_16.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,6,16,10684,0.0000000018085910958350794,0.00000027833851082400454,0.0000006058196768266803,0.0000002939845117603853,4157 diff --git a/data/fft_f64_8/grid_search_laplace_fft_0_f64_m1_6_6_16_16.csv b/data/fft_f64_8/grid_search_laplace_fft_0_f64_m1_6_6_16_16.csv deleted file mode 100644 index ad8415cb..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_0_f64_m1_6_6_16_16.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -6,6,16,20248,0.0000002852841387803858,0.000000490332516469548,0.0000006666482311861262,0.0000005076318473697164,5995 diff --git a/data/fft_f64_8/grid_search_laplace_fft_10_f64_m1_10_5_32_32.csv b/data/fft_f64_8/grid_search_laplace_fft_10_f64_m1_10_5_32_32.csv deleted file mode 100644 index 6dc26112..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_10_f64_m1_10_5_32_32.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,10,32,18611,0.000000000003209449173744516,0.00000000001977977698348399,0.00000000006610840164089346,0.000000000020958030444558576,6028 diff --git a/data/fft_f64_8/grid_search_laplace_fft_10_f64_m1_3_6_64_8000000.csv b/data/fft_f64_8/grid_search_laplace_fft_10_f64_m1_3_6_64_8000000.csv deleted file mode 100644 index f823bbbb..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_10_f64_m1_3_6_64_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -6,3,64,2375,0.0005657645803314287,0.0005748734450373959,0.0005876071582981932,0.0005746622153030085,5097 diff --git a/data/fft_f64_8/grid_search_laplace_fft_11_f64_m1_11_5_32_32.csv b/data/fft_f64_8/grid_search_laplace_fft_11_f64_m1_11_5_32_32.csv deleted file mode 100644 index 00ef28f4..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_11_f64_m1_11_5_32_32.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,11,32,25233,0.0000000000002474688982449729,0.0000000000018509659491341897,0.000000000004404429213456524,0.0000000000019383191959881632,7460 diff --git a/data/fft_f64_8/grid_search_laplace_fft_11_f64_m1_4_6_64_8000000.csv b/data/fft_f64_8/grid_search_laplace_fft_11_f64_m1_4_6_64_8000000.csv deleted file mode 100644 index 7d5a3ecc..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_11_f64_m1_4_6_64_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -6,4,64,4881,0.00008435830741809251,0.00010984211976832667,0.00012372103266019484,0.00011054320386726452,5239 diff --git a/data/fft_f64_8/grid_search_laplace_fft_12_f64_m1_3_5_128_8000000.csv b/data/fft_f64_8/grid_search_laplace_fft_12_f64_m1_3_5_128_8000000.csv deleted file mode 100644 index b2699eca..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_12_f64_m1_3_5_128_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,3,128,9461,0.0004302094661582775,0.00046954050705483785,0.0005225590372846689,0.0004693975719874745,3894 diff --git a/data/fft_f64_8/grid_search_laplace_fft_12_f64_m1_6_5_64_64.csv b/data/fft_f64_8/grid_search_laplace_fft_12_f64_m1_6_5_64_64.csv deleted file mode 100644 index 7ac2c814..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_12_f64_m1_6_5_64_64.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,6,64,11284,0.0000000018085910958350794,0.00000027833851082400454,0.0000006058196768266803,0.0000002939845117603853,4300 diff --git a/data/fft_f64_8/grid_search_laplace_fft_13_f64_m1_4_5_128_8000000.csv b/data/fft_f64_8/grid_search_laplace_fft_13_f64_m1_4_5_128_8000000.csv deleted file mode 100644 index 1505c239..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_13_f64_m1_4_5_128_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,4,128,9659,0.000018623691843535644,0.00006629082280926517,0.00012655915349306347,0.00006863001374941591,3883 diff --git a/data/fft_f64_8/grid_search_laplace_fft_13_f64_m1_7_5_64_64.csv b/data/fft_f64_8/grid_search_laplace_fft_13_f64_m1_7_5_64_64.csv deleted file mode 100644 index 403156db..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_13_f64_m1_7_5_64_64.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,7,64,12626,0.000000008499656438237967,0.00000002967474956988892,0.00000006065169002641236,0.000000030592830030632814,4489 diff --git a/data/fft_f64_8/grid_search_laplace_fft_14_f64_m1_3_6_128_8000000.csv b/data/fft_f64_8/grid_search_laplace_fft_14_f64_m1_3_6_128_8000000.csv deleted file mode 100644 index b2b18e14..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_14_f64_m1_3_6_128_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -6,3,128,2367,0.0005657645803314287,0.0005748734450373959,0.0005876071582981932,0.0005746622153030085,5106 diff --git a/data/fft_f64_8/grid_search_laplace_fft_14_f64_m1_8_5_64_64.csv b/data/fft_f64_8/grid_search_laplace_fft_14_f64_m1_8_5_64_64.csv deleted file mode 100644 index 178fbc2d..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_14_f64_m1_8_5_64_64.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,8,64,12236,0.00000000030899035730904755,0.0000000024785245691022726,0.000000005816543064295436,0.0000000026090605207527885,5124 diff --git a/data/fft_f64_8/grid_search_laplace_fft_15_f64_m1_4_6_128_8000000.csv b/data/fft_f64_8/grid_search_laplace_fft_15_f64_m1_4_6_128_8000000.csv deleted file mode 100644 index 6eeee5ad..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_15_f64_m1_4_6_128_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -6,4,128,5090,0.00008435830741809251,0.00010984211976832667,0.00012372103266019484,0.00011054320386726452,5259 diff --git a/data/fft_f64_8/grid_search_laplace_fft_15_f64_m1_9_5_64_64.csv b/data/fft_f64_8/grid_search_laplace_fft_15_f64_m1_9_5_64_64.csv deleted file mode 100644 index 477125bb..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_15_f64_m1_9_5_64_64.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,9,64,13827,0.0000000001292219574041934,0.000000000336548279925672,0.000000000881821704938404,0.0000000003581873967442577,5856 diff --git a/data/fft_f64_8/grid_search_laplace_fft_16_f64_m1_10_5_64_64.csv b/data/fft_f64_8/grid_search_laplace_fft_16_f64_m1_10_5_64_64.csv deleted file mode 100644 index 312dee77..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_16_f64_m1_10_5_64_64.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,10,64,18719,0.000000000003209449173744516,0.00000000001977977698348399,0.00000000006610840164089346,0.000000000020958030444558576,6122 diff --git a/data/fft_f64_8/grid_search_laplace_fft_17_f64_m1_11_5_64_64.csv b/data/fft_f64_8/grid_search_laplace_fft_17_f64_m1_11_5_64_64.csv deleted file mode 100644 index 75123944..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_17_f64_m1_11_5_64_64.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,11,64,23998,0.0000000000002474688982449729,0.0000000000018509659491341897,0.000000000004404429213456524,0.0000000000019383191959881632,7480 diff --git a/data/fft_f64_8/grid_search_laplace_fft_18_f64_m1_6_5_128_128.csv b/data/fft_f64_8/grid_search_laplace_fft_18_f64_m1_6_5_128_128.csv deleted file mode 100644 index bc0388e2..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_18_f64_m1_6_5_128_128.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,6,128,13085,0.0000000018085910958350794,0.00000027833851082400454,0.0000006058196768266803,0.0000002939845117603853,4243 diff --git a/data/fft_f64_8/grid_search_laplace_fft_19_f64_m1_7_5_128_128.csv b/data/fft_f64_8/grid_search_laplace_fft_19_f64_m1_7_5_128_128.csv deleted file mode 100644 index 3f556c28..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_19_f64_m1_7_5_128_128.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,7,128,12442,0.000000008499656438237967,0.00000002967474956988892,0.00000006065169002641236,0.000000030592830030632814,4416 diff --git a/data/fft_f64_8/grid_search_laplace_fft_1_f64_m1_4_5_16_8000000.csv b/data/fft_f64_8/grid_search_laplace_fft_1_f64_m1_4_5_16_8000000.csv deleted file mode 100644 index 292aef63..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_1_f64_m1_4_5_16_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,4,16,9716,0.000018623691843535644,0.00006629082280926517,0.00012655915349306347,0.00006863001374941591,3908 diff --git a/data/fft_f64_8/grid_search_laplace_fft_1_f64_m1_7_5_16_16.csv b/data/fft_f64_8/grid_search_laplace_fft_1_f64_m1_7_5_16_16.csv deleted file mode 100644 index 61471ab0..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_1_f64_m1_7_5_16_16.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,7,16,11903,0.000000008499656438237967,0.00000002967474956988892,0.00000006065169002641236,0.000000030592830030632814,4475 diff --git a/data/fft_f64_8/grid_search_laplace_fft_1_f64_m1_7_6_16_16.csv b/data/fft_f64_8/grid_search_laplace_fft_1_f64_m1_7_6_16_16.csv deleted file mode 100644 index 5a1b0b8f..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_1_f64_m1_7_6_16_16.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -6,7,16,43875,0.00000004086067052549173,0.00000005115037059510694,0.00000006422988427871442,0.000000051870925441346174,6504 diff --git a/data/fft_f64_8/grid_search_laplace_fft_20_f64_m1_8_5_128_128.csv b/data/fft_f64_8/grid_search_laplace_fft_20_f64_m1_8_5_128_128.csv deleted file mode 100644 index ae89ce8c..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_20_f64_m1_8_5_128_128.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,8,128,12760,0.00000000030899035730904755,0.0000000024785245691022726,0.000000005816543064295436,0.0000000026090605207527885,4834 diff --git a/data/fft_f64_8/grid_search_laplace_fft_21_f64_m1_9_5_128_128.csv b/data/fft_f64_8/grid_search_laplace_fft_21_f64_m1_9_5_128_128.csv deleted file mode 100644 index 576a8e63..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_21_f64_m1_9_5_128_128.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,9,128,16400,0.0000000001292219574041934,0.000000000336548279925672,0.000000000881821704938404,0.0000000003581873967442577,6289 diff --git a/data/fft_f64_8/grid_search_laplace_fft_22_f64_m1_10_5_128_128.csv b/data/fft_f64_8/grid_search_laplace_fft_22_f64_m1_10_5_128_128.csv deleted file mode 100644 index c8c913cc..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_22_f64_m1_10_5_128_128.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,10,128,19029,0.000000000003209449173744516,0.00000000001977977698348399,0.00000000006610840164089346,0.000000000020958030444558576,6189 diff --git a/data/fft_f64_8/grid_search_laplace_fft_23_f64_m1_11_5_128_128.csv b/data/fft_f64_8/grid_search_laplace_fft_23_f64_m1_11_5_128_128.csv deleted file mode 100644 index 8c05d7a3..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_23_f64_m1_11_5_128_128.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,11,128,24240,0.0000000000002474688982449729,0.0000000000018509659491341897,0.000000000004404429213456524,0.0000000000019383191959881632,7465 diff --git a/data/fft_f64_8/grid_search_laplace_fft_2_f64_m1_3_6_16_8000000.csv b/data/fft_f64_8/grid_search_laplace_fft_2_f64_m1_3_6_16_8000000.csv deleted file mode 100644 index 9e1299aa..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_2_f64_m1_3_6_16_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -6,3,16,3007,0.0005657645803314287,0.0005748734450373959,0.0005876071582981932,0.0005746622153030085,5152 diff --git a/data/fft_f64_8/grid_search_laplace_fft_2_f64_m1_8_5_16_16.csv b/data/fft_f64_8/grid_search_laplace_fft_2_f64_m1_8_5_16_16.csv deleted file mode 100644 index d75f8f4d..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_2_f64_m1_8_5_16_16.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,8,16,15089,0.00000000030899035730904755,0.0000000024785245691022726,0.000000005816543064295436,0.0000000026090605207527885,5104 diff --git a/data/fft_f64_8/grid_search_laplace_fft_2_f64_m1_8_6_16_16.csv b/data/fft_f64_8/grid_search_laplace_fft_2_f64_m1_8_6_16_16.csv deleted file mode 100644 index 7163e935..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_2_f64_m1_8_6_16_16.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -6,8,16,120908,0.0000000030530646268500647,0.000000004711174702371123,0.000000006279905852902143,0.000000004868062124009261,7352 diff --git a/data/fft_f64_8/grid_search_laplace_fft_3_f64_m1_4_6_16_8000000.csv b/data/fft_f64_8/grid_search_laplace_fft_3_f64_m1_4_6_16_8000000.csv deleted file mode 100644 index cb78c331..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_3_f64_m1_4_6_16_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -6,4,16,5335,0.00008435830741809251,0.00010984211976832667,0.00012372103266019484,0.00011054320386726452,5338 diff --git a/data/fft_f64_8/grid_search_laplace_fft_3_f64_m1_9_5_16_16.csv b/data/fft_f64_8/grid_search_laplace_fft_3_f64_m1_9_5_16_16.csv deleted file mode 100644 index e537df75..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_3_f64_m1_9_5_16_16.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,9,16,17282,0.0000000001292219574041934,0.000000000336548279925672,0.000000000881821704938404,0.0000000003581873967442577,5855 diff --git a/data/fft_f64_8/grid_search_laplace_fft_4_f64_m1_10_5_16_16.csv b/data/fft_f64_8/grid_search_laplace_fft_4_f64_m1_10_5_16_16.csv deleted file mode 100644 index 75f2341a..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_4_f64_m1_10_5_16_16.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,10,16,19936,0.000000000003209449173744516,0.00000000001977977698348399,0.00000000006610840164089346,0.000000000020958030444558576,6430 diff --git a/data/fft_f64_8/grid_search_laplace_fft_4_f64_m1_3_5_32_8000000.csv b/data/fft_f64_8/grid_search_laplace_fft_4_f64_m1_3_5_32_8000000.csv deleted file mode 100644 index 5d59e055..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_4_f64_m1_3_5_32_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,3,32,9774,0.0004302094661582775,0.00046954050705483785,0.0005225590372846689,0.0004693975719874745,3935 diff --git a/data/fft_f64_8/grid_search_laplace_fft_5_f64_m1_11_5_16_16.csv b/data/fft_f64_8/grid_search_laplace_fft_5_f64_m1_11_5_16_16.csv deleted file mode 100644 index fb2dd54d..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_5_f64_m1_11_5_16_16.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,11,16,26403,0.0000000000002474688982449729,0.0000000000018509659491341897,0.000000000004404429213456524,0.0000000000019383191959881632,7532 diff --git a/data/fft_f64_8/grid_search_laplace_fft_5_f64_m1_4_5_32_8000000.csv b/data/fft_f64_8/grid_search_laplace_fft_5_f64_m1_4_5_32_8000000.csv deleted file mode 100644 index 2570c365..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_5_f64_m1_4_5_32_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,4,32,9748,0.000018623691843535644,0.00006629082280926517,0.00012655915349306347,0.00006863001374941591,3924 diff --git a/data/fft_f64_8/grid_search_laplace_fft_6_f64_m1_3_6_32_8000000.csv b/data/fft_f64_8/grid_search_laplace_fft_6_f64_m1_3_6_32_8000000.csv deleted file mode 100644 index 054d09c1..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_6_f64_m1_3_6_32_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -6,3,32,2427,0.0005657645803314287,0.0005748734450373959,0.0005876071582981932,0.0005746622153030085,5173 diff --git a/data/fft_f64_8/grid_search_laplace_fft_6_f64_m1_6_5_32_32.csv b/data/fft_f64_8/grid_search_laplace_fft_6_f64_m1_6_5_32_32.csv deleted file mode 100644 index e506a293..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_6_f64_m1_6_5_32_32.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,6,32,10778,0.0000000018085910958350794,0.00000027833851082400454,0.0000006058196768266803,0.0000002939845117603853,4102 diff --git a/data/fft_f64_8/grid_search_laplace_fft_6_f64_m1_6_6_16_16.csv b/data/fft_f64_8/grid_search_laplace_fft_6_f64_m1_6_6_16_16.csv deleted file mode 100644 index 3831bea5..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_6_f64_m1_6_6_16_16.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -6,6,16,23075,0.0000002852841387803858,0.000000490332516469548,0.0000006666482311861262,0.0000005076318473697164,6616 diff --git a/data/fft_f64_8/grid_search_laplace_fft_7_f64_m1_4_6_32_8000000.csv b/data/fft_f64_8/grid_search_laplace_fft_7_f64_m1_4_6_32_8000000.csv deleted file mode 100644 index 1fa284be..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_7_f64_m1_4_6_32_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -6,4,32,5803,0.00008435830741809251,0.00010984211976832667,0.00012372103266019484,0.00011054320386726452,5345 diff --git a/data/fft_f64_8/grid_search_laplace_fft_7_f64_m1_7_5_32_32.csv b/data/fft_f64_8/grid_search_laplace_fft_7_f64_m1_7_5_32_32.csv deleted file mode 100644 index 9c8502eb..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_7_f64_m1_7_5_32_32.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,7,32,11952,0.000000008499656438237967,0.00000002967474956988892,0.00000006065169002641236,0.000000030592830030632814,4491 diff --git a/data/fft_f64_8/grid_search_laplace_fft_7_f64_m1_7_6_16_16.csv b/data/fft_f64_8/grid_search_laplace_fft_7_f64_m1_7_6_16_16.csv deleted file mode 100644 index 4f2349bb..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_7_f64_m1_7_6_16_16.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -6,7,16,57901,0.00000004086067052549173,0.00000005115037059510694,0.00000006422988427871442,0.000000051870925441346174,6947 diff --git a/data/fft_f64_8/grid_search_laplace_fft_8_f64_m1_3_5_64_8000000.csv b/data/fft_f64_8/grid_search_laplace_fft_8_f64_m1_3_5_64_8000000.csv deleted file mode 100644 index 284b7e7d..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_8_f64_m1_3_5_64_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,3,64,9478,0.0004302094661582775,0.00046954050705483785,0.0005225590372846689,0.0004693975719874745,3880 diff --git a/data/fft_f64_8/grid_search_laplace_fft_8_f64_m1_8_5_32_32.csv b/data/fft_f64_8/grid_search_laplace_fft_8_f64_m1_8_5_32_32.csv deleted file mode 100644 index f089f6ce..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_8_f64_m1_8_5_32_32.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,8,32,12468,0.00000000030899035730904755,0.0000000024785245691022726,0.000000005816543064295436,0.0000000026090605207527885,5179 diff --git a/data/fft_f64_8/grid_search_laplace_fft_8_f64_m1_8_6_16_16.csv b/data/fft_f64_8/grid_search_laplace_fft_8_f64_m1_8_6_16_16.csv deleted file mode 100644 index d9b29db4..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_8_f64_m1_8_6_16_16.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -6,8,16,124471,0.0000000030530646268500647,0.000000004711174702371123,0.000000006279905852902143,0.000000004868062124009261,7257 diff --git a/data/fft_f64_8/grid_search_laplace_fft_9_f64_m1_4_5_64_8000000.csv b/data/fft_f64_8/grid_search_laplace_fft_9_f64_m1_4_5_64_8000000.csv deleted file mode 100644 index efd131c3..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_9_f64_m1_4_5_64_8000000.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,4,64,9821,0.000018623691843535644,0.00006629082280926517,0.00012655915349306347,0.00006863001374941591,3894 diff --git a/data/fft_f64_8/grid_search_laplace_fft_9_f64_m1_9_5_32_32.csv b/data/fft_f64_8/grid_search_laplace_fft_9_f64_m1_9_5_32_32.csv deleted file mode 100644 index bafa748b..00000000 --- a/data/fft_f64_8/grid_search_laplace_fft_9_f64_m1_9_5_32_32.csv +++ /dev/null @@ -1,2 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,9,32,13792,0.0000000001292219574041934,0.000000000336548279925672,0.000000000881821704938404,0.0000000003581873967442577,5947 diff --git a/data/grid_search_laplace_blas_uniform_f64_m1_8000000_4_2_6_35.csv b/data/grid_search_laplace_blas_uniform_f64_m1_8000000_4_2_6_35.csv deleted file mode 100644 index b33d5559..00000000 --- a/data/grid_search_laplace_blas_uniform_f64_m1_8000000_4_2_6_35.csv +++ /dev/null @@ -1,6 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -6,2,0,4,11370,0.0000025098687611969817,0.000026132380413960342,0.0000375953401996105,0.00002841103774170237,0,20,12054,55,1 -6,2,0.0000001,4,9446,0.0000025098990891793045,0.000026132418535827266,0.000037595389268855866,0.00002841107382258229,0,20,12047,55,1 -6,2,0.00001,4,8945,0.000002510140946889874,0.000026132846873120187,0.000037596106654526306,0.000028411495282191114,0,20,12313,55,1 -6,2,0.001,4,8527,0.0000025853253866962188,0.000026241484115784815,0.00003777111037526696,0.000028518171272455034,0,20,13984,55,1 -6,2,0.1,4,6449,0.000047324530653180654,0.00007046884210677857,0.00008365934070288635,0.0000716187531594754,0,20,16988,33,1 diff --git a/data/grid_search_laplace_f32_blas_m1_1.csv b/data/grid_search_laplace_f32_blas_m1_1.csv deleted file mode 100644 index 833244b6..00000000 --- a/data/grid_search_laplace_f32_blas_m1_1.csv +++ /dev/null @@ -1,271 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0.0,2,562,0.03487918,0.040229473,0.053911474,0.03865559,0,20,449,4,1 -4,0,0.0,3,585,5.0574286e-06,0.00043314937,0.0006942123,0.00046776718,0,20,467,13,1 -4,0,0.0,4,571,3.577735e-05,8.145509e-05,0.00013965143,8.302265e-05,0,20,480,28,1 -4,0,1e-07,2,580,0.034879345,0.040229622,0.053911656,0.038655724,0,20,458,4,1 -4,0,1e-07,3,723,5.0574286e-06,0.00043317646,0.0006942877,0.0004677927,0,20,461,13,1 -4,0,1e-07,4,567,3.5928304e-05,8.150327e-05,0.00013965143,8.306787e-05,0,20,496,28,1 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@@ -1,25 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,3,128,156,0.00043466355,0.0004631569,0.00048573408,0.00046296025,1156 -5,3,64,167,0.00043466355,0.0004631569,0.00048573408,0.00046296025,1157 -4,3,64,533,0.00029853775,0.00034218995,0.00038464146,0.0003419967,926 -4,4,64,525,6.038583e-07,4.300728e-05,0.00010778607,4.613934e-05,930 -5,4,32,252,1.5539365e-05,6.2159874e-05,8.493679e-05,6.458634e-05,1243 -5,4,16,278,1.5539365e-05,6.2159874e-05,8.493679e-05,6.458634e-05,1204 -4,4,128,516,6.038583e-07,4.300728e-05,0.00010778607,4.613934e-05,913 -5,4,64,233,1.5539365e-05,6.2159874e-05,8.493679e-05,6.458634e-05,1207 -4,3,128,481,0.00029853775,0.00034218995,0.00038464146,0.0003419967,863 -4,3,16,493,0.00029853775,0.00034218995,0.00038464146,0.0003419967,855 -4,5,64,523,0.0,4.249616e-06,1.3534111e-05,5.1436073e-06,949 -4,5,128,521,0.0,4.249616e-06,1.3534111e-05,5.1436073e-06,989 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a/data/grid_search_laplace_f32_fft_m1_8.csv +++ /dev/null @@ -1,25 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,setup_time -5,5,16,4436,1.288938e-06,0.00012569428,0.00030825654,0.00014677555,3927 -6,3,16,1367,0.0003791737,0.0004618011,0.000542133,0.00046473174,5022 -6,5,64,3694,4.457746e-05,0.00011925035,0.00019870921,0.00012959247,5275 -6,3,128,1292,0.0003791737,0.0004618011,0.000542133,0.00046473174,5092 -5,5,32,4355,1.288938e-06,0.00012569428,0.00030825654,0.00014677555,3910 -6,3,32,1367,0.0003791737,0.0004618011,0.000542133,0.00046473174,5127 -5,3,32,4109,0.00014249298,0.00045863597,0.000723327,0.0004806604,3755 -5,4,32,4196,5.5630085e-07,0.00014264838,0.0003826771,0.00016835216,3856 -6,5,16,4785,4.457746e-05,0.00011925035,0.00019870921,0.00012959247,5564 -6,5,32,3814,4.457746e-05,0.00011925035,0.00019870921,0.00012959247,5275 -5,4,128,4203,5.5630085e-07,0.00014264838,0.0003826771,0.00016835216,3890 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b/data/grid_search_laplace_f64_blas_m1_1.csv deleted file mode 100644 index b93b0782..00000000 --- a/data/grid_search_laplace_f64_blas_m1_1.csv +++ /dev/null @@ -1,631 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0.0,9,2984,1.3117023e-09,4.0800472e-09,6.3909624e-09,4.250302e-09,0,10,4520,193,1 -5,0,1e-07,9,1889,1.3048894e-09,4.072757e-09,6.3835413e-09,4.2433893e-09,0,10,4442,193,1 -5,0,1e-05,9,1784,1.24796e-11,1.1139577e-09,3.2839821e-09,1.4083504e-09,0,10,4467,193,1 -5,0,0.001,9,1339,6.574857e-10,1.06249065e-08,1.9038657e-08,1.20444263e-08,0,10,4233,127,1 -5,0,0.1,9,842,7.1904522e-08,2.3254019346e-06,4.0189604146e-06,2.6276866999e-06,0,10,4183,49,1 -5,1,0.0,8,2865,3.9473129e-09,7.9961677e-09,1.11344031e-08,8.1302439e-09,0,5,5032,193,1 -5,1,1e-07,8,1799,3.9411525e-09,7.9878213e-09,1.11199707e-08,8.1217422e-09,0,5,4958,193,1 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-depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,rel_l2_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,2,0.0,8,15136,2.36339e-10,1.1860555e-09,2.6318686e-09,1.2588247e-09,0,10,9989,244,1 -5,2,1e-07,8,13497,2.412571e-10,1.1790596e-09,2.6192374e-09,1.251773e-09,0,10,10008,244,1 -5,2,1e-05,8,13206,1.0792628e-09,2.5598887e-09,4.2112699e-09,2.6146628e-09,0,10,10544,242,1 -5,2,0.001,8,12374,2.22757e-11,6.0862848e-09,2.3498389e-08,7.9356137e-09,0,10,10707,127,1 -5,2,0.1,8,11503,3.81636382e-08,2.3547737226e-06,4.0120484198e-06,2.6260249453e-06,0,10,10847,49,1 -5,1,0.0,7,12865,2.35694e-11,7.8043815e-09,2.63442818e-08,1.00255095e-08,0,10,5828,148,1 -5,1,1e-07,7,11864,1.57611e-11,7.7974878e-09,2.63295734e-08,1.00172228e-08,0,10,5817,148,1 -5,1,1e-05,7,12226,4.38799e-11,6.4081038e-09,2.23392494e-08,8.1012187e-09,0,10,5968,148,1 -5,1,0.001,7,11448,4.000352662e-07,5.59922527e-07,7.319363806e-07,5.623656846e-07,0,10,6517,116,1 -5,1,0.1,7,10839,2.24565518e-08,4.2977134855e-06,2.02287580948e-05,5.3740724347e-06,0,10,6770,39,1 -6,0,0.0,7,20390,1.537991426e-07,2.468159929e-07,3.443941891e-07,2.51237889e-07,0,5,12723,109,1 -6,0,1e-07,7,16123,1.537763627e-07,2.467902247e-07,3.443623411e-07,2.512121941e-07,0,5,13407,109,1 -6,0,1e-05,7,16125,1.494552761e-07,2.422064455e-07,3.389377908e-07,2.466616024e-07,0,5,13920,109,1 -6,0,0.001,7,15374,3.649137909e-07,4.216122555e-07,4.978071187e-07,4.231897833e-07,0,5,13655,108,1 -6,0,0.1,7,9259,4.81785104e-06,9.1469800411e-06,1.72283619473e-05,1.03373385344e-05,0,5,13810,39,1 -5,0,0.0,6,11136,8.266886354e-07,1.4759473793e-06,2.3899077878e-06,1.5187143106e-06,0,20,4487,76,1 -5,0,1e-07,6,10942,8.266476811e-07,1.4758725168e-06,2.3897742971e-06,1.5186358386e-06,0,20,4369,76,1 -5,0,1e-05,6,10729,8.176618874e-07,1.464261646e-06,2.3734640612e-06,1.5069094484e-06,0,20,4356,76,1 -5,0,0.001,6,10739,3.277315499e-07,8.809979493e-07,1.6355696427e-06,9.276815293e-07,0,20,4918,76,1 -5,0,0.1,6,10448,4.03889647551e-05,4.96674167928e-05,5.27653604647e-05,4.97181222741e-05,0,20,5202,39,1 -5,0,0.0,4,9799,6.58429399204e-05,0.0001100093894547,0.000171167537232,0.0001112151123075,0,20,3929,28,1 -5,0,1e-07,4,9861,6.58429454695e-05,0.000110009433204,0.0001711675375876,0.00011121515654,0,20,3977,28,1 -5,0,1e-05,4,9849,6.58508982151e-05,0.0001100153837162,0.0001711712487329,0.0001112211094238,0,20,3961,28,1 -5,0,0.001,4,9819,6.50279516042e-05,0.0001091596581738,0.0001703679675051,0.0001103764925509,0,20,4036,28,1 -5,0,0.1,4,9840,9.49609076943e-05,0.0002027895285734,0.0002317690741151,0.0002035791780167,0,20,4815,28,1 -5,2,0.0,10,19433,2.508e-13,1.91372e-11,3.52464e-11,1.976e-11,0,5,22901,364,1 -5,2,1e-07,10,15950,8.4794e-12,2.59748e-11,4.51208e-11,2.65787e-11,0,5,24427,364,1 -5,2,1e-05,10,14517,1.5007144e-09,2.9679965e-09,5.028415e-09,3.0462786e-09,0,5,23619,268,1 -5,2,0.001,10,13580,2.36366073e-08,3.81643735e-08,5.13964858e-08,3.89602311e-08,0,5,23020,136,1 -5,2,0.1,10,12602,3.7008779e-09,1.4473326811e-06,4.3104018999e-06,1.669955839e-06,0,5,22343,50,1 -5,2,0.0,7,13992,1.07721e-11,3.0908547e-09,1.0581354e-08,3.723618e-09,0,20,7479,193,1 -5,2,1e-07,7,12611,1.86412e-11,3.091714e-09,1.05994018e-08,3.7246837e-09,0,20,7471,193,1 -5,2,1e-05,7,12381,1.17618e-11,3.1776878e-09,1.46666451e-08,4.2265625e-09,0,20,7837,193,1 -5,2,0.001,7,11681,3.399098546e-07,4.844788111e-07,6.473669032e-07,4.870942434e-07,0,20,8285,124,1 -5,2,0.1,7,10989,4.01150212e-08,4.1369300491e-06,2.14229324111e-05,5.7277060132e-06,0,20,8267,43,1 -5,1,0.0,6,11763,3.7209866e-09,2.504932551e-07,6.546014823e-07,2.872015442e-07,0,20,5079,109,1 -5,1,1e-07,6,11266,3.6742703e-09,2.504383591e-07,6.544901896e-07,2.871463035e-07,0,20,5018,109,1 -5,1,1e-05,6,11491,7.8491848e-09,2.395214846e-07,6.363414078e-07,2.763645426e-07,0,20,4939,109,1 -5,1,0.001,6,10917,3.76788114e-08,3.437777284e-07,7.182279051e-07,3.587847111e-07,0,20,5535,108,1 -5,1,0.1,6,11008,5.4805144929e-06,2.13009601442e-05,2.59780657085e-05,2.17064638789e-05,0,20,5843,39,1 -5,0,0.0,9,14582,1.033329e-10,3.7777141e-09,6.1706993e-09,3.9119325e-09,0,10,7233,193,1 -5,0,1e-07,9,13218,9.55333e-11,3.7714228e-09,6.1638754e-09,3.9059165e-09,0,10,7189,193,1 -5,0,1e-05,9,13000,1.33288e-11,1.0105628e-09,3.9169839e-09,1.2706603e-09,0,10,7679,193,1 -5,0,0.001,9,12072,9.1961e-11,6.5161088e-09,1.6658404e-08,7.5091489e-09,0,10,7853,127,1 -5,0,0.1,9,11793,2.237717652e-07,2.4951184186e-06,4.0688095106e-06,2.7358878132e-06,0,10,8069,49,1 -5,2,0.0,3,9906,3.4008829479e-06,6.08433088796e-05,0.0001532702903648,6.64159709017e-05,0,10,4033,25,1 -5,2,1e-07,3,9735,3.4008947065e-06,6.08433171617e-05,0.0001532702969693,6.64159783728e-05,0,10,3992,25,1 -5,2,1e-05,3,9727,3.367754846e-06,6.08227713527e-05,0.0001532521504147,6.63979605894e-05,0,10,3987,25,1 -5,2,0.001,3,9761,6.1917144893e-06,6.31887174503e-05,0.0001553853042847,6.8601175683e-05,0,10,4009,25,1 -5,2,0.1,3,9902,0.0003986769652708,0.0004506604345827,0.0005201273788537,0.0004508960569925,0,10,4477,25,1 -5,0,0.0,11,18016,2.65e-14,1.424341e-10,3.402956e-10,1.588921e-10,0,10,13668,301,1 -5,0,1e-07,11,15590,5.8339e-12,1.472524e-10,3.468003e-10,1.63668e-10,0,10,15023,301,1 -5,0,1e-05,11,14812,1.5406516e-09,3.0025878e-09,5.0592527e-09,3.0795922e-09,0,10,14262,268,1 -5,0,0.001,11,13537,2.26383729e-08,3.69421389e-08,5.03577844e-08,3.77853389e-08,0,10,13755,136,1 -5,0,0.1,11,12522,9.08087424e-08,1.4537006539e-06,4.0927454254e-06,1.6717242554e-06,0,10,13461,50,1 -5,1,0.0,6,11485,2.4843652e-09,2.855434584e-07,7.843567147e-07,3.301985641e-07,0,5,4941,109,1 -5,1,1e-07,6,11193,2.4623925e-09,2.854987575e-07,7.842559646e-07,3.301514407e-07,0,5,4879,109,1 -5,1,1e-05,6,11207,1.7350413e-09,2.768278843e-07,7.672141101e-07,3.211204204e-07,0,5,4966,109,1 -5,1,0.001,6,11016,2.98034134e-08,3.089595362e-07,9.472304898e-07,3.400017419e-07,0,5,5640,107,1 -5,1,0.1,6,10871,5.4437795285e-06,2.12389814878e-05,2.6049423882e-05,2.16453808639e-05,0,5,5717,39,1 -5,2,0.0,5,11483,6.8248213e-08,7.743838667e-07,1.6279327315e-06,8.166904307e-07,0,10,4946,97,1 -5,2,1e-07,5,11187,6.83702899e-08,7.744963071e-07,1.6281367355e-06,8.168036708e-07,0,10,4699,97,1 -5,2,1e-05,5,11381,8.2560343e-08,7.862101219e-07,1.6473229087e-06,8.285162175e-07,0,10,4700,97,1 -5,2,0.001,5,11017,8.353813817e-07,1.6155770726e-06,2.7956763632e-06,1.6511807393e-06,0,10,5539,97,1 -5,2,0.1,5,10597,2.00271484012e-05,4.78515334128e-05,9.48696718917e-05,4.99971467375e-05,0,10,5656,36,1 -6,0,0.0,4,5217,2.54468880091e-05,4.7996287057e-05,6.71995764389e-05,4.92426848993e-05,0,5,10755,28,1 -6,0,1e-07,4,5399,2.5446872022e-05,4.79962766479e-05,6.7199569398e-05,4.92426757622e-05,0,5,10577,28,1 -6,0,1e-05,4,4969,2.54654854475e-05,4.80150510832e-05,6.72168390898e-05,4.92608342849e-05,0,5,10989,28,1 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a/m1/f32/sphere/blas/grid_search_laplace_blas_high_sphere_f32_m1_3.csv b/m1/f32/sphere/blas/grid_search_laplace_blas_high_sphere_f32_m1_3.csv deleted file mode 100644 index 2f5c279a..00000000 --- a/m1/f32/sphere/blas/grid_search_laplace_blas_high_sphere_f32_m1_3.csv +++ /dev/null @@ -1,61 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,2,308,0.04526942,0.045536134,0.04568718,0,20,687,4,1 -5,0,0,3,311,0.001746798,0.0017662937,0.0017983238,0,20,631,13,1 -5,0,0,4,345,0.0002571507,0.00025959764,0.00026415905,0,20,644,28,1 -5,0,0,5,365,0.000056431152,0.000057736415,0.000060290196,0,20,728,49,1 -5,0,0.0000001,2,334,0.045269202,0.045535993,0.045687072,0,20,615,4,1 -5,0,0.0000001,3,307,0.0017466992,0.0017662937,0.0017983238,0,20,622,13,1 -5,0,0.0000001,4,324,0.0002568551,0.00025936778,0.00026386365,0,20,658,28,1 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a/m1/f32/sphere/blas/grid_search_laplace_blas_low_sphere_f32_m1_0.csv b/m1/f32/sphere/blas/grid_search_laplace_blas_low_sphere_f32_m1_0.csv deleted file mode 100644 index f0150568..00000000 --- a/m1/f32/sphere/blas/grid_search_laplace_blas_low_sphere_f32_m1_0.csv +++ /dev/null @@ -1,61 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,2,1082,0.033827607,0.034574322,0.035420127,0,0,437,7,0 -4,0,0,3,1085,0.00012025899,0.0002834068,0.00054509606,0,0,440,25,0 -4,0,0,4,1066,0.0000860602,0.00016414249,0.000182165,0,0,478,55,0 -4,0,0,5,1048,0.000034930672,0.000043807366,0.00005250807,0,0,637,97,0 -4,0,0.0000001,2,1045,0.033827502,0.034574203,0.035420023,0,0,436,7,0 -4,0,0.0000001,3,1067,0.000120455996,0.00028341537,0.00054499734,0,0,420,25,0 -4,0,0.0000001,4,1030,0.0000860602,0.00016416458,0.00018219459,0,0,468,55,0 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a/m1/f32/sphere/blas/grid_search_laplace_blas_low_sphere_f32_m1_1.csv b/m1/f32/sphere/blas/grid_search_laplace_blas_low_sphere_f32_m1_1.csv deleted file mode 100644 index a3afa869..00000000 --- a/m1/f32/sphere/blas/grid_search_laplace_blas_low_sphere_f32_m1_1.csv +++ /dev/null @@ -1,61 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,2,1096,0.038609385,0.03949894,0.040651783,0,5,416,4,1 -4,0,0,3,1091,0.00140186,0.0016987284,0.0021266541,0,5,422,13,1 -4,0,0,4,1050,0.000044559936,0.00020279345,0.0002348265,0,5,480,28,1 -4,0,0,5,1069,0.000064231,0.00007412857,0.00008329941,0,5,532,49,1 -4,0,0.0000001,2,1141,0.038609277,0.0394989,0.040651675,0,5,408,4,1 -4,0,0.0000001,3,1139,0.0014017618,0.0016986509,0.0021264562,0,5,448,13,1 -4,0,0.0000001,4,1102,0.000044461358,0.00020274795,0.00023472827,0,5,470,28,1 -4,0,0.0000001,5,1080,0.000064231,0.00007410931,0.00008329941,0,5,526,49,1 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b/m1/f32/sphere/blas/grid_search_laplace_blas_low_sphere_f32_m1_2.csv deleted file mode 100644 index 697b0cbe..00000000 --- a/m1/f32/sphere/blas/grid_search_laplace_blas_low_sphere_f32_m1_2.csv +++ /dev/null @@ -1,61 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,2,1049,0.03862505,0.039514333,0.040667348,0,10,470,4,1 -4,0,0,3,1050,0.0016625945,0.0019036836,0.0021535575,0,10,434,13,1 -4,0,0,4,1042,0.0001099139,0.00024793996,0.000280634,0,10,452,28,1 -4,0,0,5,1142,0.000066835084,0.000076688586,0.00008942393,0,10,534,49,1 -4,0,0.0000001,2,1060,0.038624946,0.039514244,0.04066724,0,10,422,4,1 -4,0,0.0000001,3,1111,0.0016625945,0.0019035813,0.0021534585,0,10,440,13,1 -4,0,0.0000001,4,1110,0.0001099139,0.00024791033,0.000280634,0,10,466,28,1 -4,0,0.0000001,5,1224,0.000066736655,0.0000766415,0.00008932567,0,10,543,49,1 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b/m1/f32/sphere/blas/grid_search_laplace_blas_low_sphere_f32_m1_3.csv deleted file mode 100644 index 2074f8c9..00000000 --- a/m1/f32/sphere/blas/grid_search_laplace_blas_low_sphere_f32_m1_3.csv +++ /dev/null @@ -1,61 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,2,1060,0.038628332,0.03951796,0.0406601,0,20,415,4,1 -4,0,0,3,1196,0.0012995595,0.0015626217,0.0018064377,0,20,467,13,1 -4,0,0,4,1112,0.00014953612,0.00025387117,0.0002829875,0,20,462,28,1 -4,0,0,5,1072,0.000044179513,0.000053969823,0.000063748856,0,20,534,49,1 -4,0,0.0000001,2,1056,0.03862844,0.039518032,0.040660203,0,20,430,4,1 -4,0,0.0000001,3,1087,0.0012995595,0.0015625992,0.0018063388,0,20,449,13,1 -4,0,0.0000001,4,1094,0.00014963468,0.00025395487,0.00028308554,0,20,468,28,1 -4,0,0.0000001,5,1085,0.000044179513,0.000054053526,0.00006384738,0,20,538,49,1 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b/m1/f32/sphere/blas/grid_search_laplace_blas_sphere_f64_high_4_m1_1.csv deleted file mode 100644 index 02e8d8f7..00000000 --- a/m1/f32/sphere/blas/grid_search_laplace_blas_sphere_f64_high_4_m1_1.csv +++ /dev/null @@ -1,31 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,8,3142,0.00000003535376025899344,0.00000005390996989324901,0.00000008505814301032505,0,5,1897,148,1 -4,0,0,9,2988,0.000000000010759277765785624,0.000000001475852448477653,0.000000006114580016751747,0,5,3330,193,1 -4,0,0.00000000001,8,3039,0.000000035353759891669684,0.00000005390996978385931,0.00000008505814374375351,0,5,1851,148,1 -4,0,0.00000000001,9,3151,0.000000000010760743782206611,0.0000000014758531143701946,0.0000000061145822171327754,0,5,3428,193,1 -4,0,0.000000001,8,2840,0.000000035353737117596594,0.00000005391001913376803,0.00000008505803317941306,0,5,1860,148,1 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-4,2,0.00001,9,2916,0.0000000002603264887658357,0.0000000024028445277047695,0.000000006445178891344145,0,5,11263,237,1 diff --git a/m1/f32/sphere/blas/grid_search_laplace_blas_sphere_f64_high_4_m1_2.csv b/m1/f32/sphere/blas/grid_search_laplace_blas_sphere_f64_high_4_m1_2.csv deleted file mode 100644 index d659e9a9..00000000 --- a/m1/f32/sphere/blas/grid_search_laplace_blas_sphere_f64_high_4_m1_2.csv +++ /dev/null @@ -1,31 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,8,2898,0.000000017501157381470922,0.00000003227333594598436,0.000000059158689244241346,0,10,1999,148,1 -4,0,0,9,3142,0.000000000010903158497748516,0.0000000015528924721646248,0.000000005452918148457757,0,10,3559,193,1 -4,0,0.00000000001,8,2971,0.00000001750115848243771,0.0000000322733356775348,0.00000005915868887708035,0,10,1882,148,1 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-4,3,16,1377,0.0001246917,0.00028712722,0.00056157855,944 -4,3,32,1293,0.0001246917,0.00028712722,0.00056157855,931 -4,3,64,1208,0.0001246917,0.00028712722,0.00056157855,944 -4,3,128,1286,0.0001246917,0.00028712722,0.00056157855,976 -4,4,16,1246,0.000087243054,0.00016430664,0.0001823618,1030 -4,4,32,1189,0.000087243054,0.00016430664,0.0001823618,1003 -4,4,64,1193,0.000087243054,0.00016430664,0.0001823618,1031 -4,4,128,1191,0.000087243054,0.00016430664,0.0001823618,1040 -4,5,16,1219,0.000036603346,0.00004548062,0.00005536482,1070 -4,5,32,1217,0.000036603346,0.00004548062,0.00005536482,1100 -4,5,64,1221,0.000036603346,0.00004548062,0.00005536482,1048 -4,5,128,1227,0.000036603346,0.00004548062,0.00005536482,1074 -5,3,16,343,0.0005809314,0.00060586265,0.00065426534,1223 -5,3,32,334,0.0005809314,0.00060586265,0.00065426534,1207 -5,3,64,330,0.0005809314,0.00060586265,0.00065426534,1241 -5,3,128,335,0.0005809314,0.00060586265,0.00065426534,1185 -5,4,16,415,0.00018639571,0.00019679317,0.00020477975,1265 -5,4,32,366,0.00018639571,0.00019679317,0.00020477975,1278 -5,4,64,364,0.00018639571,0.00019679317,0.00020477975,1258 -5,4,128,362,0.00018639571,0.00019679317,0.00020477975,1354 -5,5,16,482,0.000054363107,0.000056028923,0.000058221536,1342 -5,5,32,413,0.000054363107,0.000056028923,0.000058221536,1316 -5,5,64,412,0.000054363107,0.000056028923,0.000058221536,1300 -5,5,128,407,0.000054363107,0.000056028923,0.000058221536,1366 diff --git a/m1/f32/spheroid/blas/grid_search_laplace_blas_high_spheroid_f32_m1_0.csv b/m1/f32/spheroid/blas/grid_search_laplace_blas_high_spheroid_f32_m1_0.csv deleted file mode 100644 index 700a2a15..00000000 --- a/m1/f32/spheroid/blas/grid_search_laplace_blas_high_spheroid_f32_m1_0.csv +++ /dev/null @@ -1,61 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,2,453,0.036543857,0.036874846,0.037618395,0,0,538,7,0 -5,0,0,3,421,0.00004359123,0.000072796276,0.000118307085,0,0,533,25,0 -5,0,0,4,448,0.00017432193,0.00018325791,0.00019644073,0,0,573,55,0 -5,0,0,5,474,0.000086762404,0.00009207811,0.000098614175,0,0,709,97,0 -5,0,0.0000001,2,437,0.036543857,0.03687491,0.037618395,0,0,522,7,0 -5,0,0.0000001,3,513,0.00004359123,0.00007281429,0.000118307085,0,0,521,25,0 -5,0,0.0000001,4,484,0.00017415949,0.00018319036,0.00019635954,0,0,568,55,0 -5,0,0.0000001,5,470,0.000086681175,0.00009214566,0.000098614175,0,0,714,97,0 -5,0,0.00001,2,425,0.036543857,0.036874928,0.037618395,0,0,560,7,0 -5,0,0.00001,3,442,0.000043753585,0.00007284138,0.000118469616,0,0,556,25,0 -5,0,0.00001,4,460,0.00017432193,0.00018331641,0.00019644073,0,0,571,55,0 -5,0,0.00001,5,514,0.000086681175,0.00009210959,0.000098614175,0,0,706,97,0 -5,0,0.001,2,489,0.036545604,0.036876652,0.037620056,0,0,517,7,0 -5,0,0.001,3,505,0.000044727738,0.000074169846,0.00012017618,0,0,532,25,0 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-5,2,0.1,3,475,0.0010583757,0.001070401,0.0010788619,0,5,608,20,1 -5,2,0.1,4,473,0.00027631997,0.00028945965,0.00030579366,0,5,742,25,1 -5,2,0.1,5,500,0.00023756968,0.00024722662,0.0002571677,0,5,1081,30,1 diff --git a/m1/f32/spheroid/blas/grid_search_laplace_blas_high_spheroid_f32_m1_2.csv b/m1/f32/spheroid/blas/grid_search_laplace_blas_high_spheroid_f32_m1_2.csv deleted file mode 100644 index 2d1f1c58..00000000 --- a/m1/f32/spheroid/blas/grid_search_laplace_blas_high_spheroid_f32_m1_2.csv +++ /dev/null @@ -1,61 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,2,433,0.035972074,0.036229625,0.036808416,0,10,550,4,1 -5,0,0,3,443,0.00071892305,0.0007955282,0.0009205429,0,10,541,13,1 -5,0,0,4,444,0.000277132,0.00030399757,0.00032446327,0,10,565,28,1 -5,0,0,5,465,0.000088297034,0.000095245865,0.00010283427,0,10,648,49,1 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a/m1/f32/spheroid/blas/grid_search_laplace_blas_low_spheroid_f32_m1_0.csv b/m1/f32/spheroid/blas/grid_search_laplace_blas_low_spheroid_f32_m1_0.csv deleted file mode 100644 index 29a7833e..00000000 --- a/m1/f32/spheroid/blas/grid_search_laplace_blas_low_spheroid_f32_m1_0.csv +++ /dev/null @@ -1,61 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,2,1625,0.020543665,0.02490496,0.030506812,0,0,395,7,0 -4,0,0,3,1693,0.00000194745,0.00012010343,0.00019503513,0,0,395,25,0 -4,0,0,4,1644,0.00007549633,0.00018041262,0.00023570692,0,0,445,55,0 -4,0,0,5,1673,0.00008408164,0.00009648642,0.00010740309,0,0,582,97,0 -4,0,0.0000001,2,1644,0.020543495,0.024904916,0.030506812,0,0,412,7,0 -4,0,0.0000001,3,1656,0.00000194745,0.000120117584,0.00019503513,0,0,419,25,0 -4,0,0.0000001,4,1643,0.00007541585,0.00018038398,0.00023570692,0,0,462,55,0 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b/m1/f32/spheroid/blas/grid_search_laplace_blas_low_spheroid_f32_m1_1.csv deleted file mode 100644 index ad19280a..00000000 --- a/m1/f32/spheroid/blas/grid_search_laplace_blas_low_spheroid_f32_m1_1.csv +++ /dev/null @@ -1,61 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,2,1654,0.021602618,0.025549881,0.03036154,0,5,397,4,1 -4,0,0,3,1577,0.0000004052828,0.00029557841,0.0006234574,0,5,400,13,1 -4,0,0,4,1644,0.00018829077,0.00030884522,0.00040389723,0,5,441,28,1 -4,0,0,5,1638,0.00009352362,0.00010571333,0.00011823197,0,5,512,49,1 -4,0,0.0000001,2,1601,0.021602701,0.025549946,0.030361626,0,5,409,4,1 -4,0,0.0000001,3,1839,0.0000004863394,0.00029555813,0.00062353874,0,5,417,13,1 -4,0,0.0000001,4,1660,0.00018829077,0.00030882744,0.00040389723,0,5,434,28,1 -4,0,0.0000001,5,1873,0.00009352362,0.00010571486,0.00011823197,0,5,481,49,1 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--- a/m1/f32/spheroid/blas/grid_search_laplace_blas_low_spheroid_f32_m1_2.csv +++ /dev/null @@ -1,61 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,2,1696,0.021594426,0.025542153,0.03035464,0,10,397,4,1 -4,0,0,3,1564,0.000000080900556,0.000285212,0.00092542527,0,10,411,13,1 -4,0,0,4,1568,0.0001774079,0.000295411,0.0003615021,0,10,430,28,1 -4,0,0,5,1570,0.0000904701,0.000101242185,0.00011418061,0,10,516,49,1 -4,0,0.0000001,2,1566,0.021594593,0.025542283,0.030354725,0,10,422,4,1 -4,0,0.0000001,3,1599,0.00000016180111,0.0002852157,0.00092542527,0,10,421,13,1 -4,0,0.0000001,4,1576,0.00017724547,0.00029533156,0.00036134003,0,10,441,28,1 -4,0,0.0000001,5,1847,0.00009063107,0.00010131859,0.00011418061,0,10,506,49,1 -4,0,0.00001,2,1755,0.021594593,0.025542311,0.030354813,0,10,405,4,1 -4,0,0.00001,3,1612,0.000000080900556,0.00028521693,0.00092542527,0,10,411,13,1 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a/m1/f32/spheroid/blas/grid_search_laplace_blas_low_spheroid_f32_m1_3.csv +++ /dev/null @@ -1,61 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,2,1621,0.021603884,0.025550045,0.030360678,0,20,396,4,1 -4,0,0,3,1563,0.00006757186,0.00041126748,0.0009643339,0,20,412,13,1 -4,0,0,4,1536,0.00018244328,0.0002878698,0.00035460168,0,20,462,28,1 -4,0,0,5,1584,0.00008278347,0.000096439166,0.000108860884,0,20,524,49,1 -4,0,0.0000001,2,1553,0.021603884,0.025550054,0.03036059,0,20,421,4,1 -4,0,0.0000001,3,1590,0.00006757186,0.00041128648,0.0009643339,0,20,445,13,1 -4,0,0.0000001,4,1579,0.00018244328,0.00028787783,0.00035452074,0,20,451,28,1 -4,0,0.0000001,5,1676,0.00008294593,0.00009659197,0.00010910386,0,20,517,49,1 -4,0,0.00001,2,1597,0.021603884,0.025550008,0.03036059,0,20,413,4,1 -4,0,0.00001,3,1678,0.00006765279,0.00041133453,0.0009643339,0,20,419,13,1 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/dev/null @@ -1,25 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,setup_time -4,3,16,1859,0.0000016222442,0.000107547254,0.00018472072,919 -4,3,32,1780,0.0000016222442,0.000107547254,0.00018472072,922 -4,3,64,1769,0.0000016222442,0.000107547254,0.00018472072,929 -4,3,128,1794,0.0000016222442,0.000107547254,0.00018472072,928 -4,4,16,1897,0.000075822136,0.00018070707,0.00023594993,956 -4,4,32,1830,0.000075822136,0.00018070707,0.00023594993,965 -4,4,64,1930,0.000075822136,0.00018070707,0.00023594993,989 -4,4,128,1935,0.000075822136,0.00018070707,0.00023594993,985 -4,5,16,1867,0.00008481273,0.0000972065,0.00010797001,1005 -4,5,32,2247,0.00008481273,0.0000972065,0.00010797001,992 -4,5,64,2238,0.00008481273,0.0000972065,0.00010797001,999 -4,5,128,2008,0.00008481273,0.0000972065,0.00010797001,1008 -5,3,16,558,0.00005869075,0.00008751059,0.00013244731,1156 -5,3,32,736,0.00005869075,0.00008751059,0.00013244731,1158 -5,3,64,581,0.00005869075,0.00008751059,0.00013244731,1152 -5,3,128,425,0.00005869075,0.00008751059,0.00013244731,1163 -5,4,16,463,0.000174728,0.000183636,0.0001967655,1205 -5,4,32,456,0.000174728,0.000183636,0.0001967655,1236 -5,4,64,453,0.000174728,0.000183636,0.0001967655,1235 -5,4,128,451,0.000174728,0.000183636,0.0001967655,1220 -5,5,16,526,0.00008773718,0.000092956216,0.000099425735,1252 -5,5,32,484,0.00008773718,0.000092956216,0.000099425735,1275 -5,5,64,471,0.00008773718,0.000092956216,0.000099425735,1250 -5,5,128,474,0.00008773718,0.000092956216,0.000099425735,1271 diff --git a/m1/f32/uniform/blas/grid_search_laplace_blas_high_uniform_f32_m1_0.csv b/m1/f32/uniform/blas/grid_search_laplace_blas_high_uniform_f32_m1_0.csv deleted file mode 100644 index f342e4f4..00000000 --- a/m1/f32/uniform/blas/grid_search_laplace_blas_high_uniform_f32_m1_0.csv +++ /dev/null @@ -1,61 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,2,295,0.020739423,0.02375036,0.02585594,0,0,1177,7,0 -5,0,0,3,328,0.0004360919,0.00046462138,0.00048806585,0,0,1197,25,0 -5,0,0,4,429,0.000013795175,0.00006021627,0.0000831488,0,0,1243,55,0 -5,0,0,5,668,0.0000018972031,0.000006302219,0.000010864461,0,0,1422,97,0 -5,0,0.0000001,2,290,0.020739423,0.023750314,0.025855858,0,0,1160,7,0 -5,0,0.0000001,3,310,0.00043625062,0.0004646822,0.00048822665,0,0,1202,25,0 -5,0,0.0000001,4,411,0.000013795175,0.000060183716,0.00008323007,0,0,1257,55,0 -5,0,0.0000001,5,594,0.0000015810031,0.0000059368804,0.000010473657,0,0,1385,97,0 -5,0,0.00001,2,306,0.020739507,0.023750404,0.025856022,0,0,1226,7,0 -5,0,0.00001,3,303,0.00043625062,0.00046466824,0.00048822665,0,0,1208,25,0 -5,0,0.00001,4,398,0.000013636612,0.00006003844,0.00008306753,0,0,1263,55,0 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-depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,2,294,0.054142565,0.057895232,0.062662445,0,5,1322,4,1 -5,0,0,3,317,0.0005838939,0.0007184091,0.000818618,0,5,1247,13,1 -5,0,0,4,376,0.0000028631016,0.000024883482,0.000050357976,0,5,1272,28,1 -5,0,0,5,515,0.0000010348846,0.000005762463,0.000012691554,0,5,1506,49,1 -5,0,0.0000001,2,282,0.05414265,0.057895355,0.06266272,0,5,1318,4,1 -5,0,0.0000001,3,285,0.00058413803,0.0007187656,0.0008188526,0,5,1365,13,1 -5,0,0.0000001,4,365,0.0000029426324,0.000024747696,0.000050201834,0,5,1432,28,1 -5,0,0.0000001,5,448,0.0000011144912,0.000005785814,0.0000127718795,0,5,1444,49,1 -5,0,0.00001,2,263,0.054142565,0.05789528,0.062662534,0,5,1326,4,1 -5,0,0.00001,3,287,0.0005839753,0.00071856874,0.0008187744,0,5,1299,13,1 -5,0,0.00001,4,341,0.0000027835706,0.000024935245,0.000050514114,0,5,1264,28,1 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-4,2,0.1,4,540,0.0000025912398,0.00003341107,0.00007576444,0,5,672,33,1 -4,2,0.1,5,564,0.00000022077425,0.000024894254,0.000087878056,0,5,980,36,1 diff --git a/m1/f32/uniform/blas/grid_search_laplace_blas_low_uniform_f32_m1_2.csv b/m1/f32/uniform/blas/grid_search_laplace_blas_low_uniform_f32_m1_2.csv deleted file mode 100644 index b9506761..00000000 --- a/m1/f32/uniform/blas/grid_search_laplace_blas_low_uniform_f32_m1_2.csv +++ /dev/null @@ -1,61 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,2,529,0.034881216,0.040230524,0.053912647,0,10,479,4,1 -4,0,0,3,511,0.000014306568,0.00066006987,0.000964559,0,10,482,13,1 -4,0,0,4,516,0.0000005245604,0.000038628183,0.0001385993,0,10,548,28,1 -4,0,0,5,527,0.000000075704534,0.000007642119,0.000024283767,0,10,597,49,1 -4,0,0.0000001,2,509,0.034881134,0.04023037,0.053912375,0,10,485,4,1 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-4,2,0.1,5,565,0.0000004415486,0.000024902674,0.000087796754,0,10,909,36,1 diff --git a/m1/f32/uniform/blas/grid_search_laplace_blas_low_uniform_f32_m1_3.csv b/m1/f32/uniform/blas/grid_search_laplace_blas_low_uniform_f32_m1_3.csv deleted file mode 100644 index 789ecb5d..00000000 --- a/m1/f32/uniform/blas/grid_search_laplace_blas_low_uniform_f32_m1_3.csv +++ /dev/null @@ -1,61 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,2,538,0.034879345,0.04022969,0.053911656,0,20,464,4,1 -4,0,0,3,546,0.0000049771515,0.00043308246,0.0006940614,0,20,483,13,1 -4,0,0,4,535,0.000035833415,0.000081484686,0.00013972657,0,20,537,28,1 -4,0,0,5,532,0,0.0000030850708,0.000010575866,0,20,595,49,1 -4,0,0.0000001,2,493,0.034879263,0.04022955,0.053911563,0,20,486,4,1 -4,0,0.0000001,3,511,0.0000051377056,0.00043321043,0.0006942123,0,20,491,13,1 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a/m1/f32/uniform/fft/grid_search_laplace_fft_f32_m1_uniform.csv b/m1/f32/uniform/fft/grid_search_laplace_fft_f32_m1_uniform.csv deleted file mode 100644 index 2aa9aeac..00000000 --- a/m1/f32/uniform/fft/grid_search_laplace_fft_f32_m1_uniform.csv +++ /dev/null @@ -1,25 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,setup_time -4,3,16,561,0.00029853775,0.00034218995,0.00038464146,943 -4,3,32,546,0.00029853775,0.00034218995,0.00038464146,948 -4,3,64,556,0.00029853775,0.00034218995,0.00038464146,965 -4,3,128,551,0.00029853775,0.00034218995,0.00038464146,967 -4,4,16,559,0.0000006038583,0.00004300728,0.00010778607,1021 -4,4,32,559,0.0000006038583,0.00004300728,0.00010778607,1007 -4,4,64,573,0.0000006038583,0.00004300728,0.00010778607,1019 -4,4,128,557,0.0000006038583,0.00004300728,0.00010778607,989 -4,5,16,597,0,0.000004249616,0.000013534111,1057 -4,5,32,597,0,0.000004249616,0.000013534111,1042 -4,5,64,588,0,0.000004249616,0.000013534111,1089 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a/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_high_4_m1_0.csv +++ /dev/null @@ -1,31 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,8,3265,0.00000000003814509517948264,0.000000006910811663271371,0.00000001763441356876439,0,0,4509,295,0 -4,0,0,9,3264,0.0000000000005167631939389341,0.00000000022748299282889515,0.0000000014465345945477362,0,0,9343,385,0 -4,0,0.00000000001,8,3079,0.000000000038142710182261064,0.000000006910802718937641,0.000000017634397246468752,0,0,4481,295,0 -4,0,0.00000000001,9,3159,0.000000000000516396435107182,0.00000000022748301980359143,0.0000000014465362450032607,0,0,8989,385,0 -4,0,0.000000001,8,3543,0.00000000003838377836296565,0.000000006911332048825777,0.0000000176354042770907,0,0,4483,295,0 -4,0,0.000000001,9,3194,0.0000000000006453121644680181,0.00000000022777423879406545,0.0000000014469066805765325,0,0,9399,382,0 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diff --git a/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_high_5_1_m1_0.csv b/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_high_5_1_m1_0.csv deleted file mode 100644 index 912ffc3d..00000000 --- a/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_high_5_1_m1_0.csv +++ /dev/null @@ -1,16 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,8,1513,0.000000026975762998253564,0.000000027728030412903238,0.000000028426510519812952,0,0,4728,295,0 -5,0,0.00000000001,8,1334,0.00000002697574374210684,0.000000027728012012775006,0.00000002842649309833891,0,0,4998,295,0 -5,0,0.000000001,8,1581,0.000000026976730940562114,0.00000002772901191491341,0.00000002842751711424462,0,0,4771,295,0 -5,0,0.0000001,8,1291,0.000000026956816783797622,0.000000027725301404386878,0.000000028443156829950386,0,0,4594,284,0 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-5,2,0.000000001,8,1270,0.000000003980383961974609,0.000000004028006134276595,0.000000004058169793131579,0,0,9469,295,0 -5,2,0.0000001,8,1243,0.000000004066776872574214,0.000000004111895399283304,0.000000004142070948658361,0,0,9568,295,0 -5,2,0.00001,8,1131,0.000000006209779603329367,0.0000000064413612739777265,0.0000000066947591293017845,0,0,8927,231,0 diff --git a/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_high_5_1_m1_1.csv b/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_high_5_1_m1_1.csv deleted file mode 100644 index 019c7160..00000000 --- a/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_high_5_1_m1_1.csv +++ /dev/null @@ -1,16 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,8,1074,0.00000008080950723499451,0.00000008176384541866183,0.0000000832606932481987,0,5,2207,148,1 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+++ /dev/null @@ -1,16 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,8,1247,0.00000005807628738953488,0.00000006000443137067293,0.00000006266327050701527,0,10,2322,148,1 -5,0,0.00000000001,8,1322,0.00000005807628573899166,0.0000000600044296590286,0.00000006266326830631295,0,10,2315,148,1 -5,0,0.000000001,8,1153,0.00000005807608455611266,0.00000006000421228104778,0.00000006266303741596065,0,10,2306,148,1 -5,0,0.0000001,8,1225,0.00000005814630269929392,0.00000006007502187904131,0.0000000627327943613078,0,10,2325,148,1 -5,0,0.00001,8,1446,0.000000053053356991958696,0.00000005486629174622732,0.00000005740902417194298,0,10,2330,148,1 -5,1,0,8,1623,0.000000008887988336714626,0.000000009095084581093408,0.000000009226616805363126,0,10,4737,193,1 -5,1,0.00000000001,8,1359,0.000000008887982468174462,0.000000009095078773743112,0.000000009226610753370733,0,10,4617,193,1 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a/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_high_5_1_m1_3.csv b/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_high_5_1_m1_3.csv deleted file mode 100644 index b22ffc43..00000000 --- a/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_high_5_1_m1_3.csv +++ /dev/null @@ -1,16 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,8,1368,0.00000007701286655412768,0.00000007979707906092183,0.00000008191019804895688,0,20,2516,148,1 -5,0,0.00000000001,8,1113,0.00000007701286453681727,0.00000007979707673798094,0.00000008191019584834986,0,20,2466,148,1 -5,0,0.000000001,8,1108,0.00000007701269159829782,0.00000007979690184539763,0.00000008191001081397695,0,20,2387,148,1 -5,0,0.0000001,8,1096,0.00000007709294882570213,0.00000007987728359858739,0.0000000819916278437448,0,20,2367,148,1 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-5,2,0.000000001,8,1449,0.000000004753753490197721,0.000000004814644125163869,0.000000004849103952987453,0,20,7655,244,1 -5,2,0.0000001,8,1400,0.000000004837266722560915,0.000000004896354238551958,0.000000004930470240515492,0,20,8046,244,1 -5,2,0.00001,8,1336,0.000000004986410113959526,0.000000005387807670622883,0.000000005752549632408889,0,20,7603,231,1 diff --git a/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_high_5_2_m1_0.csv b/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_high_5_2_m1_0.csv deleted file mode 100644 index 553f0cd4..00000000 --- a/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_high_5_2_m1_0.csv +++ /dev/null @@ -1,16 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,9,1967,0.0000000021644175954486454,0.00000000222603295577745,0.000000002257782360717269,0,0,9309,385,0 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a/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_high_5_2_m1_1.csv +++ /dev/null @@ -1,16 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,9,1323,0.0000000013960811640439464,0.0000000022804193842809202,0.0000000027655261899632805,0,5,3732,193,1 -5,0,0.00000000001,9,1192,0.0000000013960824477871268,0.0000000022804207291313214,0.0000000027655271069318368,0,5,3717,193,1 -5,0,0.000000001,9,1162,0.0000000013959427031723388,0.0000000022802463869139216,0.0000000027653470143073816,0,5,3753,193,1 -5,0,0.0000001,9,1119,0.000000001478308949367504,0.0000000023653298210765075,0.000000002850342113538571,0,5,3725,193,1 -5,0,0.00001,9,1130,0.000000007569251474635346,0.000000008054930204501705,0.000000008657246840230581,0,5,3740,193,1 -5,1,0,9,1587,0.0000000035724283953815973,0.000000003777349340407857,0.000000003910160798459483,0,5,7751,244,1 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-5,2,0.00001,9,1269,0.000000005229230139703364,0.0000000053545278696180365,0.0000000054978786564775505,0,5,11291,237,1 diff --git a/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_high_5_2_m1_2.csv b/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_high_5_2_m1_2.csv deleted file mode 100644 index fb84bfdf..00000000 --- a/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_high_5_2_m1_2.csv +++ /dev/null @@ -1,16 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,9,1323,0.000000004417593918064392,0.000000004759223305209695,0.000000004938380045732701,0,10,3801,193,1 -5,0,0.00000000001,9,1197,0.000000004417595385199447,0.000000004759225016833881,0.000000004938382613107943,0,10,3814,193,1 -5,0,0.000000001,9,1134,0.000000004417423730398067,0.00000000475903221331771,0.000000004938166586819692,0,10,3791,193,1 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b/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_high_5_m1_0.csv deleted file mode 100644 index 166116d0..00000000 --- a/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_high_5_m1_0.csv +++ /dev/null @@ -1,31 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,8,1651,0.000000026975762448077944,0.00000002772802998499619,0.00000002842650996966114,0,0,4963,295,0 -5,0,0,9,2206,0.00000000216441722866488,0.00000000222603216109537,0.0000000022577816271424236,0,0,10359,385,0 -5,0,0.00000000001,8,1535,0.000000026975743375323095,0.000000027728011707123313,0.000000028426492914954975,0,0,4972,295,0 -5,0,0.00000000001,9,2001,0.0000000021644197961512368,0.0000000022260345451595163,0.0000000022577836444732494,0,0,9824,385,0 -5,0,0.000000001,8,1443,0.00000002697673112395399,0.000000027729012281696953,0.000000028427517297628557,0,0,5132,295,0 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-5,2,0.00001,9,1487,0.000000005362130204415503,0.00000000548994670926105,0.000000005638082654641562,0,0,15674,239,0 diff --git a/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_high_5_m1_1.csv b/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_high_5_m1_1.csv deleted file mode 100644 index 91844962..00000000 --- a/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_high_5_m1_1.csv +++ /dev/null @@ -1,31 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,8,1248,0.00000008080950851868193,0.00000008176384664126053,0.00000008326069416515796,0,5,2209,148,1 -5,0,0,9,1569,0.0000000013960829979627757,0.000000002280420484624074,0.0000000027655269235381257,0,5,3807,193,1 -5,0,0.00000000001,8,1145,0.00000008080951071928895,0.00000008176384908646143,0.00000008326069691603575,0,5,2155,148,1 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a/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_low_4_m1_1.csv b/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_low_4_m1_1.csv deleted file mode 100644 index 1a353747..00000000 --- a/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_low_4_m1_1.csv +++ /dev/null @@ -1,46 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,5,2653,0.00002600398486513034,0.00003084082895975755,0.0000411243859168072,0,5,587,49,1 -4,0,0,6,2780,0.0000002498362965725268,0.0000014566929807816072,0.0000024322085964007042,0,5,757,76,1 -4,0,0,7,2805,0.0000000022394408599711496,0.0000001697953033464403,0.00000037183252779980813,0,5,1151,109,1 -4,0,0.00000000001,5,2812,0.000026003984865497036,0.00003084082895959064,0.00004112438591607575,0,5,566,49,1 -4,0,0.00000000001,6,2806,0.0000002498362973058539,0.0000014566929818486326,0.0000024322085993391555,0,5,750,76,1 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100644 index 127a5bd4..00000000 --- a/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_low_4_m1_2.csv +++ /dev/null @@ -1,46 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,5,2636,0.000027578430063991756,0.00003301331107051773,0.00004205133579839193,0,10,567,49,1 -4,0,0,6,2672,0.0000000024770953121716937,0.0000008817407060422769,0.000001895289262507937,0,10,776,76,1 -4,0,0,7,2687,0.0000000008423148031650915,0.00000018265355204105297,0.0000004165457141914974,0,10,1174,109,1 -4,0,0.00000000001,5,2556,0.000027578430063624614,0.0000330133110703335,0.000042051335798575227,0,10,611,49,1 -4,0,0.00000000001,6,2668,0.0000000024770943950270893,0.0000008817407061787906,0.0000018952892637939555,0,10,773,76,1 -4,0,0.00000000001,7,2719,0.0000000008423164530068688,0.00000018265355016080854,0.00000041654571180662134,0,10,1186,109,1 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index f1a5f422..00000000 --- a/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_low_5_m1_0.csv +++ /dev/null @@ -1,46 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,5,976,0.000008410024248854792,0.000008463867235825936,0.000008542078962078652,0,0,957,97,0 -5,0,0,6,1222,0.0000019397910351121084,0.0000019580617041074444,0.000001994095427460896,0,0,1555,151,0 -5,0,0,7,1528,0.00000009753452111330045,0.00000010046291496746897,0.00000010282663913254533,0,0,2692,217,0 -5,0,0.00000000001,5,942,0.000008410024253989729,0.000008463867241205271,0.000008542078967030147,0,0,991,97,0 -5,0,0.00000000001,6,1122,0.000001939791045015407,0.0000019580617138271474,0.0000019940954369968994,0,0,1488,151,0 -5,0,0.00000000001,7,1570,0.0000000975345095601095,0.00000010046290500330838,0.00000010282663069651696,0,0,2732,217,0 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a/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_max_4_m1_0.csv b/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_max_4_m1_0.csv deleted file mode 100644 index 0b7797c8..00000000 --- a/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_max_4_m1_0.csv +++ /dev/null @@ -1,31 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,10,4245,0.0000000000023431584277127717,0.00000000009933771693625672,0.0000000004099047514220809,0,0,18747,487,0 -4,0,0,11,4994,0.00000000000000643767541305312,0.000000000004509866506130022,0.000000000027865557620900428,0,0,34206,601,0 -4,0,0.00000000001,10,3707,0.00000000000234205645635627,0.00000000009933851909243325,0.00000000040990328435049815,0,0,18311,487,0 -4,0,0.00000000001,11,4774,0.000000000000005701941080132767,0.000000000004510110690090972,0.000000000027863907165371083,0,0,34311,593,0 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b/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_max_4_m1_3.csv deleted file mode 100644 index c360791e..00000000 --- a/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_max_4_m1_3.csv +++ /dev/null @@ -1,31 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,10,3178,0.00000000000028321357123442597,0.0000000002334923867024388,0.0000000011231276723583126,0,20,5809,244,1 -4,0,0,11,3394,0.00000000000016519105836800018,0.00000000004229567465854483,0.0000000001622270813869256,0,20,9527,301,1 -4,0,0.00000000001,10,2962,0.00000000000028394823678886005,0.0000000002334931624629631,0.0000000011231273046551911,0,20,5706,244,1 -4,0,0.00000000001,11,3344,0.0000000000001644568758863645,0.00000000004229554853525947,0.0000000001622281816007693,0,20,9824,301,1 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-depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,10,2450,0.0000000004833617693922673,0.0000000005123792267445712,0.0000000005365297169696699,0,0,15535,487,0 -5,0,0.00000000001,10,1807,0.0000000004833612192166165,0.0000000005123778818949786,0.0000000005365276997462431,0,0,15554,487,0 -5,0,0.000000001,10,1713,0.0000000004835040814939512,0.0000000005125345577742799,0.000000000536697146514095,0,0,14753,466,0 -5,0,0.0000001,10,1671,0.00000000045244373173487915,0.0000000004819355852302397,0.0000000005067279915986932,0,0,12963,383,0 -5,0,0.00001,10,1380,0.0000000056912172231088795,0.0000000058393233730220154,0.000000005988462264235219,0,0,11301,233,0 -5,1,0,10,2806,0.0000000000936037674900849,0.00000000010305898783088232,0.00000000011135733487481475,0,0,23656,487,0 -5,1,0.00000000001,10,1980,0.00000000009360541801703614,0.00000000010306021042736055,0.00000000011135861856244891,0,0,23987,487,0 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--git a/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_max_5_1_m1_1.csv b/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_max_5_1_m1_1.csv deleted file mode 100644 index e3f43976..00000000 --- a/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_max_5_1_m1_1.csv +++ /dev/null @@ -1,16 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,10,1687,0.000000001206710338917183,0.0000000012912012035450207,0.0000000014006819462887692,0,5,5928,244,1 -5,0,0.00000000001,10,1446,0.000000001206714923714258,0.000000001291205604917021,0.0000000014006870813126983,0,5,5882,244,1 -5,0,0.000000001,10,1376,0.0000000012064869676036972,0.0000000012909736169062282,0.0000000014004600398975414,0,5,5857,244,1 -5,0,0.0000001,10,1500,0.0000000012434796778884512,0.0000000013279569364414624,0.0000000014377735078842329,0,5,5884,244,1 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a/m1/f64/sphere/blas/grid_search_laplace_blas_sphere_f64_max_5_1_m1_3.csv +++ /dev/null @@ -1,16 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,10,1676,0.0000000003660010324399551,0.00000000045272588822587335,0.0000000005460506169716867,0,20,6117,244,1 -5,0,0.00000000001,10,1441,0.0000000003659958974160077,0.000000000452721120088607,0.0000000005460462155664794,0,20,6091,244,1 -5,0,0.000000001,10,1394,0.000000000366251915038527,0.00000000045297756002178867,0.0000000005462890264204137,0,20,6083,244,1 -5,0,0.0000001,10,1449,0.00000000032638835734917635,0.0000000004131667371120083,0.0000000005062265192660292,0,20,6078,244,1 -5,0,0.00001,10,1428,0.000000004639507491048027,0.000000004899893575680092,0.000000005139505157481476,0,20,6010,233,1 -5,1,0,10,2320,0.00000000008311356836381535,0.00000000009217462265269067,0.00000000010007940547314619,0,20,12986,301,1 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-5,2,0.0000001,10,1479,0.00000000002640245918166376,0.000000000029936650174127193,0.00000000003214823038357632,0,20,20594,364,1 -5,2,0.00001,10,1335,0.000000005574107933216422,0.000000005761423875178756,0.000000005956539886542877,0,20,19417,243,1 diff --git a/m1/f64/sphere/fft/grid_search_laplace_fft_high_f64_m1_sphere.csv b/m1/f64/sphere/fft/grid_search_laplace_fft_high_f64_m1_sphere.csv deleted file mode 100644 index 87a83b2e..00000000 --- a/m1/f64/sphere/fft/grid_search_laplace_fft_high_f64_m1_sphere.csv +++ /dev/null @@ -1,17 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,setup_time -4,9,16,3325,0.0000000000005103449143832728,0.00000000022747167249885665,0.0000000014465133220098652,2192 -4,9,32,3512,0.0000000000005103449143832728,0.00000000022747167249885665,0.0000000014465133220098652,2242 -4,9,64,4215,0.0000000000005103449143832728,0.00000000022747167249885665,0.0000000014465133220098652,2138 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-5,10,16,2800,0.0000000004833735064728186,0.0000000005123904135397439,0.0000000005365399864707519,3233 -5,10,32,2720,0.0000000004833735064728186,0.0000000005123904135397439,0.0000000005365399864707519,3284 -5,10,64,2652,0.0000000004833735064728186,0.0000000005123904135397439,0.0000000005365399864707519,3281 -5,10,128,2633,0.0000000004833735064728186,0.0000000005123904135397439,0.0000000005365399864707519,3323 diff --git a/m1/f64/sphere/fft/grid_search_laplace_fft_low_f64_m1_sphere.csv b/m1/f64/sphere/fft/grid_search_laplace_fft_low_f64_m1_sphere.csv deleted file mode 100644 index c7bda53b..00000000 --- a/m1/f64/sphere/fft/grid_search_laplace_fft_low_f64_m1_sphere.csv +++ /dev/null @@ -1,25 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,setup_time -4,6,16,2647,0.000000008391179885283765,0.0000004176163773590837,0.0000011273482854605067,1133 -4,6,32,2591,0.000000008391179885283765,0.0000004176163773590837,0.0000011273482854605067,1103 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-5,8,64,1480,0.000000026976527008798735,0.000000027728327021451804,0.000000028426378666762056,2086 -5,8,128,1679,0.000000026976527008798735,0.000000027728327021451804,0.000000028426378666762056,2067 diff --git a/m1/f64/sphere/fft/grid_search_laplace_fft_max_f64_m1_sphere.csv b/m1/f64/sphere/fft/grid_search_laplace_fft_max_f64_m1_sphere.csv deleted file mode 100644 index 0d887643..00000000 --- a/m1/f64/sphere/fft/grid_search_laplace_fft_max_f64_m1_sphere.csv +++ /dev/null @@ -1,9 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,setup_time -4,11,16,3562,0.000000000000006253741829823032,0.000000000004512567297150837,0.000000000027870508987488466,3556 -4,11,32,3364,0.000000000000006253741829823032,0.000000000004512567297150837,0.000000000027870508987488466,3488 -4,11,64,3385,0.000000000000006253741829823032,0.000000000004512567297150837,0.000000000027870508987488466,3378 -4,11,128,3510,0.000000000000006253741829823032,0.000000000004512567297150837,0.000000000027870508987488466,3333 -5,11,16,4324,0.00000000003052502882318362,0.000000000031837812769299464,0.00000000003262748859097999,4377 -5,11,32,3648,0.00000000003052502882318362,0.000000000031837812769299464,0.00000000003262748859097999,5329 -5,11,64,3571,0.00000000003052502882318362,0.000000000031837812769299464,0.00000000003262748859097999,4739 -5,11,128,3547,0.00000000003052502882318362,0.000000000031837812769299464,0.00000000003262748859097999,4621 diff --git a/m1/f64/spheroid/blas/grid_search_laplace_blas_spheroid_f64_high_4_m1_0.csv b/m1/f64/spheroid/blas/grid_search_laplace_blas_spheroid_f64_high_4_m1_0.csv deleted file mode 100644 index d87e0df1..00000000 --- a/m1/f64/spheroid/blas/grid_search_laplace_blas_spheroid_f64_high_4_m1_0.csv +++ /dev/null @@ -1,31 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,8,4331,0.00000000006446577487456752,0.000000003301994935827867,0.000000009054753153201118,0,0,4411,295,0 -4,0,0,9,4622,0.000000000002114804663962321,0.0000000002653479349674496,0.0000000006286661760240691,0,0,9002,385,0 -4,0,0.00000000001,8,4410,0.00000000006446729338490406,0.000000003301994802561227,0.000000009054747979790434,0,0,4421,295,0 -4,0,0.00000000001,9,4579,0.000000000002114956458727176,0.00000000026534757548850254,0.0000000006286669358981599,0,0,9063,385,0 -4,0,0.000000001,8,4483,0.0000000000643385237083651,0.000000003301909087996112,0.000000009054779172413672,0,0,4403,295,0 -4,0,0.000000001,9,4662,0.0000000000020067267913856523,0.000000000265287307371358,0.0000000006287856282311451,0,0,8889,382,0 -4,0,0.0000001,8,4496,0.00000000006871191927103944,0.0000000033148966696946836,0.000000009039549107838958,0,0,4278,284,0 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b/m1/f64/spheroid/blas/grid_search_laplace_blas_spheroid_f64_high_4_m1_1.csv deleted file mode 100644 index 20875983..00000000 --- a/m1/f64/spheroid/blas/grid_search_laplace_blas_spheroid_f64_high_4_m1_1.csv +++ /dev/null @@ -1,31 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,8,4107,0.000000054054446028618575,0.00000010124012513236146,0.00000019238840184613108,0,5,1866,148,1 -4,0,0,9,4242,0.000000000003891195638565783,0.0000000036000958063743927,0.000000012850839952608307,0,5,3331,193,1 -4,0,0.00000000001,8,4121,0.00000005405444663630837,0.00000010124012627377903,0.00000019238840290917792,0,5,1851,148,1 -4,0,0.00000000001,9,4320,0.000000000003889372495208647,0.00000000360009560012665,0.00000001285083964888075,0,5,3351,193,1 -4,0,0.000000001,8,4193,0.00000005405457151656064,0.00000010124030394323401,0.0000001923885131623213,0,5,1850,148,1 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a/m1/f64/spheroid/blas/grid_search_laplace_blas_spheroid_f64_high_5_1_m1_1.csv b/m1/f64/spheroid/blas/grid_search_laplace_blas_spheroid_f64_high_5_1_m1_1.csv deleted file mode 100644 index c4000067..00000000 --- a/m1/f64/spheroid/blas/grid_search_laplace_blas_spheroid_f64_high_5_1_m1_1.csv +++ /dev/null @@ -1,16 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,8,1243,0.00000006006761153679445,0.00000007440567397329162,0.00000008358398515040386,0,5,2157,148,1 -5,0,0.00000000001,8,1192,0.00000006006761184061026,0.00000007440567345305993,0.0000000835839836335957,0,5,2124,148,1 -5,0,0.000000001,8,1164,0.00000006006769599759254,0.0000000744057930610886,0.00000008358412530347756,0,5,2131,148,1 -5,0,0.0000001,8,1159,0.0000000600691281853703,0.00000007441039775354,0.00000008358955517331851,0,5,2158,148,1 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a/m1/f64/spheroid/blas/grid_search_laplace_blas_spheroid_f64_high_5_1_m1_3.csv +++ /dev/null @@ -1,16 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,8,1233,0.000000025390742776545204,0.00000003781838382979297,0.000000047660528427872116,0,20,2114,148,1 -5,0,0.00000000001,8,1195,0.000000025390743384176802,0.00000003781838378663679,0.000000047660527821311743,0,20,2280,148,1 -5,0,0.000000001,8,1282,0.000000025390854125035687,0.00000003781851816403199,0.00000004766067066627991,0,20,2241,148,1 -5,0,0.0000001,8,1169,0.000000025394206428566413,0.00000003782061035211063,0.00000004766106493052325,0,20,2189,148,1 -5,0,0.00001,8,1154,0.00000002780728240972372,0.00000004072329021614031,0.00000004998718287719211,0,20,2135,148,1 -5,1,0,8,1353,0.000000002433723582208868,0.000000004361312265621637,0.000000006412194267196172,0,20,4720,193,1 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-5,2,0.00001,8,1328,0.0000000001715328725289277,0.00000000047841726839801,0.000000000808089677797425,0,20,7139,231,1 diff --git a/m1/f64/spheroid/blas/grid_search_laplace_blas_spheroid_f64_high_5_2_m1_0.csv b/m1/f64/spheroid/blas/grid_search_laplace_blas_spheroid_f64_high_5_2_m1_0.csv deleted file mode 100644 index 29a9e75a..00000000 --- a/m1/f64/spheroid/blas/grid_search_laplace_blas_spheroid_f64_high_5_2_m1_0.csv +++ /dev/null @@ -1,16 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,9,1775,0.00000000026146280027287847,0.0000000007916191068614299,0.000000001089323475437675,0,0,9225,385,0 -5,0,0.00000000001,9,1536,0.0000000002614606735623917,0.0000000007916175227129883,0.0000000010893213467682357,0,0,9216,385,0 -5,0,0.000000001,9,1500,0.00000000026132988086745505,0.0000000007915735786825277,0.0000000010892989957391238,0,0,9120,382,0 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b/m1/f64/spheroid/blas/grid_search_laplace_blas_spheroid_f64_high_5_2_m1_2.csv deleted file mode 100644 index 74c0e345..00000000 --- a/m1/f64/spheroid/blas/grid_search_laplace_blas_spheroid_f64_high_5_2_m1_2.csv +++ /dev/null @@ -1,16 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,9,1418,0.000000007247407515918555,0.000000009949196811690223,0.00000001247973558161161,0,10,3646,193,1 -5,0,0.00000000001,9,1355,0.000000007247409188444523,0.000000009949197592776586,0.000000012479736643092138,0,10,3666,193,1 -5,0,0.000000001,9,1360,0.0000000072472848133316775,0.000000009949061124471128,0.000000012479615482671929,0,10,3680,193,1 -5,0,0.0000001,9,1341,0.000000007234060606651053,0.000000009933365909988219,0.000000012464122416172775,0,10,3699,193,1 -5,0,0.00001,9,1360,0.000000005130905116660389,0.000000008093254007827264,0.000000010862828844993863,0,10,3910,193,1 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b/m1/f64/spheroid/blas/grid_search_laplace_blas_spheroid_f64_max_4_m1_1.csv deleted file mode 100644 index 006223ec..00000000 --- a/m1/f64/spheroid/blas/grid_search_laplace_blas_spheroid_f64_max_4_m1_1.csv +++ /dev/null @@ -1,31 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,10,4465,0.00000000000388924714557223,0.00000000070074481088362,0.0000000017558542452246342,0,5,5790,244,1 -4,0,0,11,4713,0.00000000007438344388658831,0.00000000027994087197244093,0.0000000004820486221547929,0,5,9656,301,1 -4,0,0.00000000001,10,4337,0.000000000003889550507152948,0.0000000007007448475386784,0.0000000017558542452246342,0,5,5610,244,1 -4,0,0.00000000001,11,4779,0.00000000007438389985561892,0.000000000279940912856876,0.0000000004820492300540658,0,5,9470,301,1 -4,0,0.000000001,10,4395,0.000000000003958413585975801,0.0000000007007061400776639,0.000000001755836442605258,0,5,5626,244,1 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a/m1/f64/spheroid/blas/grid_search_laplace_blas_spheroid_f64_max_5_1_m1_0.csv +++ /dev/null @@ -1,16 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,10,2024,0.00000000013960814235067887,0.0000000001643211578063438,0.00000000020531016790192022,0,0,15645,487,0 -5,0,0.00000000001,10,1642,0.00000000013960844582552925,0.00000000016432148320416047,0.00000000020531077609319014,0,0,15463,487,0 -5,0,0.000000001,10,1633,0.00000000013967141685698316,0.00000000016439764277481102,0.00000000020539637901443174,0,0,14780,466,0 -5,0,0.0000001,10,1577,0.0000000001365943336115608,0.00000000016183097241428621,0.00000000020171134810998454,0,0,12905,383,0 -5,0,0.00001,10,1483,0.0000000007184697856352935,0.0000000012071119348002948,0.000000001826801160832717,0,0,11265,233,0 -5,1,0,10,2375,0.000000000000434706651276501,0.000000000007042378290873119,0.000000000015430499852516708,0,0,23626,487,0 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-depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,10,1631,0.000000000027460073567177155,0.00000000017246058895430697,0.00000000033012637333515246,0,10,5842,244,1 -5,0,0.00000000001,10,1534,0.000000000027459770092326844,0.00000000017245998120298663,0.0000000003301250049047948,0,10,5778,244,1 -5,0,0.000000001,10,1562,0.00000000002752835540849742,0.00000000017251717026173463,0.00000000033020163700482416,0,10,5862,244,1 -5,0,0.0000001,10,1465,0.00000000002362096497331648,0.00000000017023958626866256,0.00000000032719397912649793,0,10,5769,244,1 -5,0,0.00001,10,1475,0.0000000010903103670061469,0.0000000015485725568080672,0.0000000020189518949295406,0,10,5725,233,1 -5,1,0,10,2152,0.00000000021593934703825819,0.0000000002577027094267525,0.0000000002866504529168076,0,10,12372,301,1 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100644 index d9651689..00000000 --- a/m1/f64/spheroid/fft/grid_search_laplace_fft_low_f64_m1_spheroid.csv +++ /dev/null @@ -1,25 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,setup_time -4,6,16,3908,0.00000037560804230294346,0.0000007475333502813584,0.0000011253939164124917,1189 -4,6,32,3807,0.00000037560804230294346,0.0000007475333502813584,0.0000011253939164124917,1120 -4,6,64,3807,0.00000037560804230294346,0.0000007475333502813584,0.0000011253939164124917,1106 -4,6,128,3926,0.00000037560804230294346,0.0000007475333502813584,0.0000011253939164124917,1116 -4,7,16,4147,0.0000000005889426506218525,0.000000025383661277167844,0.00000004945221017016544,1292 -4,7,32,3945,0.0000000005889426506218525,0.000000025383661277167844,0.00000004945221017016544,1274 -4,7,64,3999,0.0000000005889426506218525,0.000000025383661277167844,0.00000004945221017016544,1275 -4,7,128,3868,0.0000000005889426506218525,0.000000025383661277167844,0.00000004945221017016544,1289 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b/m1/f64/uniform/blas/grid_search_laplace_blas_uniform_f64_high_4_m1_0.csv deleted file mode 100644 index b4c3660e..00000000 --- a/m1/f64/uniform/blas/grid_search_laplace_blas_uniform_f64_high_4_m1_0.csv +++ /dev/null @@ -1,31 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -4,0,0,8,1922,0.000000000027796469038384202,0.0000000014982963101784915,0.000000006541327778324487,0,0,4706,295,0 -4,0,0,9,2380,0.00000000006672791610113964,0.0000000001653466606364729,0.000000000571745268350071,0,0,9465,385,0 -4,0,0.00000000001,8,1787,0.000000000027795631858381563,0.000000001498294858339944,0.0000000065413271026100564,0,0,4933,295,0 -4,0,0.00000000001,9,2051,0.00000000006672819631982916,0.0000000001653468207655849,0.0000000005717423898446107,0,0,9622,385,0 -4,0,0.000000001,8,1686,0.000000000027703123468089978,0.000000001498209972759679,0.0000000065412093931563065,0,0,4884,295,0 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-5,2,0.00001,10,3277,0.0000000017588543701967042,0.000000003109823240124882,0.000000004272399899794847,0,0,24811,268,0 diff --git a/m1/f64/uniform/blas/grid_search_laplace_blas_uniform_f64_max_5_1_m1_1.csv b/m1/f64/uniform/blas/grid_search_laplace_blas_uniform_f64_max_5_1_m1_1.csv deleted file mode 100644 index 4a24275f..00000000 --- a/m1/f64/uniform/blas/grid_search_laplace_blas_uniform_f64_max_5_1_m1_1.csv +++ /dev/null @@ -1,16 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,10,4461,0.000000000022255234488323103,0.00000000043631802228893916,0.0000000010140832327094938,0,5,6982,244,1 -5,0,0.00000000001,10,3712,0.000000000022255534171669193,0.0000000004363178714727403,0.00000000101408399021093,0,5,6588,244,1 -5,0,0.000000001,10,3474,0.00000000002222886235386741,0.0000000004363057122221443,0.0000000010141468628301353,0,5,6665,244,1 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b/m1/f64/uniform/blas/grid_search_laplace_blas_uniform_f64_max_5_1_m1_3.csv deleted file mode 100644 index 46533295..00000000 --- a/m1/f64/uniform/blas/grid_search_laplace_blas_uniform_f64_max_5_1_m1_3.csv +++ /dev/null @@ -1,16 +0,0 @@ -depth,surface_diff,svd_threshold,expansion_order,runtime,min_rel_err,mean_rel_err,max_rel_err,n_iter,n_oversamples,setup_time,cutoff_rank,rsvd -5,0,0,10,4396,0.000000000026396071154366918,0.00000000039828576099619637,0.0000000015938074140186408,0,20,6915,244,1 -5,0,0.00000000001,10,3625,0.000000000026396362757607677,0.0000000003982854856188178,0.0000000015938063535166314,0,20,6880,244,1 -5,0,0.000000001,10,3481,0.00000000002639184290737592,0.00000000039831501056677865,0.0000000015938734681438017,0,20,6875,244,1 -5,0,0.0000001,10,3229,0.00000000002491560150103911,0.0000000004003869893938374,0.0000000015967300060564187,0,20,6864,244,1 -5,0,0.00001,10,3079,0.0000000019848306969466277,0.000000003767071667552059,0.000000006107812835370922,0,20,6834,244,1 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-5,9,128,5620,0.00000000021553372389520778,0.00000000037141593359165344,0.0000000006141323217507616,3029 -5,10,16,9947,0.000000000014970181371864145,0.000000000021195864797384797,0.00000000003488288867966985,3714 -5,10,32,8079,0.000000000014970181371864145,0.000000000021195864797384797,0.00000000003488288867966985,3734 -5,10,64,7248,0.000000000014970181371864145,0.000000000021195864797384797,0.00000000003488288867966985,3686 -5,10,128,7324,0.000000000014970181371864145,0.000000000021195864797384797,0.00000000003488288867966985,3692 diff --git a/m1/f64/uniform/fft/grid_search_laplace_fft_low_f64_m1_uniform.csv b/m1/f64/uniform/fft/grid_search_laplace_fft_low_f64_m1_uniform.csv deleted file mode 100644 index bf4a3259..00000000 --- a/m1/f64/uniform/fft/grid_search_laplace_fft_low_f64_m1_uniform.csv +++ /dev/null @@ -1,25 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,setup_time -4,6,16,1598,0.000000016686702299490217,0.0000001806345983488817,0.0000007584427734354027,1167 -4,6,32,1357,0.000000016686702299490217,0.0000001806345983488817,0.0000007584427734354027,1135 -4,6,64,1394,0.000000016686702299490217,0.0000001806345983488817,0.0000007584427734354027,1125 -4,6,128,1400,0.000000016686702299490217,0.0000001806345983488817,0.0000007584427734354027,1131 -4,7,16,1517,0.0000000012761168495142224,0.00000001493895643652047,0.00000004286895467535286,1313 -4,7,32,1476,0.0000000012761168495142224,0.00000001493895643652047,0.00000004286895467535286,1313 -4,7,64,1499,0.0000000012761168495142224,0.00000001493895643652047,0.00000004286895467535286,1304 -4,7,128,1507,0.0000000012761168495142224,0.00000001493895643652047,0.00000004286895467535286,1296 -4,8,16,1951,0.00000000002776493525828481,0.0000000014979825499069186,0.000000006541376024334807,1571 -4,8,32,1810,0.00000000002776493525828481,0.0000000014979825499069186,0.000000006541376024334807,1562 -4,8,64,1707,0.00000000002776493525828481,0.0000000014979825499069186,0.000000006541376024334807,1537 -4,8,128,1736,0.00000000002776493525828481,0.0000000014979825499069186,0.000000006541376024334807,1542 -5,6,16,2002,0.00000007363947317807973,0.00000027384696535735265,0.0000004085263879221838,1538 -5,6,32,1537,0.00000007363947317807973,0.00000027384696535735265,0.0000004085263879221838,1525 -5,6,64,1548,0.00000007363947317807973,0.00000027384696535735265,0.0000004085263879221838,1690 -5,6,128,1650,0.00000007363947317807973,0.00000027384696535735265,0.0000004085263879221838,1779 -5,7,16,2749,0.000000020760057606409505,0.0000000313831060299183,0.000000046302488262898814,2104 -5,7,32,2370,0.000000020760057606409505,0.0000000313831060299183,0.000000046302488262898814,1975 -5,7,64,2440,0.000000020760057606409505,0.0000000313831060299183,0.000000046302488262898814,2005 -5,7,128,2362,0.000000020760057606409505,0.0000000313831060299183,0.000000046302488262898814,1932 -5,8,16,3995,0.0000000006775990793078764,0.0000000026624143464007455,0.000000004205206987239361,2367 -5,8,32,3515,0.0000000006775990793078764,0.0000000026624143464007455,0.000000004205206987239361,2345 -5,8,64,3516,0.0000000006775990793078764,0.0000000026624143464007455,0.000000004205206987239361,2385 -5,8,128,3850,0.0000000006775990793078764,0.0000000026624143464007455,0.000000004205206987239361,2322 diff --git a/m1/f64/uniform/fft/grid_search_laplace_fft_max_f64_m1_uniform.csv b/m1/f64/uniform/fft/grid_search_laplace_fft_max_f64_m1_uniform.csv deleted file mode 100644 index dbe29157..00000000 --- a/m1/f64/uniform/fft/grid_search_laplace_fft_max_f64_m1_uniform.csv +++ /dev/null @@ -1,9 +0,0 @@ -depth,expansion_order,block_size,runtime,min_rel_err,mean_rel_err,max_rel_err,setup_time -4,11,16,2838,0.00000000000016981362434069548,0.0000000000011634261801119752,0.0000000000060448245956206634,3623 -4,11,32,2772,0.00000000000016981362434069548,0.0000000000011634261801119752,0.0000000000060448245956206634,3664 -4,11,64,2790,0.00000000000016981362434069548,0.0000000000011634261801119752,0.0000000000060448245956206634,3475 -4,11,128,2804,0.00000000000016981362434069548,0.0000000000011634261801119752,0.0000000000060448245956206634,3430 -5,11,16,15399,0.0000000000013449553261033692,0.000000000001930021538710101,0.0000000000028292678699804097,5028 -5,11,32,10807,0.0000000000013449553261033692,0.000000000001930021538710101,0.0000000000028292678699804097,5413 -5,11,64,11245,0.0000000000013449553261033692,0.000000000001930021538710101,0.0000000000028292678699804097,5157 -5,11,128,11925,0.0000000000013449553261033692,0.000000000001930021538710101,0.0000000000028292678699804097,5096 From c4779b9e61e4a068aee6014e032340b99433090d Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Fri, 3 Oct 2025 15:42:11 +0100 Subject: [PATCH 19/52] Notebooks --- hpc/archer2/data/ijhpca/Strong Scaling.ipynb | 2 - hpc/archer2/data/ijhpca/Weak Scaling.ipynb | 890 +++++++++++++++++++ 2 files changed, 890 insertions(+), 2 deletions(-) create mode 100644 hpc/archer2/data/ijhpca/Weak Scaling.ipynb diff --git a/hpc/archer2/data/ijhpca/Strong Scaling.ipynb b/hpc/archer2/data/ijhpca/Strong Scaling.ipynb index 296bccf0..d3c16656 100644 --- a/hpc/archer2/data/ijhpca/Strong Scaling.ipynb +++ b/hpc/archer2/data/ijhpca/Strong Scaling.ipynb @@ -99,7 +99,6 @@ "total_ranks = n_nodes*ranks_per_node\n", "points_per_rank = np.int64(points_per_node/ranks_per_node)\n", "\n", - "\n", "# The global depth is a function of the ranks per node (as will need enough global leaves to cover this many ranks (local trees))\n", "global_depth = v_global_depth(total_ranks)\n", "print(f\"global depth {global_depth}\")\n", @@ -108,7 +107,6 @@ "min_local_depth = 3\n", "max_points_per_leaf = points_per_rank[-1] / 8**min_local_depth\n", "\n", - "\n", "local_depth = v_local_depth(points_per_rank, max_points_per_leaf = 2000, min_local_depth=min_local_depth)\n", "print(f\"local depth {local_depth}\")\n", "\n", diff --git a/hpc/archer2/data/ijhpca/Weak Scaling.ipynb b/hpc/archer2/data/ijhpca/Weak Scaling.ipynb new file mode 100644 index 00000000..550dbf18 --- /dev/null +++ b/hpc/archer2/data/ijhpca/Weak Scaling.ipynb @@ -0,0 +1,890 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 36, + "id": "2ae1633f-50c6-48ec-ad4b-d899118e24c8", + "metadata": {}, + "outputs": [], + "source": [ + "import pathlib\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "89326e5d-3c39-43f0-b114-4d41e8f091de", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[34mUntitled\u001b[m\u001b[m \u001b[34mweak_fft_n=32768000000_p=512_11030112\u001b[m\u001b[m\n", + "\u001b[34mweak_fft_n=1024000000_p=128_11029691\u001b[m\u001b[m \u001b[34mweak_fft_n=4096000000_p=128_11029701\u001b[m\u001b[m\n", + "\u001b[34mweak_fft_n=1024000000_p=16_11029907\u001b[m\u001b[m \u001b[34mweak_fft_n=4096000000_p=512_11030104\u001b[m\u001b[m\n", + "\u001b[34mweak_fft_n=128000000_p=16_11029687\u001b[m\u001b[m \u001b[34mweak_fft_n=512000000_p=16_11029699\u001b[m\u001b[m\n", + "\u001b[34mweak_fft_n=16384000000_p=512_11030108\u001b[m\u001b[m \u001b[34mweak_fft_n=8192000000_p=128_11029908\u001b[m\u001b[m\n", + "\u001b[34mweak_fft_n=250000_p=16_11018099\u001b[m\u001b[m\n" + ] + } + ], + "source": [ + "! ls weak_scaling_data/" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "09f4cb6e-d46a-48a5-9d6c-f880ab68856e", + "metadata": {}, + "outputs": [], + "source": [ + "def global_depth(rank):\n", + " \"\"\"\n", + " will return the first power of 8 greater than rank, the power corresponding to the level\n", + " \"\"\"\n", + " level = 1\n", + " loop = True\n", + "\n", + " while loop:\n", + "\n", + " if rank <= 8**level:\n", + " loop = False\n", + " result = level\n", + " else:\n", + " level += 1\n", + " \n", + " return result\n", + "\n", + "v_global_depth = np.vectorize(global_depth)\n", + "\n", + "def pow2(x): return np.log2(x).is_integer()" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "762508be-775e-4f58-8942-f2d69b0ae615", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total problem size n=1.024B\n", + "points per rank 0.25M\n", + "points per node 8.0M\n", + "global depth [4 4 4]\n", + "points per leaf 61.03515625\n", + "MPI tasks [1024 2048 4096]\n" + ] + } + ], + "source": [ + "min_nodes = 32\n", + "max_nodes = 128\n", + "\n", + "ranks_per_node = 32 # Say each rank gets a whole AMD EPYC ROME CPU\n", + "total_ranks = ranks_per_node * max_nodes\n", + "\n", + "points_per_rank = 250e3\n", + "n = points_per_rank*total_ranks # total problem size\n", + "print(f\"Total problem size n={n/1e9}B\")\n", + "\n", + "points_per_node = points_per_rank * ranks_per_node\n", + "print(f\"points per rank {points_per_rank/1e6}M\")\n", + "print(f\"points per node {points_per_node/1e6}M\")\n", + "\n", + "# Double number of nodes used until we reach max_nodes\n", + "n_nodes = np.array(list(filter(pow2, [i for i in range(min_nodes, max_nodes+1)])))\n", + "\n", + "# The global depth is a function of the ranks per node (as will need enough global leaves to cover this many ranks (local trees))\n", + "global_depth = v_global_depth(n_nodes*ranks_per_node)\n", + "print(f\"global depth {global_depth}\")\n", + "\n", + "# local depth is a constant, chosen to balance M2L and P2P\n", + "local_depth = 4\n", + "points_per_leaf = points_per_rank / 8**local_depth \n", + "print(f\"points per leaf {points_per_leaf}\")\n", + "print(f\"MPI tasks {n_nodes*ranks_per_node}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "74eb7d8a-606d-4658-9ce3-0ad588d933c4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 32 64 128]\n" + ] + } + ], + "source": [ + "print(n_nodes)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "60dd15a0-cc36-46f9-8c60-e8f11a33b1b4", + "metadata": {}, + "outputs": [], + "source": [ + "t1 = pd.read_csv('weak_scaling_data/weak_fft_n=128000000_p=16_11029687/weak_fft_11029687.csv')\n", + "# t2 = pd.read_csv('weak_scaling_data/weak_fft_n=1024000000_p=128_11029691/weak_fft_11029691.csv')\n", + "t2 = pd.read_csv('weak_scaling_data/weak_fft_n=1024000000_p=128_11029691/test.csv')\n", + "t2['experiment_id'] += t1['experiment_id'].max()+1\n", + "\n", + "df_250k = pd.concat([t1, t2])\n", + "\n", + "# Calculate nodes and ranks\n", + "df_250k[\"n_nodes\"] = df_250k.groupby(\"experiment_id\")[\"experiment_id\"].transform(\n", + " lambda x: 1 if len(x) / 32 < 1 else len(x)/32\n", + ")\n", + "df_250k[\"n_ranks\"] = df_250k.groupby(\"experiment_id\")[\"experiment_id\"].transform(\n", + " lambda x: len(x)\n", + ")\n", + "df_250k['total_points'] = df_250k['n_points'] * df_250k['n_ranks']\n", + "\n", + "#### \n", + "t1 = pd.read_csv('weak_scaling_data/weak_fft_n=512000000_p=16_11029699/weak_fft_11029699.csv')\n", + "t2 = pd.read_csv('weak_scaling_data/weak_fft_n=4096000000_p=128_11029701/weak_fft_11029701.csv')\n", + "t2['experiment_id'] += t1['experiment_id'].max()+1\n", + "\n", + "df_1e6 = pd.concat([t1, t2])\n", + "\n", + "df_1e6[\"n_nodes\"] = df_1e6.groupby(\"experiment_id\")[\"experiment_id\"].transform(\n", + " lambda x: 1 if len(x) / 32 < 1 else len(x)/32\n", + ")\n", + "\n", + "df_1e6[\"n_ranks\"] = df_1e6.groupby(\"experiment_id\")[\"experiment_id\"].transform(\n", + " lambda x: len(x)\n", + ")\n", + "df_1e6['total_points'] = df_1e6['n_points'] * df_1e6['n_ranks']\n", + "\n", + "##### \n", + "t1 = pd.read_csv('weak_scaling_data/weak_fft_n=1024000000_p=16_11029907/weak_fft_n=1024000000_p=16_11029907.csv')\n", + "t2 = pd.read_csv('weak_scaling_data/weak_fft_n=8192000000_p=128_11029908/weak_fft_n=8192000000_p=128_11029908.csv')\n", + "t3 = pd.read_csv('weak_scaling_data/weak_fft_n=32768000000_p=512_11030112/weak_fft_n=32768000000_p=512_11030112.csv')\n", + "\n", + "t2['experiment_id'] += t1['experiment_id'].max()+1\n", + "t3['experiment_id'] += t2['experiment_id'].max()+1\n", + "df_2e6 = pd.concat([t1, t2, t3])\n", + "\n", + "df_2e6[\"n_nodes\"] = df_2e6.groupby(\"experiment_id\")[\"experiment_id\"].transform(\n", + " lambda x: 1 if len(x) / 32 < 1 else len(x)/32\n", + ")\n", + "\n", + "df_2e6[\"n_ranks\"] = df_2e6.groupby(\"experiment_id\")[\"experiment_id\"].transform(\n", + " lambda x: len(x)\n", + ")\n", + "\n", + "df_2e6['total_points'] = df_2e6['n_points'] * df_2e6['n_ranks']" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "aae5401c-84f3-4769-9096-b6d45c6b7c81", + "metadata": {}, + "outputs": [], + "source": [ + "plt.rcParams.update({\n", + " \"font.size\": 9, # base font size\n", + " \"axes.labelsize\": 9, # x/y labels\n", + " \"axes.titlesize\": 9,\n", + " \"xtick.labelsize\": 9,\n", + " \"ytick.labelsize\": 9,\n", + " \"legend.fontsize\": 9,\n", + " \"font.family\": \"serif\", # or \"sans-serif\" for a cleaner look\n", + " \"text.usetex\": False # True if you want full LaTeX rendering\n", + "})\n", + "\n", + "\n", + "def set_size(width_cm, height_cm):\n", + " \"\"\"Helper to set figure size in cm.\"\"\"\n", + " return (width_cm / 2.54, height_cm / 2.54)\n", + "\n", + "\n", + "def weak_scaling_plot(df_vec, shrink=0.08, band_alpha=0.2):\n", + " \"\"\"\n", + " df_vec: list of dataframes (each with experiment_id, total_points, n_nodes, runtime, ...)\n", + " shrink: fraction to inset band edges on a log scale (per band)\n", + " band_alpha: transparency of the shaded bands\n", + " \"\"\"\n", + " fig, ax = plt.subplots(1, 3, figsize=set_size(35, 10)) # double column width\n", + " \n", + " shade_colors = [\"tab:blue\", \"tab:orange\"]\n", + "\n", + " for (i, df) in enumerate(df_vec):\n", + " # aggregate per experiment\n", + " grp = df.groupby('experiment_id')\n", + " runtime = grp.mean()[['total_points', 'n_points', 'n_nodes', 'n_ranks', 'runtime']]\n", + " runtime_err = grp.std()[['total_points', 'n_nodes', 'n_ranks', 'runtime']]\n", + "\n", + " # plot means + error bars\n", + " ax[i].errorbar(\n", + " runtime['total_points'],\n", + " runtime['runtime'],\n", + " yerr=runtime_err['runtime'],\n", + " fmt='o-', capsize=4, label=\"Runtime\", zorder=3\n", + " )\n", + "\n", + " ax[i].set_xscale('log')\n", + " ax[i].grid(True, zorder=0)\n", + "\n", + " # ----- build non-overlapping n_nodes regions on the x-axis -----\n", + " # collect per-n_nodes x ranges + a representative x (median) to order regions along x\n", + " bands = []\n", + " for n, sub in runtime.groupby('n_nodes'):\n", + " if sub.empty:\n", + " continue\n", + " x_min = sub['total_points'].min()\n", + " x_max = sub['total_points'].max()\n", + " x_rep = sub['total_points'].median()\n", + " bands.append((int(n), x_min, x_max, x_rep))\n", + "\n", + " if not bands:\n", + " continue\n", + "\n", + " # order the bands by their representative x (so bands won't overlap visually)\n", + " bands.sort(key=lambda t: t[3])\n", + "\n", + " # compute region edges: [left edge of first band] + midpoints between reps + [right edge of last band]\n", + " edges = [bands[0][1]]\n", + " for k in range(len(bands) - 1):\n", + " # geometric midpoint on log scale between representative x's\n", + " mid = (bands[k][3] * bands[k+1][3]) ** 0.5\n", + " edges.append(mid)\n", + " edges.append(bands[-1][2])\n", + "\n", + " # now shade each band between consecutive edges, alternating colors\n", + " # inset edges slightly in log-space so bands don't sit exactly on data limits\n", + " # now shade each band between consecutive edges, alternating colors\n", + " y0, y1 = ax[i].get_ylim()\n", + " for j, (n, _, _, _) in enumerate(bands):\n", + " left = edges[j]\n", + " right = edges[j+1]\n", + " \n", + " if left <= 0 or right <= 0 or left >= right:\n", + " continue\n", + " \n", + " # shrink inward on left, but extend right edge slightly\n", + " log_left, log_right = np.log10(left), np.log10(right)\n", + " pad = (log_right - log_left) * shrink\n", + " log_left_adj = log_left + pad\n", + " log_right_adj = log_right - pad\n", + " \n", + " # right-inclusive extension (+5% of width)\n", + " # if j == (len(bands)-1):\n", + " log_right_adj += (log_right - log_left) * 0.1\n", + "\n", + " if j == 0:\n", + " log_left_adj -= (log_right - log_left) * 0.15\n", + "\n", + " if j == (len(bands)-1):\n", + " log_right_adj += (log_right - log_left) * 0.1\n", + "\n", + "\n", + " left_adj = 10 ** log_left_adj\n", + " right_adj = 10 ** log_right_adj\n", + " \n", + " ax[i].axvspan(\n", + " left_adj, right_adj,\n", + " color=shade_colors[j % 2], alpha=band_alpha, zorder=1\n", + " )\n", + " \n", + " # label in log-midpoint\n", + " xmid = (left_adj * right_adj) ** 0.5\n", + " y_label = y0 + 0.92 * (y1 - y0)\n", + " y_label = y1 + 0.3*y1\n", + " ax[i].text(\n", + " xmid, y_label, f\"{n} nodes\",\n", + " ha=\"center\", va=\"top\", rotation=90,\n", + " fontsize=9, fontweight=\"bold\",\n", + " bbox=dict(facecolor=\"white\", alpha=0.7, edgecolor=\"none\"),\n", + " zorder=4\n", + " )\n", + "\n", + "\n", + " # keep limits stable if shading changed them\n", + " ax[i].set_ylim(y0, y1)\n", + "\n", + " ax[0].set_ylabel(\"Runtime (ms)\")\n", + " ax[1].set_xlabel(\"Number of Particles\")\n", + " fig.tight_layout()\n", + " # fig.legend(loc=\"upper right\", bbox_to_anchor=(0.98, 1.05))\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "58ab246a-9568-47c4-9906-ee50f9c4823b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " n_ranks n_nodes runtime total_points source_tree \\\n", + "experiment_id \n", + "0 8.0 1.0 2707.625000 1.600000e+07 24182.500000 \n", + "1 16.0 1.0 6299.187500 3.200000e+07 15686.187500 \n", + "2 32.0 1.0 3750.375000 6.400000e+07 18838.062500 \n", + "3 64.0 2.0 2709.453125 1.280000e+08 23951.734375 \n", + "4 128.0 4.0 6400.523438 2.560000e+08 16258.140625 \n", + "5 256.0 8.0 3830.542969 5.120000e+08 18787.957031 \n", + "6 512.0 16.0 2761.445312 1.024000e+09 25013.923828 \n", + "7 1024.0 32.0 6407.586914 2.048000e+09 17878.924805 \n", + "8 2048.0 64.0 3876.565918 4.096000e+09 22884.522461 \n", + "9 4096.0 128.0 2883.618652 8.192000e+09 32766.833008 \n", + "10 8192.0 256.0 7236.480835 1.638400e+10 34051.090088 \n", + "\n", + " m2l p2p local_depth \n", + "experiment_id \n", + "0 1273.625000 1207.250000 5.0 \n", + "1 5404.250000 629.250000 5.0 \n", + "2 2746.531250 827.031250 5.0 \n", + "3 1325.796875 1225.593750 5.0 \n", + "4 5476.750000 630.390625 5.0 \n", + "5 2765.609375 829.410156 5.0 \n", + "6 1358.531250 1229.859375 5.0 \n", + "7 5184.622070 625.692383 5.0 \n", + "8 2639.126953 826.509766 5.0 \n", + "9 1316.570557 1227.698242 5.0 \n", + "10 5327.551514 625.129883 5.0 " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tmp = df_2e6.groupby('experiment_id').mean()[['n_ranks', 'n_nodes', 'runtime', 'total_points', 'source_tree', 'm2l', 'p2p', 'local_depth']]\n", + "tmp" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "133ce93f-58c9-4485-a84d-4e570426e8bd", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "3c464fe6-d84e-42ec-8bac-8d31fc7f6f7f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['experiment_id', 'rank', 'runtime', 'p2m', 'm2m', 'l2l', 'm2l', 'p2p',\n", + " 'source_tree', 'target_tree', 'source_domain', 'target_domain',\n", + " 'layout', 'ghost_exchange_v', 'ghost_exchange_v_runtime',\n", + " 'ghost_exchange_u', 'gather_global_fmm', 'scatter_global_fmm',\n", + " 'source_to_target_data', 'source_data', 'target_data', 'global_fmm',\n", + " 'ghost_fmm_v', 'ghost_fmm_u', 'displacement_map', 'metadata_creation',\n", + " 'expansion_order', 'n_points', 'local_depth', 'global_depth',\n", + " 'block_size', 'n_threads', 'n_samples', 'n_nodes', 'n_ranks',\n", + " 'total_points'],\n", + " dtype='object')" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_250k.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "25a6fbdc-26bd-481e-8784-ac9f6c4fc34b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['experiment_id', 'rank', 'runtime', 'p2m', 'm2m', 'l2l', 'm2l', 'p2p',\n", + " 'source_tree', 'target_tree', 'source_domain', 'target_domain',\n", + " 'layout', 'ghost_exchange_v', 'ghost_exchange_v_runtime',\n", + " 'ghost_exchange_u', 'gather_global_fmm', 'scatter_global_fmm',\n", + " 'source_to_target_data', 'source_data', 'target_data', 'global_fmm',\n", + " 'ghost_fmm_v', 'ghost_fmm_u', 'displacement_map', 'metadata_creation',\n", + " 'expansion_order', 'n_points', 'local_depth', 'global_depth',\n", + " 'block_size', 'n_threads', 'n_samples'],\n", + " dtype='object')" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "1f03d766-8c9a-494b-bb8a-e7542c56138f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " rank runtime p2m m2m l2l m2l \\\n", + "experiment_id \n", + "0 511.5 910.956055 12.051758 1.006836 1.280273 657.564453 \n", + "2 2047.5 493.990723 4.479004 0.001953 0.004639 160.172119 \n", + "\n", + " p2p source_tree target_tree source_domain ... \\\n", + "experiment_id ... \n", + "0 111.202148 6297.000000 6253.0000 340.969727 ... \n", + "2 191.774902 22184.558594 22150.0354 522.776123 ... \n", + "\n", + " ghost_fmm_u displacement_map metadata_creation \\\n", + "experiment_id \n", + "0 0.0 3.0 250000.0 \n", + "2 0.0 3.0 250000.0 \n", + "\n", + " expansion_order n_points local_depth global_depth \\\n", + "experiment_id \n", + "0 4.0 4.0 128.0 4.0 \n", + "2 4.0 4.0 128.0 4.0 \n", + "\n", + " block_size n_threads n_samples \n", + "experiment_id \n", + "0 5000.0 NaN NaN \n", + "2 5000.0 NaN NaN \n", + "\n", + "[2 rows x 32 columns]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby('experiment_id').mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "dd40834f-91f4-4d27-ae73-0b0f66f70c7c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "experiment_id\n", + "0 2000000.0\n", + "1 2000000.0\n", + "2 2000000.0\n", + "3 2000000.0\n", + "4 2000000.0\n", + "5 2000000.0\n", + "6 2000000.0\n", + "7 2000000.0\n", + "8 2000000.0\n", + "9 2000000.0\n", + "10 2000000.0\n", + "dtype: float64" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tmp['total_points']/[tmp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4033f28b-40e7-44cb-90c9-a6ec449443c9", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.17" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 60f82ee683c6c01ed5e4ee484e7f39a932e7489d Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Sat, 4 Oct 2025 16:57:19 +0100 Subject: [PATCH 20/52] Update weak scaling notebook --- hpc/archer2/data/ijhpca/Weak Scaling.ipynb | 627 +++++++++++++-------- 1 file changed, 393 insertions(+), 234 deletions(-) diff --git a/hpc/archer2/data/ijhpca/Weak Scaling.ipynb b/hpc/archer2/data/ijhpca/Weak Scaling.ipynb index 550dbf18..ecd20935 100644 --- a/hpc/archer2/data/ijhpca/Weak Scaling.ipynb +++ b/hpc/archer2/data/ijhpca/Weak Scaling.ipynb @@ -39,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 105, "id": "09f4cb6e-d46a-48a5-9d6c-f880ab68856e", "metadata": {}, "outputs": [], @@ -63,12 +63,14 @@ "\n", "v_global_depth = np.vectorize(global_depth)\n", "\n", - "def pow2(x): return np.log2(x).is_integer()" + "def pow2(x): return np.log2(x).is_integer()\n", + "\n", + "def pow8(x): return np.emath.logn(8, x).is_integer()" ] }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 194, "id": "762508be-775e-4f58-8942-f2d69b0ae615", "metadata": {}, "outputs": [ @@ -76,23 +78,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "Total problem size n=1.024B\n", - "points per rank 0.25M\n", - "points per node 8.0M\n", - "global depth [4 4 4]\n", - "points per leaf 61.03515625\n", - "MPI tasks [1024 2048 4096]\n" + "Total problem size n=8.192B\n", + "points per rank 0.5M\n", + "points per node 16.0M\n", + "global depth [2 3 4 5]\n", + "points per leaf 122.0703125\n", + "MPI tasks [ 32 256 2048 16384]\n" ] } ], "source": [ - "min_nodes = 32\n", - "max_nodes = 128\n", + "min_nodes = 1\n", + "max_nodes = 512\n", "\n", "ranks_per_node = 32 # Say each rank gets a whole AMD EPYC ROME CPU\n", "total_ranks = ranks_per_node * max_nodes\n", "\n", - "points_per_rank = 250e3\n", + "points_per_rank = 500e3\n", "n = points_per_rank*total_ranks # total problem size\n", "print(f\"Total problem size n={n/1e9}B\")\n", "\n", @@ -101,7 +103,7 @@ "print(f\"points per node {points_per_node/1e6}M\")\n", "\n", "# Double number of nodes used until we reach max_nodes\n", - "n_nodes = np.array(list(filter(pow2, [i for i in range(min_nodes, max_nodes+1)])))\n", + "n_nodes = np.array(list(filter(pow8, [i for i in range(min_nodes, max_nodes+1)])))\n", "\n", "# The global depth is a function of the ranks per node (as will need enough global leaves to cover this many ranks (local trees))\n", "global_depth = v_global_depth(n_nodes*ranks_per_node)\n", @@ -116,25 +118,7 @@ }, { "cell_type": "code", - "execution_count": 91, - "id": "74eb7d8a-606d-4658-9ce3-0ad588d933c4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 32 64 128]\n" - ] - } - ], - "source": [ - "print(n_nodes)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, + "execution_count": 129, "id": "60dd15a0-cc36-46f9-8c60-e8f11a33b1b4", "metadata": {}, "outputs": [], @@ -193,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 130, "id": "aae5401c-84f3-4769-9096-b6d45c6b7c81", "metadata": {}, "outputs": [], @@ -328,7 +312,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 131, "id": "58ab246a-9568-47c4-9906-ee50f9c4823b", "metadata": {}, "outputs": [ @@ -349,8 +333,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "id": "ae9259b6-6e2b-44cb-83ba-0e73d8baa628", + "execution_count": 132, + "id": "b7cb9620-59bd-4881-beed-c3f97eb8abf8", "metadata": {}, "outputs": [ { @@ -368,7 +352,7 @@ " dtype='object')" ] }, - "execution_count": 16, + "execution_count": 132, "metadata": {}, "output_type": "execute_result" } @@ -377,9 +361,17 @@ "df_2e6.columns" ] }, + { + "cell_type": "markdown", + "id": "77ac42c5-f42d-44c9-b93c-233b19884c71", + "metadata": {}, + "source": [ + "#### " + ] + }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 239, "id": "08c36c16-3525-4f04-9c83-35934ffd2755", "metadata": {}, "outputs": [ @@ -408,10 +400,12 @@ " n_nodes\n", " runtime\n", " total_points\n", + " n_points\n", " source_tree\n", " m2l\n", " p2p\n", " local_depth\n", + " global_depth\n", " \n", " \n", " experiment_id\n", @@ -423,6 +417,8 @@ " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -432,10 +428,12 @@ " 1.0\n", " 2707.625000\n", " 1.600000e+07\n", + " 2000000.0\n", " 24182.500000\n", " 1273.625000\n", " 1207.250000\n", " 5.0\n", + " 1.0\n", " \n", " \n", " 1\n", @@ -443,10 +441,12 @@ " 1.0\n", " 6299.187500\n", " 3.200000e+07\n", + " 2000000.0\n", " 15686.187500\n", " 5404.250000\n", " 629.250000\n", " 5.0\n", + " 2.0\n", " \n", " \n", " 2\n", @@ -454,10 +454,12 @@ " 1.0\n", " 3750.375000\n", " 6.400000e+07\n", + " 2000000.0\n", " 18838.062500\n", " 2746.531250\n", " 827.031250\n", " 5.0\n", + " 2.0\n", " \n", " \n", " 3\n", @@ -465,10 +467,12 @@ " 2.0\n", " 2709.453125\n", " 1.280000e+08\n", + " 2000000.0\n", " 23951.734375\n", " 1325.796875\n", " 1225.593750\n", " 5.0\n", + " 2.0\n", " \n", " \n", " 4\n", @@ -476,10 +480,12 @@ " 4.0\n", " 6400.523438\n", " 2.560000e+08\n", + " 2000000.0\n", " 16258.140625\n", " 5476.750000\n", " 630.390625\n", " 5.0\n", + " 3.0\n", " \n", " \n", " 5\n", @@ -487,10 +493,12 @@ " 8.0\n", " 3830.542969\n", " 5.120000e+08\n", + " 2000000.0\n", " 18787.957031\n", " 2765.609375\n", " 829.410156\n", " 5.0\n", + " 3.0\n", " \n", " \n", " 6\n", @@ -498,10 +506,12 @@ " 16.0\n", " 2761.445312\n", " 1.024000e+09\n", + " 2000000.0\n", " 25013.923828\n", " 1358.531250\n", " 1229.859375\n", " 5.0\n", + " 3.0\n", " \n", " \n", " 7\n", @@ -509,10 +519,12 @@ " 32.0\n", " 6407.586914\n", " 2.048000e+09\n", + " 2000000.0\n", " 17878.924805\n", " 5184.622070\n", " 625.692383\n", " 5.0\n", + " 4.0\n", " \n", " \n", " 8\n", @@ -520,10 +532,12 @@ " 64.0\n", " 3876.565918\n", " 4.096000e+09\n", + " 2000000.0\n", " 22884.522461\n", " 2639.126953\n", " 826.509766\n", " 5.0\n", + " 4.0\n", " \n", " \n", " 9\n", @@ -531,10 +545,12 @@ " 128.0\n", " 2883.618652\n", " 8.192000e+09\n", + " 2000000.0\n", " 32766.833008\n", " 1316.570557\n", " 1227.698242\n", " 5.0\n", + " 4.0\n", " \n", " \n", " 10\n", @@ -542,126 +558,85 @@ " 256.0\n", " 7236.480835\n", " 1.638400e+10\n", + " 2000000.0\n", " 34051.090088\n", " 5327.551514\n", " 625.129883\n", " 5.0\n", + " 5.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " n_ranks n_nodes runtime total_points source_tree \\\n", - "experiment_id \n", - "0 8.0 1.0 2707.625000 1.600000e+07 24182.500000 \n", - "1 16.0 1.0 6299.187500 3.200000e+07 15686.187500 \n", - "2 32.0 1.0 3750.375000 6.400000e+07 18838.062500 \n", - "3 64.0 2.0 2709.453125 1.280000e+08 23951.734375 \n", - "4 128.0 4.0 6400.523438 2.560000e+08 16258.140625 \n", - "5 256.0 8.0 3830.542969 5.120000e+08 18787.957031 \n", - "6 512.0 16.0 2761.445312 1.024000e+09 25013.923828 \n", - "7 1024.0 32.0 6407.586914 2.048000e+09 17878.924805 \n", - "8 2048.0 64.0 3876.565918 4.096000e+09 22884.522461 \n", - "9 4096.0 128.0 2883.618652 8.192000e+09 32766.833008 \n", - "10 8192.0 256.0 7236.480835 1.638400e+10 34051.090088 \n", + " n_ranks n_nodes runtime total_points n_points \\\n", + "experiment_id \n", + "0 8.0 1.0 2707.625000 1.600000e+07 2000000.0 \n", + "1 16.0 1.0 6299.187500 3.200000e+07 2000000.0 \n", + "2 32.0 1.0 3750.375000 6.400000e+07 2000000.0 \n", + "3 64.0 2.0 2709.453125 1.280000e+08 2000000.0 \n", + "4 128.0 4.0 6400.523438 2.560000e+08 2000000.0 \n", + "5 256.0 8.0 3830.542969 5.120000e+08 2000000.0 \n", + "6 512.0 16.0 2761.445312 1.024000e+09 2000000.0 \n", + "7 1024.0 32.0 6407.586914 2.048000e+09 2000000.0 \n", + "8 2048.0 64.0 3876.565918 4.096000e+09 2000000.0 \n", + "9 4096.0 128.0 2883.618652 8.192000e+09 2000000.0 \n", + "10 8192.0 256.0 7236.480835 1.638400e+10 2000000.0 \n", + "\n", + " source_tree m2l p2p local_depth \\\n", + "experiment_id \n", + "0 24182.500000 1273.625000 1207.250000 5.0 \n", + "1 15686.187500 5404.250000 629.250000 5.0 \n", + "2 18838.062500 2746.531250 827.031250 5.0 \n", + "3 23951.734375 1325.796875 1225.593750 5.0 \n", + "4 16258.140625 5476.750000 630.390625 5.0 \n", + "5 18787.957031 2765.609375 829.410156 5.0 \n", + "6 25013.923828 1358.531250 1229.859375 5.0 \n", + "7 17878.924805 5184.622070 625.692383 5.0 \n", + "8 22884.522461 2639.126953 826.509766 5.0 \n", + "9 32766.833008 1316.570557 1227.698242 5.0 \n", + "10 34051.090088 5327.551514 625.129883 5.0 \n", "\n", - " m2l p2p local_depth \n", - "experiment_id \n", - "0 1273.625000 1207.250000 5.0 \n", - "1 5404.250000 629.250000 5.0 \n", - "2 2746.531250 827.031250 5.0 \n", - "3 1325.796875 1225.593750 5.0 \n", - "4 5476.750000 630.390625 5.0 \n", - "5 2765.609375 829.410156 5.0 \n", - "6 1358.531250 1229.859375 5.0 \n", - "7 5184.622070 625.692383 5.0 \n", - "8 2639.126953 826.509766 5.0 \n", - "9 1316.570557 1227.698242 5.0 \n", - "10 5327.551514 625.129883 5.0 " + " global_depth \n", + "experiment_id \n", + "0 1.0 \n", + "1 2.0 \n", + "2 2.0 \n", + "3 2.0 \n", + "4 3.0 \n", + "5 3.0 \n", + "6 3.0 \n", + "7 4.0 \n", + "8 4.0 \n", + "9 4.0 \n", + "10 5.0 " ] }, - "execution_count": 25, + "execution_count": 239, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "tmp = df_2e6.groupby('experiment_id').mean()[['n_ranks', 'n_nodes', 'runtime', 'total_points', 'source_tree', 'm2l', 'p2p', 'local_depth']]\n", + "tmp = df_2e6.groupby('experiment_id').mean()[['n_ranks', 'n_nodes', 'runtime', 'total_points', 'n_points', 'source_tree', 'm2l', 'p2p', 'local_depth', 'global_depth']]\n", "tmp" ] }, { "cell_type": "code", - "execution_count": 26, - "id": "133ce93f-58c9-4485-a84d-4e570426e8bd", + "execution_count": 240, + "id": "0406a78a-bc2b-4038-b7a4-b674e2d4a48c", "metadata": {}, "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "3c464fe6-d84e-42ec-8bac-8d31fc7f6f7f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['experiment_id', 'rank', 'runtime', 'p2m', 'm2m', 'l2l', 'm2l', 'p2p',\n", - " 'source_tree', 'target_tree', 'source_domain', 'target_domain',\n", - " 'layout', 'ghost_exchange_v', 'ghost_exchange_v_runtime',\n", - " 'ghost_exchange_u', 'gather_global_fmm', 'scatter_global_fmm',\n", - " 'source_to_target_data', 'source_data', 'target_data', 'global_fmm',\n", - " 'ghost_fmm_v', 'ghost_fmm_u', 'displacement_map', 'metadata_creation',\n", - " 'expansion_order', 'n_points', 'local_depth', 'global_depth',\n", - " 'block_size', 'n_threads', 'n_samples', 'n_nodes', 'n_ranks',\n", - " 'total_points'],\n", - " dtype='object')" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "df_250k.columns" + "tmp['local_trees_per_rank'] = 8**tmp['global_depth']/tmp['n_ranks']" ] }, { "cell_type": "code", - "execution_count": 31, - "id": "25a6fbdc-26bd-481e-8784-ac9f6c4fc34b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['experiment_id', 'rank', 'runtime', 'p2m', 'm2m', 'l2l', 'm2l', 'p2p',\n", - " 'source_tree', 'target_tree', 'source_domain', 'target_domain',\n", - " 'layout', 'ghost_exchange_v', 'ghost_exchange_v_runtime',\n", - " 'ghost_exchange_u', 'gather_global_fmm', 'scatter_global_fmm',\n", - " 'source_to_target_data', 'source_data', 'target_data', 'global_fmm',\n", - " 'ghost_fmm_v', 'ghost_fmm_u', 'displacement_map', 'metadata_creation',\n", - " 'expansion_order', 'n_points', 'local_depth', 'global_depth',\n", - " 'block_size', 'n_threads', 'n_samples'],\n", - " dtype='object')" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "1f03d766-8c9a-494b-bb8a-e7542c56138f", + "execution_count": 241, + "id": "69802397-a9fc-4f87-96b7-146e2c9fe9cb", "metadata": {}, "outputs": [ { @@ -685,27 +660,17 @@ " \n", " \n", " \n", - " rank\n", + " n_ranks\n", + " n_nodes\n", " runtime\n", - " p2m\n", - " m2m\n", - " l2l\n", + " total_points\n", + " n_points\n", + " source_tree\n", " m2l\n", " p2p\n", - " source_tree\n", - " target_tree\n", - " source_domain\n", - " ...\n", - " ghost_fmm_u\n", - " displacement_map\n", - " metadata_creation\n", - " expansion_order\n", - " n_points\n", " local_depth\n", " global_depth\n", - " block_size\n", - " n_threads\n", - " n_samples\n", + " local_trees_per_rank\n", " \n", " \n", " experiment_id\n", @@ -720,147 +685,341 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " 0\n", - " 511.5\n", - " 910.956055\n", - " 12.051758\n", - " 1.006836\n", - " 1.280273\n", - " 657.564453\n", - " 111.202148\n", - " 6297.000000\n", - " 6253.0000\n", - " 340.969727\n", - " ...\n", - " 0.0\n", - " 3.0\n", - " 250000.0\n", - " 4.0\n", + " 8.0\n", + " 1.0\n", + " 2707.625000\n", + " 1.600000e+07\n", + " 2000000.0\n", + " 24182.500000\n", + " 1273.625000\n", + " 1207.250000\n", + " 5.0\n", + " 1.0\n", + " 1.0\n", + " \n", + " \n", + " 1\n", + " 16.0\n", + " 1.0\n", + " 6299.187500\n", + " 3.200000e+07\n", + " 2000000.0\n", + " 15686.187500\n", + " 5404.250000\n", + " 629.250000\n", + " 5.0\n", + " 2.0\n", " 4.0\n", + " \n", + " \n", + " 2\n", + " 32.0\n", + " 1.0\n", + " 3750.375000\n", + " 6.400000e+07\n", + " 2000000.0\n", + " 18838.062500\n", + " 2746.531250\n", + " 827.031250\n", + " 5.0\n", + " 2.0\n", + " 2.0\n", + " \n", + " \n", + " 3\n", + " 64.0\n", + " 2.0\n", + " 2709.453125\n", + " 1.280000e+08\n", + " 2000000.0\n", + " 23951.734375\n", + " 1325.796875\n", + " 1225.593750\n", + " 5.0\n", + " 2.0\n", + " 1.0\n", + " \n", + " \n", + " 4\n", " 128.0\n", " 4.0\n", - " 5000.0\n", - " NaN\n", - " NaN\n", + " 6400.523438\n", + " 2.560000e+08\n", + " 2000000.0\n", + " 16258.140625\n", + " 5476.750000\n", + " 630.390625\n", + " 5.0\n", + " 3.0\n", + " 4.0\n", " \n", " \n", - " 2\n", - " 2047.5\n", - " 493.990723\n", - " 4.479004\n", - " 0.001953\n", - " 0.004639\n", - " 160.172119\n", - " 191.774902\n", - " 22184.558594\n", - " 22150.0354\n", - " 522.776123\n", - " ...\n", - " 0.0\n", + " 5\n", + " 256.0\n", + " 8.0\n", + " 3830.542969\n", + " 5.120000e+08\n", + " 2000000.0\n", + " 18787.957031\n", + " 2765.609375\n", + " 829.410156\n", + " 5.0\n", + " 3.0\n", + " 2.0\n", + " \n", + " \n", + " 6\n", + " 512.0\n", + " 16.0\n", + " 2761.445312\n", + " 1.024000e+09\n", + " 2000000.0\n", + " 25013.923828\n", + " 1358.531250\n", + " 1229.859375\n", + " 5.0\n", " 3.0\n", - " 250000.0\n", + " 1.0\n", + " \n", + " \n", + " 7\n", + " 1024.0\n", + " 32.0\n", + " 6407.586914\n", + " 2.048000e+09\n", + " 2000000.0\n", + " 17878.924805\n", + " 5184.622070\n", + " 625.692383\n", + " 5.0\n", " 4.0\n", " 4.0\n", + " \n", + " \n", + " 8\n", + " 2048.0\n", + " 64.0\n", + " 3876.565918\n", + " 4.096000e+09\n", + " 2000000.0\n", + " 22884.522461\n", + " 2639.126953\n", + " 826.509766\n", + " 5.0\n", + " 4.0\n", + " 2.0\n", + " \n", + " \n", + " 9\n", + " 4096.0\n", " 128.0\n", + " 2883.618652\n", + " 8.192000e+09\n", + " 2000000.0\n", + " 32766.833008\n", + " 1316.570557\n", + " 1227.698242\n", + " 5.0\n", + " 4.0\n", + " 1.0\n", + " \n", + " \n", + " 10\n", + " 8192.0\n", + " 256.0\n", + " 7236.480835\n", + " 1.638400e+10\n", + " 2000000.0\n", + " 34051.090088\n", + " 5327.551514\n", + " 625.129883\n", + " 5.0\n", + " 5.0\n", " 4.0\n", - " 5000.0\n", - " NaN\n", - " NaN\n", " \n", " \n", "\n", - "

2 rows × 32 columns

\n", "" ], "text/plain": [ - " rank runtime p2m m2m l2l m2l \\\n", - "experiment_id \n", - "0 511.5 910.956055 12.051758 1.006836 1.280273 657.564453 \n", - "2 2047.5 493.990723 4.479004 0.001953 0.004639 160.172119 \n", - "\n", - " p2p source_tree target_tree source_domain ... \\\n", - "experiment_id ... \n", - "0 111.202148 6297.000000 6253.0000 340.969727 ... \n", - "2 191.774902 22184.558594 22150.0354 522.776123 ... \n", - "\n", - " ghost_fmm_u displacement_map metadata_creation \\\n", - "experiment_id \n", - "0 0.0 3.0 250000.0 \n", - "2 0.0 3.0 250000.0 \n", - "\n", - " expansion_order n_points local_depth global_depth \\\n", - "experiment_id \n", - "0 4.0 4.0 128.0 4.0 \n", - "2 4.0 4.0 128.0 4.0 \n", + " n_ranks n_nodes runtime total_points n_points \\\n", + "experiment_id \n", + "0 8.0 1.0 2707.625000 1.600000e+07 2000000.0 \n", + "1 16.0 1.0 6299.187500 3.200000e+07 2000000.0 \n", + "2 32.0 1.0 3750.375000 6.400000e+07 2000000.0 \n", + "3 64.0 2.0 2709.453125 1.280000e+08 2000000.0 \n", + "4 128.0 4.0 6400.523438 2.560000e+08 2000000.0 \n", + "5 256.0 8.0 3830.542969 5.120000e+08 2000000.0 \n", + "6 512.0 16.0 2761.445312 1.024000e+09 2000000.0 \n", + "7 1024.0 32.0 6407.586914 2.048000e+09 2000000.0 \n", + "8 2048.0 64.0 3876.565918 4.096000e+09 2000000.0 \n", + "9 4096.0 128.0 2883.618652 8.192000e+09 2000000.0 \n", + "10 8192.0 256.0 7236.480835 1.638400e+10 2000000.0 \n", "\n", - " block_size n_threads n_samples \n", - "experiment_id \n", - "0 5000.0 NaN NaN \n", - "2 5000.0 NaN NaN \n", + " source_tree m2l p2p local_depth \\\n", + "experiment_id \n", + "0 24182.500000 1273.625000 1207.250000 5.0 \n", + "1 15686.187500 5404.250000 629.250000 5.0 \n", + "2 18838.062500 2746.531250 827.031250 5.0 \n", + "3 23951.734375 1325.796875 1225.593750 5.0 \n", + "4 16258.140625 5476.750000 630.390625 5.0 \n", + "5 18787.957031 2765.609375 829.410156 5.0 \n", + "6 25013.923828 1358.531250 1229.859375 5.0 \n", + "7 17878.924805 5184.622070 625.692383 5.0 \n", + "8 22884.522461 2639.126953 826.509766 5.0 \n", + "9 32766.833008 1316.570557 1227.698242 5.0 \n", + "10 34051.090088 5327.551514 625.129883 5.0 \n", "\n", - "[2 rows x 32 columns]" + " global_depth local_trees_per_rank \n", + "experiment_id \n", + "0 1.0 1.0 \n", + "1 2.0 4.0 \n", + "2 2.0 2.0 \n", + "3 2.0 1.0 \n", + "4 3.0 4.0 \n", + "5 3.0 2.0 \n", + "6 3.0 1.0 \n", + "7 4.0 4.0 \n", + "8 4.0 2.0 \n", + "9 4.0 1.0 \n", + "10 5.0 4.0 " + ] + }, + "execution_count": 241, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# tmp = df_2e6.groupby('experiment_id').mean()[['n_ranks', 'n_nodes', 'runtime', 'total_points', 'n_points', 'source_tree', 'm2l', 'p2p', 'local_depth', 'global_depth', 'local_trees_per_rank']]\n", + "tmp" + ] + }, + { + "cell_type": "code", + "execution_count": 232, + "id": "f4732f41-6b6b-49c3-a222-0dd35d953e29", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('weak_scaling_data/weak_fft_n=1024000000_p=64_11033767.csv')\n", + "df[\"n_nodes\"] = df.groupby(\"experiment_id\")[\"experiment_id\"].transform(\n", + " lambda x: 1 if len(x) / 32 < 1 else len(x)/32\n", + ")\n", + "df[\"n_ranks\"] = df.groupby(\"experiment_id\")[\"experiment_id\"].transform(\n", + " lambda x: len(x)\n", + ")\n", + "df['total_points'] = df['n_points'] * df['n_ranks']\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "id": "0d0a83cd-74a0-4af9-96e3-f50f4ecb8246", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 16000000\n", + "1 16000000\n", + "2 16000000\n", + "3 16000000\n", + "4 16000000\n", + " ... \n", + "2331 1024000000\n", + "2332 1024000000\n", + "2333 1024000000\n", + "2334 1024000000\n", + "2335 1024000000\n", + "Name: total_points, Length: 2336, dtype: int64" ] }, - "execution_count": 30, + "execution_count": 163, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.groupby('experiment_id').mean()" + "df['total_points']" ] }, { "cell_type": "code", - "execution_count": 23, - "id": "dd40834f-91f4-4d27-ae73-0b0f66f70c7c", + "execution_count": 159, + "id": "31b6e6d3-8b91-4b03-846b-ef034c18d860", "metadata": {}, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "experiment_id\n", - "0 2000000.0\n", - "1 2000000.0\n", - "2 2000000.0\n", - "3 2000000.0\n", - "4 2000000.0\n", - "5 2000000.0\n", - "6 2000000.0\n", - "7 2000000.0\n", - "8 2000000.0\n", - "9 2000000.0\n", - "10 2000000.0\n", - "dtype: float64" + "
" ] }, - "execution_count": 23, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "runtime = df.groupby('experiment_id').mean()[['n_ranks', 'n_nodes', 'runtime', 'total_points', 'source_tree', 'm2l', 'p2p', 'local_depth', 'global_depth', 'ghost_exchange_v_runtime']]\n", + "# Calculate nodes and ranks\n", + "runtime_err = df.groupby('experiment_id').std()[['total_points', 'n_nodes', 'n_ranks', 'runtime']]\n", + "\n", + "plt.xscale('log')\n", + "plt.xlim(runtime['total_points'].min()-0.3*runtime['total_points'].min(), runtime['total_points'].max()+0.3*runtime['total_points'].max())\n", + "# plot means + error bars\n", + "plt.errorbar(\n", + " runtime['total_points'],\n", + " runtime['runtime'],\n", + " yerr=runtime_err['runtime'],\n", + " fmt='o-', capsize=4, label=\"Runtime\", zorder=3\n", + " )\n", + "\n", + "plt.xlabel('Number of Points')\n", + "plt.ylabel('Runtime (ms)')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "id": "c89c4033-1506-43ba-ade9-5f9c56462b5a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "512" + ] + }, + "execution_count": 141, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "tmp['total_points']/[tmp" + "plt.errorbar(tmp['runtime'" ] }, { "cell_type": "code", "execution_count": null, - "id": "4033f28b-40e7-44cb-90c9-a6ec449443c9", + "id": "67d9abc3-f9bb-41ec-9778-9912fecbc924", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5551fd95-6e65-4501-9ffc-95fe01a78b71", "metadata": {}, "outputs": [], "source": [] From 060e76a5ae358f605bceb647ee8fff83f9f9c9e0 Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Sat, 4 Oct 2025 17:11:04 +0100 Subject: [PATCH 21/52] Add roots per rank to script output --- hpc/archer2/slurm/weak_1_fft.slurm | 2 +- hpc/archer2/slurm/weak_2_fft.slurm | 2 +- hpc/archer2/slurm/weak_3_fft.slurm | 2 +- scripts/src/bin/fmm_m2l_fft_mpi_f32.rs | 42 ++++++++++++++++++++++++-- 4 files changed, 42 insertions(+), 6 deletions(-) diff --git a/hpc/archer2/slurm/weak_1_fft.slurm b/hpc/archer2/slurm/weak_1_fft.slurm index e891a464..ad54be91 100644 --- a/hpc/archer2/slurm/weak_1_fft.slurm +++ b/hpc/archer2/slurm/weak_1_fft.slurm @@ -78,7 +78,7 @@ source_tree,target_tree,source_domain,target_domain,layout,\ ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\ source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\ displacement_map,metadata_creation,\ -expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples" >> ${OUTPUT} +expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples,source_local_trees_per_rank,target_local_trees_per_rank" >> ${OUTPUT} # Perform weak scaling for i in ${!n_tasks[@]}; do diff --git a/hpc/archer2/slurm/weak_2_fft.slurm b/hpc/archer2/slurm/weak_2_fft.slurm index 260f62c6..0509eafe 100644 --- a/hpc/archer2/slurm/weak_2_fft.slurm +++ b/hpc/archer2/slurm/weak_2_fft.slurm @@ -77,7 +77,7 @@ source_tree,target_tree,source_domain,target_domain,layout,\ ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\ source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\ displacement_map,metadata_creation,\ -expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples" >> ${OUTPUT} +expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples,source_local_trees_per_rank,target_local_trees_per_rank" >> ${OUTPUT} # Perform weak scaling for i in ${!n_tasks[@]}; do diff --git a/hpc/archer2/slurm/weak_3_fft.slurm b/hpc/archer2/slurm/weak_3_fft.slurm index 79d641e9..2462da2a 100644 --- a/hpc/archer2/slurm/weak_3_fft.slurm +++ b/hpc/archer2/slurm/weak_3_fft.slurm @@ -79,7 +79,7 @@ source_tree,target_tree,source_domain,target_domain,layout,\ ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\ source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\ displacement_map,metadata_creation,\ -expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples" >> ${OUTPUT} +expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples,source_local_trees_per_rank,target_local_trees_per_rank" >> ${OUTPUT} # Perform weak scaling for i in ${!n_tasks[@]}; do diff --git a/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs b/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs index 595bbcfa..84815b98 100644 --- a/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs +++ b/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs @@ -2,7 +2,7 @@ use clap::Parser; use green_kernels::laplace_3d::Laplace3dKernel; use kifmm::{ - traits::types::{CommunicationType, FmmOperatorType, MetadataType}, + traits::{tree::MultiFmmTree, types::{CommunicationType, FmmOperatorType, MetadataType}}, tree::{helpers::points_fixture, types::SortKind}, DataAccessMulti, EvaluateMulti, FftFieldTranslation, MultiNodeBuilder, }; @@ -112,6 +112,40 @@ fn main() { multi_fmm.evaluate().unwrap(); let runtime = start.elapsed().as_millis(); + let mut mean_roots_per_rank_source_tree = 0.; + if multi_fmm.rank() == 0 { + + let mut m: HashMap = HashMap::new(); + for x in multi_fmm.tree().source_tree().all_roots_ranks.iter() { + *m.entry(*x).or_default() += 1.0; + } + + let n_ranks = m.len(); + let mut tmp = 0.0; + for (_rank, n_keys) in m.iter() { + tmp += n_keys; + } + + mean_roots_per_rank_source_tree = tmp / (n_ranks as f64); + } + + let mut mean_roots_per_rank_target_tree= 0.; + if multi_fmm.rank() == 0 { + + let mut m: HashMap = HashMap::new(); + for x in multi_fmm.tree().target_tree().all_roots_ranks.iter() { + *m.entry(*x).or_default() += 1.0; + } + + let n_ranks = m.len(); + let mut tmp = 0.0; + for (_rank, n_keys) in m.iter() { + tmp += n_keys; + } + + mean_roots_per_rank_target_tree = tmp / (n_ranks as f64); + } + // Destructure operator times let mut operator_times = HashMap::new(); @@ -225,7 +259,7 @@ fn main() { "{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},\ {:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?}, \ {:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?}, \ - {:?},{:?},{:?},{:?},{:?},{:?},{:?}", + {:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?}hpc/archer2/slurm/weak_1_fft.slurm", id, multi_fmm.rank(), runtime, @@ -260,6 +294,8 @@ fn main() { args.global_depth, args.block_size, args.n_threads, - args.n_samples + args.n_samples, + mean_roots_per_rank_source_tree, + mean_roots_per_rank_target_tree ); } From e1e32e4d0be1349cede3dd020197f2c8b3db46ee Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Sat, 4 Oct 2025 18:15:00 +0100 Subject: [PATCH 22/52] Auto generation of slurm scripts --- .gitignore | 5 +- hpc/archer2/data/ijhpca/Weak Scaling.ipynb | 156 +- hpc/archer2/data/ijhpca/job.slurm | 56 + .../data/ijhpca/make_slurm_weak_scaling.py | 149 ++ .../strong_fft_p=153600000_n=128_11018635.csv | 2034 ----------------- hpc/archer2/data/ijhpca/weak_fft_10984742.csv | 1018 --------- hpc/archer2/data/ijhpca/weak_fft_10984754.csv | 1018 --------- hpc/archer2/data/ijhpca/weak_fft_10984759.csv | 1018 --------- hpc/archer2/data/ijhpca/weak_fft_11004445.csv | 1018 --------- .../weak_fft_11018099_non_contiguous.csv | 1018 --------- hpc/archer2/slurm/grid_search_1.slurm | 84 - hpc/archer2/slurm/grid_search_2.slurm | 88 - hpc/archer2/slurm/grid_search_3.slurm | 85 - .../slurm/grid_search_fft_m2l_1e6.slurm | 91 - .../slurm/grid_search_fft_m2l_1e7.slurm | 91 - hpc/archer2/slurm/strong_1_nodes_fft.slurm | 83 - hpc/archer2/slurm/weak_1_blas.slurm | 93 - hpc/archer2/slurm/weak_1_fft.slurm | 95 - hpc/archer2/slurm/weak_2_blas.slurm | 93 - hpc/archer2/slurm/weak_2_fft.slurm | 94 - hpc/archer2/slurm/weak_3_fft.slurm | 96 - 21 files changed, 322 insertions(+), 8161 deletions(-) create mode 100644 hpc/archer2/data/ijhpca/job.slurm create mode 100644 hpc/archer2/data/ijhpca/make_slurm_weak_scaling.py delete mode 100644 hpc/archer2/data/ijhpca/strong_fft_p=153600000_n=128_11018635.csv delete mode 100644 hpc/archer2/data/ijhpca/weak_fft_10984742.csv delete mode 100644 hpc/archer2/data/ijhpca/weak_fft_10984754.csv delete mode 100644 hpc/archer2/data/ijhpca/weak_fft_10984759.csv delete mode 100644 hpc/archer2/data/ijhpca/weak_fft_11004445.csv delete mode 100644 hpc/archer2/data/ijhpca/weak_fft_11018099_non_contiguous.csv delete mode 100644 hpc/archer2/slurm/grid_search_1.slurm delete mode 100644 hpc/archer2/slurm/grid_search_2.slurm delete mode 100644 hpc/archer2/slurm/grid_search_3.slurm delete mode 100644 hpc/archer2/slurm/grid_search_fft_m2l_1e6.slurm delete mode 100644 hpc/archer2/slurm/grid_search_fft_m2l_1e7.slurm delete mode 100644 hpc/archer2/slurm/strong_1_nodes_fft.slurm delete mode 100644 hpc/archer2/slurm/weak_1_blas.slurm delete mode 100644 hpc/archer2/slurm/weak_1_fft.slurm delete mode 100644 hpc/archer2/slurm/weak_2_blas.slurm delete mode 100644 hpc/archer2/slurm/weak_2_fft.slurm delete mode 100644 hpc/archer2/slurm/weak_3_fft.slurm diff --git a/.gitignore b/.gitignore index 696dd937..d8ef49ed 100644 --- a/.gitignore +++ b/.gitignore @@ -12,6 +12,9 @@ Cargo.lock # nano swp files .*.swp +# SLURM scripts +*.slurm + # Local VSCode files .vscode/ @@ -19,7 +22,7 @@ Cargo.lock fftw-src/fftw-* # Data -# *.csv +*.csv # Python *.ipynb_checkpoints/ diff --git a/hpc/archer2/data/ijhpca/Weak Scaling.ipynb b/hpc/archer2/data/ijhpca/Weak Scaling.ipynb index ecd20935..f140a966 100644 --- a/hpc/archer2/data/ijhpca/Weak Scaling.ipynb +++ b/hpc/archer2/data/ijhpca/Weak Scaling.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 36, + "execution_count": 1, "id": "2ae1633f-50c6-48ec-ad4b-d899118e24c8", "metadata": {}, "outputs": [], @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 2, "id": "89326e5d-3c39-43f0-b114-4d41e8f091de", "metadata": {}, "outputs": [ @@ -24,12 +24,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[34mUntitled\u001b[m\u001b[m \u001b[34mweak_fft_n=32768000000_p=512_11030112\u001b[m\u001b[m\n", - "\u001b[34mweak_fft_n=1024000000_p=128_11029691\u001b[m\u001b[m \u001b[34mweak_fft_n=4096000000_p=128_11029701\u001b[m\u001b[m\n", - "\u001b[34mweak_fft_n=1024000000_p=16_11029907\u001b[m\u001b[m \u001b[34mweak_fft_n=4096000000_p=512_11030104\u001b[m\u001b[m\n", - "\u001b[34mweak_fft_n=128000000_p=16_11029687\u001b[m\u001b[m \u001b[34mweak_fft_n=512000000_p=16_11029699\u001b[m\u001b[m\n", - "\u001b[34mweak_fft_n=16384000000_p=512_11030108\u001b[m\u001b[m \u001b[34mweak_fft_n=8192000000_p=128_11029908\u001b[m\u001b[m\n", - "\u001b[34mweak_fft_n=250000_p=16_11018099\u001b[m\u001b[m\n" + "\u001b[34mUntitled\u001b[m\u001b[m \u001b[34mweak_fft_n=250000_p=16_11018099\u001b[m\u001b[m\n", + "\u001b[34mweak_fft_n=1024000000_p=128_11029691\u001b[m\u001b[m \u001b[34mweak_fft_n=32768000000_p=512_11030112\u001b[m\u001b[m\n", + "\u001b[34mweak_fft_n=1024000000_p=16_11029907\u001b[m\u001b[m \u001b[34mweak_fft_n=4096000000_p=128_11029701\u001b[m\u001b[m\n", + "weak_fft_n=1024000000_p=64_11033767.csv \u001b[34mweak_fft_n=4096000000_p=512_11030104\u001b[m\u001b[m\n", + "\u001b[34mweak_fft_n=128000000_p=16_11029687\u001b[m\u001b[m \u001b[34mweak_fft_n=512000000_p=16_11029699\u001b[m\u001b[m\n", + "\u001b[34mweak_fft_n=16384000000_p=512_11030108\u001b[m\u001b[m \u001b[34mweak_fft_n=8192000000_p=128_11029908\u001b[m\u001b[m\n" ] } ], @@ -39,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 3, "id": "09f4cb6e-d46a-48a5-9d6c-f880ab68856e", "metadata": {}, "outputs": [], @@ -70,50 +70,120 @@ }, { "cell_type": "code", - "execution_count": 194, - "id": "762508be-775e-4f58-8942-f2d69b0ae615", + "execution_count": 70, + "id": "b2219ad2-3f03-4f41-9e91-964c6a5b0b62", + "metadata": {}, + "outputs": [], + "source": [ + "def experiment_parameters(min_nodes, max_nodes, ranks_per_node, points_per_rank, local_depth=4, scaling_func=pow8):\n", + "\n", + " max_cpus=128 # AMD EPYC rome\n", + " \n", + " total_ranks = ranks_per_node * max_nodes\n", + "\n", + " n = points_per_rank*total_ranks # total problem size\n", + " print(f\"Total problem size n={n/1e9}B\")\n", + " \n", + " points_per_node = points_per_rank * ranks_per_node\n", + " print(f\"points per rank {points_per_rank/1e6}M\")\n", + " print(f\"points per node {points_per_node/1e6}M\")\n", + " \n", + " # Double number of nodes used until we reach max_nodes\n", + " n_nodes = np.array(list(filter(scaling_func, [i for i in range(min_nodes, max_nodes+1)])))\n", + " \n", + " # The global depth is a function of the ranks per node (as will need enough global leaves to cover this many ranks (local trees))\n", + " global_depth = v_global_depth(n_nodes*ranks_per_node)\n", + " print(f\"global depth {global_depth}\")\n", + " print(f\"local depth {local_depth}\")\n", + " \n", + " # local depth is a constant, chosen to balance M2L and P2P\n", + " points_per_leaf = points_per_rank / 8**local_depth \n", + " print(f\"points per leaf {points_per_leaf}\")\n", + "\n", + " n_tasks = n_nodes*ranks_per_node\n", + " print(f\"MPI tasks {n_tasks}\")\n", + "\n", + " print(f\"number of nodes {n_nodes}\")\n", + "\n", + " local_trees_per_rank = (8**global_depth)/(n_tasks)\n", + " print(f\"local trees per rank {local_trees_per_rank}\")\n", + "\n", + " max_threads_per_rank = max_cpus/ranks_per_node\n", + " print(f\"max threads per rank {max_threads_per_rank}\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d6b9585-7da2-48ea-add2-185bc200ef90", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "22ca7892-b74b-40c7-9446-76f1d69d75bd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Total problem size n=8.192B\n", - "points per rank 0.5M\n", - "points per node 16.0M\n", - "global depth [2 3 4 5]\n", - "points per leaf 122.0703125\n", - "MPI tasks [ 32 256 2048 16384]\n" + "Total problem size n=0.128B\n", + "points per rank 1.0M\n", + "points per node 2.0M\n", + "global depth [1 2 3]\n", + "local depth 4\n", + "points per leaf 244.140625\n", + "MPI tasks [ 2 16 128]\n", + "number of nodes [ 1 8 64]\n", + "local trees per rank [4. 4. 4.]\n", + "max threads per rank 64.0\n" ] } ], "source": [ - "min_nodes = 1\n", - "max_nodes = 512\n", - "\n", - "ranks_per_node = 32 # Say each rank gets a whole AMD EPYC ROME CPU\n", - "total_ranks = ranks_per_node * max_nodes\n", - "\n", - "points_per_rank = 500e3\n", - "n = points_per_rank*total_ranks # total problem size\n", - "print(f\"Total problem size n={n/1e9}B\")\n", - "\n", - "points_per_node = points_per_rank * ranks_per_node\n", - "print(f\"points per rank {points_per_rank/1e6}M\")\n", - "print(f\"points per node {points_per_node/1e6}M\")\n", - "\n", - "# Double number of nodes used until we reach max_nodes\n", - "n_nodes = np.array(list(filter(pow8, [i for i in range(min_nodes, max_nodes+1)])))\n", - "\n", - "# The global depth is a function of the ranks per node (as will need enough global leaves to cover this many ranks (local trees))\n", - "global_depth = v_global_depth(n_nodes*ranks_per_node)\n", - "print(f\"global depth {global_depth}\")\n", - "\n", - "# local depth is a constant, chosen to balance M2L and P2P\n", - "local_depth = 4\n", - "points_per_leaf = points_per_rank / 8**local_depth \n", - "print(f\"points per leaf {points_per_leaf}\")\n", - "print(f\"MPI tasks {n_nodes*ranks_per_node}\")" + "# 32 ranks per node -> 1 rank per CCX\n", + "experiment_parameters(1, 64, 2, 1e6, local_depth=4, scaling_func=pow8)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bba669ab-adf1-4fd8-8d08-9757a599e3a8", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6da6193f-690d-49c3-82ef-1930aae667ee", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "178da76c-f2f3-4e55-859b-37cccde9f575", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "128" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "8*16" ] }, { diff --git a/hpc/archer2/data/ijhpca/job.slurm b/hpc/archer2/data/ijhpca/job.slurm new file mode 100644 index 00000000..b86a79eb --- /dev/null +++ b/hpc/archer2/data/ijhpca/job.slurm @@ -0,0 +1,56 @@ +#!/bin/bash +#SBATCH --job-name=weak_scaling_fft +#SBATCH --time=00:30:00 +#SBATCH --nodes=64 +#SBATCH --ntasks-per-node=4 +#SBATCH --cpus-per-task=32 +#SBATCH --account=e738 +#SBATCH --partition=standard +#SBATCH --qos=standard + +module load PrgEnv-aocc +module load craype-network-ucx +module load cray-mpich-ucx + +export HOME="/home/e738/e738/skailasa" +export WORK="/work/e738/e738/skailasa" + +script_name="fmm_m2l_fft_mpi_f32" + +export SCRATCH=$WORK/weak_fft_n=64000000_p=64_${SLURM_JOBID} +mkdir -p $SCRATCH +cd $SCRATCH + +export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK +export OMP_NUM_THREADS=1 + +export OUTPUT=$SCRATCH/weak_fft_n=64000000_p=64_${SLURM_JOBID}.csv +touch $OUTPUT +echo " +experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,source_tree,target_tree,source_domain,target_domain,layout,ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,displacement_map,metadata_creation,expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples,source_local_trees_per_rank,target_local_trees_per_rank" >> ${OUTPUT} + +# Perform weak scaling + +srun --nodes=1 --ntasks=4 --cpus-per-task=32 \ + --distribution=block:block --hint=nomultithread \ + $WORK/$script_name --id 0 --n-points 250000 \ + --expansion-order 3 --prune-empty \ + --global-depth 1 --local-depth 3 \ + --n-samples 500 --block-size 128 --n-threads 32 \ + >> $OUTPUT 2> $SCRATCH/err_run_0.log + +srun --nodes=8 --ntasks=32 --cpus-per-task=32 \ + --distribution=block:block --hint=nomultithread \ + $WORK/$script_name --id 1 --n-points 250000 \ + --expansion-order 3 --prune-empty \ + --global-depth 2 --local-depth 3 \ + --n-samples 500 --block-size 128 --n-threads 32 \ + >> $OUTPUT 2> $SCRATCH/err_run_1.log + +srun --nodes=64 --ntasks=256 --cpus-per-task=32 \ + --distribution=block:block --hint=nomultithread \ + $WORK/$script_name --id 2 --n-points 250000 \ + --expansion-order 3 --prune-empty \ + --global-depth 3 --local-depth 3 \ + --n-samples 500 --block-size 128 --n-threads 32 \ + >> $OUTPUT 2> $SCRATCH/err_run_2.log diff --git a/hpc/archer2/data/ijhpca/make_slurm_weak_scaling.py b/hpc/archer2/data/ijhpca/make_slurm_weak_scaling.py new file mode 100644 index 00000000..d989a998 --- /dev/null +++ b/hpc/archer2/data/ijhpca/make_slurm_weak_scaling.py @@ -0,0 +1,149 @@ +#!/usr/bin/env python3 +import numpy as np +import argparse +from pathlib import Path + + +def global_depth(rank): + """ + Return the first power of 8 >= rank, the exponent (tree level). + """ + level = 1 + while True: + if rank <= 8**level: + return level + level += 1 + + +v_global_depth = np.vectorize(global_depth) + + +def pow2(x): + return np.log2(x).is_integer() + + +def pow8(x): + return np.emath.logn(8, x).is_integer() + + +def experiment_parameters(min_nodes, max_nodes, ranks_per_node, points_per_rank, local_depth=4, scaling_func=pow8): + + max_cpus=128 # AMD EPYC rome + + total_ranks = ranks_per_node * max_nodes + + n = points_per_rank*total_ranks # total problem size + print(f"Total problem size n={n/1e9}B") + + points_per_node = points_per_rank * ranks_per_node + print(f"points per rank {points_per_rank/1e6}M") + print(f"points per node {points_per_node/1e6}M") + + # Double number of nodes used until we reach max_nodes + n_nodes = np.array(list(filter(scaling_func, [i for i in range(min_nodes, max_nodes+1)]))) + + # The global depth is a function of the ranks per node (as will need enough global leaves to cover this many ranks (local trees)) + global_depth = v_global_depth(n_nodes*ranks_per_node) + print(f"global depth {global_depth}") + print(f"local depth {local_depth}") + + # local depth is a constant, chosen to balance M2L and P2P + points_per_leaf = points_per_rank / 8**local_depth + print(f"points per leaf {points_per_leaf}") + + n_tasks = n_nodes*ranks_per_node + print(f"MPI tasks {n_tasks}") + + print(f"number of nodes {n_nodes}") + + local_trees_per_rank = (8**global_depth)/(n_tasks) + print(f"local trees per rank {local_trees_per_rank}") + + max_threads_per_rank = max_cpus/ranks_per_node + print(f"max threads per rank {max_threads_per_rank}") + + return n_nodes.tolist(), n_tasks.tolist(), global_depth.tolist(), max_threads_per_rank + + + +def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, points_per_rank, local_depth, script_name="fmm_m2l_fft_mpi_f32"): + expansion_order = 3 + n_points = points_per_rank + n_samples = 500 + block_size = 128 + cpus_per_task = int(max_threads) + + + last_nodes = n_nodes[-1] + last_tasks = n_tasks[-1] + max_points = last_tasks * n_points + + slurm = f"""#!/bin/bash +#SBATCH --job-name=weak_scaling_fft +#SBATCH --time=00:30:00 +#SBATCH --nodes={last_nodes} +#SBATCH --ntasks-per-node={int(n_tasks[-1] // n_nodes[-1])} +#SBATCH --cpus-per-task={cpus_per_task} +#SBATCH --account=e738 +#SBATCH --partition=standard +#SBATCH --qos=standard + +module load PrgEnv-aocc +module load craype-network-ucx +module load cray-mpich-ucx + +export HOME="/home/e738/e738/skailasa" +export WORK="/work/e738/e738/skailasa" + +script_name="{script_name}" + +export SCRATCH=$WORK/weak_fft_n={max_points}_p={last_nodes}_${{SLURM_JOBID}} +mkdir -p $SCRATCH +cd $SCRATCH + +export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK +export OMP_NUM_THREADS=1 + +export OUTPUT=$SCRATCH/weak_fft_n={max_points}_p={last_nodes}_${{SLURM_JOBID}}.csv +touch $OUTPUT +echo " +experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ +source_tree,target_tree,source_domain,target_domain,layout,\ +ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\ +source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\ +displacement_map,metadata_creation,\ +expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples,source_local_trees_per_rank,target_local_trees_per_rank" >> ${{OUTPUT}} +""" + + # loop of runs + slurm += "\n# Perform weak scaling\n" + for i, (nn, nt, gd) in enumerate(zip(n_nodes, n_tasks, global_depths)): + slurm += f""" +srun --nodes={nn} --ntasks={nt} --cpus-per-task={cpus_per_task} \\ + --distribution=block:block --hint=nomultithread \\ + $WORK/$script_name --id {i} --n-points {n_points} \\ + --expansion-order {expansion_order} --prune-empty \\ + --global-depth {gd} --local-depth {local_depth} \\ + --n-samples {n_samples} --block-size {block_size} --n-threads {int(max_threads)} \\ + >> $OUTPUT 2> $SCRATCH/err_run_{i}.log +""" + + Path(script_path).write_text(slurm) + print(f"✅ Wrote SLURM script to {script_path}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Generate a SLURM script for weak scaling runs.") + parser.add_argument("--min-nodes", type=int, default=1) + parser.add_argument("--max-nodes", type=int, default=16) + parser.add_argument("--ranks-per-node", type=int, default=32) + parser.add_argument("--points-per-rank", type=int, default=250000) + parser.add_argument("--local-depth", type=int, default=4) + parser.add_argument("--output", type=str, default="job.slurm") + args = parser.parse_args() + + n_nodes, n_tasks, global_depths, max_threads = experiment_parameters( + args.min_nodes, args.max_nodes, args.ranks_per_node, args.points_per_rank, args.local_depth + ) + + write_slurm(args.output, n_nodes, n_tasks, global_depths, max_threads, args.points_per_rank, args.local_depth) diff --git a/hpc/archer2/data/ijhpca/strong_fft_p=153600000_n=128_11018635.csv b/hpc/archer2/data/ijhpca/strong_fft_p=153600000_n=128_11018635.csv deleted file mode 100644 index 3654593a..00000000 --- a/hpc/archer2/data/ijhpca/strong_fft_p=153600000_n=128_11018635.csv +++ /dev/null @@ -1,2034 +0,0 @@ - -experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,source_tree,target_tree,source_domain,target_domain,layout,ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,displacement_map,metadata_creation,expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples -"0",14,6784,77,13,27,3928,2556,74311,73812,121,120,2,906,115,399,0,21, 381,0,0,0,0,0,29,3207, 3,9600000,5,2,128,8,500 -"0",4,8776,69,13,24,5233,3273,74311,73813,122,120,1,234,90,1110,0,13, 380,0,0,0,0,0,31,3247, 3,9600000,5,2,128,8,500 -"0",6,8795,69,13,27,5260,3267,74311,73813,123,120,1,250,85,1095,0,12, 380,0,0,0,0,0,36,3248, 3,9600000,5,2,128,8,500 -"0",0,8837,71,13,33,5255,3271,74311,73813,122,120,0,1024,109,282,23,0, 380,0,0,0,0,0,38,3208, 3,9600000,5,2,128,8,500 -"0",11,8845,88,16,32,5270,3264,74312,73812,123,120,0,1026,90,276,0,24, 381,0,0,0,0,0,40,3205, 3,9600000,5,2,128,8,500 -"0",2,8809,81,13,38,5258,3272,74311,73813,123,120,0,226,80,1118,0,6, 380,1,0,0,0,0,37,3247, 3,9600000,5,2,128,8,500 -"0",13,8831,69,13,25,5298,3266,74311,73813,122,120,0,259,90,1083,0,11, 381,1,0,0,0,0,36,3244, 3,9600000,5,2,128,8,500 -"0",9,8906,107,17,39,5322,3266,74312,73813,122,120,0,1030,78,272,0,17, 380,1,0,0,0,0,39,3205, 3,9600000,5,2,128,8,500 -"0",12,8874,53,10,22,5243,3378,74310,73814,122,120,0,122,101,1236,0,6, 381,0,0,0,0,0,34,3260, 3,9600000,5,2,128,8,500 -"0",8,8937,111,14,40,5266,3405,74311,73813,122,120,0,130,37,1227,0,1, 380,0,0,0,0,0,39,3260, 3,9600000,5,2,128,8,500 -"0",7,8948,84,13,40,5296,3378,74311,73813,123,120,1,165,69,1187,0,5, 381,1,0,0,0,0,37,3255, 3,9600000,5,2,128,8,500 -"0",1,8961,62,11,39,5315,3372,74310,73814,124,120,0,127,95,1229,0,6, 380,1,0,0,0,0,33,3258, 3,9600000,5,2,128,8,500 -"0",5,8994,70,13,27,5354,3379,74311,73813,121,120,0,146,87,1205,0,4, 380,1,0,0,0,0,36,3255, 3,9600000,5,2,128,8,500 -"0",3,9032,74,13,23,5302,3483,74311,73813,123,120,0,141,74,1210,0,2, 380,1,0,0,0,0,46,3255, 3,9600000,5,2,128,8,500 -"0",10,9044,56,9,26,5301,3481,74310,73814,122,120,0,138,101,1217,0,7, 381,1,0,0,0,0,33,3257, 3,9600000,5,2,128,8,500 -"0",15,10885,104,16,46,6484,4086,74311,73813,121,120,0,974,54,348,0,22, 381,1,0,0,0,0,43,3224, 3,9600000,5,2,128,8,500 -"1",0,4895,23,0,0,156,4676,42275,43840,65,62,2,1295,14,198,5,0, 385,1,0,0,0,0,2,2272, 3,4800000,4,2,128,8,500 -"1",14,5572,19,0,0,165,5348,42275,43840,64,62,2,1046,18,450,0,3, 385,1,0,0,0,0,2,2276, 3,4800000,4,2,128,8,500 -"1",13,9935,19,1,1,322,9544,42275,43841,65,62,1,395,14,1105,0,3, 387,1,0,0,0,0,2,2278, 3,4800000,4,2,128,8,500 -"1",9,9945,19,0,1,323,9555,42275,43841,63,62,1,397,14,1103,0,3, 385,1,0,0,0,0,2,2280, 3,4800000,4,2,128,8,500 -"1",31,9961,31,1,1,324,9565,42276,43841,64,62,0,1177,4,319,0,2, 390,1,0,0,0,0,3,2272, 3,4800000,4,2,128,8,500 -"1",23,10127,22,0,1,325,9732,42275,43841,64,62,0,994,12,504,0,3, 388,1,0,0,0,0,3,2276, 3,4800000,4,2,128,8,500 -"1",19,10142,22,0,1,322,9749,42275,43841,63,62,0,998,12,500,0,3, 389,1,0,0,0,0,3,2274, 3,4800000,4,2,128,8,500 -"1",1,10140,11,0,1,319,9754,42273,43843,65,62,0,230,22,1272,0,3, 384,1,0,0,0,0,2,2281, 3,4800000,4,2,128,8,500 -"1",27,10162,22,0,1,320,9771,42275,43841,63,62,0,989,12,509,0,3, 388,1,0,0,0,0,2,2276, 3,4800000,4,2,128,8,500 -"1",16,10164,19,0,1,321,9772,42275,43841,63,62,0,986,15,513,0,3, 389,1,0,0,0,0,2,2275, 3,4800000,4,2,128,8,500 -"1",5,10169,19,0,1,325,9774,42275,43841,64,62,1,994,13,504,0,5, 384,1,0,0,0,0,2,2279, 3,4800000,4,2,128,8,500 -"1",6,10354,19,1,1,321,9963,42275,43841,65,62,1,922,12,578,0,5, 385,1,0,0,0,0,2,2280, 3,4800000,4,2,128,8,500 -"1",30,10363,25,0,1,326,9970,42275,43841,64,62,0,272,7,1230,0,1, 388,1,0,0,0,0,2,2280, 3,4800000,4,2,128,8,500 -"1",22,10372,21,1,1,323,9980,42275,43841,64,62,1,910,12,590,0,2, 387,1,0,0,0,0,3,2278, 3,4800000,4,2,128,8,500 -"1",12,10376,19,0,1,329,9979,42275,43841,64,62,1,238,13,1263,0,2, 385,1,0,0,0,0,2,2281, 3,4800000,4,2,128,8,500 -"1",24,10378,19,0,1,324,9986,42275,43841,63,62,0,920,14,580,0,2, 387,1,0,0,0,0,2,2278, 3,4800000,4,2,128,8,500 -"1",26,10379,27,1,1,322,9988,42275,43841,63,62,0,919,6,581,0,2, 388,1,0,0,0,0,2,2278, 3,4800000,4,2,128,8,500 -"1",2,10381,22,1,1,320,9992,42275,43841,65,62,1,926,10,574,0,3, 384,1,0,0,0,0,2,2281, 3,4800000,4,2,128,8,500 -"1",8,10384,21,0,1,325,9991,42275,43841,63,62,1,223,11,1279,0,2, 385,1,0,0,0,0,2,2281, 3,4800000,4,2,128,8,500 -"1",18,10416,25,1,1,325,10023,42275,43841,64,62,0,902,8,598,0,3, 387,1,0,0,0,0,2,2278, 3,4800000,4,2,128,8,500 -"1",11,10454,19,0,1,323,10064,42275,43841,64,62,1,278,13,1223,0,2, 385,1,0,0,0,0,2,2281, 3,4800000,4,2,128,8,500 -"1",15,10590,22,0,1,322,10200,42275,43841,64,62,1,862,11,638,0,3, 385,1,0,0,0,0,3,2280, 3,4800000,4,2,128,8,500 -"1",20,10603,21,0,1,323,10211,42275,43841,62,62,1,865,11,635,0,2, 387,1,0,0,0,0,3,2279, 3,4800000,4,2,128,8,500 -"1",28,10604,19,0,1,324,10212,42275,43841,64,62,0,849,13,652,0,2, 388,1,0,0,0,0,2,2278, 3,4800000,4,2,128,8,500 -"1",3,10606,23,0,1,323,10214,42275,43841,64,62,1,873,9,627,0,3, 384,1,0,0,0,0,3,2281, 3,4800000,4,2,128,8,500 -"1",21,10624,23,0,1,327,10228,42275,43841,63,62,1,851,10,649,0,2, 387,1,0,0,0,0,2,2278, 3,4800000,4,2,128,8,500 -"1",4,10625,21,0,1,322,10234,42275,43841,64,62,1,865,10,636,0,3, 384,1,0,0,0,0,2,2282, 3,4800000,4,2,128,8,500 -"1",17,10657,22,1,1,325,10263,42275,43841,63,62,0,877,12,623,0,2, 387,1,0,0,0,0,2,2278, 3,4800000,4,2,128,8,500 -"1",10,10835,22,0,1,328,10442,42275,43841,63,62,1,100,8,1403,0,2, 385,1,0,0,0,0,2,2283, 3,4800000,4,2,128,8,500 -"1",25,11082,22,1,1,321,10692,42275,43841,63,62,0,861,12,640,0,2, 387,1,0,0,0,0,2,2278, 3,4800000,4,2,128,8,500 -"1",29,15610,21,0,1,473,15060,42275,43842,64,62,0,131,8,1374,0,1, 388,1,0,0,0,0,3,2282, 3,4800000,4,2,128,8,500 -"1",7,15883,19,1,1,474,15331,42275,43842,64,62,0,17,10,1488,0,2, 385,1,0,0,0,0,3,2285, 3,4800000,4,2,128,8,500 -"2",0,1453,0,0,0,0,0,28136,28657,34,32,2,0,0,0,1451,0, 371,1,0,0,0,0,0,627, 3,2400000,4,2,128,8,500 -"2",7,39,14,0,0,0,0,28137,28656,34,32,2,1318,19,95,2,2, 387,1,0,0,0,0,0,2025, 3,2400000,4,2,128,8,500 -"2",14,38,10,0,0,0,0,28137,28656,34,32,2,1317,22,97,2,2, 386,1,0,0,0,0,0,2027, 3,2400000,4,2,128,8,500 -"2",17,38,16,0,0,0,0,28137,28656,34,32,2,1327,17,87,2,2, 385,1,0,0,0,0,1,2028, 3,2400000,4,2,128,8,500 -"2",62,39,11,0,0,0,0,28136,28656,33,32,2,1316,21,97,2,2, 379,1,0,0,0,0,0,2034, 3,2400000,4,2,128,8,500 -"2",22,39,11,0,0,0,0,28137,28656,34,32,2,1318,22,95,2,2, 384,1,0,0,0,0,0,2029, 3,2400000,4,2,128,8,500 -"2",30,39,14,0,0,0,0,28137,28656,34,32,2,1315,19,99,2,2, 383,1,0,0,0,0,0,2030, 3,2400000,4,2,128,8,500 -"2",9,4871,10,0,0,160,4660,28137,28657,33,32,1,1177,19,240,2,2, 387,1,0,0,0,0,1,2029, 3,2400000,4,2,128,8,500 -"2",54,4867,11,0,0,164,4656,28137,28658,33,32,1,397,15,1025,1,2, 380,1,0,0,0,0,1,2041, 3,2400000,4,2,128,8,500 -"2",45,4909,10,0,0,165,4695,28137,28657,33,32,1,1195,18,223,2,2, 382,1,0,0,0,0,1,2036, 3,2400000,4,2,128,8,500 -"2",50,5022,0,0,0,89,4888,28136,28658,34,32,1,328,25,1096,1,2, 382,1,0,0,0,0,1,2042, 3,2400000,4,2,128,8,500 -"2",4,5082,10,0,0,159,4872,28137,28657,33,32,1,1132,18,286,2,2, 388,1,0,0,0,0,1,2029, 3,2400000,4,2,128,8,500 -"2",61,5078,14,0,0,164,4868,28136,28658,34,32,1,377,10,1045,0,2, 380,1,0,0,0,0,1,2043, 3,2400000,4,2,128,8,500 -"2",11,5083,10,0,0,161,4872,28137,28657,33,32,1,1123,18,295,2,2, 387,1,0,0,0,0,1,2030, 3,2400000,4,2,128,8,500 -"2",41,5084,9,0,0,162,4872,28137,28657,34,32,1,1133,18,286,2,2, 383,1,0,0,0,0,1,2036, 3,2400000,4,2,128,8,500 -"2",47,5085,13,0,0,163,4871,28137,28657,32,32,1,1134,14,284,2,2, 382,1,0,0,0,0,1,2037, 3,2400000,4,2,128,8,500 -"2",2,5084,9,0,0,162,4876,28136,28657,34,32,1,345,16,1076,1,2, 388,1,0,0,0,0,1,2033, 3,2400000,4,2,128,8,500 -"2",33,5087,11,0,0,163,4877,28137,28658,33,32,1,371,13,1052,1,2, 383,1,0,0,0,0,1,2039, 3,2400000,4,2,128,8,500 -"2",38,5091,10,0,0,163,4879,28137,28657,33,32,1,1125,17,294,2,2, 383,1,0,0,0,0,1,2036, 3,2400000,4,2,128,8,500 -"2",59,5090,11,0,0,165,4880,28136,28657,34,32,1,361,13,1062,0,2, 380,1,0,0,0,0,1,2043, 3,2400000,4,2,128,8,500 -"2",37,5098,10,0,0,160,4888,28137,28657,32,32,1,1129,17,290,2,2, 383,1,0,0,0,0,1,2036, 3,2400000,4,2,128,8,500 -"2",44,5099,10,0,0,164,4886,28137,28658,33,32,1,1127,17,291,2,2, 383,1,0,0,0,0,1,2036, 3,2400000,4,2,128,8,500 -"2",55,5096,9,0,0,164,4886,28137,28658,33,32,1,368,16,1055,0,2, 380,1,0,0,0,0,1,2042, 3,2400000,4,2,128,8,500 -"2",32,5099,10,0,0,164,4890,28137,28657,33,32,1,318,14,1104,1,1, 384,1,0,0,0,0,1,2039, 3,2400000,4,2,128,8,500 -"2",13,5105,10,0,0,161,4893,28137,28657,33,32,1,1124,17,294,2,2, 387,1,0,0,0,0,1,2031, 3,2400000,4,2,128,8,500 -"2",52,5101,11,0,0,163,4894,28137,28657,35,32,1,325,13,1098,0,2, 381,1,0,0,0,0,1,2043, 3,2400000,4,2,128,8,500 -"2",23,5104,10,0,0,166,4893,28137,28657,35,32,1,374,14,1049,1,2, 385,1,0,0,0,0,1,2038, 3,2400000,4,2,128,8,500 -"2",26,5160,12,0,0,162,4953,28137,28657,34,32,1,328,12,1095,1,2, 384,1,0,0,0,0,1,2038, 3,2400000,4,2,128,8,500 -"2",34,5306,12,0,0,166,5095,28137,28657,33,32,1,255,12,1169,0,2, 383,1,0,0,0,0,1,2040, 3,2400000,4,2,128,8,500 -"2",25,5309,11,0,0,162,5103,28137,28657,33,32,1,290,13,1133,0,2, 385,1,0,0,0,0,1,2038, 3,2400000,4,2,128,8,500 -"2",39,5315,10,0,0,163,5104,28137,28657,33,32,1,1066,17,354,1,2, 383,1,0,0,0,0,1,2037, 3,2400000,4,2,128,8,500 -"2",46,5316,10,0,0,163,5104,28137,28657,33,32,1,1069,16,351,1,2, 382,1,0,0,0,0,1,2038, 3,2400000,4,2,128,8,500 -"2",5,5317,10,0,0,160,5108,28137,28657,33,32,1,1065,16,354,1,2, 388,1,0,0,0,0,1,2031, 3,2400000,4,2,128,8,500 -"2",51,5314,11,0,0,163,5106,28137,28658,33,32,1,302,12,1122,0,2, 382,1,0,0,0,0,1,2042, 3,2400000,4,2,128,8,500 -"2",12,5320,10,0,0,162,5108,28136,28657,33,32,1,1065,16,355,1,2, 387,1,0,0,0,0,1,2032, 3,2400000,4,2,128,8,500 -"2",40,5320,11,0,0,163,5108,28137,28657,32,32,1,1072,15,347,1,2, 383,1,0,0,0,0,1,2037, 3,2400000,4,2,128,8,500 -"2",18,5317,12,0,0,162,5109,28137,28657,34,32,1,257,11,1166,0,2, 386,1,0,0,0,0,1,2038, 3,2400000,4,2,128,8,500 -"2",48,5320,11,0,0,160,5110,28137,28657,33,32,1,1065,15,355,1,1, 381,1,0,0,0,0,1,2039, 3,2400000,4,2,128,8,500 -"2",35,5318,11,0,0,165,5107,28137,28657,33,32,1,297,12,1127,0,2, 383,1,0,0,0,0,1,2040, 3,2400000,4,2,128,8,500 -"2",6,5324,11,0,0,158,5116,28137,28657,34,32,1,1075,16,344,1,2, 388,1,0,0,0,0,1,2031, 3,2400000,4,2,128,8,500 -"2",20,5323,13,0,0,163,5113,28137,28657,33,32,1,1066,13,354,1,2, 385,1,0,0,0,0,2,2035, 3,2400000,4,2,128,8,500 -"2",57,5320,14,0,0,166,5108,28136,28658,33,32,1,296,11,1127,0,2, 380,1,0,0,0,0,1,2043, 3,2400000,4,2,128,8,500 -"2",3,5325,18,0,0,162,5114,28137,28657,35,32,1,1121,6,297,1,2, 388,1,0,0,0,0,1,2032, 3,2400000,4,2,128,8,500 -"2",43,5326,12,0,0,161,5116,28137,28657,32,32,1,1071,14,349,1,2, 382,1,0,0,0,0,1,2037, 3,2400000,4,2,128,8,500 -"2",53,5326,11,0,0,164,5116,28137,28658,34,32,1,260,12,1163,0,2, 381,1,0,0,0,0,1,2042, 3,2400000,4,2,128,8,500 -"2",24,5326,13,0,0,162,5119,28137,28657,34,32,1,242,10,1182,0,2, 385,1,0,0,0,0,1,2039, 3,2400000,4,2,128,8,500 -"2",10,5331,13,0,0,162,5120,28137,28657,33,32,1,1062,13,357,1,2, 387,1,0,0,0,0,1,2032, 3,2400000,4,2,128,8,500 -"2",31,5331,13,0,0,166,5121,28137,28657,34,32,1,309,11,1114,1,2, 384,1,0,0,0,0,1,2039, 3,2400000,4,2,128,8,500 -"2",29,5337,10,0,0,161,5126,28137,28657,34,32,1,1070,17,349,1,2, 384,1,0,0,0,0,1,2036, 3,2400000,4,2,128,8,500 -"2",60,5372,12,0,0,166,5161,28136,28658,34,32,1,256,11,1168,0,2, 380,1,0,0,0,0,1,2044, 3,2400000,4,2,128,8,500 -"2",58,5538,9,0,0,165,5328,28136,28657,34,32,1,259,14,1165,0,2, 380,1,0,0,0,0,1,2043, 3,2400000,4,2,128,8,500 -"2",21,5551,11,0,0,162,5342,28137,28657,33,32,1,994,14,427,1,2, 384,1,0,0,0,0,1,2036, 3,2400000,4,2,128,8,500 -"2",15,5549,11,0,0,162,5341,28137,28657,34,32,1,244,12,1179,0,2, 386,1,0,0,0,0,1,2037, 3,2400000,4,2,128,8,500 -"2",49,5558,10,0,0,164,5346,28137,28657,33,32,1,989,16,431,1,2, 381,1,0,0,0,0,1,2039, 3,2400000,4,2,128,8,500 -"2",28,5563,11,0,0,159,5355,28137,28657,35,32,1,996,14,424,1,2, 384,1,0,0,0,0,1,2036, 3,2400000,4,2,128,8,500 -"2",56,5565,9,0,0,165,5356,28136,28658,33,32,1,180,13,1245,0,2, 380,1,0,0,0,0,1,2044, 3,2400000,4,2,128,8,500 -"2",42,5591,11,0,0,163,5380,28137,28657,32,32,1,1007,14,414,1,2, 383,1,0,0,0,0,1,2038, 3,2400000,4,2,128,8,500 -"2",1,9914,9,0,1,320,9537,28136,28658,34,32,1,308,14,1115,0,2, 388,1,0,0,0,0,2,2036, 3,2400000,4,2,128,8,500 -"2",27,9948,11,0,1,322,9570,28137,28658,33,32,1,279,10,1146,0,2, 384,1,0,0,0,0,2,2041, 3,2400000,4,2,128,8,500 -"2",19,10105,10,0,1,322,9726,28137,28658,34,32,0,293,12,1131,0,2, 384,1,0,0,0,0,2,2040, 3,2400000,4,2,128,8,500 -"2",16,10357,9,0,1,322,9980,28137,28658,34,32,0,80,10,1347,0,1, 385,1,0,0,0,0,2,2043, 3,2400000,4,2,128,8,500 -"2",36,10385,11,0,1,326,10004,28137,28658,33,32,0,44,8,1384,0,2, 383,1,0,0,0,0,2,2045, 3,2400000,4,2,128,8,500 -"2",8,10613,11,0,1,322,10236,28137,28658,34,32,1,16,9,1411,0,2, 387,1,0,0,0,0,2,2040, 3,2400000,4,2,128,8,500 -"2",63,10625,19,1,1,323,10245,28137,28657,34,32,1,1085,2,335,1,2, 379,1,0,0,0,0,4,2043, 3,2400000,4,2,128,8,500 -"3",120,842,9,1,1,479,310,9210,9145,18,17,1,155,23,109,2,6, 384,1,0,0,0,0,4,579, 3,1200000,4,3,128,8,500 -"3",8,844,10,1,1,481,309,9210,9145,17,17,1,179,20,82,6,6, 397,1,0,0,0,0,4,563, 3,1200000,4,3,128,8,500 -"3",81,842,10,1,1,479,310,9210,9145,18,17,1,164,16,99,7,6, 388,1,0,0,0,0,5,574, 3,1200000,4,3,128,8,500 -"3",5,846,10,1,1,476,316,9210,9144,18,17,1,173,17,89,8,6, 397,1,0,0,0,0,5,564, 3,1200000,4,3,128,8,500 -"3",46,850,9,1,1,481,316,9210,9145,17,17,1,151,17,113,7,6, 393,1,1,0,0,0,5,570, 3,1200000,4,3,128,8,500 -"3",42,851,12,1,1,477,322,9210,9145,18,17,1,152,14,113,7,6, 394,1,1,0,0,0,5,570, 3,1200000,4,3,128,8,500 -"3",49,850,9,1,1,485,315,9210,9145,18,17,1,153,17,114,5,6, 392,1,0,0,0,0,4,573, 3,1200000,4,3,128,8,500 -"3",0,1058,8,1,2,620,380,9210,9145,18,17,1,195,18,66,11,0, 376,1,1,0,0,0,5,580, 3,1200000,4,3,128,8,500 -"3",36,1081,10,1,2,646,381,9210,9145,18,17,1,107,14,158,8,6, 394,1,0,0,0,0,5,569, 3,1200000,4,3,128,8,500 -"3",18,1081,10,1,2,647,379,9210,9145,18,17,1,109,13,156,9,6, 396,1,0,0,0,0,5,567, 3,1200000,4,3,128,8,500 -"3",2,1084,9,1,2,636,391,9210,9145,18,17,1,178,17,84,9,6, 397,1,0,0,0,0,5,563, 3,1200000,4,3,128,8,500 -"3",109,1084,9,1,2,641,388,9210,9145,18,17,1,162,20,103,3,6, 385,1,0,0,0,0,5,578, 3,1200000,4,3,128,8,500 -"3",16,1085,10,1,2,639,393,9210,9145,17,17,1,90,15,177,5,6, 396,1,0,0,0,0,5,569, 3,1200000,4,3,128,8,500 -"3",91,1085,9,1,2,647,386,9210,9145,18,17,1,89,15,178,6,6, 387,1,0,0,0,0,5,578, 3,1200000,4,3,128,8,500 -"3",38,1088,9,1,2,645,392,9210,9145,17,17,1,96,15,170,7,6, 394,1,0,0,0,0,5,570, 3,1200000,4,3,128,8,500 -"3",32,1090,9,1,2,644,394,9210,9145,19,17,1,92,14,174,8,6, 395,1,0,0,0,0,5,570, 3,1200000,4,3,128,8,500 -"3",22,1092,10,1,2,649,391,9210,9145,18,17,1,97,12,170,8,6, 396,1,0,0,0,0,5,569, 3,1200000,4,3,128,8,500 -"3",64,1090,17,2,2,650,392,9210,9145,18,17,0,87,6,183,3,6, 389,1,0,0,0,0,5,579, 3,1200000,4,3,128,8,500 -"3",83,1095,9,1,2,640,405,9210,9145,18,17,1,148,14,119,6,6, 388,1,0,0,0,0,6,578, 3,1200000,4,3,128,8,500 -"3",125,1097,14,1,2,647,398,9210,9145,18,17,1,153,14,113,3,6, 384,1,0,0,0,0,6,580, 3,1200000,4,3,128,8,500 -"3",123,1097,11,2,2,648,398,9210,9145,18,17,1,154,11,112,7,6, 384,1,0,0,0,0,6,581, 3,1200000,4,3,128,8,500 -"3",111,1099,9,1,2,647,398,9210,9145,18,17,1,151,20,114,2,6, 385,1,0,0,0,0,5,579, 3,1200000,4,3,128,8,500 -"3",73,1098,8,1,2,654,392,9210,9145,18,17,1,86,19,180,5,6, 389,1,0,0,0,0,5,576, 3,1200000,4,3,128,8,500 -"3",95,1095,9,1,2,647,398,9210,9145,18,17,1,75,13,195,5,6, 387,1,0,0,0,0,5,582, 3,1200000,4,3,128,8,500 -"3",55,1101,11,2,2,646,402,9210,9145,19,17,1,170,16,94,5,6, 391,1,1,0,0,0,5,571, 3,1200000,4,3,128,8,500 -"3",77,1102,11,1,2,643,405,9210,9145,18,17,1,186,19,77,4,6, 388,1,0,0,0,0,6,573, 3,1200000,4,3,128,8,500 -"3",48,1099,9,1,2,642,406,9210,9145,17,17,1,140,17,127,5,6, 392,1,0,0,0,0,5,574, 3,1200000,4,3,128,8,500 -"3",26,1098,9,1,2,643,405,9210,9145,17,17,1,78,13,190,7,6, 396,1,0,0,0,0,5,571, 3,1200000,4,3,128,8,500 -"3",45,1099,9,1,2,640,407,9210,9145,18,17,1,147,15,120,7,6, 393,1,0,0,0,0,5,572, 3,1200000,4,3,128,8,500 -"3",90,1095,9,1,2,644,405,9210,9146,18,17,1,59,11,213,6,6, 387,1,0,0,0,0,5,583, 3,1200000,4,3,128,8,500 -"3",20,1098,10,1,2,645,404,9210,9145,18,17,1,78,10,192,8,6, 396,1,0,0,0,0,5,572, 3,1200000,4,3,128,8,500 -"3",118,1100,10,1,2,638,411,9210,9145,18,17,0,140,16,127,3,6, 384,1,0,0,0,0,5,582, 3,1200000,4,3,128,8,500 -"3",37,1102,9,1,2,644,405,9210,9145,18,17,1,88,14,178,8,6, 394,1,1,0,0,0,6,571, 3,1200000,4,3,128,8,500 -"3",119,1102,9,1,2,645,405,9210,9145,18,17,1,147,17,119,4,6, 384,1,0,0,0,0,5,580, 3,1200000,4,3,128,8,500 -"3",126,1103,10,1,2,646,405,9210,9145,18,17,1,150,17,117,3,6, 384,1,0,0,0,0,5,582, 3,1200000,4,3,128,8,500 -"3",65,1100,9,1,2,647,405,9210,9145,18,16,0,66,15,204,3,6, 389,1,0,0,0,0,5,579, 3,1200000,4,3,128,8,500 -"3",108,1104,8,1,2,648,404,9210,9145,18,17,1,142,20,125,2,6, 385,1,0,0,0,0,5,580, 3,1200000,4,3,128,8,500 -"3",72,1099,9,1,2,647,405,9210,9145,17,17,1,59,15,212,2,6, 389,1,0,0,0,0,5,581, 3,1200000,4,3,128,8,500 -"3",9,1108,9,1,2,645,409,9210,9145,18,17,1,170,20,94,5,6, 397,1,0,0,0,0,5,566, 3,1200000,4,3,128,8,500 -"3",104,1101,9,1,2,642,411,9210,9145,18,17,1,52,16,220,1,6, 386,1,0,0,0,0,5,584, 3,1200000,4,3,128,8,500 -"3",63,1102,10,1,2,649,405,9210,9145,18,17,1,75,14,194,3,6, 390,1,0,0,0,0,5,578, 3,1200000,4,3,128,8,500 -"3",101,1101,10,2,2,649,406,9210,9145,18,17,1,71,13,200,2,6, 386,1,0,0,0,0,6,583, 3,1200000,4,3,128,8,500 -"3",1,1109,10,2,2,650,405,9210,9145,18,17,1,171,16,93,8,6, 397,1,0,0,0,0,5,565, 3,1200000,4,3,128,8,500 -"3",19,1105,12,1,2,648,406,9210,9145,17,17,1,88,9,179,8,6, 396,1,0,0,0,0,7,569, 3,1200000,4,3,128,8,500 -"3",82,1106,10,1,2,643,411,9210,9145,18,17,1,139,13,129,7,6, 388,1,0,0,0,0,6,578, 3,1200000,4,3,128,8,500 -"3",89,1104,9,1,2,655,400,9210,9145,18,17,1,73,13,197,5,6, 387,1,0,0,0,0,5,582, 3,1200000,4,3,128,8,500 -"3",44,1103,9,1,2,642,412,9210,9145,18,17,1,136,12,135,6,6, 394,1,0,0,0,0,5,575, 3,1200000,4,3,128,8,500 -"3",105,1104,9,1,2,649,406,9210,9145,18,17,1,74,17,196,1,6, 386,1,0,0,0,0,5,583, 3,1200000,4,3,128,8,500 -"3",124,1104,9,1,2,644,412,9210,9145,17,17,1,140,16,131,2,6, 384,1,0,0,0,0,5,585, 3,1200000,4,3,128,8,500 -"3",11,1109,9,1,2,638,418,9210,9145,18,17,1,156,18,110,5,6, 397,1,1,0,0,0,5,568, 3,1200000,4,3,128,8,500 -"3",107,1107,21,2,2,645,410,9210,9144,18,17,1,150,6,116,2,6, 385,1,0,0,0,0,6,579, 3,1200000,4,3,128,8,500 -"3",34,1105,9,1,2,650,405,9210,9145,18,17,1,78,11,191,8,6, 395,1,0,0,0,0,5,574, 3,1200000,4,3,128,8,500 -"3",115,1105,9,1,2,640,416,9210,9145,18,17,1,135,14,135,4,6, 385,1,0,0,0,0,5,584, 3,1200000,4,3,128,8,500 -"3",74,1103,11,1,2,646,411,9210,9145,18,17,1,48,11,225,3,6, 388,1,0,0,0,0,5,583, 3,1200000,4,3,128,8,500 -"3",12,1111,10,1,2,641,417,9210,9145,17,17,1,154,17,112,5,6, 397,1,0,0,0,0,5,568, 3,1200000,4,3,128,8,500 -"3",79,1110,10,1,2,653,405,9210,9145,18,17,1,85,18,182,2,6, 388,1,0,0,0,0,5,577, 3,1200000,4,3,128,8,500 -"3",51,1106,9,1,2,652,407,9210,9145,17,17,1,69,12,202,4,6, 392,1,0,0,0,0,5,578, 3,1200000,4,3,128,8,500 -"3",3,1114,9,1,2,644,417,9210,9145,18,17,1,158,15,107,7,6, 397,1,0,0,0,0,5,567, 3,1200000,4,3,128,8,500 -"3",27,1113,9,1,2,657,404,9210,9145,18,17,1,86,14,181,8,6, 395,1,0,0,0,0,5,570, 3,1200000,4,3,128,8,500 -"3",56,1107,9,1,2,642,420,9210,9145,18,17,1,51,11,222,3,6, 391,1,0,0,0,0,5,581, 3,1200000,4,3,128,8,500 -"3",25,1111,8,1,2,645,417,9210,9145,17,17,1,66,14,205,6,6, 396,1,0,0,0,0,4,574, 3,1200000,4,3,128,8,500 -"3",92,1110,11,2,2,645,417,9210,9145,18,17,1,139,11,133,2,6, 387,1,0,0,0,0,6,583, 3,1200000,4,3,128,8,500 -"3",23,1113,9,1,2,643,421,9210,9145,17,17,1,71,11,199,8,6, 396,1,1,0,0,0,5,573, 3,1200000,4,3,128,8,500 -"3",112,1112,9,1,2,646,418,9210,9146,18,17,1,131,15,140,3,6, 385,1,0,0,0,0,5,585, 3,1200000,4,3,128,8,500 -"3",24,1119,11,2,2,648,416,9210,9145,18,17,1,156,13,109,8,6, 396,1,1,0,0,0,5,568, 3,1200000,4,3,128,8,500 -"3",88,1112,11,1,2,653,412,9210,9145,18,17,1,52,9,221,4,6, 388,1,0,0,0,0,5,583, 3,1200000,4,3,128,8,500 -"3",66,1112,10,1,2,648,417,9210,9145,18,16,1,47,11,226,3,6, 389,1,0,0,0,0,5,582, 3,1200000,4,3,128,8,500 -"3",50,1113,11,1,2,654,412,9210,9145,18,17,1,48,11,224,3,6, 392,1,1,0,0,0,5,579, 3,1200000,4,3,128,8,500 -"3",52,1113,9,2,2,656,411,9210,9145,18,17,1,50,12,222,4,6, 392,1,0,0,0,0,5,579, 3,1200000,4,3,128,8,500 -"3",17,1116,9,1,2,650,417,9210,9145,17,17,1,68,11,202,7,6, 396,1,0,0,0,0,5,573, 3,1200000,4,3,128,8,500 -"3",41,1115,9,1,2,649,418,9210,9145,18,17,1,68,11,202,7,6, 394,1,0,0,0,0,5,575, 3,1200000,4,3,128,8,500 -"3",68,1114,11,1,2,650,418,9210,9145,19,17,0,42,12,232,2,6, 389,1,0,0,0,0,4,583, 3,1200000,4,3,128,8,500 -"3",102,1115,10,1,2,651,418,9210,9145,18,17,1,49,13,223,2,6, 386,1,0,0,0,0,5,585, 3,1200000,4,3,128,8,500 -"3",100,1115,9,1,2,657,413,9210,9145,19,17,1,57,16,215,0,6, 386,1,0,0,0,0,5,585, 3,1200000,4,3,128,8,500 -"3",7,1116,12,1,2,649,420,9210,9145,18,17,1,54,10,218,3,6, 397,1,0,0,0,0,5,574, 3,1200000,4,3,128,8,500 -"3",33,1118,9,1,2,650,419,9210,9145,18,17,1,68,11,203,7,6, 395,1,0,0,0,0,5,574, 3,1200000,4,3,128,8,500 -"3",110,1121,11,1,2,639,430,9210,9145,18,17,1,136,17,131,2,6, 385,1,0,0,0,0,5,581, 3,1200000,4,3,128,8,500 -"3",103,1118,10,1,2,654,417,9210,9145,19,17,1,55,15,216,1,6, 386,1,0,0,0,0,5,584, 3,1200000,4,3,128,8,500 -"3",122,1120,14,1,2,652,419,9210,9145,18,17,1,135,11,135,2,6, 384,1,0,0,0,0,5,585, 3,1200000,4,3,128,8,500 -"3",13,1124,10,1,2,642,429,9210,9145,18,17,1,154,17,112,5,6, 397,1,0,0,0,0,5,568, 3,1200000,4,3,128,8,500 -"3",87,1119,9,1,2,654,418,9210,9145,19,17,1,52,13,220,4,6, 388,1,0,0,0,0,5,583, 3,1200000,4,3,128,8,500 -"3",10,1126,9,1,2,655,417,9210,9145,17,17,1,156,18,109,5,6, 397,1,1,0,0,0,4,568, 3,1200000,4,3,128,8,500 -"3",93,1123,9,1,2,645,426,9210,9145,18,17,1,140,18,128,3,6, 387,1,0,0,0,0,5,579, 3,1200000,4,3,128,8,500 -"3",53,1122,9,1,2,668,406,9210,9145,18,17,1,69,14,202,4,6, 391,1,0,0,0,0,5,578, 3,1200000,4,3,128,8,500 -"3",94,1118,13,1,2,654,418,9210,9145,18,17,1,42,9,232,2,6, 387,1,0,0,0,0,5,586, 3,1200000,4,3,128,8,500 -"3",39,1122,10,1,2,656,418,9210,9145,19,17,1,72,11,198,6,6, 394,1,0,0,0,0,5,575, 3,1200000,4,3,128,8,500 -"3",116,1119,6,1,2,657,417,9210,9146,18,17,1,44,17,230,1,6, 385,1,0,0,0,0,4,588, 3,1200000,4,3,128,8,500 -"3",97,1121,10,1,2,656,418,9210,9145,18,17,1,53,13,220,2,6, 386,1,0,0,0,0,5,585, 3,1200000,4,3,128,8,500 -"3",114,1123,9,1,2,650,424,9210,9146,18,17,1,124,15,147,2,6, 385,1,0,0,0,0,5,584, 3,1200000,4,3,128,8,500 -"3",60,1121,9,1,2,654,421,9210,9145,18,17,1,50,12,223,3,6, 391,1,0,0,0,0,5,580, 3,1200000,4,3,128,8,500 -"3",106,1120,10,1,2,652,425,9210,9145,18,17,1,40,13,234,0,6, 386,1,0,0,0,0,5,587, 3,1200000,4,3,128,8,500 -"3",80,1125,12,1,2,646,431,9210,9145,17,17,1,58,13,213,1,6, 388,1,0,0,0,0,5,581, 3,1200000,4,3,128,8,500 -"3",76,1125,9,1,2,659,418,9210,9145,18,17,1,65,15,207,2,6, 388,1,0,0,0,0,5,581, 3,1200000,4,3,128,8,500 -"3",40,1125,9,1,2,660,419,9210,9145,19,17,1,63,12,208,5,6, 394,1,0,0,0,0,5,575, 3,1200000,4,3,128,8,500 -"3",14,1129,10,1,2,644,433,9210,9145,17,17,1,144,15,124,4,6, 397,1,0,0,0,0,5,569, 3,1200000,4,3,128,8,500 -"3",61,1125,10,1,2,660,417,9210,9145,18,17,1,56,12,216,3,6, 391,1,0,0,0,0,5,580, 3,1200000,4,3,128,8,500 -"3",84,1124,11,1,2,654,425,9210,9145,18,17,1,125,12,147,2,6, 388,1,0,0,0,0,6,583, 3,1200000,4,3,128,8,500 -"3",35,1126,9,1,2,646,432,9210,9145,18,17,1,53,11,219,6,6, 394,1,1,0,0,0,5,576, 3,1200000,4,3,128,8,500 -"3",58,1125,9,1,2,654,424,9210,9145,18,17,1,41,11,233,4,6, 391,1,0,0,0,0,5,581, 3,1200000,4,3,128,8,500 -"3",86,1124,7,1,2,650,428,9210,9146,18,17,1,31,15,244,2,6, 388,1,0,0,0,0,4,585, 3,1200000,4,3,128,8,500 -"3",69,1127,9,1,2,654,427,9210,9146,18,17,0,55,14,217,2,6, 389,1,0,0,0,0,5,582, 3,1200000,4,3,128,8,500 -"3",30,1128,9,1,2,653,428,9210,9146,18,17,1,66,10,207,6,6, 395,1,0,0,0,0,5,576, 3,1200000,4,3,128,8,500 -"3",85,1128,10,1,2,651,430,9210,9145,18,17,1,119,14,153,2,6, 388,1,0,0,0,0,5,582, 3,1200000,4,3,128,8,500 -"3",96,1128,9,1,2,649,431,9210,9145,20,17,1,60,14,213,2,6, 386,1,0,0,0,0,5,585, 3,1200000,4,3,128,8,500 -"3",117,1134,11,1,2,654,427,9210,9145,18,17,1,144,16,123,3,6, 384,1,1,0,0,0,5,581, 3,1200000,4,3,128,8,500 -"3",43,1131,10,1,2,651,432,9210,9145,18,17,1,119,10,153,5,6, 394,1,0,0,0,0,5,576, 3,1200000,4,3,128,8,500 -"3",31,1132,9,1,2,656,428,9210,9145,18,17,1,72,10,198,8,6, 395,1,0,0,0,0,5,574, 3,1200000,4,3,128,8,500 -"3",62,1132,9,1,2,650,433,9210,9145,17,17,1,61,14,211,3,6, 390,1,0,0,0,0,4,580, 3,1200000,4,3,128,8,500 -"3",113,1132,9,1,2,654,431,9210,9145,18,17,0,118,15,154,1,6, 385,1,0,0,0,0,5,585, 3,1200000,4,3,128,8,500 -"3",59,1133,11,1,2,655,431,9210,9145,18,17,1,58,12,213,3,6, 391,1,0,0,0,0,5,579, 3,1200000,4,3,128,8,500 -"3",70,1130,10,1,2,655,431,9210,9146,18,17,0,33,11,241,2,6, 389,1,0,0,0,0,5,584, 3,1200000,4,3,128,8,500 -"3",99,1131,9,1,2,656,431,9210,9145,17,17,1,33,14,242,0,6, 386,1,0,0,0,0,5,587, 3,1200000,4,3,128,8,500 -"3",67,1134,9,1,2,659,427,9210,9146,19,16,0,57,14,215,3,6, 389,1,0,0,0,0,5,581, 3,1200000,4,3,128,8,500 -"3",29,1134,9,1,2,655,431,9210,9145,18,17,1,53,11,219,5,6, 395,1,0,0,0,0,5,576, 3,1200000,4,3,128,8,500 -"3",98,1130,9,1,2,657,430,9210,9145,18,17,1,28,11,248,1,6, 386,1,0,0,0,0,5,588, 3,1200000,4,3,128,8,500 -"3",71,1131,10,1,2,659,429,9210,9146,19,16,0,41,10,234,2,6, 389,1,1,0,0,0,5,585, 3,1200000,4,3,128,8,500 -"3",78,1133,8,1,2,656,431,9210,9145,18,17,1,49,16,224,1,6, 388,1,0,0,0,0,5,584, 3,1200000,4,3,128,8,500 -"3",28,1136,9,1,2,659,431,9210,9145,17,17,1,48,10,224,6,6, 395,1,0,0,0,0,5,576, 3,1200000,4,3,128,8,500 -"3",57,1136,9,1,2,660,430,9210,9145,18,17,1,38,13,236,3,6, 391,1,0,0,0,0,4,581, 3,1200000,4,3,128,8,500 -"3",15,1137,8,1,2,659,433,9210,9145,18,17,1,52,14,220,3,6, 396,1,0,0,0,0,5,575, 3,1200000,4,3,128,8,500 -"3",21,1141,10,2,2,661,434,9210,9145,18,17,1,55,8,218,6,6, 396,1,1,0,0,0,5,575, 3,1200000,4,3,128,8,500 -"3",127,1326,11,1,3,796,475,9210,9145,18,17,1,185,12,80,9,6, 384,1,0,0,0,0,6,580, 3,1200000,4,3,128,8,500 -"3",4,1338,9,1,3,796,493,9210,9145,18,17,1,81,11,190,6,6, 397,1,0,0,0,0,6,572, 3,1200000,4,3,128,8,500 -"3",54,1336,9,1,3,796,495,9210,9145,18,17,1,57,9,216,4,6, 391,1,0,0,0,0,7,581, 3,1200000,4,3,128,8,500 -"3",75,1348,9,1,3,796,502,9210,9145,17,17,1,67,13,204,4,6, 389,1,0,0,0,0,6,581, 3,1200000,4,3,128,8,500 -"3",121,1358,9,1,3,794,519,9210,9145,18,17,1,38,12,237,0,6, 384,1,1,0,0,0,6,590, 3,1200000,4,3,128,8,500 -"3",6,1381,8,1,3,806,527,9210,9146,18,17,1,53,10,219,5,6, 397,1,0,0,0,0,6,574, 3,1200000,4,3,128,8,500 -"3",47,1382,8,1,3,795,541,9210,9145,18,17,1,42,8,233,4,6, 393,1,1,0,0,0,7,580, 3,1200000,4,3,128,8,500 -"4",105,604,3,0,0,26,555,5495,5540,10,9,2,259,6,150,3,7, 397,1,0,0,0,0,0,636, 3,600000,3,3,128,8,500 -"4",209,665,4,0,0,27,612,5495,5540,10,9,2,329,6,79,2,7, 387,1,0,0,0,0,0,646, 3,600000,3,3,128,8,500 -"4",48,692,5,0,0,28,639,5495,5540,10,9,1,328,6,81,1,7, 402,1,0,0,0,0,0,630, 3,600000,3,3,128,8,500 -"4",66,717,3,0,0,24,669,5495,5541,10,9,1,303,8,106,0,7, 401,1,0,0,0,0,0,632, 3,600000,3,3,128,8,500 -"4",206,721,4,0,0,28,668,5495,5540,9,9,2,309,4,101,3,7, 388,1,0,0,0,0,0,646, 3,600000,3,3,128,8,500 -"4",113,725,3,0,0,29,673,5495,5540,10,9,1,213,7,197,0,7, 397,1,0,0,0,0,0,637, 3,600000,3,3,128,8,500 -"4",213,725,5,0,0,27,675,5495,5541,10,9,1,311,4,98,2,7, 386,1,0,0,0,0,0,648, 3,600000,3,3,128,8,500 -"4",226,725,3,0,0,27,673,5495,5541,9,9,1,309,5,100,2,7, 385,1,0,0,0,0,0,649, 3,600000,3,3,128,8,500 -"4",29,726,5,0,0,25,676,5495,5540,10,9,2,303,5,106,1,7, 405,1,0,0,0,0,0,628, 3,600000,3,3,128,8,500 -"4",21,726,4,0,0,28,674,5495,5541,9,9,1,307,6,103,1,7, 406,1,0,0,0,0,0,628, 3,600000,3,3,128,8,500 -"4",42,726,5,0,0,26,676,5495,5541,10,9,2,307,6,103,0,7, 404,1,0,0,0,0,0,630, 3,600000,3,3,128,8,500 -"4",240,726,3,0,0,29,675,5495,5541,10,9,1,298,4,113,2,7, 384,1,0,0,0,0,0,651, 3,600000,3,3,128,8,500 -"4",0,1120,3,0,0,39,1055,5495,5541,10,9,1,323,7,85,1,0, 375,1,1,0,0,0,0,654, 3,600000,3,3,128,8,500 -"4",73,1131,3,0,0,45,1060,5495,5541,10,9,1,239,4,170,4,7, 401,1,0,0,0,0,0,633, 3,600000,3,3,128,8,500 -"4",146,1136,5,0,0,43,1066,5495,5541,10,9,1,324,5,84,2,7, 393,1,0,0,0,0,0,639, 3,600000,3,3,128,8,500 -"4",219,1137,4,0,0,47,1065,5494,5541,9,9,1,328,4,80,4,7, 386,1,0,0,0,0,1,647, 3,600000,3,3,128,8,500 -"4",182,1150,3,0,0,44,1079,5495,5541,10,9,1,329,6,79,3,7, 390,1,0,0,0,0,0,643, 3,600000,3,3,128,8,500 -"4",4,1173,3,0,0,44,1104,5495,5541,10,9,1,312,6,97,1,7, 407,1,0,0,0,0,0,627, 3,600000,3,3,128,8,500 -"4",150,1180,4,0,0,44,1109,5495,5541,10,9,1,312,5,97,2,7, 393,1,0,0,0,0,0,640, 3,600000,3,3,128,8,500 -"4",77,1183,3,0,0,45,1113,5495,5541,10,9,1,225,4,185,3,7, 400,1,0,0,0,0,0,634, 3,600000,3,3,128,8,500 -"4",108,1183,4,0,0,45,1114,5495,5541,10,9,1,118,4,292,2,7, 397,1,0,0,0,0,0,638, 3,600000,3,3,128,8,500 -"4",36,1191,5,0,0,45,1121,5495,5541,10,9,1,207,3,203,3,7, 404,1,0,0,0,0,0,629, 3,600000,3,3,128,8,500 -"4",178,1194,4,0,0,46,1122,5495,5541,11,9,1,310,6,98,2,7, 390,1,1,0,0,0,0,642, 3,600000,3,3,128,8,500 -"4",166,1225,3,0,0,46,1154,5495,5541,10,9,1,209,4,201,3,7, 392,1,0,0,0,0,1,642, 3,600000,3,3,128,8,500 -"4",75,1231,3,0,0,45,1162,5495,5541,10,9,1,207,4,202,3,7, 400,1,0,0,0,0,1,634, 3,600000,3,3,128,8,500 -"4",183,1233,4,0,0,45,1162,5495,5541,10,9,1,302,3,107,3,7, 390,1,0,0,0,0,1,643, 3,600000,3,3,128,8,500 -"4",190,1232,3,0,0,44,1163,5495,5541,10,9,1,301,4,109,3,7, 390,1,0,0,0,0,0,645, 3,600000,3,3,128,8,500 -"4",100,1233,3,0,0,44,1164,5495,5541,10,9,1,195,7,214,0,7, 398,1,0,0,0,0,0,636, 3,600000,3,3,128,8,500 -"4",1,1234,4,0,0,45,1164,5495,5541,9,9,1,296,6,112,1,7, 407,1,0,0,0,0,0,626, 3,600000,3,3,128,8,500 -"4",52,1234,3,0,0,45,1165,5494,5541,11,9,1,296,4,113,4,7, 402,1,0,0,0,0,0,631, 3,600000,3,3,128,8,500 -"4",154,1235,3,0,0,44,1166,5495,5541,10,9,1,295,5,114,2,7, 393,1,0,0,0,0,0,641, 3,600000,3,3,128,8,500 -"4",2,1236,4,0,0,45,1165,5495,5541,10,9,1,298,6,111,1,7, 407,1,0,0,0,0,0,626, 3,600000,3,3,128,8,500 -"4",8,1236,3,0,0,45,1165,5495,5541,10,9,1,294,7,115,1,7, 407,1,0,0,0,0,0,627, 3,600000,3,3,128,8,500 -"4",9,1236,4,0,0,45,1165,5495,5541,10,9,1,295,7,114,0,7, 406,1,0,0,0,0,0,627, 3,600000,3,3,128,8,500 -"4",155,1236,3,0,0,44,1165,5495,5541,10,9,1,295,6,114,2,7, 393,1,0,0,0,0,0,641, 3,600000,3,3,128,8,500 -"4",54,1236,3,0,0,46,1165,5495,5541,11,9,1,296,5,113,4,7, 402,1,0,0,0,0,0,631, 3,600000,3,3,128,8,500 -"4",217,1236,3,0,0,46,1164,5495,5541,9,9,1,298,5,111,3,7, 386,1,0,0,0,0,0,647, 3,600000,3,3,128,8,500 -"4",130,1236,4,0,0,46,1165,5495,5541,11,9,1,191,6,218,2,7, 395,1,0,0,0,0,0,639, 3,600000,3,3,128,8,500 -"4",218,1237,3,0,0,45,1165,5495,5541,9,9,1,301,5,108,3,7, 386,1,0,0,0,0,0,647, 3,600000,3,3,128,8,500 -"4",101,1236,3,0,0,44,1167,5495,5541,11,9,1,209,4,200,3,7, 398,1,0,0,0,0,0,636, 3,600000,3,3,128,8,500 -"4",254,1236,4,0,0,48,1163,5495,5541,9,9,1,266,4,143,3,7, 382,1,0,0,0,0,0,652, 3,600000,3,3,128,8,500 -"4",251,1237,4,0,0,46,1165,5495,5541,9,9,1,270,3,139,3,7, 383,1,0,0,0,0,0,652, 3,600000,3,3,128,8,500 -"4",147,1237,3,0,0,45,1167,5495,5541,10,9,1,296,6,113,2,7, 393,1,0,0,0,0,0,640, 3,600000,3,3,128,8,500 -"4",203,1238,3,0,0,47,1165,5495,5541,9,9,1,297,4,112,3,7, 388,1,0,0,0,0,0,645, 3,600000,3,3,128,8,500 -"4",72,1237,4,0,0,47,1166,5495,5541,11,9,1,193,6,217,0,7, 401,1,0,0,0,0,0,633, 3,600000,3,3,128,8,500 -"4",89,1238,3,0,0,45,1167,5495,5541,11,9,1,207,4,202,3,7, 398,1,0,0,0,0,0,636, 3,600000,3,3,128,8,500 -"4",247,1238,3,0,0,47,1166,5495,5541,11,9,1,270,4,140,3,7, 383,1,0,0,0,0,0,651, 3,600000,3,3,128,8,500 -"4",191,1238,3,0,0,46,1167,5495,5541,10,9,1,299,4,111,3,7, 389,1,0,0,0,0,1,645, 3,600000,3,3,128,8,500 -"4",180,1240,4,0,0,44,1169,5495,5541,10,9,1,298,5,111,3,7, 390,1,0,0,0,0,0,643, 3,600000,3,3,128,8,500 -"4",128,1239,4,0,0,46,1167,5495,5541,11,9,1,195,4,215,3,7, 395,1,0,0,0,0,0,640, 3,600000,3,3,128,8,500 -"4",144,1238,7,0,0,45,1168,5495,5541,11,9,1,293,1,116,2,7, 394,1,0,0,0,0,1,640, 3,600000,3,3,128,8,500 -"4",16,1239,3,0,0,49,1166,5495,5541,11,9,1,297,6,113,1,7, 406,1,0,0,0,0,0,628, 3,600000,3,3,128,8,500 -"4",127,1240,4,0,0,46,1169,5495,5541,10,9,1,209,3,201,3,7, 395,1,0,0,0,0,1,639, 3,600000,3,3,128,8,500 -"4",37,1242,3,0,0,45,1172,5495,5541,10,9,1,177,4,233,3,7, 404,1,0,0,0,0,0,630, 3,600000,3,3,128,8,500 -"4",164,1243,4,0,0,47,1170,5495,5541,10,9,1,298,4,110,3,7, 392,1,0,0,0,0,0,642, 3,600000,3,3,128,8,500 -"4",91,1243,3,0,0,47,1171,5495,5541,9,9,1,203,4,206,4,7, 398,1,0,0,0,0,0,636, 3,600000,3,3,128,8,500 -"4",223,1243,3,0,0,46,1172,5495,5541,9,9,1,312,4,97,3,7, 385,1,0,0,0,0,1,648, 3,600000,3,3,128,8,500 -"4",109,1243,3,0,0,46,1173,5495,5541,10,9,1,91,3,320,3,7, 397,1,0,0,0,0,0,638, 3,600000,3,3,128,8,500 -"4",64,1244,5,0,0,45,1174,5495,5541,10,9,1,293,4,116,0,7, 401,1,0,0,0,0,1,633, 3,600000,3,3,128,8,500 -"4",201,1259,5,0,0,49,1184,5495,5541,9,9,1,297,2,112,3,7, 388,1,0,0,0,0,0,645, 3,600000,3,3,128,8,500 -"4",132,1281,5,0,0,45,1212,5495,5541,10,9,1,180,3,231,2,7, 395,1,0,0,0,0,0,641, 3,600000,3,3,128,8,500 -"4",148,1282,3,0,0,46,1212,5495,5541,10,9,1,275,6,135,2,7, 393,1,0,0,0,0,0,641, 3,600000,3,3,128,8,500 -"4",5,1287,3,0,0,46,1216,5495,5541,9,9,1,280,6,130,1,7, 407,1,0,0,0,0,0,627, 3,600000,3,3,128,8,500 -"4",110,1286,3,0,0,45,1216,5495,5541,10,9,1,73,4,337,2,7, 397,1,0,0,0,0,0,638, 3,600000,3,3,128,8,500 -"4",50,1287,3,0,0,44,1217,5495,5541,10,9,1,180,7,229,0,7, 403,1,0,0,0,0,0,632, 3,600000,3,3,128,8,500 -"4",179,1287,4,0,0,44,1218,5495,5541,10,9,1,280,4,130,2,7, 390,1,0,0,0,0,0,644, 3,600000,3,3,128,8,500 -"4",6,1288,5,0,0,48,1216,5495,5541,10,9,1,280,4,131,1,7, 407,1,0,0,0,0,0,628, 3,600000,3,3,128,8,500 -"4",125,1290,3,0,0,46,1219,5495,5541,10,9,1,209,4,201,3,7, 395,1,0,0,0,0,1,639, 3,600000,3,3,128,8,500 -"4",39,1289,6,0,0,46,1219,5495,5541,10,9,1,159,4,252,0,7, 404,1,0,0,0,0,0,631, 3,600000,3,3,128,8,500 -"4",160,1290,4,0,0,45,1221,5495,5541,10,9,1,275,4,134,2,7, 392,1,0,0,0,0,1,642, 3,600000,3,3,128,8,500 -"4",158,1290,3,0,0,48,1218,5495,5541,9,9,1,278,4,132,2,7, 392,1,0,0,0,0,0,642, 3,600000,3,3,128,8,500 -"4",40,1291,3,0,0,48,1219,5495,5541,9,9,1,174,7,236,0,7, 404,1,0,0,0,0,0,630, 3,600000,3,3,128,8,500 -"4",176,1291,4,0,0,45,1220,5495,5541,10,9,1,280,4,130,2,7, 391,1,0,0,0,0,0,644, 3,600000,3,3,128,8,500 -"4",162,1291,4,0,0,46,1221,5495,5541,10,9,1,276,4,134,2,7, 392,1,0,0,0,0,0,642, 3,600000,3,3,128,8,500 -"4",33,1291,3,0,0,47,1220,5495,5541,10,9,1,162,7,248,0,7, 404,1,0,0,0,0,0,630, 3,600000,3,3,128,8,500 -"4",215,1291,3,0,0,48,1219,5495,5541,9,9,1,283,5,127,2,7, 386,1,0,0,0,0,1,648, 3,600000,3,3,128,8,500 -"4",12,1290,2,0,0,45,1222,5495,5541,10,9,1,181,6,231,0,7, 406,1,0,0,0,0,0,630, 3,600000,3,3,128,8,500 -"4",93,1292,2,0,0,47,1221,5495,5541,10,9,1,188,5,222,3,7, 398,1,0,0,0,0,0,636, 3,600000,3,3,128,8,500 -"4",134,1292,3,0,0,46,1221,5495,5541,10,9,1,179,5,231,2,7, 394,1,0,0,0,0,0,640, 3,600000,3,3,128,8,500 -"4",186,1292,3,0,0,46,1221,5495,5541,10,9,1,278,5,131,2,7, 390,1,0,0,0,0,0,644, 3,600000,3,3,128,8,500 -"4",107,1291,3,0,0,46,1221,5495,5541,10,9,1,73,5,338,2,7, 397,1,0,0,0,0,0,638, 3,600000,3,3,128,8,500 -"4",243,1292,3,0,0,47,1220,5495,5541,9,9,1,256,5,155,2,7, 383,1,0,0,0,0,0,652, 3,600000,3,3,128,8,500 -"4",187,1292,4,0,0,45,1223,5495,5541,10,9,1,279,3,132,2,7, 390,1,0,0,0,0,0,646, 3,600000,3,3,128,8,500 -"4",45,1291,4,0,0,45,1222,5495,5541,10,9,1,153,2,257,4,7, 404,1,0,0,0,0,1,631, 3,600000,3,3,128,8,500 -"4",151,1293,4,0,0,44,1223,5495,5541,10,9,1,279,4,131,2,7, 393,1,0,0,0,0,0,641, 3,600000,3,3,128,8,500 -"4",18,1293,2,0,0,45,1223,5495,5541,10,9,1,177,8,233,1,7, 406,1,0,0,0,0,0,628, 3,600000,3,3,128,8,500 -"4",205,1292,3,0,0,48,1221,5495,5541,9,9,1,279,2,131,3,7, 388,1,0,0,0,0,2,647, 3,600000,3,3,128,8,500 -"4",47,1292,4,0,0,46,1223,5495,5541,10,9,1,158,5,253,0,7, 403,1,0,0,0,0,0,632, 3,600000,3,3,128,8,500 -"4",96,1293,3,0,0,45,1224,5495,5541,10,9,1,177,7,233,0,7, 398,1,0,0,0,0,0,637, 3,600000,3,3,128,8,500 -"4",95,1293,4,0,0,45,1223,5495,5541,10,9,1,189,2,222,3,7, 398,1,0,0,0,0,1,637, 3,600000,3,3,128,8,500 -"4",35,1293,4,0,0,45,1224,5495,5541,10,9,1,160,4,250,1,7, 404,1,0,0,0,0,1,630, 3,600000,3,3,128,8,500 -"4",69,1295,3,0,0,45,1224,5495,5541,11,9,1,189,7,221,0,7, 401,1,0,0,0,0,0,633, 3,600000,3,3,128,8,500 -"4",237,1295,2,0,0,48,1223,5495,5541,10,9,1,248,5,162,3,7, 384,1,0,0,0,0,0,650, 3,600000,3,3,128,8,500 -"4",121,1295,4,0,0,46,1224,5495,5541,10,9,1,187,3,223,2,7, 396,1,0,0,0,0,0,639, 3,600000,3,3,128,8,500 -"4",222,1297,3,0,0,47,1224,5495,5541,9,9,1,282,4,128,3,7, 385,1,0,0,0,0,0,649, 3,600000,3,3,128,8,500 -"4",253,1296,4,0,0,48,1224,5495,5541,9,9,1,255,3,155,3,7, 382,1,0,0,0,0,0,652, 3,600000,3,3,128,8,500 -"4",221,1298,4,0,0,48,1224,5495,5541,10,9,1,277,3,132,3,7, 385,1,0,0,0,0,0,649, 3,600000,3,3,128,8,500 -"4",250,1297,5,0,0,47,1226,5495,5541,10,9,1,248,2,162,3,7, 383,1,0,0,0,0,0,652, 3,600000,3,3,128,8,500 -"4",76,1299,4,0,0,49,1224,5495,5541,10,9,1,176,4,234,3,7, 400,1,0,0,0,0,0,634, 3,600000,3,3,128,8,500 -"4",248,1297,3,0,0,47,1226,5495,5541,9,9,1,245,4,166,2,7, 383,1,0,0,0,0,1,652, 3,600000,3,3,128,8,500 -"4",97,1299,3,0,0,44,1230,5495,5541,11,9,1,187,7,223,0,7, 398,1,0,0,0,0,0,636, 3,600000,3,3,128,8,500 -"4",79,1298,3,0,0,53,1222,5494,5541,10,9,1,176,3,236,3,7, 400,1,0,0,0,0,0,636, 3,600000,3,3,128,8,500 -"4",246,1298,2,0,0,48,1226,5495,5541,9,9,1,249,3,162,3,7, 383,1,0,0,0,0,0,653, 3,600000,3,3,128,8,500 -"4",123,1300,5,0,0,55,1222,5495,5541,10,9,1,189,1,222,3,7, 395,1,0,0,0,0,0,640, 3,600000,3,3,128,8,500 -"4",38,1300,4,0,0,47,1230,5495,5541,10,9,1,167,2,244,3,7, 404,1,0,0,0,0,1,631, 3,600000,3,3,128,8,500 -"4",252,1303,4,0,0,47,1231,5495,5541,10,9,1,242,2,168,3,7, 382,1,0,0,0,0,0,653, 3,600000,3,3,128,8,500 -"4",238,1309,3,0,0,48,1236,5495,5541,10,9,1,244,4,167,3,7, 384,1,0,0,0,0,0,650, 3,600000,3,3,128,8,500 -"4",68,1310,4,0,0,45,1241,5495,5541,10,9,1,177,5,234,0,7, 401,1,0,0,0,0,0,634, 3,600000,3,3,128,8,500 -"4",210,1310,3,0,0,47,1240,5495,5541,9,9,1,158,4,253,2,7, 387,1,0,0,0,0,0,649, 3,600000,3,3,128,8,500 -"4",111,1310,4,0,0,46,1242,5495,5541,10,9,1,69,4,343,0,7, 397,1,0,0,0,0,0,640, 3,600000,3,3,128,8,500 -"4",13,1312,7,0,0,54,1235,5495,5541,10,9,1,287,1,123,0,7, 406,1,0,0,0,0,2,628, 3,600000,3,3,128,8,500 -"4",136,1343,3,0,0,46,1274,5495,5541,11,9,1,156,4,254,2,7, 394,1,0,0,0,0,0,640, 3,600000,3,3,128,8,500 -"4",3,1345,4,0,0,46,1274,5495,5541,10,9,1,261,5,149,1,7, 407,1,0,0,0,0,0,627, 3,600000,3,3,128,8,500 -"4",10,1346,3,0,0,45,1277,5495,5541,9,9,1,257,6,152,1,7, 406,1,0,0,0,0,1,627, 3,600000,3,3,128,8,500 -"4",167,1346,2,0,0,45,1277,5495,5541,10,9,1,164,6,246,3,7, 393,1,0,0,0,0,0,641, 3,600000,3,3,128,8,500 -"4",119,1346,3,0,0,44,1278,5495,5541,10,9,1,176,4,234,3,7, 396,1,0,0,0,0,1,639, 3,600000,3,3,128,8,500 -"4",117,1346,2,0,0,46,1276,5495,5541,10,9,1,172,5,239,2,7, 396,1,0,0,0,0,0,639, 3,600000,3,3,128,8,500 -"4",63,1347,4,0,0,47,1277,5495,5541,10,9,1,175,2,237,3,7, 401,1,0,0,0,0,0,635, 3,600000,3,3,128,8,500 -"4",41,1347,3,0,0,48,1275,5495,5541,10,9,1,140,6,271,0,7, 404,1,0,0,0,0,0,632, 3,600000,3,3,128,8,500 -"4",249,1348,4,0,0,48,1276,5495,5541,9,9,1,234,3,176,3,7, 383,1,0,0,0,0,0,652, 3,600000,3,3,128,8,500 -"4",200,1347,3,0,0,47,1276,5495,5541,9,9,1,261,3,149,3,7, 389,1,0,0,0,0,1,645, 3,600000,3,3,128,8,500 -"4",152,1349,3,0,0,46,1277,5495,5541,10,9,1,261,5,148,2,7, 393,1,0,0,0,0,0,641, 3,600000,3,3,128,8,500 -"4",34,1348,4,0,0,46,1278,5495,5541,10,9,1,151,5,259,0,7, 405,1,0,0,0,0,0,630, 3,600000,3,3,128,8,500 -"4",23,1347,3,0,0,46,1279,5495,5541,10,9,1,139,5,272,0,7, 406,1,0,0,0,0,0,631, 3,600000,3,3,128,8,500 -"4",202,1347,4,0,0,46,1276,5495,5541,9,9,1,263,1,148,3,7, 388,1,0,0,0,0,0,647, 3,600000,3,3,128,8,500 -"4",233,1347,3,0,0,48,1277,5495,5541,9,9,1,151,2,259,3,7, 385,1,0,0,0,0,1,650, 3,600000,3,3,128,8,500 -"4",53,1349,3,0,0,45,1279,5495,5541,10,9,1,264,4,146,3,7, 402,1,0,0,0,0,0,632, 3,600000,3,3,128,8,500 -"4",19,1348,3,0,0,46,1278,5495,5541,10,9,1,142,5,270,1,7, 406,1,0,0,0,0,0,630, 3,600000,3,3,128,8,500 -"4",55,1350,3,0,0,47,1278,5495,5541,10,9,1,262,4,147,3,7, 402,1,0,0,0,0,0,632, 3,600000,3,3,128,8,500 -"4",11,1350,4,0,0,46,1280,5495,5541,9,9,1,257,6,153,1,7, 406,1,0,0,0,0,0,628, 3,600000,3,3,128,8,500 -"4",231,1350,4,0,0,49,1276,5495,5541,11,9,1,153,4,257,3,7, 385,1,0,0,0,0,0,650, 3,600000,3,3,128,8,500 -"4",129,1350,3,0,0,46,1280,5495,5541,10,9,1,174,5,236,2,7, 395,1,0,0,0,0,0,640, 3,600000,3,3,128,8,500 -"4",229,1349,4,0,0,48,1277,5495,5541,9,9,1,156,3,254,3,7, 385,1,0,0,0,0,1,650, 3,600000,3,3,128,8,500 -"4",145,1350,2,0,0,46,1280,5494,5541,10,9,1,163,6,248,2,7, 394,1,0,0,0,0,0,641, 3,600000,3,3,128,8,500 -"4",126,1350,2,0,0,45,1280,5495,5541,10,9,1,162,4,248,3,7, 395,1,0,0,0,0,1,640, 3,600000,3,3,128,8,500 -"4",82,1349,4,0,0,47,1279,5495,5541,10,9,1,157,4,254,0,7, 399,1,0,0,0,0,0,637, 3,600000,3,3,128,8,500 -"4",124,1351,3,0,0,45,1281,5495,5541,10,9,1,163,4,248,3,7, 396,1,0,0,0,0,0,639, 3,600000,3,3,128,8,500 -"4",116,1351,3,0,0,48,1280,5495,5541,10,9,1,155,6,255,0,7, 396,1,0,0,0,0,0,639, 3,600000,3,3,128,8,500 -"4",46,1352,4,0,0,46,1281,5495,5541,10,9,1,148,3,262,3,7, 404,1,0,0,0,0,0,631, 3,600000,3,3,128,8,500 -"4",194,1352,5,0,0,49,1278,5495,5541,10,9,1,262,4,147,2,7, 389,1,0,0,0,0,0,645, 3,600000,3,3,128,8,500 -"4",102,1352,3,0,0,46,1281,5495,5541,10,9,1,162,3,248,3,7, 397,1,0,0,0,0,1,637, 3,600000,3,3,128,8,500 -"4",193,1352,4,0,0,48,1279,5495,5541,10,9,1,257,4,153,2,7, 389,1,0,0,0,0,0,645, 3,600000,3,3,128,8,500 -"4",139,1352,3,0,0,47,1281,5495,5541,10,9,1,171,5,239,2,7, 394,1,0,0,0,0,0,641, 3,600000,3,3,128,8,500 -"4",189,1352,3,0,0,44,1283,5495,5541,10,9,1,262,4,148,3,7, 390,1,0,0,0,0,0,645, 3,600000,3,3,128,8,500 -"4",62,1353,3,0,0,46,1281,5495,5541,10,9,1,163,4,247,3,7, 401,1,0,0,0,0,0,633, 3,600000,3,3,128,8,500 -"4",25,1352,4,0,0,47,1281,5495,5541,10,9,1,140,4,271,1,7, 405,1,0,0,0,0,0,630, 3,600000,3,3,128,8,500 -"4",26,1353,3,0,0,47,1281,5495,5541,10,9,1,155,7,255,1,7, 405,1,0,0,0,0,0,629, 3,600000,3,3,128,8,500 -"4",235,1353,5,0,0,48,1280,5495,5541,10,9,1,254,2,157,3,7, 384,1,0,0,0,0,0,650, 3,600000,3,3,128,8,500 -"4",230,1352,4,0,0,49,1279,5495,5541,9,9,1,140,3,272,3,7, 385,1,0,0,0,0,0,651, 3,600000,3,3,128,8,500 -"4",245,1353,4,0,0,47,1282,5495,5541,9,9,1,234,3,177,3,7, 383,1,0,0,0,0,0,652, 3,600000,3,3,128,8,500 -"4",242,1353,3,0,0,49,1280,5495,5541,10,9,1,229,5,182,2,7, 383,1,0,0,0,0,0,652, 3,600000,3,3,128,8,500 -"4",174,1353,5,0,0,44,1284,5495,5541,10,9,1,261,1,149,3,7, 391,1,0,0,0,0,0,644, 3,600000,3,3,128,8,500 -"4",232,1352,4,0,0,50,1279,5495,5541,9,9,1,136,2,276,3,7, 385,1,0,0,0,0,0,651, 3,600000,3,3,128,8,500 -"4",24,1353,4,0,0,48,1281,5495,5541,10,9,1,154,5,256,1,7, 406,1,0,0,0,0,1,629, 3,600000,3,3,128,8,500 -"4",175,1354,3,0,0,44,1284,5495,5541,10,9,1,263,4,147,3,7, 391,1,0,0,0,0,0,644, 3,600000,3,3,128,8,500 -"4",20,1354,3,0,0,46,1283,5495,5541,10,9,1,156,7,254,1,7, 406,1,0,0,0,0,0,629, 3,600000,3,3,128,8,500 -"4",208,1354,3,0,0,48,1282,5495,5541,10,9,1,265,5,145,2,7, 386,1,0,0,0,0,0,648, 3,600000,3,3,128,8,500 -"4",234,1352,3,0,0,49,1281,5495,5541,9,9,1,130,2,281,3,7, 385,1,0,0,0,0,1,651, 3,600000,3,3,128,8,500 -"4",83,1353,3,0,0,46,1283,5495,5541,10,9,1,168,6,243,0,7, 399,1,0,0,0,0,0,636, 3,600000,3,3,128,8,500 -"4",192,1354,4,0,0,48,1282,5495,5541,9,9,1,259,4,151,2,7, 389,1,0,0,0,0,0,645, 3,600000,3,3,128,8,500 -"4",195,1354,3,0,0,49,1281,5495,5541,9,9,1,262,5,148,2,7, 389,1,0,0,0,0,1,645, 3,600000,3,3,128,8,500 -"4",131,1354,3,0,0,47,1282,5495,5541,10,9,1,174,5,236,2,7, 394,1,0,0,0,0,0,640, 3,600000,3,3,128,8,500 -"4",90,1355,4,0,0,47,1283,5495,5541,9,9,1,159,3,251,3,7, 398,1,0,0,0,0,0,636, 3,600000,3,3,128,8,500 -"4",118,1354,3,0,0,48,1283,5495,5541,10,9,1,160,2,251,3,7, 396,1,0,0,0,0,1,639, 3,600000,3,3,128,8,500 -"4",67,1356,2,0,0,46,1285,5494,5541,10,9,1,162,8,248,0,7, 401,1,0,0,0,0,0,633, 3,600000,3,3,128,8,500 -"4",181,1357,4,0,0,45,1286,5495,5541,10,9,1,264,4,146,3,7, 390,1,0,0,0,0,0,643, 3,600000,3,3,128,8,500 -"4",81,1355,3,0,0,45,1286,5495,5541,10,9,1,168,6,242,0,7, 399,1,0,0,0,0,1,636, 3,600000,3,3,128,8,500 -"4",60,1356,2,0,0,47,1285,5495,5541,11,9,1,162,4,249,3,7, 402,1,0,0,0,0,0,634, 3,600000,3,3,128,8,500 -"4",165,1358,6,0,0,46,1287,5495,5541,11,9,1,265,2,145,3,7, 392,1,0,0,0,0,0,642, 3,600000,3,3,128,8,500 -"4",173,1358,4,0,0,46,1288,5495,5541,10,9,1,261,2,150,3,7, 391,1,0,0,0,0,0,644, 3,600000,3,3,128,8,500 -"4",216,1362,4,0,0,50,1287,5495,5541,9,9,1,265,3,145,3,7, 386,1,0,0,0,0,0,648, 3,600000,3,3,128,8,500 -"4",88,1361,5,0,0,52,1284,5495,5541,10,9,1,156,4,254,0,7, 399,1,0,0,0,0,0,637, 3,600000,3,3,128,8,500 -"4",153,1366,3,0,0,44,1297,5495,5541,10,9,1,260,5,149,2,7, 393,1,0,0,0,0,1,641, 3,600000,3,3,128,8,500 -"4",172,1369,3,0,0,46,1299,5495,5541,10,9,1,263,3,149,3,7, 391,1,0,0,0,0,0,645, 3,600000,3,3,128,8,500 -"4",138,1392,3,0,0,46,1321,5495,5541,10,9,1,158,4,253,2,7, 394,1,0,0,0,0,0,641, 3,600000,3,3,128,8,500 -"4",137,1392,3,0,0,47,1320,5495,5541,10,9,1,167,5,243,2,7, 394,1,0,0,0,0,0,640, 3,600000,3,3,128,8,500 -"4",244,1395,3,0,0,48,1324,5495,5541,9,9,1,227,5,184,2,7, 383,1,0,0,0,0,0,652, 3,600000,3,3,128,8,500 -"4",159,1400,3,0,0,46,1328,5495,5541,10,9,1,279,4,131,2,7, 392,1,0,0,0,0,0,642, 3,600000,3,3,128,8,500 -"4",61,1401,3,0,0,45,1332,5495,5541,10,9,1,177,2,233,3,7, 401,1,0,0,0,0,1,634, 3,600000,3,3,128,8,500 -"4",103,1401,3,0,0,44,1332,5495,5541,10,9,1,173,4,237,3,7, 397,1,0,0,0,0,0,637, 3,600000,3,3,128,8,500 -"4",149,1405,3,0,0,47,1334,5495,5541,10,9,1,241,4,169,2,7, 393,1,0,0,0,0,0,642, 3,600000,3,3,128,8,500 -"4",92,1404,3,0,0,45,1336,5495,5541,10,9,1,139,3,272,3,7, 398,1,0,0,0,0,0,638, 3,600000,3,3,128,8,500 -"4",236,1405,2,0,0,50,1331,5495,5541,10,9,1,130,3,282,3,7, 384,1,0,0,0,0,0,652, 3,600000,3,3,128,8,500 -"4",71,1406,3,0,0,45,1337,5495,5541,10,9,1,148,6,263,0,7, 401,1,0,0,0,0,0,634, 3,600000,3,3,128,8,500 -"4",122,1406,3,0,0,46,1336,5495,5541,10,9,1,142,4,269,2,7, 396,1,0,0,0,0,0,640, 3,600000,3,3,128,8,500 -"4",98,1406,3,0,0,46,1336,5495,5541,10,9,1,135,6,276,0,7, 398,1,0,0,0,0,0,638, 3,600000,3,3,128,8,500 -"4",188,1406,4,0,0,47,1336,5495,5541,10,9,1,264,2,147,3,7, 390,1,0,0,0,0,1,645, 3,600000,3,3,128,8,500 -"4",168,1407,4,0,0,45,1337,5495,5541,10,9,1,241,3,170,2,7, 392,1,0,0,0,0,0,643, 3,600000,3,3,128,8,500 -"4",80,1406,3,0,0,47,1335,5495,5541,10,9,1,150,6,261,0,7, 399,1,0,0,0,0,0,637, 3,600000,3,3,128,8,500 -"4",27,1406,4,0,0,46,1337,5495,5541,10,9,1,116,4,295,0,7, 405,1,0,0,0,0,0,631, 3,600000,3,3,128,8,500 -"4",84,1405,4,0,0,46,1337,5495,5541,10,9,1,134,4,277,0,7, 399,1,0,0,0,0,1,637, 3,600000,3,3,128,8,500 -"4",49,1408,2,0,0,44,1338,5495,5541,10,9,1,143,8,267,0,7, 403,1,0,0,0,0,0,632, 3,600000,3,3,128,8,500 -"4",196,1407,4,0,0,49,1335,5495,5541,10,9,1,239,2,171,2,7, 389,1,0,0,0,0,1,646, 3,600000,3,3,128,8,500 -"4",114,1408,3,0,0,45,1339,5495,5541,11,9,1,137,6,274,0,7, 396,1,0,0,0,0,0,639, 3,600000,3,3,128,8,500 -"4",184,1408,3,0,0,47,1338,5495,5541,10,9,1,243,4,168,2,7, 390,1,0,0,0,0,0,645, 3,600000,3,3,128,8,500 -"4",56,1409,3,0,0,46,1339,5495,5541,10,9,1,239,6,171,0,7, 402,1,0,0,0,0,0,632, 3,600000,3,3,128,8,500 -"4",141,1408,3,0,0,45,1339,5495,5541,10,9,1,151,4,260,2,7, 394,1,0,0,0,0,0,641, 3,600000,3,3,128,8,500 -"4",7,1408,4,0,0,45,1338,5495,5541,10,9,1,243,4,168,1,7, 407,1,0,0,0,0,0,629, 3,600000,3,3,128,8,500 -"4",78,1409,5,0,0,46,1339,5495,5541,10,9,1,241,2,170,2,7, 400,1,0,0,0,0,0,635, 3,600000,3,3,128,8,500 -"4",31,1408,4,0,0,45,1339,5495,5541,10,9,1,237,4,173,1,7, 405,1,0,0,0,0,1,630, 3,600000,3,3,128,8,500 -"4",169,1409,3,0,0,46,1339,5495,5541,11,9,1,239,4,171,2,7, 391,1,0,0,0,0,0,643, 3,600000,3,3,128,8,500 -"4",227,1408,4,0,0,48,1337,5495,5541,10,9,1,137,3,275,2,7, 385,1,0,0,0,0,0,651, 3,600000,3,3,128,8,500 -"4",74,1410,3,0,0,46,1339,5495,5541,10,9,1,158,4,252,3,7, 401,1,0,0,0,0,0,634, 3,600000,3,3,128,8,500 -"4",163,1408,5,0,0,45,1340,5495,5541,10,9,1,241,2,169,2,7, 392,1,0,0,0,0,1,643, 3,600000,3,3,128,8,500 -"4",177,1410,4,0,0,47,1338,5495,5541,11,9,1,244,4,166,2,7, 390,1,0,0,0,0,0,644, 3,600000,3,3,128,8,500 -"4",185,1410,4,0,0,45,1340,5495,5541,10,9,1,242,4,169,2,7, 390,1,0,0,0,0,0,645, 3,600000,3,3,128,8,500 -"4",70,1410,3,0,0,46,1339,5495,5541,10,9,1,136,7,275,0,7, 401,1,0,0,0,0,0,634, 3,600000,3,3,128,8,500 -"4",228,1408,3,0,0,48,1338,5495,5541,10,9,1,120,3,292,2,7, 385,1,0,0,0,0,0,652, 3,600000,3,3,128,8,500 -"4",156,1408,4,0,0,45,1340,5495,5541,10,9,1,243,2,169,2,7, 393,1,0,0,0,0,0,644, 3,600000,3,3,128,8,500 -"4",51,1411,4,0,0,45,1341,5495,5541,10,9,1,254,6,156,0,7, 402,1,0,0,0,0,0,632, 3,600000,3,3,128,8,500 -"4",43,1410,3,0,0,47,1339,5495,5541,9,9,1,239,7,171,0,7, 404,1,0,0,0,0,0,631, 3,600000,3,3,128,8,500 -"4",197,1410,4,0,0,48,1339,5495,5541,10,9,1,241,4,169,2,7, 389,1,0,0,0,0,0,646, 3,600000,3,3,128,8,500 -"4",198,1410,4,0,0,49,1337,5495,5541,9,9,1,241,3,169,2,7, 389,1,0,0,0,0,0,646, 3,600000,3,3,128,8,500 -"4",135,1410,4,0,0,47,1339,5495,5541,11,9,1,156,4,254,2,7, 394,1,0,0,0,0,0,641, 3,600000,3,3,128,8,500 -"4",224,1410,4,0,0,48,1338,5495,5541,9,9,1,240,4,170,2,7, 385,1,0,0,0,0,0,650, 3,600000,3,3,128,8,500 -"4",14,1411,3,0,0,48,1339,5495,5541,10,9,1,240,6,170,0,7, 406,1,0,0,0,0,0,628, 3,600000,3,3,128,8,500 -"4",171,1410,3,0,0,45,1342,5495,5541,10,9,1,243,4,168,2,7, 391,1,0,0,0,0,1,644, 3,600000,3,3,128,8,500 -"4",225,1411,4,0,0,46,1340,5495,5541,9,9,1,240,4,170,2,7, 385,1,0,0,0,0,0,649, 3,600000,3,3,128,8,500 -"4",86,1411,5,0,0,45,1342,5495,5541,10,9,1,138,4,273,0,7, 398,1,0,0,0,0,0,637, 3,600000,3,3,128,8,500 -"4",112,1411,3,0,0,45,1343,5495,5541,10,9,1,24,5,388,0,7, 397,1,0,0,0,0,0,640, 3,600000,3,3,128,8,500 -"4",58,1412,4,0,0,45,1343,5495,5541,10,9,1,239,5,172,0,7, 402,1,0,0,0,0,0,634, 3,600000,3,3,128,8,500 -"4",115,1412,3,0,0,47,1342,5495,5541,10,9,1,144,6,267,0,7, 396,1,0,0,0,0,0,639, 3,600000,3,3,128,8,500 -"4",57,1413,4,0,0,46,1343,5495,5541,10,9,1,236,5,174,0,7, 402,1,0,0,0,0,0,633, 3,600000,3,3,128,8,500 -"4",120,1413,3,0,0,46,1343,5495,5541,10,9,1,138,5,273,2,7, 396,1,0,0,0,0,0,639, 3,600000,3,3,128,8,500 -"4",142,1413,3,0,0,49,1340,5495,5541,10,9,1,135,4,275,2,7, 394,1,0,0,0,0,0,641, 3,600000,3,3,128,8,500 -"4",161,1414,4,0,0,45,1345,5495,5541,11,9,1,241,4,169,2,7, 392,1,0,0,0,0,0,643, 3,600000,3,3,128,8,500 -"4",170,1414,3,0,0,46,1344,5495,5541,10,9,1,241,4,169,2,7, 391,1,0,0,0,0,1,644, 3,600000,3,3,128,8,500 -"4",204,1414,4,0,0,47,1343,5495,5541,10,9,1,243,2,168,3,7, 388,1,0,0,0,0,0,647, 3,600000,3,3,128,8,500 -"4",85,1414,5,0,0,54,1335,5495,5541,10,9,1,153,4,258,0,7, 399,1,0,0,0,0,0,637, 3,600000,3,3,128,8,500 -"4",140,1415,4,0,0,47,1344,5495,5541,10,9,1,139,4,271,2,7, 394,1,0,0,0,0,0,641, 3,600000,3,3,128,8,500 -"4",30,1415,5,0,0,47,1344,5495,5541,10,9,1,237,3,174,1,7, 405,1,0,0,0,0,0,630, 3,600000,3,3,128,8,500 -"4",94,1415,4,0,0,47,1345,5495,5541,10,9,1,145,3,266,2,7, 398,1,0,0,0,0,0,637, 3,600000,3,3,128,8,500 -"4",199,1415,5,0,0,48,1343,5495,5541,10,9,1,240,3,171,2,7, 389,1,0,0,0,0,0,647, 3,600000,3,3,128,8,500 -"4",220,1416,3,0,0,48,1344,5495,5541,9,9,1,243,4,168,3,7, 386,1,0,0,0,0,0,650, 3,600000,3,3,128,8,500 -"4",99,1416,3,0,0,45,1346,5495,5541,11,9,1,148,6,263,0,7, 398,1,0,0,0,0,0,638, 3,600000,3,3,128,8,500 -"4",15,1416,4,0,0,45,1347,5495,5541,9,9,1,239,5,172,0,7, 406,1,0,0,0,0,0,629, 3,600000,3,3,128,8,500 -"4",133,1417,3,0,0,46,1347,5495,5541,10,9,1,154,4,256,2,7, 394,1,0,0,0,0,0,640, 3,600000,3,3,128,8,500 -"4",241,1416,4,0,0,49,1344,5495,5541,9,9,1,213,2,199,2,7, 383,1,0,0,0,0,0,653, 3,600000,3,3,128,8,500 -"4",87,1418,4,0,0,48,1346,5495,5541,10,9,1,151,5,260,0,7, 398,1,0,0,0,0,0,637, 3,600000,3,3,128,8,500 -"4",212,1419,4,0,0,49,1346,5495,5541,10,9,1,247,3,164,2,7, 386,1,0,0,0,0,1,649, 3,600000,3,3,128,8,500 -"4",28,1419,3,0,0,50,1346,5495,5541,10,9,1,138,5,273,0,7, 405,1,0,0,0,0,0,631, 3,600000,3,3,128,8,500 -"4",157,1420,3,0,0,48,1348,5495,5541,10,9,1,243,4,167,2,7, 392,1,0,0,0,0,0,643, 3,600000,3,3,128,8,500 -"4",143,1420,4,0,0,53,1345,5495,5541,10,9,1,151,3,260,1,7, 394,1,0,0,0,0,1,642, 3,600000,3,3,128,8,500 -"4",59,1423,3,0,0,48,1350,5494,5541,10,9,1,239,6,171,0,7, 401,1,0,0,0,0,0,633, 3,600000,3,3,128,8,500 -"4",255,1704,6,0,0,65,1613,5495,5541,10,9,1,300,1,110,3,7, 382,1,0,0,0,0,1,652, 3,600000,3,3,128,8,500 -"4",32,1817,3,0,0,63,1727,5495,5541,10,9,1,168,7,242,1,7, 405,1,0,0,0,0,0,630, 3,600000,3,3,128,8,500 -"4",65,1865,3,0,0,67,1773,5495,5541,10,9,1,139,6,272,0,7, 401,1,0,0,0,0,0,635, 3,600000,3,3,128,8,500 -"4",211,1866,3,0,0,68,1773,5495,5541,10,9,1,161,5,250,2,7, 386,1,0,0,0,0,1,648, 3,600000,3,3,128,8,500 -"4",239,1868,3,0,0,65,1776,5495,5541,9,9,1,159,4,251,3,7, 384,1,0,0,0,0,0,651, 3,600000,3,3,128,8,500 -"4",104,1869,3,0,0,65,1780,5495,5541,10,9,1,64,4,347,2,7, 397,1,0,0,0,0,1,638, 3,600000,3,3,128,8,500 -"4",17,1920,4,0,0,65,1830,5495,5541,10,9,1,126,4,285,0,7, 406,1,0,0,0,0,0,629, 3,600000,3,3,128,8,500 -"4",44,1967,4,0,0,63,1877,5495,5541,10,9,1,155,3,255,3,7, 404,1,0,0,0,0,1,631, 3,600000,3,3,128,8,500 -"4",207,1983,3,0,0,68,1890,5495,5541,10,9,1,125,3,286,3,7, 388,1,0,0,0,0,0,648, 3,600000,3,3,128,8,500 -"4",22,1982,4,0,0,65,1892,5495,5541,10,9,1,126,3,286,1,7, 406,1,0,0,0,0,1,630, 3,600000,3,3,128,8,500 -"4",214,1984,3,0,0,66,1893,5495,5541,9,9,1,99,3,313,2,7, 386,1,0,0,0,0,0,650, 3,600000,3,3,128,8,500 -"4",106,2042,4,0,0,65,1953,5495,5541,10,9,1,5,2,406,2,7, 397,1,0,0,0,0,0,639, 3,600000,3,3,128,8,500 -"5",224,17,2,0,0,0,0,3844,3819,148,5,1,238,4,12,2,7, 412,2,1,0,0,0,0,691, 3,300000,3,3,128,8,500 -"5",429,17,3,0,0,0,0,3844,3819,147,4,2,233,4,18,1,7, 391,2,1,0,0,0,0,713, 3,300000,3,3,128,8,500 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3,300000,3,3,128,8,500 -"5",365,602,2,0,0,28,555,3844,3819,148,5,2,98,4,155,1,7, 398,2,1,0,0,0,0,708, 3,300000,3,3,128,8,500 -"5",183,602,2,0,0,27,556,3844,3819,148,5,1,191,4,62,1,7, 416,2,1,0,0,0,0,690, 3,300000,3,3,128,8,500 -"5",32,603,2,0,0,28,556,3844,3818,147,5,1,197,3,55,2,7, 432,2,1,0,0,0,0,673, 3,300000,3,3,128,8,500 -"5",9,604,1,0,0,25,559,3844,3819,148,5,2,195,4,56,2,7, 434,2,1,0,0,0,0,671, 3,300000,3,3,128,8,500 -"5",508,603,2,0,0,27,557,3844,3819,5,5,2,194,3,58,2,7, 383,2,1,0,0,0,0,722, 3,300000,3,3,128,8,500 -"5",18,603,3,0,0,28,556,3844,3819,147,5,1,193,3,59,2,7, 433,2,1,0,0,0,0,672, 3,300000,3,3,128,8,500 -"5",510,603,2,0,0,29,555,3844,3818,58,5,2,195,3,57,2,7, 383,2,1,0,0,0,0,722, 3,300000,3,3,128,8,500 -"5",36,604,3,0,0,29,555,3844,3819,147,5,1,195,3,57,2,7, 432,2,1,0,0,0,0,673, 3,300000,3,3,128,8,500 -"5",447,604,2,0,0,28,557,3844,3819,147,5,2,189,3,63,2,7, 389,2,1,0,0,0,0,716, 3,300000,3,3,128,8,500 -"5",65,603,2,0,0,27,557,3844,3819,148,5,1,192,3,60,1,7, 428,2,1,0,0,0,0,678, 3,300000,3,3,128,8,500 -"5",89,604,1,0,0,27,557,3844,3818,148,4,2,191,4,61,2,7, 426,2,1,0,0,0,0,679, 3,300000,3,3,128,8,500 -"5",72,604,1,0,0,26,558,3844,3819,148,5,2,192,4,60,2,7, 427,2,1,0,0,0,0,678, 3,300000,3,3,128,8,500 -"5",252,604,1,0,0,26,558,3844,3819,5,5,2,193,4,59,2,7, 410,2,1,0,0,0,0,695, 3,300000,3,3,128,8,500 -"5",2,604,2,0,0,24,561,3844,3819,62,5,1,195,4,57,2,7, 435,2,1,0,0,0,0,671, 3,300000,3,3,128,8,500 -"5",257,604,2,0,0,29,556,3844,3819,61,5,1,195,4,57,1,7, 409,2,1,0,0,0,0,696, 3,300000,3,3,128,8,500 -"5",218,604,2,0,0,26,558,3844,3819,148,5,2,193,3,60,2,7, 413,2,1,0,0,0,0,693, 3,300000,3,3,128,8,500 -"5",436,604,2,0,0,27,558,3844,3819,148,5,1,189,3,63,2,7, 390,2,1,0,0,0,0,715, 3,300000,3,3,128,8,500 -"5",330,604,3,0,0,28,558,3844,3818,147,5,2,191,3,61,1,7, 401,2,1,0,0,0,0,705, 3,300000,3,3,128,8,500 -"5",109,605,2,0,0,28,558,3844,3819,148,5,2,189,4,63,2,7, 424,2,1,0,0,0,0,681, 3,300000,3,3,128,8,500 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3,300000,3,3,128,8,500 -"5",155,606,3,0,0,29,558,3844,3818,148,5,2,190,2,62,1,7, 419,2,1,0,0,0,0,687, 3,300000,3,3,128,8,500 -"5",154,606,3,0,0,29,558,3844,3818,148,5,2,190,2,62,1,7, 419,2,1,0,0,0,0,687, 3,300000,3,3,128,8,500 -"5",150,607,4,0,0,28,559,3844,3818,148,5,1,191,1,61,1,7, 419,2,1,0,0,0,0,686, 3,300000,3,3,128,8,500 -"5",16,606,3,0,0,28,559,3844,3818,53,5,1,193,2,60,2,7, 433,2,1,0,0,0,0,673, 3,300000,3,3,128,8,500 -"5",223,607,3,0,0,27,560,3844,3818,147,5,2,108,2,144,2,7, 412,2,1,0,0,0,0,693, 3,300000,3,3,128,8,500 -"5",384,607,2,1,0,27,560,3844,3819,17,5,1,109,4,143,1,6, 395,2,1,0,0,0,0,710, 3,300000,3,3,128,8,500 -"5",77,608,1,0,0,26,561,3844,3819,147,5,2,191,4,61,2,7, 427,2,1,0,0,0,0,679, 3,300000,3,3,128,8,500 -"5",179,608,3,0,0,29,559,3844,3818,147,5,1,191,3,61,1,7, 416,2,1,0,0,0,0,689, 3,300000,3,3,128,8,500 -"5",358,607,1,0,0,27,561,3844,3819,147,5,1,108,4,145,1,7, 398,2,1,0,0,0,0,707, 3,300000,3,3,128,8,500 -"5",382,607,2,0,0,28,560,3844,3819,58,5,2,107,4,145,1,7, 396,2,1,0,0,0,1,709, 3,300000,3,3,128,8,500 -"5",301,609,3,0,0,29,560,3844,3818,147,5,2,192,4,60,1,7, 404,2,1,0,0,0,0,701, 3,300000,3,3,128,8,500 -"5",261,609,2,0,0,27,562,3844,3818,148,5,1,197,3,55,1,7, 409,2,1,0,0,0,0,696, 3,300000,3,3,128,8,500 -"5",309,608,2,0,0,27,561,3844,3819,147,5,1,191,4,60,1,7, 403,2,1,0,0,0,0,702, 3,300000,3,3,128,8,500 -"5",147,609,2,0,0,26,562,3844,3819,148,5,1,191,4,61,1,7, 419,2,1,0,0,0,0,686, 3,300000,3,3,128,8,500 -"5",311,608,2,0,0,29,561,3844,3819,147,5,1,190,4,62,1,7, 403,2,1,0,0,0,0,702, 3,300000,3,3,128,8,500 -"5",295,608,2,0,0,28,561,3844,3819,148,5,1,195,3,57,1,7, 405,2,1,0,0,0,1,700, 3,300000,3,3,128,8,500 -"5",504,609,3,0,0,29,561,3844,3818,147,5,2,193,2,59,2,7, 384,2,1,0,0,0,0,721, 3,300000,3,3,128,8,500 -"5",368,610,2,0,0,30,561,3844,3818,53,5,1,108,4,144,1,7, 397,2,1,0,0,0,0,708, 3,300000,3,3,128,8,500 -"5",256,610,3,0,0,29,561,3844,3818,13,5,1,193,3,59,2,6, 409,2,1,0,0,0,0,696, 3,300000,3,3,128,8,500 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3,300000,3,3,128,8,500 -"5",476,631,2,0,0,27,585,3844,3818,5,5,2,196,3,56,2,7, 386,2,1,0,0,0,0,719, 3,300000,3,3,128,8,500 -"5",302,636,2,0,0,34,583,3844,3818,57,5,2,189,4,63,1,7, 404,2,1,0,0,0,0,701, 3,300000,3,3,128,8,500 -"5",124,647,0,0,0,18,609,3844,3819,6,5,1,78,5,175,2,7, 423,2,1,0,0,0,0,683, 3,300000,3,3,128,8,500 -"5",319,653,2,0,0,28,606,3844,3819,148,5,2,174,4,79,1,7, 402,2,1,0,0,0,0,704, 3,300000,3,3,128,8,500 -"5",151,653,0,0,0,20,614,3843,3820,148,5,1,79,6,174,1,7, 419,2,1,0,0,0,0,687, 3,300000,3,3,128,8,500 -"5",10,654,3,0,0,28,607,3844,3819,148,5,2,176,2,77,2,7, 434,2,1,0,0,0,0,671, 3,300000,3,3,128,8,500 -"5",131,653,3,0,0,27,608,3844,3819,148,5,1,181,1,72,1,7, 421,2,1,0,0,0,0,684, 3,300000,3,3,128,8,500 -"5",12,655,2,0,0,24,611,3843,3818,148,5,2,177,4,75,1,7, 434,2,1,0,0,0,0,672, 3,300000,3,3,128,8,500 -"5",291,655,3,0,0,27,609,3844,3818,147,5,1,178,3,74,1,7, 405,2,1,0,0,0,1,700, 3,300000,3,3,128,8,500 -"5",6,655,2,0,0,26,611,3844,3819,61,5,1,179,3,73,2,7, 434,2,1,0,0,0,0,671, 3,300000,3,3,128,8,500 -"5",437,655,3,0,0,26,609,3844,3819,147,5,1,173,2,80,2,7, 390,2,1,0,0,0,0,715, 3,300000,3,3,128,8,500 -"5",251,656,2,0,0,27,609,3844,3818,148,5,2,172,3,80,2,7, 410,2,1,0,0,0,0,696, 3,300000,3,3,128,8,500 -"5",13,656,3,0,0,24,612,3844,3818,12,5,2,174,3,79,1,7, 434,2,1,0,0,0,0,672, 3,300000,3,3,128,8,500 -"5",312,655,3,0,0,28,609,3844,3819,147,5,2,174,3,79,1,7, 403,2,1,0,0,0,0,702, 3,300000,3,3,128,8,500 -"5",3,656,2,0,0,24,611,3844,3819,148,5,1,179,4,73,2,7, 435,2,1,0,0,0,0,671, 3,300000,3,3,128,8,500 -"5",296,656,2,0,0,27,610,3844,3818,147,5,2,177,4,75,1,7, 405,2,1,0,0,0,0,700, 3,300000,3,3,128,8,500 -"5",38,656,3,0,0,29,608,3844,3818,147,5,1,179,3,73,2,7, 432,2,1,0,0,0,0,674, 3,300000,3,3,128,8,500 -"5",193,654,3,0,0,27,610,3844,3819,148,5,1,175,1,79,1,7, 415,2,1,0,0,0,0,693, 3,300000,3,3,128,8,500 -"5",305,656,2,0,0,29,608,3844,3819,148,5,1,174,4,78,1,7, 403,2,1,0,0,0,0,702, 3,300000,3,3,128,8,500 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3,300000,3,3,128,8,500 -"5",26,656,2,0,0,28,610,3844,3819,147,5,2,176,1,78,2,7, 433,2,1,0,0,0,0,674, 3,300000,3,3,128,8,500 -"5",373,656,2,0,0,29,609,3844,3818,148,5,1,80,4,173,1,7, 397,2,1,0,0,0,0,709, 3,300000,3,3,128,8,500 -"5",265,656,3,0,0,30,608,3844,3818,148,5,2,177,2,77,1,7, 408,2,1,0,0,0,0,698, 3,300000,3,3,128,8,500 -"5",136,658,2,0,0,28,611,3844,3819,31,4,2,173,4,79,1,7, 421,2,1,0,0,0,0,684, 3,300000,3,3,128,8,500 -"5",472,657,3,0,0,27,611,3844,3818,148,5,2,173,3,79,1,7, 387,2,1,0,0,0,0,719, 3,300000,3,3,128,8,500 -"5",417,657,4,0,0,28,611,3844,3818,147,4,1,75,2,178,1,7, 392,2,1,0,0,0,0,715, 3,300000,3,3,128,8,500 -"5",360,656,2,0,0,28,610,3844,3819,148,4,2,88,4,166,0,7, 398,2,1,0,0,0,0,708, 3,300000,3,3,128,8,500 -"5",275,656,2,0,0,28,611,3844,3819,148,5,1,174,3,79,1,7, 408,2,1,0,0,0,0,699, 3,300000,3,3,128,8,500 -"5",20,657,1,0,0,29,610,3844,3819,147,5,1,176,4,76,2,7, 433,2,1,0,0,0,0,672, 3,300000,3,3,128,8,500 -"5",82,657,2,0,0,27,612,3844,3819,147,5,1,172,3,81,1,7, 426,2,1,0,0,0,0,680, 3,300000,3,3,128,8,500 -"5",322,658,2,0,0,29,610,3844,3819,147,5,1,173,4,79,1,7, 401,2,1,0,0,0,0,704, 3,300000,3,3,128,8,500 -"5",243,657,2,0,0,29,611,3844,3818,147,5,1,174,3,78,1,7, 410,2,1,0,0,0,0,696, 3,300000,3,3,128,8,500 -"5",96,658,3,0,0,26,612,3844,3818,147,5,1,174,3,78,1,7, 425,2,1,0,0,0,0,680, 3,300000,3,3,128,8,500 -"5",52,656,2,0,0,27,611,3844,3818,148,5,1,176,2,77,2,7, 430,2,1,0,0,0,1,676, 3,300000,3,3,128,8,500 -"5",54,658,3,0,0,28,610,3844,3819,148,5,1,174,3,79,2,7, 430,2,1,0,0,0,0,676, 3,300000,3,3,128,8,500 -"5",321,658,2,0,0,29,610,3844,3819,148,5,1,171,3,81,1,7, 402,2,1,0,0,0,0,704, 3,300000,3,3,128,8,500 -"5",273,657,2,0,0,28,611,3844,3819,147,5,1,174,2,79,1,7, 408,2,1,0,0,0,0,699, 3,300000,3,3,128,8,500 -"5",105,658,2,0,0,27,611,3844,3819,147,5,2,90,3,163,1,7, 425,2,1,0,0,0,0,681, 3,300000,3,3,128,8,500 -"5",50,658,2,0,0,28,611,3844,3818,148,5,1,175,3,77,2,7, 430,2,1,0,0,0,0,675, 3,300000,3,3,128,8,500 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3,300000,3,3,128,8,500 -"5",298,658,2,0,0,28,611,3844,3819,147,5,2,174,5,78,1,7, 404,2,1,0,0,0,0,701, 3,300000,3,3,128,8,500 -"5",374,657,2,0,0,26,612,3844,3818,148,5,1,86,3,167,0,7, 397,2,1,0,0,0,1,709, 3,300000,3,3,128,8,500 -"5",158,658,2,0,0,26,612,3844,3818,58,5,2,173,3,80,1,7, 418,2,1,0,0,0,0,687, 3,300000,3,3,128,8,500 -"5",430,658,3,0,0,27,611,3844,3818,58,4,2,171,3,81,1,7, 390,2,1,0,0,0,0,715, 3,300000,3,3,128,8,500 -"5",318,658,3,0,0,29,611,3844,3819,57,5,2,173,3,80,1,7, 402,2,1,0,0,0,0,704, 3,300000,3,3,128,8,500 -"5",244,658,1,0,0,28,611,3844,3818,148,5,1,174,4,79,1,7, 410,2,1,0,0,0,0,696, 3,300000,3,3,128,8,500 -"5",24,658,2,0,0,27,612,3844,3819,147,5,2,175,2,78,2,7, 433,2,1,0,0,0,0,673, 3,300000,3,3,128,8,500 -"5",34,659,2,0,0,28,611,3844,3819,148,5,1,178,4,74,2,7, 432,2,1,0,0,0,0,673, 3,300000,3,3,128,8,500 -"5",95,659,3,0,0,27,612,3844,3818,148,4,2,173,2,79,2,7, 425,2,1,0,0,0,0,680, 3,300000,3,3,128,8,500 -"5",468,658,2,0,0,28,612,3844,3819,148,5,1,173,3,80,1,7, 387,2,1,0,0,0,0,719, 3,300000,3,3,128,8,500 -"5",416,659,2,0,0,27,612,3844,3819,148,5,1,86,4,167,1,6, 392,2,1,0,0,0,0,714, 3,300000,3,3,128,8,500 -"5",33,659,2,0,0,28,612,3844,3818,147,5,1,180,3,72,2,7, 432,2,1,0,0,0,0,674, 3,300000,3,3,128,8,500 -"5",160,659,2,0,0,27,612,3844,3818,148,5,1,174,4,79,1,7, 418,2,1,0,0,0,0,688, 3,300000,3,3,128,8,500 -"5",266,659,2,0,0,27,612,3844,3818,148,5,2,175,4,78,1,7, 408,2,1,0,0,0,0,697, 3,300000,3,3,128,8,500 -"5",101,659,3,0,0,27,612,3844,3818,147,5,1,173,2,79,1,7, 425,2,1,0,0,0,0,681, 3,300000,3,3,128,8,500 -"5",177,659,2,0,0,27,612,3844,3818,148,5,1,174,4,78,1,7, 416,2,1,0,0,0,0,690, 3,300000,3,3,128,8,500 -"5",478,659,2,0,0,29,611,3844,3819,58,5,2,179,2,74,2,7, 386,2,1,0,0,0,0,720, 3,300000,3,3,128,8,500 -"5",138,659,2,0,0,27,613,3844,3819,148,4,2,173,3,79,1,7, 420,2,1,0,0,0,0,685, 3,300000,3,3,128,8,500 -"5",375,659,3,0,0,27,612,3844,3818,148,5,1,78,3,175,0,7, 397,2,1,0,0,0,0,709, 3,300000,3,3,128,8,500 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3,300000,3,3,128,8,500 -"5",132,660,3,0,0,27,613,3844,3819,148,5,1,173,3,79,1,7, 421,2,1,0,0,0,0,684, 3,300000,3,3,128,8,500 -"5",263,660,2,0,0,29,612,3844,3818,148,5,1,178,3,74,1,7, 409,2,1,0,0,0,0,697, 3,300000,3,3,128,8,500 -"5",389,659,2,0,0,28,612,3844,3818,148,5,1,78,4,174,1,7, 395,2,1,0,0,0,0,711, 3,300000,3,3,128,8,500 -"5",68,660,2,0,0,29,613,3844,3819,147,4,1,175,4,77,1,7, 428,2,1,0,0,0,0,678, 3,300000,3,3,128,8,500 -"5",259,660,3,0,0,28,613,3844,3818,148,5,1,178,3,74,1,7, 409,2,1,0,0,0,0,696, 3,300000,3,3,128,8,500 -"5",79,660,2,0,0,28,613,3844,3819,147,5,2,174,3,78,2,7, 427,2,1,0,0,0,0,679, 3,300000,3,3,128,8,500 -"5",424,659,2,0,0,27,612,3844,3818,148,5,2,88,3,165,1,7, 391,2,1,0,0,0,0,715, 3,300000,3,3,128,8,500 -"5",444,660,1,0,0,26,613,3844,3819,6,5,2,171,4,81,2,7, 389,2,1,0,0,0,0,716, 3,300000,3,3,128,8,500 -"5",48,660,3,0,0,29,611,3844,3819,5,5,1,176,3,76,2,7, 430,2,1,0,0,0,0,675, 3,300000,3,3,128,8,500 -"5",194,659,2,0,0,28,612,3844,3819,147,5,1,174,3,79,1,7, 415,2,1,0,0,0,0,691, 3,300000,3,3,128,8,500 -"5",208,659,3,0,0,28,613,3844,3819,54,5,1,174,2,79,2,7, 413,2,1,0,0,0,0,693, 3,300000,3,3,128,8,500 -"5",196,659,2,0,0,28,612,3844,3819,147,5,1,174,3,79,1,7, 414,2,1,0,0,0,0,691, 3,300000,3,3,128,8,500 -"5",488,660,2,0,0,30,611,3844,3818,147,5,2,173,3,79,1,7, 385,2,1,0,0,0,0,720, 3,300000,3,3,128,8,500 -"5",44,660,2,0,0,28,613,3844,3818,148,5,2,175,3,77,1,7, 431,2,1,0,0,0,0,675, 3,300000,3,3,128,8,500 -"5",145,660,2,0,0,28,613,3844,3818,148,5,1,174,4,78,1,7, 419,2,1,0,0,0,0,686, 3,300000,3,3,128,8,500 -"5",162,660,2,0,0,28,613,3844,3819,147,5,1,175,4,78,1,7, 418,2,1,0,0,0,0,688, 3,300000,3,3,128,8,500 -"5",352,660,2,0,0,28,613,3844,3819,147,5,1,87,4,166,1,7, 399,2,1,0,0,0,0,707, 3,300000,3,3,128,8,500 -"5",201,660,3,0,0,29,613,3844,3819,148,5,2,175,2,78,2,7, 414,2,1,0,0,0,0,692, 3,300000,3,3,128,8,500 -"5",254,660,1,0,0,27,613,3844,3818,58,5,2,175,3,77,2,7, 409,2,1,0,0,0,0,696, 3,300000,3,3,128,8,500 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3,300000,3,3,128,8,500 -"5",380,660,2,0,0,28,613,3844,3819,6,5,2,87,4,165,0,7, 396,2,1,0,0,0,0,709, 3,300000,3,3,128,8,500 -"5",40,660,3,0,0,27,614,3843,3819,147,5,2,176,2,76,1,7, 432,2,1,0,0,0,0,674, 3,300000,3,3,128,8,500 -"5",80,661,1,0,0,28,614,3844,3818,53,5,1,174,4,78,1,7, 426,2,1,0,0,0,0,679, 3,300000,3,3,128,8,500 -"5",474,660,1,0,0,28,613,3844,3819,148,5,2,178,4,75,2,7, 386,2,1,0,0,0,0,719, 3,300000,3,3,128,8,500 -"5",431,661,3,0,0,28,613,3844,3819,147,4,2,171,3,81,1,7, 390,2,1,0,0,0,0,715, 3,300000,3,3,128,8,500 -"5",27,660,2,0,0,29,613,3844,3819,147,5,2,176,3,77,1,7, 433,2,1,0,0,0,0,673, 3,300000,3,3,128,8,500 -"5",460,660,3,0,0,30,612,3844,3819,147,5,2,175,2,78,2,7, 388,2,1,0,0,0,0,718, 3,300000,3,3,128,8,500 -"5",432,661,2,0,0,29,613,3844,3819,27,5,1,171,4,81,1,7, 390,2,1,0,0,0,0,715, 3,300000,3,3,128,8,500 -"5",74,661,2,0,0,28,614,3844,3819,148,5,2,174,2,78,2,7, 427,2,1,0,0,0,0,679, 3,300000,3,3,128,8,500 -"5",215,661,2,0,0,29,613,3844,3819,147,5,1,173,3,80,1,7, 413,2,1,0,0,0,0,693, 3,300000,3,3,128,8,500 -"5",408,661,1,0,0,29,614,3844,3818,149,4,2,87,4,166,1,7, 393,2,1,0,0,0,0,712, 3,300000,3,3,128,8,500 -"5",422,660,2,0,0,27,615,3844,3819,147,4,1,88,2,165,1,7, 391,2,1,0,0,0,0,715, 3,300000,3,3,128,8,500 -"5",350,660,2,0,0,29,613,3844,3818,58,5,2,81,4,172,0,7, 399,2,1,0,0,0,0,708, 3,300000,3,3,128,8,500 -"5",216,661,2,0,0,28,614,3844,3819,148,5,2,171,3,82,1,7, 413,2,1,0,0,0,0,693, 3,300000,3,3,128,8,500 -"5",385,661,2,0,0,30,612,3844,3819,74,5,1,75,4,178,1,7, 395,2,1,0,0,0,0,711, 3,300000,3,3,128,8,500 -"5",248,661,1,0,0,28,614,3844,3818,148,5,2,173,4,80,1,7, 410,2,1,0,0,0,0,696, 3,300000,3,3,128,8,500 -"5",187,660,2,0,0,29,613,3844,3819,148,5,2,174,3,79,1,7, 415,2,1,0,0,0,1,690, 3,300000,3,3,128,8,500 -"5",333,661,3,0,0,29,613,3844,3819,147,5,2,173,3,79,1,7, 400,2,1,0,0,0,0,705, 3,300000,3,3,128,8,500 -"5",137,661,3,0,0,27,615,3844,3819,149,5,2,174,3,79,1,7, 421,2,1,0,0,0,0,685, 3,300000,3,3,128,8,500 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3,300000,3,3,128,8,500 -"5",100,662,2,0,0,27,615,3844,3819,147,5,1,173,3,79,1,7, 425,2,1,0,0,0,0,681, 3,300000,3,3,128,8,500 -"5",500,662,2,0,0,28,614,3844,3819,148,5,1,175,3,78,2,7, 384,2,1,0,0,0,0,721, 3,300000,3,3,128,8,500 -"5",195,661,2,0,0,27,615,3844,3819,148,5,1,175,3,79,1,7, 414,2,1,0,0,0,0,692, 3,300000,3,3,128,8,500 -"5",238,661,2,0,0,28,614,3844,3818,58,5,2,175,3,78,1,7, 411,2,1,0,0,0,0,695, 3,300000,3,3,128,8,500 -"5",501,661,3,0,0,28,614,3844,3818,148,5,1,175,1,78,2,7, 384,2,1,0,0,0,0,722, 3,300000,3,3,128,8,500 -"5",509,662,2,0,0,30,614,3844,3819,148,5,2,177,2,75,2,7, 383,2,1,0,0,0,1,722, 3,300000,3,3,128,8,500 -"5",336,662,2,0,0,27,615,3844,3818,54,4,1,172,3,81,1,7, 400,2,1,0,0,0,0,706, 3,300000,3,3,128,8,500 -"5",139,661,2,0,0,27,615,3844,3819,148,5,2,175,2,79,1,7, 420,2,1,0,0,0,0,686, 3,300000,3,3,128,8,500 -"5",184,662,2,0,0,29,615,3844,3818,147,5,2,173,3,80,1,7, 416,2,1,0,0,0,0,690, 3,300000,3,3,128,8,500 -"5",25,662,2,0,0,27,616,3844,3819,147,5,2,175,3,78,2,7, 433,2,1,0,0,0,0,673, 3,300000,3,3,128,8,500 -"5",485,661,2,0,0,28,615,3844,3819,147,5,1,173,2,80,1,7, 385,2,1,0,0,0,0,721, 3,300000,3,3,128,8,500 -"5",502,662,3,0,0,27,616,3844,3819,147,5,1,175,2,77,2,7, 384,2,1,0,0,0,1,722, 3,300000,3,3,128,8,500 -"5",122,662,3,0,0,27,616,3844,3818,148,5,2,171,2,82,2,7, 422,2,1,0,0,0,0,683, 3,300000,3,3,128,8,500 -"5",507,662,3,0,0,28,615,3844,3819,147,5,2,177,1,75,2,7, 383,2,1,0,0,0,0,722, 3,300000,3,3,128,8,500 -"5",37,663,3,0,0,28,615,3844,3818,147,5,1,179,2,73,2,7, 432,2,1,0,0,0,0,673, 3,300000,3,3,128,8,500 -"5",467,662,3,0,0,27,615,3844,3818,148,5,1,172,3,81,1,7, 387,2,1,0,0,0,0,719, 3,300000,3,3,128,8,500 -"5",269,662,2,0,0,28,615,3844,3818,13,5,2,176,3,77,1,7, 408,2,1,0,0,0,0,698, 3,300000,3,3,128,8,500 -"5",492,662,2,0,0,28,616,3844,3818,147,4,2,173,2,80,2,7, 385,2,1,0,0,0,0,721, 3,300000,3,3,128,8,500 -"5",445,662,2,0,0,26,616,3844,3819,147,5,2,172,2,80,2,7, 389,2,1,0,0,0,0,716, 3,300000,3,3,128,8,500 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3,300000,3,3,128,8,500 -"5",66,663,3,0,0,29,616,3844,3819,147,4,1,175,2,77,1,7, 428,2,1,0,0,0,0,678, 3,300000,3,3,128,8,500 -"5",462,661,2,0,0,30,614,3844,3819,58,5,2,173,1,80,1,7, 387,2,1,0,0,0,1,719, 3,300000,3,3,128,8,500 -"5",475,663,2,0,0,28,616,3844,3819,148,5,2,178,3,75,2,7, 386,2,1,0,0,0,0,720, 3,300000,3,3,128,8,500 -"5",126,663,2,0,0,26,617,3844,3818,57,5,2,174,3,79,2,7, 422,2,1,0,0,0,0,684, 3,300000,3,3,128,8,500 -"5",419,662,3,0,0,28,616,3844,3818,147,4,1,79,2,174,1,7, 391,2,1,0,0,0,0,715, 3,300000,3,3,128,8,500 -"5",45,662,3,0,0,29,616,3844,3818,147,5,2,176,1,78,1,7, 431,2,1,0,0,0,0,676, 3,300000,3,3,128,8,500 -"5",255,664,2,0,0,28,616,3844,3819,148,5,2,174,3,79,2,7, 409,2,1,0,0,0,0,696, 3,300000,3,3,128,8,500 -"5",262,664,3,0,0,30,615,3844,3819,62,5,1,178,3,74,1,7, 409,2,1,0,0,0,0,696, 3,300000,3,3,128,8,500 -"5",76,664,3,0,0,29,616,3844,3819,147,5,2,174,2,78,2,7, 427,2,1,0,0,0,0,679, 3,300000,3,3,128,8,500 -"5",332,664,2,0,0,27,617,3844,3819,148,5,2,173,4,80,1,7, 401,2,1,0,0,0,0,705, 3,300000,3,3,128,8,500 -"5",361,664,2,0,0,29,616,3844,3819,147,4,2,76,4,177,0,7, 398,2,1,0,0,0,1,708, 3,300000,3,3,128,8,500 -"5",19,665,2,0,0,27,618,3844,3819,148,5,1,175,4,77,1,7, 433,2,1,0,0,0,0,672, 3,300000,3,3,128,8,500 -"5",270,664,3,0,0,26,619,3844,3818,58,5,2,174,3,78,1,7, 408,2,1,0,0,0,0,698, 3,300000,3,3,128,8,500 -"5",181,665,2,0,0,28,618,3844,3819,148,5,1,173,4,80,1,7, 416,2,1,0,0,0,0,690, 3,300000,3,3,128,8,500 -"5",404,665,3,0,0,29,618,3844,3818,148,5,1,88,3,165,1,7, 394,2,1,0,0,0,0,712, 3,300000,3,3,128,8,500 -"5",320,666,2,0,0,29,618,3844,3819,148,5,1,172,4,80,1,7, 402,2,1,0,0,0,0,704, 3,300000,3,3,128,8,500 -"5",307,667,3,0,0,29,618,3844,3819,148,5,1,171,3,81,1,7, 403,2,1,0,0,0,0,702, 3,300000,3,3,128,8,500 -"5",479,667,1,0,0,29,619,3844,3819,148,5,2,176,4,77,2,7, 386,2,1,0,0,0,0,720, 3,300000,3,3,128,8,500 -"5",411,667,3,0,0,29,621,3844,3818,148,4,2,77,2,177,1,7, 393,2,1,0,0,0,0,714, 3,300000,3,3,128,8,500 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3,300000,3,3,128,8,500 -"5",326,673,3,0,0,30,625,3844,3819,147,5,1,172,3,80,1,7, 401,2,1,0,0,0,0,704, 3,300000,3,3,128,8,500 -"5",297,673,3,0,0,28,626,3844,3818,148,5,2,176,3,77,1,7, 404,2,1,0,0,0,0,701, 3,300000,3,3,128,8,500 -"5",290,676,2,0,0,30,627,3844,3819,147,5,1,178,3,74,1,7, 405,2,1,0,0,0,0,700, 3,300000,3,3,128,8,500 -"5",279,676,2,0,0,26,630,3844,3818,148,5,1,173,3,80,1,7, 407,2,1,0,0,0,0,699, 3,300000,3,3,128,8,500 -"5",148,691,3,0,0,31,641,3844,3819,148,5,1,174,2,79,1,7, 419,2,1,0,0,0,0,686, 3,300000,3,3,128,8,500 -"5",344,705,0,0,0,18,668,3844,3819,148,5,1,47,7,206,0,7, 400,2,1,0,0,0,0,706, 3,300000,3,3,128,8,500 -"5",118,707,0,0,0,19,670,3843,3819,148,4,1,49,5,204,1,7, 423,2,1,0,0,0,0,684, 3,300000,3,3,128,8,500 -"5",165,707,0,0,0,18,672,3843,3819,147,5,1,53,5,201,1,7, 418,2,1,0,0,0,0,689, 3,300000,3,3,128,8,500 -"5",142,708,0,0,0,19,671,3844,3819,58,5,1,49,5,205,1,7, 420,2,1,0,0,0,0,687, 3,300000,3,3,128,8,500 -"5",167,711,0,0,0,18,674,3843,3819,147,5,1,49,5,205,1,7, 418,2,1,0,0,0,0,689, 3,300000,3,3,128,8,500 -"5",92,714,2,0,0,26,667,3844,3819,6,4,2,152,3,100,2,7, 425,2,1,0,0,0,0,680, 3,300000,3,3,128,8,500 -"5",456,713,2,0,0,31,664,3844,3819,148,4,2,152,3,101,1,7, 388,2,1,0,0,0,0,718, 3,300000,3,3,128,8,500 -"5",192,713,2,0,0,27,667,3844,3819,148,5,1,173,3,80,1,7, 415,2,1,0,0,0,0,692, 3,300000,3,3,128,8,500 -"5",284,713,2,0,0,27,667,3844,3819,6,5,2,149,3,104,1,7, 406,2,1,0,0,0,0,700, 3,300000,3,3,128,8,500 -"5",220,713,2,0,0,27,668,3844,3818,6,5,2,52,2,202,1,7, 412,2,1,0,0,0,0,694, 3,300000,3,3,128,8,500 -"5",149,714,0,0,0,20,675,3843,3820,148,5,1,52,6,201,1,7, 419,2,1,0,0,0,0,687, 3,300000,3,3,128,8,500 -"5",342,713,0,0,0,19,675,3844,3819,148,4,1,48,5,206,1,7, 400,2,1,0,0,0,0,708, 3,300000,3,3,128,8,500 -"5",402,714,2,0,0,27,668,3844,3819,148,5,1,62,3,191,1,7, 394,2,1,0,0,0,0,713, 3,300000,3,3,128,8,500 -"5",482,714,2,0,0,28,667,3844,3818,147,5,1,149,2,104,1,7, 386,2,1,0,0,0,0,721, 3,300000,3,3,128,8,500 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3,300000,3,3,128,8,500 -"5",62,716,2,0,0,28,669,3844,3819,57,5,2,150,4,103,0,7, 429,2,1,0,0,0,0,677, 3,300000,3,3,128,8,500 -"5",274,716,2,0,0,28,669,3844,3818,147,5,1,155,3,98,1,7, 408,2,1,0,0,0,0,699, 3,300000,3,3,128,8,500 -"5",55,716,1,0,0,28,669,3844,3819,148,5,1,153,3,100,1,7, 430,2,1,0,0,0,0,676, 3,300000,3,3,128,8,500 -"5",7,717,2,0,0,26,671,3844,3819,148,5,1,157,4,95,1,7, 434,2,1,0,0,0,0,671, 3,300000,3,3,128,8,500 -"5",127,716,2,0,0,27,670,3844,3818,147,5,2,150,2,103,1,7, 422,2,1,0,0,0,0,684, 3,300000,3,3,128,8,500 -"5",58,716,2,0,0,28,669,3844,3819,148,5,2,150,4,103,1,7, 429,2,1,0,0,0,0,677, 3,300000,3,3,128,8,500 -"5",292,715,1,0,0,29,669,3844,3819,147,5,1,158,3,96,1,7, 405,2,1,0,0,0,0,702, 3,300000,3,3,128,8,500 -"5",233,716,3,0,0,28,670,3844,3818,147,5,2,151,2,102,2,7, 411,2,1,0,0,0,0,695, 3,300000,3,3,128,8,500 -"5",133,716,2,0,0,29,670,3844,3819,149,5,1,153,2,100,1,7, 421,2,1,0,0,0,0,685, 3,300000,3,3,128,8,500 -"5",331,716,3,0,0,28,669,3844,3818,147,5,2,152,3,101,1,7, 401,2,1,0,0,0,0,705, 3,300000,3,3,128,8,500 -"5",71,717,2,0,0,26,670,3844,3819,147,5,1,152,3,101,1,7, 427,2,1,0,0,0,0,679, 3,300000,3,3,128,8,500 -"5",455,716,2,0,0,28,670,3844,3819,147,5,1,149,3,103,1,7, 388,2,1,0,0,0,0,718, 3,300000,3,3,128,8,500 -"5",182,716,2,0,0,27,670,3844,3819,147,5,1,152,3,101,1,7, 416,2,1,0,0,0,0,690, 3,300000,3,3,128,8,500 -"5",161,716,2,0,0,28,671,3844,3818,148,5,1,153,2,100,1,7, 418,2,1,0,0,0,0,688, 3,300000,3,3,128,8,500 -"5",102,717,3,0,0,28,670,3844,3819,147,5,1,150,3,103,1,7, 425,2,1,0,0,0,0,681, 3,300000,3,3,128,8,500 -"5",173,716,2,0,0,27,671,3844,3819,148,5,2,150,3,103,1,7, 417,2,1,0,0,0,1,689, 3,300000,3,3,128,8,500 -"5",227,716,2,0,0,26,671,3844,3819,147,5,1,152,3,102,1,7, 412,2,1,0,0,0,0,695, 3,300000,3,3,128,8,500 -"5",35,717,2,0,0,28,670,3844,3818,147,5,1,158,3,94,1,7, 432,2,1,0,0,0,0,674, 3,300000,3,3,128,8,500 -"5",241,716,3,0,0,28,670,3844,3818,148,5,1,152,2,101,1,7, 410,2,1,0,0,0,0,696, 3,300000,3,3,128,8,500 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3,300000,3,3,128,8,500 -"5",306,716,4,0,0,29,670,3844,3819,147,5,1,155,1,98,1,7, 403,2,1,0,0,0,1,703, 3,300000,3,3,128,8,500 -"5",209,717,2,0,0,27,672,3844,3819,148,5,1,174,2,79,1,7, 413,2,1,0,0,0,1,693, 3,300000,3,3,128,8,500 -"5",83,718,3,0,0,28,671,3844,3818,148,5,1,174,3,79,1,7, 426,2,1,0,0,0,0,680, 3,300000,3,3,128,8,500 -"5",345,717,2,0,0,29,671,3844,3818,148,5,2,49,3,205,1,7, 399,2,1,0,0,0,0,707, 3,300000,3,3,128,8,500 -"5",484,718,2,0,0,29,670,3844,3819,147,5,1,153,3,100,1,7, 386,2,1,0,0,0,0,720, 3,300000,3,3,128,8,500 -"5",84,718,2,0,0,28,671,3844,3818,148,5,1,151,3,102,1,7, 426,2,1,0,0,0,0,680, 3,300000,3,3,128,8,500 -"5",205,718,2,0,0,29,670,3844,3819,148,5,2,153,2,100,2,7, 414,2,1,0,0,0,0,692, 3,300000,3,3,128,8,500 -"5",46,718,2,0,0,28,671,3844,3818,58,4,2,151,3,102,1,7, 431,2,1,0,0,0,0,675, 3,300000,3,3,128,8,500 -"5",178,718,2,0,0,28,671,3844,3819,148,5,1,154,3,100,0,7, 416,2,1,0,0,0,0,690, 3,300000,3,3,128,8,500 -"5",163,717,3,0,0,26,672,3844,3818,148,5,1,153,2,100,1,7, 418,2,1,0,0,0,0,689, 3,300000,3,3,128,8,500 -"5",505,718,1,0,0,27,672,3844,3819,147,5,2,155,2,99,2,7, 383,2,1,0,0,0,0,723, 3,300000,3,3,128,8,500 -"5",413,718,3,0,0,29,671,3844,3818,148,4,2,49,2,204,1,7, 393,2,1,0,0,0,0,714, 3,300000,3,3,128,8,500 -"5",343,718,3,0,0,26,672,3844,3818,148,4,1,149,3,104,1,7, 399,2,1,0,0,0,0,706, 3,300000,3,3,128,8,500 -"5",186,716,2,0,0,28,671,3844,3819,147,5,2,153,1,100,1,7, 415,2,1,0,0,0,1,691, 3,300000,3,3,128,8,500 -"5",370,718,2,0,0,27,671,3844,3818,148,5,1,58,3,195,0,7, 397,2,1,0,0,0,0,709, 3,300000,3,3,128,8,500 -"5",398,718,2,0,0,28,672,3844,3819,57,5,2,60,3,194,1,7, 394,2,1,0,0,0,0,713, 3,300000,3,3,128,8,500 -"5",390,718,2,0,0,28,671,3844,3818,61,5,1,61,3,192,1,7, 395,2,1,0,0,0,0,712, 3,300000,3,3,128,8,500 -"5",406,718,2,0,0,28,672,3844,3819,148,5,1,60,4,193,1,7, 393,2,1,0,0,0,0,713, 3,300000,3,3,128,8,500 -"5",450,718,2,0,0,28,672,3844,3819,147,5,1,151,3,102,1,7, 388,2,1,0,0,0,0,718, 3,300000,3,3,128,8,500 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400,2,1,0,0,0,0,706, 3,300000,3,3,128,8,500 -"5",339,719,2,0,0,29,671,3844,3818,148,4,1,150,4,103,1,7, 400,2,1,0,0,0,0,706, 3,300000,3,3,128,8,500 -"5",104,719,2,0,0,27,673,3844,3818,148,5,2,48,3,205,1,7, 425,2,1,0,0,0,0,682, 3,300000,3,3,128,8,500 -"5",454,718,3,0,0,30,670,3844,3819,148,5,1,151,2,102,1,7, 388,2,1,0,0,0,0,718, 3,300000,3,3,128,8,500 -"5",245,718,2,0,0,28,672,3844,3818,148,5,1,154,3,99,1,7, 410,2,1,0,0,0,0,696, 3,300000,3,3,128,8,500 -"5",363,718,3,0,0,29,670,3844,3818,147,4,2,52,2,202,1,7, 398,2,1,0,0,0,0,709, 3,300000,3,3,128,8,500 -"5",117,718,3,0,0,26,673,3844,3819,148,5,1,63,2,190,1,7, 423,2,1,0,0,0,0,684, 3,300000,3,3,128,8,500 -"5",249,718,2,0,0,29,672,3844,3819,148,5,2,152,2,101,1,7, 410,2,1,0,0,0,0,696, 3,300000,3,3,128,8,500 -"5",465,719,3,0,0,28,671,3844,3819,148,5,1,151,3,102,1,7, 387,2,1,0,0,0,0,719, 3,300000,3,3,128,8,500 -"5",278,718,2,0,0,28,672,3844,3818,147,5,1,154,2,99,1,7, 407,2,1,0,0,0,0,699, 3,300000,3,3,128,8,500 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3,300000,3,3,128,8,500 -"5",171,719,2,0,0,26,674,3844,3818,148,5,2,151,3,102,1,7, 417,2,1,0,0,0,0,689, 3,300000,3,3,128,8,500 -"5",379,719,2,0,0,27,673,3844,3819,148,5,2,52,3,201,0,7, 397,2,1,0,0,0,0,710, 3,300000,3,3,128,8,500 -"5",400,719,3,0,0,28,673,3844,3818,53,5,1,61,2,192,1,7, 394,2,1,0,0,0,0,712, 3,300000,3,3,128,8,500 -"5",469,719,1,0,0,26,674,3844,3819,148,5,1,151,4,102,1,7, 387,2,1,0,0,0,0,719, 3,300000,3,3,128,8,500 -"5",246,719,2,0,0,27,673,3844,3819,148,5,1,153,3,100,1,7, 410,2,1,0,0,0,0,696, 3,300000,3,3,128,8,500 -"5",141,720,2,0,0,28,672,3844,3819,62,5,2,152,4,101,1,7, 420,2,1,0,0,0,0,686, 3,300000,3,3,128,8,500 -"5",477,719,3,0,0,29,672,3844,3818,148,5,2,157,2,96,2,7, 386,2,1,0,0,0,0,720, 3,300000,3,3,128,8,500 -"5",498,719,3,0,0,30,671,3844,3818,147,5,1,155,2,98,2,7, 384,2,1,0,0,0,0,722, 3,300000,3,3,128,8,500 -"5",31,719,2,0,0,29,673,3844,3819,148,5,2,151,4,102,1,7, 432,2,1,0,0,0,0,674, 3,300000,3,3,128,8,500 -"5",372,719,2,0,0,28,673,3844,3818,147,5,1,59,4,194,1,7, 397,2,1,0,0,0,0,709, 3,300000,3,3,128,8,500 -"5",459,719,2,0,0,28,673,3844,3818,147,4,2,153,2,100,2,7, 388,2,1,0,0,0,0,718, 3,300000,3,3,128,8,500 -"5",355,719,2,0,0,30,671,3844,3818,147,5,1,52,4,201,1,7, 398,2,1,0,0,0,0,708, 3,300000,3,3,128,8,500 -"5",435,720,2,0,0,28,673,3844,3819,148,5,1,151,3,102,2,7, 390,2,1,0,0,0,0,716, 3,300000,3,3,128,8,500 -"5",324,719,2,0,0,28,673,3844,3819,148,5,1,154,3,100,1,7, 401,2,1,0,0,0,0,705, 3,300000,3,3,128,8,500 -"5",483,720,2,0,0,28,673,3844,3818,147,5,1,151,3,102,1,7, 385,2,1,0,0,0,0,721, 3,300000,3,3,128,8,500 -"5",288,720,2,0,0,30,672,3844,3819,147,5,1,151,4,101,1,7, 405,2,1,0,0,0,0,701, 3,300000,3,3,128,8,500 -"5",189,720,2,0,0,27,674,3844,3818,147,5,2,152,3,101,1,7, 415,2,1,0,0,0,0,691, 3,300000,3,3,128,8,500 -"5",175,719,3,0,0,28,673,3844,3819,147,5,2,149,1,104,1,7, 417,2,1,0,0,0,1,690, 3,300000,3,3,128,8,500 -"5",451,720,2,0,0,28,673,3844,3818,147,5,1,150,3,103,1,7, 388,2,1,0,0,0,0,718, 3,300000,3,3,128,8,500 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3,300000,3,3,128,8,500 -"5",170,718,2,0,0,28,673,3844,3819,148,5,2,153,1,102,1,7, 417,2,1,0,0,0,0,690, 3,300000,3,3,128,8,500 -"5",197,720,2,0,0,28,673,3844,3819,147,5,1,153,3,100,1,7, 414,2,1,0,0,0,0,692, 3,300000,3,3,128,8,500 -"5",229,719,2,0,0,27,674,3844,3819,147,5,1,150,3,103,1,7, 412,2,1,0,0,0,0,695, 3,300000,3,3,128,8,500 -"5",449,720,2,0,0,28,674,3844,3819,147,5,1,152,3,101,1,7, 389,2,1,0,0,0,0,717, 3,300000,3,3,128,8,500 -"5",120,720,2,0,0,27,674,3844,3819,147,5,2,149,3,104,1,7, 423,2,1,0,0,0,0,683, 3,300000,3,3,128,8,500 -"5",61,720,2,0,0,27,674,3844,3818,147,5,2,151,3,102,1,7, 429,2,1,0,0,0,0,677, 3,300000,3,3,128,8,500 -"5",172,719,2,0,0,27,674,3844,3819,147,5,2,152,2,103,1,7, 417,2,1,0,0,0,0,691, 3,300000,3,3,128,8,500 -"5",285,719,2,0,0,28,673,3844,3819,148,5,2,151,3,103,1,7, 406,2,1,0,0,0,0,700, 3,300000,3,3,128,8,500 -"5",457,720,2,0,0,27,674,3844,3818,148,4,2,150,3,103,1,7, 388,2,1,0,0,0,0,718, 3,300000,3,3,128,8,500 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3,300000,3,3,128,8,500 -"5",335,720,3,0,0,29,673,3844,3819,148,5,2,154,2,99,1,7, 400,2,1,0,0,0,1,706, 3,300000,3,3,128,8,500 -"5",115,720,2,0,0,26,675,3844,3819,148,5,1,62,2,192,1,7, 423,2,1,0,0,0,1,684, 3,300000,3,3,128,8,500 -"5",176,720,2,0,0,29,672,3844,3819,28,5,1,152,3,101,1,7, 416,2,1,0,0,0,0,690, 3,300000,3,3,128,8,500 -"5",392,721,2,0,0,29,674,3844,3819,31,5,2,59,3,194,1,7, 395,2,1,0,0,0,0,711, 3,300000,3,3,128,8,500 -"5",39,721,2,0,0,28,674,3844,3819,148,5,1,158,3,95,1,7, 432,2,1,0,0,0,0,675, 3,300000,3,3,128,8,500 -"5",470,721,2,0,0,28,674,3844,3819,148,5,1,153,3,100,1,7, 387,2,1,0,0,0,0,719, 3,300000,3,3,128,8,500 -"5",491,721,2,0,0,28,674,3844,3819,147,4,2,152,3,101,2,7, 385,2,1,0,0,0,0,721, 3,300000,3,3,128,8,500 -"5",121,721,2,0,0,27,675,3844,3819,148,5,2,149,3,104,2,7, 423,2,1,0,0,0,0,684, 3,300000,3,3,128,8,500 -"5",414,721,2,0,0,27,674,3844,3819,58,4,2,58,3,196,1,7, 392,2,1,0,0,0,0,714, 3,300000,3,3,128,8,500 -"5",283,721,2,0,0,28,673,3844,3819,148,5,2,147,3,106,1,7, 407,2,1,0,0,0,0,700, 3,300000,3,3,128,8,500 -"5",14,721,2,0,0,25,677,3844,3819,57,5,2,153,3,100,1,7, 434,2,1,0,0,0,0,672, 3,300000,3,3,128,8,500 -"5",23,722,2,0,0,29,674,3844,3818,147,5,1,155,3,98,1,7, 433,2,1,0,0,0,0,673, 3,300000,3,3,128,8,500 -"5",281,721,2,0,0,29,674,3844,3819,148,5,2,150,3,103,1,7, 407,2,1,0,0,0,0,700, 3,300000,3,3,128,8,500 -"5",497,722,2,0,0,28,675,3844,3818,147,5,1,153,3,100,2,7, 384,2,1,0,0,0,0,722, 3,300000,3,3,128,8,500 -"5",418,721,2,0,0,27,675,3844,3819,148,4,1,61,3,192,1,7, 391,2,1,0,0,0,0,715, 3,300000,3,3,128,8,500 -"5",156,720,2,0,0,29,674,3844,3819,6,5,2,152,2,102,1,7, 419,2,1,0,0,0,1,688, 3,300000,3,3,128,8,500 -"5",328,722,2,0,0,28,675,3844,3818,147,5,2,152,3,101,1,7, 401,2,1,0,0,0,0,705, 3,300000,3,3,128,8,500 -"5",99,722,2,0,0,27,675,3844,3818,147,5,1,151,3,102,1,7, 425,2,1,0,0,0,0,681, 3,300000,3,3,128,8,500 -"5",271,722,3,0,0,28,675,3844,3819,148,5,2,154,3,99,1,7, 408,2,1,0,0,0,0,698, 3,300000,3,3,128,8,500 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3,300000,3,3,128,8,500 -"5",282,721,3,0,0,28,676,3844,3819,148,5,2,154,2,100,1,7, 407,2,1,0,0,0,0,701, 3,300000,3,3,128,8,500 -"5",338,723,3,0,0,27,676,3844,3819,149,4,1,151,3,102,1,7, 400,2,1,0,0,0,0,706, 3,300000,3,3,128,8,500 -"5",410,722,1,0,0,29,675,3844,3819,148,5,2,58,4,195,1,7, 393,2,1,0,0,0,0,713, 3,300000,3,3,128,8,500 -"5",168,722,2,0,0,27,676,3844,3818,147,5,2,53,2,200,0,7, 418,2,1,0,0,0,0,689, 3,300000,3,3,128,8,500 -"5",409,722,2,0,0,29,676,3844,3818,148,4,2,49,3,204,1,7, 393,2,1,0,0,0,0,713, 3,300000,3,3,128,8,500 -"5",200,722,2,0,0,27,677,3844,3818,147,5,2,151,2,102,1,7, 414,2,1,0,0,0,0,692, 3,300000,3,3,128,8,500 -"5",428,722,2,0,0,31,674,3844,3819,148,4,2,61,2,192,1,7, 391,2,1,0,0,0,0,716, 3,300000,3,3,128,8,500 -"5",356,722,2,0,0,28,676,3844,3818,148,5,1,58,3,195,0,7, 398,2,1,0,0,0,1,708, 3,300000,3,3,128,8,500 -"5",394,722,2,0,0,30,674,3844,3818,148,5,2,59,3,194,1,7, 394,2,1,0,0,0,0,712, 3,300000,3,3,128,8,500 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-"6",175,15877,52,3,16,2690,12923,59670,62703,259,72,1,250,40,2423,11,21, 432,2,1,0,0,0, 13, 9576,3,5000000,5,3,128,4,500 -"6",54,15905,50,3,16,2703,12937,59670,62703,256,72,1,109,40,2565,10,21, 448,1,1,0,0,0, 13, 9563,3,5000000,5,3,128,4,500 -"6",118,15925,52,3,18,2709,12955,59671,62703,258,72,1,181,35,2497,9,21, 440,2,1,0,0,0, 13, 9575,3,5000000,5,3,128,4,500 diff --git a/hpc/archer2/data/ijhpca/weak_fft_10984754.csv b/hpc/archer2/data/ijhpca/weak_fft_10984754.csv deleted file mode 100644 index f1763f7a..00000000 --- a/hpc/archer2/data/ijhpca/weak_fft_10984754.csv +++ /dev/null @@ -1,1018 +0,0 @@ - -experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,source_tree,target_tree,source_domain,target_domain,layout,ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,displacement_map,metadata_creation,expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples -"0",2,21,9,0,0,0,0,6299,11839,16,15,0,424,10,32,0,0, 390,1,0,0,0,0, 0, 534,3,1000000,4,1,128,4,500 -"0",1,1977,9,0,0,160,1784,6299,11839,15,15,0,367,7,93,0,1, 386,1,0,0,0,0, 1, 541,3,1000000,4,1,128,4,500 -"0",3,1976,9,0,0,161,1784,6299,11839,15,15,0,62,5,400,0,0, 386,1,0,0,0,0, 1, 543,3,1000000,4,1,128,4,500 -"0",6,1975,9,0,0,163,1783,6299,11839,16,15,0,23,5,440,0,0, 386,1,0,0,0,0, 1, 544,3,1000000,4,1,128,4,500 -"0",4,1978,9,0,0,160,1788,6299,11839,16,15,0,26,5,437,0,0, 386,1,0,0,0,0, 1, 544,3,1000000,4,1,128,4,500 -"0",5,1979,9,0,0,161,1788,6299,11839,16,15,0,47,5,416,0,0, 386,1,0,0,0,0, 1, 543,3,1000000,4,1,128,4,500 -"0",0,1985,9,0,0,159,1793,6299,11840,16,14,0,367,7,93,1,0, 387,1,0,0,0,0, 1, 540,3,1000000,4,1,128,4,500 -"0",7,4046,9,0,0,312,3696,6299,11839,15,15,0,10,2,456,0,0, 386,1,0,0,0,0, 2, 547,3,1000000,4,1,128,4,500 -"1",0,962,11,1,1,469,440,7690,7646,16,15,0,137,21,51,3,0, 381,0,0,0,0,0, 3, 327,3,1000000,4,2,128,4,500 -"1",11,1292,15,1,2,661,583,7690,7646,17,14,0,40,10,155,0,3, 382,1,0,0,0,0, 3, 334,3,1000000,4,2,128,4,500 -"1",7,1295,15,1,1,651,598,7690,7647,16,14,0,28,10,168,0,2, 382,0,0,0,0,0, 4, 335,3,1000000,4,2,128,4,500 -"1",15,1303,15,1,1,644,605,7691,7647,16,14,0,130,15,59,0,4, 382,0,0,0,0,0, 4, 328,3,1000000,4,2,128,4,500 -"1",4,1298,16,1,1,662,590,7690,7646,16,15,0,29,9,167,0,3, 382,1,0,0,0,0, 4, 336,3,1000000,4,2,128,4,500 -"1",9,1300,15,1,1,667,586,7690,7647,17,14,0,36,10,160,0,3, 382,0,0,0,0,0, 3, 335,3,1000000,4,2,128,4,500 -"1",6,1301,15,1,1,667,588,7690,7646,16,15,0,26,9,171,0,3, 382,1,0,0,0,0, 4, 336,3,1000000,4,2,128,4,500 -"1",13,1303,15,1,2,665,591,7690,7646,17,15,0,47,10,149,0,2, 382,0,0,0,0,0, 4, 334,3,1000000,4,2,128,4,500 -"1",8,1302,15,1,2,655,603,7690,7646,16,15,0,33,9,165,0,1, 382,0,0,0,0,0, 4, 337,3,1000000,4,2,128,4,500 -"1",5,1309,15,1,2,659,603,7690,7646,16,14,0,27,10,169,0,2, 382,0,0,0,0,0, 4, 336,3,1000000,4,2,128,4,500 -"1",1,1313,19,2,2,653,610,7691,7646,16,14,0,115,7,78,0,4, 381,0,0,0,0,0, 4, 332,3,1000000,4,2,128,4,500 -"1",3,1313,15,1,1,664,603,7690,7647,17,14,0,37,10,159,0,2, 382,0,0,0,0,0, 4, 336,3,1000000,4,2,128,4,500 -"1",10,1316,15,1,2,663,609,7690,7646,17,15,0,23,8,176,0,1, 382,1,0,0,0,0, 4, 338,3,1000000,4,2,128,4,500 -"1",12,1330,15,1,1,655,632,7690,7646,16,15,0,33,8,167,0,1, 382,0,0,0,0,0, 4, 338,3,1000000,4,2,128,4,500 -"1",2,1338,15,1,1,664,630,7690,7646,15,15,0,36,7,163,0,3, 382,0,0,0,0,0, 4, 338,3,1000000,4,2,128,4,500 -"1",14,1599,15,1,4,794,760,7690,7647,17,15,0,22,6,179,0,1, 383,0,0,0,0,0, 4, 339,3,1000000,4,2,128,4,500 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1031,3,1000000,4,3,128,4,500 -"5",71,1486,11,0,1,336,1090,9382,9341,100,15,1,361,17,166,3,9, 404,1,0,0,0,0, 2, 1031,3,1000000,4,3,128,4,500 -"5",115,1480,17,1,0,350,1078,9383,9341,98,15,1,179,7,354,1,9, 400,1,1,0,0,0, 2, 1041,3,1000000,4,3,128,4,500 -"5",255,1932,16,1,1,485,1385,9382,9341,100,15,1,412,2,112,12,9, 383,1,0,0,0,0, 4, 1050,3,1000000,4,3,128,4,500 -"5",38,1985,11,0,1,482,1444,9383,9341,100,15,1,239,9,292,6,9, 407,1,0,0,0,0, 2, 1032,3,1000000,4,3,128,4,500 -"5",36,1995,17,1,1,491,1443,9382,9341,99,15,1,365,4,163,7,9, 407,1,1,0,0,0, 4, 1029,3,1000000,4,3,128,4,500 -"5",223,2004,11,0,1,479,1465,9382,9341,101,15,1,250,4,281,11,9, 387,1,0,0,0,0, 3, 1052,3,1000000,4,3,128,4,500 -"5",191,2015,11,0,1,483,1472,9382,9341,98,15,1,218,8,313,7,9, 390,1,1,0,0,0, 3, 1048,3,1000000,4,3,128,4,500 -"5",91,2077,11,0,1,486,1533,9382,9341,100,15,1,193,5,339,10,9, 402,1,1,0,0,0, 3, 1037,3,1000000,4,3,128,4,500 -"5",60,2122,11,0,1,491,1576,9382,9341,100,15,1,161,9,375,1,9, 405,2,0,0,0,0, 3, 1039,3,1000000,4,3,128,4,500 -"5",26,2136,11,0,1,481,1599,9382,9341,39,15,1,165,6,371,4,9, 408,2,0,0,0,0, 2, 1035,3,1000000,4,3,128,4,500 -"5",52,2139,11,0,1,485,1597,9382,9341,100,15,1,199,5,336,6,9, 405,2,0,0,0,0, 3, 1037,3,1000000,4,3,128,4,500 -"5",32,2152,16,1,1,489,1603,9383,9341,99,15,1,348,7,182,3,9, 407,1,0,0,0,0, 3, 1031,3,1000000,4,3,128,4,500 -"6",1,49,8,0,0,0,0,12543,12434,100,17,2,870,17,58,13,9, 444,1,1,0,0,0, 0, 1983,3,1000000,4,3,128,4,500 -"6",273,48,10,0,0,0,0,12543,12434,14,17,2,835,22,94,6,9, 414,2,1,0,0,0, 0, 2013,3,1000000,4,3,128,4,500 -"6",7,49,9,0,0,0,0,12543,12434,107,17,2,862,16,66,12,9, 443,1,1,0,0,0, 0, 1984,3,1000000,4,3,128,4,500 -"6",241,48,9,1,0,0,0,12543,12434,160,17,2,841,23,88,5,9, 417,1,1,0,0,0, 0, 2011,3,1000000,4,3,128,4,500 -"6",270,49,9,0,0,0,0,12543,12434,163,17,2,839,22,90,6,9, 414,2,1,0,0,0, 0, 2013,3,1000000,4,3,128,4,500 -"6",22,49,9,0,0,0,0,12543,12434,162,17,2,853,16,76,13,9, 442,1,1,0,0,0, 0, 1985,3,1000000,4,3,128,4,500 -"6",14,49,9,0,0,0,0,12543,12434,163,17,2,863,18,66,11,9, 443,1,1,0,0,0, 0, 1984,3,1000000,4,3,128,4,500 -"6",369,48,9,0,0,0,0,12543,12434,159,17,2,841,24,88,4,9, 403,1,1,0,0,0, 0, 2024,3,1000000,4,3,128,4,500 -"6",11,49,9,0,0,0,0,12543,12434,118,17,2,863,19,65,10,9, 443,1,1,0,0,0, 0, 1984,3,1000000,4,3,128,4,500 -"6",384,47,9,0,0,0,0,12543,12434,105,17,2,867,24,63,3,9, 401,1,1,0,0,0, 0, 2028,3,1000000,4,3,128,4,500 -"6",387,49,9,0,0,0,0,12543,12434,96,17,2,856,22,72,7,9, 401,1,1,0,0,0, 0, 2026,3,1000000,4,3,128,4,500 -"6",239,49,9,0,0,0,0,12543,12434,160,17,2,843,23,86,5,9, 417,1,1,0,0,0, 0, 2011,3,1000000,4,3,128,4,500 -"6",244,48,9,0,0,0,0,12543,12434,53,17,2,837,23,92,5,9, 417,2,1,0,0,0, 0, 2011,3,1000000,4,3,128,4,500 -"6",0,1819,8,0,0,156,1605,12543,12435,104,17,1,815,12,117,13,0, 379,1,1,0,0,0, 1, 2048,3,1000000,4,3,128,4,500 -"6",74,1829,8,0,0,167,1604,12543,12435,163,17,1,815,15,117,10,9, 437,1,1,0,0,0, 1, 1994,3,1000000,4,3,128,4,500 -"6",147,1834,8,0,0,171,1606,12543,12435,36,17,1,838,5,94,20,9, 429,1,1,0,0,0, 1, 2002,3,1000000,4,3,128,4,500 -"6",439,1835,8,0,0,169,1607,12543,12435,161,17,1,836,15,97,10,9, 397,1,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 -"6",366,1836,8,0,0,167,1612,12543,12435,160,17,1,798,22,134,3,9, 405,1,1,0,0,0, 0, 2026,3,1000000,4,3,128,4,500 -"6",219,1833,9,0,0,171,1607,12543,12435,129,17,1,530,14,406,5,9, 421,1,1,0,0,0, 1, 2014,3,1000000,4,3,128,4,500 -"6",293,1833,9,0,0,173,1606,12543,12435,160,17,1,488,15,448,5,9, 412,1,1,0,0,0, 1, 2023,3,1000000,4,3,128,4,500 -"6",78,1864,0,0,0,94,1717,12542,12435,162,17,1,469,20,469,9,9, 437,2,1,0,0,0, 1, 2000,3,1000000,4,3,128,4,500 -"6",365,1934,8,0,0,167,1709,12543,12435,161,17,1,774,20,160,3,9, 405,1,1,0,0,0, 1, 2027,3,1000000,4,3,128,4,500 -"6",437,1936,9,0,0,171,1708,12543,12435,160,17,1,813,14,121,10,9, 397,2,1,0,0,0, 1, 2035,3,1000000,4,3,128,4,500 -"6",4,1937,9,0,0,166,1713,12543,12435,95,17,1,787,11,147,12,9, 443,1,1,0,0,0, 1, 1989,3,1000000,4,3,128,4,500 -"6",65,1938,9,0,0,167,1714,12543,12435,101,17,1,787,14,147,10,9, 436,1,1,0,0,0, 1, 1996,3,1000000,4,3,128,4,500 -"6",73,1938,9,0,0,166,1714,12543,12435,163,17,1,786,14,147,10,9, 437,1,1,0,0,0, 1, 1995,3,1000000,4,3,128,4,500 -"6",17,1935,9,0,0,169,1711,12543,12435,14,17,1,506,9,430,11,9, 443,1,1,0,0,0, 1, 1992,3,1000000,4,3,128,4,500 -"6",156,1938,9,0,0,167,1713,12543,12435,128,17,1,779,18,155,6,9, 428,2,1,0,0,0, 1, 2004,3,1000000,4,3,128,4,500 -"6",66,1939,9,0,0,168,1713,12543,12435,76,17,1,787,14,146,10,9, 438,1,1,0,0,0, 1, 1994,3,1000000,4,3,128,4,500 -"6",110,1935,9,0,0,172,1709,12543,12434,160,17,2,458,11,479,8,9, 433,1,1,0,0,0, 1, 2003,3,1000000,4,3,128,4,500 -"6",260,1935,10,0,0,168,1713,12543,12435,95,17,1,446,14,492,4,9, 414,1,1,0,0,0, 1, 2022,3,1000000,4,3,128,4,500 -"6",37,1936,9,0,0,173,1708,12543,12435,159,17,1,497,14,440,6,9, 441,2,1,0,0,0, 1, 1995,3,1000000,4,3,128,4,500 -"6",212,1940,9,0,0,168,1715,12543,12435,53,17,1,777,17,157,6,9, 422,1,1,0,0,0, 1, 2011,3,1000000,4,3,128,4,500 -"6",9,1941,9,0,0,167,1716,12543,12435,162,17,1,785,14,148,10,9, 442,1,1,0,0,0, 1, 1990,3,1000000,4,3,128,4,500 -"6",510,1941,9,0,0,172,1711,12543,12435,160,17,1,794,15,140,8,9, 390,1,1,0,0,0, 1, 2042,3,1000000,4,3,128,4,500 -"6",256,1936,9,0,0,168,1715,12543,12435,105,17,1,444,14,494,5,9, 416,1,1,0,0,0, 1, 2021,3,1000000,4,3,128,4,500 -"6",211,1941,9,0,0,168,1716,12543,12435,129,17,1,776,16,158,6,9, 422,2,1,0,0,0, 1, 2011,3,1000000,4,3,128,4,500 -"6",90,1938,9,0,0,172,1712,12543,12435,17,17,1,468,14,469,5,9, 435,1,1,0,0,0, 1, 2001,3,1000000,4,3,128,4,500 -"6",289,1937,9,0,0,170,1715,12542,12435,101,17,1,447,13,491,5,9, 413,1,0,0,0,0, 1, 2023,3,1000000,4,3,128,4,500 -"6",382,1937,9,0,0,170,1714,12543,12435,159,17,1,481,17,458,1,9, 403,1,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 -"6",179,1942,9,0,0,170,1714,12543,12435,161,17,1,813,13,121,10,9, 423,1,1,0,0,0, 1, 2009,3,1000000,4,3,128,4,500 -"6",294,1939,8,0,0,168,1718,12543,12434,94,17,2,479,15,458,5,9, 411,1,0,0,0,0, 1, 2025,3,1000000,4,3,128,4,500 -"6",202,1942,9,0,0,168,1716,12543,12435,162,17,1,787,17,147,6,9, 421,1,1,0,0,0, 1, 2012,3,1000000,4,3,128,4,500 -"6",334,1939,9,0,0,170,1714,12543,12435,163,17,1,472,18,465,2,9, 408,1,1,0,0,0, 1, 2027,3,1000000,4,3,128,4,500 -"6",330,1944,17,0,0,171,1716,12543,12434,163,17,1,792,11,141,3,9, 408,1,1,0,0,0, 1, 2024,3,1000000,4,3,128,4,500 -"6",311,1939,9,0,0,172,1714,12543,12435,160,17,1,443,14,495,5,9, 411,1,1,0,0,0, 1, 2026,3,1000000,4,3,128,4,500 -"6",33,1941,9,0,0,172,1715,12543,12435,101,17,1,501,14,437,5,9, 439,1,1,0,0,0, 1, 1996,3,1000000,4,3,128,4,500 -"6",92,1939,9,0,0,172,1715,12543,12435,128,17,1,475,13,464,5,9, 435,1,1,0,0,0, 1, 2003,3,1000000,4,3,128,4,500 -"6",148,1945,9,0,0,168,1718,12543,12435,53,16,1,814,5,119,19,9, 427,1,1,0,0,0, 1, 2004,3,1000000,4,3,128,4,500 -"6",358,1941,8,0,0,168,1719,12543,12435,94,17,1,484,18,454,2,9, 404,2,1,0,0,0, 1, 2032,3,1000000,4,3,128,4,500 -"6",421,1944,9,0,0,172,1715,12543,12435,159,17,1,774,17,160,6,9, 397,1,1,0,0,0, 1, 2035,3,1000000,4,3,128,4,500 -"6",407,1944,9,0,0,170,1717,12543,12435,36,17,1,801,13,134,10,9, 399,2,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 -"6",204,1945,9,0,0,169,1718,12543,12435,161,17,1,788,17,146,6,9, 423,2,1,0,0,0, 1, 2009,3,1000000,4,3,128,4,500 -"6",357,1941,9,0,0,172,1717,12543,12435,160,17,1,465,17,473,2,9, 405,1,1,0,0,0, 1, 2032,3,1000000,4,3,128,4,500 -"6",301,1943,9,0,0,172,1718,12543,12435,38,17,1,443,15,495,3,9, 412,1,1,0,0,0, 1, 2024,3,1000000,4,3,128,4,500 -"6",447,1942,9,0,0,175,1715,12543,12435,127,17,1,509,11,430,8,9, 394,1,1,0,0,0, 1, 2043,3,1000000,4,3,128,4,500 -"6",503,1947,9,0,0,172,1718,12543,12435,159,17,1,782,15,152,8,9, 390,2,0,0,0,0, 1, 2044,3,1000000,4,3,128,4,500 -"6",76,1944,9,0,0,172,1718,12543,12435,162,17,1,472,11,466,8,9, 435,1,1,0,0,0, 1, 2001,3,1000000,4,3,128,4,500 -"6",183,1948,9,0,0,169,1721,12543,12435,160,17,1,803,13,131,10,9, 425,1,1,0,0,0, 1, 2008,3,1000000,4,3,128,4,500 -"6",494,1947,8,0,0,173,1718,12543,12435,160,16,1,794,20,141,2,9, 390,1,1,0,0,0, 1, 2043,3,1000000,4,3,128,4,500 -"6",440,1949,9,0,0,171,1721,12543,12435,160,17,1,806,14,127,9,9, 397,1,1,0,0,0, 1, 2035,3,1000000,4,3,128,4,500 -"6",403,1949,10,0,0,169,1722,12543,12435,36,17,1,802,12,132,10,9, 400,1,1,0,0,0, 1, 2033,3,1000000,4,3,128,4,500 -"6",302,1946,9,0,0,173,1719,12543,12435,160,17,1,476,15,462,5,9, 412,1,1,0,0,0, 1, 2024,3,1000000,4,3,128,4,500 -"6",504,1949,10,0,0,171,1721,12543,12435,160,17,1,784,13,150,8,9, 389,1,1,0,0,0, 1, 2044,3,1000000,4,3,128,4,500 -"6",129,1950,9,0,0,169,1724,12543,12435,101,17,1,784,10,149,13,9, 429,1,1,0,0,0, 1, 2003,3,1000000,4,3,128,4,500 -"6",448,1948,9,0,0,175,1720,12543,12435,104,17,1,449,16,490,2,9, 395,1,1,0,0,0, 1, 2042,3,1000000,4,3,128,4,500 -"6",476,1948,10,0,0,175,1719,12543,12435,128,17,1,458,14,480,4,9, 394,1,1,0,0,0, 1, 2043,3,1000000,4,3,128,4,500 -"6",480,1948,9,0,0,176,1720,12543,12435,105,17,1,480,16,459,2,9, 392,2,1,0,0,0, 1, 2045,3,1000000,4,3,128,4,500 -"6",223,1949,9,0,0,171,1723,12543,12435,162,17,1,476,14,462,5,9, 418,1,1,0,0,0, 1, 2019,3,1000000,4,3,128,4,500 -"6",251,1955,9,0,0,170,1730,12543,12435,161,17,1,474,16,463,4,9, 415,1,1,0,0,0, 1, 2021,3,1000000,4,3,128,4,500 -"6",508,1959,10,0,0,182,1719,12543,12435,159,17,1,786,13,148,8,9, 389,1,1,0,0,0, 1, 2044,3,1000000,4,3,128,4,500 -"6",106,1957,9,0,0,171,1732,12543,12435,161,17,1,462,11,476,8,9, 433,1,1,0,0,0, 1, 2003,3,1000000,4,3,128,4,500 -"6",362,1958,9,0,0,170,1734,12543,12435,160,17,1,491,18,446,2,9, 403,1,1,0,0,0, 1, 2032,3,1000000,4,3,128,4,500 -"6",145,1963,9,0,0,169,1736,12543,12435,128,17,1,814,4,120,19,9, 428,1,1,0,0,0, 1, 2004,3,1000000,4,3,128,4,500 -"6",155,1966,9,0,0,167,1743,12543,12435,40,17,1,816,17,118,6,9, 426,2,1,0,0,0, 1, 2006,3,1000000,4,3,128,4,500 -"6",70,1970,0,0,0,94,1822,12542,12435,61,17,2,436,19,503,9,9, 437,2,1,0,0,0, 0, 2000,3,1000000,4,3,128,4,500 -"6",322,1977,0,0,0,95,1830,12542,12436,77,17,1,441,26,498,3,9, 410,1,1,0,0,0, 1, 2028,3,1000000,4,3,128,4,500 -"6",55,1980,0,0,0,94,1832,12542,12436,160,17,1,425,23,514,6,9, 439,1,1,0,0,0, 1, 1998,3,1000000,4,3,128,4,500 -"6",84,1983,0,0,0,95,1834,12542,12436,53,17,1,441,23,498,5,9, 436,1,1,0,0,0, 0, 2001,3,1000000,4,3,128,4,500 -"6",131,2046,9,0,0,169,1818,12543,12435,96,17,1,801,10,133,13,9, 430,1,1,0,0,0, 1, 2001,3,1000000,4,3,128,4,500 -"6",196,2048,9,0,0,167,1825,12543,12435,127,17,1,752,15,183,6,9, 421,1,1,0,0,0, 1, 2012,3,1000000,4,3,128,4,500 -"6",298,2047,9,0,0,169,1825,12543,12435,162,17,1,426,15,512,4,9, 411,1,1,0,0,0, 1, 2026,3,1000000,4,3,128,4,500 -"6",333,2048,9,0,0,170,1827,12543,12435,162,17,2,414,16,525,2,9, 407,1,0,0,0,0, 1, 2031,3,1000000,4,3,128,4,500 -"6",367,2054,9,0,0,164,1833,12543,12435,159,17,1,743,19,191,3,9, 402,1,1,0,0,0, 1, 2031,3,1000000,4,3,128,4,500 -"6",6,2055,9,0,0,168,1830,12543,12435,93,17,1,754,11,180,12,9, 443,1,1,0,0,0, 1, 1990,3,1000000,4,3,128,4,500 -"6",139,2054,9,0,0,168,1829,12543,12435,130,17,1,769,16,166,6,9, 428,2,1,0,0,0, 1, 2005,3,1000000,4,3,128,4,500 -"6",336,2056,18,0,0,169,1829,12543,12435,127,17,1,764,10,170,3,9, 407,2,1,0,0,0, 1, 2026,3,1000000,4,3,128,4,500 -"6",109,2052,9,0,0,169,1830,12543,12435,160,17,1,442,11,496,8,9, 431,1,1,0,0,0, 1, 2005,3,1000000,4,3,128,4,500 -"6",13,2056,9,0,0,166,1832,12543,12435,162,17,1,762,13,173,10,9, 442,1,1,0,0,0, 1, 1991,3,1000000,4,3,128,4,500 -"6",146,2055,9,0,0,168,1830,12543,12435,163,17,1,790,3,145,19,9, 427,2,1,0,0,0, 1, 2006,3,1000000,4,3,128,4,500 -"6",194,2056,9,0,0,168,1830,12543,12435,76,17,1,758,16,177,6,9, 421,1,1,0,0,0, 1, 2012,3,1000000,4,3,128,4,500 -"6",165,2056,9,0,0,168,1830,12543,12435,161,17,1,751,16,184,6,9, 425,1,1,0,0,0, 1, 2008,3,1000000,4,3,128,4,500 -"6",317,2052,9,0,0,172,1827,12543,12435,160,17,1,410,14,529,4,9, 410,1,1,0,0,0, 1, 2027,3,1000000,4,3,128,4,500 -"6",203,2055,9,0,0,169,1829,12543,12435,162,17,1,757,16,179,6,9, 420,1,1,0,0,0, 1, 2013,3,1000000,4,3,128,4,500 -"6",19,2057,9,0,0,167,1832,12543,12435,36,17,1,760,10,174,12,9, 441,1,0,0,0,0, 1, 1991,3,1000000,4,3,128,4,500 -"6",184,2056,9,0,0,167,1831,12543,12435,162,17,1,778,12,157,10,9, 423,2,0,0,0,0, 1, 2010,3,1000000,4,3,128,4,500 -"6",101,2053,9,0,0,172,1827,12543,12435,159,17,1,441,13,497,5,9, 432,1,1,0,0,0, 1, 2004,3,1000000,4,3,128,4,500 -"6",167,2057,9,0,0,171,1829,12543,12435,160,17,1,752,16,183,6,9, 424,2,1,0,0,0, 1, 2008,3,1000000,4,3,128,4,500 -"6",312,2057,18,0,0,173,1827,12543,12435,161,17,1,763,8,172,6,9, 408,1,1,0,0,0, 1, 2024,3,1000000,4,3,128,4,500 -"6",361,2053,9,0,0,169,1832,12543,12435,160,17,1,433,16,506,2,9, 403,2,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 -"6",360,2054,9,0,0,169,1830,12543,12434,160,17,2,440,17,499,2,9, 404,1,1,0,0,0, 1, 2033,3,1000000,4,3,128,4,500 -"6",458,2056,9,0,0,170,1830,12543,12435,161,17,1,743,18,192,3,9, 393,1,1,0,0,0, 1, 2041,3,1000000,4,3,128,4,500 -"6",375,2058,9,0,0,171,1830,12543,12435,159,17,1,745,19,190,3,9, 402,1,1,0,0,0, 1, 2031,3,1000000,4,3,128,4,500 -"6",506,2057,9,0,0,172,1828,12543,12435,41,17,1,756,14,178,8,9, 389,2,1,0,0,0, 1, 2044,3,1000000,4,3,128,4,500 -"6",159,2058,9,0,0,171,1829,12543,12435,163,17,1,755,16,180,6,9, 425,1,1,0,0,0, 1, 2008,3,1000000,4,3,128,4,500 -"6",105,2055,9,0,0,170,1832,12543,12435,161,17,1,449,11,489,8,9, 432,1,1,0,0,0, 1, 2005,3,1000000,4,3,128,4,500 -"6",215,2058,9,0,0,170,1831,12543,12435,128,17,1,736,17,199,4,9, 419,1,1,0,0,0, 1, 2015,3,1000000,4,3,128,4,500 -"6",153,2058,9,0,0,168,1833,12543,12435,94,17,1,788,16,146,6,9, 426,2,1,0,0,0, 1, 2007,3,1000000,4,3,128,4,500 -"6",412,2058,10,0,0,171,1831,12543,12435,128,17,1,750,15,185,6,9, 398,1,1,0,0,0, 1, 2036,3,1000000,4,3,128,4,500 -"6",435,2059,9,0,0,170,1831,12543,12435,160,17,1,802,13,132,10,9, 397,1,1,0,0,0, 1, 2035,3,1000000,4,3,128,4,500 -"6",374,2059,9,0,0,170,1831,12543,12435,160,17,1,744,20,191,2,9, 402,1,1,0,0,0, 1, 2031,3,1000000,4,3,128,4,500 -"6",326,2055,9,0,0,175,1828,12543,12434,94,16,2,435,16,504,2,9, 407,1,1,0,0,0, 1, 2029,3,1000000,4,3,128,4,500 -"6",80,2054,10,0,0,173,1830,12543,12435,128,17,1,433,13,506,4,9, 435,1,1,0,0,0, 1, 2003,3,1000000,4,3,128,4,500 -"6",5,2059,9,0,0,167,1835,12543,12435,162,17,1,761,12,173,11,9, 443,1,1,0,0,0, 1, 1990,3,1000000,4,3,128,4,500 -"6",417,2059,9,0,0,172,1830,12543,12435,101,17,1,746,17,188,6,9, 397,1,1,0,0,0, 1, 2036,3,1000000,4,3,128,4,500 -"6",192,2058,9,0,0,170,1833,12543,12435,105,17,1,743,16,192,5,9, 421,1,1,0,0,0, 1, 2012,3,1000000,4,3,128,4,500 -"6",193,2059,9,0,0,168,1833,12543,12435,101,17,1,757,16,178,6,9, 422,1,1,0,0,0, 1, 2012,3,1000000,4,3,128,4,500 -"6",34,2056,9,0,0,171,1831,12543,12435,76,17,1,433,13,505,5,9, 439,1,1,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 -"6",98,2056,9,0,0,172,1832,12543,12435,76,17,1,420,13,519,5,9, 434,2,1,0,0,0, 1, 2003,3,1000000,4,3,128,4,500 -"6",376,2060,9,0,0,168,1834,12543,12435,160,17,1,755,20,179,2,9, 402,2,1,0,0,0, 1, 2031,3,1000000,4,3,128,4,500 -"6",268,2056,9,0,0,173,1831,12543,12435,163,17,1,421,15,518,3,9, 415,1,1,0,0,0, 1, 2022,3,1000000,4,3,128,4,500 -"6",23,2057,9,0,0,168,1835,12543,12435,36,17,1,463,8,475,11,9, 442,1,1,0,0,0, 1, 1994,3,1000000,4,3,128,4,500 -"6",3,2061,9,0,0,167,1836,12543,12435,95,17,1,769,11,165,12,9, 443,1,1,0,0,0, 1, 1990,3,1000000,4,3,128,4,500 -"6",354,2056,9,0,0,173,1831,12543,12435,76,17,1,433,17,505,2,9, 404,1,1,0,0,0, 1, 2033,3,1000000,4,3,128,4,500 -"6",137,2060,9,0,0,168,1834,12543,12434,162,16,2,759,16,176,6,9, 428,1,1,0,0,0, 1, 2005,3,1000000,4,3,128,4,500 -"6",216,2060,9,0,0,170,1833,12543,12434,128,17,1,747,17,188,5,9, 419,1,1,0,0,0, 1, 2015,3,1000000,4,3,128,4,500 -"6",222,2056,9,0,0,172,1830,12543,12435,161,17,1,414,13,525,5,9, 418,1,1,0,0,0, 1, 2020,3,1000000,4,3,128,4,500 -"6",160,2060,9,0,0,169,1833,12543,12435,105,17,1,743,17,191,5,9, 425,1,1,0,0,0, 1, 2008,3,1000000,4,3,128,4,500 -"6",460,2059,9,0,0,171,1833,12543,12434,161,17,2,738,18,198,3,9, 393,1,1,0,0,0, 1, 2041,3,1000000,4,3,128,4,500 -"6",305,2057,9,1,0,171,1833,12543,12435,162,17,1,393,13,545,5,9, 409,2,1,0,0,0, 1, 2028,3,1000000,4,3,128,4,500 -"6",97,2057,9,0,0,172,1831,12543,12435,101,17,1,442,13,496,5,9, 432,1,1,0,0,0, 1, 2004,3,1000000,4,3,128,4,500 -"6",264,2056,9,0,0,173,1830,12543,12435,95,17,1,403,14,536,4,9, 414,2,1,0,0,0, 1, 2024,3,1000000,4,3,128,4,500 -"6",67,2061,9,0,0,169,1834,12543,12435,97,17,1,759,14,175,9,9, 436,1,0,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 -"6",163,2060,9,0,0,169,1836,12543,12435,98,17,1,765,16,170,6,9, 425,1,1,0,0,0, 1, 2008,3,1000000,4,3,128,4,500 -"6",191,2061,9,0,0,168,1835,12543,12435,127,17,1,754,16,181,6,9, 422,2,1,0,0,0, 1, 2011,3,1000000,4,3,128,4,500 -"6",83,2057,9,0,0,170,1835,12543,12435,129,17,1,456,14,482,5,9, 434,2,1,0,0,0, 1, 2002,3,1000000,4,3,128,4,500 -"6",132,2061,9,0,0,170,1833,12543,12435,95,17,1,772,9,162,13,9, 429,1,1,0,0,0, 1, 2004,3,1000000,4,3,128,4,500 -"6",262,2058,8,0,0,168,1837,12543,12434,93,17,2,437,14,501,5,9, 414,1,1,0,0,0, 1, 2022,3,1000000,4,3,128,4,500 -"6",53,2058,9,0,0,174,1831,12543,12435,161,17,1,431,15,506,4,9, 439,1,1,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 -"6",25,2058,9,0,0,171,1833,12543,12435,94,17,1,450,11,488,8,9, 441,1,1,0,0,0, 1, 1996,3,1000000,4,3,128,4,500 -"6",94,2057,9,0,0,173,1831,12543,12435,129,17,1,420,14,518,4,9, 435,1,1,0,0,0, 1, 2002,3,1000000,4,3,128,4,500 -"6",490,2059,9,0,0,172,1831,12543,12434,159,17,2,752,18,185,2,9, 390,1,1,0,0,0, 1, 2045,3,1000000,4,3,128,4,500 -"6",195,2060,9,0,0,169,1835,12543,12435,97,17,1,751,16,184,5,9, 421,1,1,0,0,0, 1, 2013,3,1000000,4,3,128,4,500 -"6",415,2060,9,0,0,171,1833,12543,12435,161,17,1,763,16,173,5,9, 398,2,1,0,0,0, 1, 2036,3,1000000,4,3,128,4,500 -"6",277,2057,9,0,0,171,1834,12543,12435,130,17,1,393,15,546,3,9, 414,2,1,0,0,0, 1, 2023,3,1000000,4,3,128,4,500 -"6",217,2060,9,0,0,169,1835,12543,12435,95,17,1,750,15,186,6,9, 419,1,1,0,0,0, 1, 2015,3,1000000,4,3,128,4,500 -"6",235,2058,9,0,0,168,1838,12543,12435,160,17,1,429,15,509,3,9, 419,1,1,0,0,0, 1, 2018,3,1000000,4,3,128,4,500 -"6",177,2062,9,0,0,172,1832,12543,12435,161,17,1,775,12,160,10,9, 423,1,1,0,0,0, 1, 2009,3,1000000,4,3,128,4,500 -"6",350,2058,9,0,0,172,1833,12542,12435,129,17,1,424,16,514,2,9, 407,1,1,0,0,0, 1, 2030,3,1000000,4,3,128,4,500 -"6",430,2061,9,0,0,170,1834,12543,12434,160,17,2,746,16,190,6,9, 396,1,1,0,0,0, 1, 2038,3,1000000,4,3,128,4,500 -"6",445,2062,9,0,0,170,1835,12543,12435,161,17,1,755,13,179,9,9, 394,1,1,0,0,0, 1, 2039,3,1000000,4,3,128,4,500 -"6",502,2061,9,0,0,173,1832,12543,12435,160,17,1,757,14,178,8,9, 390,1,1,0,0,0, 1, 2044,3,1000000,4,3,128,4,500 -"6",68,2062,9,0,0,168,1837,12543,12435,127,17,1,761,13,174,9,9, 436,2,0,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 -"6",41,2059,9,0,0,171,1836,12543,12435,161,17,1,442,14,496,5,9, 439,1,1,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 -"6",45,2059,9,0,0,172,1833,12543,12435,37,17,1,446,14,492,5,9, 439,1,1,0,0,0, 1, 1998,3,1000000,4,3,128,4,500 -"6",353,2058,9,0,0,172,1833,12543,12435,101,17,1,410,16,529,2,9, 404,1,1,0,0,0, 1, 2033,3,1000000,4,3,128,4,500 -"6",210,2062,9,0,0,169,1835,12543,12435,163,17,1,747,17,188,5,9, 420,1,1,0,0,0, 1, 2013,3,1000000,4,3,128,4,500 -"6",509,2062,9,0,0,173,1833,12543,12435,159,17,1,761,14,174,8,9, 389,2,1,0,0,0, 1, 2045,3,1000000,4,3,128,4,500 -"6",39,2059,9,0,0,173,1832,12543,12435,161,17,1,430,15,508,4,9, 440,1,1,0,0,0, 1, 1996,3,1000000,4,3,128,4,500 -"6",209,2061,9,0,0,166,1839,12543,12435,128,17,1,749,16,186,6,9, 420,1,1,0,0,0, 1, 2014,3,1000000,4,3,128,4,500 -"6",296,2059,9,0,0,172,1833,12543,12435,161,17,1,435,14,503,5,9, 411,2,1,0,0,0, 1, 2026,3,1000000,4,3,128,4,500 -"6",492,2061,10,0,0,173,1832,12543,12435,159,16,2,756,17,181,2,9, 391,1,0,0,0,0, 1, 2044,3,1000000,4,3,128,4,500 -"6",233,2059,9,0,0,171,1835,12543,12435,161,17,1,438,13,501,5,9, 419,1,1,0,0,0, 1, 2018,3,1000000,4,3,128,4,500 -"6",91,2059,9,0,0,172,1833,12543,12435,111,17,1,434,14,504,5,9, 433,1,1,0,0,0, 1, 2003,3,1000000,4,3,128,4,500 -"6",265,2059,9,0,0,174,1833,12543,12435,162,17,1,396,12,543,5,9, 416,1,1,0,0,0, 1, 2022,3,1000000,4,3,128,4,500 -"6",479,2060,9,0,0,175,1831,12543,12435,162,17,1,446,15,492,4,9, 390,1,1,0,0,0, 1, 2046,3,1000000,4,3,128,4,500 -"6",208,2062,9,0,0,168,1838,12543,12435,128,17,1,740,16,195,6,9, 420,1,1,0,0,0, 1, 2014,3,1000000,4,3,128,4,500 -"6",401,2064,9,0,0,171,1835,12543,12435,128,17,1,752,13,182,9,9, 399,1,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 -"6",181,2063,9,0,0,170,1836,12543,12435,160,17,1,757,12,177,9,9, 423,2,1,0,0,0, 1, 2011,3,1000000,4,3,128,4,500 -"6",373,2063,9,0,0,169,1836,12543,12435,160,17,1,743,19,192,3,9, 402,1,1,0,0,0, 1, 2031,3,1000000,4,3,128,4,500 -"6",380,2059,9,0,0,172,1836,12543,12435,160,16,1,442,17,497,1,9, 403,1,1,0,0,0, 0, 2035,3,1000000,4,3,128,4,500 -"6",385,2063,9,0,0,170,1837,12543,12435,101,17,1,754,16,181,6,9, 400,2,1,0,0,0, 1, 2033,3,1000000,4,3,128,4,500 -"6",237,2060,9,0,0,170,1837,12543,12435,160,17,1,412,16,527,3,9, 417,1,1,0,0,0, 1, 2020,3,1000000,4,3,128,4,500 -"6",389,2064,9,0,0,170,1836,12543,12435,161,17,1,750,17,185,6,9, 400,1,1,0,0,0, 1, 2033,3,1000000,4,3,128,4,500 -"6",257,2059,9,0,0,174,1834,12543,12435,101,17,1,406,13,533,5,9, 414,1,1,0,0,0, 1, 2023,3,1000000,4,3,128,4,500 -"6",149,2065,9,0,0,170,1837,12543,12435,131,16,1,787,4,147,19,9, 426,1,1,0,0,0, 1, 2006,3,1000000,4,3,128,4,500 -"6",247,2061,9,0,0,171,1836,12543,12435,160,17,1,426,15,513,3,9, 418,1,1,0,0,0, 1, 2019,3,1000000,4,3,128,4,500 -"6",269,2060,9,0,0,171,1836,12543,12435,162,17,1,416,14,522,4,9, 415,1,1,0,0,0, 1, 2022,3,1000000,4,3,128,4,500 -"6",436,2065,9,0,0,172,1834,12543,12435,53,17,1,777,13,158,9,9, 395,1,1,0,0,0, 1, 2038,3,1000000,4,3,128,4,500 -"6",321,2065,9,0,0,170,1838,12543,12435,101,17,1,739,19,196,3,9, 407,1,1,0,0,0, 1, 2026,3,1000000,4,3,128,4,500 -"6",140,2065,9,0,0,168,1839,12543,12435,162,17,1,752,16,183,6,9, 428,1,1,0,0,0, 1, 2005,3,1000000,4,3,128,4,500 -"6",253,2061,9,0,0,170,1837,12543,12435,160,17,1,408,13,531,5,9, 415,1,1,0,0,0, 1, 2022,3,1000000,4,3,128,4,500 -"6",423,2066,9,0,0,171,1834,12543,12435,159,17,1,780,18,153,6,9, 398,2,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 -"6",124,2065,10,0,0,170,1838,12543,12435,161,17,1,746,15,189,6,9, 429,1,1,0,0,0, 1, 2004,3,1000000,4,3,128,4,500 -"6",468,2060,9,0,0,174,1833,12543,12435,53,17,1,414,14,526,4,9, 395,1,1,0,0,0, 1, 2043,3,1000000,4,3,128,4,500 -"6",309,2062,9,0,0,171,1834,12543,12435,160,17,1,443,14,494,5,9, 411,1,1,0,0,0, 1, 2025,3,1000000,4,3,128,4,500 -"6",496,2065,9,0,0,175,1833,12543,12435,160,16,1,796,19,139,3,9, 391,1,1,0,0,0, 1, 2042,3,1000000,4,3,128,4,500 -"6",378,2062,9,0,0,172,1837,12543,12435,41,17,1,453,17,486,1,9, 403,1,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 -"6",507,2066,9,0,0,173,1836,12543,12435,160,17,1,757,15,178,8,9, 389,1,1,0,0,0, 1, 2045,3,1000000,4,3,128,4,500 -"6",206,2064,9,0,0,169,1839,12543,12435,162,17,1,747,16,189,6,9, 421,2,1,0,0,0, 1, 2013,3,1000000,4,3,128,4,500 -"6",433,2066,9,0,0,171,1838,12543,12435,161,16,1,776,13,159,9,9, 396,1,1,0,0,0, 1, 2037,3,1000000,4,3,128,4,500 -"6",405,2066,9,0,0,171,1837,12543,12435,130,17,1,751,13,183,9,9, 399,2,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 -"6",38,2062,9,0,0,174,1835,12543,12434,94,17,2,411,15,527,4,9, 439,2,1,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 -"6",138,2066,10,0,0,167,1841,12543,12435,163,17,1,757,15,178,6,9, 428,1,1,0,0,0, 1, 2005,3,1000000,4,3,128,4,500 -"6",161,2067,9,0,0,167,1842,12543,12435,100,17,1,742,17,192,6,9, 425,1,1,0,0,0, 1, 2008,3,1000000,4,3,128,4,500 -"6",243,2062,9,0,0,169,1840,12543,12435,160,17,1,433,15,505,3,9, 418,1,1,0,0,0, 1, 2019,3,1000000,4,3,128,4,500 -"6",364,2066,9,0,0,166,1842,12543,12435,160,17,1,750,19,185,3,9, 403,1,1,0,0,0, 1, 2030,3,1000000,4,3,128,4,500 -"6",221,2062,9,0,0,173,1835,12543,12435,68,17,1,433,14,506,4,9, 420,1,1,0,0,0, 1, 2017,3,1000000,4,3,128,4,500 -"6",102,2062,9,0,0,172,1837,12543,12435,94,17,1,429,13,510,5,9, 434,2,1,0,0,0, 1, 2003,3,1000000,4,3,128,4,500 -"6",133,2066,9,0,0,168,1841,12543,12435,163,17,1,748,9,186,13,9, 428,1,1,0,0,0, 1, 2004,3,1000000,4,3,128,4,500 -"6",77,2063,8,0,0,169,1839,12543,12435,162,17,1,461,11,477,8,9, 435,1,1,0,0,0, 1, 2002,3,1000000,4,3,128,4,500 -"6",325,2062,9,0,0,173,1836,12542,12435,162,17,1,419,16,520,2,9, 409,1,1,0,0,0, 1, 2028,3,1000000,4,3,128,4,500 -"6",26,2062,9,0,0,169,1840,12543,12435,41,17,1,433,9,506,9,9, 441,1,1,0,0,0, 1, 1996,3,1000000,4,3,128,4,500 -"6",275,2062,9,0,0,169,1839,12543,12435,36,17,1,422,14,517,4,9, 412,1,1,0,0,0, 1, 2025,3,1000000,4,3,128,4,500 -"6",381,2061,9,0,0,173,1836,12543,12435,160,17,1,424,16,516,1,9, 401,1,1,0,0,0, 1, 2038,3,1000000,4,3,128,4,500 -"6",391,2066,9,0,0,170,1839,12543,12435,129,17,1,751,17,183,6,9, 400,2,0,0,0,0, 1, 2033,3,1000000,4,3,128,4,500 -"6",135,2067,9,0,0,168,1841,12543,12435,128,17,1,763,9,171,13,9, 428,2,0,0,0,0, 1, 2004,3,1000000,4,3,128,4,500 -"6",319,2063,9,0,0,174,1836,12543,12435,127,17,1,417,14,521,4,9, 407,2,1,0,0,0, 1, 2030,3,1000000,4,3,128,4,500 -"6",429,2067,9,0,1,172,1837,12543,12434,37,17,1,746,16,189,5,9, 396,2,1,0,0,0, 1, 2037,3,1000000,4,3,128,4,500 -"6",27,2064,9,0,0,174,1837,12543,12435,40,17,1,450,11,488,8,9, 440,1,1,0,0,0, 1, 1996,3,1000000,4,3,128,4,500 -"6",318,2064,9,0,0,172,1838,12543,12435,160,17,1,426,15,512,5,9, 408,1,1,0,0,0, 1, 2028,3,1000000,4,3,128,4,500 -"6",424,2067,9,0,0,172,1838,12543,12435,159,16,1,756,16,179,6,9, 396,1,1,0,0,0, 1, 2037,3,1000000,4,3,128,4,500 -"6",254,2064,9,0,0,170,1839,12543,12435,160,17,1,426,15,512,3,9, 417,1,1,0,0,0, 1, 2020,3,1000000,4,3,128,4,500 -"6",303,2064,8,0,0,170,1839,12543,12435,162,17,1,420,14,518,5,9, 410,1,1,0,0,0, 1, 2027,3,1000000,4,3,128,4,500 -"6",51,2065,9,0,0,170,1840,12543,12435,161,17,1,446,14,491,5,9, 440,2,1,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 -"6",122,2068,9,0,0,167,1844,12543,12435,41,17,1,750,16,185,6,9, 430,1,1,0,0,0, 1, 2004,3,1000000,4,3,128,4,500 -"6",346,2064,9,0,0,172,1840,12543,12435,17,17,1,436,16,502,2,9, 407,1,1,0,0,0, 1, 2030,3,1000000,4,3,128,4,500 -"6",128,2068,9,0,0,170,1840,12543,12435,105,17,1,743,16,192,6,9, 429,2,1,0,0,0, 1, 2005,3,1000000,4,3,128,4,500 -"6",419,2068,9,0,0,171,1840,12543,12435,97,17,1,752,16,183,6,9, 396,2,1,0,0,0, 1, 2037,3,1000000,4,3,128,4,500 -"6",304,2065,9,0,0,171,1839,12543,12435,160,17,1,425,14,513,5,9, 410,2,0,0,0,0, 1, 2026,3,1000000,4,3,128,4,500 -"6",416,2068,9,0,0,173,1839,12543,12435,105,17,1,760,16,176,6,9, 396,2,1,0,0,0, 1, 2037,3,1000000,4,3,128,4,500 -"6",352,2064,9,0,0,167,1844,12543,12435,104,17,1,439,15,501,2,9, 405,1,1,0,0,0, 1, 2033,3,1000000,4,3,128,4,500 -"6",486,2067,9,0,0,175,1838,12542,12435,94,17,1,754,18,182,2,9, 390,2,1,0,0,0, 1, 2044,3,1000000,4,3,128,4,500 -"6",329,2065,9,0,0,174,1838,12543,12435,162,17,1,410,16,529,2,9, 408,1,1,0,0,0, 1, 2029,3,1000000,4,3,128,4,500 -"6",297,2065,9,0,0,172,1840,12543,12435,161,17,1,410,13,529,5,9, 411,2,1,0,0,0, 1, 2026,3,1000000,4,3,128,4,500 -"6",443,2070,9,0,0,172,1840,12543,12435,161,17,1,759,13,176,9,9, 394,1,1,0,0,0, 1, 2038,3,1000000,4,3,128,4,500 -"6",475,2066,9,0,0,176,1837,12543,12435,128,17,1,442,14,497,4,9, 393,1,1,0,0,0, 1, 2044,3,1000000,4,3,128,4,500 -"6",151,2070,9,0,0,169,1843,12543,12435,36,17,1,816,4,118,19,9, 426,1,1,0,0,0, 1, 2006,3,1000000,4,3,128,4,500 -"6",332,2066,9,0,0,171,1841,12543,12435,162,16,1,435,17,503,2,9, 407,1,1,0,0,0, 1, 2030,3,1000000,4,3,128,4,500 -"6",255,2067,9,0,0,171,1841,12543,12435,160,17,1,465,16,473,3,9, 415,1,1,0,0,0, 1, 2022,3,1000000,4,3,128,4,500 -"6",307,2066,9,0,0,172,1840,12543,12435,161,17,1,404,14,534,5,9, 411,2,1,0,0,0, 1, 2026,3,1000000,4,3,128,4,500 -"6",130,2070,9,0,0,170,1842,12543,12434,76,17,1,762,9,172,13,9, 428,1,1,0,0,0, 1, 2004,3,1000000,4,3,128,4,500 -"6",359,2066,9,0,0,172,1840,12543,12435,160,17,1,424,17,515,2,9, 403,1,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 -"6",501,2070,9,0,0,172,1840,12543,12435,160,17,1,756,15,179,8,9, 390,1,1,0,0,0, 1, 2043,3,1000000,4,3,128,4,500 -"6",348,2067,9,0,0,171,1843,12543,12435,127,17,1,439,16,499,2,9, 407,1,1,0,0,0, 1, 2030,3,1000000,4,3,128,4,500 -"6",462,2065,9,0,0,174,1839,12543,12435,161,17,1,438,15,501,2,9, 395,1,1,0,0,0, 1, 2044,3,1000000,4,3,128,4,500 -"6",261,2067,9,0,0,170,1844,12543,12435,162,17,1,405,14,534,4,9, 416,2,1,0,0,0, 1, 2021,3,1000000,4,3,128,4,500 -"6",485,2069,9,0,0,173,1840,12543,12435,158,17,1,752,18,185,3,9, 390,1,1,0,0,0, 1, 2044,3,1000000,4,3,128,4,500 -"6",500,2070,9,0,0,171,1842,12543,12435,53,17,1,758,13,178,8,9, 390,2,1,0,0,0, 1, 2044,3,1000000,4,3,128,4,500 -"6",49,2068,9,0,0,171,1843,12543,12435,161,17,1,440,14,498,5,9, 440,1,1,0,0,0, 1, 1996,3,1000000,4,3,128,4,500 -"6",96,2067,9,0,0,174,1841,12543,12435,105,17,1,423,12,516,5,9, 433,1,1,0,0,0, 1, 2005,3,1000000,4,3,128,4,500 -"6",310,2068,9,0,0,175,1841,12543,12435,160,17,1,425,14,513,5,9, 409,1,1,0,0,0, 1, 2028,3,1000000,4,3,128,4,500 -"6",42,2069,9,0,0,173,1842,12543,12435,161,17,1,422,13,516,5,9, 440,1,1,0,0,0, 1, 1996,3,1000000,4,3,128,4,500 -"6",422,2072,9,0,0,171,1844,12543,12434,94,17,1,750,17,185,5,9, 396,1,1,0,0,0, 1, 2037,3,1000000,4,3,128,4,500 -"6",474,2072,18,0,0,177,1838,12543,12435,17,17,1,749,9,186,4,9, 391,1,1,0,0,0, 1, 2042,3,1000000,4,3,128,4,500 -"6",478,2069,9,0,0,179,1838,12543,12435,162,17,1,408,14,531,4,9, 392,1,1,0,0,0, 1, 2045,3,1000000,4,3,128,4,500 -"6",295,2070,9,0,0,173,1841,12543,12435,160,17,1,460,14,478,5,9, 411,1,1,0,0,0, 1, 2026,3,1000000,4,3,128,4,500 -"6",291,2069,9,0,0,172,1845,12543,12435,97,17,1,420,13,519,5,9, 411,1,0,0,0,0, 1, 2026,3,1000000,4,3,128,4,500 -"6",35,2070,9,0,0,174,1843,12543,12435,97,17,1,442,13,496,5,9, 441,1,1,0,0,0, 1, 1996,3,1000000,4,3,128,4,500 -"6",464,2068,9,0,0,175,1842,12543,12435,128,17,1,415,14,525,2,9, 395,1,1,0,0,0, 1, 2044,3,1000000,4,3,128,4,500 -"6",69,2074,18,0,0,178,1839,12543,12435,162,17,1,779,4,155,9,9, 437,1,1,0,0,0, 1, 1996,3,1000000,4,3,128,4,500 -"6",112,2071,9,0,0,173,1846,12543,12435,160,17,1,420,13,519,5,9, 433,1,1,0,0,0, 1, 2005,3,1000000,4,3,128,4,500 -"6",126,2076,9,0,0,168,1847,12543,12435,161,17,1,750,17,184,6,9, 429,2,1,0,0,0, 1, 2004,3,1000000,4,3,128,4,500 -"6",487,2074,9,0,0,175,1842,12543,12435,159,17,1,756,18,180,2,9, 391,1,1,0,0,0, 1, 2044,3,1000000,4,3,128,4,500 -"6",108,2071,9,0,0,172,1847,12543,12435,160,17,1,422,10,517,8,9, 432,1,1,0,0,0, 1, 2006,3,1000000,4,3,128,4,500 -"6",467,2072,9,0,0,174,1845,12543,12435,129,17,1,437,14,501,4,9, 392,2,1,0,0,0, 1, 2045,3,1000000,4,3,128,4,500 -"6",20,2079,9,0,0,168,1854,12543,12435,52,17,1,752,11,182,12,9, 441,1,1,0,0,0, 1, 1991,3,1000000,4,3,128,4,500 -"6",279,2076,9,0,0,170,1853,12543,12435,37,17,1,391,14,547,4,9, 414,1,1,0,0,0, 1, 2023,3,1000000,4,3,128,4,500 -"6",75,2081,18,0,0,174,1849,12543,12435,162,17,1,780,5,154,9,9, 435,1,1,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 -"6",28,2077,9,0,0,171,1853,12543,12435,97,17,1,415,8,525,9,9, 442,1,1,0,0,0, 1, 1996,3,1000000,4,3,128,4,500 -"6",201,2085,9,0,0,168,1861,12543,12435,162,17,1,759,16,176,6,9, 421,1,1,0,0,0, 1, 2013,3,1000000,4,3,128,4,500 -"6",404,2085,9,0,0,194,1834,12543,12434,53,17,2,776,12,159,10,9, 399,1,1,0,0,0, 1, 2035,3,1000000,4,3,128,4,500 -"6",320,2086,18,0,0,171,1858,12543,12435,104,17,1,764,7,170,6,9, 408,1,1,0,0,0, 1, 2025,3,1000000,4,3,128,4,500 -"6",258,2086,9,0,0,182,1851,12543,12435,76,17,1,446,12,492,5,9, 415,2,1,0,0,0, 1, 2022,3,1000000,4,3,128,4,500 -"6",472,2102,0,0,0,96,1952,12542,12436,128,17,1,445,24,495,5,9, 394,1,1,0,0,0, 0, 2043,3,1000000,4,3,128,4,500 -"6",290,2103,8,0,0,171,1877,12543,12435,76,17,1,427,14,511,5,9, 411,1,1,0,0,0, 1, 2025,3,1000000,4,3,128,4,500 -"6",481,2104,0,0,0,99,1955,12542,12436,100,17,1,406,24,535,2,9, 393,1,1,0,0,0, 0, 2047,3,1000000,4,3,128,4,500 -"6",455,2107,0,0,0,96,1958,12542,12436,128,17,1,373,24,568,2,9, 395,2,1,0,0,0, 0, 2044,3,1000000,4,3,128,4,500 -"6",377,2110,0,0,0,95,1961,12542,12436,160,17,2,393,26,547,2,9, 403,2,1,0,0,0, 0, 2035,3,1000000,4,3,128,4,500 -"6",331,2112,0,0,0,96,1963,12542,12436,162,17,1,391,25,549,2,9, 409,1,1,0,0,0, 0, 2030,3,1000000,4,3,128,4,500 -"6",236,2115,0,0,0,93,1969,12542,12436,161,17,1,383,23,557,5,9, 419,1,1,0,0,0, 0, 2019,3,1000000,4,3,128,4,500 -"6",121,2116,0,0,0,95,1968,12542,12436,160,17,1,399,22,541,5,9, 431,2,1,0,0,0, 0, 2007,3,1000000,4,3,128,4,500 -"6",81,2118,0,0,0,94,1969,12542,12436,128,17,1,444,23,495,5,9, 436,1,1,0,0,0, 0, 2001,3,1000000,4,3,128,4,500 -"6",313,2118,0,0,0,96,1969,12542,12436,160,17,1,377,22,563,5,9, 411,2,1,0,0,0, 0, 2028,3,1000000,4,3,128,4,500 -"6",337,2119,0,0,0,93,1973,12542,12436,128,17,1,370,25,570,2,9, 408,1,1,0,0,0, 0, 2031,3,1000000,4,3,128,4,500 -"6",328,2120,0,0,0,95,1972,12542,12436,127,17,1,388,25,552,2,9, 408,1,1,0,0,0, 0, 2030,3,1000000,4,3,128,4,500 -"6",46,2126,9,0,0,173,1900,12543,12435,161,17,1,419,13,519,5,9, 440,1,1,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 -"6",432,2171,9,0,0,172,1941,12543,12434,160,17,2,754,16,181,6,9, 395,2,0,0,0,0, 1, 2038,3,1000000,4,3,128,4,500 -"6",188,2171,9,0,0,170,1944,12543,12435,160,17,1,740,18,195,4,9, 422,1,1,0,0,0, 1, 2011,3,1000000,4,3,128,4,500 -"6",197,2171,9,0,0,166,1950,12543,12435,162,17,1,711,15,225,5,9, 421,2,1,0,0,0, 1, 2014,3,1000000,4,3,128,4,500 -"6",198,2173,9,0,0,169,1948,12543,12435,94,17,1,715,16,221,5,9, 421,1,1,0,0,0, 1, 2013,3,1000000,4,3,128,4,500 -"6",495,2172,9,0,0,172,1944,12543,12435,160,17,1,757,18,179,2,9, 390,1,1,0,0,0, 1, 2045,3,1000000,4,3,128,4,500 -"6",471,2171,9,0,0,175,1942,12543,12435,128,17,1,453,14,486,4,9, 392,1,1,0,0,0, 1, 2045,3,1000000,4,3,128,4,500 -"6",205,2174,9,0,0,167,1951,12543,12435,162,16,1,717,15,219,5,9, 420,1,1,0,0,0, 1, 2014,3,1000000,4,3,128,4,500 -"6",187,2177,9,0,0,167,1954,12543,12435,160,17,1,753,16,181,6,9, 423,2,0,0,0,0, 1, 2011,3,1000000,4,3,128,4,500 -"6",185,2178,9,0,0,167,1954,12543,12435,161,17,1,726,16,210,5,9, 422,1,1,0,0,0, 1, 2012,3,1000000,4,3,128,4,500 -"6",154,2179,9,0,0,170,1951,12543,12435,41,17,1,753,16,182,6,9, 426,1,1,0,0,0, 1, 2007,3,1000000,4,3,128,4,500 -"6",402,2178,9,0,0,171,1951,12543,12435,162,17,1,725,13,211,8,9, 398,1,1,0,0,0, 1, 2035,3,1000000,4,3,128,4,500 -"6",47,2176,9,0,0,171,1952,12543,12435,160,17,1,391,13,548,5,9, 438,1,1,0,0,0, 1, 1999,3,1000000,4,3,128,4,500 -"6",125,2180,9,0,0,170,1952,12543,12435,160,17,1,721,16,215,5,9, 429,1,1,0,0,0, 1, 2005,3,1000000,4,3,128,4,500 -"6",59,2180,9,0,0,169,1953,12543,12435,161,17,1,711,15,224,6,9, 437,1,1,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 -"6",438,2180,9,0,0,170,1952,12543,12435,160,17,1,778,13,157,9,9, 395,1,1,0,0,0, 1, 2038,3,1000000,4,3,128,4,500 -"6",499,2180,9,0,0,177,1947,12543,12435,159,17,1,759,14,176,8,9, 390,2,1,0,0,0, 1, 2044,3,1000000,4,3,128,4,500 -"6",238,2177,9,0,0,169,1955,12543,12435,161,17,1,390,15,550,3,9, 416,2,1,0,0,0, 1, 2021,3,1000000,4,3,128,4,500 -"6",213,2180,9,0,0,169,1954,12543,12435,162,17,1,710,17,226,3,9, 419,1,1,0,0,0, 1, 2015,3,1000000,4,3,128,4,500 -"6",371,2181,9,0,0,170,1955,12543,12435,160,17,1,708,19,228,3,9, 402,1,1,0,0,0, 1, 2032,3,1000000,4,3,128,4,500 -"6",396,2181,9,0,0,171,1953,12543,12435,162,16,1,717,16,219,5,9, 400,2,1,0,0,0, 1, 2035,3,1000000,4,3,128,4,500 -"6",484,2180,10,0,0,172,1952,12543,12435,159,16,1,732,17,205,2,9, 390,1,1,0,0,0, 1, 2045,3,1000000,4,3,128,4,500 -"6",411,2182,9,0,0,171,1954,12543,12435,40,17,1,764,16,171,6,9, 398,2,1,0,0,0, 1, 2035,3,1000000,4,3,128,4,500 -"6",113,2178,9,0,0,169,1955,12543,12435,160,17,1,394,13,545,4,9, 431,1,1,0,0,0, 1, 2007,3,1000000,4,3,128,4,500 -"6",143,2182,9,0,0,167,1958,12543,12435,129,17,1,727,16,208,5,9, 427,1,1,0,0,0, 1, 2007,3,1000000,4,3,128,4,500 -"6",57,2182,9,0,0,171,1957,12543,12435,161,17,1,711,15,225,6,9, 437,2,1,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 -"6",431,2183,9,0,0,171,1955,12543,12435,161,17,1,752,16,183,6,9, 396,2,1,0,0,0, 1, 2038,3,1000000,4,3,128,4,500 -"6",104,2179,9,0,0,171,1955,12543,12435,160,17,1,379,13,560,5,9, 432,1,1,0,0,0, 1, 2006,3,1000000,4,3,128,4,500 -"6",123,2183,9,0,0,170,1956,12543,12435,161,17,1,719,15,217,5,9, 429,1,1,0,0,0, 1, 2005,3,1000000,4,3,128,4,500 -"6",10,2184,9,0,0,166,1960,12543,12435,162,17,1,761,12,173,10,9, 442,1,1,0,0,0, 1, 1991,3,1000000,4,3,128,4,500 -"6",250,2180,9,0,0,170,1958,12543,12435,41,17,1,431,14,508,4,9, 417,1,1,0,0,0, 1, 2020,3,1000000,4,3,128,4,500 -"6",229,2179,9,0,0,169,1959,12543,12435,161,17,1,374,12,566,5,9, 418,1,1,0,0,0, 1, 2020,3,1000000,4,3,128,4,500 -"6",61,2183,9,0,0,170,1958,12543,12435,160,17,1,718,15,217,6,9, 437,1,1,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 -"6",207,2183,9,0,0,170,1957,12543,12435,162,17,1,717,15,219,5,9, 420,1,1,0,0,0, 1, 2014,3,1000000,4,3,128,4,500 -"6",134,2184,9,0,0,170,1956,12543,12435,94,17,1,724,9,211,13,9, 428,1,1,0,0,0, 1, 2006,3,1000000,4,3,128,4,500 -"6",115,2180,9,0,0,173,1954,12543,12435,160,17,1,388,13,552,4,9, 431,1,1,0,0,0, 1, 2008,3,1000000,4,3,128,4,500 -"6",107,2181,9,0,0,173,1955,12543,12435,160,17,1,398,10,541,8,9, 432,1,1,0,0,0, 1, 2006,3,1000000,4,3,128,4,500 -"6",231,2180,9,0,0,173,1954,12543,12435,160,17,1,378,14,562,2,9, 418,1,1,0,0,0, 1, 2020,3,1000000,4,3,128,4,500 -"6",178,2184,9,0,0,171,1957,12543,12435,128,17,1,749,11,187,10,9, 423,1,1,0,0,0, 1, 2011,3,1000000,4,3,128,4,500 -"6",227,2180,9,0,0,171,1957,12543,12435,97,17,1,374,14,565,3,9, 418,1,1,0,0,0, 1, 2020,3,1000000,4,3,128,4,500 -"6",48,2181,9,0,0,172,1956,12543,12435,161,16,1,380,13,560,5,9, 438,1,1,0,0,0, 1, 2000,3,1000000,4,3,128,4,500 -"6",111,2181,9,0,0,172,1956,12543,12435,160,17,1,396,10,543,8,9, 431,1,1,0,0,0, 1, 2007,3,1000000,4,3,128,4,500 -"6",446,2184,18,0,0,176,1953,12543,12435,160,17,1,755,3,181,8,9, 395,1,1,0,0,0, 1, 2039,3,1000000,4,3,128,4,500 -"6",54,2181,9,0,0,173,1956,12543,12435,160,17,1,367,14,573,3,9, 437,1,1,0,0,0, 1, 2000,3,1000000,4,3,128,4,500 -"6",16,2181,9,0,0,171,1957,12543,12435,36,17,1,405,9,534,9,9, 442,2,1,0,0,0, 1, 1996,3,1000000,4,3,128,4,500 -"6",176,2185,9,0,0,170,1958,12543,12435,161,17,1,718,17,218,4,9, 423,1,1,0,0,0, 1, 2011,3,1000000,4,3,128,4,500 -"6",118,2181,9,0,0,173,1957,12543,12435,160,17,1,367,13,573,4,9, 430,1,1,0,0,0, 1, 2009,3,1000000,4,3,128,4,500 -"6",459,2184,9,0,0,173,1956,12542,12435,161,17,1,715,17,222,3,9, 393,1,1,0,0,0, 1, 2043,3,1000000,4,3,128,4,500 -"6",71,2186,9,0,0,169,1959,12543,12435,128,17,1,728,13,207,8,9, 436,2,1,0,0,0, 1, 1998,3,1000000,4,3,128,4,500 -"6",168,2186,9,0,0,169,1959,12542,12435,161,17,1,725,15,210,6,9, 424,1,1,0,0,0, 1, 2009,3,1000000,4,3,128,4,500 -"6",338,2181,9,0,0,172,1958,12543,12435,163,17,1,396,15,544,2,9, 406,1,1,0,0,0, 1, 2033,3,1000000,4,3,128,4,500 -"6",370,2186,9,0,0,168,1960,12543,12435,160,17,1,720,19,216,3,9, 402,1,1,0,0,0, 1, 2032,3,1000000,4,3,128,4,500 -"6",274,2182,9,0,0,172,1958,12543,12435,163,17,1,394,14,545,3,9, 413,1,1,0,0,0, 1, 2025,3,1000000,4,3,128,4,500 -"6",164,2186,9,0,0,168,1961,12543,12435,161,17,1,735,15,201,6,9, 424,1,1,0,0,0, 1, 2009,3,1000000,4,3,128,4,500 -"6",355,2182,9,0,0,173,1957,12543,12435,98,17,1,364,16,575,2,9, 404,1,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 -"6",60,2186,9,0,0,172,1959,12543,12435,160,17,1,712,15,224,6,9, 437,1,1,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 -"6",395,2185,9,0,0,172,1958,12543,12435,130,17,1,724,15,212,5,9, 399,2,1,0,0,0, 1, 2035,3,1000000,4,3,128,4,500 -"6",400,2185,9,0,0,171,1958,12543,12435,37,17,1,728,15,209,5,9, 399,2,1,0,0,0, 1, 2036,3,1000000,4,3,128,4,500 -"6",232,2182,9,0,0,172,1958,12543,12435,161,17,1,374,13,566,3,9, 417,1,1,0,0,0, 1, 2022,3,1000000,4,3,128,4,500 -"6",152,2187,9,0,0,170,1960,12543,12435,95,17,1,790,3,144,19,9, 426,2,1,0,0,0, 1, 2007,3,1000000,4,3,128,4,500 -"6",175,2186,9,0,0,170,1960,12543,12435,161,17,1,726,15,209,5,9, 423,1,1,0,0,0, 1, 2011,3,1000000,4,3,128,4,500 -"6",220,2182,8,0,0,174,1956,12543,12435,128,17,1,387,14,553,4,9, 419,1,1,0,0,0, 1, 2020,3,1000000,4,3,128,4,500 -"6",170,2186,9,0,0,170,1960,12543,12435,161,17,1,708,15,227,5,9, 424,1,1,0,0,0, 1, 2010,3,1000000,4,3,128,4,500 -"6",174,2186,9,0,0,168,1962,12543,12435,161,17,1,709,15,227,5,9, 423,1,1,0,0,0, 1, 2011,3,1000000,4,3,128,4,500 -"6",85,2187,18,0,0,172,1959,12543,12435,162,17,1,749,6,186,6,9, 434,1,1,0,0,0, 1, 2000,3,1000000,4,3,128,4,500 -"6",56,2187,9,0,0,168,1961,12543,12435,160,17,1,707,17,228,5,9, 437,1,1,0,0,0, 1, 1996,3,1000000,4,3,128,4,500 -"6",234,2182,9,0,0,173,1958,12543,12435,161,17,1,370,14,571,2,9, 417,1,1,0,0,0, 0, 2022,3,1000000,4,3,128,4,500 -"6",226,2182,9,0,0,170,1960,12543,12435,77,17,1,363,12,577,4,9, 418,1,1,0,0,0, 1, 2021,3,1000000,4,3,128,4,500 -"6",349,2182,12,0,0,174,1957,12543,12435,68,17,1,375,12,565,2,9, 405,1,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 -"6",418,2187,9,0,0,172,1958,12543,12435,76,17,1,712,16,224,5,9, 396,1,1,0,0,0, 1, 2038,3,1000000,4,3,128,4,500 -"6",225,2183,9,0,0,170,1961,12543,12435,101,17,1,382,12,558,4,9, 418,1,1,0,0,0, 1, 2020,3,1000000,4,3,128,4,500 -"6",477,2183,9,0,0,175,1956,12543,12435,68,17,1,403,13,536,4,9, 392,1,1,0,0,0, 1, 2046,3,1000000,4,3,128,4,500 -"6",392,2187,11,0,0,170,1960,12543,12435,96,17,1,723,14,212,6,9, 400,2,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 -"6",103,2184,9,0,0,172,1959,12543,12435,160,17,1,398,13,541,5,9, 432,1,1,0,0,0, 1, 2006,3,1000000,4,3,128,4,500 -"6",171,2187,9,0,0,170,1961,12543,12435,160,17,1,720,15,216,5,9, 424,1,1,0,0,0, 1, 2010,3,1000000,4,3,128,4,500 -"6",173,2187,9,0,0,168,1962,12542,12435,38,17,1,721,15,214,5,9, 424,1,1,0,0,0, 1, 2011,3,1000000,4,3,128,4,500 -"6",29,2185,9,0,0,172,1960,12543,12435,35,17,1,407,11,532,7,9, 441,1,1,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 -"6",43,2185,9,0,0,173,1960,12543,12435,161,17,1,395,13,544,5,9, 439,1,1,0,0,0, 1, 1999,3,1000000,4,3,128,4,500 -"6",162,2189,9,0,0,169,1964,12543,12435,77,17,1,719,15,216,6,9, 425,1,1,0,0,0, 1, 2009,3,1000000,4,3,128,4,500 -"6",372,2188,9,0,0,169,1963,12543,12435,53,17,1,722,19,214,2,9, 401,1,1,0,0,0, 1, 2033,3,1000000,4,3,128,4,500 -"6",306,2185,9,0,0,173,1960,12543,12435,129,17,1,384,13,555,5,9, 409,2,1,0,0,0, 1, 2029,3,1000000,4,3,128,4,500 -"6",157,2188,9,0,0,170,1961,12543,12435,36,17,1,726,16,210,5,9, 425,1,1,0,0,0, 1, 2009,3,1000000,4,3,128,4,500 -"6",30,2185,9,0,0,174,1959,12543,12435,163,17,1,373,10,567,7,9, 440,1,1,0,0,0, 1, 1998,3,1000000,4,3,128,4,500 -"6",248,2186,9,0,0,171,1961,12543,12435,161,17,1,435,15,504,3,9, 416,1,0,0,0,0, 1, 2021,3,1000000,4,3,128,4,500 -"6",190,2188,9,0,0,170,1962,12543,12435,161,17,1,719,16,217,4,9, 422,1,1,0,0,0, 1, 2013,3,1000000,4,3,128,4,500 -"6",327,2189,18,0,0,174,1959,12543,12435,129,17,1,709,8,227,3,9, 407,1,1,0,0,0, 1, 2028,3,1000000,4,3,128,4,500 -"6",390,2189,9,0,0,173,1961,12543,12435,94,17,1,708,16,227,5,9, 400,1,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 -"6",450,2184,9,0,0,176,1957,12543,12435,76,17,1,372,14,569,2,9, 393,1,1,0,0,0, 1, 2046,3,1000000,4,3,128,4,500 -"6",182,2190,9,0,0,171,1961,12543,12435,162,17,1,731,13,204,9,9, 422,1,1,0,0,0, 1, 2011,3,1000000,4,3,128,4,500 -"6",456,2188,9,0,0,173,1960,12543,12435,127,17,1,719,17,218,3,9, 393,1,1,0,0,0, 1, 2042,3,1000000,4,3,128,4,500 -"6",142,2189,9,0,0,168,1963,12543,12435,162,17,1,717,16,219,5,9, 427,1,1,0,0,0, 1, 2007,3,1000000,4,3,128,4,500 -"6",498,2189,9,0,0,172,1961,12543,12435,160,17,1,732,13,204,8,9, 389,1,1,0,0,0, 1, 2045,3,1000000,4,3,128,4,500 -"6",489,2188,10,0,0,173,1959,12543,12435,159,17,1,726,17,211,2,9, 390,1,1,0,0,0, 1, 2045,3,1000000,4,3,128,4,500 -"6",393,2188,9,0,0,170,1962,12542,12435,162,17,1,708,15,229,5,9, 400,1,1,0,0,0, 1, 2035,3,1000000,4,3,128,4,500 -"6",493,2189,9,0,0,174,1960,12543,12435,160,17,1,756,19,180,2,9, 390,1,1,0,0,0, 1, 2044,3,1000000,4,3,128,4,500 -"6",397,2189,9,0,0,170,1962,12543,12435,162,17,1,706,15,230,5,9, 400,1,1,0,0,0, 1, 2035,3,1000000,4,3,128,4,500 -"6",150,2190,9,0,0,172,1961,12543,12435,162,17,1,761,3,175,19,9, 426,2,1,0,0,0, 1, 2008,3,1000000,4,3,128,4,500 -"6",292,2186,9,0,0,172,1961,12543,12435,160,17,1,393,13,546,5,9, 410,2,1,0,0,0, 1, 2027,3,1000000,4,3,128,4,500 -"6",413,2189,10,0,0,171,1962,12543,12435,35,17,1,733,14,203,5,9, 398,1,1,0,0,0, 1, 2037,3,1000000,4,3,128,4,500 -"6",351,2186,9,0,0,174,1959,12543,12435,163,17,1,379,16,561,1,9, 404,1,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 -"6",505,2190,9,0,0,173,1961,12543,12435,160,17,1,733,13,202,8,9, 388,1,1,0,0,0, 1, 2046,3,1000000,4,3,128,4,500 -"6",246,2186,9,0,0,171,1962,12543,12435,161,17,1,393,15,547,3,9, 416,1,1,0,0,0, 1, 2022,3,1000000,4,3,128,4,500 -"6",230,2186,9,0,0,174,1960,12543,12435,94,17,1,360,12,580,5,9, 418,1,1,0,0,0, 1, 2021,3,1000000,4,3,128,4,500 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2033,3,1000000,4,3,128,4,500 -"6",224,2186,9,0,0,168,1966,12543,12435,104,17,1,360,12,580,4,9, 418,1,0,0,0,0, 1, 2021,3,1000000,4,3,128,4,500 -"6",95,2187,9,0,0,173,1961,12543,12435,161,17,1,394,13,545,5,9, 433,1,1,0,0,0, 1, 2005,3,1000000,4,3,128,4,500 -"6",347,2186,9,0,0,174,1960,12543,12435,111,17,1,374,16,566,2,9, 405,1,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 -"6",281,2187,9,0,0,174,1961,12543,12435,94,17,1,361,12,579,5,9, 411,1,1,0,0,0, 1, 2027,3,1000000,4,3,128,4,500 -"6",141,2190,9,0,0,169,1964,12543,12435,162,17,1,722,15,215,5,9, 427,2,1,0,0,0, 1, 2008,3,1000000,4,3,128,4,500 -"6",426,2191,9,0,0,171,1964,12543,12435,159,17,1,709,15,226,5,9, 395,1,1,0,0,0, 1, 2039,3,1000000,4,3,128,4,500 -"6",406,2191,9,0,0,170,1964,12543,12435,162,17,1,730,12,205,9,9, 398,1,1,0,0,0, 1, 2036,3,1000000,4,3,128,4,500 -"6",263,2187,9,0,0,172,1963,12543,12435,107,17,1,390,13,549,5,9, 413,1,1,0,0,0, 1, 2024,3,1000000,4,3,128,4,500 -"6",463,2186,9,0,0,177,1959,12543,12435,162,17,1,414,15,527,2,9, 392,1,1,0,0,0, 1, 2046,3,1000000,4,3,128,4,500 -"6",442,2191,9,0,0,172,1962,12543,12435,41,17,1,732,13,204,8,9, 394,1,1,0,0,0, 1, 2040,3,1000000,4,3,128,4,500 -"6",473,2191,9,0,0,173,1962,12543,12435,94,17,1,719,16,217,4,9, 392,1,1,0,0,0, 1, 2042,3,1000000,4,3,128,4,500 -"6",324,2188,9,0,0,172,1964,12543,12435,127,17,1,400,16,540,1,9, 407,1,1,0,0,0, 1, 2031,3,1000000,4,3,128,4,500 -"6",399,2191,9,0,0,172,1963,12543,12435,129,17,1,727,15,209,5,9, 399,1,1,0,0,0, 1, 2036,3,1000000,4,3,128,4,500 -"6",449,2186,9,0,0,177,1960,12543,12435,101,17,1,394,14,547,2,9, 394,1,1,0,0,0, 1, 2046,3,1000000,4,3,128,4,500 -"6",93,2188,9,0,0,174,1960,12543,12435,68,17,1,399,13,540,4,9, 433,1,1,0,0,0, 1, 2005,3,1000000,4,3,128,4,500 -"6",408,2193,9,0,0,171,1964,12543,12435,96,17,1,777,13,158,10,9, 399,2,0,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 -"6",340,2188,9,0,0,172,1963,12543,12435,53,17,1,389,16,551,2,9, 406,1,1,0,0,0, 1, 2032,3,1000000,4,3,128,4,500 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2003,3,1000000,4,3,128,4,500 -"6",116,2189,9,0,0,173,1964,12543,12435,53,17,1,374,12,566,5,9, 431,1,1,0,0,0, 1, 2008,3,1000000,4,3,128,4,500 -"6",410,2193,9,0,0,171,1965,12543,12435,41,17,1,721,15,216,5,9, 398,1,1,0,0,0, 1, 2037,3,1000000,4,3,128,4,500 -"6",40,2190,9,0,0,174,1963,12543,12435,160,17,1,387,13,552,5,9, 439,1,1,0,0,0, 1, 1999,3,1000000,4,3,128,4,500 -"6",285,2189,9,0,0,172,1965,12543,12435,36,17,1,363,12,577,5,9, 411,1,1,0,0,0, 1, 2027,3,1000000,4,3,128,4,500 -"6",287,2189,9,0,0,174,1964,12543,12435,162,17,1,355,14,585,3,9, 411,2,1,0,0,0, 1, 2028,3,1000000,4,3,128,4,500 -"6",63,2194,9,0,0,173,1965,12543,12435,127,17,1,709,16,226,6,9, 437,1,1,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 -"6",200,2193,9,0,0,169,1966,12543,12435,127,17,1,713,17,223,4,9, 421,1,1,0,0,0, 1, 2014,3,1000000,4,3,128,4,500 -"6",252,2190,9,0,0,175,1962,12543,12435,161,17,1,370,13,570,4,9, 415,2,1,0,0,0, 1, 2023,3,1000000,4,3,128,4,500 -"6",482,2192,9,0,0,173,1964,12543,12435,76,17,1,726,17,211,2,9, 390,1,1,0,0,0, 1, 2045,3,1000000,4,3,128,4,500 -"6",245,2189,9,0,0,172,1965,12543,12435,160,17,1,375,14,565,2,9, 416,1,1,0,0,0, 1, 2023,3,1000000,4,3,128,4,500 -"6",169,2194,9,0,0,171,1966,12543,12435,161,17,1,721,16,214,5,9, 424,1,1,0,0,0, 1, 2010,3,1000000,4,3,128,4,500 -"6",24,2190,9,0,0,170,1967,12543,12435,94,17,1,389,9,551,9,9, 441,1,1,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 -"6",99,2190,9,0,0,172,1964,12543,12435,97,17,1,389,12,550,5,9, 432,1,1,0,0,0, 1, 2005,3,1000000,4,3,128,4,500 -"6",356,2191,9,0,0,172,1966,12543,12435,160,16,1,389,16,551,2,9, 404,1,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 -"6",454,2189,9,0,0,174,1964,12543,12435,94,17,1,371,14,570,2,9, 393,1,1,0,0,0, 1, 2046,3,1000000,4,3,128,4,500 -"6",114,2190,9,0,0,173,1965,12543,12435,161,17,1,367,13,573,4,9, 431,1,1,0,0,0, 1, 2008,3,1000000,4,3,128,4,500 -"6",414,2194,9,0,0,171,1966,12543,12435,162,17,1,727,15,210,5,9, 398,1,1,0,0,0, 1, 2037,3,1000000,4,3,128,4,500 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2026,3,1000000,4,3,128,4,500 -"6",259,2191,9,0,0,173,1966,12543,12435,96,17,1,384,12,556,5,9, 414,1,1,0,0,0, 1, 2024,3,1000000,4,3,128,4,500 -"6",491,2194,9,0,0,173,1966,12543,12435,159,17,1,729,18,208,2,9, 390,1,1,0,0,0, 1, 2045,3,1000000,4,3,128,4,500 -"6",158,2195,9,0,0,169,1969,12543,12435,162,17,1,718,17,218,4,9, 425,1,1,0,0,0, 1, 2009,3,1000000,4,3,128,4,500 -"6",276,2192,9,0,0,172,1968,12543,12435,53,17,1,375,14,565,3,9, 412,2,1,0,0,0, 1, 2026,3,1000000,4,3,128,4,500 -"6",300,2192,9,0,0,170,1970,12543,12435,160,17,1,391,14,548,4,9, 410,1,1,0,0,0, 1, 2028,3,1000000,4,3,128,4,500 -"6",299,2192,9,0,0,174,1965,12543,12435,160,17,1,368,14,572,3,9, 410,1,1,0,0,0, 1, 2028,3,1000000,4,3,128,4,500 -"6",457,2195,9,0,0,175,1966,12543,12435,161,17,1,715,17,222,3,9, 393,1,1,0,0,0, 1, 2042,3,1000000,4,3,128,4,500 -"6",314,2193,9,0,0,171,1969,12543,12435,41,17,1,405,14,535,3,9, 408,1,1,0,0,0, 1, 2030,3,1000000,4,3,128,4,500 -"6",469,2193,9,0,0,176,1964,12543,12435,161,17,1,395,13,545,4,9, 392,1,1,0,0,0, 1, 2046,3,1000000,4,3,128,4,500 -"6",470,2193,9,0,0,178,1963,12543,12435,161,17,1,379,13,561,4,9, 392,2,1,0,0,0, 1, 2046,3,1000000,4,3,128,4,500 -"6",316,2194,10,0,0,172,1969,12543,12435,160,16,1,366,13,573,4,9, 408,1,1,0,0,0, 1, 2030,3,1000000,4,3,128,4,500 -"6",136,2197,9,0,0,170,1970,12543,12435,95,17,1,737,8,198,13,9, 428,1,1,0,0,0, 1, 2006,3,1000000,4,3,128,4,500 -"6",339,2193,9,0,0,172,1968,12543,12435,128,17,1,386,15,554,2,9, 406,1,1,0,0,0, 1, 2033,3,1000000,4,3,128,4,500 -"6",280,2194,9,0,0,174,1967,12543,12435,95,17,1,363,14,576,4,9, 412,2,1,0,0,0, 1, 2026,3,1000000,4,3,128,4,500 -"6",88,2193,9,0,0,176,1964,12543,12435,128,17,1,383,13,557,5,9, 433,1,1,0,0,0, 1, 2005,3,1000000,4,3,128,4,500 -"6",119,2194,9,0,0,171,1971,12543,12435,161,17,1,389,13,551,4,9, 430,1,1,0,0,0, 1, 2008,3,1000000,4,3,128,4,500 -"6",335,2194,9,0,0,171,1970,12543,12435,162,17,1,378,16,562,1,9, 406,1,1,0,0,0, 1, 2032,3,1000000,4,3,128,4,500 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1999,3,1000000,4,3,128,4,500 -"6",344,2195,9,0,0,173,1969,12543,12435,127,17,1,385,15,555,2,9, 405,1,1,0,0,0, 1, 2033,3,1000000,4,3,128,4,500 -"6",425,2199,9,0,0,171,1971,12543,12435,160,17,1,708,16,228,5,9, 396,1,1,0,0,0, 1, 2039,3,1000000,4,3,128,4,500 -"6",441,2199,9,0,0,173,1969,12543,12435,161,17,1,723,13,213,8,9, 394,2,1,0,0,0, 1, 2040,3,1000000,4,3,128,4,500 -"6",420,2199,9,0,0,172,1971,12543,12435,159,17,1,729,16,206,5,9, 396,2,1,0,0,0, 1, 2038,3,1000000,4,3,128,4,500 -"6",434,2200,9,0,0,172,1971,12543,12435,128,17,1,749,12,187,9,9, 395,1,1,0,0,0, 1, 2039,3,1000000,4,3,128,4,500 -"6",266,2196,9,0,0,173,1970,12543,12435,162,17,1,385,13,554,5,9, 413,1,1,0,0,0, 1, 2024,3,1000000,4,3,128,4,500 -"6",427,2200,9,0,0,170,1973,12543,12435,160,17,1,727,16,209,5,9, 395,1,1,0,0,0, 1, 2039,3,1000000,4,3,128,4,500 -"6",271,2196,9,0,0,172,1972,12543,12435,114,17,1,373,14,567,4,9, 413,1,1,0,0,0, 1, 2025,3,1000000,4,3,128,4,500 -"6",452,2196,9,0,0,176,1969,12543,12435,127,17,1,370,15,571,2,9, 393,1,1,0,0,0, 0, 2046,3,1000000,4,3,128,4,500 -"6",82,2201,18,0,0,171,1974,12543,12435,162,17,1,760,7,174,6,9, 435,1,1,0,0,0, 1, 1998,3,1000000,4,3,128,4,500 -"6",117,2197,9,0,0,173,1972,12543,12435,160,17,1,392,13,548,4,9, 430,1,1,0,0,0, 1, 2008,3,1000000,4,3,128,4,500 -"6",214,2200,9,0,0,170,1974,12543,12435,162,17,1,715,17,221,4,9, 419,1,1,0,0,0, 1, 2015,3,1000000,4,3,128,4,500 -"6",315,2197,9,0,0,173,1972,12543,12435,160,17,1,381,14,559,3,9, 408,1,1,0,0,0, 1, 2030,3,1000000,4,3,128,4,500 -"6",497,2201,9,0,0,173,1971,12543,12435,159,17,1,726,14,210,7,9, 389,1,1,0,0,0, 1, 2045,3,1000000,4,3,128,4,500 -"6",199,2200,9,0,0,169,1974,12543,12435,128,17,1,714,17,222,4,9, 421,1,1,0,0,0, 1, 2014,3,1000000,4,3,128,4,500 -"6",50,2197,9,0,0,174,1970,12543,12435,128,17,1,385,13,554,5,9, 438,1,1,0,0,0, 1, 2000,3,1000000,4,3,128,4,500 -"6",267,2198,9,0,0,172,1973,12543,12435,118,17,1,379,15,560,3,9, 414,2,1,0,0,0, 1, 2024,3,1000000,4,3,128,4,500 -"6",466,2197,9,0,0,176,1969,12543,12435,161,17,1,374,13,566,4,9, 392,1,1,0,0,0, 1, 2046,3,1000000,4,3,128,4,500 -"6",288,2198,9,0,0,171,1975,12543,12435,105,17,1,383,12,557,5,9, 411,1,1,0,0,0, 1, 2027,3,1000000,4,3,128,4,500 -"6",345,2198,9,0,0,172,1973,12543,12435,94,17,1,371,15,570,2,9, 405,1,1,0,0,0, 1, 2034,3,1000000,4,3,128,4,500 -"6",453,2198,9,0,0,174,1972,12543,12435,160,17,1,391,15,549,2,9, 393,1,1,0,0,0, 1, 2046,3,1000000,4,3,128,4,500 -"6",79,2203,18,0,0,172,1975,12543,12435,162,17,1,747,3,188,9,9, 435,1,1,0,0,0, 1, 1999,3,1000000,4,3,128,4,500 -"6",228,2198,9,0,0,173,1974,12543,12435,160,17,1,361,14,578,3,9, 418,1,1,0,0,0, 1, 2021,3,1000000,4,3,128,4,500 -"6",283,2198,9,0,0,173,1973,12543,12435,40,17,1,357,14,583,3,9, 411,2,1,0,0,0, 1, 2027,3,1000000,4,3,128,4,500 -"6",172,2203,9,0,0,169,1976,12543,12435,161,17,1,714,17,221,4,9, 424,1,1,0,0,0, 1, 2010,3,1000000,4,3,128,4,500 -"6",379,2199,9,0,0,173,1973,12543,12435,160,17,1,391,17,548,1,9, 401,1,1,0,0,0, 1, 2037,3,1000000,4,3,128,4,500 -"6",308,2200,9,0,0,173,1974,12543,12435,53,17,1,391,13,548,5,9, 409,1,1,0,0,0, 1, 2029,3,1000000,4,3,128,4,500 -"6",31,2200,9,0,0,171,1976,12543,12435,163,17,1,384,10,555,8,9, 440,1,1,0,0,0, 1, 1998,3,1000000,4,3,128,4,500 -"6",409,2203,9,0,0,172,1976,12543,12435,94,17,1,735,15,202,5,9, 398,1,1,0,0,0, 1, 2037,3,1000000,4,3,128,4,500 -"6",32,2200,9,0,0,175,1973,12543,12435,105,17,1,384,12,556,5,9, 439,1,1,0,0,0, 1, 1999,3,1000000,4,3,128,4,500 -"6",64,2205,9,0,0,170,1975,12543,12435,104,16,1,718,15,217,6,9, 436,1,1,0,0,0, 1, 1998,3,1000000,4,3,128,4,500 -"6",323,2203,9,0,0,175,1971,12543,12435,97,17,1,388,16,552,2,9, 407,1,1,0,0,0, 1, 2031,3,1000000,4,3,128,4,500 -"6",15,2227,9,0,0,170,2004,12543,12435,113,17,1,421,10,518,8,9, 442,2,1,0,0,0, 1, 1996,3,1000000,4,3,128,4,500 -"6",511,3727,17,0,0,331,3331,12543,12435,159,17,1,771,4,165,8,9, 388,1,1,0,0,0, 2, 2047,3,1000000,4,3,128,4,500 -"6",2,3830,8,0,0,328,3443,12543,12435,76,17,1,431,5,511,10,9, 445,1,1,0,0,0, 1, 1995,3,1000000,4,3,128,4,500 -"6",218,3860,9,0,0,331,3469,12543,12435,17,17,1,405,11,537,4,9, 422,1,1,0,0,0, 1, 2019,3,1000000,4,3,128,4,500 -"6",18,3939,9,0,1,328,3550,12543,12435,162,17,1,382,4,560,10,9, 444,1,1,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 -"6",383,3955,18,0,0,336,3558,12543,12435,160,17,1,418,4,523,1,9, 401,1,1,0,0,0, 2, 2039,3,1000000,4,3,128,4,500 -"6",8,4062,8,0,0,329,3674,12543,12435,95,17,1,345,6,598,8,9, 445,1,1,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 -"6",12,4066,9,0,0,326,3680,12543,12435,162,17,1,358,5,585,8,9, 444,1,1,0,0,0, 1, 1997,3,1000000,4,3,128,4,500 -"6",386,4189,9,0,0,331,3799,12543,12435,76,17,1,339,8,606,4,9, 402,1,1,0,0,0, 1, 2041,3,1000000,4,3,128,4,500 -"6",272,4197,9,0,1,337,3804,12543,12435,37,17,1,26,7,922,1,9, 416,1,1,0,0,0, 1, 2031,3,1000000,4,3,128,4,500 -"6",242,4204,10,0,1,333,3814,12543,12435,161,17,1,27,8,921,0,9, 419,1,1,0,0,0, 1, 2028,3,1000000,4,3,128,4,500 -"6",240,4300,9,0,0,334,3912,12543,12435,160,17,1,21,7,928,0,9, 417,2,1,0,0,0, 1, 2030,3,1000000,4,3,128,4,500 -"6",249,4305,9,0,1,328,3921,12543,12435,160,17,1,57,7,891,2,9, 418,1,1,0,0,0, 1, 2028,3,1000000,4,3,128,4,500 -"6",368,4325,9,0,0,331,3934,12543,12435,161,17,1,321,12,622,1,9, 404,1,1,0,0,0, 1, 2038,3,1000000,4,3,128,4,500 diff --git a/hpc/archer2/data/ijhpca/weak_fft_10984759.csv b/hpc/archer2/data/ijhpca/weak_fft_10984759.csv deleted file mode 100644 index 8aa63b13..00000000 --- a/hpc/archer2/data/ijhpca/weak_fft_10984759.csv +++ /dev/null @@ -1,1018 +0,0 @@ - -experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,source_tree,target_tree,source_domain,target_domain,layout,ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,displacement_map,metadata_creation,expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples -"0",0,121,0,0,0,0,0,2970,2935,4,3,0,0,0,0,121,0, 386,0,0,0,0,0, 0, 40,3,250000,4,1,128,4,500 -"0",5,241,0,0,0,84,141,2970,2935,4,3,0,8,9,103,0,0, 386,1,0,0,0,0, 0, 151,3,250000,4,1,128,4,500 -"0",1,325,8,0,0,161,143,2970,2934,4,3,0,86,3,20,0,0, 386,1,0,0,0,0, 1, 147,3,250000,4,1,128,4,500 -"0",2,322,4,0,0,162,144,2970,2934,4,3,0,9,4,101,0,0, 386,1,0,0,0,0, 1, 151,3,250000,4,1,128,4,500 -"0",4,322,4,0,0,164,143,2970,2934,4,3,0,8,4,103,0,0, 386,1,0,0,0,0, 1, 151,3,250000,4,1,128,4,500 -"0",6,329,4,0,0,161,148,2970,2934,4,3,0,83,7,24,0,0, 386,1,0,0,0,0, 1, 147,3,250000,4,1,128,4,500 -"0",3,330,4,0,0,162,151,2970,2934,4,3,0,13,4,97,0,0, 386,1,0,0,0,0, 1, 151,3,250000,4,1,128,4,500 -"0",7,645,8,0,0,329,296,2970,2934,4,3,0,78,1,30,0,0, 386,1,0,0,0,0, 2, 149,3,250000,4,1,128,4,500 -"1",0,566,7,1,1,471,55,1919,1898,4,3,0,66,20,17,4,0, 382,1,0,0,0,0, 3, 155,3,250000,4,2,128,4,500 -"1",4,579,8,1,1,482,55,1919,1898,4,3,0,64,19,19,0,5, 382,1,0,0,0,0, 3, 155,3,250000,4,2,128,4,500 -"1",7,579,12,2,1,480,59,1919,1898,4,3,0,63,11,22,0,4, 382,1,0,0,0,0, 4, 157,3,250000,4,2,128,4,500 -"1",1,761,11,1,2,649,77,1919,1898,4,3,0,59,10,30,0,3, 382,1,0,0,0,0, 4, 162,3,250000,4,2,128,4,500 -"1",2,770,10,1,2,659,77,1919,1898,4,3,0,60,10,29,0,4, 382,1,0,0,0,0, 4, 162,3,250000,4,2,128,4,500 -"1",5,773,12,2,2,658,78,1919,1898,4,3,0,63,9,25,0,3, 382,1,0,0,0,0, 4, 160,3,250000,4,2,128,4,500 -"1",11,776,10,1,1,666,75,1919,1898,5,3,0,44,11,46,0,3, 382,1,0,0,0,0, 4, 162,3,250000,4,2,128,4,500 -"1",9,779,10,1,2,665,78,1919,1898,5,3,0,44,11,46,0,3, 382,1,0,0,0,0, 4, 162,3,250000,4,2,128,4,500 -"1",12,774,11,1,2,666,79,1919,1898,4,3,0,44,7,51,0,1, 383,1,0,0,0,0, 4, 165,3,250000,4,2,128,4,500 -"1",13,780,10,1,2,670,74,1919,1898,4,3,0,47,11,44,0,2, 383,1,0,0,0,0, 4, 161,3,250000,4,2,128,4,500 -"1",14,776,8,1,2,669,77,1919,1898,4,3,0,43,11,52,0,1, 383,1,0,0,0,0, 4, 166,3,250000,4,2,128,4,500 -"1",10,779,10,1,2,674,76,1919,1898,4,3,0,43,8,51,0,1, 383,1,0,0,0,0, 4, 166,3,250000,4,2,128,4,500 -"1",8,782,10,1,2,674,77,1919,1898,4,3,0,46,9,47,0,1, 382,1,0,0,0,0, 4, 165,3,250000,4,2,128,4,500 -"1",6,902,7,1,2,781,94,1919,1898,5,3,0,25,6,74,0,2, 382,1,0,0,0,0, 4, 170,3,250000,4,2,128,4,500 -"1",15,920,13,2,2,790,93,1919,1898,4,3,0,65,8,23,0,3, 383,1,0,0,0,0, 5, 159,3,250000,4,2,128,4,500 -"1",3,922,13,2,3,792,95,1919,1898,4,3,0,62,5,28,0,4, 382,1,0,0,0,0, 5, 162,3,250000,4,2,128,4,500 -"2",0,238,3,0,0,158,47,2295,2281,5,3,1,71,18,17,3,0, 388,0,0,0,0,0, 1, 154,3,250000,4,2,128,4,500 -"2",1,453,6,0,1,328,97,2295,2280,5,3,1,60,9,32,0,4, 388,0,0,0,0,0, 2, 160,3,250000,4,2,128,4,500 -"2",2,453,6,0,0,328,99,2295,2280,4,3,1,59,8,36,0,3, 388,1,0,0,0,0, 2, 162,3,250000,4,2,128,4,500 -"2",31,459,9,1,1,333,97,2295,2280,5,3,1,70,8,22,0,3, 392,1,0,0,0,0, 2, 154,3,250000,4,2,128,4,500 -"2",22,459,6,0,1,332,100,2295,2280,4,3,1,58,8,36,0,3, 391,1,0,0,0,0, 2, 159,3,250000,4,2,128,4,500 -"2",13,462,9,1,1,332,100,2295,2280,4,3,1,65,7,27,0,4, 390,1,0,0,0,0, 2, 157,3,250000,4,2,128,4,500 -"2",10,457,3,0,0,332,101,2295,2281,6,3,1,20,10,78,0,2, 389,1,0,0,0,0, 1, 163,3,250000,4,2,128,4,500 -"2",17,460,6,0,0,332,101,2295,2280,4,3,1,55,9,40,0,3, 390,1,0,0,0,0, 2, 160,3,250000,4,2,128,4,500 -"2",4,459,6,0,1,333,100,2295,2281,5,3,1,57,8,40,0,2, 388,1,0,0,0,0, 2, 162,3,250000,4,2,128,4,500 -"2",9,463,9,1,1,334,99,2295,2280,4,3,1,64,7,28,0,4, 389,1,0,0,0,0, 2, 157,3,250000,4,2,128,4,500 -"2",5,463,9,1,0,335,99,2295,2280,5,3,1,64,7,28,0,4, 389,1,0,0,0,0, 2, 158,3,250000,4,2,128,4,500 -"2",3,461,6,0,1,332,102,2295,2280,5,3,1,57,9,38,0,3, 389,1,0,0,0,0, 2, 161,3,250000,4,2,128,4,500 -"2",29,464,6,0,0,337,98,2295,2280,4,3,1,62,11,31,0,4, 391,1,0,0,0,0, 2, 155,3,250000,4,2,128,4,500 -"2",30,460,3,0,1,334,101,2295,2281,5,3,1,28,11,69,0,3, 391,1,0,0,0,0, 1, 160,3,250000,4,2,128,4,500 -"2",20,461,6,0,1,333,102,2295,2280,6,3,0,57,9,39,0,2, 390,1,0,0,0,0, 2, 160,3,250000,4,2,128,4,500 -"2",27,466,6,0,0,335,100,2295,2280,5,3,1,67,12,24,0,4, 391,1,0,0,0,0, 2, 155,3,250000,4,2,128,4,500 -"2",15,464,9,1,1,334,102,2295,2280,5,3,1,59,7,34,0,3, 390,1,0,0,0,0, 2, 158,3,250000,4,2,128,4,500 -"2",8,460,3,0,0,337,100,2295,2281,4,3,1,21,9,78,0,3, 390,1,0,0,0,0, 1, 164,3,250000,4,2,128,4,500 -"2",21,463,6,0,0,332,105,2295,2280,4,3,1,56,9,39,0,2, 391,0,0,0,0,0, 2, 159,3,250000,4,2,128,4,500 -"2",6,462,6,0,0,337,101,2295,2280,5,3,1,24,6,74,0,3, 389,1,0,0,0,0, 2, 164,3,250000,4,2,128,4,500 -"2",26,467,6,0,0,338,99,2295,2280,4,3,1,63,11,30,0,3, 391,1,0,0,0,0, 2, 156,3,250000,4,2,128,4,500 -"2",16,463,4,0,1,341,97,2295,2281,7,3,1,26,8,72,0,3, 390,1,0,0,0,0, 2, 162,3,250000,4,2,128,4,500 -"2",28,466,6,0,1,336,103,2295,2280,6,3,0,59,9,36,0,3, 391,1,0,0,0,0, 2, 158,3,250000,4,2,128,4,500 -"2",19,464,3,0,1,339,101,2295,2281,5,3,1,24,10,73,0,3, 390,1,0,0,0,0, 2, 162,3,250000,4,2,128,4,500 -"2",11,467,6,0,1,339,101,2295,2280,4,3,1,56,9,39,0,2, 389,1,0,0,0,0, 2, 160,3,250000,4,2,128,4,500 -"2",12,467,6,0,1,340,101,2295,2280,5,3,1,58,9,38,0,2, 389,1,0,0,0,0, 2, 161,3,250000,4,2,128,4,500 -"2",18,467,9,1,1,341,99,2295,2280,4,3,1,61,5,34,0,1, 390,1,0,0,0,0, 2, 160,3,250000,4,2,128,4,500 -"2",24,469,6,0,1,343,101,2295,2280,6,3,1,21,8,76,0,1, 391,1,0,0,0,0, 1, 161,3,250000,4,2,128,4,500 -"2",7,470,6,0,1,341,104,2295,2281,4,3,1,22,7,75,0,2, 389,1,0,0,0,0, 2, 164,3,250000,4,2,128,4,500 -"2",14,470,3,0,1,341,105,2295,2281,5,3,1,21,10,77,0,2, 390,1,0,0,0,0, 1, 163,3,250000,4,2,128,4,500 -"2",23,477,9,1,1,343,104,2295,2280,4,3,1,66,7,26,0,4, 391,1,0,0,0,0, 2, 156,3,250000,4,2,128,4,500 -"2",25,654,6,0,1,479,152,2295,2281,5,3,0,16,4,84,0,1, 391,1,0,0,0,0, 3, 164,3,250000,4,2,128,4,500 -"3",40,25,4,0,0,0,0,2909,2983,6,4,1,136,14,14,3,1, 387,1,0,0,0,0, 0, 227,3,250000,4,2,128,4,500 -"3",28,25,4,0,0,0,0,2909,2983,5,3,1,131,16,19,1,3, 389,1,0,0,0,0, 0, 224,3,250000,4,2,128,4,500 -"3",1,26,4,0,0,0,0,2909,2983,5,3,1,133,15,17,2,3, 392,1,0,0,0,0, 0, 221,3,250000,4,2,128,4,500 -"3",53,26,4,0,0,0,0,2909,2983,6,3,1,135,15,15,3,1, 386,1,0,0,0,0, 0, 228,3,250000,4,2,128,4,500 -"3",60,26,4,0,0,0,0,2909,2983,5,3,1,134,15,17,3,1, 385,1,0,0,0,0, 0, 229,3,250000,4,2,128,4,500 -"3",57,26,5,0,0,0,0,2909,2983,5,3,1,133,14,17,3,1, 385,1,0,0,0,0, 0, 228,3,250000,4,2,128,4,500 -"3",35,272,0,0,0,94,157,2909,2984,6,3,1,31,10,130,2,1, 388,1,0,0,0,0, 0, 236,3,250000,4,2,128,4,500 -"3",0,323,4,0,0,157,139,2909,2983,4,3,1,122,10,32,3,0, 375,1,1,0,0,0, 1, 242,3,250000,4,2,128,4,500 -"3",27,334,4,0,0,168,138,2909,2983,6,3,1,119,11,35,1,3, 389,1,0,0,0,0, 1, 227,3,250000,4,2,128,4,500 -"3",19,332,4,0,0,169,138,2909,2983,6,3,1,48,9,109,1,3, 390,1,0,0,0,0, 1, 230,3,250000,4,2,128,4,500 -"3",55,336,4,0,0,169,139,2909,2983,6,3,1,123,12,31,2,1, 386,1,0,0,0,0, 1, 231,3,250000,4,2,128,4,500 -"3",10,334,4,0,0,169,140,2909,2983,6,3,1,41,9,116,1,3, 391,1,0,0,0,0, 1, 229,3,250000,4,2,128,4,500 -"3",9,335,4,0,0,169,144,2909,2983,5,3,1,42,7,117,1,3, 391,1,0,0,0,0, 1, 230,3,250000,4,2,128,4,500 -"3",36,336,4,0,0,173,140,2909,2983,5,3,1,44,9,114,1,1, 387,1,0,0,0,0, 1, 234,3,250000,4,2,128,4,500 -"3",5,336,4,0,0,170,144,2909,2983,6,3,1,42,6,117,1,3, 392,1,0,0,0,0, 1, 230,3,250000,4,2,128,4,500 -"3",42,341,4,0,0,170,143,2909,2983,5,3,1,118,10,36,3,1, 387,1,0,0,0,0, 1, 231,3,250000,4,2,128,4,500 -"3",62,341,4,0,0,171,143,2909,2983,5,3,1,120,11,35,2,1, 385,1,0,0,0,0, 1, 233,3,250000,4,2,128,4,500 -"3",14,337,4,0,0,170,144,2909,2983,6,3,1,32,7,126,0,3, 391,1,0,0,0,0, 1, 231,3,250000,4,2,128,4,500 -"3",12,337,4,0,0,171,144,2909,2983,4,3,1,34,7,125,0,3, 391,1,0,0,0,0, 1, 231,3,250000,4,2,128,4,500 -"3",37,338,4,0,0,172,143,2909,2983,5,3,1,43,7,116,2,1, 387,1,0,0,0,0, 1, 235,3,250000,4,2,128,4,500 -"3",38,338,4,0,0,171,144,2909,2983,5,3,1,37,7,121,2,1, 387,1,0,0,0,0, 1, 235,3,250000,4,2,128,4,500 -"3",47,339,4,0,0,171,144,2909,2983,5,3,1,52,8,106,1,1, 387,1,0,0,0,0, 1, 235,3,250000,4,2,128,4,500 -"3",2,339,4,0,0,171,144,2909,2983,4,3,1,42,6,117,1,3, 392,1,1,0,0,0, 1, 230,3,250000,4,2,128,4,500 -"3",32,338,4,0,0,173,144,2909,2984,7,3,1,44,6,116,2,1, 388,1,0,0,0,0, 1, 235,3,250000,4,2,128,4,500 -"3",45,344,4,0,0,170,148,2909,2983,5,3,1,122,10,33,3,1, 387,1,0,0,0,0, 1, 231,3,250000,4,2,128,4,500 -"3",52,340,4,0,0,173,145,2909,2983,5,3,1,48,8,112,1,1, 386,1,0,0,0,0, 1, 236,3,250000,4,2,128,4,500 -"3",17,341,4,0,0,168,150,2909,2983,4,3,1,41,7,117,1,3, 390,1,0,0,0,0, 1, 231,3,250000,4,2,128,4,500 -"3",50,340,4,0,0,172,145,2909,2983,7,3,1,37,8,123,1,1, 386,1,0,0,0,0, 1, 236,3,250000,4,2,128,4,500 -"3",3,341,4,0,0,168,150,2909,2983,6,3,1,45,7,113,1,3, 392,1,0,0,0,0, 1, 230,3,250000,4,2,128,4,500 -"3",4,341,4,0,0,171,150,2909,2983,6,3,1,27,7,133,0,3, 392,1,0,0,0,0, 1, 231,3,250000,4,2,128,4,500 -"3",25,342,4,0,0,171,149,2909,2983,6,3,1,37,7,122,0,3, 389,1,0,0,0,0, 1, 233,3,250000,4,2,128,4,500 -"3",41,346,4,0,0,170,150,2909,2983,6,3,1,113,9,43,2,1, 387,1,0,0,0,0, 1, 232,3,250000,4,2,128,4,500 -"3",7,343,4,0,0,170,150,2909,2983,6,3,1,31,7,128,0,3, 392,1,0,0,0,0, 1, 230,3,250000,4,2,128,4,500 -"3",13,344,4,0,0,172,150,2909,2983,8,3,1,30,7,129,0,3, 391,1,0,0,0,0, 1, 231,3,250000,4,2,128,4,500 -"3",11,344,4,0,0,172,151,2909,2983,5,3,1,35,7,124,0,3, 391,1,0,0,0,0, 1, 231,3,250000,4,2,128,4,500 -"3",31,344,4,0,0,172,151,2909,2983,5,3,1,35,7,124,0,3, 389,1,0,0,0,0, 1, 234,3,250000,4,2,128,4,500 -"3",44,348,4,0,0,171,151,2909,2983,6,4,1,113,11,43,1,1, 387,1,0,0,0,0, 1, 232,3,250000,4,2,128,4,500 -"3",23,346,4,0,0,172,152,2909,2983,8,3,1,37,8,122,0,3, 390,1,0,0,0,0, 1, 232,3,250000,4,2,128,4,500 -"3",30,345,4,0,0,172,150,2909,2983,4,3,1,30,7,129,0,3, 389,1,0,0,0,0, 1, 234,3,250000,4,2,128,4,500 -"3",6,345,4,0,0,170,153,2909,2983,4,3,1,27,6,132,0,3, 392,2,0,0,0,0, 1, 231,3,250000,4,2,128,4,500 -"3",16,345,4,0,0,172,152,2909,2983,7,3,1,25,7,135,0,3, 390,1,0,0,0,0, 0, 232,3,250000,4,2,128,4,500 -"3",21,346,4,0,0,173,152,2909,2983,5,3,1,32,7,127,0,3, 390,1,0,0,0,0, 1, 232,3,250000,4,2,128,4,500 -"3",48,347,4,0,0,175,151,2909,2983,5,3,1,26,6,134,2,1, 387,1,0,0,0,0, 1, 237,3,250000,4,2,128,4,500 -"3",61,351,4,0,0,170,155,2909,2983,5,3,1,115,10,41,1,1, 385,2,0,0,0,0, 1, 234,3,250000,4,2,128,4,500 -"3",20,348,4,0,0,171,156,2909,2983,6,3,1,35,7,124,0,3, 390,1,0,0,0,0, 1, 233,3,250000,4,2,128,4,500 -"3",58,352,4,0,0,170,156,2909,2983,6,3,1,114,10,42,2,1, 385,1,0,0,0,0, 1, 234,3,250000,4,2,128,4,500 -"3",8,349,4,0,0,171,158,2909,2983,7,3,1,22,6,138,0,3, 391,1,0,0,0,0, 1, 232,3,250000,4,2,128,4,500 -"3",34,353,5,0,0,176,151,2909,2983,6,3,1,114,8,42,2,1, 388,1,0,0,0,0, 1, 231,3,250000,4,2,128,4,500 -"3",29,349,4,0,0,172,157,2909,2983,6,3,1,27,6,134,0,3, 388,1,0,0,0,0, 1, 235,3,250000,4,2,128,4,500 -"3",24,350,4,0,0,171,158,2909,2983,5,3,1,32,6,128,0,3, 389,1,0,0,0,0, 1, 234,3,250000,4,2,128,4,500 -"3",18,350,4,0,0,172,156,2909,2983,6,3,1,27,7,133,0,3, 390,1,0,0,0,0, 1, 232,3,250000,4,2,128,4,500 -"3",15,350,4,0,0,172,157,2909,2983,6,3,1,24,6,136,0,3, 391,1,0,0,0,0, 1, 232,3,250000,4,2,128,4,500 -"3",54,355,4,0,0,171,157,2909,2983,5,3,1,115,11,41,2,1, 386,1,0,0,0,0, 1, 233,3,250000,4,2,128,4,500 -"3",43,354,4,0,0,171,158,2909,2983,6,3,1,108,10,49,1,1, 387,1,0,0,0,0, 1, 233,3,250000,4,2,128,4,500 -"3",51,353,4,0,0,174,156,2909,2983,5,3,1,37,8,123,1,1, 386,1,0,0,0,0, 1, 236,3,250000,4,2,128,4,500 -"3",39,352,4,0,0,175,155,2909,2983,5,3,1,36,7,123,2,1, 387,1,0,0,0,0, 1, 235,3,250000,4,2,128,4,500 -"3",22,352,4,0,0,173,158,2909,2983,5,3,1,19,6,141,0,3, 390,1,0,0,0,0, 0, 234,3,250000,4,2,128,4,500 -"3",49,353,4,0,0,176,156,2909,2983,6,3,1,25,6,136,1,1, 386,1,0,0,0,0, 1, 237,3,250000,4,2,128,4,500 -"3",63,643,8,0,0,326,291,2909,2984,5,3,1,116,4,41,1,1, 384,1,0,0,0,0, 2, 236,3,250000,4,2,128,4,500 -"3",26,648,4,0,0,338,289,2909,2984,6,3,1,21,4,142,0,3, 389,1,0,0,0,0, 1, 236,3,250000,4,2,128,4,500 -"3",59,658,4,0,0,337,298,2909,2984,5,3,1,32,5,130,0,1, 385,1,0,0,0,0, 1, 240,3,250000,4,2,128,4,500 -"3",33,661,4,0,0,339,301,2909,2984,5,3,1,29,4,133,1,1, 388,1,0,0,0,0, 1, 238,3,250000,4,2,128,4,500 -"3",56,673,4,0,0,341,312,2909,2984,6,3,1,15,4,149,0,1, 385,1,0,0,0,0, 1, 242,3,250000,4,2,128,4,500 -"3",46,677,4,0,1,341,314,2909,2984,6,3,1,25,3,137,1,1, 387,1,0,0,0,0, 1, 239,3,250000,4,2,128,4,500 -"4",107,601,10,1,1,484,57,2008,1954,51,4,1,99,24,30,6,9, 385,1,0,0,0,0, 3, 254,3,250000,4,3,128,4,500 -"4",4,605,10,1,1,487,56,2007,1954,50,3,1,102,23,24,9,9, 397,1,1,0,0,0, 3, 239,3,250000,4,3,128,4,500 -"4",19,602,10,1,1,487,57,2007,1954,49,3,1,99,20,30,9,9, 395,1,0,0,0,0, 3, 245,3,250000,4,3,128,4,500 -"4",10,602,10,1,1,485,58,2008,1954,50,3,1,99,23,30,6,9, 396,1,0,0,0,0, 3, 244,3,250000,4,3,128,4,500 -"4",125,605,10,1,1,488,57,2007,1954,51,4,1,101,18,28,12,9, 382,1,0,0,0,0, 3, 257,3,250000,4,3,128,4,500 -"4",31,601,7,1,1,489,60,2007,1954,51,3,1,76,22,60,4,9, 394,1,0,0,0,0, 3, 252,3,250000,4,3,128,4,500 -"4",88,601,10,1,1,493,58,2008,1954,51,4,1,76,18,59,4,9, 387,1,1,0,0,0, 4, 258,3,250000,4,3,128,4,500 -"4",43,609,10,1,1,495,59,2007,1954,52,3,1,93,21,38,5,9, 393,1,0,0,0,0, 4, 249,3,250000,4,3,128,4,500 -"4",112,612,10,1,1,498,59,2007,1954,50,3,1,95,23,36,3,9, 384,1,1,0,0,0, 3, 258,3,250000,4,3,128,4,500 -"4",27,778,10,1,1,653,76,2007,1954,50,3,1,61,15,77,4,9, 394,1,1,0,0,0, 4, 255,3,250000,4,3,128,4,500 -"4",0,787,8,1,2,658,73,2007,1954,50,3,1,88,14,44,15,0, 373,1,1,0,0,0, 5, 265,3,250000,4,3,128,4,500 -"4",34,780,8,1,2,656,76,2007,1954,50,3,1,60,13,80,7,9, 393,1,0,0,0,0, 4, 257,3,250000,4,3,128,4,500 -"4",11,782,10,1,2,655,78,2008,1954,50,3,1,75,14,63,4,9, 396,1,1,0,0,0, 4, 253,3,250000,4,3,128,4,500 -"4",26,781,10,1,1,658,75,2007,1954,50,3,1,58,14,82,3,9, 394,1,0,0,0,0, 4, 256,3,250000,4,3,128,4,500 -"4",29,779,10,1,1,654,79,2007,1954,50,4,1,55,14,87,1,9, 394,1,0,0,0,0, 4, 258,3,250000,4,3,128,4,500 -"4",79,779,10,1,2,658,75,2007,1954,50,3,1,38,13,104,3,9, 388,1,0,0,0,0, 4, 264,3,250000,4,3,128,4,500 -"4",127,794,10,1,2,660,75,2007,1954,50,4,1,103,17,24,13,9, 382,1,1,0,0,0, 4, 256,3,250000,4,3,128,4,500 -"4",42,784,10,1,1,655,80,2007,1954,51,3,1,76,14,64,4,9, 393,1,1,0,0,0, 4, 257,3,250000,4,3,128,4,500 -"4",12,782,7,1,2,657,78,2007,1954,50,3,1,71,16,70,4,9, 395,1,1,0,0,0, 4, 256,3,250000,4,3,128,4,500 -"4",105,793,10,1,2,661,74,2007,1954,51,3,1,101,19,29,9,9, 385,1,0,0,0,0, 4, 255,3,250000,4,3,128,4,500 -"4",87,783,10,1,1,659,77,2007,1954,50,4,1,55,13,85,3,9, 388,1,1,0,0,0, 4, 263,3,250000,4,3,128,4,500 -"4",72,783,12,2,1,661,76,2007,1954,50,3,1,57,12,83,2,9, 389,1,0,0,0,0, 5, 261,3,250000,4,3,128,4,500 -"4",73,789,10,1,2,663,72,2007,1954,51,4,1,66,16,69,7,9, 389,1,0,0,0,0, 4, 256,3,250000,4,3,128,4,500 -"4",37,786,7,1,2,661,76,2007,1954,51,3,1,63,14,75,8,9, 393,1,1,0,0,0, 4, 256,3,250000,4,3,128,4,500 -"4",69,787,10,1,1,663,76,2007,1954,50,4,1,56,12,84,5,9, 389,1,0,0,0,0, 4, 261,3,250000,4,3,128,4,500 -"4",24,790,10,1,2,664,76,2007,1954,50,4,1,98,15,40,4,9, 394,1,1,0,0,0, 4, 254,3,250000,4,3,128,4,500 -"4",28,785,10,1,2,660,79,2007,1954,51,4,1,53,12,90,3,9, 394,1,0,0,0,0, 4, 260,3,250000,4,3,128,4,500 -"4",98,790,10,1,1,661,80,2007,1954,50,3,1,75,15,64,4,9, 386,1,1,0,0,0, 4, 264,3,250000,4,3,128,4,500 -"4",90,795,12,2,2,666,76,2007,1954,51,3,1,77,15,57,4,9, 387,1,1,0,0,0, 4, 258,3,250000,4,3,128,4,500 -"4",76,788,10,1,1,664,78,2008,1954,50,3,1,55,13,86,3,9, 389,1,1,0,0,0, 4, 263,3,250000,4,3,128,4,500 -"4",52,797,10,1,2,666,76,2007,1954,50,3,1,96,15,37,9,9, 391,1,0,0,0,0, 4, 252,3,250000,4,3,128,4,500 -"4",113,789,10,1,2,663,79,2007,1954,51,3,1,71,13,69,3,9, 384,1,1,0,0,0, 4, 267,3,250000,4,3,128,4,500 -"4",80,785,10,1,1,665,77,2007,1954,50,4,1,31,11,114,2,9, 389,1,1,0,0,0, 4, 266,3,250000,4,3,128,4,500 -"4",2,799,10,1,2,669,73,2007,1954,50,3,1,99,16,32,10,9, 396,1,1,0,0,0, 4, 245,3,250000,4,3,128,4,500 -"4",54,799,10,1,2,669,73,2007,1954,51,4,1,97,14,35,12,9, 392,1,1,0,0,0, 4, 250,3,250000,4,3,128,4,500 -"4",104,798,10,1,1,664,79,2007,1954,50,4,1,95,16,37,9,9, 385,1,0,0,0,0, 4, 258,3,250000,4,3,128,4,500 -"4",86,789,10,1,1,665,78,2007,1954,51,3,1,54,14,88,1,9, 388,1,0,0,0,0, 4, 265,3,250000,4,3,128,4,500 -"4",70,788,10,1,1,664,79,2007,1954,51,3,1,51,14,92,1,9, 389,1,0,0,0,0, 4, 264,3,250000,4,3,128,4,500 -"4",66,787,8,1,1,667,78,2007,1954,50,3,1,31,13,113,2,9, 390,1,0,0,0,0, 4, 265,3,250000,4,3,128,4,500 -"4",45,798,10,1,1,667,77,2007,1954,51,3,1,95,14,38,10,9, 392,1,1,0,0,0, 4, 251,3,250000,4,3,128,4,500 -"4",74,790,10,1,1,668,77,2007,1954,50,3,1,54,13,87,3,9, 389,1,0,0,0,0, 4, 263,3,250000,4,3,128,4,500 -"4",1,801,13,2,2,668,76,2007,1954,50,4,1,103,9,27,13,9, 396,1,0,0,0,0, 5, 245,3,250000,4,3,128,4,500 -"4",82,788,10,1,1,667,78,2007,1954,51,3,1,34,13,110,1,9, 388,1,0,0,0,0, 4, 265,3,250000,4,3,128,4,500 -"4",84,789,13,2,2,664,79,2007,1954,50,4,1,51,10,92,1,9, 388,1,1,0,0,0, 4, 265,3,250000,4,3,128,4,500 -"4",7,794,10,1,2,665,80,2007,1954,50,4,1,77,11,62,7,9, 396,1,0,0,0,0, 4, 253,3,250000,4,3,128,4,500 -"4",3,801,10,1,2,666,79,2007,1954,50,4,1,97,15,35,9,9, 396,1,0,0,0,0, 4, 246,3,250000,4,3,128,4,500 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268,3,250000,4,3,128,4,500 -"4",8,797,10,1,1,672,76,2008,1954,50,4,1,78,12,60,8,9, 396,1,1,0,0,0, 4, 253,3,250000,4,3,128,4,500 -"4",85,794,10,1,1,669,79,2008,1954,51,4,1,52,14,90,1,9, 388,1,1,0,0,0, 4, 265,3,250000,4,3,128,4,500 -"4",55,804,10,1,1,673,75,2007,1954,51,4,1,97,13,35,13,9, 391,1,1,0,0,0, 4, 251,3,250000,4,3,128,4,500 -"4",40,799,10,1,2,669,80,2008,1954,50,3,1,77,12,60,8,9, 393,1,1,0,0,0, 4, 255,3,250000,4,3,128,4,500 -"4",83,795,10,1,2,670,79,2008,1954,52,4,1,36,14,106,2,9, 388,2,0,0,0,0, 4, 264,3,250000,4,3,128,4,500 -"4",59,797,7,1,1,671,78,2007,1954,51,3,1,73,18,67,3,9, 391,1,0,0,0,0, 4, 259,3,250000,4,3,128,4,500 -"4",77,800,10,1,2,675,74,2007,1954,4,4,1,64,13,74,6,9, 388,1,0,0,0,0, 4, 260,3,250000,4,3,128,4,500 -"4",102,806,10,1,2,673,77,2007,1954,50,4,1,97,12,36,12,9, 385,1,1,0,0,0, 4, 257,3,250000,4,3,128,4,500 -"4",21,802,11,1,1,672,78,2007,1954,50,4,1,95,13,42,6,9, 395,1,0,0,0,0, 4, 253,3,250000,4,3,128,4,500 -"4",68,797,10,1,2,674,77,2007,1954,51,4,1,53,12,88,3,9, 389,1,0,0,0,0, 4, 262,3,250000,4,3,128,4,500 -"4",53,805,10,1,2,672,79,2007,1954,50,3,1,95,12,39,10,9, 391,2,0,0,0,0, 4, 253,3,250000,4,3,128,4,500 -"4",92,799,10,1,1,671,81,2008,1954,50,3,1,75,14,65,4,9, 387,1,1,0,0,0, 4, 263,3,250000,4,3,128,4,500 -"4",56,802,10,1,2,674,77,2007,1954,50,3,1,96,15,42,4,9, 391,1,0,0,0,0, 4, 257,3,250000,4,3,128,4,500 -"4",5,802,10,1,1,672,79,2007,1954,50,3,1,80,12,58,8,9, 397,1,0,0,0,0, 4, 251,3,250000,4,3,128,4,500 -"4",121,805,12,2,2,673,79,2007,1954,50,3,1,94,15,40,5,9, 383,1,0,0,0,0, 5, 262,3,250000,4,3,128,4,500 -"4",14,802,11,1,2,672,80,2007,1954,50,4,1,92,14,47,4,9, 396,1,1,0,0,0, 4, 253,3,250000,4,3,128,4,500 -"4",39,803,10,1,2,674,78,2008,1954,51,3,1,80,12,57,9,9, 393,1,0,0,0,0, 4, 254,3,250000,4,3,128,4,500 -"4",6,802,10,1,2,676,77,2007,1954,50,3,1,77,11,61,8,9, 396,1,1,0,0,0, 4, 252,3,250000,4,3,128,4,500 -"4",93,807,10,1,1,678,76,2007,1954,50,4,1,80,18,54,4,9, 387,1,1,0,0,0, 4, 258,3,250000,4,3,128,4,500 -"4",67,801,12,2,2,672,80,2007,1954,51,3,1,57,12,83,2,9, 390,1,1,0,0,0, 4, 261,3,250000,4,3,128,4,500 -"4",46,805,10,1,2,675,79,2007,1954,50,3,1,95,12,42,8,9, 392,1,1,0,0,0, 4, 255,3,250000,4,3,128,4,500 -"4",101,811,13,2,1,677,77,2007,1954,51,4,1,101,9,30,14,9, 385,1,1,0,0,0, 4, 256,3,250000,4,3,128,4,500 -"4",119,805,8,1,1,678,77,2007,1954,50,3,1,79,14,59,9,9, 383,1,0,0,0,0, 4, 265,3,250000,4,3,128,4,500 -"4",41,805,10,1,2,676,79,2008,1954,50,3,1,77,12,60,8,9, 393,1,0,0,0,0, 4, 255,3,250000,4,3,128,4,500 -"4",106,808,10,1,2,675,79,2007,1954,50,3,1,94,18,41,5,9, 385,1,1,0,0,0, 4, 260,3,250000,4,3,128,4,500 -"4",64,805,10,1,1,681,74,2007,1954,51,4,1,79,12,58,8,9, 390,1,0,0,0,0, 4, 258,3,250000,4,3,128,4,500 -"4",109,809,10,1,2,679,75,2007,1954,51,3,1,85,10,49,13,9, 384,1,1,0,0,0, 4, 260,3,250000,4,3,128,4,500 -"4",115,803,7,1,2,677,77,2007,1954,51,4,1,75,18,65,3,9, 383,1,1,0,0,0, 4, 267,3,250000,4,3,128,4,500 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249,3,250000,4,3,128,4,500 -"4",116,808,10,1,2,679,79,2007,1954,51,4,1,96,15,41,4,9, 383,1,0,0,0,0, 4, 264,3,250000,4,3,128,4,500 -"4",23,812,10,1,2,682,76,2008,1954,50,3,1,95,17,39,7,9, 395,1,0,0,0,0, 4, 249,3,250000,4,3,128,4,500 -"4",97,809,10,1,2,679,79,2007,1954,50,4,1,77,13,60,7,9, 386,1,1,0,0,0, 4, 262,3,250000,4,3,128,4,500 -"4",114,805,10,1,1,681,78,2007,1954,50,3,1,72,14,69,3,9, 384,1,1,0,0,0, 4, 267,3,250000,4,3,128,4,500 -"4",63,809,10,1,1,681,77,2007,1954,51,3,1,80,10,57,10,9, 390,1,0,0,0,0, 4, 257,3,250000,4,3,128,4,500 -"4",117,810,10,1,1,681,78,2008,1954,51,3,1,96,11,41,10,9, 383,1,1,0,0,0, 4, 264,3,250000,4,3,128,4,500 -"4",38,814,12,2,2,683,76,2007,1954,50,4,1,83,13,49,8,9, 393,1,1,0,0,0, 4, 250,3,250000,4,3,128,4,500 -"4",94,809,11,1,2,682,78,2007,1954,50,4,1,77,15,61,4,9, 387,1,0,0,0,0, 4, 261,3,250000,4,3,128,4,500 -"4",44,810,10,1,2,680,79,2007,1954,51,3,1,96,16,41,4,9, 392,1,1,0,0,0, 4, 255,3,250000,4,3,128,4,500 -"4",33,811,10,1,2,681,79,2007,1954,51,3,1,80,14,57,7,9, 393,1,1,0,0,0, 4, 254,3,250000,4,3,128,4,500 -"4",122,811,10,1,2,681,79,2007,1954,51,4,1,92,17,45,4,9, 383,1,0,0,0,0, 4, 264,3,250000,4,3,128,4,500 -"4",30,809,13,2,1,681,80,2007,1954,50,3,1,79,13,60,3,9, 394,1,0,0,0,0, 4, 255,3,250000,4,3,128,4,500 -"4",95,816,10,1,2,683,76,2007,1954,50,4,1,81,20,52,5,9, 387,1,1,0,0,0, 4, 256,3,250000,4,3,128,4,500 -"4",62,810,10,1,1,680,80,2007,1954,50,3,1,78,10,60,9,9, 391,1,1,0,0,0, 4, 258,3,250000,4,3,128,4,500 -"4",99,810,10,1,2,681,80,2007,1954,51,3,1,77,15,62,3,9, 385,1,1,0,0,0, 4, 263,3,250000,4,3,128,4,500 -"4",96,812,10,1,2,683,78,2007,1954,50,4,1,76,16,62,4,9, 386,1,1,0,0,0, 4, 262,3,250000,4,3,128,4,500 -"4",78,804,8,1,1,684,79,2007,1954,50,3,1,31,11,116,2,9, 388,1,1,0,0,0, 4, 269,3,250000,4,3,128,4,500 -"4",57,813,10,1,2,684,80,2007,1954,51,3,1,91,15,48,3,9, 391,1,1,0,0,0, 4, 258,3,250000,4,3,128,4,500 -"4",61,813,10,1,1,685,79,2007,1954,50,3,1,78,9,60,9,9, 390,1,1,0,0,0, 4, 259,3,250000,4,3,128,4,500 -"4",48,819,10,1,2,679,85,2007,1954,51,3,1,95,15,39,9,9, 392,1,0,0,0,0, 4, 252,3,250000,4,3,128,4,500 -"4",13,815,10,1,2,686,79,2007,1954,4,4,1,92,11,45,9,9, 396,1,1,0,0,0, 5, 251,3,250000,4,3,128,4,500 -"4",110,816,10,1,1,688,77,2007,1954,50,3,1,80,10,59,8,9, 384,1,0,0,0,0, 4, 265,3,250000,4,3,128,4,500 -"4",49,818,10,1,2,687,80,2007,1954,50,3,1,94,13,43,8,9, 392,1,1,0,0,0, 4, 256,3,250000,4,3,128,4,500 -"4",58,818,10,1,2,689,80,2007,1954,50,3,1,94,15,44,3,9, 391,1,1,0,0,0, 4, 258,3,250000,4,3,128,4,500 -"4",50,826,10,1,2,697,75,2008,1954,52,4,1,96,15,38,9,9, 392,1,1,0,0,0, 4, 252,3,250000,4,3,128,4,500 -"4",18,937,10,1,2,794,92,2007,1954,50,3,1,82,11,56,8,9, 395,1,1,0,0,0, 5, 253,3,250000,4,3,128,4,500 -"4",65,934,10,1,3,792,95,2007,1954,51,3,1,57,10,86,4,9, 390,2,0,0,0,0, 4, 263,3,250000,4,3,128,4,500 -"4",111,942,10,1,2,798,94,2007,1954,51,3,1,84,9,54,9,9, 384,1,1,0,0,0, 5, 264,3,250000,4,3,128,4,500 -"4",9,943,10,1,2,798,96,2007,1954,50,3,1,76,10,64,8,9, 396,1,0,0,0,0, 4, 254,3,250000,4,3,128,4,500 -"4",126,948,12,2,2,799,95,2007,1954,50,3,1,97,9,38,10,9, 382,1,1,0,0,0, 5, 263,3,250000,4,3,128,4,500 -"4",89,942,10,1,2,802,94,2007,1954,51,4,1,60,12,82,3,9, 387,1,0,0,0,0, 4, 265,3,250000,4,3,128,4,500 -"4",25,942,7,1,2,799,98,2007,1954,50,4,1,52,13,92,2,9, 394,1,1,0,0,0, 4, 261,3,250000,4,3,128,4,500 -"4",124,947,10,1,2,803,97,2007,1954,51,4,1,76,12,65,4,9, 383,1,0,0,0,0, 5, 269,3,250000,4,3,128,4,500 -"4",32,950,13,2,2,807,94,2007,1954,51,4,1,80,10,60,4,9, 393,1,1,0,0,0, 5, 257,3,250000,4,3,128,4,500 -"5",144,261,6,0,0,167,46,2365,2384,85,4,1,120,21,25,4,9, 397,1,0,0,0,0, 1, 309,3,250000,4,3,128,4,500 -"5",37,270,6,0,0,172,50,2365,2384,84,4,1,122,17,23,8,9, 409,1,1,0,0,0, 1, 297,3,250000,4,3,128,4,500 -"5",29,270,6,0,0,170,53,2365,2384,5,4,1,118,19,28,5,9, 410,1,1,0,0,0, 1, 297,3,250000,4,3,128,4,500 -"5",208,271,6,0,0,175,50,2365,2384,84,4,1,117,20,29,5,9, 390,1,1,0,0,0, 1, 316,3,250000,4,3,128,4,500 -"5",142,271,6,0,0,173,52,2365,2384,85,4,1,113,20,34,4,9, 397,1,0,0,0,0, 1, 310,3,250000,4,3,128,4,500 -"5",56,272,6,0,0,173,53,2365,2384,84,4,1,118,19,28,5,9, 407,1,1,0,0,0, 1, 300,3,250000,4,3,128,4,500 -"5",39,274,6,0,0,173,53,2365,2384,60,4,1,118,17,28,7,9, 409,1,1,0,0,0, 1, 298,3,250000,4,3,128,4,500 -"5",113,273,6,0,0,174,53,2365,2384,85,4,1,116,19,30,5,9, 401,1,1,0,0,0, 1, 307,3,250000,4,3,128,4,500 -"5",51,275,6,0,0,175,53,2365,2384,85,4,1,118,19,27,5,9, 408,1,1,0,0,0, 1, 299,3,250000,4,3,128,4,500 -"5",196,271,6,0,0,175,53,2365,2384,84,4,1,77,16,74,4,9, 391,1,1,0,0,0, 1, 320,3,250000,4,3,128,4,500 -"5",254,276,6,0,0,177,52,2365,2384,85,4,1,119,16,27,8,9, 385,1,0,0,0,0, 1, 321,3,250000,4,3,128,4,500 -"5",0,465,6,0,0,327,90,2365,2384,68,4,1,125,14,21,10,0, 376,1,1,0,0,0, 2, 327,3,250000,4,3,128,4,500 -"5",73,463,7,0,0,326,94,2365,2384,85,4,1,82,8,69,10,9, 404,1,1,0,0,0, 2, 308,3,250000,4,3,128,4,500 -"5",36,470,6,0,1,330,92,2365,2384,84,4,1,121,15,25,8,9, 409,1,0,0,0,0, 2, 298,3,250000,4,3,128,4,500 -"5",96,468,6,0,0,326,100,2365,2384,84,4,1,110,11,41,8,9, 402,1,0,0,0,0, 2, 310,3,250000,4,3,128,4,500 -"5",148,469,8,0,0,325,100,2365,2384,85,4,1,111,13,40,4,9, 397,1,1,0,0,0, 2, 314,3,250000,4,3,128,4,500 -"5",13,472,7,0,0,326,99,2365,2384,60,4,1,114,12,34,8,9, 412,1,1,0,0,0, 2, 297,3,250000,4,3,128,4,500 -"5",77,468,6,0,0,330,96,2365,2384,60,4,1,78,8,73,10,9, 404,1,0,0,0,0, 2, 308,3,250000,4,3,128,4,500 -"5",12,472,6,0,1,328,99,2365,2384,60,4,1,114,13,34,8,9, 412,1,1,0,0,0, 2, 298,3,250000,4,3,128,4,500 -"5",105,473,6,0,0,330,97,2365,2384,84,4,1,113,10,36,10,9, 401,1,0,0,0,0, 2, 309,3,250000,4,3,128,4,500 -"5",151,470,6,0,1,331,96,2365,2384,37,4,1,81,14,71,4,9, 397,1,0,0,0,0, 2, 316,3,250000,4,3,128,4,500 -"5",253,471,6,0,0,331,96,2365,2384,53,4,1,83,12,68,6,9, 385,1,0,0,0,0, 2, 327,3,250000,4,3,128,4,500 -"5",182,472,7,0,1,336,93,2365,2384,84,4,1,86,11,64,8,9, 393,1,1,0,0,0, 2, 318,3,250000,4,3,128,4,500 -"5",178,475,9,1,0,334,94,2365,2384,84,4,1,118,14,29,5,9, 394,1,0,0,0,0, 2, 314,3,250000,4,3,128,4,500 -"5",164,471,6,0,1,333,96,2365,2384,84,4,1,79,14,73,4,9, 395,1,1,0,0,0, 2, 317,3,250000,4,3,128,4,500 -"5",6,475,6,0,0,330,98,2365,2384,7,4,1,114,14,34,8,9, 412,1,1,0,0,0, 2, 297,3,250000,4,3,128,4,500 -"5",211,475,9,1,0,333,96,2365,2384,84,4,1,115,14,33,4,9, 390,1,1,0,0,0, 2, 319,3,250000,4,3,128,4,500 -"5",22,475,6,0,0,330,100,2365,2384,37,4,1,113,16,35,5,9, 411,1,1,0,0,0, 2, 298,3,250000,4,3,128,4,500 -"5",20,476,6,0,1,331,99,2365,2384,85,4,1,114,17,35,5,9, 411,1,0,0,0,0, 2, 298,3,250000,4,3,128,4,500 -"5",101,475,6,0,0,334,96,2365,2384,85,4,1,113,12,36,9,9, 402,1,1,0,0,0, 2, 308,3,250000,4,3,128,4,500 -"5",108,474,6,0,0,331,99,2365,2384,61,4,1,112,8,38,10,9, 401,1,1,0,0,0, 2, 310,3,250000,4,3,128,4,500 -"5",4,478,6,0,1,336,94,2365,2384,85,4,1,119,14,28,8,9, 412,1,1,0,0,0, 2, 296,3,250000,4,3,128,4,500 -"5",125,475,6,0,0,331,100,2365,2384,53,4,1,110,8,40,11,9, 400,1,0,0,0,0, 2, 311,3,250000,4,3,128,4,500 -"5",8,478,6,0,0,334,96,2365,2384,85,4,1,119,13,28,9,9, 412,1,1,0,0,0, 2, 297,3,250000,4,3,128,4,500 -"5",65,474,6,0,1,335,96,2365,2384,85,4,1,79,10,73,8,9, 405,1,1,0,0,0, 2, 307,3,250000,4,3,128,4,500 -"5",91,475,6,0,1,332,99,2365,2384,84,4,1,108,9,43,10,9, 403,1,1,0,0,0, 2, 308,3,250000,4,3,128,4,500 -"5",46,472,8,1,1,332,99,2365,2384,84,4,1,74,7,80,6,9, 408,1,1,0,0,0, 2, 306,3,250000,4,3,128,4,500 -"5",145,475,6,0,1,330,100,2365,2384,38,4,1,111,14,40,4,9, 397,1,1,0,0,0, 2, 315,3,250000,4,3,128,4,500 -"5",146,475,6,0,0,334,97,2365,2384,85,4,1,113,15,37,4,9, 397,1,0,0,0,0, 2, 314,3,250000,4,3,128,4,500 -"5",34,478,6,0,0,333,99,2365,2384,64,4,1,113,15,35,6,9, 409,1,0,0,0,0, 2, 300,3,250000,4,3,128,4,500 -"5",97,476,6,0,1,332,100,2365,2384,84,4,1,110,11,40,8,9, 402,1,1,0,0,0, 2, 309,3,250000,4,3,128,4,500 -"5",110,476,6,1,0,335,96,2365,2384,84,4,1,114,7,36,11,9, 401,1,0,0,0,0, 2, 310,3,250000,4,3,128,4,500 -"5",106,476,6,0,1,332,100,2365,2384,84,4,1,110,9,40,10,9, 401,1,1,0,0,0, 2, 310,3,250000,4,3,128,4,500 -"5",251,476,6,0,1,335,97,2365,2384,84,4,1,112,13,37,7,9, 386,1,1,0,0,0, 2, 325,3,250000,4,3,128,4,500 -"5",10,477,6,0,0,330,102,2365,2384,86,4,1,114,11,36,9,9, 411,1,1,0,0,0, 2, 299,3,250000,4,3,128,4,500 -"5",55,477,6,0,0,329,104,2365,2384,57,4,1,111,13,39,6,9, 407,1,1,0,0,0, 2, 303,3,250000,4,3,128,4,500 -"5",124,476,6,0,1,329,103,2365,2384,84,4,1,108,9,43,10,9, 400,1,1,0,0,0, 1, 313,3,250000,4,3,128,4,500 -"5",1,480,6,0,1,337,96,2365,2384,47,4,1,118,13,29,9,9, 412,1,1,0,0,0, 2, 296,3,250000,4,3,128,4,500 -"5",153,476,6,0,0,330,103,2365,2384,85,4,1,107,14,44,4,9, 396,1,0,0,0,0, 2, 316,3,250000,4,3,128,4,500 -"5",2,481,6,0,1,338,96,2365,2384,36,4,1,118,13,29,9,9, 413,1,1,0,0,0, 2, 295,3,250000,4,3,128,4,500 -"5",162,475,6,0,1,335,99,2365,2384,64,4,1,74,13,78,4,9, 396,1,0,0,0,0, 2, 318,3,250000,4,3,128,4,500 -"5",117,477,7,0,0,331,103,2365,2384,85,4,1,108,9,43,8,9, 400,1,1,0,0,0, 2, 311,3,250000,4,3,128,4,500 -"5",109,480,6,0,1,339,95,2365,2384,60,4,1,117,10,31,12,9, 401,1,1,0,0,0, 2, 308,3,250000,4,3,128,4,500 -"5",209,477,6,0,1,330,103,2365,2384,68,4,1,106,14,46,4,9, 390,1,1,0,0,0, 2, 322,3,250000,4,3,128,4,500 -"5",160,476,6,0,1,335,99,2365,2384,84,4,1,73,13,80,4,9, 395,1,0,0,0,0, 2, 318,3,250000,4,3,128,4,500 -"5",223,480,9,1,0,336,98,2365,2384,84,4,1,116,10,33,8,9, 389,1,0,0,0,0, 2, 320,3,250000,4,3,128,4,500 -"5",17,479,6,0,1,329,105,2365,2384,37,4,1,113,16,37,4,9, 411,1,1,0,0,0, 2, 300,3,250000,4,3,128,4,500 -"5",93,477,7,0,0,329,106,2365,2384,54,4,1,108,7,43,10,9, 403,1,0,0,0,0, 2, 309,3,250000,4,3,128,4,500 -"5",210,478,6,0,1,336,99,2365,2384,84,4,1,108,15,43,4,9, 390,1,1,0,0,0, 2, 322,3,250000,4,3,128,4,500 -"5",205,476,6,0,1,335,100,2365,2384,60,4,1,78,10,75,6,9, 391,1,1,0,0,0, 2, 323,3,250000,4,3,128,4,500 -"5",3,479,6,0,0,333,101,2365,2384,86,4,1,114,11,36,9,9, 412,1,1,0,0,0, 2, 298,3,250000,4,3,128,4,500 -"5",104,478,6,0,1,335,99,2365,2384,85,4,1,112,11,39,9,9, 401,1,1,0,0,0, 2, 310,3,250000,4,3,128,4,500 -"5",167,480,6,0,1,331,103,2365,2384,61,4,1,111,13,39,7,9, 395,1,1,0,0,0, 2, 315,3,250000,4,3,128,4,500 -"5",248,478,6,0,0,335,99,2365,2384,84,4,1,110,12,42,6,9, 386,1,0,0,0,0, 2, 326,3,250000,4,3,128,4,500 -"5",5,482,6,0,0,336,99,2365,2384,86,4,1,115,14,32,8,9, 412,1,1,0,0,0, 2, 297,3,250000,4,3,128,4,500 -"5",100,478,6,0,1,331,103,2365,2384,85,4,1,108,10,44,8,9, 402,1,0,0,0,0, 2, 311,3,250000,4,3,128,4,500 -"5",238,479,6,0,1,329,106,2365,2384,84,4,1,109,13,42,7,9, 387,1,1,0,0,0, 2, 324,3,250000,4,3,128,4,500 -"5",95,479,6,0,1,332,102,2365,2384,85,4,1,108,9,44,10,9, 402,1,1,0,0,0, 2, 310,3,250000,4,3,128,4,500 -"5",166,483,6,0,1,341,95,2365,2384,61,4,1,115,15,33,8,9, 395,1,1,0,0,0, 2, 313,3,250000,4,3,128,4,500 -"5",128,477,6,0,1,339,96,2365,2384,67,4,1,80,13,73,4,9, 398,1,1,0,0,0, 2, 315,3,250000,4,3,128,4,500 -"5",7,480,6,0,1,330,105,2365,2384,35,4,1,109,12,41,7,9, 412,1,1,0,0,0, 2, 299,3,250000,4,3,128,4,500 -"5",191,477,6,0,1,337,99,2365,2384,85,4,1,73,14,80,3,9, 392,1,0,0,0,0, 1, 322,3,250000,4,3,128,4,500 -"5",121,479,7,0,1,330,106,2365,2384,84,4,1,109,7,43,10,9, 400,1,1,0,0,0, 2, 312,3,250000,4,3,128,4,500 -"5",74,478,6,0,1,336,100,2365,2384,85,4,1,78,8,75,9,9, 404,1,1,0,0,0, 2, 309,3,250000,4,3,128,4,500 -"5",250,479,6,0,1,335,100,2365,2384,84,4,1,110,12,41,6,9, 386,1,0,0,0,0, 2, 326,3,250000,4,3,128,4,500 -"5",18,483,6,0,0,336,100,2365,2384,85,4,1,115,17,32,5,9, 411,1,1,0,0,0, 2, 297,3,250000,4,3,128,4,500 -"5",152,477,3,0,0,337,100,2365,2384,85,4,1,72,15,82,3,9, 396,1,1,0,0,0, 1, 319,3,250000,4,3,128,4,500 -"5",24,481,6,0,0,334,102,2365,2384,85,4,1,114,15,36,5,9, 410,1,0,0,0,0, 2, 301,3,250000,4,3,128,4,500 -"5",219,480,6,0,1,343,94,2365,2384,84,4,1,81,8,71,10,9, 389,1,1,0,0,0, 2, 323,3,250000,4,3,128,4,500 -"5",229,480,6,0,1,332,105,2365,2384,84,4,1,107,11,44,7,9, 388,1,1,0,0,0, 2, 324,3,250000,4,3,128,4,500 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404,1,1,0,0,0, 1, 481,3,250000,4,3,128,4,500 -"6",341,366,4,0,0,178,158,3189,3172,125,4,1,17,6,143,3,9, 404,1,1,0,0,0, 1, 481,3,250000,4,3,128,4,500 -"6",315,367,4,0,0,179,158,3189,3172,134,4,1,17,7,142,3,9, 407,1,1,0,0,0, 1, 478,3,250000,4,3,128,4,500 -"6",346,367,4,0,0,177,159,3189,3172,66,4,1,12,7,148,3,9, 403,1,1,0,0,0, 1, 482,3,250000,4,3,128,4,500 -"6",235,370,4,0,0,171,164,3189,3172,134,4,1,109,8,46,4,9, 415,1,1,0,0,0, 1, 466,3,250000,4,3,128,4,500 -"6",336,366,4,0,0,179,158,3189,3172,122,4,1,12,6,147,3,9, 405,1,1,0,0,0, 1, 481,3,250000,4,3,128,4,500 -"6",418,370,4,0,0,178,158,3189,3172,68,4,1,106,9,49,3,9, 395,1,1,0,0,0, 1, 486,3,250000,4,3,128,4,500 -"6",451,370,4,0,0,174,162,3189,3172,68,4,1,105,9,51,3,9, 392,1,1,0,0,0, 1, 490,3,250000,4,3,128,4,500 -"6",167,367,4,0,1,175,162,3189,3172,69,4,1,22,7,137,2,9, 423,1,1,0,0,0, 1, 462,3,250000,4,3,128,4,500 -"6",327,368,4,0,0,179,157,3189,3172,68,4,1,20,7,139,3,9, 406,1,1,0,0,0, 1, 479,3,250000,4,3,128,4,500 -"6",314,370,4,0,0,178,161,3189,3172,134,4,1,16,7,143,2,9, 407,1,1,0,0,0, 1, 478,3,250000,4,3,128,4,500 -"6",511,640,7,0,1,334,269,3189,3172,129,4,1,123,2,32,6,9, 385,1,1,0,0,0, 2, 496,3,250000,4,3,128,4,500 -"6",73,643,4,0,0,339,270,3189,3172,135,4,1,48,4,112,4,9, 432,1,1,0,0,0, 2, 452,3,250000,4,3,128,4,500 -"6",65,645,4,0,1,337,276,3189,3172,71,4,1,43,3,117,4,9, 433,1,1,0,0,0, 1, 453,3,250000,4,3,128,4,500 -"6",256,644,4,0,0,337,277,3189,3172,74,4,1,36,3,127,2,9, 412,1,1,0,0,0, 1, 476,3,250000,4,3,128,4,500 -"6",145,652,5,0,1,335,286,3189,3172,136,4,1,27,3,134,3,9, 424,1,1,0,0,0, 1, 462,3,250000,4,3,128,4,500 -"6",301,653,4,0,0,342,278,3189,3172,89,4,1,36,5,124,2,9, 408,1,1,0,0,0, 1, 477,3,250000,4,3,128,4,500 -"6",131,662,4,0,0,340,290,3189,3172,68,4,1,34,4,127,3,9, 426,1,1,0,0,0, 1, 461,3,250000,4,3,128,4,500 -"6",260,666,4,0,0,341,296,3189,3172,70,4,1,20,5,143,1,9, 413,1,1,0,0,0, 1, 476,3,250000,4,3,128,4,500 -"6",380,666,4,0,1,340,296,3189,3172,134,4,1,16,4,147,1,9, 400,1,1,0,0,0, 1, 488,3,250000,4,3,128,4,500 -"6",265,668,4,0,0,340,297,3189,3172,89,4,1,29,5,132,1,9, 412,2,1,0,0,0, 1, 475,3,250000,4,3,128,4,500 -"6",377,680,4,0,0,344,305,3189,3172,134,4,1,22,5,140,1,9, 400,1,1,0,0,0, 1, 488,3,250000,4,3,128,4,500 -"6",406,683,4,0,0,341,312,3189,3172,87,4,1,31,5,132,0,9, 397,1,1,0,0,0, 2, 492,3,250000,4,3,128,4,500 diff --git a/hpc/archer2/data/ijhpca/weak_fft_11004445.csv b/hpc/archer2/data/ijhpca/weak_fft_11004445.csv deleted file mode 100644 index b68f14d1..00000000 --- a/hpc/archer2/data/ijhpca/weak_fft_11004445.csv +++ /dev/null @@ -1,1018 +0,0 @@ - -experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,source_tree,target_tree,source_domain,target_domain,layout,ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,displacement_map,metadata_creation,expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples -"0",1,18,4,0,0,0,0,2980,2935,4,4,0,112,13,11,0,0, 381,1,0,0,0,0, 0, 160,3,250000,4,1,128,4,500 -"0",0,244,0,0,0,82,143,2980,2935,5,3,0,32,11,97,1,0, 381,0,0,0,0,0, 1, 168,3,250000,4,1,128,4,500 -"0",2,327,4,0,0,161,144,2980,2935,5,4,0,102,9,24,0,1, 381,1,0,0,0,0, 1, 164,3,250000,4,1,128,4,500 -"0",3,328,4,0,0,162,143,2980,2935,5,4,0,102,10,24,0,2, 382,1,0,0,0,0, 0, 163,3,250000,4,1,128,4,500 -"0",7,328,8,0,0,163,143,2981,2935,4,3,0,105,6,21,0,1, 381,1,0,0,0,0, 1, 164,3,250000,4,1,128,4,500 -"0",5,327,4,0,0,164,143,2981,2935,4,3,0,27,8,103,0,0, 381,1,0,0,0,0, 0, 167,3,250000,4,1,128,4,500 -"0",6,327,4,0,0,164,144,2981,2935,4,3,0,26,7,104,0,0, 381,1,0,0,0,0, 1, 168,3,250000,4,1,128,4,500 -"0",4,633,4,0,0,321,294,2981,2935,4,3,0,11,3,123,0,0, 381,1,0,0,0,0, 2, 172,3,250000,4,1,128,4,500 -"1",0,588,8,1,1,488,57,1954,1952,4,3,0,45,20,17,4,0, 382,0,0,0,0,0, 3, 136,3,250000,4,2,128,4,500 -"1",10,585,8,1,1,486,60,1954,1952,4,3,0,40,18,25,0,4, 383,1,0,0,0,0, 3, 140,3,250000,4,2,128,4,500 -"1",9,777,10,1,1,661,75,1954,1952,4,3,0,46,15,18,0,5, 383,1,0,0,0,0, 4, 139,3,250000,4,2,128,4,500 -"1",13,777,10,1,2,667,75,1954,1952,4,3,0,26,11,44,0,3, 383,1,0,0,0,0, 4, 144,3,250000,4,2,128,4,500 -"1",12,783,13,2,2,671,76,1954,1952,4,3,0,40,9,28,0,3, 383,1,0,0,0,0, 4, 143,3,250000,4,2,128,4,500 -"1",11,786,10,1,2,669,79,1954,1952,4,3,0,42,12,26,0,4, 383,1,0,0,0,0, 4, 142,3,250000,4,2,128,4,500 -"1",14,789,8,1,2,680,79,1954,1952,4,3,0,23,11,52,0,1, 383,1,0,0,0,0, 4, 149,3,250000,4,2,128,4,500 -"1",2,799,13,2,2,684,78,1954,1952,4,3,0,43,10,25,0,3, 382,1,0,0,0,0, 4, 143,3,250000,4,2,128,4,500 -"1",1,800,10,1,2,685,78,1954,1952,4,3,0,39,11,29,0,3, 382,1,0,0,0,0, 4, 144,3,250000,4,2,128,4,500 -"1",3,800,10,1,1,690,77,1954,1952,4,3,0,24,11,46,0,3, 382,1,0,0,0,0, 4, 146,3,250000,4,2,128,4,500 -"1",4,802,10,1,2,690,78,1954,1952,4,3,0,24,9,46,0,4, 382,1,0,0,0,0, 4, 145,3,250000,4,2,128,4,500 -"1",7,802,10,1,2,690,77,1954,1952,5,3,0,24,11,47,0,2, 383,1,0,0,0,0, 4, 145,3,250000,4,2,128,4,500 -"1",5,803,10,1,2,693,77,1954,1952,4,3,0,23,11,48,0,2, 382,1,0,0,0,0, 4, 145,3,250000,4,2,128,4,500 -"1",6,806,10,1,2,693,78,1954,1952,5,3,0,22,10,49,0,3, 383,1,0,0,0,0, 4, 145,3,250000,4,2,128,4,500 -"1",15,939,13,2,2,806,95,1954,1952,4,3,0,46,9,22,0,4, 383,1,0,0,0,0, 5, 141,3,250000,4,2,128,4,500 -"1",8,931,10,1,2,802,99,1954,1952,4,3,0,23,7,53,0,1, 383,1,0,0,0,0, 4, 150,3,250000,4,2,128,4,500 -"2",0,243,6,0,0,160,47,2256,2320,4,3,1,75,17,15,4,0, 381,0,0,0,0,0, 1, 156,3,250000,4,2,128,4,500 -"2",11,254,6,0,0,171,48,2257,2320,5,3,1,70,17,21,0,5, 383,1,0,0,0,0, 1, 156,3,250000,4,2,128,4,500 -"2",7,255,6,0,0,171,53,2257,2320,6,3,1,66,15,28,0,4, 382,1,0,0,0,0, 1, 159,3,250000,4,2,128,4,500 -"2",13,457,6,0,0,327,97,2257,2320,5,3,1,63,13,31,0,4, 383,1,0,0,0,0, 2, 159,3,250000,4,2,128,4,500 -"2",22,456,6,0,1,326,100,2257,2320,5,3,1,61,12,36,0,4, 384,1,0,0,0,0, 2, 160,3,250000,4,2,128,4,500 -"2",12,456,6,0,0,325,102,2256,2320,5,3,1,60,11,38,0,3, 383,1,0,0,0,0, 2, 162,3,250000,4,2,128,4,500 -"2",27,459,6,0,0,330,97,2257,2320,4,3,1,64,13,31,0,4, 384,1,0,0,0,0, 2, 158,3,250000,4,2,128,4,500 -"2",23,459,6,0,1,329,97,2257,2320,4,3,1,64,13,31,0,5, 384,1,0,0,0,0, 2, 158,3,250000,4,2,128,4,500 -"2",29,457,6,0,1,326,101,2257,2320,5,3,1,60,12,37,0,3, 384,1,0,0,0,0, 2, 160,3,250000,4,2,128,4,500 -"2",18,459,6,0,0,330,99,2257,2320,5,3,1,61,11,36,0,4, 384,1,0,0,0,0, 2, 161,3,250000,4,2,128,4,500 -"2",17,460,6,0,1,329,101,2257,2320,5,3,1,60,11,37,0,4, 384,1,0,0,0,0, 2, 161,3,250000,4,2,128,4,500 -"2",9,460,6,0,0,336,95,2257,2320,5,3,1,35,12,62,0,3, 383,1,0,0,0,0, 2, 162,3,250000,4,2,128,4,500 -"2",2,460,3,0,0,332,99,2256,2320,5,3,1,33,13,66,0,4, 382,1,0,0,0,0, 1, 165,3,250000,4,2,128,4,500 -"2",19,464,6,0,1,330,101,2257,2320,5,3,1,65,13,30,0,4, 384,1,0,0,0,0, 2, 159,3,250000,4,2,128,4,500 -"2",30,461,6,0,1,332,98,2257,2320,4,3,1,64,11,33,0,4, 385,1,0,0,0,0, 2, 161,3,250000,4,2,128,4,500 -"2",16,464,6,0,1,335,97,2256,2320,5,3,1,65,12,31,0,4, 384,1,0,0,0,0, 2, 159,3,250000,4,2,128,4,500 -"2",26,463,6,0,1,331,101,2257,2320,4,3,1,62,11,35,0,4, 384,1,0,0,0,0, 2, 160,3,250000,4,2,128,4,500 -"2",20,463,8,0,0,332,101,2257,2320,6,3,1,60,8,38,0,4, 384,1,0,0,0,0, 2, 161,3,250000,4,2,128,4,500 -"2",6,464,6,0,1,336,98,2257,2320,6,3,1,29,11,69,0,2, 382,1,0,0,0,0, 2, 164,3,250000,4,2,128,4,500 -"2",21,465,6,0,1,333,101,2257,2320,6,3,1,61,12,37,0,3, 384,1,0,0,0,0, 2, 161,3,250000,4,2,128,4,500 -"2",15,465,6,0,1,335,101,2256,2320,5,3,1,60,11,38,0,3, 383,1,0,0,0,0, 2, 162,3,250000,4,2,128,4,500 -"2",24,466,6,0,1,334,101,2257,2320,5,3,1,61,11,36,0,4, 384,1,0,0,0,0, 2, 160,3,250000,4,2,128,4,500 -"2",4,466,6,0,0,340,97,2256,2320,5,3,1,36,12,62,0,3, 382,1,0,0,0,0, 2, 163,3,250000,4,2,128,4,500 -"2",14,467,6,0,1,331,105,2256,2320,5,3,1,60,12,37,0,4, 383,1,0,0,0,0, 2, 161,3,250000,4,2,128,4,500 -"2",28,466,6,0,1,333,104,2257,2320,5,3,1,60,10,38,0,4, 384,1,0,0,0,0, 2, 161,3,250000,4,2,128,4,500 -"2",25,467,6,0,1,334,103,2256,2320,5,3,1,60,11,38,0,4, 384,1,0,0,0,0, 2, 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a/hpc/archer2/data/ijhpca/weak_fft_11018099_non_contiguous.csv b/hpc/archer2/data/ijhpca/weak_fft_11018099_non_contiguous.csv deleted file mode 100644 index a812204a..00000000 --- a/hpc/archer2/data/ijhpca/weak_fft_11018099_non_contiguous.csv +++ /dev/null @@ -1,1018 +0,0 @@ - -experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,source_tree,target_tree,source_domain,target_domain,layout,ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,displacement_map,metadata_creation,expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples -"0",0,125,0,0,0,0,0,2996,2993,4,4,0,0,0,0,125,0, 378,0,0,0,0,0,0,34, 3,250000,4,1,128,4,500 -"0",4,252,0,0,0,84,151,2997,2993,4,3,0,15,9,98,0,1, 379,1,0,0,0,0,0,148, 3,250000,4,1,128,4,500 -"0",1,321,4,0,0,160,142,2996,2992,4,4,0,87,7,23,0,1, 379,1,0,0,0,0,1,145, 3,250000,4,1,128,4,500 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3,250000,4,2,128,4,500 -"3",38,341,4,0,0,172,144,3054,3052,5,4,1,118,9,33,2,1, 385,2,0,0,0,0,1,226, 3,250000,4,2,128,4,500 -"3",61,338,4,0,0,174,143,3054,3052,4,4,1,45,7,110,0,1, 382,2,1,0,0,0,1,232, 3,250000,4,2,128,4,500 -"3",50,343,4,0,0,171,146,3054,3053,4,4,1,117,10,34,2,1, 384,1,0,0,0,0,1,226, 3,250000,4,2,128,4,500 -"3",56,338,4,0,0,174,143,3054,3052,4,4,1,38,7,118,0,1, 383,2,0,0,0,0,1,232, 3,250000,4,2,128,4,500 -"3",6,343,4,0,0,168,150,3054,3052,5,4,1,113,8,40,0,3, 389,2,1,0,0,0,1,223, 3,250000,4,2,128,4,500 -"3",17,344,4,0,0,169,150,3054,3052,7,4,1,118,9,34,0,3, 388,2,0,0,0,0,1,224, 3,250000,4,2,128,4,500 -"3",29,344,4,0,0,169,149,3054,3052,5,4,1,113,8,40,0,3, 387,2,0,0,0,0,1,226, 3,250000,4,2,128,4,500 -"3",20,344,4,0,0,167,152,3054,3052,5,4,1,112,9,40,0,3, 388,2,0,0,0,0,1,224, 3,250000,4,2,128,4,500 -"3",5,344,4,0,0,169,150,3054,3052,6,4,1,114,8,38,0,3, 389,2,0,0,0,0,1,223, 3,250000,4,2,128,4,500 -"3",23,345,4,0,0,170,151,3054,3052,5,4,1,113,8,40,0,3, 387,2,1,0,0,0,1,225, 3,250000,4,2,128,4,500 -"3",44,347,8,0,0,174,146,3054,3052,4,4,1,116,5,35,2,1, 385,2,0,0,0,0,1,226, 3,250000,4,2,128,4,500 -"3",30,346,4,0,0,170,151,3054,3052,5,4,1,113,9,39,0,3, 387,2,0,0,0,0,1,225, 3,250000,4,2,128,4,500 -"3",12,345,4,0,0,170,151,3054,3052,5,4,1,115,8,38,0,3, 389,2,0,0,0,0,1,224, 3,250000,4,2,128,4,500 -"3",10,346,4,0,0,167,155,3054,3052,4,4,1,117,8,36,0,3, 389,1,1,0,0,0,1,223, 3,250000,4,2,128,4,500 -"3",40,347,4,0,0,171,151,3054,3053,5,4,1,112,8,40,2,1, 385,2,1,0,0,0,1,227, 3,250000,4,2,128,4,500 -"3",34,346,4,0,0,172,151,3054,3052,4,4,1,113,8,40,2,1, 385,1,1,0,0,0,1,227, 3,250000,4,2,128,4,500 -"3",39,347,4,0,0,172,150,3054,3052,5,4,1,112,9,40,2,1, 385,2,0,0,0,0,1,227, 3,250000,4,2,128,4,500 -"3",46,347,4,0,0,172,150,3054,3053,5,4,1,112,9,40,2,1, 385,2,1,0,0,0,0,227, 3,250000,4,2,128,4,500 -"3",3,348,4,0,0,167,156,3054,3052,5,4,1,117,9,35,0,3, 389,2,0,0,0,0,1,222, 3,250000,4,2,128,4,500 -"3",19,349,5,0,0,168,156,3054,3052,6,4,1,121,8,30,0,3, 388,2,0,0,0,0,1,223, 3,250000,4,2,128,4,500 -"3",15,348,4,0,0,173,152,3054,3052,5,4,1,113,8,39,0,3, 389,2,0,0,0,0,1,223, 3,250000,4,2,128,4,500 -"3",7,348,4,0,0,169,156,3054,3052,5,4,1,108,7,45,0,3, 389,2,0,0,0,0,1,223, 3,250000,4,2,128,4,500 -"3",48,350,4,0,0,173,153,3054,3052,5,4,1,112,9,40,2,1, 385,1,1,0,0,0,1,227, 3,250000,4,2,128,4,500 -"3",43,347,4,0,0,176,151,3054,3052,4,4,1,42,7,115,0,1, 385,2,0,0,0,0,1,230, 3,250000,4,2,128,4,500 -"3",52,346,4,0,0,176,151,3054,3052,5,4,1,30,5,127,1,1, 384,1,0,0,0,0,1,233, 3,250000,4,2,128,4,500 -"3",59,351,4,0,0,171,155,3054,3052,4,4,1,111,9,41,2,1, 383,2,0,0,0,0,1,229, 3,250000,4,2,128,4,500 -"3",24,351,4,0,0,172,156,3054,3052,5,4,1,116,8,36,0,3, 387,2,0,0,0,0,1,225, 3,250000,4,2,128,4,500 -"3",54,348,4,0,0,177,151,3054,3052,5,4,1,28,6,128,1,1, 383,1,0,0,0,0,1,232, 3,250000,4,2,128,4,500 -"3",21,352,4,0,0,171,158,3054,3052,5,4,1,109,7,45,0,3, 388,2,0,0,0,0,1,226, 3,250000,4,2,128,4,500 -"3",28,352,4,0,0,172,158,3054,3052,5,4,1,107,8,46,0,3, 387,2,0,0,0,0,1,226, 3,250000,4,2,128,4,500 -"3",33,354,4,0,0,172,157,3054,3052,5,4,1,113,9,40,2,1, 386,1,1,0,0,0,1,226, 3,250000,4,2,128,4,500 -"3",62,351,4,0,0,173,156,3054,3052,4,4,1,41,7,115,0,1, 382,2,0,0,0,0,1,233, 3,250000,4,2,128,4,500 -"3",35,353,4,0,0,172,158,3054,3052,5,4,1,107,8,47,2,1, 385,1,1,0,0,0,1,227, 3,250000,4,2,128,4,500 -"3",14,353,4,0,0,171,159,3054,3052,5,4,1,107,8,46,0,3, 389,2,0,0,0,0,1,224, 3,250000,4,2,128,4,500 -"3",49,354,4,0,0,173,158,3054,3052,4,4,1,107,8,47,1,1, 384,2,0,0,0,0,1,229, 3,250000,4,2,128,4,500 -"3",57,352,4,0,0,176,156,3054,3052,5,4,1,26,6,131,0,1, 383,2,0,0,0,0,1,233, 3,250000,4,2,128,4,500 -"3",60,672,4,0,0,340,312,3054,3053,5,4,1,14,3,146,0,1, 382,1,1,0,0,0,1,237, 3,250000,4,2,128,4,500 -"4",76,595,8,1,1,485,59,2065,1980,10,6,1,61,18,55,6,9, 388,1,1,0,0,0,3,224, 3,250000,4,3,128,4,500 -"4",110,600,10,1,1,485,58,2065,1980,10,5,1,81,19,30,8,9, 384,1,1,0,0,0,4,223, 3,250000,4,3,128,4,500 -"4",72,600,10,1,1,490,58,2065,1980,11,6,1,60,19,53,5,9, 388,1,1,0,0,0,4,221, 3,250000,4,3,128,4,500 -"4",14,600,10,1,1,488,59,2065,1980,11,6,1,78,20,35,4,9, 395,1,1,0,0,0,3,214, 3,250000,4,3,128,4,500 -"4",10,603,10,1,1,489,58,2065,1980,11,6,1,82,19,29,7,9, 396,1,1,0,0,0,3,211, 3,250000,4,3,128,4,500 -"4",19,604,10,1,1,490,57,2065,1980,8,6,1,85,21,25,7,9, 395,1,0,0,0,0,4,212, 3,250000,4,3,128,4,500 -"4",95,600,10,1,1,490,57,2065,1980,10,5,1,62,18,52,6,9, 385,1,0,0,0,0,3,225, 3,250000,4,3,128,4,500 -"4",5,603,10,1,1,489,57,2065,1980,11,5,1,82,20,30,7,9, 396,1,1,0,0,0,3,211, 3,250000,4,3,128,4,500 -"4",124,603,10,1,1,491,58,2065,1980,10,6,1,78,19,34,6,9, 382,1,0,0,0,0,4,226, 3,250000,4,3,128,4,500 -"4",107,605,10,1,1,493,58,2065,1980,11,6,1,64,18,49,6,9, 384,1,0,0,0,0,4,225, 3,250000,4,3,128,4,500 -"4",99,602,7,1,1,492,60,2065,1980,11,5,1,60,19,58,4,9, 385,1,0,0,0,0,3,229, 3,250000,4,3,128,4,500 -"4",0,778,10,1,2,648,71,2065,1980,11,6,1,87,17,22,11,0, 372,1,1,0,0,0,4,229, 3,250000,4,3,128,4,500 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3,250000,4,3,128,4,500 -"4",24,789,10,1,2,659,77,2065,1980,9,6,1,80,17,34,6,9, 394,1,0,0,0,0,4,216, 3,250000,4,3,128,4,500 -"4",23,789,10,1,1,661,77,2065,1980,12,6,1,77,17,37,5,9, 394,1,0,0,0,0,5,216, 3,250000,4,3,128,4,500 -"4",67,783,10,1,2,658,78,2065,1980,10,6,1,58,13,63,3,9, 388,1,0,0,0,0,4,229, 3,250000,4,3,128,4,500 -"4",3,791,10,1,2,660,78,2065,1980,8,6,1,80,16,33,7,9, 396,1,0,0,0,0,4,214, 3,250000,4,3,128,4,500 -"4",40,781,10,1,1,658,79,2065,1980,11,6,1,36,12,88,1,9, 392,1,0,0,0,0,4,227, 3,250000,4,3,128,4,500 -"4",32,783,10,1,2,663,75,2065,1980,10,6,1,40,12,82,3,9, 393,1,1,0,0,0,4,225, 3,250000,4,3,128,4,500 -"4",111,793,10,1,1,664,75,2065,1980,11,6,1,84,16,28,8,9, 384,1,0,0,0,0,4,225, 3,250000,4,3,128,4,500 -"4",52,784,10,1,2,664,75,2065,1980,11,6,1,41,11,81,4,9, 391,1,0,0,0,0,4,227, 3,250000,4,3,128,4,500 -"4",50,783,10,1,2,662,76,2065,1980,12,6,1,42,11,80,4,9, 391,1,1,0,0,0,5,227, 3,250000,4,3,128,4,500 -"4",33,783,10,1,2,661,79,2065,1980,10,6,1,37,11,87,2,9, 393,1,0,0,0,0,4,227, 3,250000,4,3,128,4,500 -"4",48,784,10,1,2,663,76,2065,1980,11,5,1,36,11,87,3,9, 392,1,1,0,0,0,4,228, 3,250000,4,3,128,4,500 -"4",25,793,10,1,2,663,77,2065,1980,10,5,1,79,18,35,6,9, 394,1,0,0,0,0,4,216, 3,250000,4,3,128,4,500 -"4",43,782,10,1,2,660,80,2065,1980,10,5,1,34,11,91,0,9, 392,1,0,0,0,0,4,230, 3,250000,4,3,128,4,500 -"4",39,784,10,1,2,662,79,2065,1980,11,5,1,39,11,85,3,9, 392,1,0,0,0,0,4,228, 3,250000,4,3,128,4,500 -"4",84,783,8,1,1,660,80,2065,1981,10,5,1,33,13,93,2,9, 387,1,0,0,0,0,4,235, 3,250000,4,3,128,4,500 -"4",69,787,12,2,2,662,79,2065,1980,11,6,1,55,10,66,3,9, 388,1,0,0,0,0,4,229, 3,250000,4,3,128,4,500 -"4",15,786,10,1,2,662,79,2065,1980,9,5,1,55,11,67,3,9, 395,2,0,0,0,0,4,223, 3,250000,4,3,128,4,500 -"4",1,796,10,1,2,663,76,2065,1980,8,5,1,83,14,30,10,9, 396,1,1,0,0,0,4,213, 3,250000,4,3,128,4,500 -"4",88,786,10,1,1,664,77,2065,1980,10,5,1,40,11,82,4,9, 386,1,0,0,0,0,4,232, 3,250000,4,3,128,4,500 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3,250000,4,3,128,4,500 -"4",47,788,10,1,1,668,76,2065,1980,10,5,1,37,12,87,2,9, 392,1,0,0,0,0,4,228, 3,250000,4,3,128,4,500 -"4",22,799,10,1,2,669,75,2065,1981,9,6,1,84,18,29,7,9, 394,1,0,0,0,0,4,214, 3,250000,4,3,128,4,500 -"4",57,786,10,1,1,665,79,2065,1980,11,6,1,33,11,93,0,9, 390,1,0,0,0,0,4,232, 3,250000,4,3,128,4,500 -"4",87,788,10,1,1,667,77,2065,1980,12,6,1,39,11,84,2,9, 387,1,1,0,0,0,4,233, 3,250000,4,3,128,4,500 -"4",98,790,10,1,2,666,78,2065,1980,10,5,1,54,13,68,2,9, 385,2,0,0,0,0,4,234, 3,250000,4,3,128,4,500 -"4",80,798,10,1,1,668,77,2065,1980,10,6,1,79,18,35,6,9, 387,1,0,0,0,0,4,222, 3,250000,4,3,128,4,500 -"4",63,793,10,1,1,670,74,2065,1980,10,5,1,63,13,55,5,9, 389,2,0,0,0,0,4,225, 3,250000,4,3,128,4,500 -"4",85,786,10,1,2,667,79,2065,1980,11,5,1,33,10,92,1,9, 387,1,0,0,0,0,4,235, 3,250000,4,3,128,4,500 -"4",73,800,10,1,1,674,72,2065,1981,10,5,1,67,17,46,7,9, 388,1,1,0,0,0,4,221, 3,250000,4,3,128,4,500 -"4",109,795,10,1,2,673,74,2065,1980,10,5,1,63,12,55,7,9, 384,1,1,0,0,0,4,231, 3,250000,4,3,128,4,500 -"4",4,801,10,1,1,669,76,2065,1980,10,5,1,84,18,28,7,9, 396,1,1,0,0,0,4,211, 3,250000,4,3,128,4,500 -"4",29,788,10,1,1,665,81,2065,1980,10,6,1,33,11,92,1,9, 394,1,0,0,0,0,4,228, 3,250000,4,3,128,4,500 -"4",120,792,10,1,1,665,80,2065,1980,10,5,1,59,12,62,4,9, 383,1,0,0,0,0,4,234, 3,250000,4,3,128,4,500 -"4",71,790,10,1,2,668,79,2065,1980,10,5,1,36,12,89,1,9, 388,1,1,0,0,0,4,232, 3,250000,4,3,128,4,500 -"4",26,802,10,1,2,671,76,2065,1980,11,5,1,83,18,29,6,9, 394,1,0,0,0,0,4,214, 3,250000,4,3,128,4,500 -"4",97,792,10,1,1,668,79,2065,1981,11,6,1,55,10,67,4,9, 385,1,0,0,0,0,5,233, 3,250000,4,3,128,4,500 -"4",46,791,10,1,1,670,77,2065,1980,10,6,1,36,12,88,1,9, 391,1,0,0,0,0,4,228, 3,250000,4,3,128,4,500 -"4",28,790,10,1,2,669,79,2065,1980,11,6,1,34,11,91,1,9, 394,1,1,0,0,0,4,228, 3,250000,4,3,128,4,500 -"4",53,792,10,1,2,671,77,2065,1980,11,5,1,39,11,84,3,9, 391,1,0,0,0,0,4,228, 3,250000,4,3,128,4,500 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3,250000,4,3,128,4,500 -"4",56,791,7,1,1,671,80,2065,1980,10,6,1,30,12,97,1,9, 390,1,0,0,0,0,4,233, 3,250000,4,3,128,4,500 -"4",62,801,10,1,2,676,75,2065,1980,11,6,1,60,13,59,5,9, 389,1,0,0,0,0,4,225, 3,250000,4,3,128,4,500 -"4",106,795,10,1,2,673,80,2065,1980,10,5,1,38,10,86,3,9, 384,2,0,0,0,0,4,236, 3,250000,4,3,128,4,500 -"4",77,808,13,2,1,678,74,2065,1980,11,5,1,84,16,27,6,9, 387,1,0,0,0,0,4,220, 3,250000,4,3,128,4,500 -"4",90,804,10,1,2,676,77,2065,1980,10,5,1,64,16,51,6,9, 386,2,0,0,0,0,4,225, 3,250000,4,3,128,4,500 -"4",96,800,10,1,1,673,80,2065,1980,10,5,1,59,14,61,2,9, 385,1,0,0,0,0,4,232, 3,250000,4,3,128,4,500 -"4",65,801,10,1,2,674,80,2065,1980,10,6,1,59,14,61,3,9, 388,1,1,0,0,0,4,228, 3,250000,4,3,128,4,500 -"4",100,805,10,1,1,678,76,2065,1981,11,5,1,60,16,57,5,9, 385,1,0,0,0,0,4,228, 3,250000,4,3,128,4,500 -"4",119,806,10,1,2,678,76,2065,1980,10,5,1,63,16,52,6,9, 383,1,0,0,0,0,4,228, 3,250000,4,3,128,4,500 -"4",75,802,10,1,2,679,76,2065,1980,10,5,1,62,13,56,6,9, 388,1,0,0,0,0,4,227, 3,250000,4,3,128,4,500 -"4",91,803,10,1,2,680,74,2065,1980,10,6,1,63,13,55,5,9, 386,1,0,0,0,0,4,228, 3,250000,4,3,128,4,500 -"4",104,802,10,1,1,674,80,2065,1980,10,5,1,57,13,62,5,9, 384,1,0,0,0,0,4,231, 3,250000,4,3,128,4,500 -"4",115,803,10,1,2,678,77,2065,1980,10,6,1,62,13,57,5,9, 383,1,0,0,0,0,4,232, 3,250000,4,3,128,4,500 -"4",116,803,10,1,2,678,78,2065,1980,11,6,1,61,13,59,5,9, 383,1,0,0,0,0,4,233, 3,250000,4,3,128,4,500 -"4",55,802,12,2,2,681,76,2065,1980,12,5,1,61,9,60,4,9, 390,1,0,0,0,0,4,226, 3,250000,4,3,128,4,500 -"4",49,798,10,1,1,677,78,2065,1980,10,6,1,36,11,89,2,9, 391,1,0,0,0,0,4,230, 3,250000,4,3,128,4,500 -"4",121,808,10,1,1,682,76,2065,1980,11,5,1,64,16,51,5,9, 382,1,0,0,0,0,4,229, 3,250000,4,3,128,4,500 -"4",112,804,8,1,2,675,81,2065,1980,10,5,1,58,16,63,3,9, 383,1,0,0,0,0,4,234, 3,250000,4,3,128,4,500 -"4",64,807,10,1,2,679,79,2065,1980,10,6,1,59,13,59,6,9, 388,1,1,0,0,0,4,226, 3,250000,4,3,128,4,500 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3,250000,4,3,128,4,500 -"4",103,809,10,1,2,682,79,2065,1980,6,5,1,62,13,57,5,9, 385,1,0,0,0,0,5,230, 3,250000,4,3,128,4,500 -"4",89,809,12,2,2,684,76,2065,1980,10,6,1,65,10,54,4,9, 386,1,0,0,0,0,5,228, 3,250000,4,3,128,4,500 -"4",82,811,10,1,2,687,76,2065,1980,10,5,1,64,14,55,4,9, 387,1,1,0,0,0,4,228, 3,250000,4,3,128,4,500 -"4",122,812,10,1,2,685,78,2065,1980,11,5,1,63,14,55,4,9, 382,1,0,0,0,0,4,232, 3,250000,4,3,128,4,500 -"4",93,812,12,2,2,687,79,2065,1980,10,5,1,61,11,59,3,9, 386,1,0,0,0,0,5,230, 3,250000,4,3,128,4,500 -"4",113,812,10,1,1,685,79,2065,1980,10,6,1,59,13,62,3,9, 383,1,0,0,0,0,4,233, 3,250000,4,3,128,4,500 -"4",66,816,10,1,2,688,80,2065,1980,11,6,1,59,13,61,3,9, 388,1,1,0,0,0,4,227, 3,250000,4,3,128,4,500 -"4",117,821,10,1,2,694,79,2065,1980,10,5,1,62,14,57,5,9, 383,1,0,0,0,0,4,232, 3,250000,4,3,128,4,500 -"4",11,934,10,1,2,791,97,2065,1981,9,6,1,57,10,66,4,9, 396,1,0,0,0,0,5,223, 3,250000,4,3,128,4,500 -"4",27,933,7,1,2,794,96,2065,1981,12,5,1,39,11,88,3,9, 394,1,0,0,0,0,4,229, 3,250000,4,3,128,4,500 -"4",81,936,10,1,2,794,97,2065,1981,10,5,1,56,10,67,3,9, 390,1,0,0,0,0,5,230, 3,250000,4,3,128,4,500 -"4",94,940,10,1,2,801,95,2065,1981,12,6,1,44,10,80,2,9, 386,2,0,0,0,0,4,234, 3,250000,4,3,128,4,500 -"4",17,943,10,1,2,795,101,2065,1981,11,5,1,61,11,60,4,9, 400,1,0,0,0,0,4,218, 3,250000,4,3,128,4,500 -"4",105,940,10,1,2,803,94,2065,1981,10,5,1,39,9,86,4,9, 387,1,0,0,0,0,4,234, 3,250000,4,3,128,4,500 -"4",20,944,10,1,2,801,98,2065,1981,8,5,1,60,8,62,5,9, 394,1,1,0,0,0,5,224, 3,250000,4,3,128,4,500 -"4",125,950,10,1,2,807,94,2065,1981,12,6,1,65,11,54,7,9, 382,1,0,0,0,0,4,233, 3,250000,4,3,128,4,500 -"4",6,949,10,1,2,806,96,2065,1981,11,5,1,56,9,67,5,9, 396,1,1,0,0,0,5,223, 3,250000,4,3,128,4,500 -"4",108,953,12,2,2,809,96,2065,1980,10,6,1,64,9,56,5,9, 384,1,0,0,0,0,5,232, 3,250000,4,3,128,4,500 -"4",74,953,13,2,2,806,99,2065,1980,11,5,1,60,9,61,3,9, 388,1,1,0,0,0,5,229, 3,250000,4,3,128,4,500 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433,1,1,0,0,0,1,1194, 3,250000,4,3,128,4,500 -"6",345,368,4,0,0,173,157,3220,3190,120,4,1,110,9,47,7,9, 401,1,1,0,0,0,1,1222, 3,250000,4,3,128,4,500 -"6",457,369,5,0,0,173,157,3220,3190,120,4,1,106,9,51,7,9, 389,1,1,0,0,0,1,1234, 3,250000,4,3,128,4,500 -"6",287,368,4,0,0,172,157,3220,3190,67,4,1,110,10,48,6,9, 407,1,1,0,0,0,1,1216, 3,250000,4,3,128,4,500 -"6",371,369,4,0,0,174,157,3220,3190,119,4,1,111,7,47,9,9, 398,1,1,0,0,0,1,1225, 3,250000,4,3,128,4,500 -"6",340,368,4,0,0,174,156,3220,3190,120,4,1,111,9,47,7,9, 401,1,1,0,0,0,1,1222, 3,250000,4,3,128,4,500 -"6",335,369,4,0,0,174,156,3220,3190,119,4,1,113,9,44,7,9, 402,1,1,0,0,0,1,1221, 3,250000,4,3,128,4,500 -"6",200,364,4,0,0,172,158,3220,3190,120,4,1,20,7,142,5,9, 417,1,1,0,0,0,1,1211, 3,250000,4,3,128,4,500 -"6",442,366,4,0,0,173,157,3220,3190,120,4,1,34,11,127,3,9, 391,1,1,0,0,0,1,1235, 3,250000,4,3,128,4,500 -"6",126,365,4,0,0,174,157,3220,3190,120,4,1,22,7,140,5,9, 425,1,1,0,0,0,0,1202, 3,250000,4,3,128,4,500 -"6",156,369,4,0,0,171,159,3220,3190,121,4,1,108,11,50,5,9, 422,1,1,0,0,0,1,1201, 3,250000,4,3,128,4,500 -"6",226,364,4,0,0,173,157,3220,3190,120,4,1,21,9,141,2,9, 414,1,1,0,0,0,0,1214, 3,250000,4,3,128,4,500 -"6",445,366,4,0,0,176,155,3220,3190,120,4,1,39,11,121,3,9, 390,1,1,0,0,0,1,1235, 3,250000,4,3,128,4,500 -"6",427,369,4,0,0,173,158,3220,3190,120,4,1,110,13,48,3,9, 392,1,1,0,0,0,1,1231, 3,250000,4,3,128,4,500 -"6",62,369,4,0,0,171,159,3220,3190,121,4,1,108,9,50,7,9, 432,1,1,0,0,0,1,1191, 3,250000,4,3,128,4,500 -"6",482,369,4,0,0,174,157,3220,3190,119,4,1,112,13,46,3,9, 386,1,1,0,0,0,1,1237, 3,250000,4,3,128,4,500 -"6",228,364,4,0,0,175,155,3220,3190,120,4,1,21,8,141,2,9, 414,1,1,0,0,0,1,1214, 3,250000,4,3,128,4,500 -"6",347,368,4,0,0,172,158,3220,3190,70,4,1,110,8,47,7,9, 401,1,1,0,0,0,1,1222, 3,250000,4,3,128,4,500 -"6",481,368,4,0,0,174,157,3220,3190,109,4,1,110,12,48,3,9, 386,1,1,0,0,0,1,1237, 3,250000,4,3,128,4,500 -"6",235,365,4,0,0,175,157,3220,3190,120,4,1,22,6,139,6,9, 414,1,1,0,0,0,1,1213, 3,250000,4,3,128,4,500 -"6",119,369,4,0,0,171,160,3220,3190,118,4,1,108,8,50,7,9, 426,1,1,0,0,0,1,1197, 3,250000,4,3,128,4,500 -"6",341,368,4,0,0,175,156,3220,3190,121,4,1,110,8,49,7,9, 401,1,1,0,0,0,1,1222, 3,250000,4,3,128,4,500 -"6",454,369,4,0,0,172,158,3220,3190,119,4,1,111,12,47,3,9, 389,1,1,0,0,0,1,1234, 3,250000,4,3,128,4,500 -"6",498,369,4,0,0,175,156,3220,3190,120,4,1,112,13,46,3,9, 385,1,1,0,0,0,1,1238, 3,250000,4,3,128,4,500 -"6",306,369,4,0,0,173,157,3220,3190,120,4,1,109,10,49,6,9, 405,1,1,0,0,0,1,1218, 3,250000,4,3,128,4,500 -"6",78,369,4,0,0,171,160,3220,3190,121,4,1,110,10,47,7,10, 430,1,1,0,0,0,1,1192, 3,250000,4,3,128,4,500 -"6",455,368,4,0,0,173,158,3220,3190,120,4,1,110,13,48,2,9, 389,1,1,0,0,0,1,1235, 3,250000,4,3,128,4,500 -"6",383,370,4,0,0,173,156,3220,3190,81,4,1,121,7,36,9,9, 397,1,1,0,0,0,1,1225, 3,250000,4,3,128,4,500 -"6",308,368,4,0,0,173,157,3220,3190,81,4,1,109,11,49,5,9, 405,1,1,0,0,0,1,1219, 3,250000,4,3,128,4,500 -"6",465,368,4,0,0,173,158,3220,3190,120,4,1,106,13,52,2,9, 388,2,1,0,0,0,1,1235, 3,250000,4,3,128,4,500 -"6",282,369,4,0,0,173,157,3220,3190,121,4,1,109,9,49,6,9, 408,1,1,0,0,0,1,1215, 3,250000,4,3,128,4,500 -"6",227,365,4,0,0,174,158,3220,3190,120,4,1,21,8,140,3,9, 414,1,1,0,0,0,1,1213, 3,250000,4,3,128,4,500 -"6",461,369,4,0,0,173,158,3220,3190,83,4,1,111,8,46,7,9, 388,1,1,0,0,0,1,1235, 3,250000,4,3,128,4,500 -"6",505,369,4,0,0,171,158,3220,3190,120,4,1,112,7,45,9,9, 384,1,1,0,0,0,1,1239, 3,250000,4,3,128,4,500 -"6",398,369,4,0,0,172,159,3220,3190,121,4,1,113,12,46,4,9, 395,1,1,0,0,0,1,1228, 3,250000,4,3,128,4,500 -"6",324,369,4,0,0,175,157,3220,3190,120,4,1,110,9,47,7,9, 403,1,1,0,0,0,1,1220, 3,250000,4,3,128,4,500 -"6",116,365,4,0,0,173,159,3220,3190,120,4,1,32,6,130,6,9, 426,1,1,0,0,0,1,1201, 3,250000,4,3,128,4,500 -"6",351,370,4,0,0,172,158,3220,3190,120,4,1,112,9,46,7,9, 400,1,1,0,0,0,1,1223, 3,250000,4,3,128,4,500 -"6",343,369,4,0,0,174,158,3220,3190,121,4,1,112,8,47,7,9, 401,1,1,0,0,0,1,1222, 3,250000,4,3,128,4,500 -"6",466,370,4,0,0,174,158,3220,3190,120,4,1,109,12,48,3,9, 388,1,1,0,0,0,1,1235, 3,250000,4,3,128,4,500 -"6",360,370,4,0,0,173,157,3220,3190,120,4,1,119,7,37,9,9, 399,1,1,0,0,0,1,1223, 3,250000,4,3,128,4,500 -"6",233,366,4,0,0,174,158,3220,3190,119,4,1,23,7,138,5,10, 413,1,1,0,0,0,1,1213, 3,250000,4,3,128,4,500 -"6",323,369,4,0,0,173,158,3220,3190,81,4,1,110,8,48,7,9, 403,1,1,0,0,0,1,1220, 3,250000,4,3,128,4,500 -"6",349,369,4,0,0,174,157,3220,3190,72,4,1,112,9,46,7,9, 401,1,1,0,0,0,1,1222, 3,250000,4,3,128,4,500 -"6",229,366,4,0,0,175,157,3220,3190,120,4,1,27,9,135,3,9, 414,1,1,0,0,0,1,1213, 3,250000,4,3,128,4,500 -"6",189,366,4,0,0,173,159,3220,3190,121,4,1,26,11,136,1,9, 418,1,1,0,0,0,1,1209, 3,250000,4,3,128,4,500 -"6",225,366,4,0,0,174,158,3220,3190,109,4,1,21,9,141,3,10, 414,1,1,0,0,0,1,1213, 3,250000,4,3,128,4,500 -"6",87,369,4,0,0,173,159,3220,3190,121,4,1,107,9,51,6,10, 429,1,1,0,0,0,1,1194, 3,250000,4,3,128,4,500 -"6",355,370,4,0,0,175,157,3220,3190,119,4,1,111,7,47,9,9, 399,1,1,0,0,0,1,1224, 3,250000,4,3,128,4,500 -"6",133,367,4,0,0,174,158,3220,3190,122,4,1,27,8,135,4,9, 424,1,1,0,0,0,0,1203, 3,250000,4,3,128,4,500 -"6",452,366,4,0,0,175,158,3220,3190,120,4,1,35,10,127,3,9, 389,1,1,0,0,0,1,1238, 3,250000,4,3,128,4,500 -"6",377,370,4,0,0,174,157,3220,3190,120,4,1,114,7,44,9,9, 397,1,1,0,0,0,1,1225, 3,250000,4,3,128,4,500 -"6",276,369,4,0,0,174,159,3220,3190,121,4,1,109,9,49,6,9, 408,1,1,0,0,0,1,1215, 3,250000,4,3,128,4,500 -"6",491,370,4,0,0,176,157,3220,3190,120,4,1,113,7,45,9,9, 386,1,1,0,0,0,1,1237, 3,250000,4,3,128,4,500 -"6",363,370,4,0,0,173,158,3220,3190,120,4,1,115,3,42,13,9, 399,1,1,0,0,0,1,1224, 3,250000,4,3,128,4,500 -"6",470,370,4,0,0,175,157,3220,3190,120,4,1,112,11,46,3,9, 388,1,1,0,0,0,1,1235, 3,250000,4,3,128,4,500 -"6",283,369,4,0,0,175,158,3220,3190,70,4,1,109,9,49,6,9, 408,1,1,0,0,0,1,1215, 3,250000,4,3,128,4,500 -"6",450,366,4,0,0,176,157,3220,3190,121,4,1,21,9,141,2,9, 390,1,1,0,0,0,1,1238, 3,250000,4,3,128,4,500 -"6",448,366,4,0,0,177,156,3220,3190,120,4,1,22,9,140,2,9, 390,1,1,0,0,0,1,1238, 3,250000,4,3,128,4,500 -"6",281,370,4,0,0,176,158,3220,3190,121,4,1,108,10,50,6,9, 408,2,1,0,0,0,1,1215, 3,250000,4,3,128,4,500 -"6",449,366,4,0,0,175,157,3220,3190,109,4,1,24,9,138,2,9, 390,1,1,0,0,0,1,1237, 3,250000,4,3,128,4,500 -"6",370,370,4,0,0,176,157,3220,3190,120,4,1,112,7,45,9,9, 398,1,1,0,0,0,1,1225, 3,250000,4,3,128,4,500 -"6",420,370,4,0,0,175,157,3220,3190,120,4,1,110,13,47,3,9, 392,1,1,0,0,0,1,1230, 3,250000,4,3,128,4,500 -"6",174,370,4,0,0,170,161,3220,3190,120,4,1,111,13,48,3,9, 420,1,1,0,0,0,1,1204, 3,250000,4,3,128,4,500 -"6",425,370,4,0,0,175,158,3220,3190,120,4,1,110,12,48,3,10, 392,1,1,0,0,0,1,1231, 3,250000,4,3,128,4,500 -"6",135,366,4,0,0,176,158,3220,3190,121,4,1,27,8,135,4,9, 424,1,1,0,0,0,1,1203, 3,250000,4,3,128,4,500 -"6",315,370,4,0,0,174,157,3220,3190,70,4,1,110,9,48,6,9, 404,1,1,0,0,0,1,1219, 3,250000,4,3,128,4,500 -"6",483,370,4,0,0,174,158,3220,3190,119,4,1,110,12,48,3,9, 386,1,1,0,0,0,1,1237, 3,250000,4,3,128,4,500 -"6",313,367,4,0,0,177,157,3220,3190,119,4,1,31,7,130,5,9, 404,1,1,0,0,0,1,1222, 3,250000,4,3,128,4,500 -"6",280,370,4,0,0,175,158,3220,3190,121,4,1,109,11,49,5,9, 408,1,1,0,0,0,1,1215, 3,250000,4,3,128,4,500 -"6",477,370,4,0,0,174,158,3220,3190,72,4,1,112,7,46,8,9, 387,1,1,0,0,0,1,1236, 3,250000,4,3,128,4,500 -"6",356,371,4,0,0,174,158,3220,3190,119,4,1,110,7,48,9,9, 400,1,1,0,0,0,1,1223, 3,250000,4,3,128,4,500 -"6",379,371,4,0,0,177,157,3220,3190,70,4,1,113,6,45,9,9, 397,1,1,0,0,0,1,1226, 3,250000,4,3,128,4,500 -"6",437,368,4,0,0,174,158,3220,3190,59,4,1,40,11,120,3,9, 391,1,1,0,0,0,1,1234, 3,250000,4,3,128,4,500 -"6",342,371,4,0,0,173,159,3220,3190,120,4,1,111,9,47,7,9, 401,1,1,0,0,0,1,1222, 3,250000,4,3,128,4,500 -"6",268,367,4,0,0,175,158,3220,3190,79,4,1,30,5,132,7,9, 409,1,1,0,0,0,1,1218, 3,250000,4,3,128,4,500 -"6",441,367,4,0,0,177,157,3220,3190,119,4,1,27,10,135,2,9, 391,1,1,0,0,0,1,1236, 3,250000,4,3,128,4,500 -"6",337,370,4,0,0,174,159,3220,3190,74,4,1,110,8,49,7,9, 402,1,1,0,0,0,1,1222, 3,250000,4,3,128,4,500 -"6",434,371,4,0,0,174,159,3220,3190,121,4,1,110,12,47,4,9, 391,1,1,0,0,0,1,1232, 3,250000,4,3,128,4,500 -"6",440,367,4,0,0,177,158,3220,3190,120,4,1,25,9,137,3,9, 391,1,1,0,0,0,0,1236, 3,250000,4,3,128,4,500 -"6",451,368,4,0,0,174,159,3220,3190,120,4,1,23,11,139,1,9, 390,1,1,0,0,0,1,1237, 3,250000,4,3,128,4,500 -"6",278,371,8,0,0,176,158,3220,3190,71,4,1,108,6,49,5,9, 409,1,1,0,0,0,1,1215, 3,250000,4,3,128,4,500 -"6",372,371,4,0,0,178,157,3220,3190,120,4,1,113,7,44,9,9, 398,1,1,0,0,0,1,1225, 3,250000,4,3,128,4,500 -"6",285,371,5,0,0,175,158,3220,3190,70,4,1,108,8,50,6,9, 407,1,1,0,0,0,1,1216, 3,250000,4,3,128,4,500 -"6",267,368,5,0,0,176,157,3220,3190,74,4,1,25,5,137,6,9, 409,1,1,0,0,0,1,1217, 3,250000,4,3,128,4,500 -"6",271,372,8,0,0,176,158,3220,3190,120,4,1,113,4,44,7,9, 409,1,1,0,0,0,1,1213, 3,250000,4,3,128,4,500 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417,1,1,0,0,0,1,1213, 3,250000,4,3,128,4,500 -"6",416,671,4,0,0,344,295,3220,3190,120,4,1,24,6,141,1,9, 393,1,1,0,0,0,1,1238, 3,250000,4,3,128,4,500 -"6",178,675,3,0,1,339,303,3220,3190,121,4,1,15,6,150,2,9, 420,1,1,0,0,0,1,1211, 3,250000,4,3,128,4,500 -"6",123,676,4,0,1,336,305,3220,3190,70,4,1,28,4,136,5,9, 425,1,1,0,0,0,1,1204, 3,250000,4,3,128,4,500 -"6",182,689,7,0,0,339,313,3220,3190,120,4,1,98,5,64,2,9, 419,1,1,0,0,0,2,1208, 3,250000,4,3,128,4,500 -"6",412,687,4,0,0,343,314,3220,3190,120,4,1,18,5,150,0,9, 394,1,1,0,0,0,1,1239, 3,250000,4,3,128,4,500 -"6",410,691,4,0,0,345,316,3220,3190,120,4,1,20,5,147,0,9, 394,1,1,0,0,0,1,1239, 3,250000,4,3,128,4,500 diff --git a/hpc/archer2/slurm/grid_search_1.slurm b/hpc/archer2/slurm/grid_search_1.slurm deleted file mode 100644 index 7003a63d..00000000 --- a/hpc/archer2/slurm/grid_search_1.slurm +++ /dev/null @@ -1,84 +0,0 @@ -#!/bin/bash -# Here, we perform grid search where the number of MPI processes is set equal to the number of NUMA regions, i.e. 8 per node - -#SBATCH --job-name=grid_search -#SBATCH --time=00:30:00 -#SBATCH --nodes=1 -#SBATCH --ntasks-per-node=8 -#SBATCH --cpus-per-task=16 - -#SBATCH --account=e738 -#SBATCH --partition=standard -#SBATCH --qos=standard -# Development environment for KiFMM - -# Restore AMD compiler env -module load PrgEnv-aocc -module load craype-network-ucx -module load cray-mpich-ucx - -# Home and work directories -export HOME="/home/e738/e738/skailasa" -export WORK="/work/e738/e738/skailasa" - -# Create a scratch directory for this run -export SCRATCH=${WORK}/grid_search_${SLURM_JOBID} - -# Load Spack -source $HOME/spack/share/spack/setup-env.sh -. "$HOME/.cargo/env" - -# Load BLAS -spack load openblas - -# Ensure Rust can find the Cray libraries -# export RUSTFLAGS="-L $(echo $CRAY_LD_LIBRARY_PATH)" -# export LD_LIBRARY_PATH=$CRAY_LD_LIBRARY_PATH:$LD_LIBRARY_PATH -export RUSTFLAGS="-L $(spack location -i openblas)/lib" -export LD_LIBRARY_PATH=$(spack location -i openblas)/lib:$LD_LIBRARY_PATH - -mkdir -p ${SCRATCH} -cd ${SCRATCH} - -# Pass variable to SRUN from SBATCH -export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK - -# Set simulation parameters -n_points=1000000 # points per MPI process -n_tasks=8 -global_depth=1 # Number of local roots matches the number of MPI processes, therefore the number of NUMA regions -local_depth=(5 6) -n_samples=(100 1000) -block_size=(128 256) -n_threads=(8 16) # See if bandwidth saturates with different threading parameters for Rayon thread pool -export OMP_NUM_THREADS=1 # Need to set to 1 to avoid oversubsciption between Rayon and OpenMP - -# Create a CSV output file for analysis -export OUTPUT=${SCRATCH}/grid_search_fft_${SLURM_JOBID}.csv -touch ${OUTPUT} -echo " -experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ -source_tree,target_tree,source_domain,target_domain,layout,\ -ghost_exchange_v,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\ -source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u\ -expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples" >> ${OUTPUT} - -# Perform grid search -for i in ${!local_depth[@]}; do - for j in ${!n_samples[@]}; do - for k in ${!block_size[@]}; do - for l in ${!n_threads[@]}; do - experiment_id="${i}_${j}_${k}_${l}" - srun --ntasks=$n_tasks --cpus-per-task=16 --distribution=block:block --hint=nomultithread \ - ${WORK}/grid_search_mpi --id $experiment_id --n-points $n_points \ - --expansion-order 3 \ - --prune-empty \ - --global-depth $global_depth \ - --local-depth ${local_depth[$i]} \ - --n-samples ${n_samples[$j]} \ - --block-size ${block_size[$k]} \ - --n-threads ${n_threads[$l]} >> ${OUTPUT} - done - done - done -done diff --git a/hpc/archer2/slurm/grid_search_2.slurm b/hpc/archer2/slurm/grid_search_2.slurm deleted file mode 100644 index 66944cfe..00000000 --- a/hpc/archer2/slurm/grid_search_2.slurm +++ /dev/null @@ -1,88 +0,0 @@ -#!/bin/bash -# Here, we perform grid search where the number of MPI processes is set equal to the number of CCX units, each of which -# share an L3 cache, conist of 4 processors. There are 16 CCX regions per processor, and 32 per node - -#SBATCH --job-name=grid_search -#SBATCH --time=00:30:00 -#SBATCH --nodes=1 -#SBATCH --ntasks-per-node=32 -#SBATCH --cpus-per-task=4 - -#SBATCH --account=e738 -#SBATCH --partition=standard -#SBATCH --qos=standard -# Development environment for KiFMM - -# Restore AMD compiler env -module load PrgEnv-aocc -module load craype-network-ucx -module load cray-mpich-ucx - -# Home and work directories -export HOME="/home/e738/e738/skailasa" -export WORK="/work/e738/e738/skailasa" - -# Create a scratch directory for this run -export SCRATCH=${WORK}/grid_search_${SLURM_JOBID} - -# Load Spack -source $HOME/spack/share/spack/setup-env.sh -. "$HOME/.cargo/env" - -# Load BLAS -spack load openblas - -# Ensure Rust can find the Cray libraries -# export RUSTFLAGS="-L $(echo $CRAY_LD_LIBRARY_PATH)" -# export LD_LIBRARY_PATH=$CRAY_LD_LIBRARY_PATH:$LD_LIBRARY_PATH -export RUSTFLAGS="-L $(spack location -i openblas)/lib" -export LD_LIBRARY_PATH=$(spack location -i openblas)/lib:$LD_LIBRARY_PATH - -mkdir -p ${SCRATCH} -cd ${SCRATCH} - -# Pass variable to SRUN from SBATCH -export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK - -# Script being run, expected in the work directory -script_name="fmm_m2l_blas_mpi_f32" - -# Set simulation parameters -n_points=250000 # points per MPI process -n_tasks=32 -cpus_per_task=4 -global_depth=2 # Number of local roots matches the number of MPI processes, therefore the number of NUMA regions -local_depth=(4 5) -n_samples=(5000) -block_size=(128) -n_threads=(4) # See if bandwidth saturates with different threading parameters for Rayon thread pool -export OMP_NUM_THREADS=1 # Need to set to 1 to avoid oversubsciption between Rayon and OpenMP - -# Create a CSV output file for analysis -export OUTPUT=${SCRATCH}/grid_search_blas_${SLURM_JOBID}.csv -touch ${OUTPUT} -echo " -experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ -source_tree,target_tree,source_domain,target_domain, layout,\ -ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\ -source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\ -expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples" >> ${OUTPUT} - -# Perform grid search -for i in ${!local_depth[@]}; do - for j in ${!n_samples[@]}; do - for k in ${!block_size[@]}; do - for l in ${!n_threads[@]}; do - experiment_id="${i}_${j}_${k}_${l}" - srun --ntasks=$n_tasks --cpus-per-task=$cpus_per_task --distribution=block:block --hint=nomultithread \ - "${WORK}/${script_name}" --id $experiment_id --n-points $n_points \ - --expansion-order 3 \ - --prune-empty \ - --global-depth $global_depth \ - --local-depth ${local_depth[$i]} \ - --n-samples ${n_samples[$j]} \ - --n-threads ${n_threads[$l]} >> ${OUTPUT} - done - done - done -done diff --git a/hpc/archer2/slurm/grid_search_3.slurm b/hpc/archer2/slurm/grid_search_3.slurm deleted file mode 100644 index b3ad78d8..00000000 --- a/hpc/archer2/slurm/grid_search_3.slurm +++ /dev/null @@ -1,85 +0,0 @@ -#!/bin/bash -# Here we perform a naive run - -#SBATCH --job-name=grid_search -#SBATCH --time=00:30:00 -#SBATCH --nodes=1 -#SBATCH --ntasks-per-node=1 -#SBATCH --cpus-per-task=128 - -#SBATCH --account=e738 -#SBATCH --partition=standard -#SBATCH --qos=standard -# Development environment for KiFMM - -# Restore AMD compiler env -module load PrgEnv-aocc -module load craype-network-ucx -module load cray-mpich-ucx - -# Home and work directories -export HOME="/home/e738/e738/skailasa" -export WORK="/work/e738/e738/skailasa" - -# Create a scratch directory for this run -export SCRATCH=${WORK}/grid_search_${SLURM_JOBID} - -# Load Spack -source $HOME/spack/share/spack/setup-env.sh -. "$HOME/.cargo/env" - -# Load BLAS -spack load openblas - -# Ensure Rust can find the Cray libraries -# export RUSTFLAGS="-L $(echo $CRAY_LD_LIBRARY_PATH)" -# export LD_LIBRARY_PATH=$CRAY_LD_LIBRARY_PATH:$LD_LIBRARY_PATH -export RUSTFLAGS="-L $(spack location -i openblas)/lib" -export LD_LIBRARY_PATH=$(spack location -i openblas)/lib:$LD_LIBRARY_PATH - -mkdir -p ${SCRATCH} -cd ${SCRATCH} - -# Pass variable to SRUN from SBATCH -export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK - -# Set simulation parameters -n_points=8000000 # points per MPI process -n_tasks=1 -cpus_per_task=128 -global_depth=1 # Number of local roots matches the number of MPI processes, therefore the number of NUMA regions -local_depth=(5) -n_samples=(5000) -block_size=(128) -n_threads=(128) # See if bandwidth saturates with different threading parameters for Rayon thread pool -export OMP_NUM_THREADS=1 # Need to set to 1 to avoid oversubsciption between Rayon and OpenMP - -# Create a CSV output file for analysis -export OUTPUT=${SCRATCH}/grid_search_fft_${SLURM_JOBID}.csv -touch ${OUTPUT} -echo " -experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ -source_tree,target_tree,source_domain,target_domain,layout,\ -ghost_exchange_v,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\ -source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\ -expansion_order,n_points,local_depth, global_depth, block_size, n_threads, n_samples" >> ${OUTPUT} - -# Perform grid search -for i in ${!local_depth[@]}; do - for j in ${!n_samples[@]}; do - for k in ${!block_size[@]}; do - for l in ${!n_threads[@]}; do - experiment_id="${i}_${j}_${k}_${l}" - srun --ntasks=$n_tasks --cpus-per-task=$cpus_per_task --distribution=block:block --hint=nomultithread \ - ${WORK}/fmm_m2l_fft_mpi_f32 --id $experiment_id --n-points $n_points \ - --expansion-order 3 \ - --prune-empty \ - --global-depth $global_depth \ - --local-depth ${local_depth[$i]} \ - --n-samples ${n_samples[$j]} \ - --block-size ${block_size[$k]} \ - --n-threads ${n_threads[$l]} >> ${OUTPUT} - done - done - done -done diff --git a/hpc/archer2/slurm/grid_search_fft_m2l_1e6.slurm b/hpc/archer2/slurm/grid_search_fft_m2l_1e6.slurm deleted file mode 100644 index b3b29005..00000000 --- a/hpc/archer2/slurm/grid_search_fft_m2l_1e6.slurm +++ /dev/null @@ -1,91 +0,0 @@ -#!/bin/bash -# Here, we perform grid search where the number of MPI processes is set equal to the number of CCX units, each of which -# share an L3 cache, conist of 4 processors. There are 16 CCX regions per processor, and 32 per node - -#SBATCH --job-name=grid_search -#SBATCH --time=00:30:00 -#SBATCH --nodes=1 -#SBATCH --ntasks-per-node=32 -#SBATCH --cpus-per-task=4 - -#SBATCH --account=e738 -#SBATCH --partition=standard -#SBATCH --qos=standard -# Development environment for KiFMM - -# Restore AMD compiler env -module load PrgEnv-aocc -module load craype-network-ucx -module load cray-mpich-ucx - -# Home and work directories -export HOME="/home/e738/e738/skailasa" -export WORK="/work/e738/e738/skailasa" - -# Create a scratch directory for this run -export SCRATCH=${WORK}/grid_search_${SLURM_JOBID} - -# Load Spack -source $HOME/spack/share/spack/setup-env.sh -. "$HOME/.cargo/env" - -# Load BLAS -spack load openblas - -# Ensure Rust can find the Cray libraries -# export RUSTFLAGS="-L $(echo $CRAY_LD_LIBRARY_PATH)" -# export LD_LIBRARY_PATH=$CRAY_LD_LIBRARY_PATH:$LD_LIBRARY_PATH -export RUSTFLAGS="-L $(spack location -i openblas)/lib" -export LD_LIBRARY_PATH=$(spack location -i openblas)/lib:$LD_LIBRARY_PATH - -mkdir -p ${SCRATCH} -cd ${SCRATCH} - -# Pass variable to SRUN from SBATCH -export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK - -# Script being run, expected in the work directory -script_name="fmm_m2l_fft_mpi_f32" - -# Set simulation parameters -n_points=(250000 500000 1000000) # points per MPI process -n_tasks=32 -cpus_per_task=4 -global_depth=2 # Number of local roots matches the number of MPI processes, therefore the number of NUMA regions -local_depth=(4 5) -n_samples=(500) -block_size=(128) -n_threads=(4) # See if bandwidth saturates with different threading parameters for Rayon thread pool -export OMP_NUM_THREADS=1 # Need to set to 1 to avoid oversubsciption between Rayon and OpenMP - -# Create a CSV output file for analysis -export OUTPUT=${SCRATCH}/grid_search_fft_${SLURM_JOBID}.csv -touch ${OUTPUT} -echo " -experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ -source_tree,target_tree,source_domain,target_domain,layout,\ -ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\ -source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\ -displacement_map,metadata_creation,\ -expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples" >> ${OUTPUT} - -# Perform grid search -for i in ${!local_depth[@]}; do - for j in ${!n_samples[@]}; do - for k in ${!block_size[@]}; do - for l in ${!n_threads[@]}; do - for m in ${!n_points[@]}; do - experiment_id="${i}_${j}_${k}_${l}_${m}" - srun --ntasks=$n_tasks --cpus-per-task=$cpus_per_task --distribution=block:block --hint=nomultithread \ - "${WORK}/${script_name}" --id $experiment_id --n-points ${n_points[$m]} \ - --expansion-order 3 \ - --prune-empty \ - --global-depth $global_depth \ - --local-depth ${local_depth[$i]} \ - --n-samples ${n_samples[$j]} \ - --n-threads ${n_threads[$l]} >> ${OUTPUT} - done - done - done - done -done diff --git a/hpc/archer2/slurm/grid_search_fft_m2l_1e7.slurm b/hpc/archer2/slurm/grid_search_fft_m2l_1e7.slurm deleted file mode 100644 index 16a3fd6d..00000000 --- a/hpc/archer2/slurm/grid_search_fft_m2l_1e7.slurm +++ /dev/null @@ -1,91 +0,0 @@ -#!/bin/bash -# Here, we perform grid search where the number of MPI processes is set equal to the number of CCX units, each of which -# share an L3 cache, conist of 4 processors. There are 16 CCX regions per processor, and 32 per node - -#SBATCH --job-name=grid_search -#SBATCH --time=00:30:00 -#SBATCH --nodes=1 -#SBATCH --ntasks-per-node=32 -#SBATCH --cpus-per-task=4 - -#SBATCH --account=e738 -#SBATCH --partition=standard -#SBATCH --qos=standard -# Development environment for KiFMM - -# Restore AMD compiler env -module load PrgEnv-aocc -module load craype-network-ucx -module load cray-mpich-ucx - -# Home and work directories -export HOME="/home/e738/e738/skailasa" -export WORK="/work/e738/e738/skailasa" - -# Create a scratch directory for this run -export SCRATCH=${WORK}/grid_search_${SLURM_JOBID} - -# Load Spack -source $HOME/spack/share/spack/setup-env.sh -. "$HOME/.cargo/env" - -# Load BLAS -spack load openblas - -# Ensure Rust can find the Cray libraries -# export RUSTFLAGS="-L $(echo $CRAY_LD_LIBRARY_PATH)" -# export LD_LIBRARY_PATH=$CRAY_LD_LIBRARY_PATH:$LD_LIBRARY_PATH -export RUSTFLAGS="-L $(spack location -i openblas)/lib" -export LD_LIBRARY_PATH=$(spack location -i openblas)/lib:$LD_LIBRARY_PATH - -mkdir -p ${SCRATCH} -cd ${SCRATCH} - -# Pass variable to SRUN from SBATCH -export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK - -# Script being run, expected in the work directory -script_name="fmm_m2l_fft_mpi_f32" - -# Set simulation parameters -n_points=(5000000 10000000) # points per MPI process -n_tasks=32 -cpus_per_task=4 -global_depth=2 # Number of local roots matches the number of MPI processes, therefore the number of NUMA regions -local_depth=(5 6) -n_samples=(500) -block_size=(128) -n_threads=(4) # See if bandwidth saturates with different threading parameters for Rayon thread pool -export OMP_NUM_THREADS=1 # Need to set to 1 to avoid oversubsciption between Rayon and OpenMP - -# Create a CSV output file for analysis -export OUTPUT=${SCRATCH}/grid_search_fft_${SLURM_JOBID}.csv -touch ${OUTPUT} -echo " -experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ -source_tree,target_tree,source_domain,target_domain,layout,\ -ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\ -source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\ -displacement_map,metadata_creation,\ -expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples" >> ${OUTPUT} - -# Perform grid search -for i in ${!local_depth[@]}; do - for j in ${!n_samples[@]}; do - for k in ${!block_size[@]}; do - for l in ${!n_threads[@]}; do - for m in ${!n_points[@]}; do - experiment_id="${i}_${j}_${k}_${l}_${m}" - srun --ntasks=$n_tasks --cpus-per-task=$cpus_per_task --distribution=block:block --hint=nomultithread \ - "${WORK}/${script_name}" --id $experiment_id --n-points ${n_points[$m]} \ - --expansion-order 3 \ - --prune-empty \ - --global-depth $global_depth \ - --local-depth ${local_depth[$i]} \ - --n-samples ${n_samples[$j]} \ - --n-threads ${n_threads[$l]} >> ${OUTPUT} - done - done - done - done -done diff --git a/hpc/archer2/slurm/strong_1_nodes_fft.slurm b/hpc/archer2/slurm/strong_1_nodes_fft.slurm deleted file mode 100644 index 71201b92..00000000 --- a/hpc/archer2/slurm/strong_1_nodes_fft.slurm +++ /dev/null @@ -1,83 +0,0 @@ -#!/bin/bash -# Here, we perform a strong scaling run where the number of MPI processes are tied to CCX units, each of which -# share an L3 cache, consist of 4 cores. There are 16 CCX regions per processor, and 32 per node (i.e. two AMD EPYC Rome units per node) -# We start with a large (>2.5B) point problem on a single fully utilised node, and gradually double the number of nodes to identify -# the saturation point (in terms of nodes) - -#SBATCH --job-name=strong_scaling_fft -#SBATCH --time=01:00:00 -#SBATCH --nodes=128 -#SBATCH --ntasks-per-node=16 -#SBATCH --cpus-per-task=8 -#SBATCH --contiguous - -#SBATCH --account=e738 -#SBATCH --partition=standard -#SBATCH --qos=standard - -# Restore AMD compiler env -module load PrgEnv-aocc -module load craype-network-ucx -module load cray-mpich-ucx - -# Home and work directories -export HOME="/home/e738/e738/skailasa" -export WORK="/work/e738/e738/skailasa" -TOTAL_POINTS=153600000 # 150M points - -# Script being run, expected in the work directory -script_name="fmm_m2l_fft_mpi_f32" # must exist in $WORK - -# Set simulation parameters for FMM -expansion_order=3 -global_depth=(2 2 2 3 3 3 4 4) # Number of local roots -n_samples=500 -block_size=128 - -# Set parameters for strong scaling run -n_points=(9600000 4800000 2400000 1200000 600000 300000 150000 75000) -# local_depth=(5 4 4 4 3 3 3 3) slightly unbalanced -local_depth=(5 5 5 4 4 4 3 2) -n_tasks=(16 32 64 128 256 512 1024 2048) # Equal to the number of local trees -n_nodes=(1 2 4 8 16 32 64 128) -n_threads=8 -cpus_per_task=8 - -# Create a scratch directory for this run -last_nodes=${n_nodes[@]: -1} -export SCRATCH=${WORK}/strong_fft_n=${TOTAL_POINTS}_p=${last_nodes}_${SLURM_JOBID} - -mkdir -p ${SCRATCH} -cd ${SCRATCH} - -# Pass variable to SRUN from SBATCH -export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK - -export OMP_NUM_THREADS=1 # Need to set to 1 to avoid oversubsciption between Rayon and OpenMP - -# Create a CSV output file for analysis -export OUTPUT=${SCRATCH}/strong_fft_p=${TOTAL_POINTS}_n=${last_nodes}_${SLURM_JOBID}.csv - -# Create a CSV output file for analysis -touch ${OUTPUT} -echo " -experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ -source_tree,target_tree,source_domain,target_domain,layout,\ -ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\ -source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\ -displacement_map,metadata_creation,\ -expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples" >> ${OUTPUT} - -for i in ${!n_tasks[@]}; do - - experiment_id="${i}" - srun --nodes=${n_nodes[$i]} --ntasks=${n_tasks[$i]} --cpus-per-task=$cpus_per_task --distribution=block:block --hint=nomultithread \ - "${WORK}/${script_name}" --id $experiment_id --n-points ${n_points[$i]} \ - --expansion-order $expansion_order \ - --prune-empty \ - --global-depth ${global_depth[$i]} \ - --local-depth ${local_depth[$i]} \ - --n-samples $n_samples \ - --block-size $block_size \ - --n-threads $n_threads >> ${OUTPUT} 2> ${SCRATCH}/err_run_${i}.log -done diff --git a/hpc/archer2/slurm/weak_1_blas.slurm b/hpc/archer2/slurm/weak_1_blas.slurm deleted file mode 100644 index 8f20b49b..00000000 --- a/hpc/archer2/slurm/weak_1_blas.slurm +++ /dev/null @@ -1,93 +0,0 @@ -#!/bin/bash -# Here, we perform a weak scaling run where the number of MPI processes is set equal to the number of CCX units, each of which -# share an L3 cache, conist of 4 processors. There are 16 CCX regions per processor, and 32 per node - -# Running on just a single node, we try and examine the scaling performance as the number of tasks is increased -# just to see what a single node is capable of (in the best parameter settings found) - -# This should give a good baseline of what to expect when communication costs are minimal - -#SBATCH --job-name=weak_scaling -#SBATCH --time=00:30:00 -#SBATCH --nodes=16 -#SBATCH --ntasks-per-node=32 -#SBATCH --cpus-per-task=4 -#SBATCH --contiguous - -#SBATCH --account=e738 -#SBATCH --partition=standard -#SBATCH --qos=standard -# Development environment for KiFMM - -# Restore AMD compiler env -module load PrgEnv-aocc -module load craype-network-ucx -module load cray-mpich-ucx - -# Home and work directories -export HOME="/home/e738/e738/skailasa" -export WORK="/work/e738/e738/skailasa" - -# Create a scratch directory for this run -export SCRATCH=${WORK}/weak_${SLURM_JOBID} - -# Load Spack -source $HOME/spack/share/spack/setup-env.sh -. "$HOME/.cargo/env" - -# Load BLAS -spack load openblas - -# Ensure Rust can find the Cray libraries -# export RUSTFLAGS="-L $(echo $CRAY_LD_LIBRARY_PATH)" -# export LD_LIBRARY_PATH=$CRAY_LD_LIBRARY_PATH:$LD_LIBRARY_PATH -export RUSTFLAGS="-L $(spack location -i openblas)/lib" -export LD_LIBRARY_PATH=$(spack location -i openblas)/lib:$LD_LIBRARY_PATH - -mkdir -p ${SCRATCH} -cd ${SCRATCH} - -# Pass variable to SRUN from SBATCH -export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK - -# Script being run, expected in the work directory -script_name="fmm_m2l_blas_mpi_f32" - -# Set simulation parameters for FMM -expansion_order=3 -n_points=250000 # points per MPI process (n_tasks) -global_depth=(1 2 2 2 3 3 3) # Number of local roots -local_depth=4 -n_samples=5000 - -# Set parameters for weak scaling run -n_tasks=(8 16 32 64 128 256 512) -n_nodes=(1 1 1 2 4 8 16) -n_threads=4 # See if bandwidth saturates with different threading parameters for Rayon thread pool -cpus_per_task=4 -export OMP_NUM_THREADS=1 # Need to set to 1 to avoid oversubsciption between Rayon and OpenMP - -# Create a CSV output file for analysis -export OUTPUT=${SCRATCH}/weak_blas_${SLURM_JOBID}.csv -touch ${OUTPUT} -echo " -experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ -source_tree,target_tree,source_domain,target_domain,layout,\ -ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\ -source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\ -displacement_map,metadata_creation,\ -expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples" >> ${OUTPUT} - -# Perform weak scaling -for i in ${!n_tasks[@]}; do - experiment_id="${i}" - srun --nodes=${n_nodes[$i]} --ntasks=${n_tasks[$i]} --cpus-per-task=$cpus_per_task --distribution=block:block --hint=nomultithread \ - "${WORK}/${script_name}" --id $experiment_id --n-points $n_points \ - --expansion-order $expansion_order \ - --prune-empty \ - --global-depth ${global_depth[$i]} \ - --local-depth $local_depth \ - --n-samples $n_samples \ - --n-threads $n_threads >> ${OUTPUT} -done - diff --git a/hpc/archer2/slurm/weak_1_fft.slurm b/hpc/archer2/slurm/weak_1_fft.slurm deleted file mode 100644 index ad54be91..00000000 --- a/hpc/archer2/slurm/weak_1_fft.slurm +++ /dev/null @@ -1,95 +0,0 @@ -#!/bin/bash -# Here, we perform a weak scaling run where the number of MPI processes are tied to CCX units, each of which -# share an L3 cache, consist of 4 processors. There are 16 CCX regions per processor, and 32 per node (i.e. two AMD EPYC Rome units per node) - -#SBATCH --job-name=weak_scaling_fft -#SBATCH --time=00:30:00 -#SBATCH --nodes=16 -#SBATCH --ntasks-per-node=32 -#SBATCH --cpus-per-task=4 -##SBATCH --contiguous - -#SBATCH --account=e738 -#SBATCH --partition=standard -#SBATCH --qos=standard - -# Restore AMD compiler env -module load PrgEnv-aocc -module load craype-network-ucx -module load cray-mpich-ucx - -# Home and work directories -export HOME="/home/e738/e738/skailasa" -export WORK="/work/e738/e738/skailasa" - -# Script being run, expected in the work directory -script_name="fmm_m2l_fft_mpi_f32" - -# Set simulation parameters for FMM -expansion_order=3 -n_points=250000 # points per MPI process (n_tasks) -global_depth=(1 2 2 2 3 3 3) # Number of local roots -local_depth=4 -n_samples=500 -block_size=128 - -# Set parameters for weak scaling run -n_tasks=(8 16 32 64 128 256 512) -n_nodes=(1 1 1 2 4 8 16) -n_threads=4 # See if bandwidth saturates with different threading parameters for Rayon thread pool -cpus_per_task=4 -max_points=$(echo {n_tasks[@]: -1} * ${n_points} | bc -l) - -# Create a scratch directory for this run -last_nodes=${n_nodes[@]: -1} -last_tasks=${n_tasks[@]: -1} - -# Multiply -max_points=$(( last_tasks * n_points )) -export SCRATCH=${WORK}/weak_fft_n=${max_points}_p=${last_nodes}_${SLURM_JOBID} - -## Load Spack -#source $HOME/spack/share/spack/setup-env.sh -#. "$HOME/.cargo/env" -# -## Load BLAS -#spack load openblas -# -## Ensure Rust can find the Cray libraries -## export RUSTFLAGS="-L $(echo $CRAY_LD_LIBRARY_PATH)" -## export LD_LIBRARY_PATH=$CRAY_LD_LIBRARY_PATH:$LD_LIBRARY_PATH -#export RUSTFLAGS="-L $(spack location -i openblas)/lib" -#export LD_LIBRARY_PATH=$(spack location -i openblas)/lib:$LD_LIBRARY_PATH - -mkdir -p ${SCRATCH} -cd ${SCRATCH} - -# Pass variable to SRUN from SBATCH -export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK - -export OMP_NUM_THREADS=1 # Need to set to 1 to avoid oversubsciption between Rayon and OpenMP - -# Create a CSV output file for analysis -export OUTPUT=${SCRATCH}/weak_fft_n=${max_points}_p=${last_nodes}_${SLURM_JOBID}.csv -touch ${OUTPUT} -echo " -experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ -source_tree,target_tree,source_domain,target_domain,layout,\ -ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\ -source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\ -displacement_map,metadata_creation,\ -expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples,source_local_trees_per_rank,target_local_trees_per_rank" >> ${OUTPUT} - -# Perform weak scaling -for i in ${!n_tasks[@]}; do - experiment_id="${i}" - srun --nodes=${n_nodes[$i]} --ntasks=${n_tasks[$i]} --cpus-per-task=$cpus_per_task --distribution=block:block --hint=nomultithread \ - "${WORK}/${script_name}" --id $experiment_id --n-points $n_points \ - --expansion-order $expansion_order \ - --prune-empty \ - --global-depth ${global_depth[$i]} \ - --local-depth $local_depth \ - --n-samples $n_samples \ - --block-size $block_size \ - --n-threads $n_threads >> ${OUTPUT} 2> ${SCRATCH}/err_run_${i}.log -done diff --git a/hpc/archer2/slurm/weak_2_blas.slurm b/hpc/archer2/slurm/weak_2_blas.slurm deleted file mode 100644 index e30f9d61..00000000 --- a/hpc/archer2/slurm/weak_2_blas.slurm +++ /dev/null @@ -1,93 +0,0 @@ -#!/bin/bash -# Here, we perform a weak scaling run where the number of MPI processes is set equal to the number of CCX units, each of which -# share an L3 cache, conist of 4 processors. There are 16 CCX regions per processor, and 32 per node - -# Running on just a single node, we try and examine the scaling performance as the number of tasks is increased -# just to see what a single node is capable of (in the best parameter settings found) - -# This should give a good baseline of what to expect when communication costs are minimal - -#SBATCH --job-name=weak_scaling_blas -#SBATCH --time=00:30:00 -#SBATCH --nodes=128 -#SBATCH --ntasks-per-node=32 -#SBATCH --cpus-per-task=4 -#SBATCH --contiguous - -#SBATCH --account=e738 -#SBATCH --partition=standard -#SBATCH --qos=standard -# Development environment for KiFMM - -# Restore AMD compiler env -module load PrgEnv-aocc -module load craype-network-ucx -module load cray-mpich-ucx - -# Home and work directories -export HOME="/home/e738/e738/skailasa" -export WORK="/work/e738/e738/skailasa" - -# Create a scratch directory for this run -export SCRATCH=${WORK}/weak_${SLURM_JOBID} - -# Load Spack -source $HOME/spack/share/spack/setup-env.sh -. "$HOME/.cargo/env" - -# Load BLAS -spack load openblas - -# Ensure Rust can find the Cray libraries -# export RUSTFLAGS="-L $(echo $CRAY_LD_LIBRARY_PATH)" -# export LD_LIBRARY_PATH=$CRAY_LD_LIBRARY_PATH:$LD_LIBRARY_PATH -export RUSTFLAGS="-L $(spack location -i openblas)/lib" -export LD_LIBRARY_PATH=$(spack location -i openblas)/lib:$LD_LIBRARY_PATH - -mkdir -p ${SCRATCH} -cd ${SCRATCH} - -# Pass variable to SRUN from SBATCH -export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK - -# Script being run, expected in the work directory -script_name="fmm_m2l_blas_mpi_f32" - -# Set simulation parameters for FMM -expansion_order=3 -n_points=250000 # points per MPI process (n_tasks) -global_depth=(4 4 4) # Number of local roots -local_depth=4 -n_samples=5000 - -# Set parameters for weak scaling run -n_tasks=(1024 2048 4096) -n_nodes=(32 64 128) -n_threads=4 # See if bandwidth saturates with different threading parameters for Rayon thread pool -cpus_per_task=4 -export OMP_NUM_THREADS=1 # Need to set to 1 to avoid oversubsciption between Rayon and OpenMP - -# Create a CSV output file for analysis -export OUTPUT=${SCRATCH}/weak_blas_${SLURM_JOBID}.csv -touch ${OUTPUT} -echo " -experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ -source_tree,target_tree,source_domain,target_domain,layout,\ -ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\ -source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\ -displacement_map,metadata_creation,\ -expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples" >> ${OUTPUT} - -# Perform weak scaling -for i in ${!n_tasks[@]}; do - experiment_id="${i}" - srun --nodes=${n_nodes[$i]} --ntasks=${n_tasks[$i]} --cpus-per-task=$cpus_per_task --distribution=block:block --hint=nomultithread \ - "${WORK}/${script_name}" --id $experiment_id --n-points $n_points \ - --expansion-order $expansion_order \ - --prune-empty \ - --global-depth ${global_depth[$i]} \ - --local-depth $local_depth \ - --n-samples $n_samples \ - --n-threads $n_threads >> ${OUTPUT} -done - diff --git a/hpc/archer2/slurm/weak_2_fft.slurm b/hpc/archer2/slurm/weak_2_fft.slurm deleted file mode 100644 index 0509eafe..00000000 --- a/hpc/archer2/slurm/weak_2_fft.slurm +++ /dev/null @@ -1,94 +0,0 @@ -#!/bin/bash -# Here, we perform a weak scaling run where the number of MPI processes are tied to CCX units, each of which -# share an L3 cache, consist of 4 processors. There are 16 CCX regions per processor, and 32 per node (i.e. two AMD EPYC Rome units per node) - -#SBATCH --job-name=weak_scaling_fft -#SBATCH --time=00:30:00 -#SBATCH --nodes=128 -#SBATCH --ntasks-per-node=32 -#SBATCH --cpus-per-task=4 -##SBATCH --contiguous - -#SBATCH --account=e738 -#SBATCH --partition=standard -#SBATCH --qos=standard - -# Restore AMD compiler env -module load PrgEnv-aocc -module load craype-network-ucx -module load cray-mpich-ucx - -# Home and work directories -export HOME="/home/e738/e738/skailasa" -export WORK="/work/e738/e738/skailasa" - -# Script being run, expected in the work directory -script_name="fmm_m2l_fft_mpi_f32" - -# Set simulation parameters for FMM -expansion_order=3 -n_points=250000 # points per MPI process (n_tasks) -global_depth=(4 4 4) # Number of local roots -local_depth=4 -n_samples=500 -block_size=128 - -# Set parameters for weak scaling run -n_tasks=(1024 2048 4096) -n_nodes=(32 64 128) -n_threads=4 # See if bandwidth saturates with different threading parameters for Rayon thread pool -cpus_per_task=4 - -# Create a scratch directory for this run -last_nodes=${n_nodes[@]: -1} -last_tasks=${n_tasks[@]: -1} - -# Multiply -max_points=$(( last_tasks * n_points )) -export SCRATCH=${WORK}/weak_fft_n=${max_points}_p=${last_nodes}_${SLURM_JOBID} - -## Load Spack -#source $HOME/spack/share/spack/setup-env.sh -#. "$HOME/.cargo/env" -# -## Load BLAS -#spack load openblas -# -## Ensure Rust can find the Cray libraries -## export RUSTFLAGS="-L $(echo $CRAY_LD_LIBRARY_PATH)" -## export LD_LIBRARY_PATH=$CRAY_LD_LIBRARY_PATH:$LD_LIBRARY_PATH -#export RUSTFLAGS="-L $(spack location -i openblas)/lib" -#export LD_LIBRARY_PATH=$(spack location -i openblas)/lib:$LD_LIBRARY_PATH - -mkdir -p ${SCRATCH} -cd ${SCRATCH} - -# Pass variable to SRUN from SBATCH -export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK - -export OMP_NUM_THREADS=1 # Need to set to 1 to avoid oversubsciption between Rayon and OpenMP - -# Create a CSV output file for analysis -export OUTPUT=${SCRATCH}/weak_fft_n=${max_points}_p=${last_nodes}_${SLURM_JOBID}.csv -touch ${OUTPUT} -echo " -experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ -source_tree,target_tree,source_domain,target_domain,layout,\ -ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\ -source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\ -displacement_map,metadata_creation,\ -expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples,source_local_trees_per_rank,target_local_trees_per_rank" >> ${OUTPUT} - -# Perform weak scaling -for i in ${!n_tasks[@]}; do - experiment_id="${i}" - srun --nodes=${n_nodes[$i]} --ntasks=${n_tasks[$i]} --cpus-per-task=$cpus_per_task --distribution=block:block --hint=nomultithread \ - "${WORK}/${script_name}" --id $experiment_id --n-points $n_points \ - --expansion-order $expansion_order \ - --prune-empty \ - --global-depth ${global_depth[$i]} \ - --local-depth $local_depth \ - --n-samples $n_samples \ - --block-size $block_size \ - --n-threads $n_threads >> ${OUTPUT} 2> ${SCRATCH}/err_run_${i}.log -done \ No newline at end of file diff --git a/hpc/archer2/slurm/weak_3_fft.slurm b/hpc/archer2/slurm/weak_3_fft.slurm deleted file mode 100644 index 2462da2a..00000000 --- a/hpc/archer2/slurm/weak_3_fft.slurm +++ /dev/null @@ -1,96 +0,0 @@ -#!/bin/bash -# Here, we perform a weak scaling run where the number of MPI processes are tied to CCX units, each of which -# share an L3 cache, consist of 4 processors. There are 16 CCX regions per processor, and 32 per node (i.e. two AMD EPYC Rome units per node) - - -#SBATCH --job-name=weak_scaling_fft -#SBATCH --time=00:30:00 -#SBATCH --nodes=1024 -#SBATCH --ntasks-per-node=32 -#SBATCH --cpus-per-task=4 -# #SBATCH --contiguous - -#SBATCH --account=e738 -#SBATCH --partition=standard -#SBATCH --qos=standard - -# Restore AMD compiler env -module load PrgEnv-aocc -module load craype-network-ucx -module load cray-mpich-ucx - -# Home and work directories -export HOME="/home/e738/e738/skailasa" -export WORK="/work/e738/e738/skailasa" - -# Script being run, expected in the work directory -script_name="fmm_m2l_fft_mpi_f32" - -# Set simulation parameters for FMM -expansion_order=3 -n_points=250000 # points per MPI process (n_tasks) -global_depth=(5 5 5) # Number of local roots -local_depth=4 -n_samples=500 -block_size=128 - -# Set parameters for weak scaling run -n_tasks=(8192 16384 32768) -n_nodes=(256 512 1024) -n_threads=4 # See if bandwidth saturates with different threading parameters for Rayon thread pool -cpus_per_task=4 - - -# Create a scratch directory for this run -last_nodes=${n_nodes[@]: -1} -last_tasks=${n_tasks[@]: -1} - -# Multiply -max_points=$(( last_tasks * n_points )) -export SCRATCH=${WORK}/weak_fft_n=${max_points}_p=${last_nodes}_${SLURM_JOBID} - -## Load Spack -#source $HOME/spack/share/spack/setup-env.sh -#. "$HOME/.cargo/env" -# -## Load BLAS -#spack load openblas -# -## Ensure Rust can find the Cray libraries -## export RUSTFLAGS="-L $(echo $CRAY_LD_LIBRARY_PATH)" -## export LD_LIBRARY_PATH=$CRAY_LD_LIBRARY_PATH:$LD_LIBRARY_PATH -#export RUSTFLAGS="-L $(spack location -i openblas)/lib" -#export LD_LIBRARY_PATH=$(spack location -i openblas)/lib:$LD_LIBRARY_PATH - -mkdir -p ${SCRATCH} -cd ${SCRATCH} - -# Pass variable to SRUN from SBATCH -export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK - -export OMP_NUM_THREADS=1 # Need to set to 1 to avoid oversubsciption between Rayon and OpenMP - -# Create a CSV output file for analysis -export OUTPUT=${SCRATCH}/weak_fft_n=${max_points}_p=${last_nodes}_${SLURM_JOBID}.csv -touch ${OUTPUT} -echo " -experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ -source_tree,target_tree,source_domain,target_domain,layout,\ -ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\ -source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\ -displacement_map,metadata_creation,\ -expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples,source_local_trees_per_rank,target_local_trees_per_rank" >> ${OUTPUT} - -# Perform weak scaling -for i in ${!n_tasks[@]}; do - experiment_id="${i}" - srun --nodes=${n_nodes[$i]} --ntasks=${n_tasks[$i]} --cpus-per-task=$cpus_per_task --distribution=block:block --hint=nomultithread \ - "${WORK}/${script_name}" --id $experiment_id --n-points $n_points \ - --expansion-order $expansion_order \ - --prune-empty \ - --global-depth ${global_depth[$i]} \ - --local-depth $local_depth \ - --n-samples $n_samples \ - --block-size $block_size \ - --n-threads $n_threads >> ${OUTPUT} 2> ${SCRATCH}/err_run_${i}.log -done \ No newline at end of file From bcbd709b5474f47a012b6cb3760f24bbbc606b25 Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Mon, 6 Oct 2025 10:58:55 +0100 Subject: [PATCH 23/52] Add strong scaling script generator --- hpc/archer2/data/ijhpca/Strong Scaling.ipynb | 32 +-- hpc/archer2/data/ijhpca/fix.py | 12 -- hpc/archer2/data/ijhpca/job.slurm | 56 ----- .../data/ijhpca/make_slurm_strong_scaling.py | 192 ++++++++++++++++++ .../data/ijhpca/make_slurm_weak_scaling.py | 21 +- .../data/ijhpca/strong_scaling_ccd.json | 8 + 6 files changed, 231 insertions(+), 90 deletions(-) delete mode 100644 hpc/archer2/data/ijhpca/fix.py delete mode 100644 hpc/archer2/data/ijhpca/job.slurm create mode 100644 hpc/archer2/data/ijhpca/make_slurm_strong_scaling.py create mode 100644 hpc/archer2/data/ijhpca/strong_scaling_ccd.json diff --git a/hpc/archer2/data/ijhpca/Strong Scaling.ipynb b/hpc/archer2/data/ijhpca/Strong Scaling.ipynb index d3c16656..2292b075 100644 --- a/hpc/archer2/data/ijhpca/Strong Scaling.ipynb +++ b/hpc/archer2/data/ijhpca/Strong Scaling.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "20e41bec-f758-4668-aef5-cada4ba8652b", "metadata": {}, "outputs": [], @@ -34,7 +34,7 @@ " result = level\n", " else:\n", " level += 1\n", - " \n", + "\n", " return result\n", "\n", "v_global_depth = np.vectorize(global_depth)\n", @@ -53,7 +53,7 @@ " result = local_level\n", " else:\n", " local_level += 1\n", - " \n", + "\n", " return result\n", "\n", "v_local_depth = np.vectorize(local_depth)\n", @@ -63,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "f0abc6a1-73c9-4608-80f7-43c6c64c0257", "metadata": {}, "outputs": [ @@ -95,7 +95,7 @@ " p = points_per_node[i]/2\n", " points_per_node.append(p)\n", "points_per_node = np.array(points_per_node)\n", - " \n", + "\n", "total_ranks = n_nodes*ranks_per_node\n", "points_per_rank = np.int64(points_per_node/ranks_per_node)\n", "\n", @@ -332,7 +332,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "id": "480e7a52-3277-4f55-9286-f4baa1054f19", "metadata": {}, "outputs": [], @@ -365,11 +365,11 @@ "\n", " # Shaded regions (using geometric means for edges)\n", " region_edges = np.sqrt(n_nodes[:-1] * n_nodes[1:])\n", - " region_edges = np.concatenate([[n_nodes.min() / np.sqrt(2)], \n", - " region_edges, \n", + " region_edges = np.concatenate([[n_nodes.min() / np.sqrt(2)],\n", + " region_edges,\n", " [n_nodes.max() * np.sqrt(2)]])\n", " y0, y1 = ax1.get_ylim()\n", - " shrink = 0.1 \n", + " shrink = 0.1\n", "\n", " for i in range(len(region_edges) - 1):\n", " left, right = region_edges[i], region_edges[i+1]\n", @@ -388,19 +388,19 @@ " ha='center', va='center', rotation=90,\n", " bbox=dict(facecolor='white', alpha=0.6, edgecolor='none'))\n", "\n", - " \n", + "\n", " # group by node count (assuming df has 'nodes' column)\n", " stats = df.groupby('experiment_id')[parameter].agg(['mean','std']).reset_index()\n", - " \n", + "\n", " # n_nodes = stats['experiment_id'].to_numpy()\n", " # runtime = stats['mean'].to_numpy()\n", " runtime_err = stats['std'].to_numpy()\n", - " \n", + "\n", " # Runtime with error bars\n", " ax1.errorbar(n_nodes, runtime, yerr=runtime_err,\n", " fmt='o-', capsize=4, label=\"Measured runtime\")\n", "\n", - " \n", + "\n", " ax1.set_ylabel(\"Runtime (ms)\")\n", " ax1.legend()\n", " ax1.set_title(f\"Strong Scaling: {parameter}\")\n", @@ -410,7 +410,7 @@ " # ==============================\n", " efficiency = T1 / (n_nodes * runtime)\n", " efficiency_err = efficiency * (runtime_err / runtime) # propagate efficiency error\n", - " \n", + "\n", " ax2.plot(n_nodes, efficiency, 's--', color=\"red\", label=\"Efficiency\")\n", "\n", " ax2.set_xscale('log')\n", @@ -440,7 +440,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "dd481ef6-025e-41e6-af21-04d939989779", "metadata": {}, "outputs": [ @@ -472,7 +472,7 @@ " p = points_per_node[i]/2\n", " points_per_node.append(p)\n", "points_per_node = np.array(points_per_node)\n", - " \n", + "\n", "total_ranks = n_nodes*ranks_per_node\n", "points_per_rank = np.int64(points_per_node/ranks_per_node)\n", "\n", diff --git a/hpc/archer2/data/ijhpca/fix.py b/hpc/archer2/data/ijhpca/fix.py deleted file mode 100644 index a0ba80cc..00000000 --- a/hpc/archer2/data/ijhpca/fix.py +++ /dev/null @@ -1,12 +0,0 @@ -import re - -# Read file -with open("weak_fft_11004445.csv", "r") as f: - data = f.read() - -# Replace "digit + space + digit" with "digit, digit" -fixed_data = re.sub(r"(\d)\s+(\d)", r"\1,\2", data) - -# Write corrected CSV -with open("weak_fft_11004445.csv", "w") as f: - f.write(fixed_data) \ No newline at end of file diff --git a/hpc/archer2/data/ijhpca/job.slurm b/hpc/archer2/data/ijhpca/job.slurm deleted file mode 100644 index b86a79eb..00000000 --- a/hpc/archer2/data/ijhpca/job.slurm +++ /dev/null @@ -1,56 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=weak_scaling_fft -#SBATCH --time=00:30:00 -#SBATCH --nodes=64 -#SBATCH --ntasks-per-node=4 -#SBATCH --cpus-per-task=32 -#SBATCH --account=e738 -#SBATCH --partition=standard -#SBATCH --qos=standard - -module load PrgEnv-aocc -module load craype-network-ucx -module load cray-mpich-ucx - -export HOME="/home/e738/e738/skailasa" -export WORK="/work/e738/e738/skailasa" - -script_name="fmm_m2l_fft_mpi_f32" - -export SCRATCH=$WORK/weak_fft_n=64000000_p=64_${SLURM_JOBID} -mkdir -p $SCRATCH -cd $SCRATCH - -export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK -export OMP_NUM_THREADS=1 - -export OUTPUT=$SCRATCH/weak_fft_n=64000000_p=64_${SLURM_JOBID}.csv -touch $OUTPUT -echo " -experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,source_tree,target_tree,source_domain,target_domain,layout,ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,displacement_map,metadata_creation,expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples,source_local_trees_per_rank,target_local_trees_per_rank" >> ${OUTPUT} - -# Perform weak scaling - -srun --nodes=1 --ntasks=4 --cpus-per-task=32 \ - --distribution=block:block --hint=nomultithread \ - $WORK/$script_name --id 0 --n-points 250000 \ - --expansion-order 3 --prune-empty \ - --global-depth 1 --local-depth 3 \ - --n-samples 500 --block-size 128 --n-threads 32 \ - >> $OUTPUT 2> $SCRATCH/err_run_0.log - -srun --nodes=8 --ntasks=32 --cpus-per-task=32 \ - --distribution=block:block --hint=nomultithread \ - $WORK/$script_name --id 1 --n-points 250000 \ - --expansion-order 3 --prune-empty \ - --global-depth 2 --local-depth 3 \ - --n-samples 500 --block-size 128 --n-threads 32 \ - >> $OUTPUT 2> $SCRATCH/err_run_1.log - -srun --nodes=64 --ntasks=256 --cpus-per-task=32 \ - --distribution=block:block --hint=nomultithread \ - $WORK/$script_name --id 2 --n-points 250000 \ - --expansion-order 3 --prune-empty \ - --global-depth 3 --local-depth 3 \ - --n-samples 500 --block-size 128 --n-threads 32 \ - >> $OUTPUT 2> $SCRATCH/err_run_2.log diff --git a/hpc/archer2/data/ijhpca/make_slurm_strong_scaling.py b/hpc/archer2/data/ijhpca/make_slurm_strong_scaling.py new file mode 100644 index 00000000..abdacdc5 --- /dev/null +++ b/hpc/archer2/data/ijhpca/make_slurm_strong_scaling.py @@ -0,0 +1,192 @@ +#!/usr/bin/env python3 +import numpy as np +import argparse +from pathlib import Path +import json + +def global_depth(rank): + """ + will return the first power of 8 greater than rank, the power corresponding to the level + """ + level = 1 + loop = True + + while loop: + + if rank <= 8**level: + loop = False + result = level + else: + level += 1 + + return result + +v_global_depth = np.vectorize(global_depth) + +def local_depth(points_per_rank, max_points_per_leaf, min_local_depth): + + local_level = min_local_depth + loop = True + + while loop: + n_leaves = 8**local_level # leaves in local tree + points_per_leaf = points_per_rank / n_leaves + if points_per_leaf <= max_points_per_leaf: + loop = False + result = local_level + else: + local_level += 1 + + return result + +v_local_depth = np.vectorize(local_depth) + +def pow2(x): return np.log2(x).is_integer() + +def pow8(x): + return np.emath.logn(8, x).is_integer() + +def experiment_parameters(min_nodes, max_nodes, ranks_per_node, min_points_per_rank, local_depth=4, scaling_func=pow8): + + max_cpus=128 # AMD EPYC rome + n_nodes = np.array(list(filter(scaling_func, [i for i in range(min_nodes, max_nodes+1)]))) + total_ranks = max_nodes * ranks_per_node + + n = min_points_per_rank*total_ranks # total problem size + print(f"Total problem size n={n/1e9}B") + + points_per_node = [n] + for i in range(len(n_nodes)-1): + + if scaling_func == pow8: + p = points_per_node[i]/8 + elif scaling_func == pow2: + p = points_per_node[i]/2 + else: + raise ValueError("Unknown scaling function") + + points_per_node.append(p) + points_per_node = np.array(points_per_node) + + + + total_ranks = n_nodes * ranks_per_node + points_per_rank = np.int64(points_per_node/ranks_per_node) + print(f"points per rank {points_per_rank/1e6}M") + + # Double number of nodes used until we reach max_nodes + + # The global depth is a function of the ranks per node (as will need enough global leaves to cover this many ranks (local trees)) + global_depth = v_global_depth(total_ranks) + print(f"global depth {global_depth}") + + max_points_per_leaf = points_per_rank[-1] / 8**local_depth + print(f"points per leaf {max_points_per_leaf}") + + local_depth = v_local_depth(points_per_rank, max_points_per_leaf, local_depth) + print(f"local depth {local_depth}") + + # local depth is a constant, chosen to balance M2L and P2P + + n_tasks = n_nodes*ranks_per_node + print(f"MPI tasks {n_tasks}") + + print(f"number of nodes {n_nodes}") + + local_trees_per_rank = (8**global_depth)/(n_tasks) + print(f"local trees per rank {local_trees_per_rank}") + + max_threads_per_rank = max_cpus/ranks_per_node + print(f"max threads per rank {max_threads_per_rank}") + + return n_nodes.tolist(), n_tasks.tolist(), global_depth.tolist(), max_threads_per_rank + + +def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, points_per_rank, local_depth, script_name="fmm_m2l_fft_mpi_f32"): + expansion_order = 3 + n_points = points_per_rank + n_samples = 500 + block_size = 128 + cpus_per_task = int(max_threads) + + last_nodes = n_nodes[-1] + last_tasks = n_tasks[-1] + max_points = last_tasks * n_points + + slurm = f"""#!/bin/bash +#SBATCH --job-name=strong_scaling_fft +#SBATCH --time=00:30:00 +#SBATCH --nodes={last_nodes} +#SBATCH --ntasks-per-node={int(n_tasks[-1] // n_nodes[-1])} +#SBATCH --cpus-per-task={cpus_per_task} +#SBATCH --account=e738 +#SBATCH --partition=standard +#SBATCH --qos=standard + +module load PrgEnv-aocc +module load craype-network-ucx +module load cray-mpich-ucx + +export HOME="/home/e738/e738/skailasa" +export WORK="/work/e738/e738/skailasa" + +script_name="{script_name}" + +export SCRATCH=$WORK/strong_fft_n={max_points}_p={last_nodes}_${{SLURM_JOBID}} +mkdir -p $SCRATCH +cd $SCRATCH + +export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK +export OMP_NUM_THREADS=1 + +export OUTPUT=$SCRATCH/strong_fft_n={max_points}_p={last_nodes}_${{SLURM_JOBID}}.csv +touch $OUTPUT +echo " +experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ +source_tree,target_tree,source_domain,target_domain,layout,\ +ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\ +source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\ +displacement_map,metadata_creation,\ +expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples,source_local_trees_per_rank,target_local_trees_per_rank" >> ${{OUTPUT}} +""" + + # loop of runs + slurm += "\n# Perform strong scaling\n" + for i, (nn, nt, gd) in enumerate(zip(n_nodes, n_tasks, global_depths)): + slurm += f""" +srun --nodes={nn} --ntasks={nt} --cpus-per-task={cpus_per_task} \\ + --distribution=block:block --hint=nomultithread \\ + $WORK/$script_name --id {i} --n-points {n_points} \\ + --expansion-order {expansion_order} --prune-empty \\ + --global-depth {gd} --local-depth {local_depth} \\ + --n-samples {n_samples} --block-size {block_size} --n-threads {int(max_threads)} \\ + >> $OUTPUT 2> $SCRATCH/err_run_{i}.log +""" + + Path(script_path).write_text(slurm) + print(f"✅ Wrote SLURM script to {script_path}") + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Generate a SLURM script for weak scaling runs.") + parser.add_argument("--min-nodes", type=int, default=1) + parser.add_argument("--max-nodes", type=int, default=16) + parser.add_argument("--ranks-per-node", type=int, default=32) + parser.add_argument("--min-points-per-rank", type=int, default=250000) + parser.add_argument("--local-depth", type=int, default=4) + parser.add_argument("--output", type=str, default="job.slurm") + parser.add_argument("--config", action='append') + + args = parser.parse_args() + + if args.config is not None: + for fname in args.config: + with open(fname, 'r') as f: + parser.set_defaults(**json.load(f)) + + args = parser.parse_args() + + n_nodes, n_tasks, global_depths, max_threads = experiment_parameters( + args.min_nodes, args.max_nodes, args.ranks_per_node, args.min_points_per_rank, args.local_depth + ) + + write_slurm(args.output, n_nodes, n_tasks, global_depths, max_threads, args.min_points_per_rank, args.local_depth) diff --git a/hpc/archer2/data/ijhpca/make_slurm_weak_scaling.py b/hpc/archer2/data/ijhpca/make_slurm_weak_scaling.py index d989a998..24a9f8bf 100644 --- a/hpc/archer2/data/ijhpca/make_slurm_weak_scaling.py +++ b/hpc/archer2/data/ijhpca/make_slurm_weak_scaling.py @@ -29,26 +29,26 @@ def pow8(x): def experiment_parameters(min_nodes, max_nodes, ranks_per_node, points_per_rank, local_depth=4, scaling_func=pow8): max_cpus=128 # AMD EPYC rome - + total_ranks = ranks_per_node * max_nodes n = points_per_rank*total_ranks # total problem size print(f"Total problem size n={n/1e9}B") - + points_per_node = points_per_rank * ranks_per_node print(f"points per rank {points_per_rank/1e6}M") print(f"points per node {points_per_node/1e6}M") - + # Double number of nodes used until we reach max_nodes n_nodes = np.array(list(filter(scaling_func, [i for i in range(min_nodes, max_nodes+1)]))) - + # The global depth is a function of the ranks per node (as will need enough global leaves to cover this many ranks (local trees)) global_depth = v_global_depth(n_nodes*ranks_per_node) print(f"global depth {global_depth}") print(f"local depth {local_depth}") - + # local depth is a constant, chosen to balance M2L and P2P - points_per_leaf = points_per_rank / 8**local_depth + points_per_leaf = points_per_rank / 8**local_depth print(f"points per leaf {points_per_leaf}") n_tasks = n_nodes*ranks_per_node @@ -140,6 +140,15 @@ def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, point parser.add_argument("--points-per-rank", type=int, default=250000) parser.add_argument("--local-depth", type=int, default=4) parser.add_argument("--output", type=str, default="job.slurm") + parser.add_argument("--config", action='append') + + args = parser.parse_args() + + if args.config is not None: + for fname in args.config: + with open(fname, 'r') as f: + parser.set_defaults(**json.load(f)) + args = parser.parse_args() n_nodes, n_tasks, global_depths, max_threads = experiment_parameters( diff --git a/hpc/archer2/data/ijhpca/strong_scaling_ccd.json b/hpc/archer2/data/ijhpca/strong_scaling_ccd.json new file mode 100644 index 00000000..db00394e --- /dev/null +++ b/hpc/archer2/data/ijhpca/strong_scaling_ccd.json @@ -0,0 +1,8 @@ +{ + "min_nodes": 1, + "max_nodes": 64, + "ranks_per_node": 8, + "min_points_per_rank": 250000, + "local_depth": 3, + "output": "strong_scaling_ccd.slurm" +} \ No newline at end of file From f859fd8adbbd165b810d886012d87e255a16d529 Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Mon, 6 Oct 2025 14:30:52 +0100 Subject: [PATCH 24/52] Add strong scaling script --- .../data/ijhpca/make_slurm_strong_scaling.py | 192 ------------- .../data/ijhpca/strong_scaling_ccd.json | 8 - .../data => }/ijhpca/Strong Scaling.ipynb | 0 .../ijhpca/Weak Scaling (16 Nodes).ipynb | 0 .../data => }/ijhpca/Weak Scaling.ipynb | 0 hpc/ijhpca/strong_scaling.py | 262 ++++++++++++++++++ hpc/ijhpca/strong_scaling_ccd.json | 10 + hpc/ijhpca/strong_scaling_ccx.json | 10 + hpc/ijhpca/strong_scaling_socket.json | 10 + .../weak_scaling.py} | 0 10 files changed, 292 insertions(+), 200 deletions(-) delete mode 100644 hpc/archer2/data/ijhpca/make_slurm_strong_scaling.py delete mode 100644 hpc/archer2/data/ijhpca/strong_scaling_ccd.json rename hpc/{archer2/data => }/ijhpca/Strong Scaling.ipynb (100%) rename hpc/{archer2/data => }/ijhpca/Weak Scaling (16 Nodes).ipynb (100%) rename hpc/{archer2/data => }/ijhpca/Weak Scaling.ipynb (100%) create mode 100644 hpc/ijhpca/strong_scaling.py create mode 100644 hpc/ijhpca/strong_scaling_ccd.json create mode 100644 hpc/ijhpca/strong_scaling_ccx.json create mode 100644 hpc/ijhpca/strong_scaling_socket.json rename hpc/{archer2/data/ijhpca/make_slurm_weak_scaling.py => ijhpca/weak_scaling.py} (100%) diff --git a/hpc/archer2/data/ijhpca/make_slurm_strong_scaling.py b/hpc/archer2/data/ijhpca/make_slurm_strong_scaling.py deleted file mode 100644 index abdacdc5..00000000 --- a/hpc/archer2/data/ijhpca/make_slurm_strong_scaling.py +++ /dev/null @@ -1,192 +0,0 @@ -#!/usr/bin/env python3 -import numpy as np -import argparse -from pathlib import Path -import json - -def global_depth(rank): - """ - will return the first power of 8 greater than rank, the power corresponding to the level - """ - level = 1 - loop = True - - while loop: - - if rank <= 8**level: - loop = False - result = level - else: - level += 1 - - return result - -v_global_depth = np.vectorize(global_depth) - -def local_depth(points_per_rank, max_points_per_leaf, min_local_depth): - - local_level = min_local_depth - loop = True - - while loop: - n_leaves = 8**local_level # leaves in local tree - points_per_leaf = points_per_rank / n_leaves - if points_per_leaf <= max_points_per_leaf: - loop = False - result = local_level - else: - local_level += 1 - - return result - -v_local_depth = np.vectorize(local_depth) - -def pow2(x): return np.log2(x).is_integer() - -def pow8(x): - return np.emath.logn(8, x).is_integer() - -def experiment_parameters(min_nodes, max_nodes, ranks_per_node, min_points_per_rank, local_depth=4, scaling_func=pow8): - - max_cpus=128 # AMD EPYC rome - n_nodes = np.array(list(filter(scaling_func, [i for i in range(min_nodes, max_nodes+1)]))) - total_ranks = max_nodes * ranks_per_node - - n = min_points_per_rank*total_ranks # total problem size - print(f"Total problem size n={n/1e9}B") - - points_per_node = [n] - for i in range(len(n_nodes)-1): - - if scaling_func == pow8: - p = points_per_node[i]/8 - elif scaling_func == pow2: - p = points_per_node[i]/2 - else: - raise ValueError("Unknown scaling function") - - points_per_node.append(p) - points_per_node = np.array(points_per_node) - - - - total_ranks = n_nodes * ranks_per_node - points_per_rank = np.int64(points_per_node/ranks_per_node) - print(f"points per rank {points_per_rank/1e6}M") - - # Double number of nodes used until we reach max_nodes - - # The global depth is a function of the ranks per node (as will need enough global leaves to cover this many ranks (local trees)) - global_depth = v_global_depth(total_ranks) - print(f"global depth {global_depth}") - - max_points_per_leaf = points_per_rank[-1] / 8**local_depth - print(f"points per leaf {max_points_per_leaf}") - - local_depth = v_local_depth(points_per_rank, max_points_per_leaf, local_depth) - print(f"local depth {local_depth}") - - # local depth is a constant, chosen to balance M2L and P2P - - n_tasks = n_nodes*ranks_per_node - print(f"MPI tasks {n_tasks}") - - print(f"number of nodes {n_nodes}") - - local_trees_per_rank = (8**global_depth)/(n_tasks) - print(f"local trees per rank {local_trees_per_rank}") - - max_threads_per_rank = max_cpus/ranks_per_node - print(f"max threads per rank {max_threads_per_rank}") - - return n_nodes.tolist(), n_tasks.tolist(), global_depth.tolist(), max_threads_per_rank - - -def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, points_per_rank, local_depth, script_name="fmm_m2l_fft_mpi_f32"): - expansion_order = 3 - n_points = points_per_rank - n_samples = 500 - block_size = 128 - cpus_per_task = int(max_threads) - - last_nodes = n_nodes[-1] - last_tasks = n_tasks[-1] - max_points = last_tasks * n_points - - slurm = f"""#!/bin/bash -#SBATCH --job-name=strong_scaling_fft -#SBATCH --time=00:30:00 -#SBATCH --nodes={last_nodes} -#SBATCH --ntasks-per-node={int(n_tasks[-1] // n_nodes[-1])} -#SBATCH --cpus-per-task={cpus_per_task} -#SBATCH --account=e738 -#SBATCH --partition=standard -#SBATCH --qos=standard - -module load PrgEnv-aocc -module load craype-network-ucx -module load cray-mpich-ucx - -export HOME="/home/e738/e738/skailasa" -export WORK="/work/e738/e738/skailasa" - -script_name="{script_name}" - -export SCRATCH=$WORK/strong_fft_n={max_points}_p={last_nodes}_${{SLURM_JOBID}} -mkdir -p $SCRATCH -cd $SCRATCH - -export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK -export OMP_NUM_THREADS=1 - -export OUTPUT=$SCRATCH/strong_fft_n={max_points}_p={last_nodes}_${{SLURM_JOBID}}.csv -touch $OUTPUT -echo " -experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ -source_tree,target_tree,source_domain,target_domain,layout,\ -ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\ -source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\ -displacement_map,metadata_creation,\ -expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples,source_local_trees_per_rank,target_local_trees_per_rank" >> ${{OUTPUT}} -""" - - # loop of runs - slurm += "\n# Perform strong scaling\n" - for i, (nn, nt, gd) in enumerate(zip(n_nodes, n_tasks, global_depths)): - slurm += f""" -srun --nodes={nn} --ntasks={nt} --cpus-per-task={cpus_per_task} \\ - --distribution=block:block --hint=nomultithread \\ - $WORK/$script_name --id {i} --n-points {n_points} \\ - --expansion-order {expansion_order} --prune-empty \\ - --global-depth {gd} --local-depth {local_depth} \\ - --n-samples {n_samples} --block-size {block_size} --n-threads {int(max_threads)} \\ - >> $OUTPUT 2> $SCRATCH/err_run_{i}.log -""" - - Path(script_path).write_text(slurm) - print(f"✅ Wrote SLURM script to {script_path}") - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Generate a SLURM script for weak scaling runs.") - parser.add_argument("--min-nodes", type=int, default=1) - parser.add_argument("--max-nodes", type=int, default=16) - parser.add_argument("--ranks-per-node", type=int, default=32) - parser.add_argument("--min-points-per-rank", type=int, default=250000) - parser.add_argument("--local-depth", type=int, default=4) - parser.add_argument("--output", type=str, default="job.slurm") - parser.add_argument("--config", action='append') - - args = parser.parse_args() - - if args.config is not None: - for fname in args.config: - with open(fname, 'r') as f: - parser.set_defaults(**json.load(f)) - - args = parser.parse_args() - - n_nodes, n_tasks, global_depths, max_threads = experiment_parameters( - args.min_nodes, args.max_nodes, args.ranks_per_node, args.min_points_per_rank, args.local_depth - ) - - write_slurm(args.output, n_nodes, n_tasks, global_depths, max_threads, args.min_points_per_rank, args.local_depth) diff --git a/hpc/archer2/data/ijhpca/strong_scaling_ccd.json b/hpc/archer2/data/ijhpca/strong_scaling_ccd.json deleted file mode 100644 index db00394e..00000000 --- a/hpc/archer2/data/ijhpca/strong_scaling_ccd.json +++ /dev/null @@ -1,8 +0,0 @@ -{ - "min_nodes": 1, - "max_nodes": 64, - "ranks_per_node": 8, - "min_points_per_rank": 250000, - "local_depth": 3, - "output": "strong_scaling_ccd.slurm" -} \ No newline at end of file diff --git a/hpc/archer2/data/ijhpca/Strong Scaling.ipynb b/hpc/ijhpca/Strong Scaling.ipynb similarity index 100% rename from hpc/archer2/data/ijhpca/Strong Scaling.ipynb rename to hpc/ijhpca/Strong Scaling.ipynb diff --git a/hpc/archer2/data/ijhpca/Weak Scaling (16 Nodes).ipynb b/hpc/ijhpca/Weak Scaling (16 Nodes).ipynb similarity index 100% rename from hpc/archer2/data/ijhpca/Weak Scaling (16 Nodes).ipynb rename to hpc/ijhpca/Weak Scaling (16 Nodes).ipynb diff --git a/hpc/archer2/data/ijhpca/Weak Scaling.ipynb b/hpc/ijhpca/Weak Scaling.ipynb similarity index 100% rename from hpc/archer2/data/ijhpca/Weak Scaling.ipynb rename to hpc/ijhpca/Weak Scaling.ipynb diff --git a/hpc/ijhpca/strong_scaling.py b/hpc/ijhpca/strong_scaling.py new file mode 100644 index 00000000..da12448c --- /dev/null +++ b/hpc/ijhpca/strong_scaling.py @@ -0,0 +1,262 @@ +#!/usr/bin/env python3 +import numpy as np +import argparse +from pathlib import Path +import json + + + +AMD_EPYC_ROME = { + "cores_per_node": 128, # total number of CPU cores per node + "sockets_per_node": 2, # two sockets per node + "cores_per_socket": 64, # A socket consists of 8 CCDs (64 cores total) common IO/memory controllers + "cores_per_ccd": 8, # Each CCD is a processor die, consists of two CCDs which are infinity linked together and connected to io + "cores_per_ccx": 4, # Each CCX shares an L3 cache (16MB) + "ccx_per_ccd": 2 + } + +def global_depth(rank): + """ + will return the first power of 8 less than rank, the power corresponding to the level + """ + level = 1 + loop = True + + while rank > 8**level: + level += 1 + + return level + +v_global_depth = np.vectorize(global_depth) + +def local_depth(points_per_rank, max_points_per_leaf, min_local_depth): + + local_level = min_local_depth + + while True: + n_leaves = 8**local_level # leaves in local tree + points_per_leaf = points_per_rank / n_leaves # estimate based on uniform tree + if points_per_leaf <= max_points_per_leaf: + return local_level + else: + local_level += 1 + +v_local_depth = np.vectorize(local_depth) + +def pow2(x): return np.log2(x).is_integer() + +def pow8(x): + return np.emath.logn(8, x).is_integer() + +def parse_process_mapping(arch): + ccd_threads_per_rank = arch["cores_per_ccd"] + ccx_threads_per_rank = arch["cores_per_ccx"] + ccd_max_ranks_per_node = arch["cores_per_node"] / arch["cores_per_ccd"] # Mapping each rank to a CCD + ccx_max_ranks_per_node = arch["cores_per_node"] / arch["cores_per_ccx"] # Mapping each rank to a CCX + socket_max_ranks_per_node = arch["cores_per_node"] / arch["cores_per_socket"] # Mapping each rank to a socket + return {"socket": socket_max_ranks_per_node, "ccd": ccd_max_ranks_per_node, "ccx": ccx_max_ranks_per_node} + + +def experiment_parameters( + min_nodes, + max_nodes, + max_ranks_per_node, + min_points_per_rank, + min_local_depth=4, + scaling_func=pow8, + max_local_trees_per_rank=1 +): + + max_cpus=AMD_EPYC_ROME["cores_per_node"] + + # Calculate the min/max number of ranks required to scale this problem by scaling_func given + # the resources specified by min/max nodes + min_ranks = int(min_nodes*max_ranks_per_node) + max_ranks = int(max_nodes*max_ranks_per_node) + + # Want to scale the resources (ranks) by scaling function between min/max ranks + n_ranks = [] + curr = min_ranks + while curr <= max_ranks: + if scaling_func == pow2: + n_ranks.append(curr) + curr *= 2 + + elif scaling_func == pow8: + n_ranks.append(curr) + curr *= 8 + else: + raise ValueError("Unknown scaling func") + + n_ranks = np.array(n_ranks) + + # Can use the number of required ranks with this scaling function + # to calculate the number of required nodes + n_nodes = (n_ranks / max_ranks_per_node).astype(np.int32) + + # # Calculate the actual nodes required + + # we calculate the global depth as the least power of 8 smaller than or equal to the available + # ranks for each configuration + global_depth = v_global_depth(n_ranks) + + + local_trees_per_rank = 8**global_depth / n_ranks + + # Problem size defined by largest valid rank set + n = min_points_per_rank * n_ranks[-1] + + points_per_node = [n] + for i in range(len(n_nodes)-1): + + if scaling_func == pow8: + p = points_per_node[i]/8 + elif scaling_func == pow2: + p = points_per_node[i]/2 + else: + raise ValueError("Unknown scaling function") + + points_per_node.append(p) + + points_per_node = np.array(points_per_node) + + + ranks_per_node = n_ranks/n_nodes + points_per_rank = np.int64(points_per_node/ranks_per_node) + + min_points_per_leaf = points_per_rank[-1] / 8**min_local_depth + + local_depth = v_local_depth(points_per_rank, min_points_per_leaf, min_local_depth) + + max_threads_per_rank = int(max_cpus/ranks_per_node[0]) + + print(f"Total problem size n={n/1e6}M") + print(f"global depth {global_depth}") + print(f"local depth {local_depth}") + print(f"total depth {local_depth+global_depth}") + print(f"max local trees per rank {local_trees_per_rank}") + print(f"number of nodes {n_nodes} max ranks per node {max_ranks_per_node}") + print(f"number of ranks {n_ranks}") + print(f"points per rank {points_per_rank}") + print(f"points per node {points_per_node}") + print(f"min points per leaf {min_points_per_leaf}") + + # Test that same number of points being used in each experiment + assert(np.allclose(points_per_rank*n_ranks, points_per_node*n_nodes)) + return n_nodes.tolist(), n_ranks.tolist(), global_depth.tolist(), local_depth.tolist(), points_per_rank, max_threads_per_rank + + + +def write_slurm(script_path, n_nodes, n_tasks, local_depths, global_depths, max_threads, points_per_rank, script_name="fmm_m2l_fft_mpi_f32"): + expansion_order = 3 + n_samples = 500 + block_size = 128 + cpus_per_task = int(max_threads) + + last_nodes = n_nodes[-1] + last_tasks = n_tasks[-1] + max_points = last_tasks * points_per_rank[-1] + + slurm = f"""#!/bin/bash +#SBATCH --job-name=strong_scaling_fft +#SBATCH --time=00:30:00 +#SBATCH --nodes={last_nodes} +#SBATCH --ntasks-per-node={int(n_tasks[-1] // n_nodes[-1])} +#SBATCH --cpus-per-task={cpus_per_task} +#SBATCH --account=e738 +#SBATCH --partition=standard +#SBATCH --qos=standard + +module load PrgEnv-aocc +module load craype-network-ucx +module load cray-mpich-ucx + +export HOME="/home/e738/e738/skailasa" +export WORK="/work/e738/e738/skailasa" + +script_name="{script_name}" + +export SCRATCH=$WORK/strong_fft_n={max_points}_p={last_nodes}_${{SLURM_JOBID}} +mkdir -p $SCRATCH +cd $SCRATCH + +export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK +export OMP_NUM_THREADS=1 + +export OUTPUT=$SCRATCH/strong_fft_n={max_points}_p={last_nodes}_${{SLURM_JOBID}}.csv +touch $OUTPUT +echo " +experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ +source_tree,target_tree,source_domain,target_domain,layout,\ +ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\ +source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\ +displacement_map,metadata_creation,\ +expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples,source_local_trees_per_rank,target_local_trees_per_rank" >> ${{OUTPUT}} +""" + + # loop of runs + slurm += "\n# Perform strong scaling\n" + for i, (ppr, nn, nt, gd, ld) in enumerate(zip(points_per_rank, n_nodes, n_tasks, global_depths, local_depths)): + slurm += f""" +srun --nodes={nn} --ntasks={nt} --cpus-per-task={cpus_per_task} \\ + --distribution=block:block --hint=nomultithread \\ + $WORK/$script_name --id {i} --n-points {ppr} \\ + --expansion-order {expansion_order} --prune-empty \\ + --global-depth {gd} --local-depth {ld} \\ + --n-samples {n_samples} --block-size {block_size} --n-threads {int(max_threads)} \\ + >> $OUTPUT 2> $SCRATCH/err_run_{i}.log +""" + + Path(script_path).write_text(slurm) + print(f"✅ Wrote SLURM script to {script_path}") + +if __name__ == "__main__": + ranks_per_node = parse_process_mapping(AMD_EPYC_ROME) + + parser = argparse.ArgumentParser(description="Generate a SLURM script for weak scaling runs.") + parser.add_argument("--min-nodes", type=int, default=1) + parser.add_argument("--max-nodes", type=int, default=16) + parser.add_argument("--min-points-per-rank", type=int, default=250000) + parser.add_argument("--min-local-depth", type=int, default=4) + parser.add_argument("--scaling-func", type=int, default=2) + parser.add_argument("--method", type=str, default="ccx") + parser.add_argument("--output", type=str, default="job.slurm") + parser.add_argument("--config", action='append') + + args = parser.parse_args() + + if args.config is not None: + for fname in args.config: + with open(fname, 'r') as f: + parser.set_defaults(**json.load(f)) + + args = parser.parse_args() + + if args.scaling_func == 2: + scaling_func = pow2 + elif args.scaling_func == 8: + scaling_func = pow8 + else: + raise ValueError("Unknown scaling function") + + + if args.method == "ccx": + n_nodes, n_tasks, global_depths, local_depths, points_per_rank, max_threads = experiment_parameters( + args.min_nodes, args.max_nodes, ranks_per_node['ccx'], args.min_points_per_rank, args.min_local_depth, scaling_func=scaling_func + ) + write_slurm(f"{args.method}_"+args.output, n_nodes, n_tasks, local_depths, global_depths, max_threads, points_per_rank) + + elif args.method == "ccd": + n_nodes, n_tasks, global_depths, local_depths, points_per_rank, max_threads = experiment_parameters( + args.min_nodes, args.max_nodes, ranks_per_node["ccd"], args.min_points_per_rank, args.min_local_depth, scaling_func=scaling_func + ) + write_slurm(f"{args.method}_"+args.output, n_nodes, n_tasks, local_depths, global_depths, max_threads, points_per_rank) + + elif args.method == "socket": + n_nodes, n_tasks, global_depths, local_depths, points_per_rank, max_threads = experiment_parameters( + args.min_nodes, args.max_nodes, ranks_per_node["socket"], args.min_points_per_rank, args.min_local_depth, scaling_func=scaling_func + ) + + write_slurm(f"{args.method}_"+args.output, n_nodes, n_tasks, local_depths, global_depths, max_threads, points_per_rank) + else: + raise ValueError("Unknown method") \ No newline at end of file diff --git a/hpc/ijhpca/strong_scaling_ccd.json b/hpc/ijhpca/strong_scaling_ccd.json new file mode 100644 index 00000000..7bc04cfd --- /dev/null +++ b/hpc/ijhpca/strong_scaling_ccd.json @@ -0,0 +1,10 @@ +{ + "_comment": "Strong scaling", + "min_nodes": 1, + "max_nodes": 128, + "min_points_per_rank": 125000, + "min_local_depth": 3, + "output": "strong_scaling_ccd.slurm", + "method": "ccd", + "scaling_func": 8 +} \ No newline at end of file diff --git a/hpc/ijhpca/strong_scaling_ccx.json b/hpc/ijhpca/strong_scaling_ccx.json new file mode 100644 index 00000000..aec5aa49 --- /dev/null +++ b/hpc/ijhpca/strong_scaling_ccx.json @@ -0,0 +1,10 @@ +{ + "_comment": "Strong scaling", + "min_nodes": 1, + "max_nodes": 128, + "min_points_per_rank": 62500, + "min_local_depth": 3, + "output": "strong_scaling_ccx.slurm", + "method": "ccx", + "scaling_func": 8 +} \ No newline at end of file diff --git a/hpc/ijhpca/strong_scaling_socket.json b/hpc/ijhpca/strong_scaling_socket.json new file mode 100644 index 00000000..7a2a048f --- /dev/null +++ b/hpc/ijhpca/strong_scaling_socket.json @@ -0,0 +1,10 @@ +{ + "_comment": "Strong scaling", + "min_nodes": 1, + "max_nodes": 128, + "min_points_per_rank": 1000000, + "min_local_depth": 4, + "output": "strong_scaling.slurm", + "method": "socket", + "scaling_func": 8 +} \ No newline at end of file diff --git a/hpc/archer2/data/ijhpca/make_slurm_weak_scaling.py b/hpc/ijhpca/weak_scaling.py similarity index 100% rename from hpc/archer2/data/ijhpca/make_slurm_weak_scaling.py rename to hpc/ijhpca/weak_scaling.py From 35e25ff365b3440de278a31a8af9f9782dbc8e96 Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Mon, 6 Oct 2025 15:00:53 +0100 Subject: [PATCH 25/52] Add weak scaling scripts --- hpc/ijhpca/strong_scaling.py | 30 ++----- hpc/ijhpca/weak_scaling.py | 128 ++++++++++++++++++++-------- hpc/ijhpca/weak_scaling_ccd.json | 10 +++ hpc/ijhpca/weak_scaling_ccx.json | 10 +++ hpc/ijhpca/weak_scaling_socket.json | 10 +++ 5 files changed, 129 insertions(+), 59 deletions(-) create mode 100644 hpc/ijhpca/weak_scaling_ccd.json create mode 100644 hpc/ijhpca/weak_scaling_ccx.json create mode 100644 hpc/ijhpca/weak_scaling_socket.json diff --git a/hpc/ijhpca/strong_scaling.py b/hpc/ijhpca/strong_scaling.py index da12448c..0aa3f31e 100644 --- a/hpc/ijhpca/strong_scaling.py +++ b/hpc/ijhpca/strong_scaling.py @@ -4,8 +4,6 @@ from pathlib import Path import json - - AMD_EPYC_ROME = { "cores_per_node": 128, # total number of CPU cores per node "sockets_per_node": 2, # two sockets per node @@ -64,10 +62,10 @@ def experiment_parameters( min_points_per_rank, min_local_depth=4, scaling_func=pow8, - max_local_trees_per_rank=1 + arch=AMD_EPYC_ROME ): - max_cpus=AMD_EPYC_ROME["cores_per_node"] + max_cpus=arch["cores_per_node"] # Calculate the min/max number of ranks required to scale this problem by scaling_func given # the resources specified by min/max nodes @@ -94,13 +92,10 @@ def experiment_parameters( # to calculate the number of required nodes n_nodes = (n_ranks / max_ranks_per_node).astype(np.int32) - # # Calculate the actual nodes required - # we calculate the global depth as the least power of 8 smaller than or equal to the available # ranks for each configuration global_depth = v_global_depth(n_ranks) - local_trees_per_rank = 8**global_depth / n_ranks # Problem size defined by largest valid rank set @@ -120,7 +115,6 @@ def experiment_parameters( points_per_node = np.array(points_per_node) - ranks_per_node = n_ranks/n_nodes points_per_rank = np.int64(points_per_node/ranks_per_node) @@ -140,6 +134,7 @@ def experiment_parameters( print(f"points per rank {points_per_rank}") print(f"points per node {points_per_node}") print(f"min points per leaf {min_points_per_leaf}") + print(f"max threads per rank {max_threads_per_rank}") # Test that same number of points being used in each experiment assert(np.allclose(points_per_rank*n_ranks, points_per_node*n_nodes)) @@ -239,24 +234,11 @@ def write_slurm(script_path, n_nodes, n_tasks, local_depths, global_depths, max_ else: raise ValueError("Unknown scaling function") - - if args.method == "ccx": + valid_methods = {"ccx", "ccd" "socket"} + if any(args.method in m for m in valid_methods): n_nodes, n_tasks, global_depths, local_depths, points_per_rank, max_threads = experiment_parameters( - args.min_nodes, args.max_nodes, ranks_per_node['ccx'], args.min_points_per_rank, args.min_local_depth, scaling_func=scaling_func + args.min_nodes, args.max_nodes, ranks_per_node[args.method], args.min_points_per_rank, args.min_local_depth, scaling_func=scaling_func ) write_slurm(f"{args.method}_"+args.output, n_nodes, n_tasks, local_depths, global_depths, max_threads, points_per_rank) - - elif args.method == "ccd": - n_nodes, n_tasks, global_depths, local_depths, points_per_rank, max_threads = experiment_parameters( - args.min_nodes, args.max_nodes, ranks_per_node["ccd"], args.min_points_per_rank, args.min_local_depth, scaling_func=scaling_func - ) - write_slurm(f"{args.method}_"+args.output, n_nodes, n_tasks, local_depths, global_depths, max_threads, points_per_rank) - - elif args.method == "socket": - n_nodes, n_tasks, global_depths, local_depths, points_per_rank, max_threads = experiment_parameters( - args.min_nodes, args.max_nodes, ranks_per_node["socket"], args.min_points_per_rank, args.min_local_depth, scaling_func=scaling_func - ) - - write_slurm(f"{args.method}_"+args.output, n_nodes, n_tasks, local_depths, global_depths, max_threads, points_per_rank) else: raise ValueError("Unknown method") \ No newline at end of file diff --git a/hpc/ijhpca/weak_scaling.py b/hpc/ijhpca/weak_scaling.py index 24a9f8bf..9d224e53 100644 --- a/hpc/ijhpca/weak_scaling.py +++ b/hpc/ijhpca/weak_scaling.py @@ -2,6 +2,24 @@ import numpy as np import argparse from pathlib import Path +import json + +AMD_EPYC_ROME = { + "cores_per_node": 128, # total number of CPU cores per node + "sockets_per_node": 2, # two sockets per node + "cores_per_socket": 64, # A socket consists of 8 CCDs (64 cores total) common IO/memory controllers + "cores_per_ccd": 8, # Each CCD is a processor die, consists of two CCDs which are infinity linked together and connected to io + "cores_per_ccx": 4, # Each CCX shares an L3 cache (16MB) + "ccx_per_ccd": 2 + } + +def parse_process_mapping(arch): + ccd_threads_per_rank = arch["cores_per_ccd"] + ccx_threads_per_rank = arch["cores_per_ccx"] + ccd_max_ranks_per_node = arch["cores_per_node"] / arch["cores_per_ccd"] # Mapping each rank to a CCD + ccx_max_ranks_per_node = arch["cores_per_node"] / arch["cores_per_ccx"] # Mapping each rank to a CCX + socket_max_ranks_per_node = arch["cores_per_node"] / arch["cores_per_socket"] # Mapping each rank to a socket + return {"socket": socket_max_ranks_per_node, "ccd": ccd_max_ranks_per_node, "ccx": ccx_max_ranks_per_node} def global_depth(rank): @@ -9,61 +27,85 @@ def global_depth(rank): Return the first power of 8 >= rank, the exponent (tree level). """ level = 1 - while True: - if rank <= 8**level: - return level + while rank > 8**level: level += 1 - + return level v_global_depth = np.vectorize(global_depth) - def pow2(x): return np.log2(x).is_integer() - def pow8(x): return np.emath.logn(8, x).is_integer() -def experiment_parameters(min_nodes, max_nodes, ranks_per_node, points_per_rank, local_depth=4, scaling_func=pow8): +def experiment_parameters( + min_nodes, + max_nodes, + max_ranks_per_node, + points_per_rank, + local_depth=4, + scaling_func=pow8, + arch=AMD_EPYC_ROME + ): - max_cpus=128 # AMD EPYC rome + max_cpus=arch["cores_per_node"] - total_ranks = ranks_per_node * max_nodes + # Calculate the min/max number of ranks required to scale this problem by scaling_func given + # the resources specified by min/max nodes + min_ranks = int(min_nodes*max_ranks_per_node) + max_ranks = int(max_nodes*max_ranks_per_node) - n = points_per_rank*total_ranks # total problem size - print(f"Total problem size n={n/1e9}B") + # Want to scale the resources (ranks) by scaling function between min/max ranks + n_ranks = [] + curr = min_ranks + while curr <= max_ranks: + if scaling_func == pow2: + n_ranks.append(curr) + curr *= 2 - points_per_node = points_per_rank * ranks_per_node - print(f"points per rank {points_per_rank/1e6}M") - print(f"points per node {points_per_node/1e6}M") + elif scaling_func == pow8: + n_ranks.append(curr) + curr *= 8 + else: + raise ValueError("Unknown scaling func") - # Double number of nodes used until we reach max_nodes - n_nodes = np.array(list(filter(scaling_func, [i for i in range(min_nodes, max_nodes+1)]))) + n_ranks = np.array(n_ranks) - # The global depth is a function of the ranks per node (as will need enough global leaves to cover this many ranks (local trees)) - global_depth = v_global_depth(n_nodes*ranks_per_node) - print(f"global depth {global_depth}") - print(f"local depth {local_depth}") + # Can use the number of required ranks with this scaling function + # to calculate the number of required nodes + n_nodes = (n_ranks / max_ranks_per_node).astype(np.int32) - # local depth is a constant, chosen to balance M2L and P2P - points_per_leaf = points_per_rank / 8**local_depth - print(f"points per leaf {points_per_leaf}") + # we calculate the global depth as the least power of 8 smaller than or equal to the available + # ranks for each configuration + global_depth = v_global_depth(n_ranks) + + local_trees_per_rank = 8**global_depth / n_ranks + + # Problem size defined by largest valid rank set + n = points_per_rank * n_ranks[-1] - n_tasks = n_nodes*ranks_per_node - print(f"MPI tasks {n_tasks}") + points_per_node = max_ranks_per_node*points_per_rank - print(f"number of nodes {n_nodes}") + points_per_leaf = points_per_rank / 8**local_depth - local_trees_per_rank = (8**global_depth)/(n_tasks) - print(f"local trees per rank {local_trees_per_rank}") + max_threads_per_rank = int(max_cpus/max_ranks_per_node) - max_threads_per_rank = max_cpus/ranks_per_node + print(f"Total problem size n={n/1e6}M") + print(f"global depth {global_depth}") + print(f"local depth {local_depth}") + print(f"total depth {local_depth+global_depth}") + print(f"max local trees per rank {local_trees_per_rank}") + print(f"number of nodes {n_nodes} max ranks per node {max_ranks_per_node}") + print(f"number of ranks {n_ranks}") + print(f"points per rank {points_per_rank}") + print(f"points per node {points_per_node}") + print(f"points per leaf {points_per_leaf}") print(f"max threads per rank {max_threads_per_rank}") - return n_nodes.tolist(), n_tasks.tolist(), global_depth.tolist(), max_threads_per_rank + return n_nodes.tolist(), n_ranks.tolist(), global_depth.tolist(), max_threads_per_rank def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, points_per_rank, local_depth, script_name="fmm_m2l_fft_mpi_f32"): @@ -133,12 +175,15 @@ def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, point if __name__ == "__main__": + + ranks_per_node = parse_process_mapping(AMD_EPYC_ROME) + parser = argparse.ArgumentParser(description="Generate a SLURM script for weak scaling runs.") parser.add_argument("--min-nodes", type=int, default=1) parser.add_argument("--max-nodes", type=int, default=16) - parser.add_argument("--ranks-per-node", type=int, default=32) parser.add_argument("--points-per-rank", type=int, default=250000) parser.add_argument("--local-depth", type=int, default=4) + parser.add_argument("--method", type=str, default="ccx") parser.add_argument("--output", type=str, default="job.slurm") parser.add_argument("--config", action='append') @@ -151,8 +196,21 @@ def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, point args = parser.parse_args() - n_nodes, n_tasks, global_depths, max_threads = experiment_parameters( - args.min_nodes, args.max_nodes, args.ranks_per_node, args.points_per_rank, args.local_depth - ) + if args.scaling_func == 2: + scaling_func = pow2 + elif args.scaling_func == 8: + scaling_func = pow8 + else: + raise ValueError("Unknown scaling function") + + + valid_methods = {"ccx", "ccd" "socket"} + if any(args.method in m for m in valid_methods): + n_nodes, n_tasks, global_depths, max_threads = experiment_parameters( + args.min_nodes, args.max_nodes, ranks_per_node[args.method], args.points_per_rank, args.local_depth + ) + + write_slurm(args.output, n_nodes, n_tasks, global_depths, max_threads, args.points_per_rank, args.local_depth) - write_slurm(args.output, n_nodes, n_tasks, global_depths, max_threads, args.points_per_rank, args.local_depth) + else: + raise ValueError("Unknown method") \ No newline at end of file diff --git a/hpc/ijhpca/weak_scaling_ccd.json b/hpc/ijhpca/weak_scaling_ccd.json new file mode 100644 index 00000000..35c4fdf7 --- /dev/null +++ b/hpc/ijhpca/weak_scaling_ccd.json @@ -0,0 +1,10 @@ +{ + "_comment": "Weak scaling", + "min_nodes": 1, + "max_nodes": 128, + "points_per_rank": 500000, + "local_depth": 4, + "output": "weak_scaling_ccd.slurm", + "method": "ccd", + "scaling_func": 8 +} \ No newline at end of file diff --git a/hpc/ijhpca/weak_scaling_ccx.json b/hpc/ijhpca/weak_scaling_ccx.json new file mode 100644 index 00000000..5886f60a --- /dev/null +++ b/hpc/ijhpca/weak_scaling_ccx.json @@ -0,0 +1,10 @@ +{ + "_comment": "Weak scaling", + "min_nodes": 1, + "max_nodes": 128, + "points_per_rank": 250000, + "local_depth": 4, + "output": "weak_scaling_ccx.slurm", + "method": "ccx", + "scaling_func": 8 +} \ No newline at end of file diff --git a/hpc/ijhpca/weak_scaling_socket.json b/hpc/ijhpca/weak_scaling_socket.json new file mode 100644 index 00000000..4b4a9299 --- /dev/null +++ b/hpc/ijhpca/weak_scaling_socket.json @@ -0,0 +1,10 @@ +{ + "_comment": "Weak scaling", + "min_nodes": 1, + "max_nodes": 128, + "points_per_rank": 4000000, + "local_depth": 4, + "output": "weak_scaling_socket.slurm", + "method": "socket", + "scaling_func": 8 +} \ No newline at end of file From b60bb30e7287557fd4dc0cb8d0afe9fa7cc64bf6 Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Mon, 6 Oct 2025 15:02:29 +0100 Subject: [PATCH 26/52] A fix --- hpc/ijhpca/weak_scaling_socket.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/hpc/ijhpca/weak_scaling_socket.json b/hpc/ijhpca/weak_scaling_socket.json index 4b4a9299..eed84ef0 100644 --- a/hpc/ijhpca/weak_scaling_socket.json +++ b/hpc/ijhpca/weak_scaling_socket.json @@ -3,7 +3,7 @@ "min_nodes": 1, "max_nodes": 128, "points_per_rank": 4000000, - "local_depth": 4, + "local_depth": 5, "output": "weak_scaling_socket.slurm", "method": "socket", "scaling_func": 8 From 94bfd6a5bbd0cb4329ed46feaa057f0ccfc90182 Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Mon, 6 Oct 2025 15:53:09 +0100 Subject: [PATCH 27/52] Add option for contiguos nodes --- hpc/ijhpca/strong_scaling.py | 9 +++++++-- hpc/ijhpca/strong_scaling_ccd.json | 3 ++- hpc/ijhpca/strong_scaling_ccx.json | 3 ++- hpc/ijhpca/strong_scaling_socket.json | 3 ++- hpc/ijhpca/weak_scaling.py | 9 +++++++-- hpc/ijhpca/weak_scaling_ccd.json | 3 ++- hpc/ijhpca/weak_scaling_ccx.json | 3 ++- hpc/ijhpca/weak_scaling_socket.json | 3 ++- 8 files changed, 26 insertions(+), 10 deletions(-) diff --git a/hpc/ijhpca/strong_scaling.py b/hpc/ijhpca/strong_scaling.py index 0aa3f31e..e64d2e8e 100644 --- a/hpc/ijhpca/strong_scaling.py +++ b/hpc/ijhpca/strong_scaling.py @@ -142,7 +142,7 @@ def experiment_parameters( -def write_slurm(script_path, n_nodes, n_tasks, local_depths, global_depths, max_threads, points_per_rank, script_name="fmm_m2l_fft_mpi_f32"): +def write_slurm(script_path, n_nodes, n_tasks, local_depths, global_depths, max_threads, points_per_rank, contiguous=False, script_name="fmm_m2l_fft_mpi_f32"): expansion_order = 3 n_samples = 500 block_size = 128 @@ -161,7 +161,11 @@ def write_slurm(script_path, n_nodes, n_tasks, local_depths, global_depths, max_ #SBATCH --account=e738 #SBATCH --partition=standard #SBATCH --qos=standard +""" + if contiguous: + slurm += "\n#SBATCH --contiguous\n" + slurm += """ module load PrgEnv-aocc module load craype-network-ucx module load cray-mpich-ucx @@ -215,6 +219,7 @@ def write_slurm(script_path, n_nodes, n_tasks, local_depths, global_depths, max_ parser.add_argument("--min-local-depth", type=int, default=4) parser.add_argument("--scaling-func", type=int, default=2) parser.add_argument("--method", type=str, default="ccx") + parser.add_argument("--contiguous", type=bool, default=False) parser.add_argument("--output", type=str, default="job.slurm") parser.add_argument("--config", action='append') @@ -239,6 +244,6 @@ def write_slurm(script_path, n_nodes, n_tasks, local_depths, global_depths, max_ n_nodes, n_tasks, global_depths, local_depths, points_per_rank, max_threads = experiment_parameters( args.min_nodes, args.max_nodes, ranks_per_node[args.method], args.min_points_per_rank, args.min_local_depth, scaling_func=scaling_func ) - write_slurm(f"{args.method}_"+args.output, n_nodes, n_tasks, local_depths, global_depths, max_threads, points_per_rank) + write_slurm(f"{args.method}_"+args.output, n_nodes, n_tasks, local_depths, global_depths, max_threads, points_per_rank, contiguous=args.contiguous) else: raise ValueError("Unknown method") \ No newline at end of file diff --git a/hpc/ijhpca/strong_scaling_ccd.json b/hpc/ijhpca/strong_scaling_ccd.json index 7bc04cfd..fdb6a38a 100644 --- a/hpc/ijhpca/strong_scaling_ccd.json +++ b/hpc/ijhpca/strong_scaling_ccd.json @@ -6,5 +6,6 @@ "min_local_depth": 3, "output": "strong_scaling_ccd.slurm", "method": "ccd", - "scaling_func": 8 + "scaling_func": 8, + "contiguous": true } \ No newline at end of file diff --git a/hpc/ijhpca/strong_scaling_ccx.json b/hpc/ijhpca/strong_scaling_ccx.json index aec5aa49..8ed552da 100644 --- a/hpc/ijhpca/strong_scaling_ccx.json +++ b/hpc/ijhpca/strong_scaling_ccx.json @@ -6,5 +6,6 @@ "min_local_depth": 3, "output": "strong_scaling_ccx.slurm", "method": "ccx", - "scaling_func": 8 + "scaling_func": 8, + "contiguous": false } \ No newline at end of file diff --git a/hpc/ijhpca/strong_scaling_socket.json b/hpc/ijhpca/strong_scaling_socket.json index 7a2a048f..497d5ee9 100644 --- a/hpc/ijhpca/strong_scaling_socket.json +++ b/hpc/ijhpca/strong_scaling_socket.json @@ -6,5 +6,6 @@ "min_local_depth": 4, "output": "strong_scaling.slurm", "method": "socket", - "scaling_func": 8 + "scaling_func": 8, + "contiguous": false } \ No newline at end of file diff --git a/hpc/ijhpca/weak_scaling.py b/hpc/ijhpca/weak_scaling.py index 9d224e53..35fd8c55 100644 --- a/hpc/ijhpca/weak_scaling.py +++ b/hpc/ijhpca/weak_scaling.py @@ -108,7 +108,7 @@ def experiment_parameters( return n_nodes.tolist(), n_ranks.tolist(), global_depth.tolist(), max_threads_per_rank -def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, points_per_rank, local_depth, script_name="fmm_m2l_fft_mpi_f32"): +def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, points_per_rank, local_depth, contiguous=False, script_name="fmm_m2l_fft_mpi_f32"): expansion_order = 3 n_points = points_per_rank n_samples = 500 @@ -129,7 +129,11 @@ def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, point #SBATCH --account=e738 #SBATCH --partition=standard #SBATCH --qos=standard +""" + if contiguous: + slurm += "\n#SBATCH --contiguous\n" +""" module load PrgEnv-aocc module load craype-network-ucx module load cray-mpich-ucx @@ -184,6 +188,7 @@ def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, point parser.add_argument("--points-per-rank", type=int, default=250000) parser.add_argument("--local-depth", type=int, default=4) parser.add_argument("--method", type=str, default="ccx") + parser.add_argument("--contiguous", type=bool, default=False) parser.add_argument("--output", type=str, default="job.slurm") parser.add_argument("--config", action='append') @@ -210,7 +215,7 @@ def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, point args.min_nodes, args.max_nodes, ranks_per_node[args.method], args.points_per_rank, args.local_depth ) - write_slurm(args.output, n_nodes, n_tasks, global_depths, max_threads, args.points_per_rank, args.local_depth) + write_slurm(args.output, n_nodes, n_tasks, global_depths, max_threads, args.points_per_rank, args.local_depth, contiguous=args.contiguous) else: raise ValueError("Unknown method") \ No newline at end of file diff --git a/hpc/ijhpca/weak_scaling_ccd.json b/hpc/ijhpca/weak_scaling_ccd.json index 35c4fdf7..33be101b 100644 --- a/hpc/ijhpca/weak_scaling_ccd.json +++ b/hpc/ijhpca/weak_scaling_ccd.json @@ -6,5 +6,6 @@ "local_depth": 4, "output": "weak_scaling_ccd.slurm", "method": "ccd", - "scaling_func": 8 + "scaling_func": 8, + "contiguous": false } \ No newline at end of file diff --git a/hpc/ijhpca/weak_scaling_ccx.json b/hpc/ijhpca/weak_scaling_ccx.json index 5886f60a..f033ef3e 100644 --- a/hpc/ijhpca/weak_scaling_ccx.json +++ b/hpc/ijhpca/weak_scaling_ccx.json @@ -6,5 +6,6 @@ "local_depth": 4, "output": "weak_scaling_ccx.slurm", "method": "ccx", - "scaling_func": 8 + "scaling_func": 8, + "contiguous": false } \ No newline at end of file diff --git a/hpc/ijhpca/weak_scaling_socket.json b/hpc/ijhpca/weak_scaling_socket.json index eed84ef0..57c748a9 100644 --- a/hpc/ijhpca/weak_scaling_socket.json +++ b/hpc/ijhpca/weak_scaling_socket.json @@ -6,5 +6,6 @@ "local_depth": 5, "output": "weak_scaling_socket.slurm", "method": "socket", - "scaling_func": 8 + "scaling_func": 8, + "contiguous": false } \ No newline at end of file From be1089fa91ffeae168177e44eaeb1b677ca69453 Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Mon, 6 Oct 2025 16:27:44 +0100 Subject: [PATCH 28/52] Update nb and script --- hpc/ijhpca/Strong Scaling.ipynb | 856 ++++---------------------------- hpc/ijhpca/weak_scaling.py | 2 +- 2 files changed, 92 insertions(+), 766 deletions(-) diff --git a/hpc/ijhpca/Strong Scaling.ipynb b/hpc/ijhpca/Strong Scaling.ipynb index 2292b075..f83911a8 100644 --- a/hpc/ijhpca/Strong Scaling.ipynb +++ b/hpc/ijhpca/Strong Scaling.ipynb @@ -15,761 +15,118 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "20e41bec-f758-4668-aef5-cada4ba8652b", + "execution_count": 61, + "id": "ae668ed4", "metadata": {}, "outputs": [], "source": [ - "def global_depth(rank):\n", - " \"\"\"\n", - " will return the first power of 8 greater than rank, the power corresponding to the level\n", - " \"\"\"\n", - " level = 1\n", - " loop = True\n", - "\n", - " while loop:\n", - "\n", - " if rank <= 8**level:\n", - " loop = False\n", - " result = level\n", - " else:\n", - " level += 1\n", - "\n", - " return result\n", - "\n", - "v_global_depth = np.vectorize(global_depth)\n", - "\n", - "\n", - "def local_depth(points_per_rank, max_points_per_leaf, min_local_depth):\n", - "\n", - " local_level = min_local_depth\n", - " loop = True\n", - "\n", - " while loop:\n", - " n_leaves = 8**local_level # leaves in local tree\n", - " points_per_leaf = points_per_rank / n_leaves\n", - " if points_per_leaf <= max_points_per_leaf:\n", - " loop = False\n", - " result = local_level\n", - " else:\n", - " local_level += 1\n", - "\n", - " return result\n", - "\n", - "v_local_depth = np.vectorize(local_depth)\n", - "\n", - "def pow2(x): return np.log2(x).is_integer()" + "df0 = pd.read_csv('data/strong_fft_n=128000000_p=64_11077776/strong_fft_n=128000000_p=64_11077776.csv')\n", + "df1 = pd.read_csv('data/strong_fft_n=128000000_p=64_11077778/strong_fft_n=128000000_p=64_11077778.csv')\n" ] }, { "cell_type": "code", - "execution_count": null, - "id": "f0abc6a1-73c9-4608-80f7-43c6c64c0257", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total problem size n=0.256B\n", - "global depth [2 2 2 3 3 3 4 4]\n", - "local depth [5 4 4 4 3 3 3 3]\n", - "points per rank [16000000 8000000 4000000 2000000 1000000 500000 250000 125000]\n" - ] - } - ], - "source": [ - "max_nodes = 128\n", - "min_nodes = 1\n", - "ranks_per_node=16 # ranks on 1 node, such that 1 per CCX region\n", - "total_ranks = max_nodes*ranks_per_node # 16 nodes, 32 ranks per node (1 per CCX region)\n", - "min_points_per_rank = 125e3\n", - "n = min_points_per_rank*total_ranks # total problem size\n", - "print(f\"Total problem size n={n/1e9}B\")\n", - "\n", - "# Double number of nodes used until we reach max_nodes\n", - "n_nodes = np.array(list(filter(pow2, [i for i in range(min_nodes, max_nodes+1)])))\n", - "\n", - "points_per_node = [n]\n", - "for i in range(len(n_nodes)-1):\n", - " p = points_per_node[i]/2\n", - " points_per_node.append(p)\n", - "points_per_node = np.array(points_per_node)\n", - "\n", - "total_ranks = n_nodes*ranks_per_node\n", - "points_per_rank = np.int64(points_per_node/ranks_per_node)\n", - "\n", - "# The global depth is a function of the ranks per node (as will need enough global leaves to cover this many ranks (local trees))\n", - "global_depth = v_global_depth(total_ranks)\n", - "print(f\"global depth {global_depth}\")\n", - "\n", - "# Calculate max threshold for points per leaf from largest scale problem\n", - "min_local_depth = 3\n", - "max_points_per_leaf = points_per_rank[-1] / 8**min_local_depth\n", - "\n", - "local_depth = v_local_depth(points_per_rank, max_points_per_leaf = 2000, min_local_depth=min_local_depth)\n", - "print(f\"local depth {local_depth}\")\n", - "\n", - "print(f\"points per rank {points_per_rank}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "56128a60-0d71-4b4d-a62e-10c8684b8798", - "metadata": {}, - "outputs": [ - { - "data": { - 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experiment_idrankruntimep2mm2ml2lm2lp2psource_treetarget_tree...ghost_fmm_udisplacement_mapmetadata_creationexpansion_ordern_pointslocal_depthglobal_depthblock_sizen_threadsn_samples
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" - ], - "text/plain": [ - " experiment_id rank runtime p2m m2m l2l m2l p2p source_tree \\\n", - "0 0 14 6784 77 13 27 3928 2556 74311 \n", - "1 0 4 8776 69 13 24 5233 3273 74311 \n", - "2 0 6 8795 69 13 27 5260 3267 74311 \n", - "3 0 0 8837 71 13 33 5255 3271 74311 \n", - "4 0 11 8845 88 16 32 5270 3264 74312 \n", - "\n", - " target_tree ... ghost_fmm_u displacement_map metadata_creation \\\n", - "0 73812 ... 0 29 3207 \n", - "1 73813 ... 0 31 3247 \n", - "2 73813 ... 0 36 3248 \n", - "3 73813 ... 0 38 3208 \n", - "4 73812 ... 0 40 3205 \n", - "\n", - " expansion_order n_points local_depth global_depth block_size \\\n", - "0 3 9600000 5 2 128 \n", - "1 3 9600000 5 2 128 \n", - "2 3 9600000 5 2 128 \n", - "3 3 9600000 5 2 128 \n", - "4 3 9600000 5 2 128 \n", - "\n", - " n_threads n_samples \n", - "0 8 500 \n", - "1 8 500 \n", - "2 8 500 \n", - "3 8 500 \n", - "4 8 500 \n", - "\n", - "[5 rows x 33 columns]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "480e7a52-3277-4f55-9286-f4baa1054f19", + "execution_count": 71, + "id": "30777f75", "metadata": {}, "outputs": [], "source": [ - "def runtime_vs_nodes(df, n_nodes, parameter='runtime'):\n", - "\n", - " # --- compute average runtime per experiment_id ---\n", - " runtime = []\n", - " for (id, experiment) in df.groupby('experiment_id'):\n", - " r = experiment[parameter].mean()\n", - " runtime.append(r)\n", - " runtime = np.array(runtime)\n", - "\n", - "\n", - " # --- figure with 2 subplots ---\n", - " fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6/1.6))\n", - "\n", - " # ==============================\n", - " # Top plot: Runtime vs Nodes\n", - " # ==============================\n", - " # ax1.plot(n_nodes, runtime, marker=\"o\", label=\"Measured runtime\")\n", - " ax1.set_xscale('log')\n", - " ax1.set_yscale('log')\n", - " ax1.set_ylim(100, runtime.max() + 0.5*runtime.max())\n", - "\n", - " # Perfect scaling line\n", - " T1 = runtime[0]\n", - " ideal_runtime = T1 / n_nodes\n", - " ax1.plot(n_nodes, ideal_runtime, '--', color='black', label=\"Perfect scaling\")\n", - "\n", - " # Shaded regions (using geometric means for edges)\n", - " region_edges = np.sqrt(n_nodes[:-1] * n_nodes[1:])\n", - " region_edges = np.concatenate([[n_nodes.min() / np.sqrt(2)],\n", - " region_edges,\n", - " [n_nodes.max() * np.sqrt(2)]])\n", - " y0, y1 = ax1.get_ylim()\n", - " shrink = 0.1\n", - "\n", - " for i in range(len(region_edges) - 1):\n", - " left, right = region_edges[i], region_edges[i+1]\n", - "\n", - " # shrink inward in log space\n", - " log_left, log_right = np.log10(left), np.log10(right)\n", - " log_pad = (log_right - log_left) * shrink\n", - " left_adj, right_adj = 10**(log_left + log_pad), 10**(log_right - log_pad)\n", - "\n", - " ax1.axvspan(left_adj, right_adj, alpha=0.15,\n", - " color='orange' if i % 2 == 0 else 'lightblue')\n", - "\n", - " # label at log-midpoint\n", - " xmid = np.sqrt(left * right)\n", - " ax1.text(xmid, (y0 + y1) / 20, f\"{n_nodes[i]} nodes\",\n", - " ha='center', va='center', rotation=90,\n", - " bbox=dict(facecolor='white', alpha=0.6, edgecolor='none'))\n", - "\n", - "\n", - " # group by node count (assuming df has 'nodes' column)\n", - " stats = df.groupby('experiment_id')[parameter].agg(['mean','std']).reset_index()\n", - "\n", - " # n_nodes = stats['experiment_id'].to_numpy()\n", - " # runtime = stats['mean'].to_numpy()\n", - " runtime_err = stats['std'].to_numpy()\n", - "\n", - " # Runtime with error bars\n", - " ax1.errorbar(n_nodes, runtime, yerr=runtime_err,\n", - " fmt='o-', capsize=4, label=\"Measured runtime\")\n", - "\n", - "\n", - " ax1.set_ylabel(\"Runtime (ms)\")\n", - " ax1.legend()\n", - " ax1.set_title(f\"Strong Scaling: {parameter}\")\n", - "\n", - " # ==============================\n", - " # Bottom plot: Parallel Efficiency\n", - " # ==============================\n", - " efficiency = T1 / (n_nodes * runtime)\n", - " efficiency_err = efficiency * (runtime_err / runtime) # propagate efficiency error\n", - "\n", - " ax2.plot(n_nodes, efficiency, 's--', color=\"red\", label=\"Efficiency\")\n", - "\n", - " ax2.set_xscale('log')\n", - " ax2.set_ylim(0, 1.05)\n", - " ax2.set_xlabel(\"Number of Nodes\")\n", - " ax2.set_ylabel(\"Parallel Efficiency\")\n", - " ax2.legend()\n", - " ax2.set_title(\"Parallel Efficiency\")\n", - "\n", - " # Efficiency with error bars\n", - " ax2.errorbar(n_nodes, efficiency, yerr=efficiency_err,\n", - " fmt='s--', color=\"red\", capsize=4, label=\"Efficiency\")\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", "\n", + "def strong_scaling(dfs, names=None, title=\"Strong Scaling and Parallel Efficiency\"):\n", + " \"\"\"\n", + " Plots strong scaling (runtime) and parallel efficiency for one or more DataFrames.\n", + "\n", + " Parameters\n", + " ----------\n", + " dfs : list of pandas.DataFrame or pandas.DataFrame\n", + " One or more DataFrames with columns including:\n", + " ['experiment_id', 'runtime', 'p2m', 'm2l', 'm2m', 'l2l', 'source_tree', 'n_points', 'source_local_trees_per_rank']\n", + " names : list of str, optional\n", + " Names/labels for each DataFrame. Must be same length as dfs.\n", + " title : str\n", + " Title for the figure.\n", + " \"\"\"\n", "\n", + " # Handle single DataFrame input\n", + " if not isinstance(dfs, (list, tuple)):\n", + " dfs = [dfs]\n", + " if names is None:\n", + " names = [f\"Run {i+1}\" for i in range(len(dfs))]\n", + "\n", + " # Style setup\n", + " sns.set_context(\"talk\", font_scale=1.1)\n", + " sns.set_style(\"whitegrid\")\n", + " fig, axes = plt.subplots(1, 2, figsize=(14, 6))\n", + " ax_runtime, ax_eff = axes\n", + "\n", + " # Marker and color configuration\n", + " markers = ['o', 's', '^', 'D', 'v', 'P', '*']\n", + " colors = sns.color_palette(\"deep\", len(dfs))\n", + "\n", + " for df, label, color, marker in zip(dfs, names, colors, markers):\n", + " stats = df.groupby('experiment_id')[['runtime', 'p2m', 'm2l', 'm2m', 'l2l', 'source_tree', 'n_points', 'source_local_trees_per_rank']]\n", + " n_ranks = stats.size()\n", + " runtime = stats.mean()['runtime']\n", + " runtime_err = stats.std()['runtime']\n", + "\n", + " # Ideal scaling\n", + " T1 = runtime.iloc[0]\n", + " ideal_runtime = [T1 / (n_ranks.iloc[i] / n_ranks.iloc[0]) for i in range(len(n_ranks))]\n", + "\n", + " # Parallel efficiency\n", + " efficiency = (T1 / (runtime * (n_ranks / n_ranks.iloc[0]))) * 100 # in %\n", + "\n", + " # Runtime plot\n", + " ax_runtime.errorbar(\n", + " n_ranks, runtime, yerr=runtime_err, fmt=marker+'-', color=color,\n", + " label=label, capsize=4, linewidth=2, markersize=7\n", + " )\n", + " ax_runtime.plot(n_ranks, ideal_runtime, '--', color=color, alpha=0.7, linewidth=1.5)\n", + "\n", + " # Efficiency plot\n", + " ax_eff.plot(\n", + " n_ranks, efficiency, marker=marker, linestyle='-', color=color,\n", + " label=label, linewidth=2, markersize=7\n", + " )\n", + "\n", + " # Configure runtime plot\n", + " ax_runtime.set_xscale(\"log\", base=2)\n", + " ax_runtime.set_yscale(\"log\")\n", + " ax_runtime.set_xlabel(\"Number of Ranks\", fontsize=13)\n", + " ax_runtime.set_ylabel(\"Runtime (ms)\", fontsize=13)\n", + " ax_runtime.set_title(\"Strong Scaling (Runtime)\", fontsize=14)\n", + " ax_runtime.grid(True, which=\"both\", ls=\"--\", lw=0.5)\n", + " ax_runtime.legend(fontsize=11)\n", + "\n", + " # Configure efficiency plot\n", + " ax_eff.set_xscale(\"log\", base=2)\n", + " ax_eff.set_ylim(0, 110)\n", + " ax_eff.set_xlabel(\"Number of Ranks\", fontsize=13)\n", + " ax_eff.set_ylabel(\"Parallel Efficiency (%)\", fontsize=13)\n", + " ax_eff.set_title(\"Parallel Efficiency\", fontsize=14)\n", + " ax_eff.grid(True, which=\"both\", ls=\"--\", lw=0.5)\n", + " ax_eff.legend(fontsize=11)\n", + "\n", + " fig.suptitle(title, fontsize=16, y=1.03)\n", " plt.tight_layout()\n", - " return fig, (ax1, ax2), n_nodes, runtime, efficiency\n", + " plt.show()\n", "\n" ] }, - { - "cell_type": "markdown", - "id": "343e0505-8c95-42d5-ad49-0773686a9cb5", - "metadata": {}, - "source": [ - "# First Experiment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dd481ef6-025e-41e6-af21-04d939989779", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total problem size n=0.1536B\n", - "global depth [2 2 2 3 3 3 4 4]\n", - "local depth [5 4 4 4 3 3 3 3]\n", - "points per rank [9600000 4800000 2400000 1200000 600000 300000 150000 75000]\n" - ] - } - ], - "source": [ - "max_nodes = 128\n", - "min_nodes = 1\n", - "ranks_per_node=16 # ranks on 1 node, such that 1 per CCX region\n", - "total_ranks = max_nodes*ranks_per_node # 16 nodes, 32 ranks per node (1 per CCX region)\n", - "min_points_per_rank = 75e3\n", - "n = min_points_per_rank*total_ranks # total problem size\n", - "print(f\"Total problem size n={n/1e9}B\")\n", - "\n", - "# Double number of nodes used until we reach max_nodes\n", - "n_nodes = np.array(list(filter(pow2, [i for i in range(min_nodes, max_nodes+1)])))\n", - "\n", - "points_per_node = [n]\n", - "for i in range(len(n_nodes)-1):\n", - " p = points_per_node[i]/2\n", - " points_per_node.append(p)\n", - "points_per_node = np.array(points_per_node)\n", - "\n", - "total_ranks = n_nodes*ranks_per_node\n", - "points_per_rank = np.int64(points_per_node/ranks_per_node)\n", - "\n", - "\n", - "# The global depth is a function of the ranks per node (as will need enough global leaves to cover this many ranks (local trees))\n", - "global_depth = v_global_depth(total_ranks)\n", - "print(f\"global depth {global_depth}\")\n", - "\n", - "# Calculate max threshold for points per leaf from largest scale problem\n", - "min_local_depth = 3\n", - "max_points_per_leaf = points_per_rank[-1] / 8**min_local_depth\n", - "\n", - "\n", - "local_depth = v_local_depth(points_per_rank, max_points_per_leaf = 2000, min_local_depth=min_local_depth)\n", - "print(f\"local depth {local_depth}\")\n", - "\n", - "print(f\"points per rank {points_per_rank}\")" - ] - }, { "cell_type": "code", - "execution_count": 34, - "id": "6d60d6e9-1da3-4292-af2f-3c04a2f49291", + "execution_count": 72, + "id": "bff20d11", "metadata": {}, "outputs": [ { "data": { + "image/png": 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experiment_idrankruntimep2mm2ml2lm2lp2psource_treetarget_tree...ghost_fmm_udisplacement_mapmetadata_creationexpansion_ordern_pointslocal_depthglobal_depthblock_sizen_threadsn_samples
00146784771327392825567431173812...029320739600000521288500
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5 rows × 33 columns

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- "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -777,40 +134,9 @@ } ], "source": [ - "n_nodes = np.array([1, 2, 4, 8, 16, 32, 64])\n", - "runtime_vs_nodes(df, n_nodes, 'source_tree')" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "56e3de2d-905e-4e1f-b0f6-7019b9ee59de", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1.536e+08, 7.680e+07, 3.840e+07, 1.920e+07, 9.600e+06, 4.800e+06,\n", - " 2.400e+06, 1.200e+06])" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "points_per_node" + "strong_scaling([df0,df1], names=[\"CCD\", \"CCX\"], title=\"Strong Scaling Up to 128e6 Points\")" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "da498450-e577-4b33-a4e1-29f02554c400", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -830,7 +156,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "kifmm (3.10.17)", "language": "python", "name": "python3" }, diff --git a/hpc/ijhpca/weak_scaling.py b/hpc/ijhpca/weak_scaling.py index 35fd8c55..cccecd9d 100644 --- a/hpc/ijhpca/weak_scaling.py +++ b/hpc/ijhpca/weak_scaling.py @@ -133,7 +133,7 @@ def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, point if contiguous: slurm += "\n#SBATCH --contiguous\n" -""" + slurm += """ module load PrgEnv-aocc module load craype-network-ucx module load cray-mpich-ucx From 0a73ffeb72c68758b76da4c3694350355cb003c7 Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Mon, 6 Oct 2025 17:00:37 +0100 Subject: [PATCH 29/52] Fix scripts --- hpc/ijhpca/Strong Scaling.ipynb | 36 +- hpc/ijhpca/Weak Scaling.ipynb | 1119 +++---------------------------- hpc/ijhpca/strong_scaling.py | 2 +- hpc/ijhpca/weak_scaling.py | 2 +- 4 files changed, 137 insertions(+), 1022 deletions(-) diff --git a/hpc/ijhpca/Strong Scaling.ipynb b/hpc/ijhpca/Strong Scaling.ipynb index f83911a8..15ef8142 100644 --- a/hpc/ijhpca/Strong Scaling.ipynb +++ b/hpc/ijhpca/Strong Scaling.ipynb @@ -15,13 +15,33 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 73, + "id": "04c73ea3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[34mstrong_fft_n=128000000_p=64_11077776\u001b[m\u001b[m \u001b[34mweak_fft_n=512000000_p=64_11077797\u001b[m\u001b[m\n", + "\u001b[34mstrong_fft_n=128000000_p=64_11077778\u001b[m\u001b[m \u001b[34mweak_fft_n=512000000_p=64_11077800\u001b[m\u001b[m\n", + "\u001b[34mweak_fft_n=512000000_p=64_11077796\u001b[m\u001b[m\n" + ] + } + ], + "source": [ + "! ls data/" + ] + }, + { + "cell_type": "code", + "execution_count": null, "id": "ae668ed4", "metadata": {}, "outputs": [], "source": [ "df0 = pd.read_csv('data/strong_fft_n=128000000_p=64_11077776/strong_fft_n=128000000_p=64_11077776.csv')\n", - "df1 = pd.read_csv('data/strong_fft_n=128000000_p=64_11077778/strong_fft_n=128000000_p=64_11077778.csv')\n" + "df1 = pd.read_csv('data/strong_fft_n=128000000_p=64_11077778/strong_fft_n=128000000_p=64_11077778.csv')" ] }, { @@ -137,6 +157,18 @@ "strong_scaling([df0,df1], names=[\"CCD\", \"CCX\"], title=\"Strong Scaling Up to 128e6 Points\")" ] }, + { + "cell_type": "markdown", + "id": "32e0c4e5", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "4922e217", + "metadata": {}, + "source": [] + }, { "cell_type": "code", "execution_count": null, diff --git a/hpc/ijhpca/Weak Scaling.ipynb b/hpc/ijhpca/Weak Scaling.ipynb index f140a966..aea0b299 100644 --- a/hpc/ijhpca/Weak Scaling.ipynb +++ b/hpc/ijhpca/Weak Scaling.ipynb @@ -14,1023 +14,151 @@ "import matplotlib.pyplot as plt" ] }, - { - "cell_type": "code", - "execution_count": 2, - "id": "89326e5d-3c39-43f0-b114-4d41e8f091de", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[34mUntitled\u001b[m\u001b[m \u001b[34mweak_fft_n=250000_p=16_11018099\u001b[m\u001b[m\n", - "\u001b[34mweak_fft_n=1024000000_p=128_11029691\u001b[m\u001b[m \u001b[34mweak_fft_n=32768000000_p=512_11030112\u001b[m\u001b[m\n", - "\u001b[34mweak_fft_n=1024000000_p=16_11029907\u001b[m\u001b[m \u001b[34mweak_fft_n=4096000000_p=128_11029701\u001b[m\u001b[m\n", - "weak_fft_n=1024000000_p=64_11033767.csv \u001b[34mweak_fft_n=4096000000_p=512_11030104\u001b[m\u001b[m\n", - "\u001b[34mweak_fft_n=128000000_p=16_11029687\u001b[m\u001b[m \u001b[34mweak_fft_n=512000000_p=16_11029699\u001b[m\u001b[m\n", - "\u001b[34mweak_fft_n=16384000000_p=512_11030108\u001b[m\u001b[m \u001b[34mweak_fft_n=8192000000_p=128_11029908\u001b[m\u001b[m\n" - ] - } - ], - "source": [ - "! ls weak_scaling_data/" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "09f4cb6e-d46a-48a5-9d6c-f880ab68856e", - "metadata": {}, - "outputs": [], - "source": [ - "def global_depth(rank):\n", - " \"\"\"\n", - " will return the first power of 8 greater than rank, the power corresponding to the level\n", - " \"\"\"\n", - " level = 1\n", - " loop = True\n", - "\n", - " while loop:\n", - "\n", - " if rank <= 8**level:\n", - " loop = False\n", - " result = level\n", - " else:\n", - " level += 1\n", - " \n", - " return result\n", - "\n", - "v_global_depth = np.vectorize(global_depth)\n", - "\n", - "def pow2(x): return np.log2(x).is_integer()\n", - "\n", - "def pow8(x): return np.emath.logn(8, x).is_integer()" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "b2219ad2-3f03-4f41-9e91-964c6a5b0b62", - "metadata": {}, - "outputs": [], - "source": [ - "def experiment_parameters(min_nodes, max_nodes, ranks_per_node, points_per_rank, local_depth=4, scaling_func=pow8):\n", - "\n", - " max_cpus=128 # AMD EPYC rome\n", - " \n", - " total_ranks = ranks_per_node * max_nodes\n", - "\n", - " n = points_per_rank*total_ranks # total problem size\n", - " print(f\"Total problem size n={n/1e9}B\")\n", - " \n", - " points_per_node = points_per_rank * ranks_per_node\n", - " print(f\"points per rank {points_per_rank/1e6}M\")\n", - " print(f\"points per node {points_per_node/1e6}M\")\n", - " \n", - " # Double number of nodes used until we reach max_nodes\n", - " n_nodes = np.array(list(filter(scaling_func, [i for i in range(min_nodes, max_nodes+1)])))\n", - " \n", - " # The global depth is a function of the ranks per node (as will need enough global leaves to cover this many ranks (local trees))\n", - " global_depth = v_global_depth(n_nodes*ranks_per_node)\n", - " print(f\"global depth {global_depth}\")\n", - " print(f\"local depth {local_depth}\")\n", - " \n", - " # local depth is a constant, chosen to balance M2L and P2P\n", - " points_per_leaf = points_per_rank / 8**local_depth \n", - " print(f\"points per leaf {points_per_leaf}\")\n", - "\n", - " n_tasks = n_nodes*ranks_per_node\n", - " print(f\"MPI tasks {n_tasks}\")\n", - "\n", - " print(f\"number of nodes {n_nodes}\")\n", - "\n", - " local_trees_per_rank = (8**global_depth)/(n_tasks)\n", - " print(f\"local trees per rank {local_trees_per_rank}\")\n", - "\n", - " max_threads_per_rank = max_cpus/ranks_per_node\n", - " print(f\"max threads per rank {max_threads_per_rank}\")\n", - "\n" - ] - }, { "cell_type": "code", "execution_count": null, - "id": "7d6b9585-7da2-48ea-add2-185bc200ef90", + "id": "178da76c-f2f3-4e55-859b-37cccde9f575", "metadata": {}, "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "22ca7892-b74b-40c7-9446-76f1d69d75bd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total problem size n=0.128B\n", - "points per rank 1.0M\n", - "points per node 2.0M\n", - "global depth [1 2 3]\n", - "local depth 4\n", - "points per leaf 244.140625\n", - "MPI tasks [ 2 16 128]\n", - "number of nodes [ 1 8 64]\n", - "local trees per rank [4. 4. 4.]\n", - "max threads per rank 64.0\n" - ] - } - ], "source": [ - "# 32 ranks per node -> 1 rank per CCX\n", - "experiment_parameters(1, 64, 2, 1e6, local_depth=4, scaling_func=pow8)" + "df0 = pd.read_csv('data/weak_fft_n=512000000_p=64_11077797/weak_fft_n=512000000_p=64_11077797.csv')\n", + "df1 = pd.read_csv('data/weak_fft_n=512000000_p=64_11077800/weak_fft_n=512000000_p=64_11077800.csv')\n", + "df2 = pd.read_csv('data/weak_fft_n=512000000_p=64_11077796/weak_fft_n=512000000_p=64_11077796.csv')\n" ] }, { "cell_type": "code", - "execution_count": null, - "id": "bba669ab-adf1-4fd8-8d08-9757a599e3a8", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6da6193f-690d-49c3-82ef-1930aae667ee", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "178da76c-f2f3-4e55-859b-37cccde9f575", + "execution_count": 28, + "id": "7fa9e215", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "128" + "0 500000\n", + "1 500000\n", + "2 500000\n", + "3 500000\n", + "4 500000\n", + " ... \n", + "139 500000\n", + "140 500000\n", + "141 500000\n", + "142 500000\n", + "143 500000\n", + "Name: n_points, Length: 144, dtype: int64" ] }, - "execution_count": 43, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "8*16" - ] - }, - { - "cell_type": "code", - "execution_count": 129, - "id": "60dd15a0-cc36-46f9-8c60-e8f11a33b1b4", - "metadata": {}, - "outputs": [], - "source": [ - "t1 = pd.read_csv('weak_scaling_data/weak_fft_n=128000000_p=16_11029687/weak_fft_11029687.csv')\n", - "# t2 = pd.read_csv('weak_scaling_data/weak_fft_n=1024000000_p=128_11029691/weak_fft_11029691.csv')\n", - "t2 = pd.read_csv('weak_scaling_data/weak_fft_n=1024000000_p=128_11029691/test.csv')\n", - "t2['experiment_id'] += t1['experiment_id'].max()+1\n", - "\n", - "df_250k = pd.concat([t1, t2])\n", - "\n", - "# Calculate nodes and ranks\n", - "df_250k[\"n_nodes\"] = df_250k.groupby(\"experiment_id\")[\"experiment_id\"].transform(\n", - " lambda x: 1 if len(x) / 32 < 1 else len(x)/32\n", - ")\n", - "df_250k[\"n_ranks\"] = df_250k.groupby(\"experiment_id\")[\"experiment_id\"].transform(\n", - " lambda x: len(x)\n", - ")\n", - "df_250k['total_points'] = df_250k['n_points'] * df_250k['n_ranks']\n", - "\n", - "#### \n", - "t1 = pd.read_csv('weak_scaling_data/weak_fft_n=512000000_p=16_11029699/weak_fft_11029699.csv')\n", - "t2 = pd.read_csv('weak_scaling_data/weak_fft_n=4096000000_p=128_11029701/weak_fft_11029701.csv')\n", - "t2['experiment_id'] += t1['experiment_id'].max()+1\n", - "\n", - "df_1e6 = pd.concat([t1, t2])\n", - "\n", - "df_1e6[\"n_nodes\"] = df_1e6.groupby(\"experiment_id\")[\"experiment_id\"].transform(\n", - " lambda x: 1 if len(x) / 32 < 1 else len(x)/32\n", - ")\n", - "\n", - "df_1e6[\"n_ranks\"] = df_1e6.groupby(\"experiment_id\")[\"experiment_id\"].transform(\n", - " lambda x: len(x)\n", - ")\n", - "df_1e6['total_points'] = df_1e6['n_points'] * df_1e6['n_ranks']\n", - "\n", - "##### \n", - "t1 = pd.read_csv('weak_scaling_data/weak_fft_n=1024000000_p=16_11029907/weak_fft_n=1024000000_p=16_11029907.csv')\n", - "t2 = pd.read_csv('weak_scaling_data/weak_fft_n=8192000000_p=128_11029908/weak_fft_n=8192000000_p=128_11029908.csv')\n", - "t3 = pd.read_csv('weak_scaling_data/weak_fft_n=32768000000_p=512_11030112/weak_fft_n=32768000000_p=512_11030112.csv')\n", - "\n", - "t2['experiment_id'] += t1['experiment_id'].max()+1\n", - "t3['experiment_id'] += t2['experiment_id'].max()+1\n", - "df_2e6 = pd.concat([t1, t2, t3])\n", - "\n", - "df_2e6[\"n_nodes\"] = df_2e6.groupby(\"experiment_id\")[\"experiment_id\"].transform(\n", - " lambda x: 1 if len(x) / 32 < 1 else len(x)/32\n", - ")\n", - "\n", - "df_2e6[\"n_ranks\"] = df_2e6.groupby(\"experiment_id\")[\"experiment_id\"].transform(\n", - " lambda x: len(x)\n", - ")\n", - "\n", - "df_2e6['total_points'] = df_2e6['n_points'] * df_2e6['n_ranks']" + "df2['n_points']" ] }, { "cell_type": "code", - "execution_count": 130, - "id": "aae5401c-84f3-4769-9096-b6d45c6b7c81", + "execution_count": null, + "id": "754504a1", "metadata": {}, "outputs": [], "source": [ - "plt.rcParams.update({\n", - " \"font.size\": 9, # base font size\n", - " \"axes.labelsize\": 9, # x/y labels\n", - " \"axes.titlesize\": 9,\n", - " \"xtick.labelsize\": 9,\n", - " \"ytick.labelsize\": 9,\n", - " \"legend.fontsize\": 9,\n", - " \"font.family\": \"serif\", # or \"sans-serif\" for a cleaner look\n", - " \"text.usetex\": False # True if you want full LaTeX rendering\n", - "})\n", - "\n", - "\n", - "def set_size(width_cm, height_cm):\n", - " \"\"\"Helper to set figure size in cm.\"\"\"\n", - " return (width_cm / 2.54, height_cm / 2.54)\n", - "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", "\n", - "def weak_scaling_plot(df_vec, shrink=0.08, band_alpha=0.2):\n", + "def weak_scaling(dfs, names=None, title=\"Weak Scaling Performance\"):\n", " \"\"\"\n", - " df_vec: list of dataframes (each with experiment_id, total_points, n_nodes, runtime, ...)\n", - " shrink: fraction to inset band edges on a log scale (per band)\n", - " band_alpha: transparency of the shaded bands\n", + " Plots weak scaling results (runtime + efficiency) for one or more DataFrames.\n", + "\n", + " Parameters\n", + " ----------\n", + " dfs : list of pandas.DataFrame or pandas.DataFrame\n", + " One or more DataFrames with columns including:\n", + " ['experiment_id', 'runtime', 'p2m', 'm2l', 'm2m', 'l2l', 'source_tree', 'n_points', 'source_local_trees_per_rank']\n", + " names : list of str, optional\n", + " Names/labels for each DataFrame. Must be same length as dfs.\n", + " title : str\n", + " Figure title.\n", " \"\"\"\n", - " fig, ax = plt.subplots(1, 3, figsize=set_size(35, 10)) # double column width\n", - " \n", - " shade_colors = [\"tab:blue\", \"tab:orange\"]\n", "\n", - " for (i, df) in enumerate(df_vec):\n", - " # aggregate per experiment\n", - " grp = df.groupby('experiment_id')\n", - " runtime = grp.mean()[['total_points', 'n_points', 'n_nodes', 'n_ranks', 'runtime']]\n", - " runtime_err = grp.std()[['total_points', 'n_nodes', 'n_ranks', 'runtime']]\n", - "\n", - " # plot means + error bars\n", - " ax[i].errorbar(\n", - " runtime['total_points'],\n", - " runtime['runtime'],\n", - " yerr=runtime_err['runtime'],\n", - " fmt='o-', capsize=4, label=\"Runtime\", zorder=3\n", + " # Handle single DataFrame\n", + " if not isinstance(dfs, (list, tuple)):\n", + " dfs = [dfs]\n", + " if names is None:\n", + " names = [f\"Run {i+1}\" for i in range(len(dfs))]\n", + "\n", + " sns.set_context(\"talk\", font_scale=1.1)\n", + " sns.set_style(\"whitegrid\")\n", + " fig, axes = plt.subplots(1, 2, figsize=(14, 6))\n", + " ax_runtime, ax_eff = axes\n", + "\n", + " markers = ['o', 's', '^', 'D', 'v', 'P', '*']\n", + " colors = sns.color_palette(\"deep\", len(dfs))\n", + "\n", + " for df, label, color, marker in zip(dfs, names, colors, markers):\n", + " # Aggregate per experiment\n", + " stats = df.groupby('experiment_id')[['runtime', 'p2m', 'm2l', 'm2m', 'l2l',\n", + " 'source_tree', 'n_points', 'source_local_trees_per_rank']]\n", + " runtime = stats.mean()['runtime']\n", + " runtime_err = stats.std()['runtime']\n", + " n_ranks = stats.size()\n", + " n_points = stats.mean()['n_points'] * n_ranks # total DoFs = per-rank * ranks\n", + "\n", + " # Reference runtime (lowest rank)\n", + " T_ref = runtime.iloc[0]\n", + " efficiency = (T_ref / runtime) * 100\n", + "\n", + " # Runtime plot\n", + " ax_runtime.errorbar(\n", + " n_points, runtime, yerr=runtime_err,\n", + " fmt=marker+'-', color=color, capsize=4, label=label,\n", + " linewidth=2, markersize=7\n", " )\n", "\n", - " ax[i].set_xscale('log')\n", - " ax[i].grid(True, zorder=0)\n", - "\n", - " # ----- build non-overlapping n_nodes regions on the x-axis -----\n", - " # collect per-n_nodes x ranges + a representative x (median) to order regions along x\n", - " bands = []\n", - " for n, sub in runtime.groupby('n_nodes'):\n", - " if sub.empty:\n", - " continue\n", - " x_min = sub['total_points'].min()\n", - " x_max = sub['total_points'].max()\n", - " x_rep = sub['total_points'].median()\n", - " bands.append((int(n), x_min, x_max, x_rep))\n", - "\n", - " if not bands:\n", - " continue\n", - "\n", - " # order the bands by their representative x (so bands won't overlap visually)\n", - " bands.sort(key=lambda t: t[3])\n", - "\n", - " # compute region edges: [left edge of first band] + midpoints between reps + [right edge of last band]\n", - " edges = [bands[0][1]]\n", - " for k in range(len(bands) - 1):\n", - " # geometric midpoint on log scale between representative x's\n", - " mid = (bands[k][3] * bands[k+1][3]) ** 0.5\n", - " edges.append(mid)\n", - " edges.append(bands[-1][2])\n", - "\n", - " # now shade each band between consecutive edges, alternating colors\n", - " # inset edges slightly in log-space so bands don't sit exactly on data limits\n", - " # now shade each band between consecutive edges, alternating colors\n", - " y0, y1 = ax[i].get_ylim()\n", - " for j, (n, _, _, _) in enumerate(bands):\n", - " left = edges[j]\n", - " right = edges[j+1]\n", - " \n", - " if left <= 0 or right <= 0 or left >= right:\n", - " continue\n", - " \n", - " # shrink inward on left, but extend right edge slightly\n", - " log_left, log_right = np.log10(left), np.log10(right)\n", - " pad = (log_right - log_left) * shrink\n", - " log_left_adj = log_left + pad\n", - " log_right_adj = log_right - pad\n", - " \n", - " # right-inclusive extension (+5% of width)\n", - " # if j == (len(bands)-1):\n", - " log_right_adj += (log_right - log_left) * 0.1\n", - "\n", - " if j == 0:\n", - " log_left_adj -= (log_right - log_left) * 0.15\n", - "\n", - " if j == (len(bands)-1):\n", - " log_right_adj += (log_right - log_left) * 0.1\n", - "\n", - "\n", - " left_adj = 10 ** log_left_adj\n", - " right_adj = 10 ** log_right_adj\n", - " \n", - " ax[i].axvspan(\n", - " left_adj, right_adj,\n", - " color=shade_colors[j % 2], alpha=band_alpha, zorder=1\n", - " )\n", - " \n", - " # label in log-midpoint\n", - " xmid = (left_adj * right_adj) ** 0.5\n", - " y_label = y0 + 0.92 * (y1 - y0)\n", - " y_label = y1 + 0.3*y1\n", - " ax[i].text(\n", - " xmid, y_label, f\"{n} nodes\",\n", - " ha=\"center\", va=\"top\", rotation=90,\n", - " fontsize=9, fontweight=\"bold\",\n", - " bbox=dict(facecolor=\"white\", alpha=0.7, edgecolor=\"none\"),\n", - " zorder=4\n", - " )\n", - "\n", - "\n", - " # keep limits stable if shading changed them\n", - " ax[i].set_ylim(y0, y1)\n", + " # Efficiency plot\n", + " ax_eff.plot(\n", + " n_ranks, efficiency, marker=marker, linestyle='-', color=color,\n", + " label=label, linewidth=2, markersize=7\n", + " )\n", "\n", - " ax[0].set_ylabel(\"Runtime (ms)\")\n", - " ax[1].set_xlabel(\"Number of Particles\")\n", - " fig.tight_layout()\n", - " # fig.legend(loc=\"upper right\", bbox_to_anchor=(0.98, 1.05))\n", + " # --- Runtime plot formatting ---\n", + " ax_runtime.set_xscale('log')\n", + " ax_runtime.set_yscale('log')\n", + " ax_runtime.set_xlabel('Total Number of Points (DoFs)', fontsize=13)\n", + " ax_runtime.set_ylabel('Runtime (ms)', fontsize=13)\n", + " ax_runtime.set_title('Weak Scaling (Runtime)', fontsize=14)\n", + " ax_runtime.grid(True, which=\"both\", ls=\"--\", lw=0.5)\n", + " ax_runtime.legend(fontsize=11)\n", + "\n", + " # --- Efficiency plot formatting ---\n", + " ax_eff.set_xscale('log', base=2)\n", + " ax_eff.set_ylim(0, 110)\n", + " ax_eff.set_xlabel('Number of Ranks', fontsize=13)\n", + " ax_eff.set_ylabel('Parallel Efficiency (%)', fontsize=13)\n", + " ax_eff.set_title('Parallel Efficiency', fontsize=14)\n", + " ax_eff.grid(True, which=\"both\", ls=\"--\", lw=0.5)\n", + " ax_eff.legend(fontsize=11)\n", + "\n", + " # --- Global figure adjustments ---\n", + " fig.suptitle(title, fontsize=16, y=1.03)\n", + " plt.tight_layout()\n", " plt.show()\n" ] }, { "cell_type": "code", - "execution_count": 131, - "id": "58ab246a-9568-47c4-9906-ee50f9c4823b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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108192.0256.07236.4808351.638400e+102000000.034051.0900885327.551514625.1298835.05.04.0
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" - ], - "text/plain": [ - " n_ranks n_nodes runtime total_points n_points \\\n", - "experiment_id \n", - "0 8.0 1.0 2707.625000 1.600000e+07 2000000.0 \n", - "1 16.0 1.0 6299.187500 3.200000e+07 2000000.0 \n", - "2 32.0 1.0 3750.375000 6.400000e+07 2000000.0 \n", - "3 64.0 2.0 2709.453125 1.280000e+08 2000000.0 \n", - "4 128.0 4.0 6400.523438 2.560000e+08 2000000.0 \n", - "5 256.0 8.0 3830.542969 5.120000e+08 2000000.0 \n", - "6 512.0 16.0 2761.445312 1.024000e+09 2000000.0 \n", - "7 1024.0 32.0 6407.586914 2.048000e+09 2000000.0 \n", - "8 2048.0 64.0 3876.565918 4.096000e+09 2000000.0 \n", - "9 4096.0 128.0 2883.618652 8.192000e+09 2000000.0 \n", - "10 8192.0 256.0 7236.480835 1.638400e+10 2000000.0 \n", - "\n", - " source_tree m2l p2p local_depth \\\n", - "experiment_id \n", - "0 24182.500000 1273.625000 1207.250000 5.0 \n", - "1 15686.187500 5404.250000 629.250000 5.0 \n", - "2 18838.062500 2746.531250 827.031250 5.0 \n", - "3 23951.734375 1325.796875 1225.593750 5.0 \n", - "4 16258.140625 5476.750000 630.390625 5.0 \n", - "5 18787.957031 2765.609375 829.410156 5.0 \n", - "6 25013.923828 1358.531250 1229.859375 5.0 \n", - "7 17878.924805 5184.622070 625.692383 5.0 \n", - "8 22884.522461 2639.126953 826.509766 5.0 \n", - "9 32766.833008 1316.570557 1227.698242 5.0 \n", - "10 34051.090088 5327.551514 625.129883 5.0 \n", - "\n", - " global_depth local_trees_per_rank \n", - "experiment_id \n", - "0 1.0 1.0 \n", - "1 2.0 4.0 \n", - "2 2.0 2.0 \n", - "3 2.0 1.0 \n", - "4 3.0 4.0 \n", - "5 3.0 2.0 \n", - "6 3.0 1.0 \n", - "7 4.0 4.0 \n", - "8 4.0 2.0 \n", - "9 4.0 1.0 \n", - "10 5.0 4.0 " - ] - }, - "execution_count": 241, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# tmp = df_2e6.groupby('experiment_id').mean()[['n_ranks', 'n_nodes', 'runtime', 'total_points', 'n_points', 'source_tree', 'm2l', 'p2p', 'local_depth', 'global_depth', 'local_trees_per_rank']]\n", - "tmp" - ] - }, - { - "cell_type": "code", - "execution_count": 232, - "id": "f4732f41-6b6b-49c3-a222-0dd35d953e29", - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.read_csv('weak_scaling_data/weak_fft_n=1024000000_p=64_11033767.csv')\n", - "df[\"n_nodes\"] = df.groupby(\"experiment_id\")[\"experiment_id\"].transform(\n", - " lambda x: 1 if len(x) / 32 < 1 else len(x)/32\n", - ")\n", - "df[\"n_ranks\"] = df.groupby(\"experiment_id\")[\"experiment_id\"].transform(\n", - " lambda x: len(x)\n", - ")\n", - "df['total_points'] = df['n_points'] * df['n_ranks']\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 163, - "id": "0d0a83cd-74a0-4af9-96e3-f50f4ecb8246", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 16000000\n", - "1 16000000\n", - "2 16000000\n", - "3 16000000\n", - "4 16000000\n", - " ... \n", - "2331 1024000000\n", - "2332 1024000000\n", - "2333 1024000000\n", - "2334 1024000000\n", - "2335 1024000000\n", - "Name: total_points, Length: 2336, dtype: int64" - ] - }, - "execution_count": 163, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['total_points']" - ] - }, - { - "cell_type": "code", - "execution_count": 159, - "id": "31b6e6d3-8b91-4b03-846b-ef034c18d860", + "execution_count": 36, + "id": "67d9abc3-f9bb-41ec-9778-9912fecbc924", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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9fQX2QCQh8nwi1yym2+OYsA/Oi2hVu1b/NkRbb0KTJ5HaH5oSsryt+G6c2998nL7U3Yh4NtJN+IsxrR7RncEAi4Ah9QT84y0vaXPvK6QZwPWGY3oTxiFqQkDFAwKr2JtfjHb56pNgPHBAX4MUF3gggodFeHCEh0HYjOhxCPGEEEIICW8ooBJCCCGkkaiF6fmIbkMEHKYvYwq4e6QbhFJEL0J0WbduncrviYgtM8jRCCDymBdv8QbyH2LqPYQWTCt2jyCFyOGNjIwMNRUXkXqI7oQYglyeyFu5N9BO1D0pKUkJJ96EPbQXKQqQ79KIijWmxyPVgScWLVqkbAObYto8xFPYEgsbuS+UY0wftgOI3cixiWhGiMPuGBGqzRGyDFHSTFNTwr3ZzIiwNOdGbQ6IFoVgCB/Bpp5EdET+GtdNU0AoxwJHuN6x2JC34/iy6NHewDVh3Cd4UIA8wJ5o7n0FUP+VK1cqm5tzwhrgGvXFHoGAewXXXHZ2tsdrzlefBBuIx1h0DRtEZtyzyC2LukJANadPIIQQQkh4whyohBBCCGmEkccUOQwRkeYefWqexg+RCrkPjfeecgxClPSUk3D58uUqFyWmK0M0xVR5iKh9+vTxOP3eiIb1FMmGKD1jmjPyl2LqLRa5Qb5IX4QdRObh3HtbuAgpBYAhABkRqwsWLPC4//Tp09UCO8hlaUT1Qqx1F9xwbuN7OxauMXLdQih3B9GUWOHcVyA8A/cFtsDvv//utRxSLRiimSeh3Jc8rJ6iECGeYho27OhJdEfUMFI8APMiRJ6AoIb7AOkbPOWmhVgPvN0jvoKUFRBDId4j6tSbeOrPfQWMafnI5eoO/IZcxoj89JRXNFgY9fa24Nbnn3+uXo3F6oKFt0jVa665Rvkfgq752hk5cqRr4TVEmxNCCCGEUEAlhBBCiFdxDaIRaEpABcgdCPHSKGeAKfhHHnmkEiGmTp3aIMISog0+27hxo4qGhMhh5EBF5BeiWg0gAiESDIKoL9OlcV5EjaEcpkP7siiTsRgQIlaxIJAxxd0scCItAPKWQtiDyAuQJgDRfStWrJCnn366gbgLUWzp0qVqX0QLGu2D2Gqe+g+R+tprr3VNnw7WdPCmgIiLyExEV37zzTeuz2ErRAEbdfFlmrQRnYzp5+aIU4jXb7/9ttdysBUW7zFfFxA2USdEkZ500kl7PXdcXJzH6N3zzjtPvSIK2VjoDMCv8OOmTZukX79+st9++zV5fPgXK9sjQhPXhvm6wGJjmOYNsf6iiy4Sf8GDA+RRBajb6aef3uT+zb2vwMSJE9WCSEiR8e6777r2Ly0tVfvjFSkDUlNTxSomTJig6gDR2RCeDdCHQKjG96hHMDGuEbTR/HACkbyIPsc1Yu4jysvLXcI7csMSQgghhHAKPyGEEEIagdyKiMrEqvAQRo2IVHfwOSLmMN17n332cQmEZjAVFmIOolQR1QhBAqIOxDUIGojghHgIunTpohblQVQkcoMiEg3ix19//aWmdaNeiFo0phs3xQUXXKDyiWJ/pAMwzuENnA/teOqpp1SdH3/8cZUPFoISxESkKkB9Mf3ZPG0a9nnsscfU9F+UhUAFwRQCHURVRGciChWcdtppSnBDJC0iBGEziF/IuwjRpjntCxRMk0Y7r7/+eiU2I2ITPkc7cX5Ma0Zkni95SLF6+rPPPqvajHZh5XUcA9GnsKu7WGYAsQzTyseMGaOiE7F4EGwBQfKhhx7yKfeksRAVxExMsUc7Lr30UhXBjGvg5ZdfVoIkhFIcD4I2hEe0D34xL87kCVzfhn8hPOIaht9QV7QP9oEA521BLF/A9QlhFtcKcgpj8wTaNG7cuGbfV0b6CdgU+YHxUAEPRxB1jWhVtAVi8o033ihWgqhrow633HKLWrype/fu6oEJroOEhARlS2+LTPkL+iXcxxD3ISSjn8E9bOQjnjt3rsrhbKQ2wD0AIRrXsS8iPiGEEEKcDyNQCSGEEOIRI5oUCx15E7KwCrYRoeUtShVCHSJHr776aiXQYdoxhCcIJ4g+fOWVV5RwYgBRC9OXIe4g/yTyhiIaEUIfptdDnMRq3ntb9RxTcRHNB1HppZdeUgLN3rjssstUFOU555yjBCdELiIiEhGCQ4YMUVOr0Rb3FdkhPkEkhDiD6FEIwMg1efzxx6vIOgijAG2CCDd27FgVgYr9ENGKacQQ+iAeAYg6dgDh87XXXlORxBBu58+fr6IbIWzB78YCRHsDwh8ihLGIFwRHHMfIZztt2jSv5XBslINwhZy1EAQhEM6aNUutPu8LEDNxbSCaEEIirhfzAkHPPPOMsi/8D5EVdYWf4S8sTubrOXBdQIhFBCOOg9QDWKkd/oU/A8FI/wCbffLJJ143CPL+3lcA4jbqC79DIISQD3sg3yr84IuvAwV1QIQx7g3UAfcXhE08XEDdIKYHG4jkEEwRKY6HMbhOkHoE/RqixCdNmqREe9gD4jWEXojJsGGgi4wRQgghxBlE1AVriVVCCCGEENJiQLQtol4R7edJJDrxxBOVuIvoPIhywQSRzZiCDqHKW+5YQgghhBBCdIERqIQQQgghYQii7RDtiWneNTU1Db5DVB7EU0T7Bls8JYQQQgghpKXBHKiEEEIIIWEIxNPnnntOTaHGdHlMo8f0e0zlR05KpClAvkpCCCGEEELCHUagEkIIIYSEIch3iVyUV111lVpkB4sPIQ8psjude+65akV0Iw8qIYQQQggh4QxzoBJCCCGEEEIIIYQQQogXGIFKCCGEEEIIIYQQQgghXqCASgghhBBCCCGEEEIIIV6ggEoIIYQQQgghhBBCCCFeoIBKCCGEEEIIIYQQQgghXqCASgghhBBCCCGEEEIIIV6ggEoIIYQQQgghhBBCCCFeoIBKCCGEEEIIIYQQQgghXqCASgghhBBCCCGEEEIIIV6ggEoIIYQQQgghhBBCCCFeoIBKCCGEEEIIIYQQQgghXqCASgghhBBCCCGEEEIIIV6ggEoIIYQQQgghhBBCCCFeoIBKCCGEEEIIIYQQQgghXqCASgghhBBCCCGEEEIIIV6ggEoIIYQQQgghhBBCCCFeoIBKCCGEEEIIIYQQQgghXqCASgghhBBCCCGEEEIIIV6ggEoIIYQQQgghhBBCCCFeoIBKCCGEEEIIIYQQQgghXqCASgghhBBCCCGEEEIIIV6ggEoIIYQQQgghhBBCCCFeoIBKCCGEEEIIIYQQQgghXqCASgghhBBCCCGEEEIIIV6ggEoIIYQQQgghhBBCCCFeoIBKCCGEEEIIIYQQQgghXqCASgghhBBCCCGEEEIIIV6ggEoIIYQQQgghhBBCCCFeoIBKCCGEEEIIIYQQQgghXqCASgghhBBCCCGEEEIIIV6ggEoIIYQQQgghhBBCCCFeoIBKCCGEEEIIIYQQQgghXqCASgghhBBCCCGEEEIIIV6ggEoIIYQQQgghhBBCCCFeoIBKCCGEEEIIIYQQQgghXqCASgghhBBCCCGEEEIIIV6ggEoIIYQQQgghhBBCCCFeoIBKCCGEEEIIIYQQQgghXqCASgghhBBCCCGEEEIIIV6ggEoIIYQQQgghhBBCCCFeoIBKCCGEEEIIIYQQQgghXqCASgghhBBCCCGEEEIIIV6ggEoIIYQQQgghhBBCCCFeoIBKCCGEEEIIIYQQQgghXqCASgghhBBCCCGEEEIIIV6ggEoIIYQQQgghhBBCCCFeoIBKCCGEEEIIIYQQQgghXqCASgghhBBCCCGEEEIIIV6ggEoIIYQQQgghhBBCCCFeoIBKCCGEEEIIIYQQQgghXqCASgghhBBCCCGEEEIIIV6ggEoIIYQQQgghhBBCCCFeoIBKCCGEEEIIIYQQQgghXqCASgghhBBCCCGEEEIIIV6ggEoIIYQQQgghhBBCCCFeoIBKCCGEEEIIIYQQQgghXqCASgghhBBCCCGEEEIIIV6ggEoIIYQQQgghhBBCCCFeoIBKCCGEEEIIIYQQQgghXqCASgghhBBCCCGEEEIIIV6ggEoIIYQQQgghhBBCCCFeoIBKCCGEEEIIIYQQQgghXqCASgghhBBCCCGEEEIIIV6ggEoIIYQQQgghhBBCCCFeoIBKCCGEEEIIIYQQQgghXqCASgghhBBCCCGEEEIIIV6ggEoIIYQQQgghhBBCCCFeoIBKCCGEEEIIIYQQQgghXqCASgghhBBCCCGEEEIIIV6ggEoIIYQQQgghhBBCCCFeoIBKCCGEEEIIIYQQQgghXqCASgghhBBCCCGEEEIIIV6ggEoIIYQQQgghhBBCCCFeoIBKCCGEEEIIIYQQQgghXqCASgghhBBCCCGEEEIIIV6ggEoIIYQQQgghhBBCCCFeoIBKCCGEEEIIIYQQQgghXqCASgjxm5tuukn69u0rixcvbvRdaWmpDBo0SH3/1FNPeSx/6qmnyoABA6SwsNCyOuL8Z555pt/lKysr5ZVXXpEJEybI/vvvr9o0evRoue666+TXX38Vq/npp59UG6ZPn+767IgjjpCRI0dKqLn77rvl8ssvd70/55xzVF3dt/79+8sBBxygbPjmm29KbW2tbXXcsGFDg/ehtN2KFStkv/32ky1btoTk/IQQQgixH4yDPY2PBg4cKIceeqhcccUVsmTJEi3q+OOPP6r3GKvg/Q033ODX8Xwdb2E/T7Zx3zCGMiguLpbrr79ejcsHDx4s9957r1RVVck999wjI0aMUGN12NS9Tb7ywQcfqHLvvvuuX20nhDiX6FBXgBDScjnooINk9uzZ8ttvv6kBi7vwB/ExJiZGFi5cKFdddVWD7zH4wWAIg5zU1FTREYjA559/vvz5558ybtw4OfbYYyUxMVENKjG4+vTTT+XWW29V+9jJbbfdJnV1dRJKfv75ZzWwnDNnTqPvYJP09HTX+5qaGmWzd955R4mu27dvVwK01fz73/+WH374Qb755hstbAchGdfR1KlT5dVXX5WIiIiQ1IMQQggh9nPGGWeoB6kG1dXVkpOTox4uz5s3T5577jk57LDDJBx5+OGHm/w+KyvL9ffTTz+txp8YU0GA7t27t7z99tvyxhtvqN8jxx9/vNo/IyNDunTpor5vDsOHD1f1GTp0qN/tIYQ4EwqohBC/Ofjgg9Wrp6fmEE1jY2PVIOajjz6SvLy8BqIaykBYO+SQQ0RX3nrrLVm6dKk89NBDcvLJJzf47qKLLpLx48fLI488ImPGjJGOHTvaVq+jjjpKQgkiSCFOwiZdu3b1WL9OnTo1+vzss89WIvTLL7+sROfWrVtbWk/8GImOjtbKdniQgDrgwYP7NUUIIYQQ5zJkyBA56aSTGn1++OGHq1lZEO3CVUD1ZBdvGNGoiDxNSUlRf+MhvfEQv1+/fq59zX/7SufOndVGCCHucAo/IcRv2rdvLz169JDff/+9UVQfBFQMFI888kgluCES0Mz//vc/9aqzgGrUEQNbdxA1O2nSJCUCh3rald188cUXsn79ejnrrLOaVQ6C6dFHH62mWeGaCUc6dOig7okXXngh5FHEhBBCCAk9++yzj4qS/Pvvv6WgoCDU1dEezHADhnhq/iw5OTlk9SKEOB8KqISQgKfxI4fp6tWrXZ9t3LhRNm3apJ6iYyoNogAhqJr55Zdf1MAHuYsMMLX7jjvuUDmTkBcKwiWeLiN61ZOId8EFF8iBBx6oBp54vfTSS+WPP/5osr6YLjVlyhSV2+jxxx9vct+kpCT1iilBnsSuc889V03vP+GEExp8js9wDtgG03/wVB3RrOZjYKD34osvqogD7IP2Irfq7bffLrt27WpWXikjxxN8gMhQiNJIjXDiiSeq6F931q5dq+oH3+DcF198sfoM+WhvueUW2RvICdunTx+/nuojBYKveWqR9xXfIR2EOR/X//3f/8msWbNUdDPaielbSA2AtBDm/XA9ZWdnN8jD25Tt0HZcRxD+kc8Vn+Haw7R/fI5cW4g8hnjszieffKJyvKIsbApx+dtvv/VoA9R7zZo1Mn/+/GbbjxBCCCHOIzKy/mc5HswbLFq0SC677DI14wtjXUwtP++88xrl9MTY5sILL1QpADBWGTZsmLz++uvqux07dsh9990nY8eOlX333VdtmA00Y8YMNSZuLs0Zq1u1LoARuGDOkYpxGMBDarzHWNBbDtS5c+eqWVFIp4Ac/RjzmffxlgN12bJl6rcGymD8ifEcZlWZfebrWNUAvw2wn/F7AL8d/vWvf8ny5ctdKbNwvGnTpjWyx86dO9V1ceONNwbFvoSQvcMp/ISQgMCgDrmbMJiBqAa+//579QoBFU+CIZLiMwwSkPexvLxcDQxGjRrlmmK9efNmJaJBWESOKEyJX7lypcpptGDBAvVqTPlG/sgHHnhAiVpXXnmlyrMK4RRiIQYaX3/9tcfp4RgoIvcmxFckn7/kkkuabBvq8/nnn8uTTz6pBlMYJGLwigFXmzZtGk0PBxiA4bho98SJE1WULoS0u+66S4l5xiDn6quvlu+++06lAYDwVlFRodqJwRqEOwymmgsGXO3atVOvsONrr70mN998s/rMSLcAoRT1gi0wYEQ7MJBENK0vizthII60BpMnT252/XBOiIZRUVEqH6i/4FooKSlRdc7MzFT+xDUIIf8///mP8j2mweEHA36QYDoXBp9NARG5Z8+ecu211yqB9L///a8aJEPwRR4t+AsLUuEHCabhf/zxx64fO0jjADEcwjWuL/gS+XHxo8dTjlwMjlH2q6++UqI5IYQQQsIXjA8xPsPY1xi/YmyDsQcebmNciYf6GB9inIgxC8a85tyeGIfjezwgz8/PV2ONoqIiNabG+AhjJuQDxXcoi7EtxuMYD/tKc8bqzWX37t1ev0NKMIyrMU7D+O6ZZ55RYzL8jbErxlQzZ85UazIYefi91QPCMdqOcSHGeXFxcareeECO7zzNOgPIpw9/IEUV9sX4ELPrkOYLtodYa85tv7exqgHy4r///vtK+MbxMUsLgRsYo2PMid8d8Bt+j+CBPmxhANEYY2uIr4QQe6CASggJCIiYEMQweIAwBxBtCmHOiFCEsIQV6//66y/1pBTTtzFAME/fx1PZsrIy+fDDD9VAwQBTviHWYbCDp694yosn7BDgEAmJc5un1b/00ktKREVieTMYYGCQ+OWXX6rBCqJH9wYGMxhM3XnnneqJMgYyxhN9iMWnnHKKeoJtHsxgXwxykeMS4imAXSCiQdBEhABESIiqKIun+Aao0+mnn67sg4Fkcweh3bt3V0KeMYBDlAEGYBiYGQIqBpsYUCNXFL4HiJbEIBKD372xePFi9dpU9CkGh+aBMHyNQTfqtm7dOhU9gcGkv+Tm5spnn33myk912mmnqcgKCMFYgRWDWkT9Pvroo0rk9iWvFgblZtshgho+Qr5SXAMG27ZtU4NgXA+4ThGNgHIYIMP3BvA3fI1BMq5FTN03wI8A1B2RJYQQQggJD7A4qXl8BCESAiRm3GCshJXjDTDWxVgaYpp59g5yz2PMjDGbWUDFsSEsQjg1wMPgrVu3qjE0xkkGED8xLsSD/OYIqL6O1f3BXG93EFWKtsEeGNNBnISAah7f4bcHBFRvefgBxqIY00GUROSoMX7HjC3Y54knnvAooKLN+O2AsT/ObZTDOB6z2Z599lklcCKy19exakJCgpqNhzE6olQxXjTGoNjvmGOOUdcA6guBFNcIghCw7oIB/AARG7/FCCH2QAGVEBIQmIaPKTzGdBoMBjHFBv/gjYEAIlExqEJ0JgRUI7coprMA5HtChCqmA0FcMg8uIdRh8IFoPQzKIJhi0IjBjFk8xcARkajAfXoMnk7fcMMNatCCCFBfxFMDDKRQT4hdqCMGOxjsIk8Vnjrj6S+EUYi3EIghvGFAZYinAHZ48MEHVR2xH4RRCMpGBKMBpu4b+Zzw1Lq5AipSCZiffsMvxhQfAOEUbYBwbYinAHZEtKQvAirSMwDzwNkdCMueQCQsIjTx5D4QMMXJnNwfdoSgDtsjsgKD0uaCQa/ZdhBUIaBiAGvGWDQLU9hgA6wCa5R3j57AZ7gX8APFPUUBysIXEPY9RTITQgghxFlAOMPmTtu2bdVDWHMkISJN8UDaLJ5ijG2MHd3HuhgDQxg0g/Hucccd12ARV4DxCsab7sdoiuaM1f0BQRHeCNaio4giRSAGggvMwQ+tWrVS0aGe0kwBRJoiRQFEYnebYawHARVtNwuovoxV8UAe4LjmMSjGiO+9955kZGS4xtX4HYXgDENAxUJaq1atUrOizGUJIdbCX22EkIDBU2M8JcXTVuR2hFBoiKOGkIfBCcQkTDuCCIlBhSHCQZSDyIlV05t6Ao2p0Zhqg0EPBEg87cV0a0x9ysnJceUYdc9XiohORAoCnLu5Ah4GpRgwGrkzMYhEJCueCkM0xSumDOHJNsDCWu64R1yiDXgyjUEZ2o+IRgioxiDIl+n07uDJvPs5zMfCoA2CHSJV3enVq5dP5zAGzObE/e5gSjvqgvNiOhme8sMnEJGDsWgYfmi4Y7TVnIcqENsZ4rz758YPF8OmRj5UiObewPXpDoR02AT2hLBMCCGEEGeDmSnm8THGLhjTYDzsLoLh4SqiRxF5ien9GEtgrGiMP9zHiRhne3ogi3ELxmFIv4RxIMaqeEgPzA/790Zzx+rNxZgpZSVNjdObGgcbY73HHntMbb6M9XwZq8Kf3upjTnUFP+G6ge0hvsLXiD7FNXPyySd7rTchJPhQQCWEBE1AxeAM02fwD908QMTgDftg6gkGVtjPPO3GGARi2k1TK7sbohamGyHyD4MdLNqD5Pl4+o0BDnKNuoMBJaau4/x4eounuphK0xSIMMR0feSeMj9RBmlpaWqqPRZhwrQlYyq2r8n4IcBiyjcGxEgTgGhQPF1GknlEsyK/pj+4R7S6g+lhwIjUNRMfH9+sczQlVGLxAmP6FK4DTGFHnlfk8ML0KPjZF7ydw4on7Z5s4su5jGv36aefdi065o6ndAVG28xR1IQQQghxLhi3+ioUIg0RFiLCeApjRYyjkbcTY83LL7/cpzEgggew4CrKYMyKGWGY9o9xGqIwm/OwvrljdR0xxsHNHUcagRlYdwHrIHjCfQzoyzmaUx9EJ+N3DIIvMKbG7yBM3feWroAQYg0UUAkhAYOBGAQ4LAyF/KMQHd2n2yDyEBGjWFwH0+/NAqvxzx/J7D0NLLEolPFkHdP/MWjA1GrkAzIPOhBp6gkIlJjChDog4tOIhNxbHs4XXnhBRc+6C6gGiKLFgMmYMm60w9Mq7RBZkecI03QwAEKk7r///e9Gg1Bjur0VYPo57OWpfshN6gvGE/XmrLYKO0M4RZTmTTfdpBbk6tatW4PBNqaluWOlLYKF4XPYBWK+e7QG7OppShgiT3E9B2taGiGEEEKcASJPMQbF+BoP1s3TzZvzkB3RkphyjjLGQq+GcIdxHAICfKU5Y3VdMY/T3SNOkS8WU+Ixo8xbOUTWurcd9kVqA08Rp82pD347mUFuVaTeuv3229XYHcEiSMWAdGSYSYZZa1golhBiL02HKxFCiA9gYIcnspiijynteMLtjvEZkq9jcIUn4QaYJo3yEDcxxd4MxEYk1cdTeICpKwBP0M3iKQQpRJY2FQmKgR1ESwxIkAy+KTBdBiuk//HHH2qRIE8gmhXHMhasQn5XiIUQid3FP0yfwucYYBnio/vK8IjeNdrvazRrc8DACxEMGOhhar35yTrq15zBHgb3zQE/AhAxgWljEFHN0aWYwo7FAMx5pWAj+D4QIMz6kwqhORi+x+qrZp/hxwkG4VicC9HM7sB+WVlZzFtFCCGEkEYzlTA2g1BmFk8RgGAsZupLyiKMpRDgYORvN8AxIIQ2J+1Rc8bquoLoWYy7sDCXecwGe6PuGIcjv6s7CPpAwAREVvd895iBd/XVV/s1ZjXymeK47qkGkBMWKReMcSJmSmH2HgJJEJCBemIWHCHEXvR9REQIaVFAmMMKksAcXWqAVcixMA+m7yOxunsOTSTPR4QiIjSxOiielCN6D4IrhE/jKSuEOLzHk3kM/pA3CjmEMJiAmAmMV09gZUtsSNw+c+bMRov7mLnvvvvUivHI6YkpM1gFFMIqhD6sRo9cRKNGjXLlv4QwjBQCGEQiJ9HEiRNVhCGS1kO0vOaaa5RYiKfIGLxCSMRUfuTDhFCLfEYQ/SC+NdWGQICoh3phQ70h6KJ+xiJgexP0ELmLOmJ/82IHvgAxEQsq4Rp4/vnnXVPQYCsk4IetkVoBbYffIfi6D1SbA5LvQ9CHOIzrxj1CNFjXPeoM8R5TqhDpjB87iPbA1Dn417xgF4Cgimu2qbyphBBCCAlPEB0J0fOjjz5Ss1jwwH3Hjh1qnIj1BgAWmNobGLciTz/G1ljpHaIsFgzF+BXCKsaz+MzXh7m+jtX9AUEJTYHfEMbiqP6CXKMYi2LMifpj8VW0/5133lEBGkib4AmM0xGAgTH0iSeeqMpiPI/fAvh9gHEexnvNBb+XcDz4ddu2bcpfWEfirbfeUr8pbrnllgb7Y9z96quvqgVsMeb0Z9FUQkhgUEAlhAQFY0oLntB6E6owUEDeT08CKwaHmNqNZPkQN2fNmqXEPUT4QWgznp5DkIQghmlJGPBg6jdETYiiGNBh/4ULF6p8m97AIAjRssiLirqYV8l0f9qOQQ0GhlhdEyt0YsCKNmLQCIF1/PjxDQaeEFSxH9qBQQ6e7mPQh0EZBq+G6Ib6QwTGwBaCG6IRIbBi0Iy6ow1WCH6oNwZmOD9EXAwckUMJ6RAuu+wyr7lADSBqQoyE/ZqLkYsW+V5hH0T4YsoSBGfYEINn2BS2QG4u+PXaa6/1u63XXXedGuyjrRigWmFPcO+996pj45pFJCoEZqQowOeecu0atsNAmRBCCCHEDMZimP2EwAQIdO+++64S7JALFWMmpH9CJOjexE+MnzEmgRD7wAMPqCn7iGrF2BNptxA9idRbGAf6gq9jdX9AUEFTnHvuuQELqABjbQipGANj7AsREsd96KGHGj3wNoOH/ZhlBr8gYhRrOmC8irEzFgfzlK7JF3BerIEAH2OMDB8h0hdRre6LS2EMjzriAT1+fxBC7Ceizn25akIIIY4FkQsQht0H3IgoRTQuEuRfddVVTR4Dg3kIm5gCNXz4cItr7Dwg9CMKFbl897bwFyGEEEIIIQCRp4gcxlicEGI//OVGCCFhBJ7gI1LAPe+VsSiBL1GaKI9IWUQekOaBfFZYUAyRGhRPCSGEEEKILyDYAWmwmko/RgixFkagEkJIGIGpR8jpisjRY445Rol4mFL++eefy+GHH67yQvmSCwtTx5BqANPu3VcyJd654YYbJDs7W6V5oIBKCCGEEEKaAikTkAINuWuxhgTG7Mx/SkhooIBKCCFhBkRPLKCFxP/IIYscsFjZ8/zzz1d5Sn0F+UVzcnK0X3VVF/7880+V2xW5yLD4GSGEEEIIIU2B/PovvfSSyl+LXLb9+vULdZUICVsooBJCCCGEEEIIIYQQQogXOH+QEEIIIYQQQgghhBBCvEABlRBCCCGEEEIIIYQQQrxAAZUQQgghhBBCCCGEEEK84PtqIcRvli9fLlVVVWrF5bi4uFBXhxBCCCHEMioqKqS2tlZiYmJk0KBBoa4O8QOOXQkhhBASLlT4OHalgGoDGIBira6amhopLS0NdXUIIYQQQmwZ/5CWCceuhBBCCAk3qvYydqWAagN4eo8BaEREhCQkJDS5b3V1tURH+++WQMr7UzbQ+pLwsK1ubbG7PlaeL9jHZh9EnGhb3dri9D6orKxMiW8Y/xDnj12J3v0NaQj9oz/0kd7QP3pD//iHr2NXWtYGMPUJT+8xAO3fv3+T+65Zs0Z69erl97kCKe9P2UDrS8LDtrq1xe76WHm+YB+bfRBxom11a4vT+6AVK1aocQ+nfofH2JXo3d+QhtA/+kMf6Q39ozf0j3/4OnZlaAAhhBBCCCGEEEIIIYR4IaIOcarEFjU7MTFxr0/xkbg2kClvgZT3p2yg9SXhYVvd2mJ3faw8X7CPzT6IONG2urXF6X1Qc8Y9RE/oQ+f0N6Qh9I/+0Ed6Q//oDf1j7biHltWMzZs3h6y8P2UDrS8JD9vq1ha762Pl+YJ9bPZBxIm21a0t7IMIcS68J/SG/tEf+khv6B//QNwixE2rt02bNtlyntoWsgU7XpQ5UB22Ym0g5f0pyxV2rcNJttWtLXbXx8rzBfvY7IOIE22rW1vYBxHiXHhP6A39oz/0kd7QP82jvLxctm7dKpWVlUEX8zxRUVGhFkQi9WAxzNjYWMnKypL4+HgJFEagagZChkNV3p+ygdaXhIdtdWuL3fWx8nzBPjb7IOJE2+rWFvZBhDgX3hN6Q//oD32kN/RP88RTRIRC1LQrcybEQvIPsDvsDz/AH4HCCFTNaNOmTcjK+1M20PqS8LCtbm2xuz5Wni/Yx2YfRJxoW93awj6IEOfCe0Jv6B/9oY/0hv7xHUSe1tTUqMjHjh07SnS09fIbc6A2pLq6WrKzs12RwD169JBAoGU1A8p4qMr7UzbQ+pLwsK1ubbG7PlaeL9jHZh9EnGhb3drCPogQ58J7Qm/oH/2hj/SG/vE98hHT9gHEU0SGQti0eoNgaMd5IlvIBrvD/iAYaRQYgWojUL3XrFkj3bt3Vyo4HJiQkCBt27Z1dUTIKZKXlye7du1S77t16ybbtm1TZePi4iQzM1M2bNigvsvIyFAXRW5urnrfpUsX1zlwoXTq1EnWrVunvktPT5eYmBjZsWOHet+5c2fZvXu3lJSUqCchXbt2VX+jbKtWrdRTEpwX4IIrKCiQ4uJiiYqKUvVfu3atKxwa5XJyctS+yC2B/QoLC1W+iZ49e6o64ElISkqKpKamqraDDh06qPwcODbo1auXahtu+qSkJFXnLVu2qO/at2+v7AXbADw5QAJr2AvTCPAkzLAh7IknPWgfQH3xtAF1RbtwrI0bNzZ4grZz5071Cjts377dZW+0Z/369eq71q1bq/ab7Y1yWK0NtoVNzfaGD3AsAF+g7oa94VfYGqSlpanrwGxv2A/74nhoq2Fv2C85OVm1B+B6wH5me6O+aD/2w7HN9ka78vPz1XvsCzsY9kb7jKTg7dq1c12Lhr3hC0/XLP6Gf83XLK4Hw944r3Ft4ZpFPQ17w4awJ64Do3Mz2xu2Ml+zOIdhb5SFXQCuWfjLbG+003zNmu2NNhvv4eOioiK14X5CW83XLDazvXF+8zVrtjfqYb5mYQPUA+2H/2Az45qFLcz2Rp3M16y3PgLXLI5l2Bv1xDl97SPM9vbURxi+8tRHmO3trY+AfYD5msX15UsfYeTsaU4fYdQ3HPsI8zVrRR+B45ntbWUfYb5mregjUC/Yy9c+AteM+ZoNdh8Bn+B4Rh9h9MlW9BGwN+yH9gWjj/A0jsDxzH0E6koIIYQQEq5gLGaIdXZEnhLvGPY3fILfGv4SUWdXMoYwZsWKFeoHFX4Q9e/fv8l98aMLP978JZDy/pQNtL4kPGyrW1vsro+V5wv2sdkHESfaVre2OL0Pas64h+gJfeic/oY0hP7RH/pIb+gf38CD9lWrVqm/+/bta9u0ejzEpmDbfF/4Ou7hFH7NQJRKqMr7UzbQ+pLwsK1ubbG7PlaeL9jHZh9EnGhb3drCPogQ58J7Qm/oH/2hj/SG/iHhDAVUzTCm54WivD9lA60vCQ/b6tYWu+tj5fmCfWz2QcSJttWtLeyDCHEuvCf0hv7RH/pIb+if0FJbWyflFdXq1RNMo2QtjO0lhBBCCCGEEEIIIURD1m8tkNnz18qC37OlqrpWYqIjZeSQjnLSqJ7SPcuelArV1dXy5ptvyuzZs1WOf+TWHzBggFxyySUyYsSIZu2HPP8nnXSSHHXUUfLwww83OM8ff/whZ555ptx6660yadIk0QnmQNUsjxRC4rGghb8EUt6fsoHWl4SHbXVri931sfJ8wT42+yDiRNvq1han90HMn9nyoQ+d09+QhtA/+kMf6Q39Y38O1PlLtshjby0RrH1UY4o8jYqMEKh5100aJqOGdVKfBbpIkjew8OnkyZPVIqtTpkyRoUOHqsVn33//fXnjjTeUCHrCCSf4vB/44IMPlEg6ffp0OfbYY9VnWGT1lFNOkX322UeeeOKJoNSdOVAdjLGSbyjK+1M20PqS8LCtbm2xuz5Wni/Yx2YfRJxoW93aYkd9qovypCJnndqyl//k+tvbhv2dYFtCQg3vCb2hf/SHPtIb+sf+yFOIp7V1dQ3EU4D3+BzfYz9QVVVlST0gZq5atUreeustJXB269ZN+vXrJ1OnTpWTTz5Z7r33XikpKfF5PzB+/HgZN26cTJs2TbZt26Y+u+2229Qr9tMRTuHXDCj2oSrvT9lA60vCw7a6tcXu+lh5vmAfm30QcaJtdWuLHfUp/O1LyV/4jut99l72b3XYBGk98owWb1tCQg3vCb2hf/SHPtIb+sdeMG1fBZQ2MW8c389esFaumThMRVsGG4iyiCAdP368ZGZmNvr+mmuuUVPuY2JifNovPj7e9dndd98tJ554ohJYx44dK999950SX1NSUkRHKKBqhvlisru8P2UDrS8JD9vq1ha762Pl+YJ9bPZBxIm21a0tdtQndejRktR7uPp7+47t0r5de8l5+x6pLS2UyMRUyZx4R4P9o5LTHWFbQkIN7wm9oX/0hz7SG/oncL5fmi1vzl0pZRVNL/iE6fi7C/cuWCMS9ZtfNstvq3ao93ubwp8QFy1nj+svhwzO8qm+yFean58vw4YN8/h9+/bt1bZu3Tqf9jOTlpYmDz30kJr2v3jxYrnhhhtk3333FV2hgKoZ7heUneX9KRtofUl42Fa3tthdHyvPF+xjsw8iTrStbm2xoz7RKelqA5ltOquogIio+mEfXuMyezjStoSEGt4TekP/6A99pDf0T+B88N0a2bKjOOjH9UVsddVh3mqfBdSCggKX2BmM/dwZPHiwtGvXTrZv395gMSodYQ5Uzdi4cWPIyvtTNtD6kvCwrW5tsbs+Vp4v2MdmH0ScaFvd2sI+iBDnwntCb+gf/aGP9Ib+CZxTD+8tndolS0ZafJNb69S4Zh0X+2Pb23Fx7vGje/t+3Nat1Wt+fn5Q9nPnnnvukerqaundu7eKQMWiU7rCCFRCCCGEEEIIIYQQQiwGkZ++Rn8+PnOJzFuypdECUmaiIiNk9H6dVA5UiI/BTrPQuXNnadOmjSxZskSOPfbYRt+vXbtW7rvvPrn11lt93g9iKfjkk09U3tQZM2ZIp06d5LTTTlNT+u+8807REUagagYuuFCV96dsoPUl4WFb3dpid32sPF+wj80+iDjRtrq1hX0QIc6F94Te0D/6Qx/pDf1jLyeN6il1TSwgBfD9SSN7qr+jo4MfIxkZGamEzQ8++EBycnIaff/iiy/K8uXLpWPHjj7vZ0QzQyidOHGiHHXUUdKvXz+5+uqr1SJS8+bNEx1hBCohhDiA6qI8qSnO83l/LBhj5EckhBBCCCGEEKIX3bPS5LpJw+Sxt5YI1oYyR6Ii8hTiKb7HflZy6aWXysKFC2XSpElK5MRCUZiqP3PmTPnoo49k+vTpkpiY6PN+lZWVcu2110pmZqaKSDW48MILZf78+eozRKfqJthTQNWMnTt3SqtWrUJS3p+ygdaXhIdtdWuL3fWx8nzGsQt/+1LyF77jc7lWh02Q1iPP8Hq8QOtjV1ndri0n4STb6tYWJ/ZBhJB6eE/oDf2jP/SR3tA/9jNqWCfp0iFFZi9YKwt+y5aq6lqJiY6UkUM7qshTs3iKXKJWRKEmJCTIG2+8IS+//LK88MILsnXrVpUqYMCAAfL666/L/vvv36z9Hn74YVm9erW8++67DVIOINr1wQcflJNOOkluueUWdYwIKMeaQAGVEEIcQOrQoyWp9/AGn+W8fY/UlhZKZGKqZE68o1EEKiHE2VQX5EpNaZHI7mypyImUuppq9TleK3LWufaLSkyR6LS2IawpIYQQQgjxBkRS5DidMmGoVFbVSFxslO3CYmJiolx55ZVqC3S/22+/XW2eQC7UX3/9VXSEAqpmdO3aNWTl/SkbaH1JeNhWt7bYXR8rz2ccG9Px3afkR0RFu17jMns063iB1seusrpdW07CSbbVrS121Afi6eZnr5K6miqV8D7b9B0erGS/fKPrfURUjHS+7Cm/RFTdbBtK3nnnHbnjjjtk2rRpcuaZZ3rcp6KiQl555RWZM2eOyv2FqIu+ffuq/Y877jivx/a3HLEf3hN6Q//oD32kN/RPaImMjJD4OO8yXmxsrK31CTe4iJRmbN++PWTl/SkbaH1JeNhWt7bYXR8rzxfsY7MPIk60rW5tsaM+iDyFeOoL2E9FqjrAtqFi2bJlaspZU2Bl3MmTJ6v8X+vWrZOePXtKWlqa/PLLL3LdddfJ1KlTg1qOhAbeE3pD/+gPfaQ39I/eYAo/sQ4KqJqBQXKoyvtTNtD6kvCwrW5tsbs+Vp4v2MdmH0ScaFvd2qJbfQLBSW3xl0WLFqlFD0pKSprc795771VT0nr16iVffPGFWkzh66+/lueff17lDHvvvfdULrBglSOhgfeE3tA/+kMf6Q39oze1tbWhroKj4RR+zYiLiwtZeX/KBlpfEh621a0tdtfHyvOZj+3Kd7gHf/Idsg8iTrStbm3xVJ86LKNaVyt1tTUY/YrU1khdHV7xWS1u5PpX1+fG+1rXe3P5yp1bQtaWcKG0tFSee+45tcDB3n6wbNmyRT788EOVr+zRRx+Vzp07u74bPXq0WijhzjvvlKeeekpOPfVUtYhCIOV0xf3/lDdacl7ecL4nWgJO809tbZ3KhxgbE6Wm9joBp/nIadA/eqP7OKClQwFVM7KyskJW3p+ygdaXhIdtdWuL3fWx8nzGsc35Dt1pTr5D9kHEKts2Ev3cBL+6uhqTWGje1yQkmvdR3xvHqG14rD2fG8dIrqmW/OzfPIiPZpHSrT7menitj9vn5no0VaamRtY3OPaec7VAwvUeXLlypVx00UWSm5urVru95pprZNasWZKdbc42+w+zZ89W0+r23Xdf6devX6Pvx48fLw888ICaGvnzzz/LiBEjAiqnI039n3InkLy8oSZc74mWwLJtK+SV5e/I5JgJsm+H/tKSWb+1QGbPXysLfjetyD2ko5w0quGK3C0R3kN6Q//oTUxMTKir4GgooGrG+vXr1RStUJT3p2yg9SXhYVvd2mJ3faw8n3Fsf/Idevphyj7IPxpGDzYU4BoLcybRz124axCB6P0778duKCTWRy16/u4fMdJzfUpLiiUhPu6fzzzWx1s994iCJvGROBNd7kG7QWQoxNP9999frSLbv39/JaB647ffflOv2N/bog+DBg1SeU3NQqi/5XQkWP+ndCdc7wndwf/pmctmS3bRNvU6qH0/21ewDhbzl2yRx95aIqh+TW2d+gwi6rwlW+S7X7fIdZOGyahhnaSlwntIb+gfvcGik1hoklgDBdQWjMdpULuzpSIn0jHToAghoo046DHizi0yUIpypTI3zk2o8xTh52H6cU6OFJVudh27qchC433E7l2ya0Oqh2jCevGwqchCj5+bBb9mCJYtNXqwKfCzklmumklEJOZOSURklEhklEQY79VrlERgWlVklFRVV0tsXHz9+4h/Pkf5+rKRe77zcCzje+OYe17dy1SXFEjx71+H2iKOpUuXLvLKK6/IwQcf7NP+GzZsUK/mKfjudOrUSQmhxr6BlCOENGTpthWyNm+j+huveD8kc4C0xMhTiKe1amzW8DtDTMX3XTqktPhIVEII0Q0KqDYnXF6zZo10795dTfGqrKxUyf/btm0rmzZtUvvgaUFeXp7s2rVLve/WrZts27ZNlUW+kczMzPoBckmeRH7+H5E9OQ4NIJ26Tx6ri4yW1DPvkbjWHWTHjh2ugfju3bvVggeYeta1a1epqqpS9WvVqpWqB84LOnbsKAUFBVJcXCxRUVGq/mvXrlWiCsriGDk5Oa6QfuxXWFionupipVisGIvcYCkpKZKamuqa3tahQwcpKytTxwZ4koW2YapaUlKSpKenqwgP0L59e2Uv2Ab06NFDNm/erOqcmJgobdq0cdkQ9qypqVHtA6jv1q1bXU9jcKyNG+sHUCgHdu7cqV5hB0yDM+yN9uApG2jdurVqPyJOjB9PKIccaAiVh03RVoC6IyrEWKUQP25Qd8Pe8CtsDbCSLq4Ds71hP7QNx0NbDXvDfsnJyao9ANcDjmm2N+qL9mM/HNtsb7QrPz9fvce+sINhb7QPNgXt2rVT5zfbG77wdM3ib/jXfM3iejDsjfMa11ZGRoaqp2Fv2BD2xHUAe6HtZnvDVuZrFucw7I2ysAvANQt/me2NdpqvWXd7r/n7byV6ZXZoL0WFhVJcVChIHdWlc2fZtGG91NbALomSlJgoO7blqH3bZGRIeVmpFBcWqvedOnaUrdlb1L6I1EtKTJCduD7qaqVVaopUVVZKSXGx1FRXSmHJJtm1M1dqqqokNiZaHTdv9y61b3JSotRWV0tpaYlE1NWqOhYV5KvPoqMi1bFRR+wbHxurxMqKsnL1PjY6StYvrlbT9JsTR5E96z6JjolTdcRxIiMwEK+TiOpqWSu1Eqne1oucqJOvoA/yN/MiytbfXb6DNtf3IMR6IqROCXQREhkZLXURkfW/3SIjJTo6RmrUb7kIiYyKkqjoGKmqqVX7RsfEKlGvGu8jIyU+IVEqq6oEv/Mio2MkPiFBSnE9R0RKXHyC2qeiskqVTU5Jk7KKCqmpqVX7pqSmSkFhkdo3ITFRfVZcWqrep7fOkJLSMqmsrlbnz2jTRnbk7lLfRcXESEpKquTjPoqIlDZt26l9cezIqGjJ6thRtmRvVUJkckqKxCcmyc6du1Rd2rXvoPYtKS1V+3bt1l02bt6s6p+cmqaOm7Ntu6pvZlaW6qPM/9fQp1Xv6ZPRV6EvRZ/YrlMn1U+a+2T0q8b/tdYZGQ36ZPTV5v9r3sYR+L+G/xeFq5dJpPguoOJcXTI6NeiT0Zea/6+h73UfR6C++N+N/59GnxwOK8D26dNHbb5i/I/E/zZv4PoAxv/eQMqR0NGUr0hoQJ84a/nHagyKvyMkQl777V1pn3yZpMYlS0JMvETiYVQLANP2VeCsm3hqBt/PXrBWrpk4TFoivIf0hv7RG/x+JtZB69oIxCQj3B3ijxnjc/zogniCHyoG+AHjvi8WhMl2E0+9EVFbLalx0RKXmqoEOAOIb2YgFuLc7nUC+IFmBj/0jPpCfDPvix9++LFnAPHNU1sBflDih58BxDdv+wIIcAb4sdbUvubO3T1yw31f48eHN3ubMdvIPQeM+74Qjb3Z231fd3vjejHOZdjb076wd9s2GSoarraqQrpmdfgnSq6mQrq1S6//u7pE4iJrJLVVvHpfuWOjZMbVSl0skmQWSd2OfOkYsyfKbuc6ia6tlfg9kXilf+dKayNasKxGKnauknZGRF7+Gomsq5E2e94X71gmycg5uCd6sHB1jaSVl0lcbIzUrK8/Rqs9EXwFK2slpq5WYvZE/e38X60k76l7ZW2tVNTWShL2rauVXYvqxbyEPXXK3lM/fFeGOtbWSuyec27fU5foPcdaX1srUXvOUbTnO2OYvN0k3oFNpvdlezbju3rp4p/3W03vK/ZsxneFpu+w1UvG9X/jzi0w7VvqdlyjLMbHmPhcvOdv2XMO4ztg9ALNnoRWkq/KGuWMcbi392GBKdqvUQSgt8jABp8b7+sjBesjCz1FEyJ60HMZ4705stD9O29l3OvTKPKxqfp4OHZhUbGkpqc3jKLUgPrHXp755z9EPcZ/PON/q7nsP71+Pb06N8yH18r0L+af/5z19OjXMKqnV+o/7/E/0fx/DWJng3179VL1wf8HbMaDvOb+X/M2jjBI7Ny50QPVpsD/SeNBVlPHdR9H4H+Q8b/K2HfFihVK3CWNVy1uavEN4zsI2IGWcwLZr94ikfFJEhmXKJFxSRIZj1e3Ld79u6SG30VjkGMveHBL9I0+BXVSp6byX/3Zneo9hNXk2CRJjk2UlNhk9Zoch/dJkqI+T5KUPe/VFlf/eXx0nK1pALBgFHKeGpGm3sD33/5vs8THRkm79CRp2ypB2rRKkLbpCZKeGi9Rmi82xXtIb+gfEs5QQNUMRH6YBTpv1JQaEotv+LK/r+cOtIxfU4Ub5PRzy7NnzsPXYOqtlwVCjAU7PCxa0jB/n5cpxA3q52WBE7e6N+sce95XVVZIPv5BmVdf9lCPljJ1mFOB9SIiNl4iomMbTTGuqq6RWOTN2ZtQ52FaMt4XlZRISmqrvZRx/7z+GLm7dtc/fGlKmHT7PHtrjnTq3MVjfRrUvZFg2VBIVJ+10FxoVrFzS460attenECw/1c5rT6B4KS2WP2DE7M1fMHcF/lbzs7ZU0bU815nT+3Odj0o9InaGjW7Apu/1OF/Qky8RCemSJVEiez5OyYxRcqqakVi4yU1o71U1kVKWU2dRMQmSMduvWRr7i6pjYqT5NZtJDU9wzXzx5fZU3iPv8Nx9lRRUZFaAVqn2VOw4et/vyeRERH10949XSd1dVJUUay2HKmPsveFqIhIJajGRsRIYnSCpCWkKAE2orpOEqMSpGPbTKmrqJWomkhJjU+WXl17Sm72DomJiFb2bs7sKVwzGzbVLxjlC2jqpz80TusRGRkhaYnR0io5RomqmW1TJLquXL3v2aWDpCRESHVFscs35pkRGQHOjNhrH7EnWAbfma/Z5s6MMM+wNM9W83WGJa5ZXF+cYem5jzD6OKf0Ef7OsDRfs55mWMKGODZshjqi3bAT9kU9AL5DXXEufI73OIfqX6KiVJmyXTlSW1ak/FFbUys1tTX1+8bESkXlnn0joyQ6KU3qEtNcx8IrbIa/4XccF/Y2jmvUQR13z74A7TPvi814KB7jti+Oi++wr9E+87743JiV5G1f+PLYY49VaZGGDRvm2hf1w7mwL45l2MWIsDWOC5vBx0uWLFHHMdvQsDfeG2MpXJPwjXsf4evsKQqoLZTiVYubtf/uBTOlcvs6k9DYWAyM2L1bdq5O+Uf8a5S38B/BzsgvGFFSIlsXxbmJm+6Cny/ipnPzCPqLEX1IPGAIXg2i7EzRex4Fu39yDZZXVEp8YqJbdJ45+s9TxGDD9x7PEeEmyu2pU+7OXdKuXXu3CEOTmOflnI1FTHMexPrvN27aLN2695DKnZtl28x7fDZh1tn3SFxmw+hwgIFP5wASwxeuWSPt/Cyfu2aNpDa3bHWCxHdmIntC3EH+c6xk7uuK59ifWAd+DOMHtTGo94TxnXnWj7/l7Jw9ZbD32VORzYqKjk7vUC+iVpRKbUWZX2PECIw7K0qkuqLENaOiZs9mvC9qYnYJZoiURkZL9J4I13wV2YoIxPr3O9clSqoR7VqcKNX5SZJUlCed2vSWyMpCSYhLlPSunfc8sIxw/OwpiDgGTc2egrBhnq3mKVLfAIKJOVK/ObPVcM3+nvOXbC6uF8I80b1VZ+WbosoSKa4skbIq3x/519TVSkGFaT0KTBkyY1xMBsvrX2IioxtFuCbnmd5HJErulgJJTk1Un0XFxShRoH/f3hITvcpnEdVbFGtecZXa1m8rFVlZLxDVUy80xUZH7olY3a5e1d+tqqRtYYG0adVOvU+Mj2n2zIi99REGEK3M75s7M6KpfX2ZYelpX86w/Afz7FMn9BG+XrPNsTeuWQh2q1atcv0vhRDoKYoX97YZ8wJQWPMm9+UbfB7Ldb7sKamOS3EJnxAeDdxnspjrsLd9zXWKasa+7ikFPO1rfIbjmss2ZRf342IxT9j8lFNO8biv2f4Qv82+aO7sKQqomuF+E3sjue8IKf7tK5+PW7l1jezeWv9EyBsYSDb3GT8XGbFKDIRoFuFh8RDTvsbCI41EQPfvvIiBpuM2XuzEkxhoOocn4bHB+RsKfsiFGIvOsUlx001cbOocAUbZGE8D7SLBwvN1SWot0bGxUlNSYGsfZEV5f8oGWl8SHrbVrS121AeLR2IgjcUmq6qrJCY6RnLevkdF80UmpkrmxDuCstikbrbVFfwIgxBqRMl4woiKMf9A9recE2h/yvWuB32IWKmrLN8jppbUv5bjb9NWvufzBp81fO/Xg/ra6mZFwuJn2db57h9GN5GCwC39gPt+e9IYGCIsaX7uU2/Rp/gcuU/vH3Ozy7bVtTVSUllSL6hWlEpxZbEUV5ZKUUW9wGoIrcUN3pdKRbX3hxzuVNVWS15ZgdqaQ1xUrCQOjZHyskipq4oRqYmRuupYqauOEanG3/XvI2tjZN/uHeX4EX2luLhO8goqJTe/THLzymRnQZnszC+TgmLvQkFlda1s3VmiNm8kJcT8kxpgz6uRJgDvM9ISJCbav9Q//L+iN/SPfWAM54t4CrAf9o9NcU9oRYIJBVTNQOi3+1MfT0Qlumdk0wR3Ea7B1NumRbNG0249TCH2GInX4LjuEYEe6rGXcxjH8xR16C7qeRU3jc89iZtm4bIJMRDTGHy5FloCeZq1xdf7rCWcL9jHDvR4gZT3p6zdvgwnnGRb3dpiV30gimLbhT44M0siouqHfXj1FIHuBNvqCiJtMN3PmHboCWNapzl6xt9yTgNjpYi4BImMQ8SYfz8OlQhbVd608KqJCOuRQETYPXliw02EXbJ2UYPcp+5AVMX32G+/Xgerz6IjoyQtPlVtzaGypkpKlNBaL7gqkRUCq0fhtVSKIMxWlCgx1VcqaipFIiolMnHv+67A9lv931gkS0W3dkqSNj2SpFtckiREJ0hUbZzUVUdLVXm0VJRHSVlJhGCtxfz8Wtm9u1rKKrxf6yVlVWrbkOP9mk5PiWskrJoF1/QURIU1vh75f0Vv6B+9wVR0KwJ35s+fL0888YSa8o6I7FGjRsmtt96qopfx2SOPPCK//fabOv8hhxwiN998syt6HP9///vf/8pbb72lUiEgmveyyy6T448/vtF5cKxzzz1XHeOBBx5QkanfffedPPXUU2qmJKKYjzvuOLn88stVO8855xz5+eefVVm8fvvtt2IlFFA1A/kYfKG5OVBbHXJa/Y+lBoJfQ1Fxy5Zs6dyla5MRi+7C47r166Vnr95Biwwkzb8WWgK6tcXu+lh5vmAfO9DjBVLen7K6XVtOwkm21a0t7IPCj8GDB8s333yjcnR5mxnxxx9/qL+NHGCBlCNeRNjYBImMtUCENYmvu7ZlS1pinAfx9Z/I2dCJsFFexFez8OoMEbYqf4e8+eMrEhEbJXVN1Deirk7tt2+bXhLT6p8pw80lNipGYhPSJD2heTmhK6orGwitnoXXUineI8zifVF5sdSK79cQ0hJgyy0xT9f3AjI4pIhEdI6QNjEJEh+VILER8RKJVWerY6WmMkoqy6OltCRCiotEaqqiVdSrEQErNZAX6u2dV1ShttWbPUfQYzGrDENQTasXWSGsVpXly751Sep9ckJMi7jewgn+39cbX/OmNwfk3r3yyivllltukdGjR6s8ojfddJM8/PDDSsg844wz5OCDD5bXXntNpRV68MEH5eyzz5ZPPvlEpVh48cUXZcaMGTJ16lQ58MADlRiL8ki9YE4vgfy1559/vowcOVLuu+8+NdV+wYIFcs011yixFudAntp77rlH5cWFoAth9dJLL1VT8//973+L1VBA1QxzPolg5kCtLi2Q1n0PbPrc1XESl9kw78reiElIkoho3+pMrLkWWgK6tcXu+lh5PuPYwcp3GGhdAynvT1ndri0n4STb6tYWJ/ZBpGmOOeYYeeyxx5QQipxoffv2bfD9+++/rxaZQLTGAQccEHA5HXFCXl5fRNiCjRuljVsew+aKsN4jYAMVYWvUYiTYLBNhm0hDYGwRMdavXl9Rki/5URFNiqcA32M/7B+IgOovcdGxastI/CfX4t7ANbRqc6589OMK+XnlZqmJqJDouGrp3S1JenVLlJi42voIVyW8NoyAVQv2+nIOqZOSqlK1NQCz8hEBmygS3daTmBAh0XVxKr1AbWWMVFVGmVIMNEw3UFsdI7nFJbIjP1akFvkYTb76vH4xn7jYKJe46jFlQKsEiY+jpGEn/L8fOMUrfpS8+W9LbWVZk/vV1fgeoQ6QqgmzFfbWveJ/WPqoMyW5/0E+HReLkOGBbVZWlhpvYHvuuefUYlKIKkVE6n/+8x9X5OuTTz4pRx55pMyePVsmTZqkhFVElZ5++unqe0SNYuxiXrgJs2kgqiKyFQKp8T8C55kwYYJMnDjRlULirrvukvPOO0+VgQCLaxJ5Tu1IZcTeRjPckxMHKwcq9g/WuQMtQ8LPtrq1xe76WHk+49jmfIcG/uQ7DLSugZRnH6QXTrKtbm1xYh9EmgYD/pNOOkn9mJgyZYo888wzrgU0EImBKA6AKW3mhRH8Lacjnv5PeSOQvLyhZm/3RPAiYSuaFl/LveWLDQ8RFgs1XbklT0qi9p6HM7m6Vu3fUkC7+3VpJ7d0aacWhaqsqlFC495E6dq6WhWJ+k+EqznH6z8RrioHrEl4La0sU4Kqb9RJdUS5SDQ23Ms+lqqNqBdW9+R1NUTWmuoY2V4dI9vyYqQu1xzpume/2khJSYzdI6YmSptW8XtSBiS6hNaMtHiJ9uE6sIJl21bIK7+9I5OHTpB9O/QXJ8D/+4FTsGi2VO1qzrKKvuHrDAUspFiweLbPAmr//v3VdPtLL71ULZCG6fWIRB0zZoz8/fffMnDgwAZpA7APFv7Cd8jTnpubq2bUmLn44osbpCGaNm2aVFVVqYXJzH3ZX3/9JcuWLZP33nvP9ZnxIAjT/d0XSLOalvOfIkxYt25doxXdgpED1Zf9fT13oGVI+NlWt7bYXR8rz2c+tpHv0MCffIeB1jWQ8uyD9MJJttWtLU7tg0jTYOra6tWr1Y8B/BDp3bu3isDAlDWA6AojOiMY5XTE/f+UE7HjnqgXYeMlMjZeJKW1xSJsY+G1QU7YWvwUD5UIm+BVeK2tqpBW1bVqczLIIeprBCYWzEqKTVRb++S2zZoSjEhUs9DqKdWAu/AKsdZXIiLrRGIrJUKwuJX3xavcqauNlKrqGNlaHSPZEFZ3xErd1j0i6x5BFqkHkAO2dVKKtElOk8xWraRderIrghVbWnKcx3ysgYB7bOay2ZJduE29DmrfzxHpCPh/P3DSDjpZ8ubP9CkCtTlpWxA0U5+ysenrDA/w0kacJM3h0UcflSuuuEJNqf/xxx/lxhtvlP32289rRDL6DXzna8TyKaecIn369FHT/yHM4m/jOBdddJH63h0ItXZDAbWF4oRpUIQQQggh4QIWWpg5c6a88sor8tlnn6nFoZDfa8iQIWp62vjx44NajhBbRNjqSrdoVztF2GK1BYPts5+QmJR0iYhLlCgj2hWvrsjX+hywDV7jEurXi3A46G9S4pLVlpnie5qD6toaJarWC6/ec7z+I7zWR8BWVFf4fI6IyFqR2AqJiG26DGTZbXu2P8pF6rZESd2Gf9IJRNTGSnxkgiTGJEpqfJKkJ0JsTZV2qa0kq3W6dGrdWtqlpknMnuAEX1i6bYVrETO84v2QzAE+lyfOBZGfvkR/VuSsk+yXb/T5uJhxWJeepaazB5OlS5fKp59+Krfddpv06NFD5Sn9+OOPlYiKMcj333+vpvgbUahYaAwPeTF9PyUlRdq1ayfLly9X0/oNMKsG0aaYzg+wMNTw4cNlzpw5Kt/pO++8oxaQwkNj5DvtakqN89NPP6lFqRC1ivQBdkIBVTPS09MDmgZVUJAvaWmt/JoG5eu5Ay1Dws+2urXF7vpYeb5gHzvQ4wVSnn2QXjjJtrq1hX2QM/Fl5Vf8qMF0e2zNwd9yxH7C6Z5QImxMnETGxImkpAdPhG0kvpY0/sxNqPVLhDVRvWuL2pqLSsewR2RtILy6px7w8tpSFuXyh+jIKEmLT1Vbc9iWu13ikuObWFwLQmuxlJiEWbxW1e49sMggIqpGbRJX3kBkxYYlrzbhY2w7EXJpKlcbLdESL3GR8ZIYnSgpcUnSKiFFMpJTpW0qFhFLkWRE+cYkyutL35cIiVDpDyIjImTW8o9lcIf+Ld7f4dTHtUSsSOeDhaCQ6zQmJkY9uMVCUXig261bN7WI1BdffKHEVIxRIKQ+9NBD6jqBKAouueQSlc8d0/qHDh0q8+bNUwtk4sGw+8Ma5D9FtCkWnvrXv/6lpvpjEamnn35aHQ8LWGFmDqbuGxGoSUlJkp2drb7DYlJWQgFVM8y5I/yZBpWQXCRxKSmWnzuQMiT8bKtbW+yuj5XnM45dXZQnNcV5HhOP4xVPMM1EJadLtIcfO4HWNZDy7IP0wkm21a0tdtTH3CdElJZIRWWBX31CS7MtIaGG94QeImxFzlrZ9cWLzamJyt/Z7HNXlkkNNhHxXb4zERX9j/DaILrVLQI2voko2IjQ5Pe0iqT4RElJSJH0hLRmlausrnQtltVQeK1PPVBYUSK7SwqloKx4T4qBUqmoK5c68T3NQ11ktVRJsdqKq0V2VO/JOAChtQlq6+pUFOpvW/+UYR0HSkuGfZzeWCHQI/c6Vrt/+umnlZAKoXPEiBHywgsvqJy4b7zxhjzyyCNyxhlnqOsDOVLxPjW1/uHJ2WefrVIPPfHEEyofKoTX6dOnq8UvjRyoBog4hWiKcyFiddy4cWrf559/Xi0o1apVKzniiCPkhhtucJVBKqObb75ZTjzxRFm0aJGKXLUKCqiagRXOEOYcivL+lA20viQ8bKtbW+yuj5XnM45d+NuXkr/wHY/7IHeO+/SPVodNkNYjzwh6XdkHOQcn2Va3tthRH/c+ARE1/vQJLc22hIQa3hN6iLARzVwUKuuChyS2dVbD6NbyEqmBIOspRQFezX9XlCoRt9nUVEtNSYHa/CPin1ywTUa7ehBn90TNRkTHOOIeio2OlQxsienNEt+RMqBeeC2VXcUFkpOfL9sL8mVXSZHklxZJYUWxlEJsrS2XKoSlRlWJRFdJRETzBHese3P/3NclOftwteAVFrpCHlYsfKXe71n0Ki1Z76hk9nF6p23EQkxWCIiHH3642jwxYMCARtGkDeoWEaFEUWPhKDOIJF21alWDz66++mq1GRxzzDFq8wYWtMK0fjuggEoIIQ4gdejRktR7uM/7I9qMEBIefcLmzZv3umou+wRCSDgT4RIiE0RSM/w6BkQOl9haXtpQfG3w6ibEmnLDNj8Ktu6fNAaFewmD9ALSCDSOcnWPeG0svBrvkUu3pUbBQtiJj4lXW9ukDOme3lmkc9OCa2FJpezIK5Gtuwtka16eElt3FhfK7pJ6sbUsMlei2uR4OJdIRHKh5MkW2bWxrazc2HDmmEFsdKRk7Fncyljoql5oTZC26fXvE+NDI3rX1tZJRVWteg32olvE97SNnjDSNlaX+754G2k+FFA1Awp8qMr7UzbQ+pLwsK1ubbG7Plaezzg2pt76M/3W2/FCUZ59kF44yba6tcWO+pj7hE7pWRIX5AUFdLUtIaGG90T4oiLAktLU5g91dbVSV1nuEldrPES5NhBjjZywpn18iVRrdN7qSqnBVmKeq9AcIjxGtzZIP+AuwLqlI4DtWsI9BME1LTlObb07Y/G17g2+r8rfIbf/8IxsKK7PfdqIOpGULqsk4s94ya/1HMVZWV0rOTtL1OaNxPhoj8JqveCKyNZ4iYkOXhTi+q0FMnv+Wlnwe7ZUVddKTPQqGTmko5w0qqd0z/Lveif+p21sCqZYsBYKqJqRl5enViMLRXl/ygZaXxIettWtLXbXx8rzBfvY7IOIE22rW1vYBxHiXHhPtNypr6EGUZwRexaf8pdaVy5Yd9HVQwoCcwSsipgtlTpEsjabOldKA39RUbB7xNXqyBiJT2nVWIiNS2q8UJexGJeKgg1tRGR1Qa5889p1sr5DE9dShEhlQrFc0GmuDDrxPtlVkyg788skN69McvPL6v/e81paXp+33BP4buO2IrV5o1VynLRpIKw2fE1PjZcoH6JI5y/ZIo+9tURF0NbU1ovCEFHnLdki3/26Ra6bNExGDdNX9A43qqurKaJaCAVUzSgpKQlZeX/KBlpfEh621a0tdtfHyvMF+9jsg4gTbatbW9gHEeJceE/oPfXVU0oTY+qrE4iEEJkcK5Lcyq/ydbU1UosoWPe0A94EWJM4Wx8xWwoVt/nnRQRsMbb6ae1luc08QESkl9QDDSNjvQmwSoSNCkwaqS4plC9bxUsEFjVrQszF99hvdFy1dMzyft2VllcpMRXi6k43cdV4hZDpjfziCrWt2ew5shjiaeu0eGmT5h7B+k9k666CMiWeYhEs94BaQ0zF9106pDASVRNqa31fFI00HwqomhEdHR2y8v6UDbS+JDxsq1tb7K6PlecL9rHZBxEn2la3trAPIsS58J7Qe+prdEWkxGV2C1mddCciMkqJjNjEDz0MOUIhhnqPdjWLr6Ue0xXUVZb5ceJaqS0rVpu/qIXIGomv7nlfvYuzVXXVkh8d2aR4qqoaEaH2q66raXI/5Dnt2gFbqufj1NVJQXHlHkG1dI+oWi65eaUuwXV3Ybns0TkbAQFURb7mlcmKDV5sElG/+FVTYJ/ZC9bKNROHNb0jsYVQR2I7nYg63HnEUlasWCGlpaWSmJgo/fv3D3V1CCGEEEIsg+Oelg99SAgJFSoKtqLMLQrWswgL4RVpB2oa5IUtEaltWpy0BgijEVIStfcFtZKra2XAeQ9JXGYPS2tUU1MruwrL/4lg3RPNqiJb93yGRbECJSY6Ut5/8HiKd26RoMbq8n379pXIyJa50Fq4+GKFj+MePiLVjDVr1kivXr1CUt6fsoHWl4SHbXVri931sfJ8wT42+yDiRNvq1hb2QYQ4F94TekP/6M/adeuVj6ISkv0q74qC9SS6ui3GVeNRpC2Vuip/VjKvk1bV2PSZQh0VFSnt0hPV5o3yymrZVVAuO/MaCqvbd5fK0tW+5VJAKoEP562Rw4Z0UukASOgoLy+XeIsWDiUUUAkhhBBCCCGEEOIAEAWppuPHxImktPY/CtacA9b8aqQccBNnq4t2S/XurdLSiI+Nlo5tk9Vmpra2Tk67dU6TeVbNvDLnL7X17txKDhqUKQfvm9XomIS0dCigakZaWlrIyvtTNtD6kvCwrW5tsbs+Vp4v2MdmH0ScaFvd2sI+iBDnwntCb+gf/dHBRyoXbGKK2nylImedZL98oziFyMgIGTmko8xbssW1YJQvrN6cr7b/frZCLS6lxNRBWdI9K5VT/G0gKioq1FVwNBRQNSMhISFk5f0pG2h9SXjYVre22F0fK88X7GOzDyJOtK1ubWEfRIhz4T2hN/SP/tBH+nDSqJ7y3a9bmtwnMiJCbj53f9m8vUh+XJ4j67ILXN9t2laktllf/S3tWye6xNS+XdOVQEuCD3OtWgutqxnbtm0LWXl/ygZaXxIettWtLXbXx8rzBfvY7IOIE22rW1vYBxHiXHhP6A39oz/0kT50z0qT6yYNUyJplJvgiff4HN9juv4ZY/rKE9eNlhduO0ouPHEf6d+tYfoE5FT9aP5auenphTL5ni/k2feXytK/c6W6Rp+csS2BZdtWyLWf36VePVFVVWXZuaurq+W1116T8ePHy9ChQ2XEiBFywQUXyOLFi/3a74gjjlCLOhnbwIEDZfTo0XLnnXfK7t27RUcYgUoIIYQQQgghhBBCGjBqWCc1FX/2grWy4LdslRM1JjpSRg7tKCeN7KlEVjMdMpLk5FG91La7sFwW/5Eji5blyLK1O1VeVbC7sEI++3GD2lISY+SAfTqoyNQhfdpKbAynoDe1QNrMZbMlu3Cbeh3Uvp9taREqKipk8uTJkpOTI1OmTFHCKBasev/999XnDz/8sJxwwgk+72cAYRUbwH5///23PPLII3L22WfLrFmzJCXF9zQadkABVTM6duwYsvL+lA20viQ8bKtbW+yuj5XnC/ax2QcRJ9pWt7awDyLEufCe0Bv6R39aqo+QLzUiKkbqavYeAYj9mpNfNdRAJL1m4jCZMmGoFBQVS6vUZJ+Eu9ap8XLswd3VVlRaKT//uU0WLc+RJat2uBanKiqtkm9+2ay2+Ngo2b9/eyWm7te/nSTGx9jQupbD0m0rZG3eRvU3XvF+SOaABvvExsZacu4nnnhCVq1aJXPmzJHMzEzX51OnTpXi4mK59957VUTpjBkzfNovKSlJfZ6YmCht27Z17de5c2fp37+/HHfccfLiiy/KtddeKzpBAVUzCgsLA8r7Ekh5f8oGWl8SHrbVrS1218fK8wX72OyDiBNtq1tb2AcR4lx4T+gN/aM/LdVH0WltpfNlT0lNadFe94V4iv1bGshbWlleKhFpzRd/UxJj5cjhXdRWVlEtv67criJTf1mxTcoqatQ+5ZU18v3SrWqLjoqUoX3bysGDMmX4gA6Slhwn4R59Omv5xyptQm1dnXrF+8Ed+jcQs2tqaoKeBxVpARBBOn78+AaiqME111wjZ555psTExPi0X3x8fJPny8rKkjFjxsinn35KAZU0TVFRkbRv3z4k5f0pG2h9SXjYVre22F0fK88X7GOzDyJOtK1ubWEfRIhz4T2hN/SP/rRkH0EUbYnCqN3+SYiLlkMHd1RbZVWNLF2dqyJTF/+xTUWqAuRG/eWv7WqDcDuwR4YSU0cMypSMtJYnsJtZtPlXeWf5HCmrLve5TGVNlRRXlrjeQ0RFFOqFH90osVExDYTWvUUHJ0THyxmDTpARnYf5dO7NmzdLfn6+DBvmeX9cD9jWrVvn036+0KdPH5k9e7aUlJS4olV1gAKqjSCnw5o1a6R79+6SnZ0tlZWV6ukaQpY3bdrkSribl5cnu3btUu+7deumEmmjbFxcnFLyN2zYoL7LyMhQTxdyc3PV+y5duqicEzgHQrc7deqkLmKQnp6ungjs2LHDFRqNxLy4IKOjo6Vr165SWlqqyrZq1Uo9FTASeGMaRUFBgQq5joqKUvVfu3atujnRBhwDOS6MpwXYD08OceP27NlT1aG2tlblr0hNTVVtBx06dJCysjJ1bNCrVy/VNtgANwnqvGVL/ap/uNFwLtgG9OjRQ93IeBqCsO82bdq4bAh74smLkXgY9d26dauyDdqFY23cWB/6jnJg586d6hV22L59u8veaM/69evVd61bt1btN9sb5WA32BY2NdsbPsCxAHyBuhv2hl9ha5CWlqauA7O9YT8cF8dDWw17w37JycmqPQDXA45ptjfqi/ZjPxzbbG+0C50awL6wg2FvtA82Be3atVO2NdsbvvB0zeJv+Nd8zeJ6MOyN8xrXFq5Z1NOwN2wIe+I6gL3QdrO9YSvzNYtzGPZGWdgF4JqFv8z2RjvN16zZ3qiv8R4+xkAAG+4ntNV8zWIz2xvnN1+zZnujHuZrFjZAPVAG/oPNjGsWtjDbG34wX7Pe+ghcsziWYW+Ac/raR5jt7amPMHzlqY8w29tbH4FjA/M1i+vLlz4C7UX55vQRRn3DsY8wX7NW9BHY32xvK/sI8zVrRR+BusJevvYRuGbM12yw+wjsg+MZfYTRJ1vRR8DexvgjGH2E+zgCbcHxzH0E6kpIuMIVkPWG/tEf+ii8/IOcp4gwxXbFabXy5/pdKjJ10R85squgXmBE7tRla3aq7bkPl0vfrukuMTWrTbK0ND5e+ZVkFwVnsTSzqNrcOvgqoBrj2rS0tKDs5wv4TQMwVtdJQI2owyibWMqKFSvUDwz8IEI+B0IIIYQQp8JxT8uHPiSEEBJKIJqu3pynIlN/XJYjObs8C4XdMlOVmHrQvlnStUOKbYsq7Q08aEcuUIAV5s3C8+LNS2TW8k98jkB1jz51Jzk2qUEU6t5obgQqAkuOPvpomT59uhx77LEB72eAXKinnHKKXHXVVY2+e+mll9SCU0uWLAlYQG3KF80d9zACVTMQvYEolFCU96dsoPUl4WFb3dpid32sPF+wj80+iDjRtrq1hX0QIc6F94Te0D/6Qx/pjV3+wbT9vl1bq+284wbIxm1FsmjZVvlxeY5syCl07Ye/sb315SrJbJNUL6YOypTendPVMXQEwqWv4iXiHW/76iEprSpV0/bdQS7U9klt5P4xNyvxGLOO9pZjtLlg1hFmOC1ZssSjMIpr4r777pNbb73V5/169+7d5Dn//PNPNYtKp+hTwPh4zQg0IDiQ8v6UZQCzdTjJtrq1xe76WHm+YB+bfRBxom11awv7IEKcC+8JvaF/9Ic+0ptQ+AfCICJNzxzbT5664XB5/tYjZfLxA9RUfjM5O0vk/e/WyA1PLpQL7v1Snv9gmSxbkys1NbXSUlm6bYXKdepJPDXnQsV+VoGIzdNOO00++OADV4orMy+++KIsX75cpdrydb+mQNqyb775Rk444QTRDUagaoaR6yEU5f0pG2h9SXjYVre22F0fK88X7GOzDyJOtK1ubWEfRIhz4T2hN/SP/tBHeqODf5D3dPzhvdW2q6BMFmOa//Ic+WPdLjX1HyB/6pwf1qstJTFWRgzsoCJTh/RpKzHRUdJSxOpZyz+WCImQOvEuXON77De4Q3+V298KLr30Ulm4cKFMmjRJrr76arVQFPL4z5w5Uz766CM1bR/T333dzwDT5o1c/IiexVT7xx9/XOXhnzx5sugGBVTNwCIToSrvT9lA60vCw7a6tcXu+lh5vmAfm30QcaJtdWsL+yBCnAvvCb2hf/SHPtIb3fyTkZYgxx3aQ20FxRXyy1/blJj626pcqd4TeVpUWilf/bxJbQlx0TK8f3s5aN9M2a9fe/VeV6prq2Vn6e4mxVOA73eV5an9rVqEDYuWvvHGG/Lyyy/LCy+8oBZQRaqAAQMGyOuvvy77779/s/YzwH7YABYrxWKnmP5/wQUXaDd9H3ARKc0S8WOVXKza6y+BlPenbKD1JeFhW93aYnd9rDxfsI/NPog40ba6tcXpfRAXIGr50IfO6W9IQ+gf/aGP9Kal+Ke0vEp+XbFDfly+Vf63YruUV9Y02icmOlKG9W2nIlMP2KeDilQNFr4sXOQLEFALy4v3ul9afIpkJKZbkgO1pVPLRaQIIYQQQgghhBBCCGlIYnyMHDa0o9oqqmpk6d+5Skz96Y9tUlxWpfapqq6Vn/7cpjYsOLVvzzYqMnXEwExpnaqHCNkmsbXaiB5QQNUMhCyHqrw/ZQOtLwkP2+rWFrvrY+X5gn1s9kHEibbVrS3sgwhxLrwn9Ib+0R/6SG9aon/iYqJUhCk2TOv/c+0uJaYu/iNHdhdWqH2QO/X31blqe+6DZdKva2sVmYqtQ4Z+U8m9gWnwxDqsSZBA/KakpCRk5f0pG2h9SXjYVre22F0fK88X7GOzDyJOtK1ubWEfRIhz4T2hN/SP/tBHetPS/RMdFSmD+7SVy04dLK/cMVYevvIwOXlUT2nf+p+FjZDkcsWG3fLyJ3/Kxfd/LVc/Ok/e/mqVbNpWqBZ20n26OrEOCqiaUVhYGLLy/pQNtL4kPGyrW1vsro+V5wv2sdkHESfaVre2sA8ixLnwntAb+kd/6CO9cZJ/MG2/f/fWcuGJA+WF246SJ64bLWeM6SNdOqQ02G/d1gJ5c+5KueKR7+Syh76V/372l6zenKelmFpT0zjXKwkenMKvGRERESEr70/ZQOtLwsO2urXF7vpYeb5gH5t9EHGibXVrC/sgQpwL7wm9oX/0hz7SG6f6B+3q0TFNbWeP6y9bdhTJouU5alu9Od+1X3Zusbz7zWq1tWmVIAfvmebfv3uGREU60zbkHyLqdJTNHQZXMiWEEEJIuMBxT8uHPiSEEELq2ZFXqvKlQkz9a90uqfWgoKUlx6rFpyCm7turrURHRaiV3yG39ezZU2JjY0NRdSIilZWVsnbtWiWS9+3b1+NDAF/HPZzCrxnr168PWXl/ygZaXxIettWtLXbXx8rzBfvY7IOIE22rW1vYBxHiXHhP6A39oz/0kd6Eo3/apSfKiYf1lAcuP1Reu3OcXHn6ENmvXzslkhoUFFfKF4s3yrQXFsvZd34uj721RApLa5SAmp2drUQ85Ce1eisrK7PlPLUtZIPdYX8AETvQCGpO4XdYzopAyvtTljk2rMNJttWtLXbXx8rzBfvY7IOIE22rW1vYBxHiXHhP6A39oz/0kd6Eu39apcTJ2BFd1VZSViW/rNgui5ZvlV9X7pCKynrblJZXy7wlW2TZ6mg58cB06dS2SopLStUUf6tTIEA0jIxknKQ7UVFRkpWVJYFCAVUzkpOTQ1ben7KB1peEh211a4vd9bHyfME+Nvsg4kTb6tYW9kGEOBfeE3pD/+gPfaQ39M8/JCXEyOhhndRWXlktv63KVWLqz39uk5LyatldVC0f/5QnR+xbI2lJUUpATYyLVuWwRUdFWiJwU0D9BwjWiDyFeBofHy+BQgFVM9LS0kJW3p+ygdaXhIdtdWuL3fWx8nzBPjb7IOJE2+rWFvZBhDgX3hN6Q//oD32kN/SPZ+Jjo1X+U2xV1bWyfO1OlTN18fIcee+H3YK406iof/ZHIGq/rhly4MAOcuCADtKudWJQ6oEp/AkJCUE5llME1IggRv1SmtYMIz9DKMr7UzbQ+pLwsK1ubbG7PlaeL9jHZh9EnGhb3drCPogQ58J7Qm/oH/2hj/SG/tk7MdGRMqxvO7nitMHy6p1j5cErDpUTR/aU1qkJUl0jaquqFlm+dpe8OPtPufiBb+T6JxbIe9+ukezcEhVB6u+Wk5MTUHmnbRFBTpnACFRCCCGEEEIIIYQQQoIIpu3v0yNDbReeuI+szS5QkamY6r95e7FrvzVbCtT2+ucrpHP7ZDloUJaKZu3ZMc3yvKnEdyigakaHDh1CVt6fsoHWl4SHbXVri931sfJ8wT42+yDiRNvq1hb2QYQ4F94TekP/6A99pDf0j/9ACO3VqZXazjmmv2zeXuQSUyGeGkBY3bz9b3nn67+lXXqCS0zt1621EmSbgv6xFk7h14zy8vKQlfenbKD1JeFhW93aYnd9rDxfsI/NPog40ba6tYV9ECHOhfeE3tA/+kMf6Q39Ezw6t0+RCUf1kenXjpYXp46Ri04aqCJVzQGnO/LKZPaCtXLLjO/l/Lu/kBnvLZUlq3ZIdU2tx2PSP9ZCAVUz8vPzQ1ben7KB1peEh211a4vd9bHyfME+Nvsg4kTb6tYW9kGEOBfeE3pD/+gPfaQ39I81tG+dKCeN7Knypb5251iVPxV5VM0Rp/lFFTJ30Qa58/8Wydl3zpXH3vpVRbCWV1b/sw/9Yymcwk8IIYQQQgghhBBCSIhJT4mXcQd1U1txaaX8smK7Ekp/XblDKqtq1D4lZVXy3a9b1BYXGyX79WunpvpnxNV/T6whoq6urs6iY5M9rFixQkpLSyUxMVH69+/f5L5wRyBJggMp70/ZQOtLwsO2urXF7vpYeb5gH5t9EHGibXVri9P7oOaMe4ie0IfO6W9IQ+gf/aGP9Ib+CR3lFdVq+v6Py3LklxXbpLT8n8hTg+ioCBncu60SU0cM7CBpyXEhqatTxz2cwq8ZGzduDFl5f8oGWl8SHrbVrS1218fK8wX72OyDiBNtq1tb2AcR4lx4T+gN/aM/9JHe0D+hIz4uWg7eN0tuOHs/eeOucXLnRSPk6AO7SmpSrGuf6po6Fan69Lu/y7nT5sptz/wgnyxcJ7l5ZSGtu1PgFH7NqK6uDll5f8oGWl8SHrbVrS1218fK8wX72OyDiBNtq1tb2AcR4lx4T+gN/aM/9JHe0D96EBMdJfv3b6+2y08bLH+t36Wm+S9csknyS+p9VFsnsnztTrX930fLpU+XVioy9aBBmdKxbXKom9AioYCqGUlJSSEr70/ZQOtLwsO2urXF7vpYeb5gH5t9EHGibXVrC/sgQpwL7wm9oX/0hz7SG/pHP7DQ1KCebdR2/AEZUlydoMTUH5dtla07S1z7/b0pX22vffqXdO2QosTUg/fNlG6ZqUzL4CMUUDWjdevWISvvT9lA60vCw7a6tcXu+lh5vmAfm30QcaJtdWsL+yBCnAvvCb2hf/SHPtIb+kdvMjIyJCsuTvp0SZdzj+0vm7YXKTF10bIcWbe1wLXfxm1FsnHbKnn7q1XSISOxXkwdlKnKRUZSTPUGc6BqxubNm0NW3p+ygdaXhIdtdWuL3fWx8nzBPjb7IOJE2+rWFvZBhDgX3hN6Q//oD32kN/RPy/EPokq7dkiViWP6yhPXj5YXbjtKLjhhH+nfraEIvm1XqXw4b43c+NRCmXzPF/Ls+0tl6d+5Ul1TG4IW6A0jUAkhhBBCCCGEEEIIcSgdMpLklNG91LaroEx++nObikxdtnan1CJhqojsLqyQz37coLaUxBg5YJ8OcvCgLBnSp63ExkRJuEMBVTPatWsXsvL+lA20viQ8bKtbW+yuj5XnC/ax2QcRJ9pWt7awDyLEufCe0Bv6R3/oI72hf5zhn4y0BDn24O5qKyqtlJ8hpi7PkSWrdkhVdX3kaVFplXzzy2a1JcRFyX792isxdb/+7SQxPkbCEQqomlFVVRWy8v6UDbS+JDxsq1tb7K6PlecL9rHZBxEn2la3trAPIsS58J7QG/pHf+gjvaF/nOeflMRYOXJ4F7WVVVTLryu3q8jUX1Zsk7KKGrUPXr9fulVtMdGRKiIVOVMP2CdTUpNiJVxgDlTNyMvLC1l5f8oGWl8SHrbVrS1218fK8wX72OyDiBNtq1tb2AcR4lx4T+gN/aM/9JHe0D/O9k9CXLQcOrij3HjO/vLGXcfIvy88UMYc0EWJrAaIUP3lr+3yxKzf5Zxpc2Xqsz/Ip9+vU2kBnA4jUAkhhBBCCCGEEEIIIQrkPB0+oIParjitVv5cv0tFpv64PEd2F5arfZA7ddmanWp77sPl0rdruopMPWhQlmS2SRKnEVFXV1efLZZYxooVK6S0tFQSExOlf//+Te5bW1srkZH+BwYHUt6fsoHWl4SHbXVri931sfJ8wT42+yDiRNvq1han90HNGfcQPaEPndPfkIbQP/pDH+kN/aM3dvintrZOVm/OUzlTf1yWIzm7Sjzu1y0ztV5M3TdLunZIkYiICGnp4x5e+ZqxZcuWkJX3p2yg9SXhYVvd2mJ3faw8X7CPzT6IONG2urWFfVB4T6175JFH5JhjjpFBgwbJ0KFDZfz48fLSSy9JZWWl13IVFRXy3HPPyfHHH6/KDR8+XM4++2z59NNPba0/2Tu8J/SG/tEf+khv6B+9scM/kZER0rdrazn/+H3k+VuPlKduOFwmHd1XCaZmNuQUyltfrpKr/vOd/OvBb+TVOX/Kqo27lQDrK9i3vKK6WWWshFP4NaOpwbPV5f0pG2h9SXjYVre22F0fK88X7GOzDyJOtK1ubWEfFJ5s3rxZiZ7btm2TqKgo6dq1q7LfX3/9JX/++ad8/vnn8uqrr0pycnKDcuXl5XLBBRfIr7/+qsr16dNHiouL5ZdfflHbjz/+KPfdd1/I2kUawntCb+gf/aGP9Ib+0Ru7/RMREaGEU2xnju0nW3cWy2JEpi7PkVUb/8nHmrOzRN7/bo3aMtLi5aCBiEzNlH26Z0hUVOO4zvVbC2T2/LWy4PdslXMVC1eNHNJRThrVU7pnpUmooIBqIxgAr1mzRrp37y7Z2dnq4k5ISJC2bdvKpk2bXBcgohN27dql3nfr1k0NtFE2Li5OMjMzZcOGDeq7jIwMFZ6dm5ur3nfp0kWqq6vVOWJjY6VTp06ybt069V16errExMTIjh071PvOnTvL7t27paSkRKKjo9Ug3qhfq1atJD4+Xp0XdOzYUQoKCtRgHQN31H/t2rWC7A8IEccxcnJy1L5ZWVlqv8LCQtWWnj17qjpgv5SUFElNTVVtBx06dJCysjJ1bNCrVy/VNrQhKSlJ1dl4gtK+fXtlLyMpco8ePdQPEawyhzDrNm3auGwIe9bU1Kj2AdR369atKnoD7cKxNm7cqL5DObBz5071Cjts377dZW+0Z/369eq71q1bq/ab7Y1yCPWGbWFTs73hAxwLwBeou2Fv+BW2Bmlpaeo6MNsb9kMdcDy01bA37IcfVmgPwPWAY5rtjfqi/dgPxzbbG8fMz89X77Ev7GDYG+2DTUG7du2Ubc32hi88XbP4G/41X7O4Hgx7G+dFe3HNop6GvWFD2BPXAeyFtpvtDVuZr1mcw7A3ysIuANcs/GW2N9ppvmbN9sZnxnv4uKioSG24n9BW8zWLzWxvnN98zZrtjXqYr1nYAPVA++E/2My4ZmELs73hB/M1662PwDWLYxn2RrtxTl/7CLO9PfURhq889RFme3vrI9BmYL5mcX350kdgQ92a00cY9Q3HPsJ8zVrRR8AWZntb2UeYr1kr+gjYBvbytY/ANWO+ZoPdR8DOOJ7RRxh9shV9BOyNY6J9wegj3McRaAuOZ+4jUFfSmJtuukndx/D1U089pV4BRNArr7xSli9froTQBx54oEG5e++9V4mnuJ4QhQrbg3nz5sk111wj7733ngwZMkROP/30kLSLNAT3JNEX+kd/6CO9oX/0JtT+yWqTLOMP7602LCpliKl/rN0pRhDproJymfPDerVhcaoRAzvIQYMyZUifthITHSXzl2yRx95aIpjxX7OnEETUeUu2yHe/bpHrJg2TUcM6haR9zIGqWR4p/BjCjxZ/CaS8P2UDrS8JD9vq1ha762Pl+YJ9bPZBxIm21a0tTu+DmD+zMXgoc/TRR6u/33rrLdlvv/0afP/uu+/K7bffrmwJQRUPFwAE97FjxyoR/qOPPpJ+/fo1KPf222/LnXfeqR78QFANVt4z+tA5/Q1pCP2jP/SR3tA/eqOrfwqKK+TnP7cpMfX3v3Oluqa20T4JcdHSv1tr+e3vHdKUShkZESGPXzcqqJGozIHaQjEiSEJR3p+ygdaXhIdtdWuL3fWx8nzBPjb7IOJE2+rWFvZB4YcR0QzcRVCw7777un74GBHYYPbs2SqiF3lPPZVD/lSIrYiu/vnnny2rP/Ed3hN6Q//oD32kN/SP3ujqn7TkOBlzYFe586IR8ubd4+TGs/eTQwZnSXxslGufsopqWbKqafEUIDJ19oL6WU92QwGVEEIIIYQQC0EqCHOUgzsrV65Ur0iTgJQNBr/99pt63X///T0eF1EmEFcBBVRCCCGE6E5ifIyMHNpJbjl3uLxx9zFy++QD5Ij9O0tSvG8ZRjGtf8Fv2Splld1QQNUM5DELVXl/ygZaXxIettWtLXbXx8rzBfvY7IOIE22rW1vYB4UfyC976KGHqr8x5d7I5QuWLVsmjzzyiPr7rLPOajD1zshXa+Q99QRy1Zr3JaGF94Te0D/6Qx/pDf2jNy3NP3ExUXLgwEy59sxh8uLtY3wuh5yoFVU1YjdcREozsChFqMr7UzbQ+pLwsK1ubbG7PlaeL9jHZh9EnGhb3drCPig8eeKJJ2Tq1KnyxRdfyHHHHacW3oL9kB8VC3xdcsklalEoM8ZiYFg0zRtYnAwYi7qR0MJ7Qm/oH/2hj/SG/tGbluyfxLgYiYmOVOLo3sB+EF/thhGommEMlENR3p+ygdaXhIdtdWuL3fWx8nzBPjb7IOJE2+rWFvZB4QkWeBowYIASPLEo1Lp161TUKKagJSUlSUpKSqMfPuXl5eoVAqs3jO/KysosbgHxBd4TekP/6A99pDf0j960ZP9ERkbIyCEdJSoyosn98P3IoR0lAslQbYYRqIQQQgghhFhIcXGxTJ48WU3X7927t5qyj7ymVVVVsnDhQnnwwQfl0UcflV9//VVmzJgh0dH1Q/SoqCifo0ms+CEBAXfNmjXSvXt3yc7OVotcJSQkqCmCxkIVbdq0USKw8aOtW7dusm3bNlUW4m5mZqYrvUBGRoYSknNzc12pDfA3xF+kLkA6AgjLID09XeWE3bFjhyuNwe7du6WkpETZBxG8a9fWLyIBURqLaeG8oGPHjlJQUKDsDhui/tgX9UxNTVWCtbGwF/LTYr/CwkJlw549e6o6wO4QtbE/2g46dOig6opjg169eqm2YaEvHBN1Rv1gs/bt2yt7GZHBPXr0kM2bNyufY5Vf2M2wIewJUR3tA6jv1q1bpaKiQrULx0KksmFvYCw2BjtgETHD3miPkSICkctov9neKIeVhmFb2NRsb/gAxwLwBepu2Bt+RbtAWlqaug7M9ob9ioqKlH/RVrO9k5OTVXsArgcc02xv1Bftx344ttneaFd+fr56j31hB8PeaB9sCpA7GLY123vLli2NrlmcG/6Df83XLK4Hw944r/maRT0Ne7tfs2i72d6wlfmaxTkMe6Os+ZqFv8z2RjvN16zZ3rhmzNcsbG22t/maxWa2N85vvmbN9kY9YCeA6ww2MNsbNjOuWdjCbG/4wXzNBquPwPmMtjuxjzDbuyX2EUYf59Q+wrB3S+0jYDscr6X2EUO7x8p3vzad2xT7Du0Wq9ofrD4CdfWFiLpQZF4NM7BYAC5KXFT9+/dvcl84zhg0+0Mg5f0pG2h9SXjYVre22F0fK88X7GOzDyJOtK1ubXF6H9SccU+48Pjjj8uzzz6rfrh+9NFH6seOGfzgOOmkk9Sg/t5775XTTz9dfX7AAQeoHzWY/j9u3DiPx4b4+sorr8ghhxwiL7/8clDqSx86p78hDaF/9Ic+0hv6R2+c4J/5S7bIY28tETwXxoJR5shTqJfXTRomo4bV538PFr6OeziFXzOMJ12hKO9P2UDrS8LDtrq1xe76WHm+YB+bfRBxom11awv7oPBj7ty56vWcc85pJJ4aURGnnnqq+vuTTz5xfY7oCWBEe3jCiKZpKk8qsQ/eE3pD/+gPfaQ39I/eOME/o4Z1ksevGyWj9+ukcp0CvOI9Pg+2eNocWrY07UAQEh2q8v6UDbS+JDxsq1tb7K6PlecL9rHZBxEn2la3trAPshdMLcMUOAiNmPaGKWaYLoa/7cKYKofpYt7AtDlgTE00psdhmqD5M3eMqXWYYkhCT0u4J8IZ+kd/6CO9oX/0xin+6Z6VJtdMHCZTJgyVyqoaiYuNCknOU3cooGoGcmqEqrw/ZQOtLwkP2+rWFrvrY+X5gn1s9kHEibbVrS3sg6wHYum7774r8+fPl+XLlysR1QzyZCEH6ciRI+WEE06wPHoT+cbwo8YQUj1h5FpDzjGDwYMHyzfffCNLlizxWAa5xP744w/197Bhw4Jeb+Kce4LUQ//oD32kN/SP3jjNP5GRERIfp49sqU9NiAIJiUNV3p+ygdaXhIdtdWuL3fWx8nzBPjb7IOJE2+rWFvZB1oFFHJBvFNPgsVjCoEGDZPz48WqKPIRJfIbp8FjYYunSpfLwww/L9OnTVf7Ryy67zLL2jBgxQubMmaNE3YkTJ6pFDdyF0I8//lj9fdBBB7k+P+aYY+Sxxx5TAuqqVaukb9++Dcq9//77agENLFKBfKkk9Oh2T5CG0D/6Qx/pDf2jN/SPtTAHqmYYq7mForw/ZQOtLwkP2+rWFrvrY+X5gn1s9kHEibbVrS3sg6zh9ddfVwstYbr7Aw88IL/88ou89dZbMm3aNLnwwgvljDPOkDPPPFMJpXfddZdazGnx4sVy2223qZVcjzvuOHUMK8A5sZouRNBrr73WteIxwN9XXnmlWvUWqxFPnjzZ9R1Wl4W4C+F3ypQprtViAaJrIQAbx2/pi0Y4BZ3uCdIY+kd/6CO9oX/0hv6xFo60CCGEEEJIi+fbb79Vq9Dvu+++zZpaP2HCBLX973//kyeffFIt9BRskN8UkbHXX3+9fPXVV/Ldd99Jjx49JDIyUom3WDW3VatWMmPGDGnfvn2DslOnTpXVq1fLX3/9Jccff7z07t1bRZ1u3LhRfY+I1tNPPz3odSaEEEIIIf9AAVUzsLBBqMr7UzbQ+pLwsK1ubbG7PlaeL9jHZh9EnGhb3drCPsgaXnnllYDKIyfqf//7X7GKI444Qk3Tf+2112ThwoVKAMWCCFj8afTo0XL++edL27ZtG5VLS0uTmTNnqvZ99tlnKroEwuuQIUOU8IsUBUQfdLonSGPoH/2hj/SG/tEb+sdaKKBqRqAriwVS3p+yOqyE5lScZFvd2mJ3faw8X7CPzT6IONG2urWFfVD4glyst99+u1+LQmCaPjaiN7wn9Ib+0R/6SG/oH72hf6yFOVA1A4sfhKq8P2UDrS8JD9vq1ha762Pl+YJ9bPZBxIm21a0t7INCw6JFi5R4eckll8gdd9yh8p8SEs73RDhC/+gPfaQ39I/e0D/WQgGVEEIIIYQ4GiwmdfHFF0tOTo7Ke4qcolisCZ8TQgghhBCyNyLq6urq9roXCYgVK1ZIaWmpJCYmSv/+/Zvct7KyUmJjY/0+VyDl/SkbaH1JeNhWt7bYXR8rzxfsY7MPIk60rW5tcXof1Jxxj12MHTtWrr32Whk3bpzrM6xgj5yi8+bNC2nddERHH7YUdOtvSEPoH/2hj/SG/tEb+sfacQ8jUDUjNzc3ZOX9KRtofUl42Fa3tthdHyvPF+xjsw8iTrStbm1hH2QN5513nixZssTjd1jl3j0vGN7X1NTYVDsSLuh0T5DG0D/6Qx/pDf2jN/SPtXARKc0oKysLWXl/ygZaXxIettWtLXbXx8rzBfvY7IOIE22rW1vYB1nDwIED5cILL5ShQ4fKlClT1Cr1BpMmTZLrr79e3nnnHWndurVkZ2fLb7/9pqJSCXHqPUEaQ//oD32kN/SP3tA/1sIIVM0INNw6kPL+lGV4uHU4yba6tcXu+lh5vmAfm30QcaJtdWsL+yBruPHGG+Wbb76RPn36yPnnn6/E1GXLlqnv8PeMGTOkXbt2kp+fL926dZNnn31WLShFiFPvCdIY+kd/6CO9oX/0hv6xFuZA1SyPFKaSRUVF+X2uQMr7UzbQ+pLwsK1ubbG7PlaeL9jHZh9EnGhb3dri9D5Ih/yZmML2f//3fyri9IADDlARqYMGDQpJXVoiOviwpaJbf0MaQv/oD32kN/SP3tA//sEcqC2U9evXh6y8P2UDrS8JD9vq1ha762Pl+YJ9bPZBxIm21a0t7IOsp23btjJ16lT58ssvpUuXLnLWWWfJpZdeKn/++Weoq0Ycjq73BKmH/tEf+khv6B+9oX+shQIqIYQQQghxBLW1tfL777/L3Llz5aeffpKkpCS54447lJDavn17mThxolx22WUq0oAQQgghhBBf4SJSmoGFDUJV3p+ygdaXhIdtdWuL3fWx8nzBPjb7IOJE2+rWFvZB1rB27Vq54oorZMOGDa7P0tLS5O6775axY8fKXXfdpXKePvfcczJhwgQZOXKkXHXVVdKvX7+Q1ps4C53uCdIY+kd/6CO9oX/0hv6xFkagakZ0dHTIyvtTNtD6kvCwrW5tsbs+Vp4v2MdmH0ScaFvd2sI+yBowZT8jI0M+//xzWb58uSxcuFBOPvlkufnmm6WwsFDt07FjR7nnnnvks88+k9TUVDn99NNDXW3iMHS6J0hj6B/9oY/0hv7RG/rHWiigasaOHTtCVt6fsoHWl4SHbXVri931sfJ8wT42+yDiRNvq1hb2QdawcuVKOfbYY6V79+4SExOj8qAi92l5ebls3Lixwb6dO3eWBx54QAmphDj1niCNoX/0hz7SG/pHb+gfa6E8TQghhBBCWjyDBw+WF198UWJjY1WkaVFRkbzxxhvSpk0b6d27t8cyEFIJIYQQQgjZGxF1dXV1e92LBAQWKigtLZXExETp379/k/tWVFRIXFyc3+cKpLw/ZQOtLwkP2+rWFrvrY+X5gn1s9kHEibbVrS1O74OaM+4JJrm5ufLggw+qBaOqqqokIiJChgwZoqb2Dxw40LZ6OIFQ+dAJ6NbfkIbQP/pDH+kN/aM39I+14x5O4deMXbt2hay8P2UDrS8JD9vq1ha762Pl+YJ9bPZBxIm21a0t7IOsAVP2H330UVm6dKl8//33smzZMpk5cybFU2IrOt0TpDH0j/7QR3pD/+gN/WMtFFA1A6p3qMr7UzbQ+pLwsK1ubbG7PlaeL9jHZh9EnGhb3drCPsgaSkpK1GtkZKSato88qM2luLjYgpqRcEKne4I0hv7RH/pIb+gfvaF/rIUCqmb4M9gPVnl/ygZaXxIettWtLXbXx8rzBfvY7IOIE22rW1vYB1nD2LFj5dVXX1XT1/z5wfHcc8/J0UcfbUndSPig0z1BGkP/6A99pDf0j97QP9bCHKia5ZGCO5Czy18CKe9P2UDrS8LDtrq1xe76WHm+YB+bfRBxom11a4vT+6BQ5c/866+/ZNq0abJx40Y56qijZMyYMXLwwQerRaW8RZv++uuv8umnn8pXX30l3bt3l7vvvptT/pkD1VH9DWkI/aM/9JHe0D96Q//4B3OgtlDWrl0bsvL+lA20viQ8bKtbW+yuj5XnC/ax2QcRJ9pWt7awD7KGAQMGyKxZs+TWW29VA+FLL71U9ttvPznhhBPU3zfccINcf/31cuGFF8qxxx4rBxxwgPp89erVSjh97733KJ4SR90TpDH0j/7QR3pD/+gN/WMt0aGuACGEEEIIIcEAURcnn3yy2pYsWSLz589XrytXrpT8/Hz1PfKjZmVlKWF15MiRss8++4S62oQQQgghRHMooGpGq1atQlben7KB1peEh211a4vd9bHyfME+Nvsg4kTb6tYW9kH2MGzYMLURYic63xOE/mkJ0Ed6Q//oDf1jLZzCrxlxcXEhK+9P2UDrS8LDtrq1xe76WHm+YB+bfRBxom11awv7IEKcC+8JvaF/9Ic+0hv6R2/oH2uhgKoZ27dvD1l5f8oGWl8SHrbVrS1218fK8wX72OyDiBNtq1tb2AcR4lx4T+gN/aM/9JHe0D96Q/9YCwVUQgghhBBCCCGEEEII8QIFVM3o1KlTyMr7UzbQ+pLwsK1ubbG7PlaeL9jHZh9EnGhb3drCPogQ58J7Qm/oH/2hj/SG/tEb+sdaKKBqBlaIDVV5f8oGWl8SHrbVrS1218fK8wX72OyDiBNtq1tb2AcR4lx4T+gN/aM/9JHe0D96Q/9YS3QghbOzs2XLli2Sl5cnUVFRkpGRIVlZWdKhQ4fg1TDMKC4uDll5f8oGWl8SHrbVrS1218fK8wX72OyDiBNtq1tb2AfZwyOPPCInnHCC9OvXL9RVIWGEzvcEoX9aAvSR3tA/ekP/aCag/v333/LGG2/IwoULZdu2beqzuro69RoREaFeu3TpIqNGjZLTTjtN+vTpE+w6OxoI0aEq70/ZQOtLwsO2urXF7vpYeb5gH5t9EHGibXVrC/sge3j99dfl5Zdflh49esjxxx+vts6dO4e6WsTh6HxPEPqnJUAf6Q39ozf0j7VE1Bnqpw/C6f333y+LFy9WEabDhw9X4igGosnJyVJbW6vChSGqLl26VH7//XfZvXu3HHzwwXLdddfJPvvsI+HKihUrpLS0VBITE6V///6hrg4hhBBCiOPHPYjC+OKLL+Szzz6Tn376SWpqamTgwIEqKvWYY46Rtm3bhqxuuqOLDwkhhBBCdBn3+JQD9cEHH5QzzjhDJaSdNWuWzJs3T02Luvjii2XcuHFy6KGHysiRI+XEE0+USy65RGbMmCHff/+9vPjii9K6dWuZNGmSOgbZO2vWrAlZeX/KBlpfEh621a0tdtfHyvMF+9jsg4gTbatbW9gH2QMe8J966qny0ksvyYIFC+SOO+6Q+Ph4eeihh2T06NFy/vnny3vvvSdFRUWhripxEDrfE4T+aQnQR3pD/+gN/aPBFH48wcfT+8zMTJ8PjOn8hxxyiNqmTJkizzzzTCD1JIQQQgghxC+MB/rYcnJyVCCAEZl6zz33yFFHHSUXXHBBWM+YIoQQQgghAQqo9957b0AnwTT/Bx54IKBjhAtpaWkhK+9P2UDrS8LDtrq1xe76WHm+YB+bfRBxom11awv7IPtBlOnXX38tn3/+uUpHVVlZKb1791bT+fHQ/4MPPpAJEybItGnT5PTTTw91dUkLpqXcE+EK/aM/9JHe0D96Q/9otohUU6xevVoiIyOlZ8+ewTxsWIGcC6Eq70/ZQOtLwsO2urXF7vpYeb5gH5t9EHGibXVrC/sge8AMKkM0/fHHH6WqqkqysrLk3HPPVcJp3759Xfued955arr/9OnTKaASx94ThP5pCdBHekP/6A39Yy0+5UB1B+tO/d///Z/ceuut6j0WkELuU+RAxQqnF154oZSUlAS7rmEBppWFqrw/ZQOtLwkP2+rWFrvrY+X5gn1s9kHEibbVrS3sg+zhoIMOUmNVLG46fvx4eeONN+Tbb7+VG264oYF4CmJjY1UAAFevJU6+Jwj90xKgj/SG/tEb+kfDCFQk43/sscfksMMOU+/xZB/J+ceOHaumQ73wwgtqIambbrop2PUlhBBCCCFkrxx99NHqwT7Gq9HRex/yQmxt1aqVLXUjhBBCCCFhIKB++OGHMmbMGHnqqafUeyThT0hIUKuaYnVTRJ/OnTuXAqob5eXlalW07t27S3Z2tsq/Bbu1bdtWNm3apPZJSUmRvLw82bVrl3rfrVs32bZtmyobFxenFvLasGGD+i4jI0OlTMjNzVXvu3Tpon4g4ByIpOjUqZOsW7dOfZeeni4xMTGyY8cOV17a3bt3K1+hTNeuXaWmpkaVxY8H+BHnBR07dpSCggI1FQ6RGaj/2rVrVSSy4W/jSQemxmG/wsJClVMM0RyoA6KU0bbU1FTVdtChQwcpKytTxwa9evVSbauurpakpCRV5y1btqjv2rdvr+wF24AePXrI5s2b1XQ8hKm3adPGZUPYE21B+wDqu3XrVqmoqFD1xbE2btyovkM5sHPnTvUKO2zfvt1lb7Rn/fr1rgUo0H6zvVGutLRU2RY2NdsbPsCxAHyBuhv2hl+NFfKQpwTXgdnesB/agOOhrYa9YT+sKoz2AFwPOKbZ3qgvymI/HNtsb7QrPz9fvce+sINhb7QPNgXt2rVTtjXbG77wdM3ib/jXfM3iejDsjfMa1xauWdTTsDdsCHviOoC90HazvWEr8zWLcxj2RlnYBeCahb/M9kY7zdes2d7wj/EePkZuPGy4n9BW8zWLzWxvnN98zZrtjXqYr1nYAPXA9/AfbGZcs7CF2d7wg/ma9dZH4JrFsQx7oyzO6WsfYba3pz7C8JWnPsJsb299BOoGzNcsri9f+gjUH3VrTh9h1Dcc+wjzNWtFH2FcS3b0EeZr1oo+AtcA7OVrH4H9zddssPsIvMfxjD7C6JOt6CNgb7QR7QtGH+E+jkBbcDxzH4G66sCjjz6qfPzuu++qWVK4jsF7772n2oip+rgGDXDvExIo6DOIvtA/+kMf6Q39ozf0j7VE1Bm/dpvBvvvuK3fccYcaeGKgf+CBB8rw4cPl+eefdw1M7777blm2bJkVdW5xrFixQv2gwg+i/v37N7kvftwFMoAPpLw/ZQOtLwkP2+rWFrvrY+X5gn1s9kHEibbVrS1O74OaM+6xEojM559/vhKtIaIOHDhQfX7bbbepRaMwa+q1115TYjwRLX3YEtGtvyENoX/0hz7SG/pHb+gfa8c9fuVARYQLnuiDn376SZ1o5MiRru8RBWFE7ZDmgYiUUJX3p2yg9SXhYVvd2mJ3faw8X7CPzT6IONG2urWFfZB9Eaio38svv+wST8H9998vb775pop0RkoqQsLlniD0T0uAPtIb+kdv6B8Np/APHTpUJeLHVLrnnntOTaFDnilEo3733Xcyc+ZMOeqoo4Jf2zAAU+lCVd6fsoHWl4SHbXVri931sfJ8wT42+yDiRNvq1hb2Qfbw888/ywUXXKAWk3Jnv/32k3POOUfefvvtkNSNOBed7wlC/7QE6CO9oX/0hv6xFr+si6lPyKM1ZcoUFep6/fXXq/xbS5YsUZ/h76uvvjr4tQ0DkFstVOX9KRtofUl42Fa3tthdHyvPF+xjsw8iTrStbm1hH2QPmCGFXK7eQF5a5AcmJFzuCUL/tAToI72hf/SG/tFQQMUCBB9//LG88847Mm/ePJk8ebL6vF+/fmoq1Pvvv68WhSDNx1isIRTl/SkbaH1JeNhWt7bYXR8rzxfsY7MPIk60rW5tYR9kDwMGDFALn2KxLXcwawpjWYxdCQmXe4LQPy0B+khv6B+9oX80nMKvCkZHq8WkzGDl2mOPPTYY9QpbsKpvqMr7UzbQ+pLwsK1ubbG7PlaeL9jHZh9EnGhb3drCPsgeLr74Yrn00kvljDPOUAufdu3aVSIiIlSufiwi9ddff8kzzzwT6moSh6HzPUHon5YAfaQ39I/e0D+aCqgfffSR/PDDD5Kbm+vRSRigYmVT0jxSUlJCVt6fsoHWl4SHbXVri931sfJ8wT42+yDSEmxbV1enNl/BVG2dBpR21yeY58P4DltLuE5GjRol//nPf+TBBx+Uu+++21VvXDutW7dWn48ePTrU1SQOQ+d7gtA/LQH6SG/oH72hfzQUUKdPny7PP/+8xMTESEZGBhPVBhGKF8SJttWtLRRQrTse+yDnoKNty8vLZevWrWpKdnMEVIiHeXl5ogt21yeY54MIibyiWVlZEh8fr+V1Yua4445Ts6P++OMPyc7OVrZAKqqBAweqcSwhwUb3eyLcoX/0hz7SG/pHb+gfa/FL+UQ+qUMPPVStboocqN9++63HjTQf/DAMVXl/ygZaXxIettWtLXbXx8rzBfvY7IOIrraFeIqp1xUVFc0ST0lwge3hA/jCELR1B6LvoEGDZNy4cUpMHTp0KMVTYhkt4Z4IZ+gf/aGP9Ib+0Rv6R8MI1OLiYhk7dqwkJCQEv0aEEEIIIR4GhDU1NSrqsWPHjioXu69A6EM5XbC7PsE8X3V1tYrkbCni6Zo1a2TOnDmyc+dOdf14Elfvv//+kNSNEEIIIYQ4XEA97LDDZPHixSohPwkumFYWqvL+lA20viQ8bKtbW+yuj5XnC/ax2QcRHW2LqEdjJXWIp5hC3hzi4uK0Sjdkd32CeT7YHj5Yu3at8kmnTp1EV+bOnSvXXXddk/lfKaASJ/edpDH0j/7QR3pD/+gN/aOhgHrHHXfI5MmT5frrr5ejjjpK5UE1LyhgMHz48GDUMawoLS2VpKSkkJT3p2yg9SXhYVvd2mJ3faw8X7CPzT6I6Ghb86JRzYk8NYCAFhUVJbsLy9XmK61T49UWbIz62EWwz2f4AD4pKSlRi1TpyIwZM1Su1scee0z69evXbOGdkJbed5LG0D/6Qx/pDf2jN/SPhgIqpmwVFRXJp59+Kp999lmj7zGghqC6YsWKYNQxrCgoKJC2bduGpLw/ZQOtLwkP2+rWFrvrY+X5gn1s9kHEibbF1G3kvJy7aIPM/HKVz+XOPLqvTBrbz7L62IWV5yssLJT27duLjmzYsEFuvvlm2XfffUNdFRJGOKnvdCL0j/7QR3pD/+gN/aOhgHr33XerAfOFF14o3bp18ysahBBCCCHETsYd1E0O2KdDg8+mvbBICoorJS05VqZdfFCD76yIPiX20aFDB5WrlRBCCCGEkEDxS/lcvXq1XHnllXLxxRcHXAHSkF69eoWsvD9lA60vCQ/b6tYWu+tj5fmCfWz2QcSJtjUWUPI0JT8qMsL12qtTK1vrgwWZ3nzzTZk9e7asX79e5SodMGCAXHLJJTJixAjX/r7st3nzZjnppJNUaqWHH3640UJKZ555ptx6660yadKkoLalZ8+eoitnnXWWvPbaazJ+/Hhp3bp1qKtDwgQn9Z1OhP7RH/pIb+gfvaF/rCXS3yf6Oi3G4CTwwyhU5f0pG2h9SXjYVre22F0fK88X7GOzDyJOtG1FRUWjz9ZvLZDHZy6R3YX13+EV7/G5HfXBdu6558qrr74q55xzjnz44YfqbwiSyDP/ySefuPb1Zb/OnTvL7bffrkRWc3olpFy6+uqr5Ygjjgi6eGpMk9eVqqoqlVIKojLE5ltuuUWJyObttttuC3U1icNwUt/pROgf/aGP9Ib+0Rv6R8MI1IsuukieeuopGTVqFBVuC/KUhaq8P2UDrS8JD9vq1ha762Pl+YJ9bPZBxIm2NRagMpi/ZIs89tYScV//ct6SLfLdr1vkuknDZNSwTpbW58knn5RVq1bJnDlzGqyYOnXqVCkuLpZ7771XiZ5YCMmX/bBgACIt58+fL9OmTZNhw4apB96GQIj9wu06efTRR11/L1iwwOM+EFjvv/9+G2slsmjRIhVR/Pvvv0t+fr60atVKRRL/61//kt69e3u189tvvy3vv/++rF27Vi0K1qNHD+XziRMnMrBBI3S+Jwj90xKgj/SG/tEb+kdDAXXlypVqwHniiSeqiIc2bdo0Wt0V32PaFGkega5kG0h5f8rquvKuE3CSbXVri931sfJ8wT42+yDiRNuaxyiIMIV4WgtRtaGuKjW19R/g+y4dUqR7Vpol9amtrVVCGMQvsyhqcM0116gp91j4yZf9jJQARp56jM8gsI4dO1a+++47+e9//yspKSlhd51gvKob//nPf+SFF15Qf2ORB4igiBZBJPEXX3whTz/9tApQcL9errvuOpk7d64aXyN4AZ8tX75cbd9++608++yzti5MRlrmPUHon5YAfaQ39I/e0D/W4tfjagzG8WMEkQ2YHpWTkyNbtmxpsCEXF2k+iEIIVXl/ygZaXxIettWtLXbXx8rzBfvY7IOIE21rFlBnz1/bKPLUHXw/e8Fay+qDcRMiDxEl6gmsao+V4zGe8mU/c/vS0tLkoYcekh9//FHuuusuuf7662XIkCGWtQXnawlAcNy5c6dUVlaGrA7vvfeeEk+x+Oo999wjCxculI8//li+//57FUWMut10000q7YKZ559/XomnEFw/+OADFY2MNA2zZs1SQQw4DoRXogdO6judCP2jP/SR3tA/ekP/aBiBiifdxBrwYymQtAiBlPenbKD1JeFhW93aYnd9rDxfsI/NPoi0RNt+vzRb3py7Usoqqr1OmUfkHl6NnKdNgUjUb37ZLL+t2qHKNUVCXLScPa6/HDI4y+f6QsjzRXwsKCjwS6QcPHiwtGvXTrZv366mhkOYM0epBpPs7Gyv0851YOPGjSrqEyJleXm5vPzyy+rzxx57TG6++WbZf//9bakHctk+8sgj6m+cd8KECa7v4F98N3r0aCWYf/nll3Lqqaeq75Cm4ZVXXlF/QxDH4mEGEMYffPBBlVoLs74uuOCCFiNoO5mW1HeGI/SP/tBHekP/6A39o0EEKgbHgcKIVEIIIYRYwQffrZEtO4plV0G5xw2iqfHaHIxyTW047wfzVjfruOnp6eoVYllTGCvH720/dxDdWF1drYTNG264QQmH4QgWuDr99NPl559/lsMOO8z1OSJ2161bpwRH5CG1AwQfwI9IfXXWWWd5nHKHRcCw0JVZkP7666+VkI5IU0SpuoN24ZhlZWVqX0IIIYQQEkIBFU/BsfgAIhn8EU7vuOMOOe200/ypX9iB6XihKu9P2UDrS8LDtrq1xe76WHm+YB+bfRBpibY99fDe0qldsmSkxTe5tU6Na9Zxsf/ejonzjh/dvAjM7t27K0FsyZIlHr/HIkEQ9xA56st+q1f/I+AilybypiJaEVGNiMB8/PHHxSoQ6aoriDJF5C2mu2NhLWMxsQMOOEB9BtvaNfUdKRUARFD3dQMMTj75ZJk8ebJKy2Dw22+/qdf99tvPazQ0vgMQiknoaUl9ZzhC/+gPfaQ39I/e0D8aTOGfPXu23HfffXLkkUeqqU5jxoyRkSNHqife7mBw+vfff8v//vc/NTjFoP/www9XxyC+TfEKZKGHQMr7UzbQ+pLwsK1ubbG7PlaeL9jHZh9EWqJtMX2+qSn0yNduLLDz+MwlMm/JFteCUZ6IioyQ0ft1kmsmes49GgzwYPn111+XCy+8sNECUS+++KJaHKhjx44+7wcglt55551qVfajjjpKfXb11VerKeyYHo7NiutEVxYvXqwEyYyMDMnLy2v0A2PSpEnKhnawatUq9YroUoyVEZGKiFHkw01NTZVDDjlETjnlFImNjW0URQs8jbkNOnXq1GBfElpaUt8ZjtA/+kMf6Q39ozf0jwYCKgaZTz75pPzyyy/y6quvyv33368iUhMSEtSgHdOOMBjE4HTHjh1qqhiekuMp+5tvvul18QPSGEzvQkREKMr7UzbQ+pLwsK1ubbG7PlaeL9jHZh9EnGjbmpoal4B60qie8t2vW5rcH4GKJ43saWl9Lr30UrX4D0Q8iJwYK8HmM2fOlI8++kimT58uiYmJPu+HaNVrr71Wiay33nqr61wQXrH4Jz5DdGqwfYrp5bpGocImECe9gWvCLgF469atrnOef/75Stw188UXX6g8pv/3f//nEkTB7t27G6RzaGrBCHeRmIQGJ/WdToT+0R/6SG/oH72hfzRaRGr48OFq27ZtmyxYsEBFl2KKPpwUGRmpBu2YRoQFCw499NAmB3uEEEIIIXbTPStNrps0TB57a4lgRrQ5EhWRpxBP8T32sxI8hH7jjTfUokZYmR0CG6abY5EgRJwaixv5ut/DDz+spvK/++67DRaMwvgMOVGxaBHya+IYe1sYyyn069dPRXp6yjmKHLEff/yx9O3b15a6lJSUqNeHHnpILQwFQfvEE09U4jfEVAQmICXDJZdcIh988IHLh8htCuLivKefcN+XEEIIIYQEn4g6IyEUsYwVK1ZIaWmpGiT379+/yX2NlYL9JZDy/pQNtL4kPGyrW1vsro+V5wv2sdkHER1tW1tb65oCDcELomCgbVm/tUBmL1gr3/zyzyKXRw7vrCJPrRZPW3ofZPZHnz59GuX0bM64x0oQeXv55ZfLcccdp9JQIUIXQiUW8XrppZdUflHkhx07dqzldYHgjchj8MQTT8i4ceMafI/0C6gn0k1gMalzzjlHfY66YWo+1hM4++yzPR4bojnKYMYYAhyCgeFD3GuYCoi8vVhUFlG9EPXbtm0rmzZtUvsi0gXX2K5du9T7bt26qWALzEiD8IsACyO9ANIp4Ji5ubnqfZcuXdTfEH+RvgDRt1jgC8BPiNjF7DYjjQEiciFGR0dHS9euXZXobEThQkjGeQFmyCE6GmI1rk/UH/uinohKTkpKUukTQFZWltqvsLBQ3Sc9e/ZUdcB1jrZjf2NB3Q4dOqi64tgAKx2jbRDkcUzUGSsgA/gD9jIig3v06KECT+Bj3Buwm2FD2BPXhxFxjPriYQkipNEuHAvXiGFvsHPnTvUKO2CdCsPeaM/69evVdwhmQfvN9kY5+Ba2hU3N9oYPjDUv4AvU3bA3/LpmzRr1XVpamroOzPaG/YqKipR/0VazvTFb0YjCxvWAY5rtjfqi/dgPxzbbG+0yFtLDvrCDYW+0z1ioGJHwsK3Z3vCFp2sWf8O/5msW14Nhb5zXfM2inoa93a9ZtN1sb9jKfM3iHIa9UdZ8zcJfZnujneZr1mxvXDPmaxa2NtvbfM1iM9sb5zdfs2Z7ox7maxY2MNsbNjOuWdjCbG/4wXzNBquPaMre7CPYR7CPYB8RacE4AuVRt72NXSmg2kBzfkjghocj/SWQ8v6UDbS+JDxsq1tb7K6PlecL9rHZBxEdbRuogIrBHgZguwvL1Wbmzv/7UQpLqiQ1KUbuuuTgBt+1TsXCU/9EcwYLoz52Eezzmf2BQTAGujoKqADRnEg9hcGyISTjFfaAoIrp9HaA1AuoA3Kgzpkzx+M+iBD+8MMP5aCDDlIpswDyov71119y0003qXQMnkAkMoRh+AGpAIKBTj5saejUd5LG0D/6Qx/pDf2jN/SPteOeZk3hJ9YD9T5U5f0pG2h9SXjYVre22F0fK88X7GOzDyJOtK3xrHjuog0y88t64c8diKjXTp/f4LMzj+4rk8b2s6w+dmHl+XS/TsaPHy9HH320/PDDDyoqAuIvokEOPvhgFZlgF4iwgYCKtALegLgKjOgNYNTRiPbwhBFNw9RZeqD7PRHu0D/6Qx/pDf2jN/SPtVBA1Qwo3qEq70/ZQOtLwsO2urXF7vpYeb5gH5t9EHGibY2I1XEHdZMD9ungczkrok/N9bELK8/XEq4TTEGzY5p+U2CaG6bWYXqYN4xUCJiCZi4H8deYrugJY2qdeyQwCQ0t4Z4IZ+gf/aGP9Ib+0Rv6x1oooGoGcjqEqrw/ZQOtLwkP2+rWFrvrY+X5gn1s9kHEibZFniMrp+T7Wx8nnE+nqEcszDRx4kQZPHiw6/3ewJR+TPG3miFDhsj3338vy5Yt85qT1sjZhXxeBkZbkK/VG1jU1UgTQEKPk/pOJ0L/6A99pDf0j97QP9ZibwgE2SvmaVt2l/enbKD1JeFhW93aYnd9rDxfsI/NPog40bZNRf2FQ32sPJ8R/agDyB9qLEhgvPdls4Pjjz9eiaaIQv30008bfY+FE4zcqOZo2cMPP1wtWIHFHubNm9eoHBaNwr2KxTKQqoCEHif1nU6E/tEf+khv6B+9oX+sJeCQBKxohcEgVvZCQn5EOdg9NY0QQgghhIQ3K1eubPJ9KMHqs2eccYa8/fbbcscdd6hp+obgiZV7saAVcqRi9dvjjjvOVQ7C6AUXXCDPPPOMiqidMWOGK9J06dKlrijbc889V63CSwghhBBCNBNQf/31V7nvvvvUalXg5ZdflpqaGrntttvUKqLHHntsMOsZNrRr1y5k5f0pG2h9SXjYVre22F0fK88X7GOzDyJOtG1MTIyEc32sPF/btm1FZ7BoFKbOH3jggepBP/jmm2/Uw35Ed9oJxsgIPPj222/lqquukg4dOqipdn///bda9CEzM1Mef/xxVz0NLrvsMjX1H+0488wzVV5URLOuWbNGfY92XHnllba2hYRH3+lE6B/9oY/0hv7RG/pHQwEVg7jJkyergd55550nr732mvocT74RgXrDDTeoJ+ajRo0Kdn0dT3V1dcjK+1M20PqS8LCtbm2xuz5Wni/Yx2YfRJxoW2MV+uqiPKkprl+x3BeiktMlOiXdsvrYhZXn0/k6wer0//rXv2T58uXywQcfSP/+/dXnn3zyicydO1cOOuggFdFp14ILEEYRSYop/O+++64KQoAI2qlTJxWNev7553vMKYto1eeff15mzZql2oFcqQha6Nevn5x88sly9tln255Xl7TMe4LQPy0B+khv6B+9oX+sxa/R1hNPPKEGexjElZaWyquvvqo+HzRokHz88cfq6TgGehRQm8/u3bsDWpAhkPL+lA20viQ8bKtbW+yuj5XnC/ax2QcRJ9oWg0kITIW/fSn5C9/xuVyrwyZI65FnWFYfu7DyfBAp27RpIzoyffp0Wb16tdx9990qatPg4YcfliOOOELuvPNOeeqpp+Tmm2+2rU6IHEU+VGzNAf4766yz1Eb0xkl9pxOhf/SHPtIb+kdv6B9r8Ws0jZVAL7/8cpXUvqysrMF3ycnJMmHCBHnyySeDVUdCCCGEkIBJHXq0xGX2ktryEtdnO798UerKSyQiPknaHH2R6/PI+CSJ69AjRDUlwWD+/PkqqvP0009vFNF54oknKnH1s88+s1VAJYQQQgghLRO/wxEw+PRGRUWFyjlF/FtkIFTl/SkbaH1JeNhWt7bYXR8rzxfsY7MPIk60rSunZG217Hj/EamrqWq0D0TU3I+fcL2PiIqRzpc9ZW19LGDLli1y5JFHyn//+1+V99Of82HFdzwsNy9m5I1u3bqJrhQWFqoco95ADlIs4ERIMHFS3+lE6B/9oY/0hv7RG/rHWiL9KTR48GCZM2eOx+8wpR95nTCdnzSf7OzskJX3p2yg9SXhYVvd2mJ3faw8X7CPzT6IONG2lZWV6rWmtMijeOoJ7If9rayPXTT3fIjIXLhwoc9iq65g2v4XX3zhNQcsFpPiDw0SbJzUdzoR+kd/6CO9oX/0hv7RUECdMmWK/PXXXypp/UcffaTyOWFhKUQ7nHTSSSr64dJLLw1+bcOAQH9UBVLen7J2/wgMJ5xkW93aort4Ecpjsw8iTrSt3Ys2hdMiUjpfJ+ecc4788ssvakyK6fwbNmyQjRs3KnH4qquukkWLFsm5554b6moSh6HzPUHon5YAfaQ39I/e0D8aTuEfOnSoWiQKyfcfeughV6J+0LZtW3nsscdkxIgRwa1pmJCQkBCy8v6UDbS+JDxsq1tb7K6PlecL9rHZBxEn2jYy0q/nxZbWB4IeFuVcu3atWgUeC2/eeuutkpaWpj575JFH1DR6LAB1yCGHqKjQjh07ugRRPLR+6623JCcnRzp37iyXXXaZx4WJcCwIiYceeqg88MADEhUVJd99951aPAkrwLdv315N1Udue6Rnwr4///yzKovXb7/9tsVeJ3iov2PHDnn66adlwYIFjRZluvrqq2X8+PEhqx9xJjrfE4T+aQnQR3pD/+gN/aNpDlQM5r/66iv5888/ZfPmzSrnKQb2AwcOtHVlWacBATpU5f0pG2h9SXjYVre22F0fK88X7GOzDyIt0bbFK36UvPlvS21lw4Ut3amrqW7WcXPevkciopoe00TGJkj6qDMluf9BPh+3qKhIrrzySrnllltk9OjRsm3bNrnpppvU6vAQMs844ww5+OCD5bXXXlN55R988EE16+eTTz5Ri3W++OKLMmPGDJk6darKcwoxFuXbtGkjnTp1cp0H0ZZYRAni7H333aeEWwiJ11xzjRJrcY5NmzbJPffcI+vXr1eCLoRVRGwiP+i///3vvbYF59SZiy++WNnzhx9+UOkGampqJCsrS7Wdq9SScO87wxH6R3/oI72hf/SG/rGWgJROTN2HYIqNBAf8kOnVq1dIyvtTNtD6kvCwrW5tsbs+Vp4v2MdmH0Raom0LFs2Wql3Bz/lUW1q4131qcP7Fs5sloOLBM6ZYQcjDw2dszz33nBL3EFWKiNT//Oc/rgU7n3zySbUw1OzZs2XSpElKWMXUc2N1eUSNlpeXq2hVA6RTgqgK8RRCqxGFi/NMmDBBJk6cqN536dJF7rrrLjnvvPNUGQiwMTExEh8f75PAiLb07t1bdCY1NVWOOeaYUFeDhAktqe8MR+gf/aGP9Ib+0Rv6R0MBFYP+F154QT3Nz83NVdGnnsTVr7/+Ohh1JIQQQgjxStpBJ0ve/JleI1Ax5R3jEkSg+iKKGkQmpvoUgZo24qRm1bdfv35quj0iPREpgFk9iEQdM2aM/P333+rBtCGeAuyDxY7wXV5enhp7YUFP90hLABEUTJs2TaqqqiQzM1O13QA57JG3/r333muUIxXT/c0RrC0NTNU/+uijpU+fPq73ewO2ueKKK2yoHSGEEEIICTsBFdPAZs2apaZ3IWpCt9xiLZlAp8IFUt6fsrpP3WvJOMm2urXF7vpYeb5gH5t9EGmJtkX0Z1MRoIjMRHqhipx1kv3yjT4fN3PiHRKX2UOCDery6KOPKuEOU+p//PFHufHGG2W//fZT0Z+ewMNqfOfte3dOOeUUJSRi+v8RRxwh/fv3dx3noosuUt8HY9pXRkaG6AIE065du1JAJSGlJfWd4Qj9oz/0kd7QP3pD/2gooCL3KSInML2M6LVSbiDl/Smr28rGTsJJttWtLU5aATvYx2YfRAxoW+tABOjcuXPltttukx49eqg8pR9//LESUbGo0ffff69m+xhRqDt37lT5TDF9PyUlRdq1ayfLly9X0/oNpkyZoqJNMZ0fYGGo4cOHy5w5c9QU/nfffVctIIXp9sh3CqHR4KefflKLUiFqFekDWup1gqha87S1b775JqT1IeGJTvcEaQz9oz/0kd7QP3pD/1iLX6GjiOTAoJwEn127doWsvD9lA60vCQ/b6tYWu+tj5fmCfWz2QcSJtjXnBtUB5BdFrtNHHnlECaOYmv/ZZ59Jt27d1CJSJSUlSkxduXKlEluxWnx6eroSRcEll1yi8qAiJypyXUH8hFhoFlQBZghhgahVq1aphaeMqf5ffPGFis6EkLpo0SK1oBQWtjIiUJOSkiQ7O1stbrU3du/eLbqAtmGhLYMPP/xQ2dLIM+ttIySYOKnvdCL0j/7QR3pD/+gN/aOhgDpu3DgVhUoIIYQQ0lKISkyRiCjfpsBjP+xvBYg6xWr3ixcvlpNPPlnOPPNMFR2K/PKdO3eWN954QwoLC9Xq8RdeeKESNmfOnKkWQwJnn322ElqfeOIJJaoiunT69OlywAEHNDoXIk4vuOACJZiuWbNGjeGwL/LUn3DCCUqoPfTQQxtMd8cCUxB1TzzxRLWwVUuhtLS0wQ8HtAntIIQQQgghJFAi6vyI8cXTfEQ/YFB91FFHqfxX5gUKDPCjgIisWLFCDeoxLc7IQba3PG3+Ekh5f8oGWl8SHrbVrS1218fK8wX72OyDiI62Rd5ORFGCvn37Njv3urGIFKguyJWa0iLXdzlv36MWlsKCUch5agDxNDqt+TlBm1sfOwj2+cz+6NmzZ4MFr5o77gkmSF+wZMkStUgXomh//vlnVb+m8rTCLojmJaKFD52ATn0naQz9oz/0kd7QP3pD/1g77vHLsr/++qtaxbWsrEx+//13rwNSCqjNB9PlAlkBN5Dy/pQNtL4kPGyrW1vsro+V5wv2sdkHESfaFqvRQ+SrLsprIJ42hdovMlqiU9Itq49dWHm+7du3q6hZHXjooYfUolkQd/Py8tRYFCkGMF4lxC6c1Hc6EfpHf+gjvaF/9Ib+sRa/BNSHH35YKbOY9tW9e3c17YwEh/Ly8pCV96dsoPUl4WFb3dpid32sPF+wj80+iDjRtoiYBIW/fSn5C9/xvE9poWS/fGODz1odNkFajzzDsvrYhZXn0+k6mT9/vlx33XUqlyxAJCoW6kKqAkLsQqd7gjSG/tEf+khv6B+9oX80FFCxYAHEU6wGS4JLXFxcyMr7UzbQ+pLwsK1ubbG7PlaeL9jHZh9EnGhbY8p/6tCjJam374tgRiWnW1ofu7DyfDpdJ3jAf/vtt7sE1KysLE5jI7aj0z1BGkP/6A99pDf0j97QP9bi16gSUadYrZUEn8zMzJCV96dsoPUl4WFb3dpid32sPF+wj80+iDjRtjEx9QtHYTq+FVPy/a2PE87XoUMH0QWkKcAip4MHD5aEhATZunWr7Ny5U702BYRWQoKFk/pOJ0L/6A99pDf0j97QP9biV0jClClTVMJ9TJWyexqa09mwYUPIyvtTNtD6kvCwrW5tsbs+Vp4v2MdmH0ScaNuKigoJ5/pYeb6NGzeKLpx22mkyb948NWUfi5yC+++/X4488sgmN0KCiZP6TidC/+gPfaQ39I/e0D8aRqC+++676in/pZdeqkKEW7Vq1SgPKhL3f/3118GqJyGEEEIIIV5Beqnhw4erRaQqKytlxowZSkjt27dvqKtGCCGEEELCUUAtKSlR+aWMHFMkeGRkZISsvD9lA60vCQ/b6tYWu+tj5fmCfWz2QcSJtjXyYOaVFajNV9IT0tRmVX3swsrztW7dWnRi9OjRagMffvihnHzyyYwyJbbipL7TidA/+kMf6Q39ozf0j7X4NaJ+/fXXg18TEpSFHgIp709ZuxfCCCecZFvd2uKkBVyCfWz2QcTJtv1q7UJ5789Pfd7/tH2OkwkDj7e0Ti0dna+Tb7/9NtRVIGGIzvcEoX9aAvSR3tA/ekP/WAuXJtWM3NxcSUtLC0l5f8oGWl8SHrbVrS1218fK8wX72OyDiBNtW11draIwx/Q8TPbP2rfBd/cveEoKK4olNS5Zbht5VYPvrIg+NdfHLqw8HxZpSk8P/cJc4Morr5Tzzz9f9t9/f9dnyNX/999/S9euXdXCUmY+/vhjufnmm2XFihUhqC1xKk7qO50I/aM/9JHe0D96Q/9Yi0+jaUx9uu2221xToHyZCsUcqIQQQgjRCU9T8qMjo12vPVp3CVHNSDDAuHPs2LENPisoKJBTTjlFXn75ZTnooINCVjdCCCGEEBIGAmpWVpYkJiY2eE+soUuXLiEr70/ZQOtLwsO2urXF7vpYeb5gH5t9EHGibbHwpTeqaqoavNpZH0SGvvnmmzJ79mxZv369WphzwIABcskll8iIESNc+/u63xFHHCHZ2dmu9zExMdKmTRsZOXKkXHPNNZbkK+3cubPoTl1dXairQMIIJ/WdToT+0R/6SG/oH72hfzQQUN1znjIHqrUh1x07dgxJeX/KBlpfEh621a0tdtfHyvMF+9jsg4gTbQsB0pOICmGttKpc/Y1XvMcMGjvqg3NNnjxZcnJyZMqUKTJ06FApLy+X999/X33+8MMPywknnCAVFRU+7WdwwQUXqA1gP0xfxz5nn322zJo1S1JSUoI+hb9Tp05BPSYhLRkn9Z1OhP7RH/pIb+gfvaF/rMWvhFi33nqrTJw4UQYPHuzx+8WLF8tLL70kL7zwQqD1CzvKyspCVt6fsoHWl4SHbXVri931sfJ8wT42+yDiRNsiD6Ynlm5bITV1NepvvOL9kMwBttTnySeflFWrVsmcOXMkMzPT9d3UqVOluLhY7r33XhVROmPGDJ/2S0pKUp9jxlDbtm0bRIj26NFDxo8fLy+++KJce+21QW2Lk64TQoIB7wm9oX/0hz7SG/pHb+gfa/Fria4PP/xQNm/e7PX7n376SW0kuNMMrS7vT9lA60vCw7a6tcXu+lh5vmAfm30QcaJtPUWVIgJ01vKPG3yG93ZM90YEKiJIIWqaRVEDTLfHQ2hMwfdlv/j4+CbPh9RLY8aMkU8//VSCjZOuE0KCAe8JvaF/9Ic+0hv6R2/oHw0iUCGWHn/88VJZWen67MYbb1SbNwYNGhScGoYZgU6DC6S8P2U5bc86nGRb3dpid32sPF+wj80+iLRE2y7a/Ku8s3yOlFXXT8f3hcqaKimuLGnw2dq8jXLhRzdKbFSMz8dJiI6XMwadICM6D/O5zPbt2yU/P1+GDfNcpn379mpbt26dT/v5Mpju06ePyqFaUlLiilYNBpwmRkjL7TvDEfpHf+gjvaF/9Ib+0UBAxfSvf//73/K///1PRWZ89NFHst//t3ceYFJV5/9/t7C7LAtL772jYkHUqFHUROxBsWLQoImxd2PvP0FjjcEajGgsiCVqYomxBPRv7KBRQJTe6y67sGzf+T/fA3e8MzuzOzt37r3v3Pl+eOaZ2Zl7znnP+557OPPOe96z994xDw7Izs42hxRMmDDBDXkDD74sDR482JfyyZR1Ki/JDN1q64vX8rjZXqrr5hxE0lG3//j+XVm9dV1K6op2qibafkscqMgbCoqLi5u8DifIJ3JdcyCPart27cxrbPtPpQMVh1oNGTJEtACH85o1axrpsKSkJOJ9UFpa6rl8JPik09yZidA++qGNdEP76Ib2UZID9cQTTzQPgBNeL7jgAtl///3dlI0QQgghpFnGDR8rM7/9Z9wI1OjDoWJFn9opymuTcBQqIlB/NfzwFsnboUOHsLOvKfCDdCLXJcLWrVvNc1FRkQSZKVOmmEc0V111lS/yEEIIIYSQDD5E6plnnkm9JCTiS5Uf5ZMp61Rekhm61dYXr+Vxs71U1805iKSjbhH92VQEKHKO5ubmhp2p17/7R9leu10aYuQ7zc7Kkm5tOsuUw6+JmTs1FfTv3186d+4sc+bMkaOPPrrR54sXL5bJkyebQzsTva6pKFD0fd68eabdVEafgvbt24sWTjjhBL9FICSt5s5MhPbRD22kG9pHN7SPQgcqWLRokTkRFtvQ6ut3nGBrB186YkUAkKbBgRF+lU+mrFN5SWboVltfvJbHzfZSXTfnIBJE3dodod+sW2ByncYDTlV8juv27LGLK/Lk5OTISSedZH6Q/u1vf9vogKgnnnhCvv32W5NfNNHrmsu5+v7778s555wT6HFy5513+i0CIaruCdIY2kc/tJFuaB/d0D4KHaj/+te/5IorrpCGhoa419CBmhwbNmwI5ynzunwyZZ3KSzJDt9r64rU8braX6ro5B5Eg6ra2ttY4LRF9OvPbf0iWZElIGkefWuBzXLdH9xGuRKFCnvPOO08++ugjOf300+XSSy81B0Vhq/6MGTNMrvkHHnhACgsLE77OYvv27bJx40bzuqqqShYuXCj333+/OVTgrLPOSnlf0JamKFRC/CZIc2cQoX30QxvphvbRDe2j0IH68MMPS8+ePc2CfPjw4eZ0V0IIIYQQzdQ11Mmm7SVNOk8BPt9cWWqub5VgLtSW0rp1a3n22WflySeflGnTppkDjgoKCmSXXXYxEaejR49u0XUWuA4PKwoBUatjx46V3//+9ynfvk8IIYQQQkimkBVCOEYLGTlypFxzzTUyceJEd6QKGAsWLDARIYgQGTFiRLMn5ebn5yfdlpPyyZR1Ki/JDN1q64vX8rjZXqrr5hxENOoWO14QSQmGDRsm2dnZLS5vlYEDtbxqW/izKR9OlfLqbdIuv0iuP/ji8PvFBW2lU6E7eaTs8nhBqtuz2wN5VeHkTXbdQ3RCGwZj7iSNoX30QxvphvbRDe3j7ronqdV09+7dzbYwknpKSkp8K59MWafykszQrba+eC2Pm+2lum7OQSSIusUhUqC0sizCedoUZVVbzfVuyuMVbrZXWlrqWt2EpCNBmjuDCO2jH9pIN7SPbmgfhVv4f/3rX8vTTz8t48ePl44dO6ZeqgymoqLCt/LJlHUqL8kM3Wrri9fyuNlequvmHESCqFsrZ/u7iz+Sl+e9GfMaRKFe+27kIUQn7XqMnLLbsa7J4xVuthekcUJIKuA9oRvaRz+0kW5oH93QPgodqDj8AIcq/PKXvzS5t+BEjT5kgYdIJUdubq5v5ZMp61Rekhm61dYXr+Vxs71U1805iARRt9Ya5fBBB8nonrsnXK5D62JX5fEKN9sL0jghJBXwntAN7aMf2kg3tI9uaB+FOVBxcFSzFWdlmTwC6cCHH34oU6dOlUWLFkmHDh1MZO25555rDl/wOo8UzOHki46T8smUdSovyQzdauuL1/K42V6q6+YcRDTq1mkOVE19CcIcZLfH0KFDJScnR0X+zDPPPLPFZaAX7KoikTAHavJom29IJLSPfmgj3dA+uqF9FOZA/f7775t9pIvz9OOPPzYn08JZetVVV8mhhx4qjzzyiNx4442+yLN48WLfyidT1qm8JDN0q60vXsvjZnuprptzEAmibpFQP5PlcbO9JUuWiBZWrVrV4sfKlSv9FpsEjCDNnUGE9tEPbaQb2kc3tI+7ZHx87z333CP9+vWTp556SvLy8sx7OE32iSeekIsuukj69Onjt4iEEEJIRoNf0vHAr+o4EMn6/5r4g3UolbYIhw8++MBvEQghhBBCSEBJyoF63XXXJXTdnXdGHsagjaqqKunUqZMccMABEV/GkNd12rRpZnua1w7U9u3b+1Y+mbJO5SWZoVttffFaHjfbS3XdnIOIRt3CUYf/pxFJuXr1aunVq1eLcjyhvNcHN2mSJ5XtwXkKGwDYpF27dpIObNiwQdauXSsDBw6U/Px8M35amgqCkHSbO0ljaB/90Ea6oX10Q/sodKC++uqrTX4OpyQOltJOQUGB/PWvf230PlIQgB49evgik1/lkynrVF6SGbrV1hev5XGzvVTXzTmIaNVtz549ZcWKFebHz5ZuT9KWDyrdc6AC5D2FTaxoVK189dVXMnny5HBqqSeffFLq6+vl+uuvl2uvvVaOPvpov0UkAUPb3EkioX30QxvphvbRDe3jLinLgTp//nyZPXu2XHPNNWZheu+990o6gS8XyIP17LPPmhyoP//5z2XXXXf1XI5169b5Vj6Zsk7lJZmhW2198VoeN9tLdd2cg4hW3WJB2LdvXxM92FJnYE1NjWjCa3lS2R50DxvAFrCJtnFi53//+5+cddZZUlFRIb/5zW/C7xcXF5sIVOS+x9qVkFSi+Z4gtE86QBvphvbRDe2TJjlQsQ2qW7duZqGKCJG77rpLpk+fLunC8uXL5YgjjjCvET0LRzAhhBBC9ACHHbZg40dPPBIFEauDBg0SLXgtTyrbs/LRpgMPPvig9O7dW/7+97+bk1WR7x6MHDlS/vGPf8iECRPk8ccflzFjxvgtKiGEEEIIUY4ryZ922WUX+frrryWdKCwsNAvtO+64w+TzOvXUU33pA/K6+VU+mbJO5SWZoVttffFaHjfbS3XdnINIOugWDjz8cJvoA060llzv9sNreVLZXrTzVPM4mTt3rowfP9443qPlLioqklNOOUV+/PFH3+QjwUTzPUFon3SANtIN7aMb2icNHajYDtWmTRtJJ7p27SpHHnmknHzyyfLcc8+ZrV1/+tOfPJejrKzMt/LJlHUqL8kM3Wrri9fyuNlequvmHESCqFttfeEc5B32Q0KjwaFkmg4XI8FA+z2R6dA++qGNdEP76Ib2UbiF/7rrroubYwsn12Or2JlnninpSufOnWX06NEyZ84cz9vetm2bb+WTKetUXpIZutXWF6/lcbO9VNfNOYgEUbfa+sI5yBv22GMPeeONN2KuSbGl/6WXXjLb+QnJlHuC0D7pAG2kG9pHN7SPQgfqq6++GvN9bO2C83HSpEly2WWXiXbg6D3nnHPkggsukJNOOqnRwtqPE8xwqq1f5ZMp61Rekhm61dYXr+Vxs71U1805iARRt9r6wjnIGy655BI544wzZOLEifKLX/zCbOPHwVLYtv/MM8/ImjVr5LbbbvNbTBIwNN8ThPZJB2gj3dA+uqF93CUr1JJTGAJGbW2t7L///tKvXz958cUXw4Nt3rx5Ji8W8mb93//9n+N2FixYYByyyLM6YsSIFEhOCCGEEKITTeuejz/+WG655RZZtWpVxPtdunSRG2+8MXyAKNFrQ0IIIYQQDeseV3Kggtdff12006pVK7nhhhvku+++M1Gzzz//vDzwwAMmWgELa0Qu+BEV61f5ZMo6lZdkhm619cVredxsL9V1cw4iQdSttr5wDvKOAw88UN599115+eWXzRrvvvvukxdeeEH+85//0HlKMvKeyHRoH/3QRrqhfXRD+yjZwl9XVyfvvfeefPPNN4KgVXhljz322EYhwqtXr5abb75Z/vvf/8q4ceNSJigiRG+66Sa59dZbZcKECU0eCDB9+nST82r58uVmG/6wYcNMmWOOOabR9SeccII5MGratGkyZcoUcyrr2LFj5fLLLzdOVK9xGhDspHwyZTM4gNl1gqRbbX3xWh4320t13ZyDSBB1q60vnIO8BVv3d9ttN/MgxG3S4Z7IZGgf/dBGuqF9dEP7KHCgbt68WX7729+aA6Isg2Ax+sQTT8izzz4rxcXF5r2nn37anFxfWVkpe++9d8qERL6qu+66q9nrqqqq5Oyzz5avvvrKOHaHDh1qkuh+8cUX5gGn7uTJkxuVO+6448xDA+3atfOtfDJlncpLMkO32vritTxutpfqujkHkSDqVltfOAe5QzIHmGI9i/Wr32DX0zvvvGNy81911VWNPq+vrzeRs6+88oqJLsE6d+DAgSbd1GmnnWbOISA60HRPkMbQPvqhjXRD++iG9lHgQL3//vvl+++/N1GciNhs3bq1fPjhh/LQQw/JHXfcYZySiNj84IMPjMGuv/56Ofnkk1Mi4CeffGIWlRUVFc1eC1ngPB08eLA89thj0qdPH/P+rFmzzKFW2L615557pkw2N2jTpo1v5ZMp61Rekhm61dYXr+Vxs71U1805iARRt9r6wjnIHaLznKYLr732mnGexqOhoUGuuOIK+de//mUcvljn4r1vv/3WPLD+fvTRR01qKuI/mu4J0hjaRz+0kW5oH93QPgocqHBiYls7kvBbDBkyxGyPv/fee82C7f333zcnnN5+++3SqVMnx4IhgSucoNhaj0ViIovmV1991Swskd/Kcp6CQw45RK699loj/9SpU+XEE0/05Zd6RMguWrRIBgwYYFId1NTUGGc0UgWsWLEifLBV9+7dTdQv6N+/v6xbt86Uzc/Plx49esiyZcvMZ9Az+rFx40bzd9++fWXp0qXGLnl5edK7d29ZsmSJ+axDhw7GThs2bDB/Qz8lJSXGMY0UBjhIC7Lhhmvfvr2pA+2CXr16SVlZmYnmRcQD5Ef0A6KRkTIBMq5du9Zc27NnT3NdeXm5scWgQYOMDLBh27ZtjYMdfQfoJ6KVUTfAFwL0DekiIAdktr4MdevWzeirtLTU/I2oi5UrVxp9IdFv586dwzqEPhGpgf4ByIuTdiEr+oW6kN4BoBzYtGmTeYYe1q9fH9Y3+gOdgo4dO5r+2/WNchir0C10atc3bIC6AGwB2S19Q2fQN0AEN8aBXd/QH/6GztBXS9/QH9JMoD8A4wF12vUNedF/XIe67fpGv7Zs2WL+xrXQg6Vv9A86BV27djW6tesbtog1ZvEa9rWPWYwHS99o1xpbGLOQ09I3dAh9YhxAX+i7Xd/QlX3Mog1L3yhr5XjBmIW97PpGP+1j1q5v1IO6rTG7detW88D9hL7axywedn2jffuYtesbctjHLHQAOWCj3Xff3ejMGrPQhV3fsIN9zMabIzBmMRYsfUNO1JfoHGHXd6w5Av2BrWLNEXZ9x5sjoB/01T5mUV8icwT0Bf23ZI6wxlYmzhH2MevGHIH6ILcXc4R9zLoxR0AufJ7oHIExYx+zqZ4jYBPUY80R1pzsxhwBfUNeXJeKOSJ6HYG/oTP7HAFZ/QCOxHQD4wwBAE3x+OOPG+cp7PyXv/xFdtllF/P+119/LRdeeKF89NFHJqgBwQxEh01x7xOd0D76oY10Q/vohvZxl6xQAkkS8GX4uuuua5R7FAv7ww8/3Hz5wOcTJ05MiVCIdv3d735nFvH4coQI1JkzZ5ovC/FyoD788MPy5z//2cj60ksvNfocXzL22Wcf8+UQW7V+9rOficaTTPEFzsmAd1I+mbJO5SWZoVttffFaHjfbS3XdnINIEHWrrS9Bn4N4gntiYAmOQ0w//fRT4+CG4zp6Cz+c/Icddphxyj/yyCMmWMEOnKdYM6P87Nmzw2m1nEIbBme+IZHQPvqhjXRD++iG9nF33ZNQBCqcj4iciMZ67/TTT0+Z8xQgOgPO09GjR8uNN95oOgAHalPMnTvXPKNMLBBJMXLkSJML9fPPP/fUgdoSEOniV/lkyjqVl2SGbrX1xWt53Gwv1XVzDiJB1K22vnAOcod0y4GKduE8PeKII0w0N9an0eAAVzhPEVkMR2o0Bx10kIkGRlADrsUuK+Ivmu4J0hjaRz+0kW5oH93QPu6Skn3sBx98sKQSbCGbPn26PPfccwn/6m1tR7Nv3Y8GW9Hs12oEkQZ+lU+mrFN5SWboVltfvJbHzfZSXTfnIBJE3WrrC+cgd8AP8C19WGkSvAbpDnDGANIoYHdVcwECOJwVzt5YWAe3xnLAEu/RdE+QxtA++qGNdEP76Ib2cZfclFSyM5dgqhg6dKh5tAQr1xdyosUDuceAlbNNI8hTh1xnfpRPpqxTeUlm6FZbX7yWx832Ul035yASRN1q6wvnoMzOgYo8t3/4wx9MDlw4UZtauwYlQCCT0HRPkMbQPvqhjXRD++iG9nGXhD2fOOTAOiTBwjokAYcIRH/mdfgwcpsCHJAQD+sz5JjSSrzoAi/KJ1PWqbwkM3SrrS9ey+Nme6mum3MQCaJutfWFc5AesIZtyoHpBsjbP2/ePDn++OPll7/8ZZPXWgeHpXuAQCaR7vdE0KF99EMb6Yb20Q3to8SBOmXKFPOIhT3Zvd1w8+fPF6/AQVY4ETfdBxVO4fWrfDJlncpLMkO32vritTxutpfqujkHkSDqVltfOAd5x4wZM8xBSzgYwL5OrK+vl4qKCnPYwnfffeeZPN9884385S9/ke7du5s8/81h/ejfVIBAQUFBxLXEX7TfE5kO7aMf2kg3tI9uaB8FDtQTTjhBtIPTshARi+1Q8bA+w0mlWlmyZIkMHDjQl/LJlHUqL8kM3Wrri9fyuNlequvmHESCqFttfeEc5A3Tpk2T++67zxwkioNPEaEJxyV2VcHZCMfjGWec4Zk8aPPqq682jlwEJbRt2zahAIFEcSNAADu84GQeMGCArF692hwsi3V0ly5dZMWKFeYaHHAVCoXC6bT69+8v69atM2Xh+O3Ro0c4vQByvmZnZ5vDYq1zD/AauoGdkI4AYwp06NBBWrVqJRs2bAinMUBELhzfSB/Wr18/k0vWisKFPdEu6NWrl/legFxw0CHkx7WQs127dtKmTRtZu3ZteMccrsO2R+gQXz4hA+wEG+F69B1g/EBWaxceTjpG3+rq6kydkPmHH34w30u6detm9GVFBuM+Qc5dpHDA59CbpUPoE059K+IY8mJ3H767oF+oa/ny5WF9g02bNpln6GH9+vVhfaM/S5cuDUcuo/92faMcflCAbqFTu75hA9QFYAvIbukbdsVYAMXFxWYc2PUN/W3dutXYF3216xv3n7VbEeMBddr1DXnRf1yHuu36Rr9wzwJcCz1Y+kb/rDzG2LIK3dr1jTzH0WMWfYfOYF/7mMV4sPSNdu1jFnJa+o4es+i7Xd/QlX3Mog1L3yhrH7Owl13f6Kd9zNr1jTFjH7PQtV3f9jGLh13faN8+Zu36hhzQE8A4gw7s+obOrDELXdj1DTvYx2yq5gjoz5r7gjhH2PWdjnMEykK+oM4Rlr7TdY5AXRh3QZ4jsl1YR0DWhAilCYceemho6NChoeeffz7m52PHjjWfz5gxI24dV199tbnmyiuvDHnJ/PnzQ19++aV5bo4ff/zRUVtOyidT1qm8JDN0q60vXsvjZnuprptzEAmibrX1JehzUEvWPW5y5JFHho4//vjQ1q1bQytWrAgNGzbMPNfV1YWee+650PDhw0Nvv/22Z/LccsstZh166623Nvps4sSJ5rN77rkn4n3Ij/efeOKJuPX+7W9/M9dgLZwqtNgwHdE235BIaB/90Ea6oX10Q/u4u+7JloCFKlu/RsTC8pzDo62VRKIR3CqfTFmn8pLM0K22vngtj5vtpbpuzkEkiLrV1hfOQd6ANeG4ceNMBAWiDhCh8eWXX5rIjdNPP12OPvpoefrppz2R5cMPPzTpBBApgQOkEgXRE8CK9oiFFU3jdS5Xkn73BKF90gHaSDe0j25oH3cJjAN1jz32MM9z5syJ+TlCha0cV6NGjRKtIITdr/LJlHUqL8kM3Wrri9fyuNlequvmHESCqFttfeEc5A3YnoUtfBbYqrVw4cLw3/vtt59nJ9e/9dZb5hnb1fbaay8ZNmxYxOPzzz8Ppx3A34cddligAgQyCc33BKF90gHaSDe0j25oH3cJjAP1qKOOCjtQ7Ytji1deecXkUEAOin333Ve00tQC2e3yyZR1Ki/JDN1q64vX8rjZXqrr5hxEgqhbbX3hHOQNcD7OnTs3/Dfyb9kPjEK+L/zA7gVwbuIH/HgPRMkC5FXD37vttltEgIC9H9FYwQOaAwQyCc33BKF90gHaSDe0j25oHwWHSKUD2BKFbVqvv/66XHLJJfLII4+Ef7WfPXu23H333eb1+eefbyISCCGEEEJIcBk/frzcdtttxkl6++23m6jOSy+9VB566CFzoAK27w8fPtwTWc477zzziAcOs0IU6nHHHSdXXXVV+P1DDz3UHFiBwx5mzZolhxxySKPUADiwAZG2Y8eOdbUPhBBCCCGZTKA8iTfccIP8+OOPMn/+fDn22GNlyJAhJurUOpHutNNOk5NPPtk3+RI5ydQ6JTbZE8jgHEYbyZxAhpPHULYlpxRCJtTBUwpTf5Ip9GCdbpzupxRaY0vLKYV4WH97cZIp+g/7uXFKIf5Gm6k6pdCyVbInmUJvIJmTTCETZGvJHGHJm4lzhNsnmUIPdn2n80mmkAn60nLaMXSK+rw6yRRyo39unGQKuVBfUieZusyECRPMffPcc8+ZsQIHIxyQcKAC2MTurNQI7r2zzz7bBAZcd9118vDDD4cjTb/55hvzHjjzzDPN2CX+gzmN6IX20Q9tpBvaRze0j7tk4SQpSQMQNYAvC7feeqtZEMcDX/6mT59uck3hCzC+GCCX1CmnnGIiEfDFzGsWLFhgvlDhC9GIESOavBZfXPBFKFmclE+mrFN5SWboVltfvJbHzfZSXTfnIBJE3WrrS9DnoJase9wEzmk4reHQte8+wkFS+Ay5SOFE1oAVgXrOOec0curCUY4dVP/v//2/sLMd61nL6Y8oVTiFU7nDSosN0xFt8w2JhPbRD22kG9pHN7SPu+uetMmB+sEHH5jcpk05TwGiWbDI/Oc//yn/+9//5Ouvv5aZM2fKiSee6IvztKVYUSt+lE+mrFN5SWboVltfvJbHzfZSXTfnIBJE3WrrC+cgbzj++ONNxGa0Y3H06NHyy1/+Uo3ztDkQDfz444/LzTffbHKjIjoa0clIP3DttdfK1KlTmZ5KEZrvCUL7pAO0kW5oH93QPu7C1RYhhASA0soy80iUDq2LzYMQQoIKUkykSxTGM8880+TncJD++te/Ng9CCCGEEOI9abOFP51xYxsUnSWEEDsvfveGvDzvzYSvP2nXY+SU3Y51VSZCSGaiZfs3cuP/8MMP8uijj4bzGZP0siEhhBBCiJZ1DyNQlYGDGHDgQ3O8u/ijlDtLEm3baRmSebrV1hev5XGzPavuwwcdJKN77h7x2ZQPp0p59TZpl18k1x98ccRn8X5QcSqrk/Kcg3QRJN1q60sQ5yCNWIdnjRkzxhyQZR2aZQfpnZ5++mnfZCTBQ/M9QWifdIA20g3toxvax13oQFVGoifXpspZkkzbTsuQzNOttr54LY+b7Vl1x4oyz83ODT8P7Ni3RfU5lcerstrGVpAIkm619SWIc5BGPv74Y+nQoYN5XV1dLWvWrPFbJJIBaL4nCO2TDtBGuqF9dEP7uAsdqMpo06ZNQtelylmSTNtOy5DM0622vngtj5vtpbpup/U5Kc85SBdB0q22vnAO8u4AUkK8RvM9QWifdIA20g3toxvax10i9zER37EiJdKlbT/lDTpB0q22vngtj5vtpbpup/U5Kc85SBdB0q22vnAO0kNJSYnfIpCAke73RNChffRDG+mG9tEN7eMudKAqY9WqVWnVtp/yBp0g6VZbX7yWx832Ul230/qclOccpIsg6VZbXzgHeceMGTPkggsukEmTJsmZZ54ZfuA0++OPP14OPvhgv0UkAUP7PZHp0D76oY10Q/vohvZxF27hJ4SQgFJbXxvxTAghmcS0adPkvvvuk7y8PCkqKpLS0lLp3r27bNmyRSorK6WgoEDOOOMMv8UkhBBCCCFpACNQldGtW7e0attPeYNOkHSrrS9ey+Nme/HqDoVCsr22yrzGM/52Up9Tedwqq21sBYkg6VZbXzJhDtLA3//+dxkxYoT897//lZkzZ5p58G9/+5t8+eWXcvPNN5uDpfbYYw+/xSQBQ/M9QWifdIA20g3toxvax13oQFVGTU1NWrXtp7xBJ0i61dYXr+Vxs714dX+zboHUh+rNazzjbyf1OZXHrbLaxlaQCJJutfUlE+YgDaxevVrGjRtnok/79OkjxcXFxnmak5Mjp59+uhx99NHy9NNP+y0mCRia7wlC+6QDtJFuaB/d0D7uQgeqMrC9LJ3a9lPeoBMk3Wrri9fyuNlerLobGhrkhW9fj3hv5rf/SCgK1amsTspzDtJFkHSrrS9Bn4O0kJubG3Eabb9+/WThwoXhv/fbbz9ZtmyZT9KRoKL5niC0TzpAG+mG9tEN7eMuzIFKCAksDaEGaQiFpKGhXupC9caxiGjM8pptsqmixLyuxzU736+3PTfs/Czydb2p86frdpbf+Vnj1zvq2lyyWdqWzzFldrTVEP+1KRvZTuPrfpK1pq5Wsv6HKNMd16CveI5mcelyE4W6Z49dfLEFIYR4zaBBg2Tu3Lly8sknm78HDBgg3333XfjzsrIyRmoQQgghhJCEoAPVQ6qqqmTRokVmAY9tZVi0t27dWrp06SIrVqww13Tq1Mn8arB582bzd//+/WXdunWmbH5+vvTo0SMcLYFrs7OzZePGjeZvOFJMOzVVpr7evXvLkiVLzHsdOnSQVq1ayYYNG8zf2MpWUlIiFRUVJkIDURmIToN87du3NwcroF3Qq1cv8yVj27ZtZtsb5F+8eLG5vm3btqaOtWvXmmt79uxprisvL5esrCzz5QUywOmDa9u1a2f6DnCQAw5xQN1g8ODBpm91dXUmYgQyW6fIIZcH9GX9ojJw4EBZuXKl1NbWSmFhoXTu3DmsQ+izvr7e9A9A3jVr1phcZ+gX6lq+fLn5DOXApk2bzDP0sH79+rC+0Z+lS5eazzp27Gj6b+m7b9++ptz27duNbqFTu75xaAXqArAFZLf0DbtC1wBbCjEO7PqG/qBf1Ie+WvqG/rAVEf0BGA+o065vyIv+4zrUDR2GJCRdunaVisoKKd1SKg3SYORfvnKF1NbVSn7rAmnbrq2sXrvGOO6K2xdLTW2NbCkvM9d2695N1m/YYN7LbZUrRe2KZMPGDcY5WVhUKHUN9bJ161ZzbXH79rKlfIuxTVZOthS2KZSS0hJ5e9VsycvPN9dUbN9unlsXtpbtlduNDJKVJXkFebKtYptxAObk5kiDhKS6uso8o93q2hqpq68z/cnOzZHqmiojA/7hGnyGevE3HIx4jsu34i07bj3fyJIsmfG/16RzfTszvjE+Y80R1jwQa47AOARNzRGYk4B9zOJ+TmSOwHWYE1oyR1jyZuIcgXsO+nY6R9j1jX7hgB1LL3Z9o3/QKejatavRrV3fmGti/b+G17Cv/f81jAdL32jX/v8a5LT0DR1CnxgH0Bf6btc3dGX/fw1tWPpGWWvMop/Ql13f6Kf9/zW7vjFm7GMWZe36tv+/hodd32jfPmbt+sa9Y+bkUMjUBx1Y+oZtoDNrzEIXdn3DDvYxG28dgTGL+u36xlhF/5pbR0TrO5F1BNpCffY5ArJqYPz48XLbbbcZPd1+++1y2GGHyaWXXioPPfSQsSO27w8fPtxvMUnAwNgieqF99EMb6Yb20Q3t4y5ZoURPFiFJs2DBAvOFCl+IcJhBU+BLO76EtBSY8fSXLjYRaTlZOfL8yVPNl9CWkEzbycrrWzRirEg+ewRiRDRirAjExtGIcB7Gj0CMjEasQ3mr3iaiEVGmYnuFcTg2F41oj6y0RyA2FY1I0gfcx7inc7KyjaMj+nVDfYMU5OXvfC9HquuqZd22Hc6QWFx/8MVNRqE6vaedlA/6HJRuBEm32vritTxuther7pase9zmgQcekOeee84cJAUH8Pnnny+zZs0yn8GpPW3aNNlrr718lVEjmmyYbmibb0gktI9+aCPd0D66oX3cXfcwAlUZiDxJhlgHxsRyksDRapxxMRxt2NLcuqLop/dtzzvKWFubf3q9esNqWZO9ace1Ox171rblRo6/WFufm3Bi7nAMRm6FjucgrE/AKcnfCtIby0GI58jXOcaZmLvzuZGzMTtbsrMiX1dtr5J2bdvFdUpa9eea63e2k52zs57Yr3Ot93bKZcqZ+rJl7Zq10rdP3/D1lsx2+SNf/yQP6mwKRJUhyg1gjF//7h8lOyvLROZGg/eRC3WP7iPi/sCS7ByUivLJlHUqL8kM3Wrri9fyuNmeNt1Gc/nll8vFF19sopXBY489Zg6SQuQvHKeIwiUkk+6JTIf20Q9tpBvaRze0j7vQgaoMeLxbCpwmcIrY+eNHD0vrVq0ltNPRmHA0YjLbmnfs7iMKohEt511cZ2OUQ7B5R95P18VyRP702lbnTll2OBN3vC4tKZWuXbqE32/kbGxCbjhG0e+WRlQ3BbbbYluuV7SvK5KenXq6PmfghxPkOo0HnKrN5UJNZg5KVflkyjqVl2SGbrX1xWt53GxPm26tLw/4cQmpBPADE9Ic2Bk9erRvspHgo/GeID9B++iHNtIN7aMb2sdd6EBVhpVvryXEcprAUbqtpkIyMRox7MBrNhqxCUdk1GvLMZhINGJEOzGiEX96HS1r5Ov6unopyC/YGVmJNrKajUbUSk3HGpNPL53vM63tWXVbP6Qg12lTuV/xeVNRqE5ldVI+mbJe2zKTCJJutfUliHOQFp566il5+OGHTX5bgP97Tj/9dLnyyivDUaiEuIm2e4JEQvvohzbSDe2jG9rHXbiSVAYOgbC24yaC5TSJtWU3NztXuhR23BHtFycacYfDbsfflRWV5gCSeNGI4ahBm4Nwi4ks7BrxvuWctEcgxnv9k4OxmWhEm4M01dGIWrFvzc60cR00edxsz6q7rqFONm0vafrgLMwZEpLNlaXm+lY5rVIuq5PyyZTVNraCRJB0q60vQZyDNPDaa6/JXXfdZQ4bGzdunFm7fPbZZ8apioO8rr/+er9FJBmApnuCNIb20Q9tpBvaRze0j7vQgZrmNLVlFw6Ss0ad2uSBMU4ddkFy8hGSzsAZeufYa6W8akfUFZjy4VQpr94m7fKLzOFRFsUFbWM6TwkhJJ15/vnnZc8995Snn35a8vPzwz80Iw/qzJkz5aqrrlK1G4IQQgghhKQP6bkfOMB06dIlqejTWFgHxiR6eFJL2nZShmSebrX1xWt53GzPXnfnwo4ysGPf8ANR6ADP9vc7FXZwTVYn5TkH6SJIutXWl6DOQX6zePFiOe6448LOU4AdK5MmTZKamhpZsoRJ24n7aLonSGNoH/3QRrqhfXRD+7gLHajKwBazlkafxjptO/rAmFS37aQMyTzdauuL1/K42V6q63Zan5PynIN0ESTdausL5yB3qKyslLZt2zZ6v3fv3ubH5PLycl/kIpmFpnuCNIb20Q9tpBvaRze0j7vQgaqMkpKShK6zHxjTFNaBMYlEoSbattMyJPN0q60vXsvjZnuprttpfU7Kcw7SRZB0q60vnIPcoaGhIWaO9JycHPPMLxXECzTdE6QxtI9+aCPd0D66oX3chQ7UNCWZA2MIIYQQQgghhBBCCCEtg4dIKWPAgAG+HRiTaNtOy5DM0622vngtj5vtWXWXVpaZhx3rhxM8LylZEfFZh9bF5pFqWZ2U5xykiyDpVltfgjgHaWHLli2yZs2aiPfKysrCURnRn4GePXt6Jh8JPtruCRIJ7aMf2kg3tI9uaB93oQNVGVjY9+nTJ6FrcWAMHhbRB8a42baTMiTzdKutL17L42Z7Vt3vLv5IXp73Zsxr8MPKte/eGfHeSbseI6fsdmzKZXVSnnOQLoKkW219CeIcpIUpU6aYRyyuuuqqRu9hy//8+fM9kIxkCtruCRIJ7aMf2kg3tI9uaB93oQNVGdXV1WnVtp/yBp0g6VZbX7yWx832rLoPH3SQjO65e8LlYkWf2utzKo9XZbWNrSARJN1q60sQ5yANnHDCCX6LQIiqe4I0hvbRD22kG9pHN7SPu9CBqoyCgoK0attPeYNOkHSrrS9ey+Nme1bd8bbkJ1ufH+U5B+kiSLrV1pcgzkEauPPOyEh7QvxA0z1BGkP76Ic20g3toxvax114iJQyunXrllZt+ylv0AmSbrX1xWt53Gwv1XU7rc9Jec5BugiSbrX1hXMQIcGF94RuaB/90Ea6oX10Q/u4Cx2oyli+fHlate2nvEEnSLrV1hev5XGzvVTX7bQ+J+U5B+kiSLrV1hfOQYQEF94TuqF99EMb6Yb20Q3t4y50oBJCCCGEEEIIIYQQQkgcmAPVQ6qqqmTRokUyYMAAWb16tdTU1Ejr1q2lS5cusmLFCnMN/i4tLZXNmzebv/v37y/r1q0zZfPz86VHjx6ybNky81mnTp0kOztbNm7caP4OhULmua6uztTXu3dvWbJkiXmvQ4cO0qpVK9mwYYP5GyezlZSUSEVFheTm5kq/fv2ktrbWyNe+fXuTOwPtgl69eklZWZls27ZNcnJyjPyLFy827aFO1LF27Vpzbc+ePc115eXl5mTbQYMGGRkaGhqkbdu20q5dO9N30L17d6msrDR1g8GDB5u+Qf42bdoYmVetWhUORYe+oBswcOBAWblypZG5sLBQOnfuHNYh9FlfX2/6ByAvTqNDQmX0C3VZv8ygHNi0aZN5hh7Wr18f1jf6s3TpUvNZx44dTf8tffft29eU2759u9EDdGrXd15enqkLwBaQ3dI37Apdg+LiYmN3u76hP/QN9aGvlr6hv6KiItMfgPGAOu36hrzoP65D3XZ9o19btmwxf+Na6MHSN/oHnYKuXbua9u36hi1ijVm8hn3tYxbjwdI32rXGFsYs5LT0DR1CnxgH0Bf6btc3dGUfs2jD0jfKQi8AYxb2susb/bSPWbu+oRvrb9h469at5oH7CX21j1k87PpG+/Yxa9c35LCPWegAcqD/sB90Zo1Z6MKub9jBPmbjzREYs6jL0rd1nyQyR0TrO9YcYdkq1hxh13e8OQK6APYxi/GVyByBdiBbS+YIS95MnCPsY9aNOQLjx65vN+cI+5h1Y45AWegr0TkCY8Y+ZlM9R0B3qM+aI6w52Y05AvoG6F8q5ojodQTkRX32OQKyEpKpWP9vEJ3QPvqhjXRD++iG9nGXrJDldSOusWDBAvOFCl+IRowY0eS1+CKFL1jJcN4/rpOSyi3SsXV7eexXLT9IIZm2nchLMke32vritTxutpfqup3W56Q85yBdBEm32voS9DmoJeseohPaMDjzDYmE9tEPbaQb2kc3tI+76x5u4VeGFXXTHKWVZbKkZEXEo65hR8QHnqM/w/WpattpGZJ5utXWF6/lcbO9VNfttD4n5TkH6SJIutXWF85BhAQX3hO6oX30QxvphvbRDe3jLtzCn6a8u/gjeXnemzE/K6/eJte+GxmBetKux8gpux3rkXSEEEIIIYQQQgghhAQDbuFXtg0KucSQY6w5EFEaK6oUeceQEy6aDq2LzSMVbTstQzJPt9r64rU8braX6rqd1uekPOcgXQRJt9r6EvQ5iNu/0x/aMDjzDYmE9tEPbaQb2kc3tE9ycAt/mmIdcNEccIYO7Ni30aOwJi/m+805T1vSttMyJPN0q60vXsvjZnuprttpfU7Kcw7SRZB0q60vnIMICS68J3RD++iHNtIN7aMb2sdd6EBVBk5A9qt8MmWdyksyQ7fa+uK1PG62l+q6OQeRIOpWW184BxESXHhP6Ib20Q9tpBvaRze0j7vQgaqM/Px838onU9apvCQzdKutL17L42Z7qa6bcxAJom619YVzECHBhfeEbmgf/dBGuqF9dEP7uAtzoCrLI1VfXy85OTlJt+WkfDJlncpLMkO32vritTxutpfqujkHkSDqVltfgj4HMX9m+kMbBme+IZHQPvqhjXRD++iG9kkO5kBNU5YuXepb+WTKOpWXZIZutfXFa3ncbC/VdXMOIkHUrba+cA4iJLjwntAN7aMf2kg3tI9uaB93oQOVEEIIIYQQQgghhBBC4kAHqjI6duzoW/lkyjqVl2SGbrX1xWt53Gwv1XVzDiJB1K22vnAOIiS48J7QDe2jH9pIN7SPbmgfd8l1uX7SQpzmq3BSPpmyzK/hHkHSrba+eC2Pm+2lum7OQSSIutXWFy/kKSmvMg+wbdt2Kdne9G/mHdsVmEe665YQv+E9oRvaRz+0kW5oH93QPu5CB6oyNm7cKMXFxb6UT6asU3lJZuhWW1+8lsfN9lJdN+cgEkTdauuLF/L865NlMuPfCxO+fsLYYXL6EcPTXreE+A3vCd3QPvqhjXRD++iG9nEXOlAJIYQQQgLGkfv3l3137W5er1y5Uvr06SO3TvtEyrbVSHFRntx6zv4R1ycTfUoIIYQQQkimQAeqMvr27etb+WTKOpWXZIZutfXFa3ncbC/VdXMOIkHUrba+eCGPfUt+366FkpeXJ7k5O7bx43lw7/aB1C0hfsN7Qje0j35oI93QPrqhfdyFh0gpY9OmTb6VT6asU3lJZuhWW1+8lsfN9lJdN+cgEkTdausL5yBCggvvCd3QPvqhjXRD++iG9nEXOlCVsX37dt/KJ1PWqbwkM3SrrS9ey+Nme6mum3MQCaJutfWFcxAhwYX3hG5oH/3QRrqhfXRD+7gLHajKaNWqlW/lkynrVF6SGbrV1hev5XGzvVTXzTmIBFG32vrCOYiQ4MJ7Qje0j35oI93QPrqhfdyFDlRl4JAHv8onU9apvCQzdKutL17L42Z7qa6bcxAJom619YVzECHBhfeEbmgf/dBGuqF9dEP7uAsPkfKQqqoqWbRokQwYMEBWr14tNTU10rp1a+nSpYusWLHCXFNbWyvdu3eXzZs3m7/79+8v69atM2Xz8/OlR48esmzZMvNZp06dJDs7WzZu3BhOGPzDDz9IQUGBOSyid+/esmTJEvNZhw4dzK8RGzZsCN9YJSUlUlFRIbm5udKvXz/59ttvpU2bNtK+fXtTB9oFvXr1krKyMtm2bZvk5OQY+RcvXiyhUEiqq6uNjGvXrjXX9uzZ01xXXl4uWVlZMmjQICNDQ0ODtG3bVtq1a2f6DtDPyspKUzcYPHiw6VtdXZ2RAzKvWrXKfNatWzejr9LSUvP3wIEDzanC0FdhYaF07tw5rEPos76+3vQPQN41a9YYWdEv1LV8+XLzGcrZc4VAD+vXrw/rG/1ZunSp+axjx46m/3Z9oxzC5KFb6NSub9gAdQHYArJb+obOMBZAcXGxGQd2fUN/+Bs6Q18tfUN/RUVFpj8A4wF12vUNedF/XIe67fpGv7Zs2WL+xrXQg6Vv9A86BV27djW6tesbtog1ZvEa9rWPWYwHS99od968eaYNjFnIaekbOoQ+MQ6gL/Tdrm/oyj5m0Yalb5SFXgDGLOxl1zf6aR+zdn2jHtRtjdmtW7eaB+4n9NU+ZvGw6xvt28esXd+Qwz5moQPIARvtvvvuRmfWmIUu7PqGHexjNt4cgTGLsWDpG3KivkTnCLu+Y80R6A9sFWuOsOs73hwB/aCv9jGL+hKZI6Av6L8lc8TChQvN60ycI+xj1o05AvVBbi/mCPuYdWOOgFz4PNE5AmPGPmZTPUfAJqjHmiOsOdmNOQL6XrBggbkOdQC0gf4mM0dEryPwN3RmnyOsdgjJRHDP4N4nOqF99EMb6Yb20Q3t4y5ZIayyiavgiwu+UOEL0YgRI5q8Fl9onAx4J+WTKetUXpIZutXWF6/lcbO9VNfNOYgEUbfa+uK1PLM//U7mLq2R97/c4ZAFvxjdR8aNGSQDehanvC8tWfcQndCGwZlvSCS0j35oI93QPrqhfdxd9zACVRmI8PCrfDJlncpLMkO32vritTxutpfqujkHkSDqVltfvJRn9pxVcv/Li01kr51Zc1bJf75aJVecPkrGjOodGN0S4je8J3RD++iHNtIN7aMb2sdd6EBVBrbM+VU+mbJO5SWZoVttffFaHjfbS3XdnINIEHWrrS9eybN0TZnc//wcacBeo6gNR/XmTTGf9+3eNulIVG26TQeQjmP69Ony0UcfhVNoID3FmDFj5OyzzzYpGaJBugeUeeONN0yKEaS/GDZsmEyYMEGOOeYYH3pB4sF7Qje0j35oI93QPrqhfdyFh0gpw8rP5kf5ZMo6lZdkhm619cVredxsL9V1cw4iQdSttr54IU9VdZ288O5CeE6bvA6Bqa9/uCN3aRB0q50vv/xSjjvuOHnqqadM7l/kALbyAD/55JPyq1/9Sr777ruIMshNfNZZZ8kDDzxgcpshXy7yzn7xxRdyxRVXyA033OBbf0hjeE/ohvbRD22kG9pHN7SPuzAClRBCCCFEMfX1DbJ1e62UVVRLeUXNjse2n16XbcNztZRZn1XUSE1tfWJ1N4Tkw7mr5dJT92q0zZ+kFhzmdvHFF5vngw46SKZMmWIOCAM4MOzqq6+WOXPmyIUXXihvv/22ycMF7rjjDvnqq69MTrPHHnssfMLurFmz5LLLLpOXX35Z9txzTzn55JN97R8hhBBCSJDhIVLKEvEjygDbspLFSflkyjqVl2SGbrX1xWt53Gwv1XVzDiJB1K2mvmDZtaW8QqrrsnY6P+2OUJuD1DyqjXN0W2Wt63K9dOcxUpCXmxLd8gCi2CDq9M477zROUzhIi4qKIj4vKSmRI488UsrKyozTFA7RVatWyRFHHCH19fXy2muvyfDhwyPKvPDCC3LLLbdIt27djEM1Ozs1m8tow2DMN6QxtI9+aCPd0D66oX2Sg4dIpSmlpaXSo0ePZq8rKa8yj2g2bdoknTt3bvR+x3YF5pGKtp2WIZmnW2198VoeN9tLdd1O63NSnnOQLoKkWzf7UluH6NAoB6jdKbrTEfpTpGiN1NU3uCJLdnaWtGuTt+NRmCfzlm6OTn0ak1a52ZLfKkcyfZy4zWeffWaeDz300EbOU9CxY0fZa6+9jCP022+/NQ7U119/Xerq6mT33Xdv5DwF48ePN05ZbNn7/PPP5Wc/+5knfSHx4T2hG9pHP7SRbmgf3dA+7kIHqjIqKioSuu5fnyyTGf9GbrPEmDB2mJx+xPCUtO20DMk83Wrri9fyuNlequt2Wp+T8pyDdBEk3SbaF0SHVlTVRW2Pr27kCC3f6QjF37jeLQoLcqW4Tb5xiLZtkyfFRXCO7vi7eKejtLhox994FBa0Mk5Uiz/NmCOz5qwKHxgVi5zsLDl4r15Jb98P0jhxm/PPP99Ekw4YMCDuNdbGMEScgrlz55rn0aNHxz0sYuTIkSYfKh2oOuA9oRvaRz+0kW5oH93QPu5CB6oycnMTM8mR+/eXfXftHvHerdM+MdEt+IJ16zn7R3zWXPRpS9p2WoZknm619cVredxsL9V1O63PSXnOQboIgm6RBxSOznWltbJ14QZbVKg9l+hP+UO3VtQ06Wx0Qm4OokN3ODvzchqkW+fiCEeo+azoJ6do28I8ExnqhHFjBsl/vlrV5DXw1407eFBGjxOvQBQpHvHAFn44QcHQoUPNMw6XAlbe01j07t3bOFCta4m/8J7QDe2jH9pIN7SPbmgfd6F2ldG/f/+Erou1JT83Jzv8PLh3e9fadlqGZJ5utfXFa3ncbM+qO15aj3jES+vhVFYn5TkH6UKbbhsaQiYXaGSe0Nj5Q3c4Q6ulsjqxg5SSoah1q4iI0J8e+Tvfj4wQbZ2f6/khTQN6FssVp4+S+5+fI2ja7hxG5Cmcp/gc1wVlnKQzkydPlsrKSpO77KijjjLvbd68Oby9Px7t27cPb9sj/sN7Qje0j35oI93QPrqhfdyFDlRlLFq0yJyymi5t+ylv0AmSbrX1xWt53GzPqjtVaT2cyuqkPOcgXbit26oabJWvaRQVGusgpfKd0aEuBYdKXm62tNvp6Cy2RYPGig618ovm7PzRMhm8HLdjRvUWqdkic5fVyPtfrAy/f8jevU3kqRPnKeA9mBoeeeQReeONN8zr8847zxw0ZR0GAfLz8+OWtT6D85X4D+8J3dA++qGNdEP76Ib2cRc6UAkhJAC4kdaDkESpr8dBSrXhrfA/bY+3H55ULeXbfzpICdvr3QCRltj+bo/+lPoq6d2jc+Po0J3Ro/l5OZ5Hh3pJr84FMuZnu8nchRukpLxaOrbLl8tOG+W3WGQnDz30kEydOtW8PuSQQ+Tcc88Nf5aTkyMNDYkdOubGGIYDF1/GkLd19erVUlNTI61bt5YuXbrIihUrzDU4vBS5W61oWUS/rFu3zpSFcxeHWVjpBTp16iTZ2dmyceNG83ffvn3Nazh/kc8V6QiWLFliPuvQoYO0atVKNmzYEE5jgDQHyO+GLYr9+vWTxYsXh6NwEbmLdkGvXr2krKxMtm3bZnQI+XEt5GzXrp20adNG1q5da67t2bOnua68vNzocNCgQUYG6L1t27bmevQddO/e3ciKugG+pKJvOOgLdUJmyAeddevWzejLigweOHCgrFy5Umpra80pv9CbpUPoE3lv0T8AedesWSPV1dWmX6hr+fLlYX1bB8cC6AGHiFn6Rn+WLl0ajlxG/+36RjmcNAzdQqd2fcMGqAvAFpDd0jfsin6B4uJiMw7s+ob+tm7dauyLvtr1jUPT0B+A8YA67fqGvOg/rkPddn2jX1u2bDF/41rowdI3+gedAvzoAN3a9b1q1apGYxZtw36wr33MYjxY+ka79jELOS19R49Z9N2ub+jKPmbRhqVvlLWPWdjLrm/00z5m7frGmLGPWejarm/7mMXDrm+0bx+zdn1DDugJYJxBB3Z9Q2fWmIUu7PqGHexjNlVzBNqz+h7EOcKu73ScI6w5LqhzhKXvdJ0joDvUF+Q5ItuFdQRkTYSskJWtnrjGggULzKDEoBoxYkST18L4GEzJMOn2d2RzWZV0Ki6Qp24+osXlk2nbibwkc3SrrS9ey+Nme03Vncyc4FRWJ+U5B+kAy4LK6jpZunKdtMovarw9PsYWemytd2s1UZCXE44OtUeI2h2h9m30RYgOtR2kpHGc+DUHOV0nNFV3suueTAaL9dtvv11mzpxp/j7ggAPk0UcfNV/OLPbdd1/zpebBBx+UI488MmY9d911l0yfPl0OPPBAefLJJ1MiG22YPNrmGxIJ7aMf2kg3tI9uaB931z2MQFUGvPHp1Laf8gadIOlWW1+8lsfN9lJdt9P6nJTnHOQOtXWIDo3h/IzIGWqPFK2RuvrEIt5aCk6Ij47+NI+ixu+Zg5QQHdoqx3G72sYJ5yCCCJJLLrlEPv74Y/P3EUccIffee6+JnrCD6Ak4UK1oj1hY0TRN5Ukl3sF7Qje0j35oI93QPrqhfdyFDlRlIFTZr5wVybTtp7xBJ0i61dYXr+Vxs71U1+20PiflOQclFh1aUVW3I/JzW6zDkxqfLo/r3aKwIDfs9ISz036okpU/1NpGj0dhQSvjRPUabeOEc1BmA1jLXMQAAEHTSURBVJ2dc8458sMPP5i/zzrrLLn66qvNlrRosD0O29as7YqxsLbW8eAIHfCe0A3tox/aSDe0j25oH3ehA5UQQgjxCeQBtW+HtyJAI5yhVv7Qne/bT1JPJbk5WT85P4vyJDtUIz27dYp5kJKJDi3Mk1a5yR+kREgmglxmZ5xxhsn7BYfpDTfcIBMnTox7/R577CHvv/++zJkzJ+bnyCX23XffmdejRjGvLSGEEEKIW9CBqgwkGU6ntv2UN+gESbfa+uK1PG62l+q6ndbnpHy6z0ENDSGTCzTaEWp3flrRojhoaWtFtVRWu3OQEihq3SoiItSeLzTiIKWdEaKt83MjDqFBMvagbAPSNE4A56DMBM7O888/3zhPcajBfffdZ7buN8VRRx0l999/v3GgLly4UIYNGxbx+SuvvGIOWYAdkC+V+A/vCd3QPvqhjXRD++iG9nEXOlCVgdPm/PrCmkzbfsobdIKkW2198VoeN9tLdd1O63NSXtscVFVTF94mH709PtZBSlsrasSl4FDJy82OOkhpRzRorOhQ88BBSjnZgbpvg9QXzkGZybRp02TevHnm9U033dSs89Q6XXbcuHHy+uuvm5ypjzzyiNnWD2bPni133323eQ3HLE6UJf7De0I3tI9+aCPd0D66oX3chSstZWzdulW6deuWNm37KW/QCZJutfXFa3ncbC/VdTutz0l5N+cgbHvfanN2lsU6SAmvw4ct1Zjt9W6AIE9sf4/ODxozOnRn9Gh+Xk5EdGgm3rdB6gvnoMyMPn366afNazg6X3vtNfOIxwEHHCAXX3yxeY1t/j/++KPMnz9fjj32WBkyZIiJOl2+fLn5/LTTTpOTTz7Zo56Q5uA9oRvaRz+0kW5oH93QPu5CB6oyYh0goLltP+UNOkHSrba+eC2Pm+2lum6n9Tkpn2hZHKRUWY2DlGpk5cYq2VK7PmLbfKzoUGytD7kUHVqQlxMVHWo7SCnsDP3p8yJEh/pwkFK637dB6osX8pSUV5kHWL25WqRgi9TVN5i/8bxoVeSp7h3bFZhHuutWKzgwqqyszLyuq6uLm9PUonv37uHXxcXFMmPGDJk+fbq89dZb5lAp6H3PPfeUU045RcaPH++6/CRxeE/ohvbRD22kG9pHN7SPu2SF8E2UuMqCBQtk+/btUlhYKCNGjHCtnUm3vyOby6qkU3GBPHVz89vCCCHB5je3/UtKyqulY7t8efqWI0UrcObEO0jJihCNOF2+okZq63Y4glINToiPjv40j6LG75mDlBAd2irHFVkIccLz73wvM/69MOHrJ4wdJqcfMTyt1j3EPWhDQgghhGQKCxJc9zACVRmLFy8O57ZKh7b9lDfoBEm32vritTxuther7qVryuT12YuN8xTg+U8z5si4MYNkQM9iV2VdtGiRdO/Vb0fk57bETpevqKoTtygsyA07PeHstB+qZM8fakWKFha0Mk5Uou++DVJfvJDnyP37y7677ohiXLVqlfTu3bvJ65OJPtWoW0L8hveEbmgf/dBGuqF9dEP7uAsdqB6CfFVwLgwYMEBWr15t8mEhwW+XLl3MiaygtrZWSktLZfPmzebv/v37y7p160zZ/Px86dGjh9m6BTp16mRCtDdu3Gj+toKJsTUM9eHL0pIlS8x7HTp0MCe+btiwwfzdp08fKSkpkYqKCpOLq1+/frJt2zYjX/v27aWgoMC0a53khm1n+DwnJ8fIjxsT7VVXV5s61q5da67t2bOnuQ7Ji5GvDzcvZGhoaJC2bdtKu3btTN+t7Wk45dna0jZ48GDTN8jfpk0bIzO+9AHk8YC+oBswcOBAWblypdEXfiXo3LlzWIfQZ319vekfgLxr1qwxsqJfqMvKG4ZyYNOmTeYZeli/fn1Y3+jP0qVLzWcdO3Y0/bf0jYMdUA6/VEC30Kld33l5eaYuAFtAdkvfsCt0bW3Nwziw6xv6gx5RH/pq6Rv6KyoqMv0BGA+o065vyIv+4zrUbdc3+rVly45tm7gWerD0jf5Bp6Br167hsWjpG7aINWbxGva1j1mMB0vfaNcaWxizkNPSN3QIfWIcQF/ou13f0JV9zKINS98oC70AjFnYy65v9NM+Zu36Rt+sv2Fj5IrBA/cT+mofs3jY9Y327WPWrm/IYR+z0AHkgI1gP+jMGrPQhV3fsIN9zMabIzBmUZelb8iJNq0xu2h9gzzwwtcS7QL8z1crzeOsowbIbv0Kjb5jzRGWraw5oqx8q1TVZknb9l3k+0XLpKKyXhqyWklljcj6TVtkW1W91DXkyJatVVK+vVYqKuukIbTjkJRUk5uTJW0KcswDTs+OxYVSX1Mh7dsWSI+uHaR1XpZk1VdJm9a5MmLoQCkvXS+hhvpm5ohaGdC3q7HxurL0miPsY9aNOQL12edkN+cI+/9rbswRkAv6SnSOwJix/7+W6jkCNkF91hwBYJtUzhElNn1XloZEqjY1uY4oqRIpKojUdyLrCOgQ9eHesPQNWQnJVLi5Tze0j35oI93QPrqhfdyFW/iVbYPCFxN8UfJjC38ybTuRl2SObrX1xWt53GzPXjciTy+7f7Y0NDGtI7jyukn7GAfkT1GhP22P31gCh6mE36+sducgJVDUulXEQUp5OQ3StVO7xgcp7fy8dX5uo4OUtI2tIBEk3WrrS1DnIAtu/05/aMPgzDckEtpHP7SRbmgf3dA+ycEt/GkKIlTSqW0/5Q06QdKttr54LY/VHn6vqqsPSW1dfdxn5PZETlDzXNcgtfU7n+3v73zGo7KqRiRrvbnu6x82NOk8BQ0hkcnTv0h5H/Nys81BSkWtc01EqNk2XxS5Td7KI2oeOEgpJzLJufWfVjqPrSARJN1q64tfc1C61U1IOsJ7Qje0j35oI93QPrqhfdyFDlRlYBsgtvylS9t+yht0gqRbL/uyw0n5k4MxltNx2fKV0q17j0afWQ7LhJyXMT6zv2d/XVNbJzj82joBWzsI8mxbGBn9+dMjKjp0Zz7R/LwcEx2KbdDJ2ppzkC6CpFttffFaHjfb06ZbQvyG94RuaB/90Ea6oX10Q/u4Cx2oAcLKxsCsDMQvJ2W81ytXlcvabaubdGzGc1g2vtYWrVkfkrroqM36RMf/jjyKmcoxBw4wh8b85Azd4QjdvGG1jNx1mOTwICVCCCGEEEIIIcRAB6oycLhDS3Fy4rbTtpMpQxKjW7fuUlMLZ2EzTkp71GRTDspYDktTtl7q6qK3kzddX+JOSjs7DkMJMji9PTcnW1rlZkurnGzJ3fmMnep5ebkR75nnqNfhsrbX8Z6t1/V1NVJUVGgcnldP/Sgh26DsuSeMbJRPFHQs6uPIeepkTuAcpIsg6VZbX7yWx832tOmWEL/hPaEb2kc/tJFuaB/d0D7uQgeqMnAyL047TpTZc1bJ/c/PMVtu7cyas0r+89UqueL0UTJmVG9X2k62jCYaGn6KpEwkOvKn14h63Bn9mGxOyzjbva1r65GsksR0UibiWIx4L8phWVNTJcXtimI6LxOpb4cTNEdycyFLTvizeI5H9w9w6Whej9mrt7n3mxo7kPHgvXrFdJ6m4p52Uj4T5yDNBEm32vritTxutqdNt4T4De8J3dA++qGNdEP76Ib2cRc6UJVRXl6esKMFkadwnppDY6L8JZYDBZ/37d42oUjUlrTdkjJwUkY4H2NsxU7E6djk62ae4zkx6aSM72SLjpRMyLEYx2FZVlYq3bt1aaa+HMnNyYp6tjksm3FStgQneTqTIZl7K5m6EXWOH06aAtPFuIMHJVSfU3m8KOumbjOdIOlWW1+8lserOYgQwntCO7SPfmgj3dA+uqF93IUOVGXEiwqLBbbtm8ub8QE++/YCOeWXQ5uNsly3frPMXd4yh+W2iu2Sk7uqSeclHKikMXAENo5q/MlZWFdbI22LChOMkvzJ6dgqB85PREVmRUZJJlBPbgqdlHYWL14sgwbFd9ppvs+0t2evGz+UIOrcikq3/0AAm8J5is+b+kHFqaxOyidT1mtbZhJB0q22vgR1DiKE8J7QDu2jH9pIN7SPbmgfd8kK8cQh11mwYIFs375dCgsLZcSIESmpE07Jk657wzgoSXzgVGzKWQinYkTEY5TTMeyUbEG0Zfh1M3kssRWdEDcweZE/XCzvf/FT3tlf7NPHRJ62JC8yIYRoWfcQb6ENCSGEEJIpLEhw3cMIVGUsXbpUBgwY0Ox1OFxIi/N0h5OxuUNv4jghHRye09RrPKe7kzLRsZAOaOuL1/K42V6suuEkvey0UTJ34QZzqFzHdvnmby9kdVI+mbLaxlaQCJJutfUl6HMQIZkM7wnd0D76oY10Q/vohvZxFzpQlVFfX5/QdXmtdjgkE3Giwod49IEDJM9EWMY/+XvTxg3Sp3fPpg/jiXq9bOkSGTJkSAp6TpIdC+mAtr54LY+b7TVVt7WFoyVbOZzK6qR8MmW1ja0gESTdautLpsxBhGQivCd0Q/vohzbSDe2jG9rHXehAVUZRUVFC1yGy8uA9eyV04vYhe/eWc0/Yvdk6161rJd27d2+RvG3btm3R9ST1YyEd0NYXr+Vxs71U1+20PiflkymrbWwFiSDpVltfOAcRElx4T+iG9tEPbaQb2kc3tI+7ZLtcP2khxcWJ5yfEidvNZbBt7sTtZNt2UoZknm619cVredxsL9V1O63PSXnOQboIkm619YVzECHBhfeEbmgf/dBGuqF9dEP7uAsdqMpYvXp1wtdaJ25nZ2U1OjUdf+P95k7cTrZtJ2VI5ulWW1+8lsfN9lJdt9P6nJTnHKSLIOlWW184BxESXHhP6Ib20Q9tpBvaRze0j7twC3+aM2ZUb+nbvW2jE7exbZ8nbhNCCCGEEEIIIYQQ4gxGoCqjpTlI7Sdu46RtYJ243VLnaTJtJ1OGZJ5utfXFa3ncbC/VdTutz0l5zkG6CJJutfWFcxAhwYX3hG5oH/3QRrqhfXRD+7gLHajKqKqqSrpsMiduO23bibwkc3SrrS9ey+Nme6mu22l9TspzDtJFkHSrrS+cgwgJLrwndEP76Ic20g3toxvax13oQFXGli1b0qptP+UNOkHSrba+eC2Pm+2lum6n9TkpzzlIF0HSrba+cA4iJLjwntAN7aMf2kg3tI9uaB93oQOVEEIIIYQQQgghhBBC4kAHqjIGDRqUVm37KW/QCZJutfXFa3ncbC/VdTutz0l5zkG6CJJutfWFcxAhwYX3hG5oH/3QRrqhfXRD+7gLHajKWL58eVq17ae8QSdIutXWF6/lcbM9q+6S8ipZtGpLxKOuvsF8hufoz3C9G7I6Kc85SBdB0q22vgRxDiKE7ID3hG5oH/3QRrqhfXRD+7hLrsv1kxZSV1eXVm37KW/QCZJutfXFa3ncbM+q+1+fLJMZ/14Y85qybTVy+QOzI96bMHaYnH7E8Lj1OZXHq7LaxlaQCJJutfUliHMQIWQHvCd0Q/vohzbSDe2jG9rHXehAVUabNm3Sqm0/5Q06QdKttr54LY+b7Vl1H7l/f9l31+4Jl+vYrqDJ+pzK41VZbWMrSARJt9r6EsQ5iBCyA94TuqF99EMb6Yb20Q3t4y50oCqjY8eOadW2n/IGnSDpVltfvJbHzfasuuEQjecUTaY+P8pzDtJFkHSrrS9BnIMIITvgPaEb2kc/tJFuaB/d0D7uwhyoyli5cmVate2nvEEnSLrV1hev5XGzvVTX7bQ+J+U5B+kiSLrV1hfOQYQEF94TuqF99EMb6Yb20Q3t4y50oBJCCCGEEEIIIYQQQkgc6EBVRteuXdOqbT/lDTpB0q22vngtj5vtpbpup/U5Kc85SBdB0q22vnAOIiS48J7QDe2jH9pIN7SPbmgfd6EDVRm1tbVp1baf8gadIOlWW1+8lsfN9lJdt9P6nJTnHKSLIOlWW184BxESXHhP6Ib20Q9tpBvaRze0j7vwECkPqaqqkkWLFsmAAQNk9erVUlNTI61bt5YuXbrIihUrwgM+OztbNm/ebP7u37+/rFu3zpTNz8+XHj16yLJly8xnnTp1Mtdu3LjR/B0KhcxzXV2dqa93796yZMkS816HDh2kVatWsmHDBvN3nz59pKSkRCoqKiQ3N1f69esnq1atktLSUmnfvr0UFBSYdkGvXr2krKxMtm3bJjk5OUb+xYsXm/aqq6vNtWvXrjXX9uzZ01xXXl4uWVlZMmjQICNDQ0ODtG3bVtq1a2f6Drp37y6VlZWmbjB48GDTN8iP0+MgM2QC3bp1M/qCfGDgwIEmvwf0VVhYKJ07dw7rEPqsr683/QOQd82aNWFZUdfy5cvNZygHNm3aZJ6hh/Xr14f1jf4sXbo0nJAZ/bf03bdvX1Nu+/btRrfQqV3feXl5pi4AW0B2S9+wK8YCKC4uNuPArm/oD39DN+irpW/or6ioyPQHYDygTru+IS/6j+tQt13f6NeWLVvM37gWerD0jf5ZOVPwyxV0a9c3bBFrzOI17GsfsxgPlr7RrjW2MGYhp6Vv6BD6xDiAvtB3u76hK/uYRRuWvlEWegEYs7CXXd/op33M2vWNeqy+wcZbt241D9xP6Kt9zOJh1zfat49Zu74hh33MQgeQAzZCf6Aza8xCF3Z9ww72MRtvjsCYxViw9A05ob9E5ohofceaIyxbxZoj7PqON0dAP2jXPmYxvhKZI6Av6KalcwTkzcQ5wj5m3ZgjUB/a8GKOsI9ZN+YIyBWt76bmCIwZ+5hN9RwBm0Aea46w5mQ35gjoG/LBVqmYI6LXEfgburHPEZCVkEzFWu8QndA++qGNdEP76Ib2cZeskOV1I66xYMEC84UKX4hGjBjR5LX4AocvXMkw6fZ3ZHNZlXQqLpCnbj6ixeWTaduJvCRzdKutL17L42Z7qa7baX1OynMO0kWQdKutL0Gfg1qy7iE6oQ2DM9+QSGgf/dBGuqF9dEP7uLvuoQNV2SIUES2IBvHDgZpM207kJZmjW2198VoeN9tLdd1O63NSnnOQLoKkW219CfocROdb+kMbBme+IZHQPvqhjXRD++iG9nF33UPNKsPa/psubfspb9AJkm619cVredxsL9V1O63PSXnOQboIkm619YVzECHBhfeEbmgf/dBGuqF9dEP7uAsdqMpAPrN0attPeYNOkHSrrS9ey+Nme6mu22l9TspzDtJFkHSrrS+cgwgJLrwndEP76Ic20g3toxvax13oQFUGDoNIp7b9lDfoBEm32vritTxutpfqup3W56Q85yBdBEm32vrCOYiQ4MJ7Qje0j35oI93QPrqhfdyFDlRl4CTddGrbT3mDTpB0q60vXsvjZnuprttpfU7Kcw7SRZB0q60vnIMICS68J3RD++iHNtIN7aMb2sdd6EBVxooVK9KqbT/lDTpB0q22vngtj5vtpbpup/U5Kc85SBdB0q22vnAOIiS48J7QDe2jH9pIN7SPbmgfd6EDlRBCCCGEEEIIIYQQQuJAB6oyuIWfBFG32vrC7bPu1cct/MEhSLrV1hfOQYQEF94TuqF99EMb6Yb20Q3t4y50oCqjoaEhrdr2U96gEyTdauuL1/K42V6q63Zan5PynIN0ESTdausL5yBCggvvCd3QPvqhjXRD++iG9nEXOlCVsXnz5rRq2095g06QdKutL17L42Z7qa7baX1OynMO0kWQdKutL5yDCAkuvCd0Q/vohzbSDe2jG9rHXXJdrp+4REl5lXnYqatvCD8vWrUl4rOO7QrMgxBCCCGEEEIIIYQQkjhZoVAo1ILrSRIsWLBAtm/fLoWFhTJixIgmr62rq5Pc3Ob92s+/873M+PfChGWYMHaYnH7E8JS07bQMyTzdauuL1/K42V6q63Zan5PynIN0ESTdautL0Oeglqx7iE5ow+DMNyQS2kc/tJFuaB/d0D7urnuoWWWsXbtW+vTp0+x1R+7fX/bdtXuj99evXy/dunVr9H4i0aeJtu20DMk83Wrri9fyuNlequt2Wp+T8pyDdBEk3WrrC+cgQoIL7wnd0D76oY10Q/vohvZxFzpQlVFdXZ3QdXG35FdtksG927vattMyJPN0q60vXsvjZnuprttpfU7Kcw7SRZB0q60vnIMICS68J3RD++iHNtIN7aMb2sddeIiUMgoKCnwrn0xZp/KSzNCttr54LY+b7aW6bs5BJIi61dYXzkGEBBfeE7qhffRDG+mG9tEN7eMudKAqo3v37r6VT6asU3lJZuhWW1+8lsfN9lJdN+cgEkTdausL5yBCggvvCd3QPvqhjXRD++iG9nEXOlCVsWzZMt/KJ1PWqbwkM3SrrS9ey+Nme6mum3MQCaJutfWFcxAhwYX3hG5oH/3QRrqhfXRD+7gLHaiEEEIIIYQQQgghhBASBzpQldGpUyffyidT1qm8JDN0q60vXsvjZnuprptzEAmibrX1hXMQIcGF94RuaB/90Ea6oX10Q/u4Cx2oysjKyvKtfDJlncpLMkO32vritTxutpfqujkHkSDqVltfOAcRElx4T+iG9tEPbaQb2kc3tI+70IGqjE2bNvlWPpmyTuUlmaFbbX3xWh4320t13ZyDSBB1q60vnINIS6murpbHHntMjj32WBk5cqTss88+MnHiRHnzzTf9Fo1EwXtCN7SPfmgj3dA+uqF93CXX5foJIYQQQgghSVJVVSVnn322fPXVV5KTkyNDhw6Vbdu2yRdffGEe//3vf2Xy5Ml+i0kIIYQQEmgYgaqMvn37+lY+mbJO5SWZoVttffFaHjfbS3XdnINIEHWrrS+cg0hLuOOOO4zzdPDgwfLOO+/Ia6+9Ju+99548/vjj0rp1a3n55ZflpZde8ltMshPeE7qhffRDG+mG9tEN7eMudKAqY+PGjb6VT6asU3lJZuhWW1+8lsfN9lJdN+cgEkTdausL5yCSKKtWrZJXX33V5DS77777pE+fPuHPDjnkELn22mvN66lTp0pDQ4OPkhIL3hO6oX30QxvphvbRDe3jLnSgKqOystK38smUdSovyQzdauuL1/K42V6q6+YcRIKoW2194RxEEuX111+Xuro6k/d0+PDhjT4fP368FBQUyPr16+Xzzz/3RUYSCe8J3dA++qGNdEP76Ib2cRc6UJWRl5fnW/lkyjqVl2SGbrX1xWt53Gwv1XVzDiJB1K22vnAOIokyd+5c8zx69Oi4+odzFdCBqgPeE7qhffRDG+mG9tEN7eMudKAqo1evXr6VT6asU3lJZuhWW1+8lsfN9lJdN+cgEkTdausL5yCSKMuWLTPP9q370fTu3TviWuIvvCd0Q/vohzbSDe2jG9rHXehAVcbSpUt9K59MWafykszQrba+eC2Pm+2lum7OQSSIutXWF85BJFE2b95snjt27Bj3mvbt25vn0tJSz+Qi8eE9oRvaRz+0kW5oH93QPu6SFQqFQi63kfF8/fXXUl9fbw4AwGmpTVFVVWVyWSWLk/LJlHUqL8kM3Wrri9fyuNlequvmHESCqFttfQn6HIT8W1he5uTkyJ577ulKu5nCiBEjzOFQjz32mBx66KExr3nggQfM53vttZe88MILnq9die75hkRC++iHNtIN7aMb2ic5El275iZZP2kB1qmoMMj27dubvT6Ra9wqn0xZp/KSzNCttr54LY+b7aW6bs5BJIi61daXTJiDeCq8c7CQT1SPcHb6tXYlkVBnuqF99EMb6Yb20Q3tkzzNrbnoQPWAVq1aSW1trWRnZ0t+fr7f4hBCCCGEuEZ1dbVZgGL9Q5xRWFgoZWVlRqfxsD5LZaQo166EEEIIyRSqE1y70oHqAdbpqIQQQgghhCRKhw4djAN1y5Ytca+xcp82lSe1pXDtSgghhBASCQ+RIoQQQgghRCGDBg0yz6tXr457zapVq8xz//79PZOLEEIIISTToAOVEEIIIYQQheyxxx7mec6cOTE/r6mpke+++868HjVqlKeyEUIIIYRkEnSgEkIIIYQQopCjjjoq7EBduHBho89feeUVc+Jur169ZN999/VBQkIIIYSQzIAOVEIIIYQQQhTSt29fGTdunDnY4JJLLpHFixeHP5s9e7bcfffd5vX5558vubk82oAQQgghxC2yQqFQyLXaCSGEEEIIIUmDQ6QmTZok8+fPl+zsbBkyZIiJOl2+fLn5/LTTTpPbbrvNbzEJIYQQQgINHaiEEEIIIYQoBg7T6dOny1tvvWUcp3CkDhs2TE455RQZP368ZGVl+S0iIYQQQkigoQOVEEIIIYQQQgghhBBC4sAcqIQQQgghhBBCCCGEEBIHOlAJIYQQQgghhBBCCCEkDnSgEkIIIYQQQgghhBBCSBzoQCWEEEIIIYQQQgghhJA40IFKCCGEEEIIIYQQQgghcaADlRBCCCGEEEIIIYQQQuJAByohhBBCCCGEEEIIIYTEgQ5UQgghhBBCCCGEEEIIiQMdqMQTVq1aJcOGDWvyQQghbvK///1PzjjjDNljjz1kn332kSuuuEI2bNjgt1iEEEICzHvvvScnn3yy7L777jJ69Gg5//zzZcmSJX6LReLw2muvme8ln332md+ikJ1s3LhRrrnmGtlvv/3MPXTOOefI4sWL/RaL7ITra71MmjRJbr755pifLVu2TM477zxzT+2///5y++23S0VFhecyphu5fgtAMoOOHTvK3Xff3ej91atXy4MPPihjxozxRS5CSGaAuQaLiKKiIrnsssvMAuGvf/2rzJs3T15//XUpKCjwW0RCCCEBY/bs2XLhhRca5+lVV10l27Ztk7/97W8yYcIEefXVV6Vnz55+i0hslJSUyJ133um3GMTG1q1bZeLEibJlyxazjsvLy5Pp06cbh90///lP6dSpk98iZjRcX+tl6tSp8sknn0jfvn0bfbZ582Y588wzJTc31/yoV15eLk8++aSsWLFCnnjiCV/kTRfoQCWeUFhYKOPGjWv0PiZcOFenTJnii1yEkMzg6aeflsrKSnnppZdk0KBB5r0hQ4bIJZdcYhbgiA4ihBBCUgmccUOHDpUZM2aYL6rg8MMPl+OPP958SY0XGUT8YfLkyYzAUsbjjz8uK1eulJkzZ8rIkSPNez//+c/lV7/6lXnvggsu8FvEjIbra33U1NSYwLVnnnkm7jVwlpaWlsrbb78tvXv3Nu/h+cYbbzROV0SkkthwCz/xjbfeesvcoJhgO3fu7Lc4hJAAs3TpUunWrVt4cWctwMEPP/zgo2SEEEKCuu0Y//ccc8wxYeep5VzA4+uvv/ZVPhLJhx9+KP/617/k7LPP9lsUspNQKGSiGPGjg+U8BUixcOWVV5r7iPgL19e6KCsrk+OOO844T3/3u9/FvQ6O0wMPPDDsPAUnnHCCCXrDZyQ+dKASX6ivrzdh5QMGDOAvU4QQ1+nTp49s2rTJbAWz52YGXbp08VEyQgghQaRDhw7GIRdrnYvtyDk5Ob7IRRqDqNNbb71VzjrrLJ7LoAis05BL84ADDjB/NzQ0yPbt283r3//+98axSvyF62tdwA5ZWVlmh8Mf/vCHmNfg/x+kXth1110j3scPfZj/kH6BxIcOVOIL//73v00CfSQutv8qTwghboBfYZEnCznoFi1aJN99951cf/315j384koIIYSkEqxvESgQnaPxP//5j6xdu1b22msv32QjkfzpT3+S7Oxsueiii/wWhUQdcgPat29vDrjZe++9zX2D7fuM4NYB19e66N69u9nle9BBB8W9xjrgC5HD0cDpvW7dOldlTHfouSK+gJw12LZ/9NFH+y0KISQDwEEdWOQh3/KsWbPMe61bt5annnqKv5ATQgjxbFs/Ih1xsAoOwSE6ThB/7rnn5C9/+QsPvFGGFdV47733mkOKcO8gv+Ojjz5qooVffvnliK3jxHu4vtZFIoFpVp7nWPNdfn5+OMqbxIYRqMRz1qxZI59++qmcdNJJ5iRFQghxm/vvv1/uuOMOsw3sgQceMAdF9OrVS37729/Kt99+67d4hBBCAg62TcLRgOgeHNSBra/EX2pra+WGG24wAR1W3kaiBzhLQVVVlTz77LPmQGKkxMDBRbAdHKnEX7i+Ts/cwk2BaHwSH0agEs/Br1O4cceOHeu3KISQDKC8vFymT59utn4hJxByA4EjjzzSfGnCtjCcHkoIIYS4FXmKw4lwqMrFF1/M/P9K+Otf/2ryNWILf0lJSUR0FqIf8V7Hjh19ljJzQSQjOOKII6RNmzbh9/Hjw6hRo+SLL77wUTrC9XV6goOiQHV1daPP8B6ivUl86EAlnvPRRx+ZcP/oxMWEEOJWDi1EMeAkZGtxB7BA+MUvfiEzZsww0Q3cukcIISTVIOL0N7/5jfm/6JJLLpELL7zQb5HITj7++GOzXTVWSjHLTgsXLvRBMmLP0RidRxjAsT1//nwfpCIWXF+nJz169IjIhWoH73Xt2tUHqdIHOlCJ5yDp98EHH+y3GISQDMFKFYLTW6Opr683EfHNbWchhBBCkonQQuQpHA1XXnmlOTmc6OGaa64xNrLz2WefyWOPPWY+Gz58uG+yEZEhQ4ZIq1atzMHD0eAUccsRRPyB6+v0pLi42KRZ+P777yPer6urM7skcEgbiQ8THBBPwa8a2A7DBQkhxMsFOBLZ//3vfzc5syxKS0vlvffek9133z28TYwQQghJFbfccossXrxYLr30UjpPFbLbbruZ3I32x9ChQ81n2CmHv4l/YNv+mDFj5N///resXLky/P4333xjDv9ClCPxD66v0xekUpw9e7ZJYWLx6quvxo3IJz/BCFTiKStWrDDP3bt391sUQkiGkJOTI9dff71cccUVcuqpp8r48eOlsrLSbC1C5MnUqVP9FpEQQkjAWLBggbz11lvGwYDUVa+//nrE523btpXDDjvMN/kISQf+8Ic/yJdffim//vWv5cwzzzSOOuTdtA4qIv7B9XX6ggMNX3vtNXNPTZo0yQS4ISf0oYceKvvtt5/f4qmGDlTSiBdffFFuuukmufXWW2XChAlxr0OSYfwH9sYbb8jy5ctNfpNhw4aZMsiFEu8EUsDkxIQQL+cf/JqKeeeRRx6Re+65x5wwiQMIcHAEfiEnhBBCEslniv97kM8fW4gBHDmIksNWfThL7VvBrQOksB08mgEDBtCB6rONiH779O/f3zjk7r33XrOGg9PuwAMPlGuvvdb8CEH8tQ/X1+k5n3Xu3FmeeeYZmTJlitx3333mXoIT/PLLL09xD4JHVoiJKYgNbIfArxA4gbIpBwYSQuOm/eqrr8x/ZNjusm3btvD2ipNOOkkmT57ssfSEkHSG8w8hhBCtIAru/PPPN5FV+L8HJ4ED/N+DfH841GbatGlmWzjxB9pIN7SPbmgfXdAeOmEOVBLmk08+MVsh4LxojjvuuMM4LwYPHizvvPOOCQFHrpPHH3/c5Dp5+eWX5aWXXvJEbkJI+sP5hxBCiFbwBfbiiy82zwcddJDMmjXL/P9jPRBxhS2QOLkdOeSI99BGuqF9dEP76IL20AsdqMTcdPfff7+J6Io+iTIWSDaMJMNZWVkm5Nv6NQQccsghZksFQN6TWKfyEUKIBecfQggh2sEhKfiy2rVrV7M1Fc8W+H/o4YcfNicbY7vlm2++6ausmQptpBvaRze0jy5oD73QgZrhfP/99+YUNkRuIWcJkkAjr0ZTIAl+XV2djBw5UoYPH97ocySQRj7C9evXy+eff+6i9ISQdIbzDyGEkHTAymeKAzZi5fHHVsq99trLvP722289l4/QRtqhfXRD++iC9tALD5HKcBDNheT2o0ePlhtvvFFGjBghM2fObLLM3LlzzTPKxCIvL884N7744gvjwPjZz37miuyEkPSG8w8hhJB0AHnojjjiCHPwUzysYyWQm454D22kG9pHN7SPLmgPvdCBmuH07dvXnOx2wAEHJFxm2bJl5tm+dTaa3r17GweGdS0hhETD+YcQQkg6gNOkmzpRGlstrV0PONiQeA9tpBvaRze0jy5oD73QgZrh4IZr6U23efPmcOh4PNq3b2+eS0tLHUpICAkqnH8IIYQEgcmTJ0tlZaVJIXPUUUf5LQ6JAW2kG9pHN7SPLmgP/2AOVNJiqqqqzHN+fn7ca6zPcGMTQkiq4PxDCCFEE4888oi88cYb5vV5550XcdgH0QFtpBvaRze0jy5oD39hBCppMTk5OQmfbo2TsgkhJFVw/iGEEKKFhx56SKZOnWpeH3LIIXLuuef6LRKJgjbSDe2jG9pHF7SH/9CBSlpMYWGhlJWVSXV1ddxrrM9at27toWSEkKDD+YcQQojf1NXVye233x4++BC5vB988EHJzubmPi3QRrqhfXRD++iC9tADHaikxXTo0ME4MLZs2RL3Giv3YFN5CgkhpKVw/iGEEOIn27Ztk0suuUQ+/vhj8zdOSr733nslLy/Pb9HITmgj3dA+uqF9dEF76IIua9JiBg0aZJ5Xr14d95pVq1aZ5/79+3smFyEk+HD+IYQQ4hfr1q2TCRMmhL/InnXWWfKnP/2JX2QVQRvphvbRDe2jC9pDH4xAJS1mjz32kPfff1/mzJkT8/Oamhr57rvvzOtRo0Z5LB0hJMhw/iGEEOIH69evlzPOOENWrFhhtk3ecMMNMnHiRL/FIjZoI93QPrqhfXRBe+iEEaikxRx11FHmGQ6MhQsXNvr8lVdeMSdl9+rVS/bdd18fJCSEBBXOP4QQQrwGP86df/755otsq1atTAQQv8jqgjbSDe2jG9pHF7SHXuhAJS2mb9++Mm7cOHMSNvJxLF68OPzZ7Nmz5e677zavcdPn5jLImRCSOjj/EEII8Zpp06bJvHnzzOubbrrJ5KAjuqCNdEP76Ib20QXtoZesUCgU8lsIoovDDjvM5Be89dZbTc6NWOAQl0mTJsn8+fNNSPmQIUNM1Nfy5cvN56eddprcdtttHktOCEl3OP8QQgjRFgn085//3Pzfgx/mdt999yavx+nIF198sWfyEdpIO7SPbmgfXdAeumF4DkmK4uJimTFjhkyfPl3eeustWbZsmXFk7LnnnnLKKafI+PHj/RaREBJQOP8QQgjxih9++MF8kQV1dXVxc3BbdO/e3SPJiAVtpBvaRze0jy5oD90wApUQQgghhBBCCCGEEELiwByohBBCCCGEEEIIIYQQEgc6UAkhhBBCCCGEEEIIISQOdKASQgghhBBCCCGEEEJIHOhAJYQQQgghhBBCCCGEkDjQgUoIIYQQQgghhBBCCCFxoAOVEEIIIYQQQgghhBBC4kAHKiGEEEIIIYQQQgghhMSBDlRCCCGEEEIIIYQQQgiJAx2ohBBCCCGEEEIIIYQQEgc6UAkhhBBCCCGEEEIIISQOdKASksFce+21MmzYsGYfuC5RNm/eLNu3b2+xLGeccYYcdthhTV4zdepUI8/dd98d95qWypsKIDfk10woFJJ77rlH9ttvP9lzzz3lueeeS3hMjBgxQkaNGiUnn3yyvPrqq0m1n4h947Ft2zYpKSmRVHLRRRfJo48+al5/9tlnjfq8yy67yL777iu//vWv5fXXX0+6nVWrVjV7f02ePDnh+lauXGlsuH79+qRlIoQQQogOrHVXvHWZtY7AGthL/FhPt5Samhq57rrrzBoVjw8++CDuGjTW+gvr4cMPP1ymTJli1pqZrk9CSPPkJnANISSgnHrqqbL//vuH//7qq69k5syZ5v299947/H7fvn0Tqm/27Nly1VVXGSdbYWGhuMXTTz8txx9/vAwdOtS1NoLGrFmz5IknnpBDDjlEfvnLX0bYNxZYkHbo0CHsfMXC8h//+IdZ/JWWlsrZZ5/dovbPO+88qaysbLHc3333nZx//vly7733GsdhqnQxZ86cRo54LKLxAHV1debHgPfee0+uvvpqc/1tt92WdJujR4+WU045JeZngwYNSriePn36yJFHHmkW+w8++GDS8hBCCCFED3/605/kiCOOkM6dO/stStrw4osvyt///ncZN26c7LPPPrLbbrs1eX30um/Lli3G6YrvFUuWLDHrZEIIaQo6UAnJYPbaay/zsKivrzcOVPwii8VIS/nf//4n5eXl4jZwbt1yyy3y/PPPS1ZWluvtBYGFCxea5yuuuML8Ct4ccLL27t074r2TTjpJjj76aHn44Ydl4sSJkpeXl3D7Bx54YBJSi/zwww+yYcMGSRUNDQ3G+fib3/ymkZMfeoke97/73e/kmmuukRdeeME4cNH/ZIDjM5l7Kha///3vjaP3yy+/NI5ZQgghhKQ3WD/feeedct999/ktStqtbW+++WYpKipq9vpY67AzzzxTzj33XBMEgu8xu+++uyuyEkKCAbfwE0LSDmwFR0Tgyy+/7LcoaUNtba15btOmTdJ1FBQUGN0jGvXHH3+UdASRBsuXL5fjjjsuoeuzs7ONs764uFimTZsmGujVq5eJHH/qqaf8FoUQQgghKQDrqzfeeEM++eQTv0VJu7VtIs7TeCAQA7vawNdff50y2QghwYQOVEJIQiDabdKkSeGoVfxi+8UXX4Q/x9buhx56yLz+xS9+EZET9O233zYRi9g2ju01WCRiGw1yFyXDpZdeKl26dDHbupvLjRkv92b0+/gbv0Bjy/avfvUrGTlypBxzzDHmF2k4DPHrNrYHwXGF11VVVY3qfOmll0zfURb5Qj/66KNG18ydO1fOOuussB6xFR6/eNuBXDfeeKNcf/315pfwgw8+uMl+Nmcb1Ge3TbK5SIEV8Yto5UTbj6fv3/72t/Lhhx/K+PHjjc7GjBljcnwhShTgNVIJANRplUdKAfQHW91Q7oADDpA//OEPsnbt2mblR9Qy8pv27Nkz4T5jYX7ooYfK/PnzzbZ+C6QyuPXWW+Wggw4y4xry/OUvf4nQTUuZMWOGce7uscceJuL1wgsvjOmsxjZ+OIMT6TMhhBBCdIN1X+vWrc26orn1cbzc+9Hv4+/bb7/drE+xRsGa8sQTTzTrzo0bN5r1NNZtWMfcf//94fWXnccee8x8jnUJ1mLRa1bwn//8R0477TRzDdbKF198sSxdurTRLh+kKUBKJ6yZsMbGjrJ4YD2OOiEzdtug3Pfffx9Rn5WXH6+dnEUAvVvrSwus/RENjPUW1prQE1Ixvf/++43y07722mvywAMPmPW69R3g008/bbLNxYsXm3Ue6t+0aZN5b82aNUZ3P//5z0092PWEH+9j2YUQ4g90oBJCmgWLBSxM4KxBPko88BpOM2shgbypVv5IOL2w0AFYtF122WXStm1bkx8V+SQRQffXv/7VLKSSAQ4tOBeRuwgHI6WKefPmmXrHjh1rZIWDDLJjy/Tq1avN9nc465DmIDpPEnJ13nHHHWaxg+uwFQsO2f/+97/haz7++GOjx61bt5pFK/SIxRIOKoIT0s6bb75ptiZBHizYOnbsmLRtUIfdNvg7GbCA+/zzz83WfStvZyLtN7U9H/rFAhJfHJBrF45ROBEBZMa4AhhPltxYzCONABb0cGZjoYqFNpzRTTkvkYMV8sNR21KGDBlinq3Fe1lZmVnYIwoaX0qgV+gEi+0rr7yyUXl8GYITPPpRUVERvgY5ZvHFCQ7eG264wTjaEWltjRk7OOAKff1//+//tbgvhBBCCNEF1sYXXHCBLFu2zPwYmyqwPkLOdKRhwgGayPUJJx3WGNhlgwAInCnw+OOPNzo085133pHp06eb9Q5+0EVZOFHtP+wiBynWfnBC4sdsrP8QLIC1a7QTFblGETWKNR/Wbrm5sbMJ4kAttIdrsaZGnXDcTpgwIezARSCGlcYIr63vHclgBTxg/WU5UrGGf/bZZ81aFGtNrDHxXQA6tFIHWEC/7777rrnmkksuMY5VlMf3iFhg7Y9r8X0GOkHeW/QVaaPwXQT9vemmm2TAgAEmWCSV44EQ4pAQIYTs5JVXXgkNHTrUPFvU1taGDj744NCYMWNCW7duDb9fVlYWOuigg8yjpqbGvPfnP//ZlF+5cmX4uiOPPDJ06qmnhhoaGhrVeeyxx4bfmzhxYujQQw9tUr7o+s8+++zQsGHDQl988UX4Gnx+zTXXNFtv9Pv4G2U/+OCD8HvPPvusee+UU04Jv4d+QHb0yQL14LpZs2aF3ystLQ3tu+++oRNOOMH8XV9fH/rFL34ROu2000J1dXXh6yoqKkKHH354aNy4cRH1DR8+PLRu3bom9eHUNrGA7nDdvHnzQps3bzaPDRs2hObOnRu69NJLzWdTpkxpcfvx9P3++++H36uqqgrts88+Ebq1xuSnn34afu+oo44K/f73v4+Qe8aMGaFf/epXoeXLl8ft2yeffGLqeuuttyLeR914HzqKx4svvmiueeONN8zf99xzj/n73Xffjbju1ltvjRgL0Df+jvewj9Xf/e53oWOOOSaiPtRz9NFHh7788suI9zEO99hjj9DVV18dV2ZCCCGE6MZadwGsmbAOGDlyZGjZsmUR6wj7GgXrKayjool+H39jnfz999+H3/vjH/9o6rvssssi1qK77rpr6Iorrgi/h2tGjBgRURYy7bLLLqGLLrrI/I2136hRo0KXX355hBxYN2I9d8EFF0TUN3r06FBlZWWT+igpKTHrm5NOOilUXV0dfh96wPsnnnhiTN01hbXmtNa11mPRokWhRx55xPRz0qRJ4eu//vprcz3WlnY+/PBD8/6TTz4Zlgl/Yx0MHVq8+eab5v2ZM2dG9B/yot2xY8ea9bN9Tf7NN9+Ya95+++2ItR6+63CtR4geeIgUIaRJsG153bp1JiLTnmOoXbt2Zls+Iu4QfWk/jMoOouoQ+Wc/7AnboFF++/btjmTDL8LY7oyoPWzjadWqlaP68vPzTVSjBX75tba9W6AfiBJYv359RFn8em+PbGzfvr2R7ZlnnjHbpHD9ypUrza/niF60g+3hyGeJa7p162beQzSm9dot2zTFCSec0Og9RJ4iGtKKsHTaPqIVDjnkkAj9Q+fWVqZ4dO/eXT777DPzqz22gOGXe0RH4NEU0D+IPhyrJXm2rHGM7fOIOMVhW3YQPYI0AYi+tY8HbMdCyoJounbtGtEvRCkjChf5uCAn6ogVMWuNQ0Q5EEIIIST9wToWa1qsobD1Hru1nIL1pP3wUGtta+1MAjhUs1OnTma9agdrYnvZfv36mW3q2P2CXTBYs2CrO9ZC9lRTOTk58rOf/cykwcI2fSvSFNvxkU+/KZADFt8bECFrP6wUayKk2MIuMBwual8/JQrScEWDNSuiZXFgqAVSESAVlV1W9NfaSm/fPQSwTrMfTDp8+HDzHK1P6ApRpohk/ec//xmxHkV/sLZDJDDOK8DuLPQ/FWOAEJI66EAlhDSJ5aCxFlx2Bg4cGN6KEs9JhsUgFiFIjI+tPytWrAjnkYQDyAlYyGF7PXJlYosRXjsBTk/7diIsAAEWlXbwvj1PUjz9YNEKsFCCjqxtRnjEAtdYTtPoNt2wTVMgNQIckwBbvLDAhMMQTs5UtQ99o247WCw2l+sJaSCwXWzKlCnmxNpdd93V5PnCAhi5ceOBlA/JHjZgle3QoUO473ZnuwXah65g8+j3kf6hKbBdDQcYYDzjMXjwYNMvbHOzxpId9CPe9jBCCCGEpB/Ylo4fsbE1Humc4MxzQqw1LIhODRVrbWut5exgPYIfkeEwxZoeXH755XHbx3WWszNeOio71toyVttW+iisLZNxoOK7AsA5Bm+99Zb5boLABqSTil6P4vvACy+8YFI/4fBR9NU6/yBaT9H9shy/0etZbPNHO3gfAQb29TN+REcKBOSihZMVDlk4fJEa7KijjgrbjRDiL3SgEkKaJHqREOuzpiI//+///s/kEEJeoT333FPGjRtnHGp4PxUH4MBpil9xH3nkERONmCixcmXGy8Vkj55tCZZ+rMUSQO5T6CEW9sViIgslp7ZpilGjRjUbqem0/ejFaqLgl33k5ULOKhxcgOc///nPZmGMyARrgR2vvWSS8S9YsMCMAysSo6m+o/5k9I7FM/KPIboWEazoF/JeoV9PPvmkyXsa3Q4X1IQQQkiwgCMNTkr8SBydc1/L2hbrD2s9hTV9vDVjcXFx+LXTNYvTta39h2z8QI1AAUR8Ykcc8rLanb748RqRrgceeKC5FmtPBH7g/WTXszgPAnn8kdP/rrvuMtG8dv1gp9Kxxx5rHK2I3kWEL9aDOKSqJeOAEOIedKASQprEihJF9Gg0VnJ4OH5igSg8OE/hNI2Oumxum3ai4FfeW265xWz1wQIu1qIm1mmmqWrfIjriEOAgANCnT5/woha/KEdHIiIhPrb1N7etKZW2SQV+tA894iAnRF8itYKVXgGRBIiAwKFlOBChqSgMK5o0UbDlCtvV4Pi3ogzQ9+jDEaztWri+R48eLe6bdSgBIg6sbWZfffWV/OY3vzGpIKIdqOiH0yhuQgghhOgCaw2kR4JTL9aBq7HWttgqj10psXaspHptC0cgduRYaxDIG722xY/BcLDat+G3dG1pbYW3sNabqVpbQsfYJYc1FrbMW2kNkIoJkbBIr2Xf9o+DPZ2AVAeIMIZdzznnHJPqCqkarDUd1rcIYEAKBzzg2MWaFkEDWCPa0ykQQvwhufAfQkjGgO3R2H6Mk9HhGLLAayww8Nluu+0W8Qus9QuxlesTW5Ht4FdVLMCw2EsFWLThF1tEI0aDX5eRMsCesxTbZrAdJ5Xg1EzkBLU7aJH/FQslLDKhI+gKizR77iToEVuHcIp7S3+Zb4lt3MCL9qOjRuFAxQmw2L5vx9ri1lQUgLUoR97WRMFYRltYxGJLlT1v7eLFi83ptnask1LtuV0TBdHJSE9gjyBB5DYiLaL7hWvgrE3GUUsIIYQQ3Zx00knGmRZvbYsfca0t5QARq9XV1SmVATth7OvnH374wfygjIhMRLBi/Y3UToiOtHLFA5RBTnicIN/SSFerTuy+sTuJsXbDjjPkUU0kzVUiIDoXUb5YZ912221SXl4e8UO7/fsL1oMICgFOv78g8hTO2hdffDHslEW0KX4whx0tEHSBMxYAdxwRogNGoBJCmgSLCvxSiui+E0880SzowMsvv2y2tmDrtOXcsaLzsJDC4gA5Inv27Gm2q2BRh1+MEW2JA5+wOIpOwu4E/EILx+zWrVsj3odjFTmO8Esv8hzBmQonZv/+/SMWe07BFhxsvUEkLBY5zz33nFlgwTEarcfx48cbPUIHiJhELicsMuNts0qFbdzAi/atMQUnLZzSOJgLB1k9+uijJmcoxhi+QGDrPg6lghzxgJMVi9FvvvkmZroH/LqPLfSWgxLtwUGK6+G0tR8mdu6558q///1v4/zGuMJ4+vTTT817Y8eOjXnwU3Ng/ECfkyZNkiOPPNIs1iEP7p3TTz894lp8icEhC7EORCCEEEJIegPHIw6Uwpox2mGHtS12XeGHXRyshKAAOONSvSsF0aNYf2DdhR+ScXgn8rxj7WOt0a644grjhDz11FONLJAVP6Jj7WI/mClREHRg1Yn1FdZ9+L6AdSB+TLdvtU8FcFBi/YXvKsj/D73iOwy+K2Cth7Utvi+8/fbbJgAD69pUfH/BNn44o7GLDvlu8cM8cqLecMMNJigDkcSIuMX3Caz1ooNRCCH+QAcqIaRZ4MyBgxB5Rh9++GHj6IMzavLkySbC0gJOKTiQsBBA0nU4nBCRhzw/f/vb34xDCAsCLBqwwEJ5LEZSESWJaEc48qytMBZYkNx8882mfbSHxQkWpNiyM2vWLEkVcOSNHDnSnJaJX66hH2y7svfN0iOcf9AlFmFDhgwxf0NON23jFm63j0UjkucjAgMOSjgnL7nkEnMA1SuvvCJ//OMfjcMaURpY+MbLf2p9EcAWrS+//DLm58g5hYf94CxEgD7wwAMmib8dtA+nLWyM9AGIWkCqBkSQwgGaDMirBac0xioOEcAXBYyfadOmGbntYGs/ZPz5z3+eVFuEEEII0Q22bOMHXORBtwOnJtaa+MEaDj9sdX/ooYfMdXB0pgo4ReHItQIhsBZBwAKCIyyw5sEBqIgYxXoJ6aiwQwlrsr333jupdlEnDolCf7Aewg/kSGN00UUXubKNHdGycJAiqAEOWzhQ77jjDtM+vsNgnYs+Yd130003mfQEToEOcSAq+mcdhIv2EHyASFv8iI/vNrA1+k0I0UFWqKmTMAghhJAAgYhSRK7C0d+vXz9JV0477TSzhQ9fmAghhBBCCCGEuAtzoBJCCMkYEBWN7fZII5GuYKve3Llz5eyzz/ZbFEIIIYQQQgjJCOhAJYQQkjFgK9qVV17Z6OCrdAJpMZDyAWkLCCGEEEIIIYS4Dx2ohBBCMgrkUYXz8amnnpJ0Y8WKFSZPKw4dIIQQQgghhBDiDcyBSgghhBBCCCGEEEIIIXFgBCohhBBCCCGEEEIIIYTEgQ5UQgghhBBCCCGEEEIIiQMdqIQQQgghhBBCCCGEEBIHOlAJIYQQQgghhBBCCCEkDnSgEkIIIYQQQgghhBBCSBzoQCWEEEIIIYQQQgghhJA40IFKCCGEEEIIIYQQQgghcaADlRBCCCGEEEIIIYQQQuJAByohhBBCCCGEEEIIIYRIbP4/JTrXj37niicAAAAASUVORK5CYII=", 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" ] }, "metadata": {}, @@ -1038,54 +166,9 @@ } ], "source": [ - "runtime = df.groupby('experiment_id').mean()[['n_ranks', 'n_nodes', 'runtime', 'total_points', 'source_tree', 'm2l', 'p2p', 'local_depth', 'global_depth', 'ghost_exchange_v_runtime']]\n", - "# Calculate nodes and ranks\n", - "runtime_err = df.groupby('experiment_id').std()[['total_points', 'n_nodes', 'n_ranks', 'runtime']]\n", - "\n", - "plt.xscale('log')\n", - "plt.xlim(runtime['total_points'].min()-0.3*runtime['total_points'].min(), runtime['total_points'].max()+0.3*runtime['total_points'].max())\n", - "# plot means + error bars\n", - "plt.errorbar(\n", - " runtime['total_points'],\n", - " runtime['runtime'],\n", - " yerr=runtime_err['runtime'],\n", - " fmt='o-', capsize=4, label=\"Runtime\", zorder=3\n", - " )\n", - "\n", - "plt.xlabel('Number of Points')\n", - "plt.ylabel('Runtime (ms)')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 141, - "id": "c89c4033-1506-43ba-ade9-5f9c56462b5a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "512" - ] - }, - "execution_count": 141, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plt.errorbar(tmp['runtime'" + "weak_scaling([df0, df1, df2], names=[\"CCX\", \"socket\", \"CCD\"], title=\"Weak Scaling up to 512e6 Points\")" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "67d9abc3-f9bb-41ec-9778-9912fecbc924", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -1097,7 +180,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "kifmm (3.10.17)", "language": "python", "name": "python3" }, diff --git a/hpc/ijhpca/strong_scaling.py b/hpc/ijhpca/strong_scaling.py index e64d2e8e..6c995cca 100644 --- a/hpc/ijhpca/strong_scaling.py +++ b/hpc/ijhpca/strong_scaling.py @@ -165,7 +165,7 @@ def write_slurm(script_path, n_nodes, n_tasks, local_depths, global_depths, max_ if contiguous: slurm += "\n#SBATCH --contiguous\n" - slurm += """ + slurm += f""" module load PrgEnv-aocc module load craype-network-ucx module load cray-mpich-ucx diff --git a/hpc/ijhpca/weak_scaling.py b/hpc/ijhpca/weak_scaling.py index cccecd9d..a9644e48 100644 --- a/hpc/ijhpca/weak_scaling.py +++ b/hpc/ijhpca/weak_scaling.py @@ -133,7 +133,7 @@ def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, point if contiguous: slurm += "\n#SBATCH --contiguous\n" - slurm += """ + slurm += f""" module load PrgEnv-aocc module load craype-network-ucx module load cray-mpich-ucx From 0f4ae8865386ba507c291f8329a2e082d12c2dd4 Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Mon, 6 Oct 2025 17:13:31 +0100 Subject: [PATCH 30/52] Add largest runs --- hpc/ijhpca/Strong Scaling.ipynb | 70 +++++++++++++- hpc/ijhpca/Weak Scaling.ipynb | 140 +++++++++++++++++++++++++++- hpc/ijhpca/weak_scaling_ccd.json | 2 +- hpc/ijhpca/weak_scaling_ccx.json | 2 +- hpc/ijhpca/weak_scaling_socket.json | 2 +- 5 files changed, 209 insertions(+), 7 deletions(-) diff --git a/hpc/ijhpca/Strong Scaling.ipynb b/hpc/ijhpca/Strong Scaling.ipynb index 15ef8142..a778d548 100644 --- a/hpc/ijhpca/Strong Scaling.ipynb +++ b/hpc/ijhpca/Strong Scaling.ipynb @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 76, "id": "ae668ed4", "metadata": {}, "outputs": [], @@ -44,6 +44,74 @@ "df1 = pd.read_csv('data/strong_fft_n=128000000_p=64_11077778/strong_fft_n=128000000_p=64_11077778.csv')" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "08b174e4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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", 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" ] @@ -166,7 +300,7 @@ } ], "source": [ - "weak_scaling([df0, df1, df2], names=[\"CCX\", \"socket\", \"CCD\"], title=\"Weak Scaling up to 512e6 Points\")" + "weak_scaling([df0, df1, df2], names=[\"CCX\", \"Socket\", \"CCD\"], title=\"Weak Scaling up to 512e6 Points\")" ] }, { diff --git a/hpc/ijhpca/weak_scaling_ccd.json b/hpc/ijhpca/weak_scaling_ccd.json index 33be101b..b0b689bb 100644 --- a/hpc/ijhpca/weak_scaling_ccd.json +++ b/hpc/ijhpca/weak_scaling_ccd.json @@ -1,7 +1,7 @@ { "_comment": "Weak scaling", "min_nodes": 1, - "max_nodes": 128, + "max_nodes": 512, "points_per_rank": 500000, "local_depth": 4, "output": "weak_scaling_ccd.slurm", diff --git a/hpc/ijhpca/weak_scaling_ccx.json b/hpc/ijhpca/weak_scaling_ccx.json index f033ef3e..77a4175d 100644 --- a/hpc/ijhpca/weak_scaling_ccx.json +++ b/hpc/ijhpca/weak_scaling_ccx.json @@ -1,7 +1,7 @@ { "_comment": "Weak scaling", "min_nodes": 1, - "max_nodes": 128, + "max_nodes": 512, "points_per_rank": 250000, "local_depth": 4, "output": "weak_scaling_ccx.slurm", diff --git a/hpc/ijhpca/weak_scaling_socket.json b/hpc/ijhpca/weak_scaling_socket.json index 57c748a9..6ed8c9a9 100644 --- a/hpc/ijhpca/weak_scaling_socket.json +++ b/hpc/ijhpca/weak_scaling_socket.json @@ -1,7 +1,7 @@ { "_comment": "Weak scaling", "min_nodes": 1, - "max_nodes": 128, + "max_nodes": 512, "points_per_rank": 4000000, "local_depth": 5, "output": "weak_scaling_socket.slurm", From 11c7f0df7d18d2d7d4ceb0c4468b56d543e5d7fa Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Mon, 6 Oct 2025 17:20:52 +0100 Subject: [PATCH 31/52] Make output more descriptive --- hpc/ijhpca/strong_scaling.py | 4 ++-- hpc/ijhpca/weak_scaling.py | 5 ++--- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/hpc/ijhpca/strong_scaling.py b/hpc/ijhpca/strong_scaling.py index 6c995cca..49da35c0 100644 --- a/hpc/ijhpca/strong_scaling.py +++ b/hpc/ijhpca/strong_scaling.py @@ -175,14 +175,14 @@ def write_slurm(script_path, n_nodes, n_tasks, local_depths, global_depths, max_ script_name="{script_name}" -export SCRATCH=$WORK/strong_fft_n={max_points}_p={last_nodes}_${{SLURM_JOBID}} +export SCRATCH=$WORK/strong_fft_n={max_points}_p={last_nodes}_points_per_rank={points_per_rank[-1]}_${{SLURM_JOBID}} mkdir -p $SCRATCH cd $SCRATCH export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK export OMP_NUM_THREADS=1 -export OUTPUT=$SCRATCH/strong_fft_n={max_points}_p={last_nodes}_${{SLURM_JOBID}}.csv +export OUTPUT=$SCRATCH/strong_fft_n={max_points}_p={last_nodes}_points_per_rank={points_per_rank[-1]}_${{SLURM_JOBID}}.csv touch $OUTPUT echo " experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ diff --git a/hpc/ijhpca/weak_scaling.py b/hpc/ijhpca/weak_scaling.py index a9644e48..422aa48b 100644 --- a/hpc/ijhpca/weak_scaling.py +++ b/hpc/ijhpca/weak_scaling.py @@ -115,7 +115,6 @@ def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, point block_size = 128 cpus_per_task = int(max_threads) - last_nodes = n_nodes[-1] last_tasks = n_tasks[-1] max_points = last_tasks * n_points @@ -143,14 +142,14 @@ def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, point script_name="{script_name}" -export SCRATCH=$WORK/weak_fft_n={max_points}_p={last_nodes}_${{SLURM_JOBID}} +export SCRATCH=$WORK/weak_fft_n={max_points}_p={last_nodes}_points_per_rank={n_points}_${{SLURM_JOBID}} mkdir -p $SCRATCH cd $SCRATCH export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK export OMP_NUM_THREADS=1 -export OUTPUT=$SCRATCH/weak_fft_n={max_points}_p={last_nodes}_${{SLURM_JOBID}}.csv +export OUTPUT=$SCRATCH/weak_fft_n={max_points}_p={last_nodes}_points_per_rank={n_points}_${{SLURM_JOBID}}.csv touch $OUTPUT echo " experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ From ac61132e0507fb571f0dc5f20ce21a9356d7c22c Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Mon, 6 Oct 2025 17:25:55 +0100 Subject: [PATCH 32/52] Cleanup --- .../data/grid_search/grid_search.ipynb | 198 ------- .../grid_search/grid_search_fft_f32_1e6.csv | 194 ------- .../grid_search/grid_search_fft_f32_1e7.csv | 98 ---- hpc/archer2/data/weak/Weak Scaling.ipynb | 308 ----------- hpc/ijhpca/README.md | 2 + hpc/ijhpca/Weak Scaling (16 Nodes).ipynb | 501 ------------------ 6 files changed, 2 insertions(+), 1299 deletions(-) delete mode 100644 hpc/archer2/data/grid_search/grid_search.ipynb delete mode 100644 hpc/archer2/data/grid_search/grid_search_fft_f32_1e6.csv delete mode 100644 hpc/archer2/data/grid_search/grid_search_fft_f32_1e7.csv delete mode 100644 hpc/archer2/data/weak/Weak Scaling.ipynb create mode 100644 hpc/ijhpca/README.md delete mode 100644 hpc/ijhpca/Weak Scaling (16 Nodes).ipynb diff --git a/hpc/archer2/data/grid_search/grid_search.ipynb b/hpc/archer2/data/grid_search/grid_search.ipynb deleted file mode 100644 index 1f0196fc..00000000 --- a/hpc/archer2/data/grid_search/grid_search.ipynb +++ /dev/null @@ -1,198 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 13, - "id": "b165b3a0-1743-44e5-adbd-fb1fb758e8a3", - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "604d9bd3-c036-4e4e-bdb6-19a5193e8174", - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.read_csv('grid_search_fft_f32_1e6.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "37937fb4-276d-4a0e-9c31-602b8aade5c5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['experiment_id', 'rank', 'runtime', 'p2m', 'm2m', 'l2l', 'm2l', 'p2p',\n", - " 'source_tree', 'target_tree', 'source_domain', 'target_domain',\n", - " 'layout', 'ghost_exchange_v', 'ghost_exchange_v_runtime',\n", - " 'ghost_exchange_u', 'gather_global_fmm', 'scatter_global_fmm',\n", - " 'source_to_target_data', 'source_data', 'target_data', 'global_fmm',\n", - " 'ghost_fmm_v', 'ghost_fmm_u', 'displacement_map', 'metadata_creation',\n", - " 'expansion_order', 'n_points', 'local_depth', 'global_depth',\n", - " 'block_size', 'n_threads', 'n_samples'],\n", - " dtype='object')" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "a58fcee5-281b-4c8e-892b-5a438cc32616", - "metadata": {}, - "outputs": [], - "source": [ - "experiments = df.groupby('experiment_id')" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "cdd2452e-a4f0-406f-9f47-f09382f2f2c0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "n_points_per_rank, local_depth, runtime, m2l, p2p, source_tree\n", - "250000 4 652 483 148 2258\n", - "500000 4 946 484 438 4599\n", - "1000000 4 2078 479 1559 9261\n", - "250000 5 4315 4055 147 2453\n", - "500000 5 4434 4054 247 4881\n", - "1000000 5 4707 4092 479 9332\n" - ] - } - ], - "source": [ - "print(\"n_points_per_rank, local_depth, runtime, m2l, p2p, source_tree\")\n", - "for (k, i) in experiments.groups.items():\n", - " print(\n", - " df.iloc[i]['n_points'].min(), \n", - " df.iloc[i]['local_depth'].min(), \n", - " df.iloc[i]['runtime'].max(), \n", - " df.iloc[i]['m2l'].max(), \n", - " df.iloc[i]['p2p'].max(), \n", - " df.iloc[i]['source_tree'].max()\n", - " )\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "e6b552bf-97cc-40f4-9a49-3348e36996bc", - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.read_csv('grid_search_fft_f32_1e7.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "36de7ef2-b49a-4917-9572-cf6dfde8c1d3", - "metadata": {}, - "outputs": [], - "source": [ - "experiments = df.groupby('experiment_id')" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "96142445-b99f-4e44-a4a4-45f39f2d1652", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "n_points_per_rank, local_depth, runtime, m2l, p2p, source_tree\n", - "5000000 5 9557 3998 5351 47382\n", - "10000000 5 16051 2782 12970 94296\n", - "5000000 6 37248 33776 2351 48902\n" - ] - } - ], - "source": [ - "print(\"n_points_per_rank, local_depth, runtime, m2l, p2p, source_tree\")\n", - "for (k, i) in experiments.groups.items():\n", - " print(\n", - " df.iloc[i]['n_points'].min(), \n", - " df.iloc[i]['local_depth'].min(), \n", - " df.iloc[i]['runtime'].max(), \n", - " df.iloc[i]['m2l'].max(), \n", - " df.iloc[i]['p2p'].max(), \n", - " df.iloc[i]['source_tree'].max()\n", - " )\n" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "84034538-3fb6-43be-84f9-258f5d0285fe", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2.56" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "512*5e6/1e9" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6fc681a0-670e-4e0e-af42-45e669e745da", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.15" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/hpc/archer2/data/grid_search/grid_search_fft_f32_1e6.csv b/hpc/archer2/data/grid_search/grid_search_fft_f32_1e6.csv deleted file mode 100644 index 2505ea34..00000000 --- a/hpc/archer2/data/grid_search/grid_search_fft_f32_1e6.csv +++ /dev/null @@ -1,194 +0,0 @@ - -experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,source_tree,target_tree,source_domain,target_domain,layout,ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,displacement_map,metadata_creation,expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples -"0_0_0_0_0",0,237,6,0,0,158,46,2258,2266,5,4,1,74,14,16,3,0, 380,0,0,0,0,0, 1, 150, 3,250000,4,2,16,4,500 -"0_0_0_0_0",29,252,6,0,0,173,50,2258,2266,4,4,1,68,12,25,0,4, 383,1,0,0,0,0, 1, 151, 3,250000,4,2,16,4,500 -"0_0_0_0_0",5,454,6,0,1,328,97,2258,2266,6,3,1,60,10,34,0,4, 381,1,0,0,0,0, 2, 155, 3,250000,4,2,16,4,500 -"0_0_0_0_0",27,452,6,0,1,329,95,2258,2266,5,4,1,34,9,63,0,3, 383,1,0,0,0,0, 2, 155, 3,250000,4,2,16,4,500 -"0_0_0_0_0",18,453,6,0,0,333,95,2258,2266,5,3,1,31,8,66,0,3, 382,1,0,0,0,0, 2, 156, 3,250000,4,2,16,4,500 -"0_0_0_0_0",13,457,6,0,1,331,97,2258,2266,5,3,1,62,10,33,0,4, 382,0,0,0,0,0, 2, 154, 3,250000,4,2,16,4,500 -"0_0_0_0_0",9,457,6,0,1,329,98,2258,2266,5,3,1,62,10,33,0,4, 382,1,0,0,0,0, 2, 154, 3,250000,4,2,16,4,500 -"0_0_0_0_0",31,461,9,1,1,334,96,2258,2266,5,3,1,69,8,23,0,4, 383,1,0,0,0,0, 2, 150, 3,250000,4,2,16,4,500 -"0_0_0_0_0",22,457,6,0,1,332,98,2258,2266,4,3,1,32,9,65,0,3, 383,1,0,0,0,0, 2, 155, 3,250000,4,2,16,4,500 -"0_0_0_0_0",23,457,6,0,0,332,99,2258,2266,4,3,1,28,8,70,0,3, 383,1,0,0,0,0, 2, 157, 3,250000,4,2,16,4,500 -"0_0_0_0_0",19,458,6,0,0,327,105,2258,2266,5,3,1,28,8,70,0,2, 382,1,0,0,0,0, 2, 157, 3,250000,4,2,16,4,500 -"0_0_0_0_0",14,459,6,0,1,333,99,2258,2266,5,4,1,60,8,37,0,3, 382,1,0,0,0,0, 2, 156, 3,250000,4,2,16,4,500 -"0_0_0_0_0",7,460,6,0,1,328,104,2258,2266,5,4,1,58,8,39,0,4, 381,1,0,0,0,0, 2, 157, 3,250000,4,2,16,4,500 -"0_0_0_0_0",12,460,6,0,1,331,102,2258,2266,5,4,1,58,8,39,0,3, 382,1,0,0,0,0, 2, 157, 3,250000,4,2,16,4,500 -"0_0_0_0_0",6,460,6,0,1,332,101,2258,2266,5,3,1,60,9,37,0,3, 381,0,0,0,0,0, 2, 157, 3,250000,4,2,16,4,500 -"0_0_0_0_0",11,461,6,0,1,332,103,2258,2266,5,3,1,59,8,39,0,3, 381,0,0,0,0,0, 2, 157, 3,250000,4,2,16,4,500 -"0_0_0_0_0",15,463,6,0,1,333,103,2258,2266,5,4,1,57,9,39,0,3, 383,1,0,0,0,0, 2, 156, 3,250000,4,2,16,4,500 -"0_0_0_0_0",4,460,3,0,1,338,100,2258,2266,5,3,1,19,9,81,0,3, 381,1,0,0,0,0, 1, 161, 3,250000,4,2,16,4,500 -"0_0_0_0_0",26,462,6,0,1,338,99,2258,2266,4,4,1,26,6,73,0,3, 383,1,0,0,0,0, 2, 157, 3,250000,4,2,16,4,500 -"0_0_0_0_0",8,465,6,0,1,336,101,2258,2266,5,3,1,61,8,36,0,4, 381,1,0,0,0,0, 2, 157, 3,250000,4,2,16,4,500 -"0_0_0_0_0",1,464,6,0,1,341,97,2258,2266,6,4,1,31,7,67,0,3, 381,0,0,0,0,0, 2, 159, 3,250000,4,2,16,4,500 -"0_0_0_0_0",25,465,6,0,1,341,99,2258,2266,5,3,1,27,7,71,0,3, 383,1,0,0,0,0, 2, 156, 3,250000,4,2,16,4,500 -"0_0_0_0_0",20,466,6,0,1,341,99,2258,2266,4,3,1,26,8,72,0,2, 383,1,0,0,0,0, 2, 157, 3,250000,4,2,16,4,500 -"0_0_0_0_0",24,464,6,0,0,337,104,2258,2266,4,3,1,20,6,80,0,2, 383,1,0,0,0,0, 2, 158, 3,250000,4,2,16,4,500 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-"1_0_0_0_2",25,1730,48,7,3,1367,156,9331,9406,20,23,1,492,105,58,0,29, 386,1,0,0,0,0, 10, 899 ,3,1000000,5,2,16,4,500 -"1_0_0_0_2",19,1730,47,7,3,1368,155,9331,9406,20,23,1,493,104,57,0,31, 385,1,0,0,0,0, 10, 900, 3,1000000,5,2,16,4,500 -"1_0_0_0_2",11,1731,45,6,4,1366,160,9331,9406,19,23,1,494,104,56,0,33, 385,1,0,0,0,0, 10, 901, 3,1000000,5,2,16,4,500 -"1_0_0_0_2",21,1730,48,7,3,1371,162,9331,9406,17,23,1,487,98,71,0,29, 386,1,0,0,0,0, 10, 907, 3,1000000,5,2,16,4,500 -"1_0_0_0_2",18,3187,48,8,19,2678,302,9331,9407,21,23,1,503,83,64,0,29, 385,1,0,0,0,0, 15, 916, 3,1000000,5,2,16,4,500 -"1_0_0_0_2",9,3202,49,7,20,2691,305,9331,9407,17,23,0,502,78,65,0,34, 384,1,0,0,0,0, 15, 918, 3,1000000,5,2,16,4,500 -"1_0_0_0_2",23,3206,48,7,20,2693,310,9332,9406,20,23,1,486,81,84,0,27, 386,1,0,0,0,0, 15, 919, 3,1000000,5,2,16,4,500 -"1_0_0_0_2",0,3227,50,7,18,2722,300,9331,9407,19,23,1,504,78,63,32,0, 384,0,0,0,0,0, 15, 917, 3,1000000,5,2,16,4,500 -"1_0_0_0_2",17,3216,49,7,20,2701,319,9331,9406,20,23,1,479,75,99,0,25, 385,1,0,0,0,0, 16, 928, 3,1000000,5,2,16,4,500 -"1_0_0_0_2",24,3229,49,7,20,2710,322,9331,9406,21,23,1,480,76,97,0,25, 386,1,0,0,0,0, 16, 926, 3,1000000,5,2,16,4,500 -"1_0_0_0_2",8,3239,48,7,21,2727,310,9331,9406,17,23,1,492,75,79,0,33, 384,1,0,0,0,0, 15, 922, 3,1000000,5,2,16,4,500 -"1_0_0_0_2",10,3240,50,7,20,2726,319,9331,9406,18,23,1,480,74,97,0,25, 384,1,0,0,0,0, 16, 928, 3,1000000,5,2,16,4,500 -"1_0_0_0_2",4,3255,48,7,19,2746,302,9331,9407,18,22,1,503,79,63,0,33, 384,0,0,0,0,0, 15, 917, 3,1000000,5,2,16,4,500 -"1_0_0_0_2",5,3253,49,7,20,2747,306,9331,9406,21,23,1,490,74,81,0,33, 384,1,0,0,0,0, 16, 922, 3,1000000,5,2,16,4,500 -"1_0_0_0_2",31,3261,70,12,18,2749,306,9331,9406,17,23,1,495,61,67,0,28, 386,1,0,0,0,0, 18, 910, 3,1000000,5,2,16,4,500 -"1_0_0_0_2",12,3211,48,7,21,2745,311,9331,9407,19,23,1,313,44,305,0,17, 385,1,0,0,0,0, 15, 968, 3,1000000,5,2,16,4,500 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cf2dfb0c..00000000 --- a/hpc/archer2/data/grid_search/grid_search_fft_f32_1e7.csv +++ /dev/null @@ -1,98 +0,0 @@ - -experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,source_tree,target_tree,source_domain,target_domain,layout,ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,displacement_map,metadata_creation,expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples -"0_0_0_0_0",0,3300,72,7,3,1355,1710,47381,47327,73,71,4,1860,90,192,26,0, 387,1,0,0,0,0, 10, 3460, 3,5000000,5,2,16,4,500 -"0_0_0_0_0",2,3325,72,6,4,1347,1747,47381,47327,75,71,4,1806,89,249,0,26, 386,1,0,0,0,0, 10, 3465, 3,5000000,5,2,16,4,500 -"0_0_0_0_0",31,6449,110,11,17,2744,3451,47382,47329,73,71,1,1842,28,223,0,25, 391,1,0,0,0,0, 19, 3471, 3,5000000,5,2,16,4,500 -"0_0_0_0_0",9,6487,69,7,10,2733,3516,47381,47330,74,71,1,1718,68,358,0,23, 387,1,0,0,0,0, 15, 3485, 3,5000000,5,2,16,4,500 -"0_0_0_0_0",10,6494,68,6,9,2702,3554,47381,47330,74,71,1,1689,67,389,0,23, 387,1,0,0,0,0, 15, 3488, 3,5000000,5,2,16,4,500 -"0_0_0_0_0",5,6472,69,6,8,2770,3492,47381,47330,74,71,1,972,52,1132,0,13, 386,1,0,0,0,0, 15, 3513, 3,5000000,5,2,16,4,500 -"0_0_0_0_0",13,6508,68,6,9,2767,3499,47381,47330,72,71,1,1722,68,353,0,25, 388,1,0,0,0,0, 15, 3484, 3,5000000,5,2,16,4,500 -"0_0_0_0_0",16,6507,73,7,18,2756,3504,47381,47330,75,71,1,1733,61,342,0,24, 391,1,0,0,0,0, 16, 3481, 3,5000000,5,2,16,4,500 -"0_0_0_0_0",19,6515,72,7,16,2772,3495,47381,47330,72,71,1,1764,64,311,0,25, 391,1,0,0,0,0, 15, 3480, 3,5000000,5,2,16,4,500 -"0_0_0_0_0",26,6547,68,6,9,2756,3555,47381,47330,74,71,1,1701,64,379,0,24, 389,1,0,0,0,0, 15, 3488, 3,5000000,5,2,16,4,500 -"0_0_0_0_0",3,6551,68,6,9,2701,3620,47381,47329,73,71,1,1684,63,401,0,21, 386,1,0,0,0,0, 15, 3494, 3,5000000,5,2,16,4,500 -"0_0_0_0_0",22,6564,69,6,10,2723,3606,47381,47330,73,71,1,1711,62,369,0,24, 389,1,0,0,0,0, 15, 3489, 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-"1_0_0_0_0",17,25388,437,70,174,22596,1546,48901,48812,79,71,1,682,487,1824,0,9, 384,1,0,0,0,0, 139, 5259, 3,5000000,6,2,16,4,500 -"1_0_0_0_0",4,37248,418,71,220,33776,2351,48901,48812,78,71,1,861,313,1777,0,1, 382,1,0,0,0,0, 208, 5392, 3,5000000,6,2,16,4,500 diff --git a/hpc/archer2/data/weak/Weak Scaling.ipynb b/hpc/archer2/data/weak/Weak Scaling.ipynb deleted file mode 100644 index 9c32e3bc..00000000 --- a/hpc/archer2/data/weak/Weak Scaling.ipynb +++ /dev/null @@ -1,308 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "f77a94f9-96b4-4f6b-b217-f6c03a3b5160", - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib import rcParams" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "e1278eec-53ac-4890-a9c0-87a9313af665", - "metadata": {}, - "outputs": [], - "source": [ - "fft = pd.read_csv('weak_fft_7913527.csv')\n", - "\n", - "# Strategy 1, where 1 MPI process per CCX, max 32 per archer node\n", - "blas1 = pd.read_csv('weak_blas_7913901.csv')\n", - "\n", - "# Strategy 2, 1 MPI process per CCD, max 16 per archer node\n", - "blas2 = pd.read_csv('weak_blas_7920032.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "5c765e29-8f1d-4f38-98d9-a0ff75258a72", - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "from matplotlib import rcParams\n", - "\n", - "def plot_weak_scaling(df, title=\"Weak Scaling of Runtime vs. Particle Count\", save_svg=False, filename=\"weak_scaling_plot.svg\"):\n", - " # Set global font to CMU and enable LaTeX\n", - " rcParams['font.family'] = 'serif'\n", - " rcParams['font.serif'] = ['CMU Serif'] # Use 'CMU Serif' for the main font\n", - " rcParams['text.usetex'] = True\n", - " rcParams['text.latex.preamble'] = r'\\renewcommand{\\rmdefault}{cmu}'\n", - "\n", - " # Calculate total particles and runtimes for each experiment_id\n", - " group_size = df.groupby('experiment_id').size()\n", - " n_particles = group_size * df['n_points'].iloc[0]\n", - " \n", - " # Get maximum runtime per experiment_id\n", - " runtimes = df.groupby('experiment_id')['runtime'].max()\n", - " m2l_runtimes = df.groupby('experiment_id')['m2l'].max()\n", - "\n", - "\n", - " fig, ax = plt.subplots(figsize=(10, 6), dpi=300) # Larger size and higher DPI for quality\n", - "\n", - " # Set bar widths as a fraction of n_particles\n", - " relative_width = 0.3\n", - " widths = n_particles * relative_width\n", - "\n", - " # Plot bars\n", - " ax.bar(n_particles, runtimes, width=widths, align='center', color='skyblue', edgecolor='black')\n", - "\n", - " # Set x-axis to logarithmic\n", - " ax.set_xscale('log')\n", - "\n", - " # Labeling the axes with LaTeX expressions for testing\n", - " ax.set_xlabel(r'Total Number of Particles', fontsize=14)\n", - " ax.set_ylabel(r'Runtime ($ms$)', fontsize=14)\n", - " ax.set_title(title, fontsize=16)\n", - "\n", - " # Customize ticks and grid\n", - " ax.tick_params(axis='both', which='major', labelsize=12)\n", - " ax.grid(True, which=\"both\", linestyle='--', linewidth=0.5, alpha=0.7)\n", - "\n", - " # Save the plot as SVG if specified\n", - " if save_svg:\n", - " plt.savefig(filename, format=\"svg\", bbox_inches=\"tight\")\n", - "\n", - " plt.show()\n", - "\n", - "colors = [\"#87CEEB\", \"#FA8072\", \"#DAA520\", \"#3CB371\", \"#6A5ACD\", \"#FF7F50\", \"#9932CC\", \"#FF6347\", \"#40E0D0\"]\n", - "\n", - "\n", - "def plot_weak_scaling_stacked(df, ylims=None, title=\"Weak Scaling of Runtime vs. Particle Count\", save_svg=False, filename=\"weak_scaling_plot.svg\"):\n", - " # Set global font to CMU and enable LaTeX\n", - " rcParams['font.family'] = 'serif'\n", - " rcParams['font.serif'] = ['CMU Serif'] # Use 'CMU Serif' for the main font\n", - " rcParams['text.usetex'] = True\n", - " rcParams['text.latex.preamble'] = r'\\renewcommand{\\rmdefault}{cmu}'\n", - "\n", - " # Calculate total particles and runtimes for each experiment_id\n", - " group_size = df.groupby('experiment_id').size()\n", - " n_particles = group_size * df['n_points'].iloc[0]\n", - " \n", - " # Get maximum runtime per experiment_id\n", - " runtimes = df.groupby('experiment_id')['runtime'].max()\n", - " m2l_runtimes = df.groupby('experiment_id')['m2l'].max()\n", - " p2p_runtimes = df.groupby('experiment_id')['p2p'].max()\n", - " ghost_exchange_u = df.groupby('experiment_id')['ghost_exchange_u'].max()\n", - " ghost_exchange_v = df.groupby('experiment_id')['ghost_exchange_v'].max()\n", - " ghost_exchange_v_runtime = df.groupby('experiment_id')['ghost_exchange_v_runtime'].max()\n", - "\n", - "\n", - " fig, ax = plt.subplots(figsize=(10, 6), dpi=300) # Larger size and higher DPI for quality\n", - "\n", - " # Set bar widths and offsets\n", - " relative_width = 0.3\n", - " widths = n_particles * relative_width\n", - " offset = widths / 2 # Offset for second set of bars\n", - " \n", - " # Plot bars\n", - " ax.bar(n_particles, m2l_runtimes, width=widths, align='center', color='skyblue', edgecolor='black', label='M2L Runtime')\n", - " ax.bar(n_particles, p2p_runtimes, bottom=m2l_runtimes, width=widths, align='center', color='salmon', edgecolor='black', label='P2P Runtime')\n", - " ax.bar(n_particles, ghost_exchange_v_runtime, bottom=p2p_runtimes+m2l_runtimes, width=widths, align='center', color='slateblue', edgecolor='black', label='Ghost Exchange V Runtime')\n", - "\n", - " # Plot the second set of stacked bars (ghost_exchange_u + ghost_exchange_v)\n", - " ax.bar(n_particles + offset, ghost_exchange_u, width=widths, align='center', color='goldenrod', edgecolor='black', label='Ghost Exchange U')\n", - " ax.bar(n_particles + offset, ghost_exchange_v, bottom=ghost_exchange_u, width=widths, align='center', color='MediumSeaGreen', edgecolor='black', label='Ghost Exchange V')\n", - " \n", - " # Set x-axis to logarithmic\n", - " ax.set_xscale('log')\n", - "\n", - " if ylims:\n", - " ax.set_ylim(ylims)\n", - " else:\n", - " ax.set_ylim(0, runtimes.max()+150)\n", - "\n", - " # Labeling the axes with LaTeX expressions for testing\n", - " ax.set_xlabel(r'Total Number of Particles', fontsize=14)\n", - " ax.set_ylabel(r'Runtime ($ms$)', fontsize=14)\n", - " ax.set_title(title, fontsize=16)\n", - "\n", - " # Customize ticks and grid\n", - " ax.tick_params(axis='both', which='major', labelsize=12)\n", - " ax.grid(True, which=\"both\", linestyle='--', linewidth=0.5, alpha=0.7)\n", - "\n", - " ax.legend()\n", - " # Save the plot as SVG if specified\n", - " if save_svg:\n", - " plt.savefig(filename, format=\"svg\", bbox_inches=\"tight\")\n", - "\n", - " plt.show()\n", - "\n", - " return ax, fig" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "4a8b8457-1b49-4fc7-9b52-a84d66479492", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ax2, fig2 = plot_weak_scaling_stacked(blas2, ylims=(0, 2500), title=\"Weak Scaling of Runtime vs. Particle Count BLAS M2L\")" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "2d78fc93-e2b7-49a6-9b22-2255091d7702", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Create a new combined figure\n", - "combined_fig, (combined_ax1, combined_ax2) = plt.subplots(1, 2, figsize=(20, 6), dpi=300)\n", - "\n", - "# Track unique labels to prevent duplicates\n", - "unique_labels = set()\n", - "\n", - "# Set lims\n", - "ylims = (0, 2500)\n", - "\n", - "# Transfer bar data from ax1 to combined_ax1 with relative widths\n", - "for container in ax1.containers:\n", - " bars = container.get_children()\n", - " x_values = [bar.get_x() + bar.get_width() / 2 for bar in bars]\n", - " heights = [bar.get_height() for bar in bars]\n", - " bottoms = [bar.get_y() for bar in bars]\n", - " color = bars[0].get_facecolor() if bars else None\n", - " widths = [x * 0.3 for x in x_values]\n", - " \n", - " # Only add label if it hasn't been added before\n", - " label = container.get_label()\n", - " for x, height, bottom, width in zip(x_values, heights, bottoms, widths):\n", - " combined_ax1.bar(x, height, bottom=bottom, width=width, color=color, edgecolor='black', label=label if label not in unique_labels else \"\")\n", - " unique_labels.add(label) # Add label to set to prevent duplicates\n", - "\n", - "# Set labels and titles for combined_ax1\n", - "combined_ax1.set_xscale('log')\n", - "combined_ax1.set_xlabel(ax1.get_xlabel(), fontsize=14)\n", - "combined_ax1.set_ylabel(ax1.get_ylabel(), fontsize=14)\n", - "combined_ax1.set_title(ax1.get_title(), fontsize=16)\n", - "combined_ax1.set_ylim(ylims)\n", - "\n", - "# Repeat for combined_ax2 with unique labels\n", - "for container in ax2.containers:\n", - " bars = container.get_children()\n", - " x_values = [bar.get_x() + bar.get_width() / 2 for bar in bars]\n", - " heights = [bar.get_height() for bar in bars]\n", - " bottoms = [bar.get_y() for bar in bars]\n", - " color = bars[0].get_facecolor() if bars else None\n", - " widths = [x * 0.3 for x in x_values]\n", - " \n", - " label = container.get_label()\n", - " for x, height, bottom, width in zip(x_values, heights, bottoms, widths):\n", - " combined_ax2.bar(x, height, bottom=bottom, width=width, color=color, edgecolor='black', label=label if label not in unique_labels else \"\")\n", - " unique_labels.add(label)\n", - "\n", - "# Set labels and titles for combined_ax1\n", - "combined_ax2.set_xscale('log')\n", - "combined_ax2.set_xlabel(ax2.get_xlabel(), fontsize=14)\n", - "combined_ax2.set_ylabel(ax2.get_ylabel(), fontsize=14)\n", - "combined_ax2.set_title(ax2.get_title(), fontsize=16)\n", - "combined_ax2.set_ylim(ylims)\n", - "\n", - "# Combine legend entries from both axes without duplicates\n", - "handles, labels = [], []\n", - "for handle, label in zip(*combined_ax1.get_legend_handles_labels()):\n", - " if label not in labels:\n", - " handles.append(handle)\n", - " labels.append(label)\n", - "\n", - "# Create a single legend for the figure\n", - "combined_fig.legend(handles, labels, loc='upper center', bbox_to_anchor=(0.5, 1.15), ncol=3)\n", - "\n", - "# Show the combined figure\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "590b4ede-b2e4-42c1-8c94-7c68608f6945", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/hpc/ijhpca/README.md b/hpc/ijhpca/README.md new file mode 100644 index 00000000..d4e27fab --- /dev/null +++ b/hpc/ijhpca/README.md @@ -0,0 +1,2 @@ +# Experiments for Journal Submission to IJHPCA + diff --git a/hpc/ijhpca/Weak Scaling (16 Nodes).ipynb b/hpc/ijhpca/Weak Scaling (16 Nodes).ipynb deleted file mode 100644 index de5dc120..00000000 --- a/hpc/ijhpca/Weak Scaling (16 Nodes).ipynb +++ /dev/null @@ -1,501 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "e74144a3-a310-4e35-8b6f-ceadad982fc0", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a15c010f-3256-4039-97b3-4ab6c4bb403e", - "metadata": {}, - "outputs": [], - "source": [ - "# df_5e6 = pd.read_csv('weak_fft_10984742.csv')\n", - "# df_1e6 = pd.read_csv('weak_fft_10984754.csv')\n", - "df_250k = pd.read_csv('weak_fft_11004445.csv')\n", - "df_250k_nc = pd.read_csv('weak_fft_11018099_non_contiguous.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "6cbe52e4-fe73-477f-87aa-4ffd218c4542", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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experiment_idrankruntimep2mm2ml2lm2lp2psource_treetarget_tree...ghost_fmm_udisplacement_mapmetadata_creationexpansion_ordern_pointslocal_depthglobal_depthblock_sizen_threadsn_samples
001184000029802935...001603250000411284500
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20232740016114429802935...011643250000411284500
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" - ], - "text/plain": [ - " experiment_id rank runtime p2m m2m l2l m2l p2p source_tree \\\n", - "0 0 1 18 4 0 0 0 0 2980 \n", - "1 0 0 244 0 0 0 82 143 2980 \n", - "2 0 2 327 4 0 0 161 144 2980 \n", - "3 0 3 328 4 0 0 162 143 2980 \n", - "4 0 7 328 8 0 0 163 143 2981 \n", - "\n", - " target_tree ... ghost_fmm_u displacement_map metadata_creation \\\n", - "0 2935 ... 0 0 160 \n", - "1 2935 ... 0 1 168 \n", - "2 2935 ... 0 1 164 \n", - "3 2935 ... 0 0 163 \n", - "4 2935 ... 0 1 164 \n", - "\n", - " expansion_order n_points local_depth global_depth block_size \\\n", - "0 3 250000 4 1 128 \n", - "1 3 250000 4 1 128 \n", - "2 3 250000 4 1 128 \n", - "3 3 250000 4 1 128 \n", - "4 3 250000 4 1 128 \n", - "\n", - " n_threads n_samples \n", - "0 4 500 \n", - "1 4 500 \n", - "2 4 500 \n", - "3 4 500 \n", - "4 4 500 \n", - "\n", - "[5 rows x 33 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_250k.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "3bff612b-6a2b-449e-a301-cb45048cf545", - "metadata": {}, - "outputs": [], - "source": [ - "n_tasks=np.array([8, 16, 32, 64, 128, 256, 512])\n", - "n_nodes=np.array([1, 1, 1, 2, 4, 8, 16])" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "caf1a774-3904-4e65-95bf-f37f1180a3d6", - "metadata": {}, - "outputs": [], - "source": [ - "def runtime_vs_npoints(df):\n", - " npoints = []\n", - " runtime = []\n", - " for (id, experiment) in df.groupby('experiment_id'):\n", - " n = experiment['n_points'].max() * experiment.shape[0]\n", - " npoints.append(n)\n", - " r = experiment['runtime'].mean()\n", - " runtime.append(r)\n", - " \n", - " npoints = np.array(npoints)\n", - " runtime = np.array(runtime)\n", - " \n", - " # unique processor counts for each experiment (same length as npoints)\n", - " nprocs = np.array([n_tasks[i] for i in range(len(npoints))])\n", - "\n", - " # sort everything by npoints for consistent region layout\n", - " sort_idx = np.argsort(npoints)\n", - " npoints = npoints[sort_idx]\n", - " runtime = runtime[sort_idx]\n", - " nprocs = nprocs[sort_idx]\n", - "\n", - " fig, ax = plt.subplots()\n", - " ax.plot(npoints, runtime, marker=\"o\")\n", - " ax.set_xscale('log')\n", - " plt.ylim(np.min(runtime) - 10)\n", - "\n", - " # Work in log space for region boundaries\n", - " log_x = np.log10(npoints)\n", - " y0, y1 = ax.get_ylim()\n", - "\n", - " # Midpoints between data points (log space)\n", - " mids = (log_x[:-1] + log_x[1:]) / 2\n", - " # Region edges = start, mids, end\n", - " region_edges = np.concatenate([[log_x[0]], mids, [log_x[-1]]])\n", - "\n", - " # Shade per-point region\n", - " for i in range(len(npoints)):\n", - " left = 10**region_edges[i]\n", - " right = 10**region_edges[i+1]\n", - "\n", - " ax.axvspan(left, right, alpha=0.15,\n", - " color='orange' if i % 2 == 0 else 'lightblue')\n", - "\n", - " # Label at the data point’s x position\n", - " ax.text(npoints[i], (y0 + y1) / 2, f\"{nprocs[i]} ranks\",\n", - " ha='center', va='center', rotation=90,\n", - " bbox=dict(facecolor='white', alpha=0.6, edgecolor='none'))\n", - "\n", - " ax.set_title(f\"Weak Scaling to {npoints.max():.2e} points\")\n", - " ax.set_xlabel('Number of Points')\n", - " ax.set_ylabel('Runtime (ms)')\n", - "\n", - " return fig, ax, nprocs, runtime\n", - "\n", - "\n", - "\n", - "def runtime_vs_nprocesses(df):\n", - " nranks = []\n", - " runtime = []\n", - " for (id, experiment) in df.groupby('experiment_id'):\n", - " n = experiment.shape[0]\n", - " nranks.append(n)\n", - " r = experiment['runtime'].mean()\n", - " runtime.append(r)\n", - " \n", - " nranks = np.array(nranks)\n", - " runtime = np.array(runtime)\n", - "\n", - " # unique sorted processor counts\n", - " nprocs = np.unique([n_nodes[i] for i in range(len(nranks))])\n", - " boundaries = nprocs * 64\n", - "\n", - " fig, ax = plt.subplots()\n", - " ax.plot(nranks, runtime, marker=\"o\")\n", - " ax.set_xscale('log')\n", - " plt.ylim(np.min(runtime)-10)\n", - "\n", - " # Region edges (start, between boundaries, end)\n", - " region_edges = np.concatenate([[nranks.min()], boundaries, [nranks.max()]])\n", - "\n", - " # Shade regions for each processor count\n", - " y0, y1 = ax.get_ylim()\n", - "\n", - " # shrink factor in log space (e.g. 5% inside)\n", - " shrink = 0.1 \n", - "\n", - " for i in range(len(nprocs)):\n", - " left = region_edges[i]\n", - " right = region_edges[i+1]\n", - "\n", - " # shift inward in log space\n", - " log_left, log_right = np.log10(left), np.log10(right)\n", - " log_pad = (log_right - log_left) * shrink\n", - " log_left_adj = log_left - log_pad\n", - " log_right_adj = log_right - log_pad\n", - "\n", - " left_adj = 10**log_left_adj\n", - " right_adj = 10**log_right_adj\n", - "\n", - " ax.axvspan(left_adj, right_adj, alpha=0.15,\n", - " color='orange' if i % 2 == 0 else 'lightblue')\n", - "\n", - " # label in the log-midpoint\n", - " xmid = 10**((log_left + log_right) / 2)\n", - " ax.text(xmid, (y0 + y1) / 2, f\"{nprocs[i]} nodes\",\n", - " ha='center', va='center', rotation=90,\n", - " bbox=dict(facecolor='white', alpha=0.6, edgecolor='none'))\n", - "\n", - " ax.set_xlabel('Number of Ranks')\n", - " ax.set_ylabel('Runtime (ms)')\n", - "\n", - " return fig, ax, nprocs, runtime\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "022c161a-007f-4f90-8f6d-3b73ed57dc00", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "(fig, ax, nprocs_250k, runtime_250k) = runtime_vs_npoints(df_250k)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "9125de69-3963-4cb2-9d59-80ed2dc441b2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "(fig, ax, nprocs_250k, runtime_250k) = runtime_vs_npoints(df_250k_nc)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "eb5e74a3-f5b1-4188-8cc9-0887efacbb2e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "(fig, ax, nprocs_250k, runtime_250k) = runtime_vs_nprocesses(df_250k)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "fe11d91c-64e3-428e-85be-79c5d433cb4a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ], - "text/plain": [ - " n_points\n", - "experiment_id \n", - "0 128000000\n", - "1 128000000\n", - "2 128000000" - ] - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "n_points = df0.groupby('experiment_id')[['n_points']].sum()" ] }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 5, "id": "30777f75", "metadata": {}, "outputs": [], @@ -206,13 +148,13 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 7, "id": "bff20d11", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -222,29 +164,9 @@ } ], "source": [ - "strong_scaling([df0,df1], names=[\"CCD\", \"CCX\"], title=\"Strong Scaling Up to 128e6 Points\")" + "strong_scaling([df0,df1], names=[\"CCD\", \"CCX\"], title=\"Strong Scaling For 128e6 Points\")" ] }, - { - "cell_type": "markdown", - "id": "32e0c4e5", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "id": "4922e217", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "099fc685-5267-4756-ad2d-7479fde532e2", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -256,7 +178,7 @@ ], "metadata": { "kernelspec": { - "display_name": "kifmm (3.10.17)", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, diff --git a/hpc/ijhpca/Weak Scaling.ipynb b/hpc/ijhpca/Weak Scaling.ipynb index dbc7b709..627a95b7 100644 --- a/hpc/ijhpca/Weak Scaling.ipynb +++ b/hpc/ijhpca/Weak Scaling.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "178da76c-f2f3-4e55-859b-37cccde9f575", "metadata": {}, "outputs": [], @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 3, "id": "7fa9e215", "metadata": {}, "outputs": [ @@ -49,7 +49,7 @@ "Name: n_points, Length: 144, dtype: int64" ] }, - "execution_count": 28, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -60,147 +60,11 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "adfa8d3f", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "04d05dfa", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "experiment_id\n", - "0 8000000.0\n", - "1 64000000.0\n", - "2 512000000.0\n", - "dtype: float64" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "total_points" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "84c2afc0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "experiment_id\n", - "0 8000000.0\n", - "1 64000000.0\n", - "2 512000000.0\n", - "dtype: float64" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "size * n_points" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a4ca0701", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "7cb485f7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " n_points\n", - "experiment_id \n", - "0 250000.0\n", - "1 250000.0\n", - "2 250000.0" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "n_points" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "754504a1", "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "def weak_scaling(dfs, names=None, title=\"Weak Scaling Performance\"):\n", @@ -284,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 8, "id": "67d9abc3-f9bb-41ec-9778-9912fecbc924", "metadata": {}, "outputs": [ @@ -314,7 +178,7 @@ ], "metadata": { "kernelspec": { - "display_name": "kifmm (3.10.17)", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, From c97fa2520b32a75d14aef74b79d8b8cd4d833107 Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Fri, 10 Oct 2025 23:53:38 +0100 Subject: [PATCH 34/52] Notebooks --- hpc/ijhpca/Strong Scaling.ipynb | 151 +++++++++++++++++++++++++++----- hpc/ijhpca/Weak Scaling.ipynb | 59 +++++-------- 2 files changed, 154 insertions(+), 56 deletions(-) diff --git a/hpc/ijhpca/Strong Scaling.ipynb b/hpc/ijhpca/Strong Scaling.ipynb index 5ba5da2f..1309c9ca 100644 --- a/hpc/ijhpca/Strong Scaling.ipynb +++ b/hpc/ijhpca/Strong Scaling.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 10, "id": "2137b1d9-7f65-410a-b79d-7ed81dcf9b46", "metadata": {}, "outputs": [], @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 11, "id": "04c73ea3", "metadata": {}, "outputs": [ @@ -23,9 +23,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[34mstrong_fft_n=128000000_p=64_11077776\u001b[m\u001b[m \u001b[34mweak_fft_n=512000000_p=64_11077797\u001b[m\u001b[m\n", - "\u001b[34mstrong_fft_n=128000000_p=64_11077778\u001b[m\u001b[m \u001b[34mweak_fft_n=512000000_p=64_11077800\u001b[m\u001b[m\n", - "\u001b[34mweak_fft_n=512000000_p=64_11077796\u001b[m\u001b[m\n" + "\u001b[34mstrong_fft_n=128000000_p=64_11077776\u001b[m\u001b[m \u001b[34mweak_fft_n=4096000000_p=512_11079232\u001b[m\u001b[m\n", + "\u001b[34mstrong_fft_n=128000000_p=64_11077778\u001b[m\u001b[m \u001b[34mweak_fft_n=512000000_p=64_11077796\u001b[m\u001b[m\n", + "\u001b[34mstrong_fft_n=128000000_p=64_11079093\u001b[m\u001b[m \u001b[34mweak_fft_n=512000000_p=64_11077797\u001b[m\u001b[m\n", + "\u001b[34mstrong_fft_n=128000000_p=64_11079322\u001b[m\u001b[m \u001b[34mweak_fft_n=512000000_p=64_11077800\u001b[m\u001b[m\n" ] } ], @@ -35,28 +36,87 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 12, "id": "ae668ed4", "metadata": {}, "outputs": [], "source": [ "df0 = pd.read_csv('data/strong_fft_n=128000000_p=64_11077776/strong_fft_n=128000000_p=64_11077776.csv')\n", - "df1 = pd.read_csv('data/strong_fft_n=128000000_p=64_11077778/strong_fft_n=128000000_p=64_11077778.csv')" + "df1 = pd.read_csv('data/strong_fft_n=128000000_p=64_11077778/strong_fft_n=128000000_p=64_11077778.csv')\n", + "df2 = pd.read_csv('data/strong_fft_n=128000000_p=64_11079093/strong_fft_n=128000000_p=64_11079093.csv')" ] }, { "cell_type": "code", - "execution_count": 4, - "id": "08b174e4", + "execution_count": 26, + "id": "10e3bc2c-7ab5-4be6-9b7c-f056301124e7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " n_points\n", + "experiment_id \n", + "0 4000000.0\n", + "1 500000.0\n", + "2 62500.0" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "n_points = df0.groupby('experiment_id')[['n_points']].sum()" + "df1.groupby('experiment_id')[['n_points']].mean()" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 35, "id": "30777f75", "metadata": {}, "outputs": [], @@ -109,23 +169,30 @@ " # Parallel efficiency\n", " efficiency = (T1 / (runtime * (n_ranks / n_ranks.iloc[0]))) * 100 # in %\n", "\n", + " n_points = stats.mean()['n_points'] # points per rank\n", + "\n", + " print(n_points)\n", " # Runtime plot\n", " ax_runtime.errorbar(\n", - " n_ranks, runtime, yerr=runtime_err, fmt=marker+'-', color=color,\n", + " n_points, runtime, yerr=runtime_err, fmt=marker+'-', color=color,\n", " label=label, capsize=4, linewidth=2, markersize=7\n", " )\n", - " ax_runtime.plot(n_ranks, ideal_runtime, '--', color=color, alpha=0.7, linewidth=1.5)\n", + " ax_runtime.plot(n_points, ideal_runtime, '--', color=color, alpha=0.7, linewidth=1.5)\n", "\n", " # Efficiency plot\n", " ax_eff.plot(\n", - " n_ranks, efficiency, marker=marker, linestyle='-', color=color,\n", + " n_points, efficiency, marker=marker, linestyle='-', color=color,\n", " label=label, linewidth=2, markersize=7\n", " )\n", "\n", + " ax_eff.xaxis.set_inverted(True) \n", + " ax_runtime.xaxis.set_inverted(True) \n", + "\n", + "\n", " # Configure runtime plot\n", " ax_runtime.set_xscale(\"log\", base=2)\n", " ax_runtime.set_yscale(\"log\")\n", - " ax_runtime.set_xlabel(\"Number of Ranks\", fontsize=13)\n", + " ax_runtime.set_xlabel(\"Points Per Rank\", fontsize=13)\n", " ax_runtime.set_ylabel(\"Runtime (ms)\", fontsize=13)\n", " ax_runtime.set_title(\"Strong Scaling (Runtime)\", fontsize=14)\n", " ax_runtime.grid(True, which=\"both\", ls=\"--\", lw=0.5)\n", @@ -134,7 +201,7 @@ " # Configure efficiency plot\n", " ax_eff.set_xscale(\"log\", base=2)\n", " ax_eff.set_ylim(0, 110)\n", - " ax_eff.set_xlabel(\"Number of Ranks\", fontsize=13)\n", + " ax_eff.set_xlabel(\"Points Per Rank\", fontsize=13)\n", " ax_eff.set_ylabel(\"Parallel Efficiency (%)\", fontsize=13)\n", " ax_eff.set_title(\"Parallel Efficiency\", fontsize=14)\n", " ax_eff.grid(True, which=\"both\", ls=\"--\", lw=0.5)\n", @@ -148,13 +215,34 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 36, "id": "bff20d11", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "experiment_id\n", + "0 8000000.0\n", + "1 1000000.0\n", + "2 125000.0\n", + "Name: n_points, dtype: float64\n", + "experiment_id\n", + "0 4000000.0\n", + "1 500000.0\n", + "2 62500.0\n", + "Name: n_points, dtype: float64\n", + "experiment_id\n", + "0 64000000.0\n", + "1 8000000.0\n", + "2 1000000.0\n", + "Name: n_points, dtype: float64\n" + ] + }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -164,13 +252,34 @@ } ], "source": [ - "strong_scaling([df0,df1], names=[\"CCD\", \"CCX\"], title=\"Strong Scaling For 128e6 Points\")" + "strong_scaling([df0,df1,df2], names=[\"CCD\", \"CCX\",\"Socket\"], title=\"Strong Scaling For 128e6 Points on 64 Nodes of ARCHER2\")" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "fcafbe0c-4a99-41a3-a5e2-79418587afc8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "524288" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "2**19" ] }, { "cell_type": "code", "execution_count": null, - "id": "022354c9-5234-47ad-88d1-aab701839b8d", + "id": "05868177-af2a-4dea-8b02-6bdb74f23706", "metadata": {}, "outputs": [], "source": [] diff --git a/hpc/ijhpca/Weak Scaling.ipynb b/hpc/ijhpca/Weak Scaling.ipynb index 627a95b7..474e4a0e 100644 --- a/hpc/ijhpca/Weak Scaling.ipynb +++ b/hpc/ijhpca/Weak Scaling.ipynb @@ -17,50 +17,39 @@ { "cell_type": "code", "execution_count": 2, - "id": "178da76c-f2f3-4e55-859b-37cccde9f575", + "id": "86084f2e-5662-445c-8bac-e630a73d764f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[34mstrong_fft_n=128000000_p=64_11077776\u001b[m\u001b[m \u001b[34mweak_fft_n=512000000_p=64_11077796\u001b[m\u001b[m\n", + "\u001b[34mstrong_fft_n=128000000_p=64_11077778\u001b[m\u001b[m \u001b[34mweak_fft_n=512000000_p=64_11077797\u001b[m\u001b[m\n", + "\u001b[34mstrong_fft_n=128000000_p=64_11079322\u001b[m\u001b[m \u001b[34mweak_fft_n=512000000_p=64_11077800\u001b[m\u001b[m\n", + "\u001b[34mweak_fft_n=4096000000_p=512_11079232\u001b[m\u001b[m\n" + ] + } + ], "source": [ - "df0 = pd.read_csv('data/weak_fft_n=512000000_p=64_11077797/weak_fft_n=512000000_p=64_11077797.csv')\n", - "df1 = pd.read_csv('data/weak_fft_n=512000000_p=64_11077800/weak_fft_n=512000000_p=64_11077800.csv')\n", - "df2 = pd.read_csv('data/weak_fft_n=512000000_p=64_11077796/weak_fft_n=512000000_p=64_11077796.csv')\n" + "! ls data/" ] }, { "cell_type": "code", "execution_count": 3, - "id": "7fa9e215", + "id": "178da76c-f2f3-4e55-859b-37cccde9f575", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 500000\n", - "1 500000\n", - "2 500000\n", - "3 500000\n", - "4 500000\n", - " ... \n", - "139 500000\n", - "140 500000\n", - "141 500000\n", - "142 500000\n", - "143 500000\n", - "Name: n_points, Length: 144, dtype: int64" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "df2['n_points']" + "df0 = pd.read_csv('data/weak_fft_n=4096000000_p=512_11079232/weak_fft_n=4096000000_p=512_11079232.csv')\n", + "df1 = pd.read_csv('data/weak_fft_n=512000000_p=64_11077800/weak_fft_n=512000000_p=64_11077800.csv')\n", + "df2 = pd.read_csv('data/weak_fft_n=512000000_p=64_11077796/weak_fft_n=512000000_p=64_11077796.csv')\n" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "id": "754504a1", "metadata": {}, "outputs": [], @@ -148,13 +137,13 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 19, "id": "67d9abc3-f9bb-41ec-9778-9912fecbc924", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -164,13 +153,13 @@ } ], "source": [ - "weak_scaling([df0, df1, df2], names=[\"CCX\", \"Socket\", \"CCD\"], title=\"Weak Scaling up to 512e6 Points\")" + "weak_scaling([df0, df1, df2], names=[\"CCX\", \"Socket\", \"CCD\"], title=\"Weak Scaling up to 4e9 Points on 512 nodes of ARCHER2\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "5551fd95-6e65-4501-9ffc-95fe01a78b71", + "id": "447bedda-cd0e-4ff3-9201-e46d6d66c62c", "metadata": {}, "outputs": [], "source": [] From e6ecb4ffcb12806498be1202a1e7dc0e56ba8787 Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Mon, 13 Oct 2025 11:27:48 +0100 Subject: [PATCH 35/52] new graphs --- hpc/ijhpca/Strong Scaling.ipynb | 14 +- hpc/ijhpca/Weak Scaling.ipynb | 33 +- .../err_run_0.log | 0 .../err_run_1.log | 0 .../err_run_2.log | 0 .../err_run_3.log | 6552 +++++++++++++++++ .../err_run_0.log | 0 .../err_run_1.log | 0 .../err_run_2.log | 0 .../err_run_3.log | 561 ++ 10 files changed, 7141 insertions(+), 19 deletions(-) create mode 100644 hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079231/err_run_0.log create mode 100644 hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079231/err_run_1.log create mode 100644 hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079231/err_run_2.log create mode 100644 hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079231/err_run_3.log create mode 100644 hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079233/err_run_0.log create mode 100644 hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079233/err_run_1.log create mode 100644 hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079233/err_run_2.log create mode 100644 hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079233/err_run_3.log diff --git a/hpc/ijhpca/Strong Scaling.ipynb b/hpc/ijhpca/Strong Scaling.ipynb index 1309c9ca..9dcaff6c 100644 --- a/hpc/ijhpca/Strong Scaling.ipynb +++ b/hpc/ijhpca/Strong Scaling.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 10, + "execution_count": 1, "id": "2137b1d9-7f65-410a-b79d-7ed81dcf9b46", "metadata": {}, "outputs": [], @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 2, "id": "04c73ea3", "metadata": {}, "outputs": [ @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 3, "id": "ae668ed4", "metadata": {}, "outputs": [], @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 4, "id": "10e3bc2c-7ab5-4be6-9b7c-f056301124e7", "metadata": {}, "outputs": [ @@ -105,7 +105,7 @@ "2 62500.0" ] }, - "execution_count": 26, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -116,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 5, "id": "30777f75", "metadata": {}, "outputs": [], @@ -215,7 +215,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 6, "id": "bff20d11", "metadata": {}, "outputs": [ diff --git a/hpc/ijhpca/Weak Scaling.ipynb b/hpc/ijhpca/Weak Scaling.ipynb index 474e4a0e..e5c44f41 100644 --- a/hpc/ijhpca/Weak Scaling.ipynb +++ b/hpc/ijhpca/Weak Scaling.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 7, "id": "2ae1633f-50c6-48ec-ad4b-d899118e24c8", "metadata": {}, "outputs": [], @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 28, "id": "86084f2e-5662-445c-8bac-e630a73d764f", "metadata": {}, "outputs": [ @@ -24,10 +24,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[34mstrong_fft_n=128000000_p=64_11077776\u001b[m\u001b[m \u001b[34mweak_fft_n=512000000_p=64_11077796\u001b[m\u001b[m\n", - "\u001b[34mstrong_fft_n=128000000_p=64_11077778\u001b[m\u001b[m \u001b[34mweak_fft_n=512000000_p=64_11077797\u001b[m\u001b[m\n", - "\u001b[34mstrong_fft_n=128000000_p=64_11079322\u001b[m\u001b[m \u001b[34mweak_fft_n=512000000_p=64_11077800\u001b[m\u001b[m\n", - "\u001b[34mweak_fft_n=4096000000_p=512_11079232\u001b[m\u001b[m\n" + "\u001b[34mstrong_fft_n=128000000_p=64_11077776\u001b[m\u001b[m \u001b[34mweak_fft_n=4096000000_p=512_11079232\u001b[m\u001b[m\n", + "\u001b[34mstrong_fft_n=128000000_p=64_11077778\u001b[m\u001b[m \u001b[34mweak_fft_n=4096000000_p=512_11079233\u001b[m\u001b[m\n", + "\u001b[34mstrong_fft_n=128000000_p=64_11079093\u001b[m\u001b[m \u001b[34mweak_fft_n=512000000_p=64_11077796\u001b[m\u001b[m\n", + "\u001b[34mstrong_fft_n=128000000_p=64_11079322\u001b[m\u001b[m \u001b[34mweak_fft_n=512000000_p=64_11077797\u001b[m\u001b[m\n", + "\u001b[34mweak_fft_n=4096000000_p=512_11079231\u001b[m\u001b[m \u001b[34mweak_fft_n=512000000_p=64_11077800\u001b[m\u001b[m\n" ] } ], @@ -37,19 +38,19 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 29, "id": "178da76c-f2f3-4e55-859b-37cccde9f575", "metadata": {}, "outputs": [], "source": [ "df0 = pd.read_csv('data/weak_fft_n=4096000000_p=512_11079232/weak_fft_n=4096000000_p=512_11079232.csv')\n", - "df1 = pd.read_csv('data/weak_fft_n=512000000_p=64_11077800/weak_fft_n=512000000_p=64_11077800.csv')\n", - "df2 = pd.read_csv('data/weak_fft_n=512000000_p=64_11077796/weak_fft_n=512000000_p=64_11077796.csv')\n" + "df1 = pd.read_csv('data/weak_fft_n=4096000000_p=512_11079231/weak_fft_n=4096000000_p=512_11079231.csv')\n", + "df2 = pd.read_csv('data/weak_fft_n=4096000000_p=512_11079233/weak_fft_n=4096000000_p=512_11079233.csv')\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 30, "id": "754504a1", "metadata": {}, "outputs": [], @@ -137,13 +138,13 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 31, "id": "67d9abc3-f9bb-41ec-9778-9912fecbc924", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -163,6 +164,14 @@ "metadata": {}, "outputs": [], "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "add61ffe-1504-409b-95ba-8c06b3706c59", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079231/err_run_0.log b/hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079231/err_run_0.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079231/err_run_1.log b/hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079231/err_run_1.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079231/err_run_2.log b/hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079231/err_run_2.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079231/err_run_3.log b/hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079231/err_run_3.log new file mode 100644 index 00000000..2c649e1e --- /dev/null +++ b/hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079231/err_run_3.log @@ -0,0 +1,6552 @@ +srun: Job 11079231 step creation temporarily disabled, retrying (Requested nodes are busy) +srun: Step created for job 11079231 +[nid001858:72021:0:72021] ud_ep.c:255 Fatal: UD endpoint 0x55c45f30b310 to : unhandled timeout error +==== backtrace (tid: 72021) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1470ef64f454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1470ef64c400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1470ef64c529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1470eb677133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1470ef6458aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1470ef1e0962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1470f0e73295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1470f0e91870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1470f112d796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1470f11c6d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1470f0c29e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55c45ebb20f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55c45ebf1a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55c45ebf5bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55c45ec23258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55c45ebb26c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55c45ec34e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1470efd7229d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55c45eba346a] +================================= +[nid001858:72026:0:72026] ud_ep.c:255 Fatal: UD endpoint 0x56170ad44290 to : unhandled timeout error +==== backtrace (tid: 72026) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x155089e6d454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x155089e6a400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x155089e6a529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x155085e95133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x155089e638aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1550899fe962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15508b691295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15508b6af870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15508b94b796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15508b9e4d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15508b447e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56170a5db0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x56170a61aa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56170a61ebdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56170a64c258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56170a5db6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56170a65de63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15508a59029d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56170a5cc46a] +================================= +[nid001858:72022:0:72022] ud_ep.c:255 Fatal: UD endpoint 0x55f4986ab190 to : unhandled timeout error +==== backtrace (tid: 72022) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1519e229c454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1519e2299400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1519e2299529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1519de2c4133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1519e22928aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1519e1e2d962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1519e3ac0295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1519e3ade870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1519e3d7a796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1519e3e13d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1519e3876e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55f497f520f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55f497f91a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55f497f95bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55f497fc3258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55f497f526c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55f497fd4e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1519e29bf29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55f497f4346a] +================================= +[nid001660:125276:0:125276] ud_ep.c:255 Fatal: UD endpoint 0x55f83740f920 to : unhandled timeout error +==== backtrace (tid: 125276) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14c1eaed8454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14c1eaed5400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14c1eaed5529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14c1e6f00133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14c1eaece8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14c1eaa69962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14c1ec6fc295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14c1ec71a870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14c1ec9b6796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14c1eca4fd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14c1ec4b2e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55f836d190f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55f836d58a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55f836d5cbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55f836d8a258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55f836d196c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55f836d9be63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14c1eb5fb29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55f836d0a46a] +================================= +[nid001409:185379:0:185379] ud_ep.c:255 Fatal: UD endpoint 0x556a0f975f90 to : unhandled timeout error +==== backtrace (tid: 185379) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14c2b1447454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14c2b1444400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14c2b1444529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14c2ad46f133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14c2b143d8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14c2b0fd8962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14c2b2c6b295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14c2b2c89870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14c2b2f25796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14c2b2fbed35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14c2b2a21e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x556a0f2eb0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x556a0f32aa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x556a0f32ebdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x556a0f35c258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x556a0f2eb6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x556a0f36de63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14c2b1b6a29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x556a0f2dc46a] +================================= +[nid005128:172740:0:172740] ud_ep.c:255 Fatal: UD endpoint 0x5568a6f71cc0 to : unhandled timeout error +==== backtrace (tid: 172740) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1479a3909454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1479a3906400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1479a3906529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14799f931133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1479a38ff8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1479a349a962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1479a512d295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1479a514b870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1479a53e7796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1479a5480d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1479a4ee3e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5568a68e00f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5568a691fa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5568a6923bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5568a6951258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5568a68e06c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5568a6962e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1479a402c29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5568a68d146a] +================================= +[nid001660:125282:0:125282] ud_ep.c:255 Fatal: UD endpoint 0x55a0ba6c6c70 to : unhandled timeout error +==== backtrace (tid: 125282) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15360340c454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x153603409400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x153603409529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1535ff434133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1536034028aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x153602f9d962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x153604c30295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x153604c4e870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x153604eea796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x153604f83d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1536049e6e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55a0b9fdb0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55a0ba01aa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55a0ba01ebdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55a0ba04c258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55a0b9fdb6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55a0ba05de63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x153603b2f29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55a0b9fcc46a] +================================= +[nid001660:125281:0:125281] ud_ep.c:255 Fatal: UD endpoint 0x55dcde4e80e0 to : unhandled timeout error +==== backtrace (tid: 125281) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15006c2bd454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15006c2ba400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15006c2ba529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1500682e5133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15006c2b38aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15006be4e962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15006dae1295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15006daff870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15006dd9b796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15006de34d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15006d897e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55dcdddff0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55dcdde3ea81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55dcdde42bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55dcdde70258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55dcdddff6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55dcdde81e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15006c9e029d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55dcdddf046a] +================================= +[nid001918:19477:0:19477] ud_ep.c:255 Fatal: UD endpoint 0x559ea497c6d0 to : unhandled timeout error +==== backtrace (tid: 19477) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x145e46fce454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x145e46fcb400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x145e46fcb529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x145e42ff6133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x145e46fc48aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x145e46b5f962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x145e487f2295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x145e48810870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x145e48aac796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x145e48b45d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x145e485a8e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559ea426f0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559ea42aea81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559ea42b2bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559ea42e0258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559ea426f6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559ea42f1e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x145e476f129d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559ea426046a] +================================= +[nid001660:125278:0:125278] ud_ep.c:255 Fatal: UD endpoint 0x5638a70e3ae0 to : unhandled timeout error +==== backtrace (tid: 125278) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x153981b87454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x153981b84400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x153981b84529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x15397dbaf133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x153981b7d8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x153981718962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1539833ab295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1539833c9870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x153983665796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1539836fed35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x153983161e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5638a69fa0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5638a6a39a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5638a6a3dbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5638a6a6b258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5638a69fa6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5638a6a7ce63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1539822aa29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5638a69eb46a] +================================= +[nid001954:89829:0:89829] ud_ep.c:255 Fatal: UD endpoint 0x555e011d0680 to : unhandled timeout error +==== backtrace (tid: 89829) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14b3a89a2454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14b3a899f400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14b3a899f529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14b3a49ca133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14b3a89988aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14b3a8533962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14b3aa1c6295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14b3aa1e4870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14b3aa480796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14b3aa519d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14b3a9f7ce9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x555e00aad0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x555e00aeca81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x555e00af0bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x555e00b1e258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x555e00aad6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x555e00b2fe63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14b3a90c529d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x555e00a9e46a] +================================= +[nid001927:149635:0:149635] ud_ep.c:255 Fatal: UD endpoint 0x5638b4c43100 to : unhandled timeout error +==== backtrace (tid: 149635) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146bc4bb3454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146bc4bb0400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146bc4bb0529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x146bc0bdb133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146bc4ba98aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146bc4744962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x146bc63d7295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146bc63f5870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146bc6691796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146bc672ad35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146bc618de9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5638b444e0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5638b448da81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5638b4491bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5638b44bf258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5638b444e6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5638b44d0e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x146bc52d629d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5638b443f46a] +================================= +[nid001409:185384:0:185384] ud_ep.c:255 Fatal: UD endpoint 0x55a15ad7c6f0 to : unhandled timeout error +[nid001463:252762:0:252762] ud_ep.c:255 Fatal: UD endpoint 0x55d45c88ab30 to : unhandled timeout error +==== backtrace (tid: 185384) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14b330bff454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14b330bfc400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14b330bfc529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14b32cc27133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14b330bf58aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14b330790962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14b332423295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14b332441870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14b3326dd796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14b332776d35] +==== backtrace (tid: 252762) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14ffb41e0454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14ffb41dd400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14ffb41dd529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14ffb0208133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14ffb41d68aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14ffb3d71962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14ffb5a04295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14ffb5a22870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14ffb5cbe796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14ffb5d57d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14b3321d9e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55a15a7810f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55a15a7c0a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55a15a7c4bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55a15a7f2258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55a15a7816c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55a15a803e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14b33132229d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55a15a77246a] +================================= +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14ffb57bae9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55d45c2290f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55d45c268a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55d45c26cbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55d45c29a258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55d45c2296c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55d45c2abe63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14ffb490329d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55d45c21a46a] +================================= +[nid001920:170321:0:170321] ud_ep.c:255 Fatal: UD endpoint 0x5562cc22a6d0 to : unhandled timeout error +==== backtrace (tid: 170321) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x149c6195f454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x149c6195c400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x149c6195c529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x149c5d987133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x149c619558aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x149c614f0962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x149c63183295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x149c631a1870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x149c6343d796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x149c634d6d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x149c62f39e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5562cb9750f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5562cb9b4a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5562cb9b8bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5562cb9e6258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5562cb9756c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5562cb9f7e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x149c6208229d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5562cb96646a] +================================= +[nid001409:185382:0:185382] ud_ep.c:255 Fatal: UD endpoint 0x5622b9cb8770 to : unhandled timeout error +==== backtrace (tid: 185382) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x153ae3e8f454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x153ae3e8c400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x153ae3e8c529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x153adfeb7133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x153ae3e858aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x153ae3a20962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x153ae56b3295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x153ae56d1870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x153ae596d796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x153ae5a06d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x153ae5469e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5622b96b00f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5622b96efa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5622b96f3bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5622b9721258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5622b96b06c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5622b9732e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x153ae45b229d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5622b96a146a] +================================= +[nid001920:170322:0:170322] ud_ep.c:255 Fatal: UD endpoint 0x55ad7805cb70 to : unhandled timeout error +==== backtrace (tid: 170322) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1466eead1454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1466eeace400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1466eeace529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1466eaaf9133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1466eeac78aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1466ee662962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1466f02f5295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1466f0313870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1466f05af796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1466f0648d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1466f00abe9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ad777990f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ad777d8a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ad777dcbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ad7780a258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ad777996c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ad7781be63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1466ef1f429d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ad7778a46a] +================================= +[nid002270:182966:0:182966] ud_ep.c:255 Fatal: UD endpoint 0x55b5a5038890 to : unhandled timeout error +==== backtrace (tid: 182966) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14ad400c8454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14ad400c5400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14ad400c5529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14ad3c0f0133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14ad400be8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14ad3fc59962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14ad418ec295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14ad4190a870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14ad41ba6796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14ad41c3fd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14ad416a2e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55b5a48630f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55b5a48a2a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55b5a48a6bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55b5a48d4258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55b5a48636c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55b5a48e5e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14ad407eb29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55b5a485446a] +================================= +[nid001954:89827:0:89827] ud_ep.c:255 Fatal: UD endpoint 0x56290402e230 to : unhandled timeout error +==== backtrace (tid: 89827) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14b047d4e454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14b047d4b400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14b047d4b529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14b043d76133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14b047d448aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14b0478df962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14b049572295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14b049590870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14b04982c796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14b0498c5d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14b049328e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56290390b0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x56290394aa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56290394ebdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56290397c258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56290390b6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56290398de63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14b04847129d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5629038fc46a] +================================= +[nid002270:182967:0:182967] ud_ep.c:255 Fatal: UD endpoint 0x55742245a890 to : unhandled timeout error +==== backtrace (tid: 182967) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14ae5f7f0454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14ae5f7ed400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14ae5f7ed529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14ae5b818133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14ae5f7e68aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14ae5f381962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14ae61014295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14ae61032870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14ae612ce796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14ae61367d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14ae60dcae9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x557421c850f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x557421cc4a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x557421cc8bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x557421cf6258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x557421c856c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x557421d07e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14ae5ff1329d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x557421c7646a] +================================= +[nid002270:182965:0:182965] ud_ep.c:255 Fatal: UD endpoint 0x563f0fd32890 to : unhandled timeout error +==== backtrace (tid: 182965) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14eaa6e07454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14eaa6e04400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14eaa6e04529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14eaa2e2f133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14eaa6dfd8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14eaa6998962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14eaa862b295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14eaa8649870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14eaa88e5796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14eaa897ed35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14eaa83e1e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x563f0f55d0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x563f0f59ca81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x563f0f5a0bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x563f0f5ce258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x563f0f55d6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x563f0f5dfe63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14eaa752a29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x563f0f54e46a] +================================= +[nid001954:89831:0:89831] ud_ep.c:255 Fatal: UD endpoint 0x55dbb0b5b3a0 to : unhandled timeout error +==== backtrace (tid: 89831) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14d35a4bc454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14d35a4b9400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14d35a4b9529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14d3564e4133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14d35a4b28aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14d35a04d962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14d35bce0295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14d35bcfe870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14d35bf9a796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14d35c033d35] +[nid002270:182963:0:182963] ud_ep.c:255 Fatal: UD endpoint 0x55eb81a64890 to : unhandled timeout error +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14d35ba96e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55dbb04380f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55dbb0477a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55dbb047bbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55dbb04a9258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55dbb04386c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55dbb04bae63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14d35abdf29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55dbb042946a] +================================= +==== backtrace (tid: 182963) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x151edd1b5454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x151edd1b2400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x151edd1b2529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x151ed91dd133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x151edd1ab8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x151edcd46962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x151ede9d9295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x151ede9f7870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x151edec93796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x151eded2cd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x151ede78fe9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55eb8128f0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55eb812cea81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55eb812d2bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55eb81300258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55eb8128f6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55eb81311e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x151edd8d829d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55eb8128046a] +================================= +[nid001920:170319:0:170319] ud_ep.c:255 Fatal: UD endpoint 0x55fd132523a0 to : unhandled timeout error +==== backtrace (tid: 170319) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1494458e8454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1494458e5400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1494458e5529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x149441910133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1494458de8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x149445479962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14944710c295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14944712a870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1494473c6796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14944745fd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x149446ec2e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55fd129a10f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55fd129e0a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55fd129e4bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55fd12a12258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55fd129a16c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55fd12a23e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14944600b29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55fd1299246a] +================================= +[nid001954:89830:0:89830] ud_ep.c:255 Fatal: UD endpoint 0x5646a5edd230 to : unhandled timeout error +[nid001864:92460:0:92460] ud_ep.c:255 Fatal: UD endpoint 0x557af3c8dd50 to : unhandled timeout error +==== backtrace (tid: 92460) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14be093a9454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14be093a6400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14be093a6529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14be053d1133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14be0939f8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14be08f3a962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14be0abcd295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14be0abeb870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14be0ae87796] +==== backtrace (tid: 89830) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1497ddcd3454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1497ddcd0400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1497ddcd0529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1497d9cfb133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1497ddcc98aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1497dd864962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1497df4f7295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1497df515870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1497df7b1796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1497df84ad35] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14be0af20d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14be0a983e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x557af34a50f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x557af34e4a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x557af34e8bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x557af3516258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x557af34a56c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x557af3527e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14be09acc29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x557af349646a] +================================= +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1497df2ade9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5646a57ba0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5646a57f9a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5646a57fdbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5646a582b258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5646a57ba6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5646a583ce63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1497de3f629d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5646a57ab46a] +================================= +[nid001927:149634:0:149634] ud_ep.c:255 Fatal: UD endpoint 0x55b083c14da0 to : unhandled timeout error +[nid001656:201192:0:201192] ud_ep.c:255 Fatal: UD endpoint 0x564a134188d0 to : unhandled timeout error +==== backtrace (tid: 149634) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14e1c8acf454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14e1c8acc400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14e1c8acc529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14e1c4af7133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14e1c8ac58aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14e1c8660962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14e1ca2f3295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14e1ca311870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14e1ca5ad796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14e1ca646d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14e1ca0a9e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55b0834210f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55b083460a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55b083464bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55b083492258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55b0834216c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55b0834a3e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14e1c91f229d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55b08341246a] +================================= +==== backtrace (tid: 201192) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x145ce7e50454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x145ce7e4d400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x145ce7e4d529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x145ce3e78133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x145ce7e468aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x145ce79e1962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x145ce9674295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x145ce9692870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x145ce992e796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x145ce99c7d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x145ce942ae9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x564a12dae0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x564a12deda81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x564a12df1bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x564a12e1f258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x564a12dae6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x564a12e30e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x145ce857329d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x564a12d9f46a] +================================= +[nid001656:201198:0:201198] ud_ep.c:255 Fatal: UD endpoint 0x5575fa5fd8f0 to : unhandled timeout error +==== backtrace (tid: 201198) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14b4bffe4454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14b4bffe1400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14b4bffe1529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14b4bc00c133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14b4bffda8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14b4bfb75962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14b4c1808295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14b4c1826870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14b4c1ac2796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14b4c1b5bd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14b4c15bee9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5575f9fe90f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5575fa028a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5575fa02cbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5575fa05a258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5575f9fe96c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5575fa06be63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14b4c070729d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5575f9fda46a] +================================= +[nid001927:149637:0:149637] ud_ep.c:255 Fatal: UD endpoint 0x55ff57d95d20 to : unhandled timeout error +[nid002270:182964:0:182964] ud_ep.c:255 Fatal: UD endpoint 0x560f9022b890 to : unhandled timeout error +==== backtrace (tid: 149637) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14bd63aad454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14bd63aaa400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14bd63aaa529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14bd5fad5133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14bd63aa38aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14bd6363e962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14bd652d1295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14bd652ef870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14bd6558b796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14bd65624d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14bd65087e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ff575b20f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ff575f1a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ff575f5bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ff57623258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ff575b26c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ff57634e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14bd641d029d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ff575a346a] +================================= +==== backtrace (tid: 182964) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15443bd40454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15443bd3d400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15443bd3d529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x154437d68133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15443bd368aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15443b8d1962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15443d564295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15443d582870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15443d81e796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15443d8b7d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15443d31ae9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x560f8fa560f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x560f8fa95a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x560f8fa99bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x560f8fac7258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x560f8fa566c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x560f8fad8e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15443c46329d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x560f8fa4746a] +================================= +[nid002270:182962:0:182962] ud_ep.c:255 Fatal: UD endpoint 0x5581e33f4890 to : unhandled timeout error +==== backtrace (tid: 182962) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14e512698454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14e512695400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14e512695529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14e50e6c0133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14e51268e8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14e512229962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14e513ebc295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14e513eda870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14e514176796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14e51420fd35] +[nid001950:100695:0:100695] ud_ep.c:255 Fatal: UD endpoint 0x5566250090e0 to : unhandled timeout error +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14e513c72e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5581e2c1f0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5581e2c5ea81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5581e2c62bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5581e2c90258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5581e2c1f6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5581e2ca1e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14e512dbb29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5581e2c1046a] +================================= +==== backtrace (tid: 100695) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1469d8cc1454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1469d8cbe400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1469d8cbe529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1469d4ce9133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1469d8cb78aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1469d8852962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1469da4e5295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1469da503870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1469da79f796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1469da838d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1469da29be9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5566249020f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x556624941a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x556624945bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x556624973258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5566249026c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x556624984e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1469d93e429d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5566248f346a] +================================= +[nid001842:138813:0:138813] ud_ep.c:255 Fatal: UD endpoint 0x55dc28d73ba0 to : unhandled timeout error +==== backtrace (tid: 138813) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14800af0a454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14800af07400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14800af07529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x148006f32133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14800af008aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14800aa9b962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14800c72e295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14800c74c870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14800c9e8796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14800ca81d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14800c4e4e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55dc2867d0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55dc286bca81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55dc286c0bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55dc286ee258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55dc2867d6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55dc286ffe63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14800b62d29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55dc2866e46a] +================================= +[nid001409:185380:0:185380] ud_ep.c:255 Fatal: UD endpoint 0x5561e56e43e0 to : unhandled timeout error +==== backtrace (tid: 185380) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x145cf7d84454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x145cf7d81400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x145cf7d81529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x145cf3dac133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x145cf7d7a8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x145cf7915962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x145cf95a8295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x145cf95c6870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x145cf9862796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x145cf98fbd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x145cf935ee9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5561e50940f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5561e50d3a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5561e50d7bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5561e5105258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5561e50946c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5561e5116e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x145cf84a729d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5561e508546a] +================================= +[nid001660:125275:0:125275] ud_ep.c:255 Fatal: UD endpoint 0x55bff19f1180 to : unhandled timeout error +==== backtrace (tid: 125275) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x151f15e8f454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x151f15e8c400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x151f15e8c529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x151f11eb7133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x151f15e858aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x151f15a20962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x151f176b3295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x151f176d1870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x151f1796d796] +[nid001656:201194:0:201194] ud_ep.c:255 Fatal: UD endpoint 0x564f10838cb0 to : unhandled timeout error + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x151f17a06d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x151f17469e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55bff13020f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55bff1341a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55bff1345bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55bff1373258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55bff13026c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55bff1384e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x151f165b229d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55bff12f346a] +================================= +==== backtrace (tid: 201194) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14c94f610454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14c94f60d400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14c94f60d529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14c94b638133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14c94f6068aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14c94f1a1962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14c950e34295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14c950e52870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14c9510ee796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14c951187d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14c950beae9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x564f1021f0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x564f1025ea81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x564f10262bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x564f10290258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x564f1021f6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x564f102a1e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14c94fd3329d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x564f1021046a] +================================= +[nid001463:252759:0:252759] ud_ep.c:255 Fatal: UD endpoint 0x55c0eb02fef0 to : unhandled timeout error +==== backtrace (tid: 252759) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14967c530454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14967c52d400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14967c52d529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x149678558133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14967c5268aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14967c0c1962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14967dd54295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14967dd72870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14967e00e796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14967e0a7d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14967db0ae9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55c0ea9ec0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55c0eaa2ba81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55c0eaa2fbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55c0eaa5d258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55c0ea9ec6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55c0eaa6ee63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14967cc5329d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55c0ea9dd46a] +================================= +[nid001920:170323:0:170323] ud_ep.c:255 Fatal: UD endpoint 0x564221bc31f0 to : unhandled timeout error +[nid001842:138809:0:138809] ud_ep.c:255 Fatal: UD endpoint 0x5567c1eeb2e0 to : unhandled timeout error +[nid001864:92461:0:92461] ud_ep.c:255 Fatal: UD endpoint 0x55d53b8f2c70 to : unhandled timeout error +==== backtrace (tid: 92461) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14ca0a06d454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14ca0a06a400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14ca0a06a529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14ca06095133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14ca0a0638aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14ca09bfe962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14ca0b891295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14ca0b8af870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14ca0bb4b796] +[nid001950:100691:0:100691] ud_ep.c:255 Fatal: UD endpoint 0x558a6188b050 to : unhandled timeout error + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14ca0bbe4d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14ca0b647e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55d53b10e0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55d53b14da81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55d53b151bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55d53b17f258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55d53b10e6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55d53b190e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14ca0a79029d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55d53b0ff46a] +================================= +[nid001954:89826:0:89826] ud_ep.c:255 Fatal: UD endpoint 0x5620e96fcde0 to : unhandled timeout error +==== backtrace (tid: 170323) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146dc8073454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146dc8070400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146dc8070529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x146dc409b133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146dc80698aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146dc7c04962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x146dc9897295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146dc98b5870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146dc9b51796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146dc9bead35] +==== backtrace (tid: 138809) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14bae75f2454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14bae75ef400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14bae75ef529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14bae361a133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14bae75e88aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14bae7183962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14bae8e16295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14bae8e34870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14bae90d0796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14bae9169d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146dc964de9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5642213140f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x564221353a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x564221357bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x564221385258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5642213146c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x564221396e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x146dc879629d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56422130546a] +================================= +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14bae8bcce9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5567c17ec0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5567c182ba81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5567c182fbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5567c185d258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5567c17ec6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5567c186ee63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14bae7d1529d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5567c17dd46a] +================================= +==== backtrace (tid: 100691) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14d1474d4454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14d1474d1400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14d1474d1529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14d1434fc133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14d1474ca8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14d147065962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14d148cf8295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14d148d16870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14d148fb2796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14d14904bd35] +==== backtrace (tid: 89826) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14ada264f454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14ada264c400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14ada264c529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14ad9e677133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14ada26458aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14ada21e0962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14ada3e73295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14ada3e91870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14ada412d796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14ada41c6d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14d148aaee9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x558a611840f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x558a611c3a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x558a611c7bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x558a611f5258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x558a611846c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x558a61206e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14d147bf729d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x558a6117546a] +================================= +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14ada3c29e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5620e8fda0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5620e9019a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5620e901dbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5620e904b258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5620e8fda6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5620e905ce63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14ada2d7229d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5620e8fcb46a] +================================= +[nid001656:201196:0:201196] ud_ep.c:255 Fatal: UD endpoint 0x55b37b4bfe30 to : unhandled timeout error +==== backtrace (tid: 201196) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1473c3023454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1473c3020400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1473c3020529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1473bf04b133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1473c30198aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1473c2bb4962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1473c4847295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1473c4865870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1473c4b01796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1473c4b9ad35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1473c45fde9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55b37ae4f0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55b37ae8ea81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55b37ae92bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55b37aec0258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55b37ae4f6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55b37aed1e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1473c374629d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55b37ae4046a] +================================= +[nid001409:185377:0:185377] ud_ep.c:255 Fatal: UD endpoint 0x55d5e5943180 to : unhandled timeout error +==== backtrace (tid: 185377) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14d26d726454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14d26d723400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14d26d723529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14d26974e133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14d26d71c8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14d26d2b7962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14d26ef4a295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14d26ef68870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14d26f204796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14d26f29dd35] +[nid001950:100694:0:100694] ud_ep.c:255 Fatal: UD endpoint 0x559268315ce0 to : unhandled timeout error +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14d26ed00e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55d5e52d80f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55d5e5317a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55d5e531bbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55d5e5349258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55d5e52d86c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55d5e535ae63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14d26de4929d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55d5e52c946a] +================================= +==== backtrace (tid: 100694) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x148a7aa2b454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x148a7aa28400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x148a7aa28529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x148a76a53133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x148a7aa218aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x148a7a5bc962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x148a7c24f295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x148a7c26d870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x148a7c509796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x148a7c5a2d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x148a7c005e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559267c0f0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559267c4ea81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559267c52bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559267c80258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559267c0f6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559267c91e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x148a7b14e29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559267c0046a] +================================= +[nid001864:92457:0:92457] ud_ep.c:255 Fatal: UD endpoint 0x55b3e54c95a0 to : unhandled timeout error +==== backtrace (tid: 92457) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x151f6c8ba454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x151f6c8b7400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x151f6c8b7529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x151f688e2133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x151f6c8b08aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x151f6c44b962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x151f6e0de295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x151f6e0fc870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x151f6e398796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x151f6e431d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x151f6de94e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55b3e4cea0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55b3e4d29a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55b3e4d2dbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55b3e4d5b258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55b3e4cea6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55b3e4d6ce63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x151f6cfdd29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55b3e4cdb46a] +================================= +[nid001660:125280:0:125280] ud_ep.c:255 Fatal: UD endpoint 0x55ce72f4bff0 to : unhandled timeout error +==== backtrace (tid: 125280) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x149023a69454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x149023a66400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x149023a66529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14901fa91133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x149023a5f8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1490235fa962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14902528d295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1490252ab870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x149025547796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1490255e0d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x149025043e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ce728530f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ce72892a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ce72896bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ce728c4258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ce728536c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ce728d5e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14902418c29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ce7284446a] +================================= +[nid002270:182968:0:182968] ud_ep.c:255 Fatal: UD endpoint 0x55be9c5bb890 to : unhandled timeout error +==== backtrace (tid: 182968) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x147f6dfc0454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x147f6dfbd400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x147f6dfbd529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x147f69fe8133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x147f6dfb68aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x147f6db51962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x147f6f7e4295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x147f6f802870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x147f6fa9e796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x147f6fb37d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x147f6f59ae9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55be9bde60f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55be9be25a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55be9be29bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55be9be57258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55be9bde66c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55be9be68e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x147f6e6e329d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55be9bdd746a] +================================= +[nid001950:100689:0:100689] ud_ep.c:255 Fatal: UD endpoint 0x56428a51dce0 to : unhandled timeout error +[nid001858:72024:0:72024] ud_ep.c:255 Fatal: UD endpoint 0x5566b514b080 to : unhandled timeout error +==== backtrace (tid: 72024) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x152d1ed6e454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x152d1ed6b400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x152d1ed6b529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x152d1ad96133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x152d1ed648aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x152d1e8ff962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x152d20592295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x152d205b0870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x152d2084c796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x152d208e5d35] +==== backtrace (tid: 100689) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x151c70512454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x151c7050f400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x151c7050f529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x151c6c53a133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x151c705088aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x151c700a3962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x151c71d36295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x151c71d54870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x151c71ff0796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x151c72089d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x152d20348e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5566b49f10f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5566b4a30a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5566b4a34bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5566b4a62258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5566b49f16c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5566b4a73e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x152d1f49129d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5566b49e246a] +================================= +[nid001842:138814:0:138814] ud_ep.c:255 Fatal: UD endpoint 0x558835b40fe0 to : unhandled timeout error +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x151c71aece9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x564289e170f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x564289e56a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x564289e5abdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x564289e88258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x564289e176c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x564289e99e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x151c70c3529d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x564289e0846a] +================================= +==== backtrace (tid: 138814) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15446f506454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15446f503400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15446f503529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x15446b52e133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15446f4fc8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15446f097962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x154470d2a295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x154470d48870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x154470fe4796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15447107dd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x154470ae0e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55883544c0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55883548ba81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55883548fbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5588354bd258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55883544c6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5588354cee63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15446fc2929d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55883543d46a] +================================= +[nid001920:170318:0:170318] ud_ep.c:255 Fatal: UD endpoint 0x559fad1f9d00 to : unhandled timeout error +==== backtrace (tid: 170318) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14d76d316454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14d76d313400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14d76d313529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14d76933e133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14d76d30c8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14d76cea7962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14d76eb3a295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14d76eb58870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14d76edf4796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14d76ee8dd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14d76e8f0e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559fac94e0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559fac98da81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559fac991bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559fac9bf258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559fac94e6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559fac9d0e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14d76da3929d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559fac93f46a] +================================= +[nid001463:252757:0:252757] ud_ep.c:255 Fatal: UD endpoint 0x563f40864c00 to : unhandled timeout error +[nid001842:138815:0:138815] ud_ep.c:255 Fatal: UD endpoint 0x5610be6e5360 to : unhandled timeout error +[nid001463:252756:0:252756] ud_ep.c:255 Fatal: UD endpoint 0x5623c255da40 to : unhandled timeout error +[nid001842:138811:0:138811] ud_ep.c:255 Fatal: UD endpoint 0x560ca7dbbef0 to : unhandled timeout error +[nid001463:252763:0:252763] ud_ep.c:255 Fatal: UD endpoint 0x55fb64a5cde0 to : unhandled timeout error +==== backtrace (tid: 138815) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x147e841e5454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x147e841e2400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x147e841e2529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x147e8020d133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x147e841db8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x147e83d76962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x147e85a09295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x147e85a27870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x147e85cc3796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x147e85d5cd35] +==== backtrace (tid: 252757) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14c4d6577454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14c4d6574400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14c4d6574529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14c4d259f133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14c4d656d8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14c4d6108962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14c4d7d9b295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14c4d7db9870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14c4d8055796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14c4d80eed35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x147e857bfe9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5610bdfee0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5610be02da81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5610be031bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5610be05f258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5610bdfee6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5610be070e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x147e8490829d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5610bdfdf46a] +================================= +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14c4d7b51e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x563f402210f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x563f40260a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x563f40264bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x563f40292258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x563f402216c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x563f402a3e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14c4d6c9a29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x563f4021246a] +================================= +==== backtrace (tid: 138811) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14a7a6c32454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14a7a6c2f400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14a7a6c2f529] +[nid001864:92458:0:92458] ud_ep.c:255 Fatal: UD endpoint 0x560ddbfff6b0 to : unhandled timeout error +==== backtrace (tid: 252756) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14602e218454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14602e215400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14602e215529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14602a240133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14602e20e8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14602dda9962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14602fa3c295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14602fa5a870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14602fcf6796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14602fd8fd35] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14a7a2c5a133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14a7a6c288aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14a7a67c3962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14a7a8456295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14a7a8474870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14a7a8710796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14a7a87a9d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14a7a820ce9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x560ca76c70f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x560ca7706a81] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14602f7f2e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5623c1f650f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5623c1fa4a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5623c1fa8bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5623c1fd6258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5623c1f656c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5623c1fe7e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14602e93b29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5623c1f5646a] +================================= +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x560ca770abdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x560ca7738258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x560ca76c76c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x560ca7749e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14a7a735529d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x560ca76b846a] +================================= +==== backtrace (tid: 92458) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x147196d8a454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x147196d87400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x147196d87529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x147192db2133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x147196d808aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14719691b962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1471985ae295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1471985cc870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x147198868796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x147198901d35] +==== backtrace (tid: 252763) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1501aad62454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1501aad5f400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1501aad5f529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1501a6d8a133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1501aad588aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1501aa8f3962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1501ac586295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1501ac5a4870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1501ac840796] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x147198364e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x560ddb82b0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x560ddb86aa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x560ddb86ebdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x560ddb89c258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x560ddb82b6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x560ddb8ade63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1471974ad29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x560ddb81c46a] +================================= + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1501ac8d9d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1501ac33ce9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55fb644140f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55fb64453a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55fb64457bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55fb64485258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55fb644146c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55fb64496e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1501ab48529d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55fb6440546a] +================================= +[nid001409:185378:0:185378] ud_ep.c:255 Fatal: UD endpoint 0x557e11c23000 to : unhandled timeout error +==== backtrace (tid: 185378) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14a571284454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14a571281400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14a571281529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14a56d2ac133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14a57127a8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14a570e15962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14a572aa8295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14a572ac6870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14a572d62796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14a572dfbd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14a57285ee9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x557e115ca0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x557e11609a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x557e1160dbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x557e1163b258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x557e115ca6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x557e1164ce63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14a5719a729d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x557e115bb46a] +================================= +[nid001842:138810:0:138810] ud_ep.c:255 Fatal: UD endpoint 0x55dd1191bd20 to : unhandled timeout error +[nid001409:185381:0:185381] ud_ep.c:255 Fatal: UD endpoint 0x55ea7007b0f0 to : unhandled timeout error +==== backtrace (tid: 138810) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x153b5ad99454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x153b5ad96400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x153b5ad96529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x153b56dc1133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x153b5ad8f8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x153b5a92a962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x153b5c5bd295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x153b5c5db870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x153b5c877796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x153b5c910d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x153b5c373e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55dd112250f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55dd11264a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55dd11268bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55dd11296258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55dd112256c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55dd112a7e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x153b5b4bc29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55dd1121646a] +================================= +[nid001664:168290:0:168290] ud_ep.c:255 Fatal: UD endpoint 0x55bbde7c4c70 to : unhandled timeout error +==== backtrace (tid: 168290) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14939de6a454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14939de67400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14939de67529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x149399e92133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14939de608aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14939d9fb962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14939f68e295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14939f6ac870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14939f948796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14939f9e1d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14939f444e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55bbde1360f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55bbde175a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55bbde179bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55bbde1a7258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55bbde1366c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55bbde1b8e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14939e58d29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55bbde12746a] +================================= +==== backtrace (tid: 185381) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x150f114e4454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x150f114e1400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x150f114e1529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x150f0d50c133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x150f114da8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x150f11075962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x150f12d08295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x150f12d26870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x150f12fc2796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x150f1305bd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x150f12abee9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ea6fa160f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ea6fa55a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ea6fa59bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ea6fa87258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ea6fa166c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ea6fa98e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x150f11c0729d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ea6fa0746a] +================================= +[nid001754:197288:0:197288] ud_ep.c:255 Fatal: UD endpoint 0x558941bdd5e0 to : unhandled timeout error +==== backtrace (tid: 197288) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14f8d72fc454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14f8d72f9400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14f8d72f9529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14f8d3324133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14f8d72f28aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14f8d6e8d962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14f8d8b20295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14f8d8b3e870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14f8d8dda796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14f8d8e73d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14f8d88d6e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55894146e0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5589414ada81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5589414b1bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5589414df258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55894146e6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5589414f0e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14f8d7a1f29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55894145f46a] +================================= +[nid001950:100690:0:100690] ud_ep.c:255 Fatal: UD endpoint 0x5585e8333ce0 to : unhandled timeout error +[nid001409:185383:0:185383] ud_ep.c:255 Fatal: UD endpoint 0x56153fe44c50 to : unhandled timeout error +==== backtrace (tid: 100690) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15081f3ab454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15081f3a8400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15081f3a8529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x15081b3d3133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15081f3a18aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15081ef3c962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x150820bcf295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x150820bed870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x150820e89796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x150820f22d35] +==== backtrace (tid: 185383) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1553fe37f454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1553fe37c400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1553fe37c529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1553fa3a7133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1553fe3758aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1553fdf10962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1553ffba3295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1553ffbc1870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1553ffe5d796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1553ffef6d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x150820985e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5585e7c2d0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5585e7c6ca81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5585e7c70bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5585e7c9e258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5585e7c2d6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5585e7cafe63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15081face29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5585e7c1e46a] +================================= +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1553ff959e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56153f7dc0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x56153f81ba81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56153f81fbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56153f84d258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56153f7dc6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56153f85ee63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1553feaa229d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56153f7cd46a] +================================= +[nid001463:252758:0:252758] ud_ep.c:255 Fatal: UD endpoint 0x55588c10b810 to : unhandled timeout error +[nid001754:197292:0:197292] ud_ep.c:255 Fatal: UD endpoint 0x55ecf007ff80 to : unhandled timeout error +==== backtrace (tid: 252758) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1527546cf454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1527546cc400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1527546cc529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1527506f7133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1527546c58aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x152754260962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x152755ef3295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x152755f11870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1527561ad796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x152756246d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x152755ca9e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55588bb170f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55588bb56a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55588bb5abdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55588bb88258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55588bb176c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55588bb99e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x152754df229d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55588bb0846a] +================================= +==== backtrace (tid: 197292) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14a0a6a66454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14a0a6a63400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14a0a6a63529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14a0a2a8e133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14a0a6a5c8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14a0a65f7962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14a0a828a295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14a0a82a8870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14a0a8544796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14a0a85ddd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14a0a8040e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ecef9230f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ecef962a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ecef966bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ecef994258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ecef9236c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ecef9a5e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14a0a718929d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ecef91446a] +================================= +[nid001754:197291:0:197291] ud_ep.c:255 Fatal: UD endpoint 0x55c537b9c7a0 to : unhandled timeout error +[nid002406:197129:0:197129] ud_ep.c:255 Fatal: UD endpoint 0x563c647a7820 to : unhandled timeout error +==== backtrace (tid: 197291) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14f4a3dd5454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14f4a3dd2400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14f4a3dd2529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14f49fdfd133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14f4a3dcb8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14f4a3966962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14f4a55f9295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14f4a5617870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14f4a58b3796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14f4a594cd35] +==== backtrace (tid: 197129) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14a08a8b1454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14a08a8ae400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14a08a8ae529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14a0868d9133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14a08a8a78aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14a08a442962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14a08c0d5295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14a08c0f3870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14a08c38f796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14a08c428d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14f4a53afe9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55c53743b0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55c53747aa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55c53747ebdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55c5374ac258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55c53743b6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55c5374bde63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14f4a44f829d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55c53742c46a] +================================= +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14a08be8be9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x563c6410c0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x563c6414ba81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x563c6414fbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x563c6417d258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x563c6410c6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x563c6418ee63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14a08afd429d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x563c640fd46a] +================================= +[nid003252:53444:0:53444] ud_ep.c:255 Fatal: UD endpoint 0x55da18e0f0f0 to : unhandled timeout error +==== backtrace (tid: 53444) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x147306dfb454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x147306df8400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x147306df8529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x147302e23133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x147306df18aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14730698c962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14730861f295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14730863d870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1473088d9796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x147308972d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1473083d5e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55da1863f0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55da1867ea81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55da18682bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55da186b0258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55da1863f6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55da186c1e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14730751e29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55da1863046a] +================================= +[nid001858:72025:0:72025] ud_ep.c:255 Fatal: UD endpoint 0x555af3fb7860 to : unhandled timeout error +==== backtrace (tid: 72025) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14612f6bd454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14612f6ba400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14612f6ba529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14612b6e5133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14612f6b38aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14612f24e962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x146130ee1295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146130eff870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14613119b796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146131234d35] +[nid001927:149632:0:149632] ud_ep.c:255 Fatal: UD endpoint 0x5595165c9960 to : unhandled timeout error +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146130c97e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x555af384f0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x555af388ea81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x555af3892bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x555af38c0258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x555af384f6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x555af38d1e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14612fde029d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x555af384046a] +================================= +==== backtrace (tid: 149632) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14ab59aef454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14ab59aec400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14ab59aec529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14ab55b17133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14ab59ae58aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14ab59680962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14ab5b313295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14ab5b331870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14ab5b5cd796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14ab5b666d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14ab5b0c9e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559515dea0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559515e29a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559515e2dbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559515e5b258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559515dea6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559515e6ce63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14ab5a21229d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559515ddb46a] +================================= +[nid001954:89824:0:89824] ud_ep.c:255 Fatal: UD endpoint 0x55eb42bdcf30 to : unhandled timeout error +[nid001754:197293:0:197293] ud_ep.c:255 Fatal: UD endpoint 0x55ad33d20730 to : unhandled timeout error +==== backtrace (tid: 89824) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x145778bee454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x145778beb400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x145778beb529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x145774c16133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x145778be48aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14577877f962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14577a412295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14577a430870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14577a6cc796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14577a765d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14577a1c8e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55eb424bb0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55eb424faa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55eb424febdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55eb4252c258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55eb424bb6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55eb4253de63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14577931129d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55eb424ac46a] +================================= +[nid001463:252761:0:252761] ud_ep.c:255 Fatal: UD endpoint 0x55d6bd026980 to : unhandled timeout error +==== backtrace (tid: 197293) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x149eef2d5454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x149eef2d2400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x149eef2d2529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x149eeb2fd133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x149eef2cb8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x149eeee66962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x149ef0af9295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x149ef0b17870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x149ef0db3796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x149ef0e4cd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x149ef08afe9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ad335bb0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ad335faa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ad335febdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ad3362c258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ad335bb6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ad3363de63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x149eef9f829d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ad335ac46a] +================================= +==== backtrace (tid: 252761) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14ac8f938454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14ac8f935400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14ac8f935529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14ac8b960133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14ac8f92e8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14ac8f4c9962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14ac9115c295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14ac9117a870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14ac91416796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14ac914afd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14ac90f12e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55d6bca1d0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55d6bca5ca81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55d6bca60bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55d6bca8e258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55d6bca1d6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55d6bca9fe63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14ac9005b29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55d6bca0e46a] +================================= +[nid001924:245144:0:245144] ud_ep.c:255 Fatal: UD endpoint 0x55981765d7d0 to : unhandled timeout error +==== backtrace (tid: 245144) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1533e840d454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1533e840a400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1533e840a529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1533e4435133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1533e84038aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1533e7f9e962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1533e9c31295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1533e9c4f870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1533e9eeb796] +[nid001950:100693:0:100693] ud_ep.c:255 Fatal: UD endpoint 0x557bab720b70 to : unhandled timeout error + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1533e9f84d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1533e99e7e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559816fd70f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559817016a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55981701abdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559817048258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559816fd76c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559817059e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1533e8b3029d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559816fc846a] +================================= +==== backtrace (tid: 100693) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1522c4baf454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1522c4bac400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1522c4bac529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1522c0bd7133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1522c4ba58aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1522c4740962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1522c63d3295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1522c63f1870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1522c668d796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1522c6726d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1522c6189e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x557bab01a0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x557bab059a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x557bab05dbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x557bab08b258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x557bab01a6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x557bab09ce63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1522c52d229d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x557bab00b46a] +================================= +[nid002580:121316:0:121316] ud_ep.c:255 Fatal: UD endpoint 0x55ce192b3940 to : unhandled timeout error +==== backtrace (tid: 121316) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x153ba4f84454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x153ba4f81400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x153ba4f81529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x153ba0fac133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x153ba4f7a8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x153ba4b15962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x153ba67a8295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x153ba67c6870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x153ba6a62796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x153ba6afbd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x153ba655ee9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ce189a80f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ce189e7a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ce189ebbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ce18a19258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ce189a86c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ce18a2ae63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x153ba56a729d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ce1899946a] +================================= +[nid002580:121318:0:121318] ud_ep.c:255 Fatal: UD endpoint 0x55c890f60950 to : unhandled timeout error +==== backtrace (tid: 121318) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14e5a7231454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14e5a722e400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14e5a722e529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14e5a3259133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14e5a72278aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14e5a6dc2962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14e5a8a55295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14e5a8a73870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14e5a8d0f796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14e5a8da8d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14e5a880be9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55c8906550f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55c890694a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55c890698bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55c8906c6258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55c8906556c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55c8906d7e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14e5a795429d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55c89064646a] +================================= +[nid001656:201197:0:201197] ud_ep.c:255 Fatal: UD endpoint 0x5609b82701c0 to : unhandled timeout error +==== backtrace (tid: 201197) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x152498dbc454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x152498db9400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x152498db9529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x152494de4133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x152498db28aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15249894d962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15249a5e0295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15249a5fe870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15249a89a796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15249a933d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15249a396e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5609b7c010f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5609b7c40a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5609b7c44bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5609b7c72258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5609b7c016c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5609b7c83e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1524994df29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5609b7bf246a] +================================= +[nid001664:168295:0:168295] ud_ep.c:255 Fatal: UD endpoint 0x55be8ba31300 to : unhandled timeout error +==== backtrace (tid: 168295) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x152345f53454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x152345f50400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x152345f50529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x152341f7b133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x152345f498aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x152345ae4962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x152347777295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x152347795870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x152347a31796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x152347acad35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15234752de9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55be8b39e0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55be8b3dda81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55be8b3e1bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55be8b40f258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55be8b39e6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55be8b420e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15234667629d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55be8b38f46a] +================================= +[nid001754:197286:0:197286] ud_ep.c:255 Fatal: UD endpoint 0x557afdbea5d0 to : unhandled timeout error +==== backtrace (tid: 197286) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146cd527a454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146cd5277400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146cd5277529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x146cd12a2133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146cd52708aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146cd4e0b962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x146cd6a9e295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146cd6abc870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146cd6d58796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146cd6df1d35] +[nid002580:121311:0:121311] ud_ep.c:255 Fatal: UD endpoint 0x55e281e5cef0 to : unhandled timeout error +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146cd6854e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x557afd4790f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x557afd4b8a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x557afd4bcbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x557afd4ea258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x557afd4796c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x557afd4fbe63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x146cd599d29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x557afd46a46a] +================================= +==== backtrace (tid: 121311) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1455f3ff9454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1455f3ff6400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1455f3ff6529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1455f0021133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1455f3fef8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1455f3b8a962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1455f581d295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1455f583b870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1455f5ad7796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1455f5b70d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1455f55d3e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55e2815510f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55e281590a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55e281594bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55e2815c2258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55e2815516c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55e2815d3e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1455f471c29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55e28154246a] +================================= +[nid003252:53448:0:53448] ud_ep.c:255 Fatal: UD endpoint 0x561240bf40f0 to : unhandled timeout error +==== backtrace (tid: 53448) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14c5db6f4454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14c5db6f1400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14c5db6f1529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14c5d771c133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14c5db6ea8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14c5db285962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14c5dcf18295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14c5dcf36870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14c5dd1d2796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14c5dd26bd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14c5dcccee9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5612404240f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x561240463a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x561240467bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x561240495258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5612404246c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5612404a6e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14c5dbe1729d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56124041546a] +================================= +[nid003252:53450:0:53450] ud_ep.c:255 Fatal: UD endpoint 0x563edf2320f0 to : unhandled timeout error +[nid001842:138808:0:138808] ud_ep.c:255 Fatal: UD endpoint 0x55865cf711a0 to : unhandled timeout error +==== backtrace (tid: 53450) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1461464af454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1461464ac400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1461464ac529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1461424d7133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1461464a58aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146146040962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x146147cd3295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146147cf1870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146147f8d796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146148026d35] +==== backtrace (tid: 138808) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15418bfeb454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15418bfe8400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15418bfe8529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x154188013133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15418bfe18aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15418bb7c962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15418d80f295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15418d82d870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15418dac9796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15418db62d35] +[nid001924:245146:0:245146] ud_ep.c:255 Fatal: UD endpoint 0x55a5debc5b10 to : unhandled timeout error +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146147a89e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x563edea620f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x563edeaa1a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x563edeaa5bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x563edead3258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x563edea626c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x563edeae4e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x146146bd229d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x563edea5346a] +================================= +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15418d5c5e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55865c8730f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55865c8b2a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55865c8b6bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55865c8e4258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55865c8736c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55865c8f5e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15418c70e29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55865c86446a] +================================= +==== backtrace (tid: 245146) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14eee6ce5454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14eee6ce2400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14eee6ce2529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14eee2d0d133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14eee6cdb8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14eee6876962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14eee8509295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14eee8527870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14eee87c3796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14eee885cd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14eee82bfe9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55a5de53e0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55a5de57da81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55a5de581bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55a5de5af258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55a5de53e6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55a5de5c0e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14eee740829d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55a5de52f46a] +================================= +[nid003252:53445:0:53445] ud_ep.c:255 Fatal: UD endpoint 0x5637c9b8b0f0 to : unhandled timeout error +[nid001954:89825:0:89825] ud_ep.c:255 Fatal: UD endpoint 0x556eb1645ce0 to : unhandled timeout error +==== backtrace (tid: 53445) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x149e64505454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x149e64502400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x149e64502529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x149e6052d133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x149e644fb8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x149e64096962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x149e65d29295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x149e65d47870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x149e65fe3796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x149e6607cd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x149e65adfe9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5637c93bb0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5637c93faa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5637c93febdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5637c942c258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5637c93bb6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5637c943de63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x149e64c2829d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5637c93ac46a] +================================= +[nid001924:245148:0:245148] ud_ep.c:255 Fatal: UD endpoint 0x561a2f144100 to : unhandled timeout error +==== backtrace (tid: 89825) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14b67a450454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14b67a44d400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14b67a44d529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14b676478133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14b67a4468aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14b679fe1962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14b67bc74295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14b67bc92870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14b67bf2e796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14b67bfc7d35] +[nid001871:180897:0:180897] ud_ep.c:255 Fatal: UD endpoint 0x562dbe632f90 to : unhandled timeout error +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14b67ba2ae9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x556eb0f250f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x556eb0f64a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x556eb0f68bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x556eb0f96258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x556eb0f256c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x556eb0fa7e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14b67ab7329d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x556eb0f1646a] +================================= +[nid002270:182961:0:182961] ud_ep.c:255 Fatal: UD endpoint 0x55dd965b9890 to : unhandled timeout error +==== backtrace (tid: 245148) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x148b366f7454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x148b366f4400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x148b366f4529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x148b3271f133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x148b366ed8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x148b36288962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x148b37f1b295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x148b37f39870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x148b381d5796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x148b3826ed35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x148b37cd1e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x561a2eabd0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x561a2eafca81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x561a2eb00bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x561a2eb2e258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x561a2eabd6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x561a2eb3fe63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x148b36e1a29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x561a2eaae46a] +================================= +==== backtrace (tid: 180897) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14a66484d454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14a66484a400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14a66484a529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14a660875133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14a6648438aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14a6643de962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14a666071295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14a66608f870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14a66632b796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14a6663c4d35] +==== backtrace (tid: 182961) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x152121157454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x152121154400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x152121154529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x15211d17f133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15212114d8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x152120ce8962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15212297b295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x152122999870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x152122c35796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x152122cced35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14a665e27e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x562dbddd80f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x562dbde17a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x562dbde1bbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x562dbde49258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x562dbddd86c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x562dbde5ae63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14a664f7029d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x562dbddc946a] +================================= +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x152122731e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55dd95de40f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55dd95e23a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55dd95e27bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55dd95e55258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55dd95de46c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55dd95e66e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15212187a29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55dd95dd546a] +================================= +[nid002580:121313:0:121313] ud_ep.c:255 Fatal: UD endpoint 0x55954ba215f0 to : unhandled timeout error +==== backtrace (tid: 121313) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14b28ba9e454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14b28ba9b400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14b28ba9b529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14b287ac6133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14b28ba948aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14b28b62f962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14b28d2c2295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14b28d2e0870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14b28d57c796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14b28d615d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14b28d078e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55954b1160f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55954b155a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55954b159bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55954b187258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55954b1166c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55954b198e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14b28c1c129d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55954b10746a] +================================= +[nid002580:121314:0:121314] ud_ep.c:255 Fatal: UD endpoint 0x557f4cfa6660 to : unhandled timeout error +==== backtrace (tid: 121314) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1497afdf0454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1497afded400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1497afded529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1497abe18133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1497afde68aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1497af981962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1497b1614295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1497b1632870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1497b18ce796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1497b1967d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1497b13cae9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x557f4c69b0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x557f4c6daa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x557f4c6debdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x557f4c70c258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x557f4c69b6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x557f4c71de63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1497b051329d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x557f4c68c46a] +================================= +[nid003252:53449:0:53449] ud_ep.c:255 Fatal: UD endpoint 0x5613b82b20f0 to : unhandled timeout error +[nid001754:197289:0:197289] ud_ep.c:255 Fatal: UD endpoint 0x561446b2dee0 to : unhandled timeout error +==== backtrace (tid: 53449) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x149c76fe6454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x149c76fe3400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x149c76fe3529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x149c7300e133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x149c76fdc8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x149c76b77962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x149c7880a295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x149c78828870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x149c78ac4796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x149c78b5dd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x149c785c0e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5613b7ae20f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5613b7b21a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5613b7b25bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5613b7b53258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5613b7ae26c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5613b7b64e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x149c7770929d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5613b7ad346a] +================================= +==== backtrace (tid: 197289) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14fb1a442454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14fb1a43f400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14fb1a43f529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14fb1646a133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14fb1a4388aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14fb19fd3962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14fb1bc66295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14fb1bc84870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14fb1bf20796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14fb1bfb9d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14fb1ba1ce9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5614463bd0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5614463fca81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x561446400bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56144642e258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5614463bd6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56144643fe63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14fb1ab6529d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5614463ae46a] +================================= +[nid003252:53446:0:53446] ud_ep.c:255 Fatal: UD endpoint 0x5565761070f0 to : unhandled timeout error +[nid002580:121317:0:121317] ud_ep.c:255 Fatal: UD endpoint 0x55d180e49280 to : unhandled timeout error +==== backtrace (tid: 53446) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x151ac5722454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x151ac571f400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x151ac571f529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x151ac174a133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x151ac57188aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x151ac52b3962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x151ac6f46295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x151ac6f64870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x151ac7200796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x151ac7299d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x151ac6cfce9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5565759370f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x556575976a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55657597abdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5565759a8258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5565759376c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5565759b9e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x151ac5e4529d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55657592846a] +================================= +==== backtrace (tid: 121317) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x152138ce3454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x152138ce0400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x152138ce0529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x152134d0b133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x152138cd98aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x152138874962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15213a507295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15213a525870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15213a7c1796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15213a85ad35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15213a2bde9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55d18053e0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55d18057da81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55d180581bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55d1805af258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55d18053e6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55d1805c0e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15213940629d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55d18052f46a] +================================= +[nid003252:53447:0:53447] ud_ep.c:255 Fatal: UD endpoint 0x5593f904b0f0 to : unhandled timeout error +==== backtrace (tid: 53447) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x152fd902e454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x152fd902b400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x152fd902b529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x152fd5056133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x152fd90248aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x152fd8bbf962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x152fda852295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x152fda870870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x152fdab0c796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x152fdaba5d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x152fda608e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5593f887b0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5593f88baa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5593f88bebdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5593f88ec258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5593f887b6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5593f88fde63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x152fd975129d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5593f886c46a] +================================= +[nid001924:245150:0:245150] ud_ep.c:255 Fatal: UD endpoint 0x5631e1cf2270 to : unhandled timeout error +==== backtrace (tid: 245150) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15009b377454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15009b374400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15009b374529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x15009739f133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15009b36d8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15009af08962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15009cb9b295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15009cbb9870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15009ce55796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15009ceeed35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15009c951e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5631e166b0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5631e16aaa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5631e16aebdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5631e16dc258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5631e166b6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5631e16ede63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15009ba9a29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5631e165c46a] +================================= +[nid001858:72019:0:72019] ud_ep.c:255 Fatal: UD endpoint 0x55b333e26c40 to : unhandled timeout error +==== backtrace (tid: 72019) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14f367ba3454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14f367ba0400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14f367ba0529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14f363bcb133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14f367b998aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14f367734962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14f3693c7295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14f3693e5870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14f369681796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14f36971ad35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14f36917de9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55b3336ce0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55b33370da81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55b333711bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55b33373f258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55b3336ce6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55b333750e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14f3682c629d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55b3336bf46a] +================================= +[nid001924:245145:0:245145] ud_ep.c:255 Fatal: UD endpoint 0x5630ab983b10 to : unhandled timeout error +==== backtrace (tid: 245145) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14cd52f7b454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14cd52f78400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14cd52f78529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14cd4efa3133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14cd52f718aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14cd52b0c962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14cd5479f295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14cd547bd870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14cd54a59796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14cd54af2d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14cd54555e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5630ab2fc0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5630ab33ba81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5630ab33fbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5630ab36d258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5630ab2fc6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5630ab37ee63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14cd5369e29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5630ab2ed46a] +================================= +[nid001754:197287:0:197287] ud_ep.c:255 Fatal: UD endpoint 0x556d100698a0 to : unhandled timeout error +==== backtrace (tid: 197287) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1493bbd8d454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1493bbd8a400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1493bbd8a529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1493b7db5133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1493bbd838aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1493bb91e962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1493bd5b1295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1493bd5cf870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1493bd86b796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1493bd904d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1493bd367e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x556d0f9160f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x556d0f955a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x556d0f959bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x556d0f987258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x556d0f9166c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x556d0f998e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1493bc4b029d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x556d0f90746a] +================================= +[nid001754:197290:0:197290] ud_ep.c:255 Fatal: UD endpoint 0x5586e07e7cd0 to : unhandled timeout error +==== backtrace (tid: 197290) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1543b035c454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1543b0359400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1543b0359529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1543ac384133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1543b03528aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1543afeed962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1543b1b80295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1543b1b9e870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1543b1e3a796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1543b1ed3d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1543b1936e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5586e00a00f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5586e00dfa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5586e00e3bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5586e0111258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5586e00a06c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5586e0122e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1543b0a7f29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5586e009146a] +================================= +[nid001842:138812:0:138812] ud_ep.c:255 Fatal: UD endpoint 0x55c84627eed0 to : unhandled timeout error +[nid001664:168293:0:168293] ud_ep.c:255 Fatal: UD endpoint 0x5611a84d77a0 to : unhandled timeout error +==== backtrace (tid: 168293) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14af21847454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14af21844400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14af21844529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14af1d86f133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14af2183d8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14af213d8962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14af2306b295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14af23089870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14af23325796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14af233bed35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14af22e21e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5611a7e450f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5611a7e84a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5611a7e88bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5611a7eb6258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5611a7e456c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5611a7ec7e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14af21f6a29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5611a7e3646a] +================================= +==== backtrace (tid: 138812) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15350dc55454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15350dc52400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15350dc52529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x153509c7d133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15350dc4b8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15350d7e6962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15350f479295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15350f497870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15350f733796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15350f7ccd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15350f22fe9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55c845b900f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55c845bcfa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55c845bd3bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55c845c01258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55c845b906c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55c845c12e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15350e37829d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55c845b8146a] +================================= +[nid002403:117137:0:117137] ud_ep.c:255 Fatal: UD endpoint 0x556c04026410 to : unhandled timeout error +==== backtrace (tid: 117137) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14952ddcb454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14952ddc8400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14952ddc8529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x149529df3133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14952ddc18aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14952d95c962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14952f5ef295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14952f60d870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14952f8a9796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14952f942d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14952f3a5e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x556c0388f0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x556c038cea81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x556c038d2bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x556c03900258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x556c0388f6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x556c03911e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14952e4ee29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x556c0388046a] +================================= +[nid002403:117136:0:117136] ud_ep.c:255 Fatal: UD endpoint 0x5618da557410 to : unhandled timeout error +==== backtrace (tid: 117136) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1496bcb93454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1496bcb90400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1496bcb90529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1496b8bbb133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1496bcb898aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1496bc724962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1496be3b7295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1496be3d5870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1496be671796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1496be70ad35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1496be16de9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5618d9dc00f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5618d9dffa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5618d9e03bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5618d9e31258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5618d9dc06c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5618d9e42e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1496bd2b629d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5618d9db146a] +================================= +[nid001911:261135:0:261135] ud_ep.c:255 Fatal: UD endpoint 0x56097b8b3920 to : unhandled timeout error +==== backtrace (tid: 261135) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146f47531454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146f4752e400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146f4752e529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x146f43559133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146f475278aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146f470c2962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x146f48d55295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146f48d73870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146f4900f796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146f490a8d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146f48b0be9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56097b14f0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x56097b18ea81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56097b192bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56097b1c0258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56097b14f6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56097b1d1e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x146f47c5429d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56097b14046a] +================================= +[nid001664:168289:0:168289] ud_ep.c:255 Fatal: UD endpoint 0x5582b2983f90 to : unhandled timeout error +==== backtrace (tid: 168289) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14b05f4a0454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14b05f49d400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14b05f49d529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14b05b4c8133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14b05f4968aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14b05f031962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14b060cc4295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14b060ce2870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14b060f7e796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14b061017d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14b060a7ae9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5582b22f50f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5582b2334a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5582b2338bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5582b2366258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5582b22f56c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5582b2377e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14b05fbc329d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5582b22e646a] +================================= +[nid001924:245143:0:245143] ud_ep.c:255 Fatal: UD endpoint 0x55ace2b69940 to : unhandled timeout error +[nid002628:135738:0:135738] ud_ep.c:255 Fatal: UD endpoint 0x55fa9a7d3f20 to : unhandled timeout error +==== backtrace (tid: 135738) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14b8a4016454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14b8a4013400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14b8a4013529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14b8a003e133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14b8a400c8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14b8a3ba7962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14b8a583a295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14b8a5858870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14b8a5af4796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14b8a5b8dd35] +==== backtrace (tid: 245143) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14cb98e9d454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14cb98e9a400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14cb98e9a529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14cb94ec5133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14cb98e938aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14cb98a2e962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14cb9a6c1295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14cb9a6df870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14cb9a97b796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14cb9aa14d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14b8a55f0e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55fa9a0a40f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55fa9a0e3a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55fa9a0e7bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55fa9a115258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55fa9a0a46c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55fa9a126e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14b8a473929d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55fa9a09546a] +================================= +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14cb9a477e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ace24e30f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ace2522a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ace2526bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ace2554258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ace24e36c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ace2565e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14cb995c029d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ace24d446a] +================================= +[nid001664:168294:0:168294] ud_ep.c:255 Fatal: UD endpoint 0x559b32c0a3a0 to : unhandled timeout error +==== backtrace (tid: 168294) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14aa13718454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14aa13715400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14aa13715529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14aa0f740133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14aa1370e8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14aa132a9962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14aa14f3c295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14aa14f5a870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14aa151f6796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14aa1528fd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14aa14cf2e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559b325790f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559b325b8a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559b325bcbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559b325ea258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559b325796c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559b325fbe63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14aa13e3b29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559b3256a46a] +================================= +[nid001871:180898:0:180898] ud_ep.c:255 Fatal: UD endpoint 0x564b02a851b0 to : unhandled timeout error +==== backtrace (tid: 180898) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14a2ff5ed454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14a2ff5ea400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14a2ff5ea529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14a2fb615133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14a2ff5e38aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14a2ff17e962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14a300e11295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14a300e2f870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14a3010cb796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14a301164d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14a300bc7e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x564b023560f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x564b02395a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x564b02399bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x564b023c7258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x564b023566c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x564b023d8e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14a2ffd1029d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x564b0234746a] +================================= +[nid001950:100688:0:100688] ud_ep.c:255 Fatal: UD endpoint 0x55eded0d0ce0 to : unhandled timeout error +[nid001911:261136:0:261136] ud_ep.c:255 Fatal: UD endpoint 0x562df997c440 to : unhandled timeout error +==== backtrace (tid: 100688) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14caf5092454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14caf508f400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14caf508f529] +[nid001924:245149:0:245149] ud_ep.c:255 Fatal: UD endpoint 0x55dd16488c40 to : unhandled timeout error + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14caf10ba133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14caf50888aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14caf4c23962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14caf68b6295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14caf68d4870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14caf6b70796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14caf6c09d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14caf666ce9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55edec9ca0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55edeca09a81] +==== backtrace (tid: 261136) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x151e8824d454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x151e8824a400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x151e8824a529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x151e84275133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x151e882438aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x151e87dde962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x151e89a71295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x151e89a8f870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x151e89d2b796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x151e89dc4d35] +[nid001871:180899:0:180899] ud_ep.c:255 Fatal: UD endpoint 0x55a80bfd3ee0 to : unhandled timeout error +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55edeca0dbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55edeca3b258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55edec9ca6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55edeca4ce63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14caf57b529d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55edec9bb46a] +================================= +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x151e89827e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x562df92080f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x562df9247a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x562df924bbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x562df9279258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x562df92086c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x562df928ae63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x151e8897029d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x562df91f946a] +================================= +==== backtrace (tid: 245149) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1487785d9454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1487785d6400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1487785d6529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x148774601133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1487785cf8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14877816a962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x148779dfd295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x148779e1b870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14877a0b7796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14877a150d35] +[nid002628:135739:0:135739] ud_ep.c:255 Fatal: UD endpoint 0x559093c35f20 to : unhandled timeout error +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x148779bb3e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55dd15e010f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55dd15e40a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55dd15e44bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55dd15e72258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55dd15e016c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55dd15e83e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x148778cfc29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55dd15df246a] +================================= +==== backtrace (tid: 180899) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15069fa89454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15069fa86400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15069fa86529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x15069bab1133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15069fa7f8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15069f61a962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1506a12ad295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1506a12cb870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1506a1567796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1506a1600d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1506a1063e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55a80b8af0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55a80b8eea81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55a80b8f2bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55a80b920258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55a80b8af6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55a80b931e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1506a01ac29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55a80b8a046a] +================================= +==== backtrace (tid: 135739) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x153495c72454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x153495c6f400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x153495c6f529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x153491c9a133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x153495c688aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x153495803962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x153497496295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1534974b4870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x153497750796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1534977e9d35] +[nid001912:205777:0:205777] ud_ep.c:255 Fatal: UD endpoint 0x55c57119bfd0 to : unhandled timeout error +==== backtrace (tid: 205777) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x151565989454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x151565986400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x151565986529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1515619b1133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15156597f8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15156551a962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1515671ad295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1515671cb870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x151567467796] +[nid002264:48356:0:48356] ud_ep.c:255 Fatal: UD endpoint 0x5591f4a7c550 to : unhandled timeout error +==== backtrace (tid: 48356) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x151c01b8e454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x151c01b8b400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x151c01b8b529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x151bfdbb6133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x151c01b848aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x151c0171f962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x151c033b2295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x151c033d0870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x151c0366c796] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15349724ce9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5590935060f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559093545a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559093549bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559093577258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5590935066c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559093588e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15349639529d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5590934f746a] +================================= + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x151c03705d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x151c03168e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5591f41650f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5591f41a4a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5591f41a8bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5591f41d6258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5591f41656c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5591f41e7e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x151c022b129d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5591f415646a] +================================= +[nid001664:168291:0:168291] ud_ep.c:255 Fatal: UD endpoint 0x556cbec67e60 to : unhandled timeout error + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x151567500d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x151566f63e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55c5709d80f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55c570a17a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55c570a1bbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55c570a49258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55c5709d86c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55c570a5ae63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1515660ac29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55c5709c946a] +================================= +[nid002264:48361:0:48361] ud_ep.c:255 Fatal: UD endpoint 0x55671df716e0 to : unhandled timeout error +==== backtrace (tid: 168291) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1551b4346454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1551b4343400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1551b4343529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1551b036e133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1551b433c8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1551b3ed7962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1551b5b6a295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1551b5b88870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1551b5e24796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1551b5ebdd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1551b5920e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x556cbe5da0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x556cbe619a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x556cbe61dbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x556cbe64b258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x556cbe5da6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x556cbe65ce63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1551b4a6929d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x556cbe5cb46a] +================================= +==== backtrace (tid: 48361) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14d265c43454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14d265c40400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14d265c40529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14d261c6b133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14d265c398aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14d2657d4962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14d267467295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14d267485870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14d267721796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14d2677bad35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14d26721de9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55671d6590f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55671d698a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55671d69cbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55671d6ca258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55671d6596c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55671d6dbe63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14d26636629d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55671d64a46a] +================================= +[nid001871:180895:0:180895] ud_ep.c:255 Fatal: UD endpoint 0x55d1d68a0fb0 to : unhandled timeout error +[nid001912:205782:0:205782] ud_ep.c:255 Fatal: UD endpoint 0x55abfa3267e0 to : unhandled timeout error +==== backtrace (tid: 180895) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14fe063ef454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14fe063ec400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14fe063ec529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14fe02417133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14fe063e58aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14fe05f80962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14fe07c13295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14fe07c31870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14fe07ecd796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14fe07f66d35] +==== backtrace (tid: 205782) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14ca92268454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14ca92265400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14ca92265529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14ca8e290133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14ca9225e8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14ca91df9962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14ca93a8c295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14ca93aaa870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14ca93d46796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14ca93ddfd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14fe079c9e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55d1d61760f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55d1d61b5a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55d1d61b9bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55d1d61e7258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55d1d61766c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55d1d61f8e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14fe06b1229d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55d1d616746a] +================================= +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14ca93842e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55abf9b620f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55abf9ba1a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55abf9ba5bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55abf9bd3258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55abf9b626c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55abf9be4e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14ca9298b29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55abf9b5346a] +================================= +[nid001871:180892:0:180892] ud_ep.c:255 Fatal: UD endpoint 0x5618c961b730 to : unhandled timeout error +==== backtrace (tid: 180892) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14a20d989454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14a20d986400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14a20d986529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14a2099b1133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14a20d97f8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14a20d51a962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14a20f1ad295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14a20f1cb870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14a20f467796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14a20f500d35] +[nid003252:53443:0:53443] ud_ep.c:255 Fatal: UD endpoint 0x55ffb70b20f0 to : unhandled timeout error +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14a20ef63e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5618c8eef0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5618c8f2ea81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5618c8f32bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5618c8f60258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5618c8eef6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5618c8f71e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14a20e0ac29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5618c8ee046a] +================================= +==== backtrace (tid: 53443) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14bc939c4454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14bc939c1400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14bc939c1529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14bc8f9ec133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14bc939ba8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14bc93555962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14bc951e8295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14bc95206870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14bc954a2796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14bc9553bd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14bc94f9ee9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ffb68e20f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ffb6921a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ffb6925bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ffb6953258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ffb68e26c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ffb6964e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14bc940e729d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ffb68d346a] +================================= +[nid002628:135743:0:135743] ud_ep.c:255 Fatal: UD endpoint 0x557d88abef20 to : unhandled timeout error +[nid002264:48357:0:48357] ud_ep.c:255 Fatal: UD endpoint 0x55c2c1ba5d00 to : unhandled timeout error +==== backtrace (tid: 135743) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x149031b0d454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x149031b0a400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x149031b0a529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14902db35133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x149031b038aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14903169e962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x149033331295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14903334f870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1490335eb796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x149033684d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1490330e7e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x557d8838f0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x557d883cea81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x557d883d2bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x557d88400258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x557d8838f6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x557d88411e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14903223029d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x557d8838046a] +================================= +[nid001858:72020:0:72020] ud_ep.c:255 Fatal: UD endpoint 0x55de6bf168d0 to : unhandled timeout error +==== backtrace (tid: 48357) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x153d599b7454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x153d599b4400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x153d599b4529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x153d559df133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x153d599ad8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x153d59548962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x153d5b1db295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x153d5b1f9870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x153d5b495796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x153d5b52ed35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x153d5af91e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55c2c128e0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55c2c12cda81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55c2c12d1bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55c2c12ff258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55c2c128e6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55c2c1310e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x153d5a0da29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55c2c127f46a] +================================= +==== backtrace (tid: 72020) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15474406a454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x154744067400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x154744067529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x154740092133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1547440608aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x154743bfb962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15474588e295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1547458ac870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x154745b48796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x154745be1d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x154745644e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55de6b7c60f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55de6b805a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55de6b809bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55de6b837258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55de6b7c66c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55de6b848e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15474478d29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55de6b7b746a] +================================= +[nid001871:180894:0:180894] ud_ep.c:255 Fatal: UD endpoint 0x5645c0904ef0 to : unhandled timeout error +==== backtrace (tid: 180894) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x153e154bf454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x153e154bc400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x153e154bc529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x153e114e7133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x153e154b58aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x153e15050962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x153e16ce3295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x153e16d01870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x153e16f9d796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x153e17036d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x153e16a99e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5645c01dc0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5645c021ba81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5645c021fbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5645c024d258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5645c01dc6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5645c025ee63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x153e15be229d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5645c01cd46a] +================================= +[nid002628:135741:0:135741] ud_ep.c:255 Fatal: UD endpoint 0x562e2594df20 to : unhandled timeout error +==== backtrace (tid: 135741) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14b2bb048454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14b2bb045400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14b2bb045529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14b2b7070133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14b2bb03e8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14b2babd9962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14b2bc86c295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14b2bc88a870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14b2bcb26796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14b2bcbbfd35] +[nid001912:205781:0:205781] ud_ep.c:255 Fatal: UD endpoint 0x55cb8a275d60 to : unhandled timeout error +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14b2bc622e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x562e2521e0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x562e2525da81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x562e25261bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x562e2528f258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x562e2521e6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x562e252a0e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14b2bb76b29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x562e2520f46a] +================================= +==== backtrace (tid: 205781) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x150b6dd20454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x150b6dd1d400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x150b6dd1d529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x150b69d48133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x150b6dd168aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x150b6d8b1962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x150b6f544295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x150b6f562870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x150b6f7fe796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x150b6f897d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x150b6f2fae9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55cb89ab20f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55cb89af1a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55cb89af5bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55cb89b23258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55cb89ab26c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55cb89b34e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x150b6e44329d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55cb89aa346a] +================================= +[nid001912:205783:0:205783] ud_ep.c:255 Fatal: UD endpoint 0x5587e9698030 to : unhandled timeout error +[nid002264:48358:0:48358] ud_ep.c:255 Fatal: UD endpoint 0x5623268df130 to : unhandled timeout error +[nid002264:48360:0:48360] ud_ep.c:255 Fatal: UD endpoint 0x564754d08ca0 to : unhandled timeout error +==== backtrace (tid: 205783) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x148358221454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14835821e400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14835821e529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x148354249133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1483582178aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x148357db2962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x148359a45295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x148359a63870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x148359cff796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x148359d98d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1483597fbe9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5587e8ee40f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5587e8f23a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5587e8f27bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5587e8f55258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5587e8ee46c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5587e8f66e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14835894429d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5587e8ed546a] +================================= +==== backtrace (tid: 48358) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1544ad7fe454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1544ad7fb400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1544ad7fb529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1544a9826133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1544ad7f48aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1544ad38f962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1544af022295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1544af040870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1544af2dc796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1544af375d35] +[nid001911:261139:0:261139] ud_ep.c:255 Fatal: UD endpoint 0x559c744e4a80 to : unhandled timeout error +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1544aedd8e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x562325fc80f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x562326007a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56232600bbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x562326039258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x562325fc86c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56232604ae63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1544adf2129d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x562325fb946a] +================================= +==== backtrace (tid: 48360) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x148e9bbe9454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x148e9bbe6400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x148e9bbe6529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x148e97c11133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x148e9bbdf8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x148e9b77a962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x148e9d40d295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x148e9d42b870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x148e9d6c7796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x148e9d760d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x148e9d1c3e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5647543f10f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x564754430a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x564754434bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x564754462258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5647543f16c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x564754473e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x148e9c30c29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5647543e246a] +================================= +==== backtrace (tid: 261139) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14c069090454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14c06908d400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14c06908d529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14c0650b8133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14c0690868aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14c068c21962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14c06a8b4295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14c06a8d2870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14c06ab6e796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14c06ac07d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14c06a66ae9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559c73d800f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559c73dbfa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559c73dc3bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559c73df1258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559c73d806c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559c73e02e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14c0697b329d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559c73d7146a] +================================= +[nid002264:48362:0:48362] ud_ep.c:255 Fatal: UD endpoint 0x562655078650 to : unhandled timeout error +==== backtrace (tid: 48362) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x154f7a6f4454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x154f7a6f1400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x154f7a6f1529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x154f7671c133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x154f7a6ea8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x154f7a285962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x154f7bf18295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x154f7bf36870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x154f7c1d2796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x154f7c26bd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x154f7bccee9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5626547630f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5626547a2a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5626547a6bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5626547d4258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5626547636c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5626547e5e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x154f7ae1729d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56265475446a] +================================= +[nid001711:229168:0:229168] ud_ep.c:255 Fatal: UD endpoint 0x55dc74e64f50 to : unhandled timeout error +==== backtrace (tid: 229168) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14bb3ad93454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14bb3ad90400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14bb3ad90529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14bb36dbb133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14bb3ad898aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14bb3a924962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14bb3c5b7295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14bb3c5d5870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14bb3c871796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14bb3c90ad35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14bb3c36de9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55dc747b40f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55dc747f3a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55dc747f7bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55dc74825258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55dc747b46c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55dc74836e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14bb3b4b629d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55dc747a546a] +================================= +[nid002403:117135:0:117135] ud_ep.c:255 Fatal: UD endpoint 0x56378064d410 to : unhandled timeout error +[nid001912:205778:0:205778] ud_ep.c:255 Fatal: UD endpoint 0x5598f1d2ee90 to : unhandled timeout error +==== backtrace (tid: 117135) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146bd5610454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146bd560d400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146bd560d529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x146bd1638133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146bd56068aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146bd51a1962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x146bd6e34295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146bd6e52870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146bd70ee796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146bd7187d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146bd6beae9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56377feb60f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x56377fef5a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56377fef9bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56377ff27258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56377feb66c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56377ff38e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x146bd5d3329d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56377fea746a] +================================= +==== backtrace (tid: 205778) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1519766f0454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1519766ed400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1519766ed529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x151972718133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1519766e68aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x151976281962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x151977f14295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x151977f32870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1519781ce796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x151978267d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x151977ccae9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5598f157c0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5598f15bba81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5598f15bfbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5598f15ed258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5598f157c6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5598f15fee63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x151976e1329d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5598f156d46a] +================================= +[nid001911:261140:0:261140] ud_ep.c:255 Fatal: UD endpoint 0x55e7d07ede70 to : unhandled timeout error +==== backtrace (tid: 261140) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x150ee619b454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x150ee6198400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x150ee6198529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x150ee21c3133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x150ee61918aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x150ee5d2c962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x150ee79bf295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x150ee79dd870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x150ee7c79796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x150ee7d12d35] +[nid001711:229167:0:229167] ud_ep.c:255 Fatal: UD endpoint 0x565130c8bac0 to : unhandled timeout error +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x150ee7775e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55e7d00790f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55e7d00b8a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55e7d00bcbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55e7d00ea258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55e7d00796c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55e7d00fbe63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x150ee68be29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55e7d006a46a] +================================= +==== backtrace (tid: 229167) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1530a67a7454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1530a67a4400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1530a67a4529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1530a27cf133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1530a679d8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1530a6338962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1530a7fcb295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1530a7fe9870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1530a8285796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1530a831ed35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1530a7d81e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5651305d40f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x565130613a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x565130617bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x565130645258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5651305d46c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x565130656e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1530a6eca29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5651305c546a] +================================= +[nid002580:121312:0:121312] ud_ep.c:255 Fatal: UD endpoint 0x559529d49650 to : unhandled timeout error +==== backtrace (tid: 121312) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1486bd25a454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1486bd257400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1486bd257529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1486b9282133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1486bd2508aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1486bcdeb962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1486bea7e295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1486bea9c870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1486bed38796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1486bedd1d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1486be834e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55952943f0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55952947ea81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559529482bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5595294b0258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55952943f6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5595294c1e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1486bd97d29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55952943046a] +================================= +[nid002607:5716 :0:5716] ud_ep.c:255 Fatal: UD endpoint 0x5645c1c4db10 to : unhandled timeout error +==== backtrace (tid: 5716) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x153d31984454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x153d31981400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x153d31981529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x153d2d9ac133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x153d3197a8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x153d31515962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x153d331a8295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x153d331c6870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x153d33462796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x153d334fbd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x153d32f5ee9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5645c14880f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5645c14c7a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5645c14cbbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5645c14f9258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5645c14886c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5645c150ae63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x153d320a729d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5645c147946a] +================================= +[nid003241:144959:0:144959] ud_ep.c:255 Fatal: UD endpoint 0x556a4919d6c0 to : unhandled timeout error +==== backtrace (tid: 144959) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x149181384454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x149181381400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x149181381529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14917d3ac133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14918137a8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x149180f15962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x149182ba8295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x149182bc6870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x149182e62796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x149182efbd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14918295ee9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x556a48bab0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x556a48beaa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x556a48beebdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x556a48c1c258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x556a48bab6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x556a48c2de63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x149181aa729d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x556a48b9c46a] +================================= +[nid001911:261137:0:261137] ud_ep.c:255 Fatal: UD endpoint 0x561674b5ef60 to : unhandled timeout error +[nid002264:48355:0:48355] ud_ep.c:255 Fatal: UD endpoint 0x5612ccbdfb10 to : unhandled timeout error +==== backtrace (tid: 261137) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14b96eed4454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14b96eed1400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14b96eed1529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14b96aefc133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14b96eeca8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14b96ea65962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14b9706f8295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14b970716870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14b9709b2796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14b970a4bd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14b9704aee9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5616743eb0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x56167442aa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56167442ebdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56167445c258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5616743eb6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56167446de63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14b96f5f729d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5616743dc46a] +================================= +==== backtrace (tid: 48355) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14fe7121d454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14fe7121a400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14fe7121a529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14fe6d245133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14fe712138aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14fe70dae962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14fe72a41295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14fe72a5f870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14fe72cfb796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14fe72d94d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14fe727f7e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5612cc2c70f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5612cc306a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5612cc30abdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5612cc338258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5612cc2c76c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5612cc349e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14fe7194029d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5612cc2b846a] +================================= +[nid001911:261141:0:261141] ud_ep.c:255 Fatal: UD endpoint 0x55570cef21a0 to : unhandled timeout error +==== backtrace (tid: 261141) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14efcb027454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14efcb024400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14efcb024529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14efc704f133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14efcb01d8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14efcabb8962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14efcc84b295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14efcc869870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14efccb05796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14efccb9ed35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14efcc601e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55570c77e0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55570c7bda81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55570c7c1bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55570c7ef258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55570c77e6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55570c800e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14efcb74a29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55570c76f46a] +================================= +[nid002628:135742:0:135742] ud_ep.c:255 Fatal: UD endpoint 0x55961d2f7f20 to : unhandled timeout error +==== backtrace (tid: 135742) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1502f8002454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1502f7fff400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1502f7fff529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1502f402a133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1502f7ff88aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1502f7b93962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1502f9826295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1502f9844870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1502f9ae0796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1502f9b79d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1502f95dce9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55961cbc80f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55961cc07a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55961cc0bbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55961cc39258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55961cbc86c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55961cc4ae63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1502f872529d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55961cbb946a] +================================= +[nid002337:209049:0:209049] ud_ep.c:255 Fatal: UD endpoint 0x563635f7c9e0 to : unhandled timeout error +==== backtrace (tid: 209049) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146b52e6a454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146b52e67400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146b52e67529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x146b4ee92133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146b52e608aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146b529fb962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x146b5468e295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146b546ac870] +[nid002357:138937:0:138937] ud_ep.c:255 Fatal: UD endpoint 0x55e13b4d1f10 to : unhandled timeout error + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146b54948796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146b549e1d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146b54444e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5636356680f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5636356a7a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5636356abbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5636356d9258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5636356686c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5636356eae63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x146b5358d29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56363565946a] +================================= +==== backtrace (tid: 138937) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14ccac8eb454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14ccac8e8400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14ccac8e8529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14cca8913133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14ccac8e18aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14ccac47c962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14ccae10f295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14ccae12d870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14ccae3c9796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14ccae462d35] +[nid002337:209046:0:209046] ud_ep.c:255 Fatal: UD endpoint 0x55669f190020 to : unhandled timeout error +[nid002607:5717 :0:5717] ud_ep.c:255 Fatal: UD endpoint 0x55d81ac0ab10 to : unhandled timeout error +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14ccadec5e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55e13acb60f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55e13acf5a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55e13acf9bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55e13ad27258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55e13acb66c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55e13ad38e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14ccad00e29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55e13aca746a] +================================= +==== backtrace (tid: 209046) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14f132f1f454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14f132f1c400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14f132f1c529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14f12ef47133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14f132f158aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14f132ab0962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14f134743295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14f134761870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14f1349fd796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14f134a96d35] +==== backtrace (tid: 5717) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x148e48b4e454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x148e48b4b400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x148e48b4b529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x148e44b76133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x148e48b448aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x148e486df962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x148e4a372295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x148e4a390870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x148e4a62c796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x148e4a6c5d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14f1344f9e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55669e87b0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55669e8baa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55669e8bebdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55669e8ec258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55669e87b6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55669e8fde63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14f13364229d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55669e86c46a] +================================= +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x148e4a128e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55d81a4450f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55d81a484a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55d81a488bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55d81a4b6258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55d81a4456c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55d81a4c7e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x148e4927129d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55d81a43646a] +================================= +[nid003264:86504:0:86504] ud_ep.c:255 Fatal: UD endpoint 0x55ff7a0eee00 to : unhandled timeout error +==== backtrace (tid: 86504) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14ee69887454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14ee69884400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14ee69884529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14ee658af133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14ee6987d8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14ee69418962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14ee6b0ab295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14ee6b0c9870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14ee6b365796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14ee6b3fed35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14ee6ae61e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ff798b50f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ff798f4a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ff798f8bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ff79926258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ff798b56c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ff79937e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14ee69faa29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ff798a646a] +================================= +[nid003264:86506:0:86506] ud_ep.c:255 Fatal: UD endpoint 0x56480dcc7e00 to : unhandled timeout error +==== backtrace (tid: 86506) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1476307b3454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1476307b0400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1476307b0529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14762c7db133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1476307a98aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x147630344962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x147631fd7295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x147631ff5870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x147632291796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14763232ad35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x147631d8de9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56480d48e0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x56480d4cda81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56480d4d1bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56480d4ff258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56480d48e6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56480d510e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x147630ed629d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56480d47f46a] +================================= +[nid002607:5713 :0:5713] ud_ep.c:255 Fatal: UD endpoint 0x564fb6370b10 to : unhandled timeout error +[nid001912:205779:0:205779] ud_ep.c:255 Fatal: UD endpoint 0x561b4a8aa770 to : unhandled timeout error +[nid002337:209059:0:209059] ud_ep.c:255 Fatal: UD endpoint 0x564274102850 to : unhandled timeout error +==== backtrace (tid: 5713) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15140d229454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15140d226400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15140d226529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x151409251133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15140d21f8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15140cdba962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15140ea4d295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15140ea6b870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15140ed07796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15140eda0d35] +==== backtrace (tid: 205779) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14ff785ed454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14ff785ea400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14ff785ea529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14ff74615133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14ff785e38aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14ff7817e962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14ff79e11295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14ff79e2f870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14ff7a0cb796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14ff7a164d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15140e803e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x564fb5bab0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x564fb5beaa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x564fb5beebdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x564fb5c1c258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x564fb5bab6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x564fb5c2de63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15140d94c29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x564fb5b9c46a] +================================= +==== backtrace (tid: 209059) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1540f88db454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1540f88d8400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1540f88d8529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1540f4903133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1540f88d18aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1540f846c962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1540fa0ff295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1540fa11d870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1540fa3b9796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1540fa452d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14ff79bc7e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x561b4a0e80f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x561b4a127a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x561b4a12bbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x561b4a159258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x561b4a0e86c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x561b4a16ae63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14ff78d1029d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x561b4a0d946a] +================================= +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1540f9eb5e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5642738280f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x564273867a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56427386bbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x564273899258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5642738286c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5642738aae63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1540f8ffe29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56427381946a] +================================= +[nid002618:161418:0:161418] ud_ep.c:255 Fatal: UD endpoint 0x559dd8860320 to : unhandled timeout error +==== backtrace (tid: 161418) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x147c61c90454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x147c61c8d400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x147c61c8d529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x147c5dcb8133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x147c61c868aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x147c61821962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x147c634b4295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x147c634d2870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x147c6376e796] +[nid002628:135737:0:135737] ud_ep.c:255 Fatal: UD endpoint 0x559bcf42af20 to : unhandled timeout error +==== backtrace (tid: 135737) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14d9dc06f454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14d9dc06c400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14d9dc06c529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14d9d8097133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14d9dc0658aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14d9dbc00962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14d9dd893295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14d9dd8b1870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14d9ddb4d796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14d9ddbe6d35] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x147c63807d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x147c6326ae9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559dd81db0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559dd821aa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559dd821ebdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559dd824c258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559dd81db6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559dd825de63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x147c623b329d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559dd81cc46a] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14d9dd649e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559bcecfb0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559bced3aa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559bced3ebdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559bced6c258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559bcecfb6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559bced7de63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14d9dc79229d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559bcecec46a] +================================= +================================= +[nid001954:89828:0:89828] ud_ep.c:255 Fatal: UD endpoint 0x55e9be5e6ec0 to : unhandled timeout error +==== backtrace (tid: 89828) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146b0e3f3454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146b0e3f0400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146b0e3f0529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x146b0a41b133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146b0e3e98aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146b0df84962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x146b0fc17295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146b0fc35870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146b0fed1796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146b0ff6ad35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146b0f9cde9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55e9bdec80f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55e9bdf07a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55e9bdf0bbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55e9bdf39258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55e9bdec86c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55e9bdf4ae63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x146b0eb1629d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55e9bdeb946a] +[nid001711:229174:0:229174] ud_ep.c:255 Fatal: UD endpoint 0x561ad2781f80 to : unhandled timeout error +================================= +[nid002357:138931:0:138931] ud_ep.c:255 Fatal: UD endpoint 0x5562410e2f10 to : unhandled timeout error +==== backtrace (tid: 229174) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14e5daa45454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14e5daa42400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14e5daa42529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14e5d6a6d133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14e5daa3b8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14e5da5d6962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14e5dc269295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14e5dc287870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14e5dc523796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14e5dc5bcd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14e5dc01fe9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x561ad20cc0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x561ad210ba81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x561ad210fbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x561ad213d258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x561ad20cc6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x561ad214ee63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14e5db16829d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x561ad20bd46a] +================================= +==== backtrace (tid: 138931) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x148502868454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x148502865400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x148502865529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1484fe890133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14850285e8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1485023f9962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14850408c295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1485040aa870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x148504346796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1485043dfd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x148503e42e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5562408c70f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x556240906a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55624090abdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x556240938258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5562408c76c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x556240949e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x148502f8b29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5562408b846a] +================================= +[nid002337:209053:0:209053] ud_ep.c:255 Fatal: UD endpoint 0x55b458081a10 to : unhandled timeout error +[nid003489:131583:0:131583] ud_ep.c:255 Fatal: UD endpoint 0x55c48a0f3680 to : unhandled timeout error +==== backtrace (tid: 131583) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14a5eab14454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14a5eab11400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14a5eab11529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14a5e6b3c133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14a5eab0a8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14a5ea6a5962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14a5ec338295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14a5ec356870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14a5ec5f2796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14a5ec68bd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14a5ec0eee9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55c489a8f0f5] +==== backtrace (tid: 209053) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146517f6a454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146517f67400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146517f67529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x146513f92133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146517f608aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146517afb962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14651978e295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1465197ac870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146519a48796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146519ae1d35] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55c489acea81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55c489ad2bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55c489b00258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55c489a8f6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55c489b11e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14a5eb23729d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55c489a8046a] +================================= +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146519544e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55b45776e0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55b4577ada81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55b4577b1bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55b4577df258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55b45776e6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55b4577f0e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14651868d29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55b45775f46a] +================================= +[nid002337:209051:0:209051] ud_ep.c:255 Fatal: UD endpoint 0x5575e4508810 to : unhandled timeout error +==== backtrace (tid: 209051) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x152de237e454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x152de237b400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x152de237b529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x152dde3a6133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x152de23748aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x152de1f0f962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x152de3ba2295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x152de3bc0870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x152de3e5c796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x152de3ef5d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x152de3958e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5575e3bf50f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5575e3c34a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5575e3c38bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5575e3c66258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5575e3bf56c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5575e3c77e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x152de2aa129d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5575e3be646a] +================================= +[nid002337:209048:0:209048] ud_ep.c:255 Fatal: UD endpoint 0x55ff9c162e60 to : unhandled timeout error +==== backtrace (tid: 209048) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x148a5b0d0454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x148a5b0cd400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x148a5b0cd529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x148a570f8133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x148a5b0c68aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x148a5ac61962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x148a5c8f4295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x148a5c912870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x148a5cbae796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x148a5cc47d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x148a5c6aae9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ff9b84f0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ff9b88ea81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ff9b892bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ff9b8c0258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ff9b84f6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ff9b8d1e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x148a5b7f329d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ff9b84046a] +================================= +[nid002337:209060:0:209060] ud_ep.c:255 Fatal: UD endpoint 0x55d48ed6c490 to : unhandled timeout error +==== backtrace (tid: 209060) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14847d413454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14847d410400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14847d410529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14847943b133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14847d4098aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14847cfa4962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14847ec37295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14847ec55870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14847eef1796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14847ef8ad35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14847e9ede9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55d48e4910f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55d48e4d0a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55d48e4d4bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55d48e502258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55d48e4916c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55d48e513e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14847db3629d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55d48e48246a] +================================= +[nid002403:117134:0:117134] ud_ep.c:255 Fatal: UD endpoint 0x5584d7d83410 to : unhandled timeout error +==== backtrace (tid: 117134) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1514cc6da454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1514cc6d7400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1514cc6d7529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1514c8702133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1514cc6d08aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1514cc26b962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1514cdefe295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1514cdf1c870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1514ce1b8796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1514ce251d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1514cdcb4e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5584d75ec0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5584d762ba81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5584d762fbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5584d765d258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5584d75ec6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5584d766ee63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1514ccdfd29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5584d75dd46a] +================================= +[nid004481:169532:0:169532] ud_ep.c:255 Fatal: UD endpoint 0x5589c4b7ad20 to : unhandled timeout error +==== backtrace (tid: 169532) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14e440ce4454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14e440ce1400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14e440ce1529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14e43cd0c133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14e440cda8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14e440875962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14e442508295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14e442526870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14e4427c2796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14e44285bd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14e4422bee9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5589c446a0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5589c44a9a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5589c44adbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5589c44db258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5589c446a6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5589c44ece63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14e44140729d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5589c445b46a] +================================= +[nid002337:209057:0:209057] ud_ep.c:255 Fatal: UD endpoint 0x561d790a94b0 to : unhandled timeout error +[nid002607:5712 :0:5712] ud_ep.c:255 Fatal: UD endpoint 0x56416bb37b10 to : unhandled timeout error +==== backtrace (tid: 209057) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x148ec9a14454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x148ec9a11400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x148ec9a11529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x148ec5a3c133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x148ec9a0a8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x148ec95a5962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x148ecb238295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x148ecb256870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x148ecb4f2796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x148ecb58bd35] +[nid002607:5714 :0:5714] ud_ep.c:255 Fatal: UD endpoint 0x555a8b9a6b10 to : unhandled timeout error +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x148ecafeee9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x561d787cf0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x561d7880ea81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x561d78812bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x561d78840258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x561d787cf6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x561d78851e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x148eca13729d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x561d787c046a] +================================= +==== backtrace (tid: 5712) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14e4d7e57454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14e4d7e54400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14e4d7e54529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14e4d3e7f133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14e4d7e4d8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14e4d79e8962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14e4d967b295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14e4d9699870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14e4d9935796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14e4d99ced35] +[nid002337:209052:0:209052] ud_ep.c:255 Fatal: UD endpoint 0x55f051daafd0 to : unhandled timeout error +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14e4d9431e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56416b3720f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x56416b3b1a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56416b3b5bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56416b3e3258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56416b3726c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56416b3f4e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14e4d857a29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56416b36346a] +================================= +==== backtrace (tid: 5714) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x147f4b4ab454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x147f4b4a8400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x147f4b4a8529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x147f474d3133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x147f4b4a18aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x147f4b03c962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x147f4cccf295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x147f4cced870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x147f4cf89796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x147f4d022d35] +==== backtrace (tid: 209052) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x147520988454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x147520985400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x147520985529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14751c9b0133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14752097e8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x147520519962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1475221ac295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1475221ca870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x147522466796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1475224ffd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x147f4ca85e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x555a8b1e10f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x555a8b220a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x555a8b224bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x555a8b252258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x555a8b1e16c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x555a8b263e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x147f4bbce29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x555a8b1d246a] +================================= +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x147521f62e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55f0514970f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55f0514d6a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55f0514dabdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55f051508258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55f0514976c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55f051519e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1475210ab29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55f05148846a] +================================= +[nid004481:169533:0:169533] ud_ep.c:255 Fatal: UD endpoint 0x5582002bed20 to : unhandled timeout error +[nid002337:209056:0:209056] ud_ep.c:255 Fatal: UD endpoint 0x55ca9d3ac030 to : unhandled timeout error +[nid002357:138933:0:138933] ud_ep.c:255 Fatal: UD endpoint 0x55b16a50bf10 to : unhandled timeout error +==== backtrace (tid: 169533) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15032c81b454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15032c818400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15032c818529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x150328843133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15032c8118aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15032c3ac962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15032e03f295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15032e05d870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15032e2f9796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15032e392d35] +[nid002357:138932:0:138932] ud_ep.c:255 Fatal: UD endpoint 0x55c3ffa9ef10 to : unhandled timeout error +==== backtrace (tid: 209056) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14c61b8c3454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14c61b8c0400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14c61b8c0529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14c6178eb133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14c61b8b98aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14c61b454962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14c61d0e7295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14c61d105870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14c61d3a1796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14c61d43ad35] +[nid001911:261134:0:261134] ud_ep.c:255 Fatal: UD endpoint 0x55727dde1230 to : unhandled timeout error +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15032ddf5e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5581ffbae0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5581ffbeda81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5581ffbf1bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5581ffc1f258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5581ffbae6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5581ffc30e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15032cf3e29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5581ffb9f46a] +================================= +==== backtrace (tid: 138933) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14c7b676a454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14c7b6767400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14c7b6767529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14c7b2792133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14c7b67608aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14c7b62fb962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14c7b7f8e295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14c7b7fac870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14c7b8248796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14c7b82e1d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14c61ce9de9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ca9cad20f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ca9cb11a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ca9cb15bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ca9cb43258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ca9cad26c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ca9cb54e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14c61bfe629d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ca9cac346a] +================================= +[nid002607:5718 :0:5718] ud_ep.c:255 Fatal: UD endpoint 0x55c8a5163b10 to : unhandled timeout error +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14c7b7d44e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55b169cf00f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55b169d2fa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55b169d33bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55b169d61258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55b169cf06c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55b169d72e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14c7b6e8d29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55b169ce146a] +================================= +[nid001871:180893:0:180893] ud_ep.c:255 Fatal: UD endpoint 0x55c4354848d0 to : unhandled timeout error +==== backtrace (tid: 180893) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14e588e37454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14e588e34400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14e588e34529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14e584e5f133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14e588e2d8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14e5889c8962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14e58a65b295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14e58a679870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14e58a915796] +==== backtrace (tid: 138932) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14dc03b7b454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14dc03b78400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14dc03b78529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14dbffba3133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14dc03b718aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14dc0370c962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14dc0539f295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14dc053bd870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14dc05659796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14dc056f2d35] +==== backtrace (tid: 261134) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14d2fe9d1454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14d2fe9ce400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14d2fe9ce529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14d2fa9f9133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14d2fe9c78aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14d2fe562962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14d3001f5295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14d300213870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14d3004af796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14d300548d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14dc05155e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55c3ff2830f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55c3ff2c2a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55c3ff2c6bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55c3ff2f4258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55c3ff2836c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55c3ff305e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14dc0429e29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55c3ff27446a] +================================= +==== backtrace (tid: 5718) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14c4970d7454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14c4970d4400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14c4970d4529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14c4930ff133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14c4970cd8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14c496c68962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14c4988fb295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14c498919870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14c498bb5796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14c498c4ed35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14d2fffabe9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55727d66d0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55727d6aca81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55727d6b0bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55727d6de258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55727d66d6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55727d6efe63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14d2ff0f429d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55727d65e46a] +================================= + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14e58a9aed35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14e58a411e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55c434d6c0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55c434daba81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55c434dafbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55c434ddd258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55c434d6c6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55c434deee63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14e58955a29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55c434d5d46a] +================================= +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14c4986b1e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55c8a499e0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55c8a49dda81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55c8a49e1bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55c8a4a0f258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55c8a499e6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55c8a4a20e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14c4977fa29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55c8a498f46a] +================================= +[nid004481:169535:0:169535] ud_ep.c:255 Fatal: UD endpoint 0x55bf56395d20 to : unhandled timeout error +[nid002337:209047:0:209047] ud_ep.c:255 Fatal: UD endpoint 0x55a51e9ceb80 to : unhandled timeout error +==== backtrace (tid: 169535) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14bbe0e00454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14bbe0dfd400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14bbe0dfd529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14bbdce28133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14bbe0df68aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14bbe0991962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14bbe2624295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14bbe2642870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14bbe28de796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14bbe2977d35] +==== backtrace (tid: 209047) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146f42cbf454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146f42cbc400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146f42cbc529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x146f3ece7133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146f42cb58aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146f42850962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x146f444e3295] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14bbe23dae9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55bf55c850f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55bf55cc4a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55bf55cc8bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55bf55cf6258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55bf55c856c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55bf55d07e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14bbe152329d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55bf55c7646a] +================================= + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146f44501870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146f4479d796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146f44836d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146f44299e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55a51e0bb0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55a51e0faa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55a51e0febdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55a51e12c258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55a51e0bb6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55a51e13de63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x146f433e229d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55a51e0ac46a] +================================= +[nid002337:209061:0:209061] ud_ep.c:255 Fatal: UD endpoint 0x56438a80a070 to : unhandled timeout error +==== backtrace (tid: 209061) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x147fb2279454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x147fb2276400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x147fb2276529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x147fae2a1133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x147fb226f8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x147fb1e0a962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x147fb3a9d295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x147fb3abb870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x147fb3d57796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x147fb3df0d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x147fb3853e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x564389f2f0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x564389f6ea81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x564389f72bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x564389fa0258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x564389f2f6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x564389fb1e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x147fb299c29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x564389f2046a] +================================= +[nid004481:169531:0:169531] ud_ep.c:255 Fatal: UD endpoint 0x562419ee8d20 to : unhandled timeout error +[nid002357:138935:0:138935] ud_ep.c:255 Fatal: UD endpoint 0x55634238bf10 to : unhandled timeout error +==== backtrace (tid: 169531) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14d8094e9454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14d8094e6400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14d8094e6529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14d805511133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14d8094df8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14d80907a962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14d80ad0d295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14d80ad2b870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14d80afc7796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14d80b060d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14d80aac3e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5624197d80f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x562419817a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56241981bbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x562419849258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5624197d86c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56241985ae63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14d809c0c29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5624197c946a] +================================= +==== backtrace (tid: 138935) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14b84ac7b454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14b84ac78400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14b84ac78529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14b846ca3133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14b84ac718aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14b84a80c962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14b84c49f295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14b84c4bd870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14b84c759796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14b84c7f2d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14b84c255e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x556341b700f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x556341bafa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x556341bb3bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x556341be1258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x556341b706c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x556341bf2e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14b84b39e29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x556341b6146a] +================================= +[nid004830:51301:0:51301] ud_ep.c:255 Fatal: UD endpoint 0x56511ab645f0 to : unhandled timeout error +==== backtrace (tid: 51301) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x147d979d0454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x147d979cd400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x147d979cd529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x147d939f8133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x147d979c68aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x147d97561962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x147d991f4295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x147d99212870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x147d994ae796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x147d99547d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x147d98faae9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56511a36c0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x56511a3aba81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56511a3afbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56511a3dd258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56511a36c6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56511a3eee63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x147d980f329d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56511a35d46a] +================================= +[nid004481:169536:0:169536] ud_ep.c:255 Fatal: UD endpoint 0x563d5ece3d20 to : unhandled timeout error +==== backtrace (tid: 169536) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14aab712d454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14aab712a400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14aab712a529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14aab3155133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14aab71238aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14aab6cbe962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14aab8951295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14aab896f870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14aab8c0b796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14aab8ca4d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14aab8707e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x563d5e5d30f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x563d5e612a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x563d5e616bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x563d5e644258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x563d5e5d36c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x563d5e655e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14aab785029d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x563d5e5c446a] +================================= +[nid002357:138936:0:138936] ud_ep.c:255 Fatal: UD endpoint 0x55c897c86f10 to : unhandled timeout error +==== backtrace (tid: 138936) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x151ad82d7454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x151ad82d4400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x151ad82d4529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x151ad42ff133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x151ad82cd8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x151ad7e68962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x151ad9afb295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x151ad9b19870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x151ad9db5796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x151ad9e4ed35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x151ad98b1e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55c89746b0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55c8974aaa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55c8974aebdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55c8974dc258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55c89746b6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55c8974ede63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x151ad89fa29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55c89745c46a] +================================= +[nid004481:169537:0:169537] ud_ep.c:255 Fatal: UD endpoint 0x5593c118fd20 to : unhandled timeout error +==== backtrace (tid: 169537) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1505f743d454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1505f743a400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1505f743a529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1505f3465133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1505f74338aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1505f6fce962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1505f8c61295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1505f8c7f870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1505f8f1b796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1505f8fb4d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1505f8a17e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5593c0a7f0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5593c0abea81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5593c0ac2bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5593c0af0258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5593c0a7f6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5593c0b01e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1505f7b6029d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5593c0a7046a] +================================= +[nid002403:117140:0:117140] ud_ep.c:255 Fatal: UD endpoint 0x56005edf6410 to : unhandled timeout error +==== backtrace (tid: 117140) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1537f9b24454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1537f9b21400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1537f9b21529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1537f5b4c133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1537f9b1a8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1537f96b5962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1537fb348295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1537fb366870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1537fb602796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1537fb69bd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1537fb0fee9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56005e65f0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x56005e69ea81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56005e6a2bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56005e6d0258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56005e65f6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56005e6e1e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1537fa24729d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56005e65046a] +================================= +[nid001871:180896:0:180896] ud_ep.c:255 Fatal: UD endpoint 0x562ee3be72f0 to : unhandled timeout error +==== backtrace (tid: 180896) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146b8a2ff454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146b8a2fc400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146b8a2fc529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x146b86327133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146b8a2f58aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146b89e90962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x146b8bb23295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146b8bb41870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146b8bddd796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146b8be76d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146b8b8d9e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x562ee34fe0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x562ee353da81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x562ee3541bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x562ee356f258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x562ee34fe6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x562ee3580e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x146b8aa2229d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x562ee34ef46a] +================================= +[nid001463:252760:0:252760] ud_ep.c:255 Fatal: UD endpoint 0x5631e67fe510 to : unhandled timeout error +==== backtrace (tid: 252760) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14dd07648454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14dd07645400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14dd07645529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14dd03670133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14dd0763e8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14dd071d9962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14dd08e6c295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14dd08e8a870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14dd09126796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14dd091bfd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14dd08c22e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5631e62100f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5631e624fa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5631e6253bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5631e6281258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5631e62106c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5631e6292e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14dd07d6b29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5631e620146a] +================================= +[nid001912:205776:0:205776] ud_ep.c:255 Fatal: UD endpoint 0x5643afbf96b0 to : unhandled timeout error +==== backtrace (tid: 205776) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14f552470454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14f55246d400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14f55246d529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14f54e498133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14f5524668aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14f552001962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14f553c94295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14f553cb2870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14f553f4e796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14f553fe7d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14f553a4ae9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5643af4370f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5643af476a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5643af47abdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5643af4a8258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5643af4376c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5643af4b9e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14f552b9329d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5643af42846a] +================================= +[nid002337:209055:0:209055] ud_ep.c:255 Fatal: UD endpoint 0x55733bca1c70 to : unhandled timeout error +[nid002628:135736:0:135736] ud_ep.c:255 Fatal: UD endpoint 0x55ac25952f20 to : unhandled timeout error +==== backtrace (tid: 135736) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146b6e7a6454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146b6e7a3400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146b6e7a3529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x146b6a7ce133] +==== backtrace (tid: 209055) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146e8012f454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146e8012c400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146e8012c529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x146e7c157133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146e801258aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146e7fcc0962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x146e81953295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146e81971870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146e81c0d796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146e81ca6d35] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146b6e79c8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146b6e337962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x146b6ffca295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146b6ffe8870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146b70284796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146b7031dd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146b6fd80e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ac252230f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ac25262a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ac25266bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ac25294258] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146e81709e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55733b3c70f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55733b406a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55733b40abdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55733b438258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55733b3c76c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55733b449e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x146e8085229d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55733b3b846a] +================================= +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ac252236c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ac252a5e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x146b6eec929d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ac2521446a] +================================= +[nid001711:229172:0:229172] ud_ep.c:255 Fatal: UD endpoint 0x55f82fe014a0 to : unhandled timeout error +==== backtrace (tid: 229172) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14d94dce7454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14d94dce4400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14d94dce4529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14d949d0f133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14d94dcdd8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14d94d878962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14d94f50b295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14d94f529870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14d94f7c5796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14d94f85ed35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14d94f2c1e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55f82f7450f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55f82f784a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55f82f788bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55f82f7b6258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55f82f7456c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55f82f7c7e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14d94e40a29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55f82f73646a] +================================= +[nid002607:5711 :0:5711] ud_ep.c:255 Fatal: UD endpoint 0x563c55dd4b10 to : unhandled timeout error +==== backtrace (tid: 5711) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14cfdce83454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14cfdce80400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14cfdce80529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14cfd8eab133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14cfdce798aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14cfdca14962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14cfde6a7295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14cfde6c5870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14cfde961796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14cfde9fad35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14cfde45de9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x563c5560f0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x563c5564ea81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x563c55652bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x563c55680258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x563c5560f6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x563c55691e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14cfdd5a629d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x563c5560046a] +================================= +[nid001950:100692:0:100692] ud_ep.c:255 Fatal: UD endpoint 0x55ba3a577340 to : unhandled timeout error +==== backtrace (tid: 100692) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x150d470c0454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x150d470bd400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x150d470bd529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x150d430e8133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x150d470b68aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x150d46c51962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x150d488e4295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x150d48902870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x150d48b9e796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x150d48c37d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x150d4869ae9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ba39e720f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ba39eb1a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ba39eb5bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ba39ee3258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ba39e726c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ba39ef4e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x150d477e329d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ba39e6346a] +================================= +[nid001664:168288:0:168288] ud_ep.c:255 Fatal: UD endpoint 0x555b7ba72a80 to : unhandled timeout error +==== backtrace (tid: 168288) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14a0e4d35454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14a0e4d32400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14a0e4d32529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14a0e0d5d133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14a0e4d2b8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14a0e48c6962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14a0e6559295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14a0e6577870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14a0e6813796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14a0e68acd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14a0e630fe9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x555b7b3dc0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x555b7b41ba81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x555b7b41fbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x555b7b44d258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x555b7b3dc6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x555b7b45ee63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14a0e545829d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x555b7b3cd46a] +================================= +[nid003264:86501:0:86501] ud_ep.c:255 Fatal: UD endpoint 0x55c7e55a1e00 to : unhandled timeout error +==== backtrace (tid: 86501) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x152efb32b454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x152efb328400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x152efb328529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x152ef7353133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x152efb3218aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x152efaebc962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x152efcb4f295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x152efcb6d870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x152efce09796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x152efcea2d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x152efc905e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55c7e4d680f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55c7e4da7a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55c7e4dabbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55c7e4dd9258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55c7e4d686c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55c7e4deae63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x152efba4e29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55c7e4d5946a] +================================= +[nid001920:170320:0:170320] ud_ep.c:255 Fatal: UD endpoint 0x559e56a0f730 to : unhandled timeout error +==== backtrace (tid: 170320) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1500b3893454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1500b3890400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1500b3890529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1500af8bb133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1500b38898aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1500b3424962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1500b50b7295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1500b50d5870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1500b5371796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1500b540ad35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1500b4e6de9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559e562dc0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559e5631ba81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559e5631fbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559e5634d258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559e562dc6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559e5635ee63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1500b3fb629d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559e562cd46a] +================================= +[nid004752:169182:0:169182] ud_ep.c:255 Fatal: UD endpoint 0x556382fe6be0 to : unhandled timeout error +==== backtrace (tid: 169182) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14eb4ef08454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14eb4ef05400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14eb4ef05529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14eb4af30133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14eb4eefe8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14eb4ea99962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14eb5072c295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14eb5074a870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14eb509e6796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14eb50a7fd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14eb504e2e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5563829470f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x556382986a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55638298abdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5563829b8258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5563829476c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5563829c9e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14eb4f62b29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55638293846a] +================================= +[nid004480:176447:0:176447] ud_ep.c:255 Fatal: UD endpoint 0x556cae00b030 to : unhandled timeout error +==== backtrace (tid: 176447) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14fcce305454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14fcce302400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14fcce302529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14fcca32d133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14fcce2fb8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14fccde96962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14fccfb29295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14fccfb47870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14fccfde3796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14fccfe7cd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14fccf8dfe9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x556cad6e80f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x556cad727a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x556cad72bbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x556cad759258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x556cad6e86c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x556cad76ae63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14fccea2829d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x556cad6d946a] +================================= +[nid004480:176452:0:176452] ud_ep.c:255 Fatal: UD endpoint 0x5642900ea030 to : unhandled timeout error +==== backtrace (tid: 176452) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1486c5e2c454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1486c5e29400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1486c5e29529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1486c1e54133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1486c5e228aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1486c59bd962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1486c7650295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1486c766e870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1486c790a796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1486c79a3d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1486c7406e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56428f7c70f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x56428f806a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56428f80abdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56428f838258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56428f7c76c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56428f849e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1486c654f29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56428f7b846a] +================================= +[nid001711:229170:0:229170] ud_ep.c:255 Fatal: UD endpoint 0x562568553c00 to : unhandled timeout error +[nid001664:168292:0:168292] ud_ep.c:255 Fatal: UD endpoint 0x559a7f9f52a0 to : unhandled timeout error +==== backtrace (tid: 229170) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x152add326454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x152add323400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x152add323529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x152ad934e133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x152add31c8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x152adceb7962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x152adeb4a295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x152adeb68870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x152adee04796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x152adee9dd35] +[nid004480:176453:0:176453] ud_ep.c:255 Fatal: UD endpoint 0x56007d903030 to : unhandled timeout error +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x152ade900e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x562567e960f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x562567ed5a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x562567ed9bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x562567f07258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x562567e966c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x562567f18e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x152adda4929d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x562567e8746a] +================================= +==== backtrace (tid: 176453) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1505c3235454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1505c3232400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1505c3232529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1505bf25d133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1505c322b8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1505c2dc6962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1505c4a59295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1505c4a77870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1505c4d13796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1505c4dacd35] +==== backtrace (tid: 168292) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15052a67b454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15052a678400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15052a678529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1505266a3133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15052a6718aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15052a20c962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15052be9f295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15052bebd870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15052c159796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15052c1f2d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1505c480fe9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56007cfe00f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x56007d01fa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56007d023bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56007d051258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56007cfe06c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56007d062e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1505c395829d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56007cfd146a] +================================= +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15052bc55e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559a7f3bc0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559a7f3fba81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559a7f3ffbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559a7f42d258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559a7f3bc6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559a7f43ee63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15052ad9e29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559a7f3ad46a] +================================= +[nid001858:72023:0:72023] ud_ep.c:255 Fatal: UD endpoint 0x55a58efa8d60 to : unhandled timeout error +==== backtrace (tid: 72023) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x148754515454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x148754512400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x148754512529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14875053d133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14875450b8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1487540a6962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x148755d39295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x148755d57870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x148755ff3796] +[nid002337:209054:0:209054] ud_ep.c:255 Fatal: UD endpoint 0x55a05b557d00 to : unhandled timeout error + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14875608cd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x148755aefe9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55a58e8620f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55a58e8a1a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55a58e8a5bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55a58e8d3258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55a58e8626c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55a58e8e4e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x148754c3829d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55a58e85346a] +================================= +==== backtrace (tid: 209054) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x145795c87454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x145795c84400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x145795c84529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x145791caf133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x145795c7d8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x145795818962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1457974ab295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1457974c9870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x145797765796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1457977fed35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x145797261e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55a05ac7d0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55a05acbca81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55a05acc0bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55a05acee258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55a05ac7d6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55a05acffe63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1457963aa29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55a05ac6e46a] +================================= +[nid004481:169530:0:169530] ud_ep.c:255 Fatal: UD endpoint 0x55cb95847d20 to : unhandled timeout error +==== backtrace (tid: 169530) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15348bb12454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15348bb0f400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15348bb0f529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x153487b3a133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15348bb088aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15348b6a3962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15348d336295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15348d354870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15348d5f0796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15348d689d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15348d0ece9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55cb951370f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55cb95176a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55cb9517abdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55cb951a8258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55cb951376c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55cb951b9e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15348c23529d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55cb9512846a] +================================= +[nid004480:176448:0:176448] ud_ep.c:255 Fatal: UD endpoint 0x55e0a5343030 to : unhandled timeout error +==== backtrace (tid: 176448) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x153ca0643454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x153ca0640400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x153ca0640529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x153c9c66b133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x153ca06398aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x153ca01d4962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x153ca1e67295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x153ca1e85870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x153ca2121796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x153ca21bad35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x153ca1c1de9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55e0a4a200f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55e0a4a5fa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55e0a4a63bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55e0a4a91258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55e0a4a206c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55e0a4aa2e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x153ca0d6629d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55e0a4a1146a] +================================= +[nid004782:158186:0:158186] ud_ep.c:255 Fatal: UD endpoint 0x5564c1be2ef0 to : unhandled timeout error +==== backtrace (tid: 158186) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14c3eb727454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14c3eb724400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14c3eb724529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14c3e774f133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14c3eb71d8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14c3eb2b8962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14c3ecf4b295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14c3ecf69870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14c3ed205796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14c3ed29ed35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14c3ecd01e9a] +[nid002264:48359:0:48359] ud_ep.c:255 Fatal: UD endpoint 0x55f5fae6efc0 to : unhandled timeout error +==== backtrace (tid: 48359) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1464779b0454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1464779ad400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1464779ad529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1464739d8133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1464779a68aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146477541962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1464791d4295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1464791f2870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14647948e796] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5564c14230f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5564c1462a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5564c1466bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5564c1494258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5564c14236c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5564c14a5e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14c3ebe4a29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5564c141446a] +================================= +[nid004480:176450:0:176450] ud_ep.c:255 Fatal: UD endpoint 0x559a722fa030 to : unhandled timeout error + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146479527d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146478f8ae9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55f5fa5720f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55f5fa5b1a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55f5fa5b5bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55f5fa5e3258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55f5fa5726c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55f5fa5f4e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1464780d329d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55f5fa56346a] +================================= +==== backtrace (tid: 176450) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14f2273a4454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14f2273a1400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14f2273a1529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14f2233cc133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14f22739a8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14f226f35962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14f228bc8295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14f228be6870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14f228e82796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14f228f1bd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14f22897ee9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559a719d70f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559a71a16a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559a71a1abdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559a71a48258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559a719d76c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559a71a59e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14f227ac729d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559a719c846a] +================================= +[nid003479:217130:0:217130] ud_ep.c:255 Fatal: UD endpoint 0x55a77756c830 to : unhandled timeout error +==== backtrace (tid: 217130) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1530d5b93454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1530d5b90400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1530d5b90529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1530d1bbb133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1530d5b898aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1530d5724962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1530d73b7295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1530d73d5870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1530d7671796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1530d770ad35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1530d716de9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55a776dbd0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55a776dfca81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55a776e00bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55a776e2e258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55a776dbd6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55a776e3fe63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1530d62b629d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55a776dae46a] +================================= +[nid004480:176449:0:176449] ud_ep.c:255 Fatal: UD endpoint 0x559b70482030 to : unhandled timeout error +==== backtrace (tid: 176449) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14b44fdef454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14b44fdec400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14b44fdec529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14b44be17133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14b44fde58aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14b44f980962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14b451613295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14b451631870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14b4518cd796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14b451966d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14b4513c9e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559b6fb5f0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559b6fb9ea81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559b6fba2bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559b6fbd0258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559b6fb5f6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559b6fbe1e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14b45051229d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559b6fb5046a] +================================= +[nid004480:176451:0:176451] ud_ep.c:255 Fatal: UD endpoint 0x5626cc694030 to : unhandled timeout error +==== backtrace (tid: 176451) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14a6f2b72454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14a6f2b6f400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14a6f2b6f529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14a6eeb9a133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14a6f2b688aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14a6f2703962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14a6f4396295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14a6f43b4870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14a6f4650796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14a6f46e9d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14a6f414ce9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5626cbd710f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5626cbdb0a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5626cbdb4bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5626cbde2258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5626cbd716c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5626cbdf3e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14a6f329529d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5626cbd6246a] +================================= +[nid002357:138930:0:138930] ud_ep.c:255 Fatal: UD endpoint 0x5566b19f3f10 to : unhandled timeout error +[nid001711:229173:0:229173] ud_ep.c:255 Fatal: UD endpoint 0x55e95bedf4c0 to : unhandled timeout error +==== backtrace (tid: 138930) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1507cdbce454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1507cdbcb400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1507cdbcb529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1507c9bf6133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1507cdbc48aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1507cd75f962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1507cf3f2295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1507cf410870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1507cf6ac796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1507cf745d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1507cf1a8e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5566b11d80f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5566b1217a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5566b121bbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5566b1249258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5566b11d86c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5566b125ae63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1507ce2f129d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5566b11c946a] +================================= +==== backtrace (tid: 229173) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1465a1168454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1465a1165400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1465a1165529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14659d190133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1465a115e8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1465a0cf9962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1465a298c295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1465a29aa870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1465a2c46796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1465a2cdfd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1465a2742e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55e95b82b0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55e95b86aa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55e95b86ebdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55e95b89c258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55e95b82b6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55e95b8ade63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1465a188b29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55e95b81c46a] +================================= +[nid004483:58045:0:58045] ud_ep.c:255 Fatal: UD endpoint 0x560d0bfb6230 to : unhandled timeout error +==== backtrace (tid: 58045) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x152688505454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x152688502400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x152688502529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x15268452d133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1526884fb8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x152688096962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x152689d29295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x152689d47870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x152689fe3796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15268a07cd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x152689adfe9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x560d0b6b90f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x560d0b6f8a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x560d0b6fcbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x560d0b72a258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x560d0b6b96c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x560d0b73be63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x152688c2829d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x560d0b6aa46a] +================================= +[nid002607:5715 :0:5715] ud_ep.c:255 Fatal: UD endpoint 0x555efc670b10 to : unhandled timeout error +==== backtrace (tid: 5715) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14bdee4d6454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14bdee4d3400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14bdee4d3529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14bdea4fe133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14bdee4cc8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14bdee067962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14bdefcfa295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14bdefd18870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14bdeffb4796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14bdf004dd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14bdefab0e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x555efbeab0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x555efbeeaa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x555efbeeebdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x555efbf1c258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x555efbeab6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x555efbf2de63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14bdeebf929d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x555efbe9c46a] +================================= +[nid004483:58050:0:58050] ud_ep.c:255 Fatal: UD endpoint 0x55849979a230 to : unhandled timeout error +==== backtrace (tid: 58050) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15198c31f454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15198c31c400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15198c31c529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x151988347133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15198c3158aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15198beb0962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15198db43295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15198db61870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15198ddfd796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15198de96d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15198d8f9e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x558498e9d0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x558498edca81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x558498ee0bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x558498f0e258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x558498e9d6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x558498f1fe63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15198ca4229d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x558498e8e46a] +================================= +[nid001656:201191:0:201191] ud_ep.c:255 Fatal: UD endpoint 0x555734192e50 to : unhandled timeout error +[nid004483:58047:0:58047] ud_ep.c:255 Fatal: UD endpoint 0x5642fa818230 to : unhandled timeout error +==== backtrace (tid: 201191) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14a9457e9454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14a9457e6400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14a9457e6529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14a941811133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14a9457df8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14a94537a962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14a94700d295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14a94702b870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14a9472c7796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14a947360d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14a946dc3e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x555733b380f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x555733b77a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x555733b7bbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x555733ba9258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x555733b386c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x555733bbae63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14a945f0c29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x555733b2946a] +================================= +==== backtrace (tid: 58047) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x150b6e83b454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x150b6e838400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x150b6e838529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x150b6a863133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x150b6e8318aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x150b6e3cc962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x150b7005f295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x150b7007d870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x150b70319796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x150b703b2d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x150b6fe15e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5642f9f1b0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5642f9f5aa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5642f9f5ebdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5642f9f8c258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5642f9f1b6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5642f9f9de63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x150b6ef5e29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5642f9f0c46a] +================================= +[nid004483:58046:0:58046] ud_ep.c:255 Fatal: UD endpoint 0x55d6dee42230 to : unhandled timeout error +==== backtrace (tid: 58046) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14e1f3b36454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14e1f3b33400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14e1f3b33529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14e1efb5e133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14e1f3b2c8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14e1f36c7962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14e1f535a295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14e1f5378870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14e1f5614796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14e1f56add35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14e1f5110e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55d6de5450f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55d6de584a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55d6de588bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55d6de5b6258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55d6de5456c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55d6de5c7e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14e1f425929d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55d6de53646a] +================================= +[nid003542:98599:0:98599] ud_ep.c:255 Fatal: UD endpoint 0x560df733ab00 to : unhandled timeout error +==== backtrace (tid: 98599) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x147835c5f454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x147835c5c400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x147835c5c529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x147831c87133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x147835c558aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1478357f0962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x147837483295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1478374a1870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14783773d796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1478377d6d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x147837239e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x560df6d610f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x560df6da0a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x560df6da4bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x560df6dd2258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x560df6d616c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x560df6de3e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14783638229d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x560df6d5246a] +================================= +[nid004481:169534:0:169534] ud_ep.c:255 Fatal: UD endpoint 0x55d376d6dd20 to : unhandled timeout error +==== backtrace (tid: 169534) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14961708b454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x149617088400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x149617088529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1496130b3133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1496170818aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x149616c1c962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1496188af295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1496188cd870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x149618b69796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x149618c02d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x149618665e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55d37665d0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55d37669ca81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55d3766a0bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55d3766ce258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55d37665d6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55d3766dfe63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1496177ae29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55d37664e46a] +================================= +[nid003497:90310:0:90310] ud_ep.c:255 Fatal: UD endpoint 0x558710e58ef0 to : unhandled timeout error +[nid006311:117413:0:117413] ud_ep.c:255 Fatal: UD endpoint 0x55c879de3c30 to : unhandled timeout error +==== backtrace (tid: 117413) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146843d78454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146843d75400] +==== backtrace (tid: 90310) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1541b03bd454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1541b03ba400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1541b03ba529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1541ac3e5133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1541b03b38aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1541aff4e962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1541b1be1295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1541b1bff870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1541b1e9b796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1541b1f34d35] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146843d75529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14683fda0133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146843d6e8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146843909962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14684559c295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1468455ba870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146845856796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1468458efd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146845352e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55c8797e70f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55c879826a81] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1541b1997e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5587107210f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x558710760a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x558710764bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x558710792258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5587107216c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5587107a3e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1541b0ae029d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55871071246a] +================================= +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55c87982abdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55c879858258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55c8797e76c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55c879869e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14684449b29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55c8797d846a] +================================= +[nid003497:90309:0:90309] ud_ep.c:255 Fatal: UD endpoint 0x559e475249d0 to : unhandled timeout error +==== backtrace (tid: 90309) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14fa68c23454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14fa68c20400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14fa68c20529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14fa64c4b133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14fa68c198aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14fa687b4962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14fa6a447295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14fa6a465870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14fa6a701796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14fa6a79ad35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14fa6a1fde9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559e46dec0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559e46e2ba81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559e46e2fbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559e46e5d258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559e46dec6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559e46e6ee63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14fa6934629d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559e46ddd46a] +================================= +[nid001660:125279:0:125279] ud_ep.c:255 Fatal: UD endpoint 0x5599b3a1ae30 to : unhandled timeout error +==== backtrace (tid: 125279) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x147afb2f7454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x147afb2f4400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x147afb2f4529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x147af731f133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x147afb2ed8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x147afae88962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x147afcb1b295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x147afcb39870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x147afcdd5796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x147afce6ed35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x147afc8d1e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5599b344a0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5599b3489a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5599b348dbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5599b34bb258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5599b344a6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5599b34cce63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x147afba1a29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5599b343b46a] +================================= +[nid003497:90312:0:90312] ud_ep.c:255 Fatal: UD endpoint 0x55f36bf4aef0 to : unhandled timeout error +[nid006178:72398:0:72398] ud_ep.c:255 Fatal: UD endpoint 0x55b098b30630 to : unhandled timeout error +==== backtrace (tid: 72398) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14bba308a454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14bba3087400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14bba3087529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14bb9f0b2133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14bba30808aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14bba2c1b962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14bba48ae295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14bba48cc870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14bba4b68796] +==== backtrace (tid: 90312) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146bbc6e8454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146bbc6e5400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146bbc6e5529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x146bb8710133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146bbc6de8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146bbc279962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x146bbdf0c295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146bbdf2a870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146bbe1c6796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146bbe25fd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146bbdcc2e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55f36b8130f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55f36b852a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55f36b856bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55f36b884258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55f36b8136c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55f36b895e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x146bbce0b29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55f36b80446a] +================================= + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14bba4c01d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14bba4664e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55b0983350f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55b098374a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55b098378bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55b0983a6258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55b0983356c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55b0983b7e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14bba37ad29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55b09832646a] +================================= +[nid004480:176446:0:176446] ud_ep.c:255 Fatal: UD endpoint 0x555852a78030 to : unhandled timeout error +==== backtrace (tid: 176446) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14d0c51b4454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14d0c51b1400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14d0c51b1529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14d0c11dc133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14d0c51aa8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14d0c4d45962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14d0c69d8295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14d0c69f6870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14d0c6c92796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14d0c6d2bd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14d0c678ee9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5558521550f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x555852194a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x555852198bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5558521c6258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5558521556c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5558521d7e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14d0c58d729d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55585214646a] +================================= +[nid001656:201193:0:201193] ud_ep.c:255 Fatal: UD endpoint 0x55d3802f3e40 to : unhandled timeout error +[nid001656:201195:0:201195] ud_ep.c:255 Fatal: UD endpoint 0x55b5e6e99170 to : unhandled timeout error +==== backtrace (tid: 201193) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x152e91100454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x152e910fd400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x152e910fd529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x152e8d128133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x152e910f68aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x152e90c91962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x152e92924295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x152e92942870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x152e92bde796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x152e92c77d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x152e926dae9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55d37fc8a0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55d37fcc9a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55d37fccdbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55d37fcfb258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55d37fc8a6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55d37fd0ce63] +==== backtrace (tid: 201195) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x148c7998f454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x148c7998c400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x148c7998c529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x148c759b7133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x148c799858aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x148c79520962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x148c7b1b3295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x148c7b1d1870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x148c7b46d796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x148c7b506d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x148c7af69e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55b5e68750f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55b5e68b4a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55b5e68b8bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55b5e68e6258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55b5e68756c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55b5e68f7e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x148c7a0b229d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55b5e686646a] +================================= +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x152e9182329d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55d37fc7b46a] +================================= +[nid005215:181280:0:181280] ud_ep.c:255 Fatal: UD endpoint 0x55cdaa888c00 to : unhandled timeout error +[nid003497:90314:0:90314] ud_ep.c:255 Fatal: UD endpoint 0x55b9b1e22ef0 to : unhandled timeout error +==== backtrace (tid: 181280) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x151e1a074454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x151e1a071400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x151e1a071529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x151e1609c133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x151e1a06a8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x151e19c05962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x151e1b898295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x151e1b8b6870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x151e1bb52796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x151e1bbebd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x151e1b64ee9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55cdaa1c20f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55cdaa201a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55cdaa205bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55cdaa233258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55cdaa1c26c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55cdaa244e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x151e1a79729d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55cdaa1b346a] +================================= +==== backtrace (tid: 90314) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14719c581454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14719c57e400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14719c57e529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1471985a9133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14719c5778aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14719c112962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14719dda5295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14719ddc3870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14719e05f796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14719e0f8d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14719db5be9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55b9b16eb0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55b9b172aa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55b9b172ebdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55b9b175c258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55b9b16eb6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55b9b176de63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14719cca429d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55b9b16dc46a] +================================= +[nid003497:90311:0:90311] ud_ep.c:255 Fatal: UD endpoint 0x55b26662fef0 to : unhandled timeout error +==== backtrace (tid: 90311) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x153cdcd8b454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x153cdcd88400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x153cdcd88529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x153cd8db3133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x153cdcd818aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x153cdc91c962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x153cde5af295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x153cde5cd870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x153cde869796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x153cde902d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x153cde365e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55b265ef80f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55b265f37a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55b265f3bbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55b265f69258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55b265ef86c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55b265f7ae63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x153cdd4ae29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55b265ee946a] +================================= +[nid003497:90316:0:90316] ud_ep.c:255 Fatal: UD endpoint 0x560ee5a3cef0 to : unhandled timeout error +==== backtrace (tid: 90316) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x148c707bd454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x148c707ba400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x148c707ba529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x148c6c7e5133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x148c707b38aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x148c7034e962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x148c71fe1295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x148c71fff870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x148c7229b796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x148c72334d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x148c71d97e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x560ee53050f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x560ee5344a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x560ee5348bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x560ee5376258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x560ee53056c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x560ee5387e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x148c70ee029d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x560ee52f646a] +================================= +[nid003497:90315:0:90315] ud_ep.c:255 Fatal: UD endpoint 0x5586198eeef0 to : unhandled timeout error +==== backtrace (tid: 90315) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x149bb34ad454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x149bb34aa400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x149bb34aa529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x149baf4d5133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x149bb34a38aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x149bb303e962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x149bb4cd1295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x149bb4cef870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x149bb4f8b796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x149bb5024d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x149bb4a87e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5586191b70f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5586191f6a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5586191fabdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x558619228258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5586191b76c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x558619239e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x149bb3bd029d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5586191a846a] +================================= +[nid002628:135740:0:135740] ud_ep.c:255 Fatal: UD endpoint 0x564f3341fe50 to : unhandled timeout error +==== backtrace (tid: 135740) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14b8ba99e454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14b8ba99b400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14b8ba99b529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14b8b69c6133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14b8ba9948aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14b8ba52f962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14b8bc1c2295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14b8bc1e0870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14b8bc47c796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14b8bc515d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14b8bbf78e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x564f32cf00f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x564f32d2fa81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x564f32d33bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x564f32d61258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x564f32cf06c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x564f32d72e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14b8bb0c129d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x564f32ce146a] +================================= +[nid005187:87821:0:87821] ud_ep.c:255 Fatal: UD endpoint 0x55bcff47cd90 to : unhandled timeout error +==== backtrace (tid: 87821) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14babcc7e454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14babcc7b400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14babcc7b529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14bab8ca6133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14babcc748aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14babc80f962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14babe4a2295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14babe4c0870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14babe75c796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14babe7f5d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14babe258e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55bcfeced0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55bcfed2ca81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55bcfed30bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55bcfed5e258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55bcfeced6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55bcfed6fe63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14babd3a129d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55bcfecde46a] +================================= +[nid006249:147983:0:147983] ud_ep.c:255 Fatal: UD endpoint 0x55adb6b06ea0 to : unhandled timeout error +==== backtrace (tid: 147983) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14b59b760454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14b59b75d400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14b59b75d529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14b597788133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14b59b7568aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14b59b2f1962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14b59cf84295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14b59cfa2870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14b59d23e796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14b59d2d7d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14b59cd3ae9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55adb63a10f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55adb63e0a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55adb63e4bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55adb6412258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55adb63a16c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55adb6423e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14b59be8329d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55adb639246a] +================================= +[nid003265:88961:0:88961] ud_ep.c:255 Fatal: UD endpoint 0x5628ca2f7b40 to : unhandled timeout error +==== backtrace (tid: 88961) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15229837f454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15229837c400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15229837c529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1522943a7133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1522983758aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x152297f10962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x152299ba3295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x152299bc1870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x152299e5d796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x152299ef6d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x152299959e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5628c9c710f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5628c9cb0a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5628c9cb4bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5628c9ce2258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5628c9c716c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5628c9cf3e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x152298aa229d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5628c9c6246a] +================================= +[nid001912:205780:0:205780] ud_ep.c:255 Fatal: UD endpoint 0x55861f171eb0 to : unhandled timeout error +==== backtrace (tid: 205780) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14aa899da454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14aa899d7400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14aa899d7529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14aa85a02133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14aa899d08aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14aa8956b962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14aa8b1fe295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14aa8b21c870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14aa8b4b8796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14aa8b551d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14aa8afb4e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55861e9e20f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55861ea21a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55861ea25bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55861ea53258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55861e9e26c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55861ea64e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14aa8a0fd29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55861e9d346a] +================================= +[nid002630:252991:0:252991] ud_ep.c:255 Fatal: UD endpoint 0x564ec53f0620 to : unhandled timeout error +==== backtrace (tid: 252991) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14c935702454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14c9356ff400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14c9356ff529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14c93172a133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14c9356f88aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14c935293962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14c936f26295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14c936f44870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14c9371e0796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14c937279d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14c936cdce9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x564ec4c910f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x564ec4cd0a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x564ec4cd4bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x564ec4d02258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x564ec4c916c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x564ec4d13e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14c935e2529d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x564ec4c8246a] +================================= +[nid006137:129123:0:129123] ud_ep.c:255 Fatal: UD endpoint 0x559e64d3fe60 to : unhandled timeout error +==== backtrace (tid: 129123) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14a3af8db454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14a3af8d8400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14a3af8d8529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14a3ab903133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14a3af8d18aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14a3af46c962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14a3b10ff295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14a3b111d870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14a3b13b9796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14a3b1452d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14a3b0eb5e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559e6476e0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559e647ada81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559e647b1bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559e647df258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559e6476e6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559e647f0e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14a3afffe29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559e6475f46a] +================================= +[nid002580:121315:0:121315] ud_ep.c:255 Fatal: UD endpoint 0x56257307d3a0 to : unhandled timeout error +==== backtrace (tid: 121315) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14a32e76c454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14a32e769400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14a32e769529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14a32a794133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14a32e7628aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14a32e2fd962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14a32ff90295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14a32ffae870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14a33024a796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14a3302e3d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14a32fd46e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56257278d0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5625727cca81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5625727d0bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5625727fe258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56257278d6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56257280fe63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14a32ee8f29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56257277e46a] +================================= +[nid006333:103753:0:103753] ud_ep.c:255 Fatal: UD endpoint 0x55ba22c17e20 to : unhandled timeout error +==== backtrace (tid: 103753) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14cbbada6454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14cbbada3400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14cbbada3529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14cbb6dce133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14cbbad9c8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14cbba937962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14cbbc5ca295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14cbbc5e8870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14cbbc884796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14cbbc91dd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14cbbc380e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ba225b80f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ba225f7a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ba225fbbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ba22629258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ba225b86c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ba2263ae63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14cbbb4c929d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ba225a946a] +================================= +[nid004483:58048:0:58048] ud_ep.c:255 Fatal: UD endpoint 0x55fe496fc230 to : unhandled timeout error +==== backtrace (tid: 58048) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x145dee01c454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x145dee019400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x145dee019529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x145dea044133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x145dee0128aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x145dedbad962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x145def840295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x145def85e870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x145defafa796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x145defb93d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x145def5f6e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55fe48dff0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55fe48e3ea81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55fe48e42bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55fe48e70258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55fe48dff6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55fe48e81e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x145dee73f29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55fe48df046a] +================================= +[nid005216:77378:0:77378] ud_ep.c:255 Fatal: UD endpoint 0x564fe0a0af60 to : unhandled timeout error +==== backtrace (tid: 77378) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x150274391454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15027438e400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15027438e529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1502703b9133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1502743878aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x150273f22962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x150275bb5295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x150275bd3870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x150275e6f796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x150275f08d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15027596be9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x564fe02350f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x564fe0274a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x564fe0278bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x564fe02a6258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x564fe02356c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x564fe02b7e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x150274ab429d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x564fe022646a] +================================= +[nid006179:71577:0:71577] ud_ep.c:255 Fatal: UD endpoint 0x564899de5670 to : unhandled timeout error +==== backtrace (tid: 71577) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1487265c9454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1487265c6400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1487265c6529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1487225f1133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1487265bf8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14872615a962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x148727ded295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x148727e0b870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1487280a7796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x148728140d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x148727ba3e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5648997a90f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5648997e8a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5648997ecbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56489981a258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5648997a96c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56489982be63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x148726cec29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56489979a46a] +================================= +[nid005104:71892:0:71892] ud_ep.c:255 Fatal: UD endpoint 0x55e887b29fd0 to : unhandled timeout error +==== backtrace (tid: 71892) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14c138e31454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14c138e2e400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14c138e2e529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14c134e59133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14c138e278aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14c1389c2962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14c13a655295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14c13a673870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14c13a90f796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14c13a9a8d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14c13a40be9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55e8874690f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55e8874a8a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55e8874acbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55e8874da258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55e8874696c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55e8874ebe63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14c13955429d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55e88745a46a] +================================= +[nid006280:156831:0:156831] ud_ep.c:255 Fatal: UD endpoint 0x5562e0f56a20 to : unhandled timeout error +==== backtrace (tid: 156831) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14a9c92f3454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14a9c92f0400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14a9c92f0529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14a9c531b133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14a9c92e98aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14a9c8e84962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14a9cab17295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14a9cab35870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14a9cadd1796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14a9cae6ad35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14a9ca8cde9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5562e08c20f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5562e0901a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5562e0905bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5562e0933258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5562e08c26c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5562e0944e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14a9c9a1629d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5562e08b346a] +================================= +[nid004753:1774 :0:1774] ud_ep.c:255 Fatal: UD endpoint 0x5594c95af8b0 to : unhandled timeout error +==== backtrace (tid: 1774) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146df01d0454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146df01cd400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146df01cd529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x146dec1f8133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146df01c68aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146defd61962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x146df19f4295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146df1a12870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146df1cae796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146df1d47d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146df17aae9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5594c8e610f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5594c8ea0a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5594c8ea4bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5594c8ed2258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5594c8e616c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5594c8ee3e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x146df08f329d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5594c8e5246a] +================================= +[nid001924:245147:0:245147] ud_ep.c:255 Fatal: UD endpoint 0x55dd5ec27190 to : unhandled timeout error +==== backtrace (tid: 245147) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x152102017454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x152102014400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x152102014529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1520fe03f133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15210200d8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x152101ba8962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15210383b295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x152103859870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x152103af5796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x152103b8ed35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1521035f1e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55dd5e5150f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55dd5e554a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55dd5e558bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55dd5e586258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55dd5e5156c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55dd5e597e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15210273a29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55dd5e50646a] +================================= +[nid002337:209058:0:209058] ud_ep.c:255 Fatal: UD endpoint 0x564775ba7610 to : unhandled timeout error +==== backtrace (tid: 209058) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14f577c7b454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14f577c78400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14f577c78529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14f573ca3133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14f577c718aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14f57780c962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14f57949f295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14f5794bd870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14f579759796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14f5797f2d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14f579255e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56477548c0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5647754cba81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5647754cfbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5647754fd258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56477548c6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56477550ee63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14f57839e29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56477547d46a] +================================= +[nid006280:156837:0:156837] ud_ep.c:255 Fatal: UD endpoint 0x5565412c5380 to : unhandled timeout error +==== backtrace (tid: 156837) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14644dbf0454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14644dbed400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14644dbed529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x146449c18133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14644dbe68aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14644d781962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14644f414295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14644f432870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14644f6ce796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14644f767d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14644f1cae9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x556540b850f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x556540bc4a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x556540bc8bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x556540bf6258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x556540b856c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x556540c07e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14644e31329d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x556540b7646a] +================================= +[nid002337:209050:0:209050] ud_ep.c:255 Fatal: UD endpoint 0x565193160390 to : unhandled timeout error +[nid001911:261138:0:261138] ud_ep.c:255 Fatal: UD endpoint 0x5625e56403d0 to : unhandled timeout error +==== backtrace (tid: 209050) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14b7efbbe454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14b7efbbb400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14b7efbbb529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14b7ebbe6133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14b7efbb48aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14b7ef74f962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14b7f13e2295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14b7f1400870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14b7f169c796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14b7f1735d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14b7f1198e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x565192a480f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x565192a87a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x565192a8bbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x565192ab9258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x565192a486c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x565192acae63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14b7f02e129d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x565192a3946a] +================================= +==== backtrace (tid: 261138) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14e48d146454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14e48d143400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14e48d143529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14e48916e133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14e48d13c8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14e48ccd7962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14e48e96a295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14e48e988870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14e48ec24796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14e48ecbdd35] +srun: error: nid003264: tasks 4840,4843,4845: Aborted +srun: launch/slurm: _step_signal: Terminating StepId=11079231.3 +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14e48e720e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5625e4fa50f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5625e4fe4a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5625e4fe8bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5625e5016258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5625e4fa56c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5625e5027e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14e48d86929d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5625e4f9646a] +================================= +[nid006346:193005:0:193005] ud_ep.c:255 Fatal: UD endpoint 0x56225ff0f360 to : unhandled timeout error +==== backtrace (tid: 193005) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x152f0e0c7454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x152f0e0c4400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x152f0e0c4529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x152f0a0ef133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x152f0e0bd8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x152f0dc58962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x152f0f8eb295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x152f0f909870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x152f0fba5796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x152f0fc3ed35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x152f0f6a1e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56225f9310f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x56225f970a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56225f974bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56225f9a2258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56225f9316c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56225f9b3e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x152f0e7ea29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56225f92246a] +================================= +[nid002357:138934:0:138934] ud_ep.c:255 Fatal: UD endpoint 0x56547f661c30 to : unhandled timeout error +==== backtrace (tid: 138934) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14d857be3454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14d857be0400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14d857be0529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14d853c0b133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14d857bd98aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14d857774962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14d859407295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14d859425870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14d8596c1796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14d85975ad35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14d8591bde9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56547ee430f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x56547ee82a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56547ee86bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56547eeb4258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56547ee436c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56547eec5e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14d85830629d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56547ee3446a] +================================= +[nid003497:90313:0:90313] ud_ep.c:255 Fatal: UD endpoint 0x55e853133ef0 to : unhandled timeout error +==== backtrace (tid: 90313) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14fe97a00454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14fe979fd400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14fe979fd529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14fe93a28133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14fe979f68aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14fe97591962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14fe99224295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14fe99242870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14fe994de796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14fe99577d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14fe98fdae9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55e8529fc0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55e852a3ba81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55e852a3fbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55e852a6d258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55e8529fc6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55e852a7ee63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14fe9812329d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55e8529ed46a] +================================= +[nid002403:117138:0:117138] ud_ep.c:255 Fatal: UD endpoint 0x560532eb9210 to : unhandled timeout error +==== backtrace (tid: 117138) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15126c96f454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15126c96c400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15126c96c529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x151268997133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15126c9658aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15126c500962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15126e193295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15126e1b1870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15126e44d796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15126e4e6d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15126df49e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56053272a0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x560532769a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56053276dbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56053279b258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56053272a6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5605327ace63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15126d09229d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56053271b46a] +================================= +[nid003200:260344:0:260344] ud_ep.c:255 Fatal: UD endpoint 0x5611c272a790 to : unhandled timeout error +==== backtrace (tid: 260344) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14c59d4cf454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14c59d4cc400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14c59d4cc529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14c5994f7133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14c59d4c58aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14c59d060962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14c59ecf3295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14c59ed11870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14c59efad796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14c59f046d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14c59eaa9e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5611c217c0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5611c21bba81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5611c21bfbdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5611c21ed258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5611c217c6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5611c21fee63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14c59dbf229d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5611c216d46a] +================================= +[nid003261:153875:0:153875] ud_ep.c:255 Fatal: UD endpoint 0x559ae47b6f00 to : unhandled timeout error +==== backtrace (tid: 153875) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146742c57454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146742c54400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146742c54529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14673ec7f133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146742c4d8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1467427e8962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14674447b295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146744499870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146744735796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1467447ced35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146744231e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559ae420e0f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559ae424da81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559ae4251bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559ae427f258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559ae420e6c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559ae4290e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14674337a29d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559ae41ff46a] +================================= +[nid004750:243709:0:243709] ud_ep.c:255 Fatal: UD endpoint 0x55ab52d39f80 to : unhandled timeout error +==== backtrace (tid: 243709) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15401d906454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15401d903400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15401d903529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x15401992e133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15401d8fc8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15401d497962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15401f12a295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15401f148870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15401f3e4796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15401f47dd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15401eee0e9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ab527210f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ab52760a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ab52764bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ab52792258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ab527216c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ab527a3e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15401e02929d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ab5271246a] +================================= +srun: error: nid004830: task 6447: Aborted +srun: error: nid004782: task 6096: Aborted +srun: error: nid003489: task 5031: Aborted +srun: error: nid006333: task 8034: Aborted +srun: error: nid003252: tasks 4680-4687: Aborted +slurmstepd: error: *** STEP 11079231.3 ON nid001386 CANCELLED AT 2025-10-06T17:55:45 *** +srun: error: nid002580: tasks 3984-3987,3989-3991: Aborted +srun: error: nid002626: tasks 4304-4319: Terminated +srun: error: nid001775: tasks 1296-1311: Terminated +srun: error: nid004766: tasks 5904-5919: Terminated +srun: error: nid006246: tasks 7504-7519: Terminated +srun: error: nid002000: tasks 2896-2911: Terminated +srun: error: nid002249: tasks 3232-3247: Terminated +srun: error: nid003264: tasks 4832-4839,4841-4842,4844,4846-4847: Terminated +srun: error: nid002656: tasks 4496-4511: Terminated +srun: error: nid001486: tasks 224-239: Terminated +srun: error: nid004782: tasks 6097-6111: Terminated +srun: error: nid004830: tasks 6432-6446: Terminated +srun: error: nid001895: tasks 1824-1839: Terminated +srun: error: nid006273: tasks 7696-7711: Terminated +srun: error: nid006333: tasks 8032-8033,8035-8047: Terminated +srun: error: nid002266: tasks 3424-3439: Terminated +srun: error: nid003489: tasks 5024-5030,5032-5039: Terminated +srun: error: nid001652: tasks 416-431: Terminated +srun: error: nid001676: tasks 752-767: Terminated +srun: error: nid005135: tasks 6624-6639: Terminated +srun: error: nid001910: tasks 2016-2031: Terminated +srun: error: nid001932: tasks 2352-2367: Terminated +srun: error: nid001911: tasks 2040-2043,2045-2047: Aborted +srun: error: nid002341: tasks 3616-3631: Terminated +srun: error: nid002537: tasks 3952-3967: Terminated +srun: error: nid003540: tasks 5216-5231: Terminated +srun: error: nid004541: tasks 5552-5567: Terminated +srun: error: nid001732: tasks 944-959: Terminated +srun: error: nid001945: tasks 2544-2559: Terminated +srun: error: nid005603: tasks 7152-7167: Terminated +srun: error: nid002615: tasks 4144-4159: Terminated +srun: error: nid002270: tasks 3448-3455: Aborted +srun: error: nid001758: tasks 1136-1151: Terminated +srun: error: nid004751: tasks 5744-5759: Terminated +srun: error: nid001845: tasks 1472-1487: Terminated +srun: error: nid004780: tasks 6080-6095: Terminated +srun: error: nid006176: tasks 7344-7359: Terminated +srun: error: nid003252: tasks 4672-4679: Terminated +srun: error: nid006271: tasks 7680-7695: Terminated +srun: error: nid002209: tasks 3072-3087: Terminated +srun: error: nid005215: task 6846: Aborted +srun: error: nid004752: task 5760: Aborted +srun: error: nid001394: tasks 64-79: Terminated +srun: error: nid001867: tasks 1664-1679: Terminated +srun: error: nid004801: tasks 6272-6287: Terminated +srun: error: nid006288: tasks 7872-7887: Terminated +srun: error: nid002252: tasks 3264-3279: Terminated +srun: error: nid002340: tasks 3600-3615: Terminated +srun: error: nid003269: tasks 4864-4879: Terminated +srun: error: nid003539: tasks 5200-5215: Terminated +srun: error: nid001665: tasks 592-607: Terminated +srun: error: nid005189: tasks 6800-6815: Terminated +srun: error: nid004833: tasks 6464-6479: Terminated +srun: error: nid001922: tasks 2192-2207: Terminated +srun: error: nid006336: tasks 8064-8079: Terminated +srun: error: nid002367: tasks 3792-3807: Terminated +srun: error: nid004478: tasks 5392-5407: Terminated +srun: error: nid001680: tasks 784-799: Terminated +srun: error: nid001756: tasks 1120-1135: Terminated +srun: error: nid005491: tasks 6992-7007: Terminated +srun: error: nid001934: tasks 2384-2399: Terminated +srun: error: nid001956: tasks 2720-2735: Terminated +srun: error: nid002627: tasks 4320-4335: Terminated +srun: error: nid004767: tasks 5920-5935: Terminated +srun: error: nid002628: tasks 4344-4351: Aborted +srun: error: nid001812: tasks 1312-1327: Terminated +srun: error: nid006248: tasks 7520-7535: Terminated +srun: error: nid002002: tasks 2912-2927: Terminated +srun: error: nid003198: tasks 4512-4527: Terminated +srun: error: nid001488: tasks 240-255: Terminated +srun: error: nid001851: tasks 1504-1519: Terminated +srun: error: nid001896: tasks 1840-1855: Terminated +srun: error: nid006274: tasks 7712-7727: Terminated +srun: error: nid004831: tasks 6448-6463: Terminated +srun: error: nid002580: task 3988: Aborted +srun: error: nid006335: tasks 8048-8063: Terminated +srun: error: nid003493: tasks 5040-5055: Terminated +srun: error: nid001653: tasks 432-447: Terminated +srun: error: nid005137: tasks 6640-6655: Terminated +srun: error: nid006249: task 7551: Aborted +srun: error: nid001912: tasks 2056-2059,2061-2063: Aborted +srun: error: nid002344: tasks 3632-3647: Terminated +srun: error: nid004783: tasks 6112-6127: Terminated +srun: error: nid002542: tasks 3968-3983: Terminated +srun: error: nid003541: tasks 5232-5247: Terminated +srun: error: nid004544: tasks 5568-5583: Terminated +srun: error: nid001733: tasks 960-975: Terminated +srun: error: nid001946: tasks 2560-2575: Terminated +srun: error: nid005604: tasks 7168-7183: Terminated +srun: error: nid002616: tasks 4160-4175: Terminated +srun: error: nid001759: tasks 1152-1167: Terminated +srun: error: nid003542: task 5259: Aborted +srun: error: nid001846: tasks 1488-1503: Terminated +srun: error: nid006177: tasks 7360-7375: Terminated +srun: error: nid001960: tasks 2752-2767: Terminated +srun: error: nid002216: tasks 3088-3103: Terminated +srun: error: nid002629: tasks 4352-4367: Terminated +srun: error: nid003253: tasks 4688-4703: Terminated +srun: error: nid001396: tasks 80-95: Terminated +srun: error: nid001868: tasks 1680-1695: Terminated +srun: error: nid004807: tasks 6288-6303: Terminated +srun: error: nid002254: tasks 3280-3295: Terminated +srun: error: nid005216: task 6862: Aborted +srun: error: nid006289: tasks 7888-7903: Terminated +srun: error: nid006178: task 7377: Aborted +srun: error: nid003294: tasks 4880-4895: Terminated +srun: error: nid001493: tasks 272-287: Terminated +srun: error: nid003200: task 4534: Aborted +srun: error: nid001924: tasks 2232-2235,2237-2239: Aborted +srun: error: nid001666: tasks 608-623: Terminated +srun: error: nid004835: tasks 6480-6495: Terminated +srun: error: nid001900: tasks 1872-1887: Terminated +srun: error: nid005214: tasks 6816-6831: Terminated +srun: error: nid006338: tasks 8080-8095: Terminated +srun: error: nid001923: tasks 2208-2223: Terminated +srun: error: nid002383: tasks 3808-3823: Terminated +srun: error: nid004753: task 5778: Aborted +srun: error: nid004479: tasks 5408-5423: Terminated +srun: error: nid001911: tasks 2032-2039: Terminated +srun: error: nid001681: tasks 800-815: Terminated +srun: error: nid005493: tasks 7008-7023: Terminated +srun: error: nid001935: tasks 2400-2415: Terminated +srun: error: nid001957: tasks 2736-2751: Terminated +srun: error: nid002582: tasks 4000-4015: Terminated +srun: error: nid002270: tasks 3440-3447: Terminated +srun: error: nid004727: tasks 5600-5615: Terminated +srun: error: nid004768: tasks 5936-5951: Terminated +srun: error: nid001819: tasks 1328-1343: Terminated +srun: error: nid004480: tasks 5432-5439: Aborted +srun: error: nid002630: task 4370: Aborted +srun: error: nid006120: tasks 7200-7215: Terminated +srun: error: nid002004: tasks 2928-2943: Terminated +srun: error: nid001656: tasks 465,467,469-471: Aborted +srun: error: nid001489: tasks 256-271: Terminated +srun: error: nid001852: tasks 1520-1535: Terminated +srun: error: nid004785: tasks 6128-6143: Terminated +srun: error: nid001899: tasks 1856-1871: Terminated +srun: error: nid002218: tasks 3120-3135: Terminated +srun: error: nid006275: tasks 7728-7743: Terminated +srun: error: nid002580: tasks 3992-3999: Terminated +srun: error: nid002276: tasks 3456-3471: Terminated +srun: error: nid001654: tasks 448-463: Terminated +srun: error: nid003495: tasks 5056-5071: Terminated +srun: error: nid003256: tasks 4720-4735: Terminated +srun: error: nid005138: tasks 6656-6671: Terminated +srun: error: nid005215: tasks 6832-6845,6847: Terminated +srun: error: nid002347: tasks 3648-3663: Terminated +srun: error: nid001669: tasks 640-655: Terminated +srun: error: nid001736: tasks 976-991: Terminated +srun: error: nid001409: tasks 112-119: Aborted +srun: error: nid004723: tasks 5584-5599: Terminated +srun: error: nid001947: tasks 2576-2591: Terminated +srun: error: nid004752: tasks 5761-5775: Terminated +srun: error: nid005605: tasks 7184-7199: Terminated +srun: error: nid002617: tasks 4176-4191: Terminated +srun: error: nid002618: task 4196: Aborted +srun: error: nid001760: tasks 1168-1183: Terminated +srun: error: nid001961: tasks 2768-2783: Terminated +srun: error: nid002217: tasks 3104-3119: Terminated +srun: error: nid001408: tasks 96-111: Terminated +srun: error: nid003255: tasks 4704-4719: Terminated +srun: error: nid001871: tasks 1712-1719: Aborted +srun: error: nid004771: tasks 5968-5983: Terminated +srun: error: nid001869: tasks 1696-1711: Terminated +srun: error: nid004809: tasks 6304-6319: Terminated +srun: error: nid006255: tasks 7568-7583: Terminated +srun: error: nid002255: tasks 3296-3311: Terminated +srun: error: nid006290: tasks 7904-7919: Terminated +srun: error: nid003299: tasks 4896-4911: Terminated +srun: error: nid001494: tasks 288-303: Terminated +srun: error: nid001667: tasks 624-639: Terminated +srun: error: nid001901: tasks 1888-1903: Terminated +srun: error: nid004837: tasks 6496-6511: Terminated +srun: error: nid006339: tasks 8096-8111: Terminated +srun: error: nid002309: tasks 3488-3503: Terminated +srun: error: nid006179: task 7403: Aborted +srun: error: nid002384: tasks 3824-3839: Terminated +srun: error: nid001688: tasks 816-831: Terminated +srun: error: nid005494: tasks 7024-7039: Terminated +srun: error: nid001936: tasks 2416-2431: Terminated +srun: error: nid001950: tasks 2632-2639: Aborted +srun: error: nid002583: tasks 4016-4031: Terminated +srun: error: nid004481: tasks 5448-5455: Aborted +srun: error: nid001738: tasks 1008-1023: Terminated +srun: error: nid001820: tasks 1344-1359: Terminated +srun: error: nid004769: tasks 5952-5967: Terminated +srun: error: nid004729: tasks 5616-5631: Terminated +srun: error: nid006122: tasks 7216-7231: Terminated +srun: error: nid002005: tasks 2944-2959: Terminated +srun: error: nid006254: tasks 7552-7567: Terminated +srun: error: nid005104: task 6526: Aborted +srun: error: nid003241: task 4574: Aborted +srun: error: nid003231: tasks 4544-4559: Terminated +srun: error: nid004786: tasks 6144-6159: Terminated +srun: error: nid001854: tasks 1536-1551: Terminated +srun: error: nid006276: tasks 7744-7759: Terminated +srun: error: nid002220: tasks 3136-3151: Terminated +srun: error: nid002308: tasks 3472-3487: Terminated +srun: error: nid001911: task 2044: Aborted +srun: error: nid001927: tasks 2273,2275-2276,2278: Aborted +srun: error: nid003496: tasks 5072-5087: Terminated +srun: error: nid003257: tasks 4736-4751: Terminated +srun: error: nid005139: tasks 6672-6687: Terminated +srun: error: nid004816: tasks 6336-6351: Terminated +srun: error: nid002628: tasks 4336-4343: Terminated +srun: error: nid006137: task 7238: Aborted +srun: error: nid001913: tasks 2064-2079: Terminated +srun: error: nid002351: tasks 3664-3679: Terminated +srun: error: nid003549: tasks 5264-5279: Terminated +srun: error: nid001670: tasks 656-671: Terminated +srun: error: nid001737: tasks 992-1007: Terminated +srun: error: nid001926: tasks 2256-2271: Terminated +srun: error: nid005218: tasks 6864-6879: Terminated +srun: error: nid001948: tasks 2592-2607: Terminated +srun: error: nid002389: tasks 3856-3871: Terminated +srun: error: nid003497: tasks 5088-5091,5093-5095: Aborted +srun: error: nid004754: tasks 5792-5807: Terminated +srun: error: nid005128: task 6537: Aborted +srun: error: nid001761: tasks 1184-1199: Terminated +srun: error: nid001962: tasks 2784-2799: Terminated +srun: error: nid002631: tasks 4384-4399: Terminated +srun: error: nid001822: tasks 1376-1391: Terminated +srun: error: nid004773: tasks 5984-5999: Terminated +srun: error: nid006249: tasks 7536-7550: Terminated +srun: error: nid004483: tasks 5481-5484,5486: Aborted +srun: error: nid006256: tasks 7584-7599: Terminated +srun: error: nid004815: tasks 6320-6335: Terminated +srun: error: nid006292: tasks 7920-7935: Terminated +srun: error: nid002256: tasks 3312-3327: Terminated +srun: error: nid001497: tasks 304-319: Terminated +srun: error: nid001902: tasks 1904-1919: Terminated +srun: error: nid001925: tasks 2240-2255: Terminated +srun: error: nid006340: tasks 8112-8127: Terminated +srun: error: nid002310: tasks 3504-3519: Terminated +srun: error: nid002385: tasks 3840-3855: Terminated +srun: error: nid003498: tasks 5104-5119: Terminated +srun: error: nid005165: tasks 6704-6719: Terminated +srun: error: nid001691: tasks 832-847: Terminated +srun: error: nid001912: tasks 2048-2055: Terminated +srun: error: nid005495: tasks 7040-7055: Terminated +srun: error: nid001937: tasks 2432-2447: Terminated +srun: error: nid003441: tasks 4912-4927: Terminated +srun: error: nid002593: tasks 4032-4047: Terminated +srun: error: nid004736: tasks 5632-5647: Terminated +srun: error: nid001744: tasks 1024-1039: Terminated +srun: error: nid001821: tasks 1360-1375: Terminated +srun: error: nid002007: tasks 2960-2975: Terminated +srun: error: nid002621: tasks 4224-4239: Terminated +srun: error: nid001463: tasks 168-175: Aborted +srun: error: nid001855: tasks 1552-1567: Terminated +srun: error: nid004788: tasks 6160-6175: Terminated +srun: error: nid006277: tasks 7760-7775: Terminated +srun: error: nid002227: tasks 3152-3167: Terminated +srun: error: nid003200: tasks 4528-4533,4535-4543: Terminated +srun: error: nid003542: tasks 5248-5258,5260-5263: Terminated +srun: error: nid001429: tasks 144-159: Terminated +srun: error: nid003258: tasks 4752-4767: Terminated +srun: error: nid001657: tasks 480-495: Terminated +srun: error: nid005140: tasks 6688-6703: Terminated +srun: error: nid001914: tasks 2080-2095: Terminated +srun: error: nid004821: tasks 6352-6367: Terminated +srun: error: nid006297: tasks 7952-7967: Terminated +srun: error: nid002352: tasks 3680-3695: Terminated +srun: error: nid003550: tasks 5280-5295: Terminated +srun: error: nid001671: tasks 672-687: Terminated +srun: error: nid005216: tasks 6848-6861,6863: Terminated +srun: error: nid005480: tasks 6880-6895: Terminated +srun: error: nid001949: tasks 2608-2623: Terminated +srun: error: nid002400: tasks 3872-3887: Terminated +srun: error: nid002620: tasks 4208-4223: Terminated +srun: error: nid004756: tasks 5808-5823: Terminated +srun: error: nid001762: tasks 1200-1215: Terminated +srun: error: nid005507: tasks 7072-7087: Terminated +srun: error: nid001963: tasks 2800-2815: Terminated +srun: error: nid001711: tasks 880-881,883,885-887: Aborted +srun: error: nid006186: tasks 7408-7423: Terminated +srun: error: nid006178: tasks 7376,7378-7391: Terminated +srun: error: nid002632: tasks 4400-4415: Terminated +srun: error: nid001426: tasks 128-143: Terminated +srun: error: nid001823: tasks 1392-1407: Terminated +srun: error: nid004775: tasks 6000-6015: Terminated +srun: error: nid001660: tasks 536-537,539,541-543: Aborted +srun: error: nid001872: tasks 1728-1743: Terminated +srun: error: nid001918: task 2135: Aborted +srun: error: nid006257: tasks 7600-7615: Terminated +srun: error: nid002009: tasks 2992-3007: Terminated +srun: error: nid004753: tasks 5776-5777,5779-5791: Terminated +srun: error: nid006294: tasks 7936-7951: Terminated +srun: error: nid002259: tasks 3328-3343: Terminated +srun: error: nid003476: tasks 4928-4943: Terminated +srun: error: nid001498: tasks 320-335: Terminated +srun: error: nid001903: tasks 1920-1935: Terminated +srun: error: nid006341: tasks 8128-8143: Terminated +srun: error: nid002311: tasks 3520-3535: Terminated +srun: error: nid003499: tasks 5120-5135: Terminated +srun: error: nid001659: tasks 512-527: Terminated +srun: error: nid001924: tasks 2224-2231: Terminated +srun: error: nid004482: tasks 5456-5471: Terminated +srun: error: nid001704: tasks 848-863: Terminated +srun: error: nid005180: tasks 6720-6735: Terminated +srun: error: nid005503: tasks 7056-7071: Terminated +srun: error: nid001938: tasks 2448-2463: Terminated +srun: error: nid002630: tasks 4368-4369,4371-4383: Terminated +srun: error: nid002595: tasks 4048-4063: Terminated +srun: error: nid001747: tasks 1040-1055: Terminated +srun: error: nid004737: tasks 5648-5663: Terminated +srun: error: nid001951: tasks 2640-2655: Terminated +srun: error: nid006138: tasks 7248-7263: Terminated +srun: error: nid002008: tasks 2976-2991: Terminated +srun: error: nid002622: tasks 4240-4255: Terminated +srun: error: nid003242: tasks 4576-4591: Terminated +srun: error: nid004758: tasks 5840-5855: Terminated +srun: error: nid004789: tasks 6176-6191: Terminated +srun: error: nid001856: tasks 1568-1583: Terminated +srun: error: nid006279: tasks 7776-7791: Terminated +srun: error: nid002238: tasks 3168-3183: Terminated +srun: error: nid004480: tasks 5424-5431: Terminated +srun: error: nid003259: tasks 4768-4783: Terminated +srun: error: nid001658: tasks 496-511: Terminated +srun: error: nid001885: tasks 1760-1775: Terminated +srun: error: nid004824: tasks 6368-6383: Terminated +srun: error: nid001915: tasks 2096-2111: Terminated +srun: error: nid006299: tasks 7968-7983: Terminated +srun: error: nid002261: tasks 3360-3375: Terminated +srun: error: nid002353: tasks 3696-3711: Terminated +srun: error: nid003551: tasks 5296-5311: Terminated +srun: error: nid001672: tasks 688-703: Terminated +srun: error: nid001928: tasks 2288-2303: Terminated +srun: error: nid005481: tasks 6896-6911: Terminated +srun: error: nid002402: tasks 3888-3903: Terminated +srun: error: nid002403: tasks 3912-3915,3918: Aborted +srun: error: nid006311: task 7998: Aborted +srun: error: nid004484: tasks 5488-5503: Terminated +srun: error: nid001763: tasks 1216-1231: Terminated +srun: error: nid004757: tasks 5824-5839: Terminated +srun: error: nid006280: tasks 7794,7800: Aborted +srun: error: nid005511: tasks 7088-7103: Terminated +srun: error: nid006188: tasks 7424-7439: Terminated +srun: error: nid001964: tasks 2816-2831: Terminated +srun: error: nid002635: tasks 4416-4431: Terminated +srun: error: nid001826: tasks 1408-1423: Terminated +srun: error: nid004776: tasks 6016-6031: Terminated +srun: error: nid001876: tasks 1744-1759: Terminated +srun: error: nid001656: tasks 464,466,468: Aborted +srun: error: nid002011: tasks 3008-3023: Terminated +srun: error: nid006258: tasks 7616-7631: Terminated +srun: error: nid002260: tasks 3344-3359: Terminated +srun: error: nid003244: tasks 4608-4623: Terminated +srun: error: nid001504: tasks 336-351: Terminated +srun: error: nid003478: tasks 4944-4959: Terminated +srun: error: nid005129: tasks 6544-6559: Terminated +srun: error: nid004794: tasks 6208-6223: Terminated +srun: error: nid001904: tasks 1936-1951: Terminated +srun: error: nid002312: tasks 3536-3551: Terminated +srun: error: nid003479: task 4973: Aborted +srun: error: nid006344: tasks 8144-8159: Terminated +srun: error: nid003500: tasks 5136-5151: Terminated +srun: error: nid001858: tasks 1603,1605-1607: Aborted +srun: error: nid001710: tasks 864-879: Terminated +srun: error: nid005182: tasks 6736-6751: Terminated +srun: error: nid001939: tasks 2464-2479: Terminated +srun: error: nid002603: tasks 4064-4079: Terminated +srun: error: nid001751: tasks 1056-1071: Terminated +srun: error: nid004739: tasks 5664-5679: Terminated +srun: error: nid006139: tasks 7264-7279: Terminated +srun: error: nid001952: tasks 2656-2671: Terminated +srun: error: nid002623: tasks 4256-4271: Terminated +srun: error: nid003243: tasks 4592-4607: Terminated +srun: error: nid004761: tasks 5856-5871: Terminated +srun: error: nid004790: tasks 6192-6207: Terminated +srun: error: nid001857: tasks 1584-1599: Terminated +srun: error: nid006212: tasks 7456-7471: Terminated +srun: error: nid002241: tasks 3184-3199: Terminated +srun: error: nid001468: tasks 176-191: Terminated +srun: error: nid001912: task 2060: Aborted +srun: error: nid003260: tasks 4784-4799: Terminated +srun: error: nid006346: task 8170: Aborted +srun: error: nid002618: tasks 4192-4195,4197-4207: Terminated +srun: error: nid001888: tasks 1776-1791: Terminated +srun: error: nid004827: tasks 6384-6399: Terminated +srun: error: nid001917: tasks 2112-2127: Terminated +srun: error: nid002356: tasks 3712-3727: Terminated +srun: error: nid001409: tasks 120-127: Terminated +srun: error: nid003481: tasks 4976-4991: Terminated +srun: error: nid002264: tasks 3400-3407: Aborted +srun: error: nid003552: tasks 5312-5327: Terminated +srun: error: nid001673: tasks 704-719: Terminated +srun: error: nid002357: tasks 3736-3739,3741-3743: Aborted +srun: error: nid005482: tasks 6912-6927: Terminated +srun: error: nid001929: tasks 2304-2319: Terminated +srun: error: nid004486: tasks 5504-5519: Terminated +srun: error: nid001713: tasks 896-911: Terminated +srun: error: nid002262: tasks 3376-3391: Terminated +srun: error: nid001765: tasks 1232-1247: Terminated +srun: error: nid001942: tasks 2496-2511: Terminated +srun: error: nid005519: tasks 7104-7119: Terminated +srun: error: nid006189: tasks 7440-7455: Terminated +srun: error: nid001991: tasks 2832-2847: Terminated +srun: error: nid002649: tasks 4432-4447: Terminated +srun: error: nid001871: tasks 1720-1727: Terminated +srun: error: nid001832: tasks 1424-1439: Terminated +srun: error: nid004777: tasks 6032-6047: Terminated +srun: error: nid002197: tasks 3024-3039: Terminated +srun: error: nid006259: tasks 7632-7647: Terminated +srun: error: nid006179: tasks 7392-7402,7404-7407: Terminated +srun: error: nid003245: tasks 4624-4639: Terminated +srun: error: nid001387: tasks 16-31: Terminated +srun: error: nid001920: tasks 2162-2163,2165-2167: Aborted +srun: error: nid001505: tasks 352-367: Terminated +srun: error: nid004795: tasks 6224-6239: Terminated +srun: error: nid005130: tasks 6560-6575: Terminated +srun: error: nid001905: tasks 1952-1967: Terminated +srun: error: nid006285: tasks 7824-7839: Terminated +srun: error: nid002313: tasks 3552-3567: Terminated +srun: error: nid003501: tasks 5152-5167: Terminated +srun: error: nid001661: tasks 544-559: Terminated +srun: error: nid003261: task 4801: Aborted +srun: error: nid005185: tasks 6752-6767: Terminated +srun: error: nid001954: tasks 2688-2695: Aborted +srun: error: nid001919: tasks 2144-2159: Terminated +srun: error: nid001940: tasks 2480-2495: Terminated +srun: error: nid002358: tasks 3744-3759: Terminated +srun: error: nid001754: tasks 1088-1095: Aborted +srun: error: nid002606: tasks 4080-4095: Terminated +srun: error: nid003555: tasks 5344-5359: Terminated +srun: error: nid001752: tasks 1072-1087: Terminated +srun: error: nid001864: tasks 1618-1619,1621-1622: Aborted +srun: error: nid004740: tasks 5680-5695: Terminated +srun: error: nid002607: tasks 4104-4111: Aborted +srun: error: nid001953: tasks 2672-2687: Terminated +srun: error: nid006149: tasks 7280-7295: Terminated +srun: error: nid002624: tasks 4272-4287: Terminated +srun: error: nid005187: task 6777: Aborted +srun: error: nid001768: tasks 1264-1279: Terminated +srun: error: nid001386: tasks 0-15: Terminated +srun: error: nid004763: tasks 5872-5887: Terminated +srun: error: nid004481: tasks 5440-5447: Terminated +srun: error: nid001998: tasks 2864-2879: Terminated +srun: error: nid006281: tasks 7808-7823: Terminated +srun: error: nid005104: tasks 6512-6525,6527: Terminated +srun: error: nid006213: tasks 7472-7487: Terminated +srun: error: nid002246: tasks 3200-3215: Terminated +srun: error: nid001474: tasks 192-207: Terminated +srun: error: nid001892: tasks 1792-1807: Terminated +srun: error: nid004828: tasks 6400-6415: Terminated +srun: error: nid001950: tasks 2624-2631: Terminated +srun: error: nid006312: tasks 8000-8015: Terminated +srun: error: nid003485: tasks 4992-5007: Terminated +srun: error: nid002406: task 3945: Aborted +srun: error: nid003553: tasks 5328-5343: Terminated +srun: error: nid001674: tasks 720-735: Terminated +srun: error: nid005132: tasks 6592-6607: Terminated +srun: error: nid001767: tasks 1248-1263: Terminated +srun: error: nid001930: tasks 2320-2335: Terminated +srun: error: nid002404: tasks 3920-3935: Terminated +srun: error: nid005484: tasks 6928-6943: Terminated +srun: error: nid001725: tasks 912-927: Terminated +srun: error: nid004487: tasks 5520-5535: Terminated +srun: error: nid001943: tasks 2512-2527: Terminated +srun: error: nid005525: tasks 7120-7135: Terminated +srun: error: nid001842: tasks 1464-1471: Aborted +srun: error: nid001994: tasks 2848-2863: Terminated +srun: error: nid002611: tasks 4112-4127: Terminated +srun: error: nid002650: tasks 4448-4463: Terminated +srun: error: nid004749: tasks 5712-5727: Terminated +srun: error: nid004778: tasks 6048-6063: Terminated +srun: error: nid006137: tasks 7232-7237,7239-7247: Terminated +srun: error: nid002202: tasks 3040-3055: Terminated +srun: error: nid006266: tasks 7648-7663: Terminated +srun: error: nid001924: task 2236: Aborted +srun: error: nid001391: tasks 32-47: Terminated +srun: error: nid003241: tasks 4560-4573,4575: Terminated +srun: error: nid003246: tasks 4640-4655: Terminated +srun: error: nid001509: tasks 368-383: Terminated +srun: error: nid004796: tasks 6240-6255: Terminated +srun: error: nid001865: tasks 1632-1647: Terminated +srun: error: nid005131: tasks 6576-6591: Terminated +srun: error: nid001906: tasks 1968-1983: Terminated +srun: error: nid006286: tasks 7840-7855: Terminated +srun: error: nid002331: tasks 3568-3583: Terminated +srun: error: nid001664: tasks 584-591: Aborted +srun: error: nid006350: tasks 8176-8191: Terminated +srun: error: nid001837: tasks 1440-1455: Terminated +srun: error: nid003518: tasks 5168-5183: Terminated +srun: error: nid001663: tasks 560-575: Terminated +srun: error: nid002337: tasks 3584-3587,3589-3595,3597-3599: Aborted +srun: error: nid002359: tasks 3760-3775: Terminated +srun: error: nid001927: tasks 2272,2274,2277,2279-2287: Terminated +srun: error: nid003556: tasks 5360-5375: Terminated +srun: error: nid004743: tasks 5696-5711: Terminated +srun: error: nid003497: task 5092: Aborted +srun: error: nid005487: tasks 6960-6975: Terminated +srun: error: nid006151: tasks 7296-7311: Terminated +srun: error: nid003265: task 4863: Aborted +srun: error: nid002625: tasks 4288-4303: Terminated +srun: error: nid001773: tasks 1280-1295: Terminated +srun: error: nid004764: tasks 5888-5903: Terminated +srun: error: nid006230: tasks 7488-7503: Terminated +srun: error: nid001999: tasks 2880-2895: Terminated +srun: error: nid002247: tasks 3216-3231: Terminated +srun: error: nid002652: tasks 4480-4495: Terminated +srun: error: nid003263: tasks 4816-4831: Terminated +srun: error: nid001480: tasks 208-223: Terminated +srun: error: nid001893: tasks 1808-1823: Terminated +srun: error: nid004829: tasks 6416-6431: Terminated +srun: error: nid002265: tasks 3408-3423: Terminated +srun: error: nid006332: tasks 8016-8031: Terminated +srun: error: nid004750: task 5743: Aborted +srun: error: nid003486: tasks 5008-5023: Terminated +srun: error: nid001650: tasks 400-415: Terminated +srun: error: nid001909: tasks 2000-2015: Terminated +srun: error: nid005133: tasks 6608-6623: Terminated +srun: error: nid001675: tasks 736-751: Terminated +srun: error: nid005486: tasks 6944-6959: Terminated +srun: error: nid001931: tasks 2336-2351: Terminated +srun: error: nid004483: tasks 5472-5480,5485,5487: Terminated +srun: error: nid004488: tasks 5536-5551: Terminated +srun: error: nid001728: tasks 928-943: Terminated +srun: error: nid005602: tasks 7136-7151: Terminated +srun: error: nid001944: tasks 2528-2543: Terminated +srun: error: nid002614: tasks 4128-4143: Terminated +srun: error: nid005128: tasks 6528-6536,6538-6543: Terminated +srun: error: nid002651: tasks 4464-4479: Terminated +srun: error: nid004779: tasks 6064-6079: Terminated +srun: error: nid006157: tasks 7328-7343: Terminated +srun: error: nid002203: tasks 3056-3071: Terminated +srun: error: nid006269: tasks 7664-7679: Terminated +srun: error: nid003247: tasks 4656-4671: Terminated +srun: error: nid001392: tasks 48-63: Terminated +srun: error: nid001647: tasks 384-399: Terminated +srun: error: nid001866: tasks 1648-1663: Terminated +srun: error: nid004799: tasks 6256-6271: Terminated +srun: error: nid006287: tasks 7856-7871: Terminated +srun: error: nid001907: tasks 1984-1999: Terminated +srun: error: nid002250: tasks 3248-3263: Terminated +srun: error: nid003526: tasks 5184-5199: Terminated +srun: error: nid005188: tasks 6784-6799: Terminated +srun: error: nid001921: tasks 2176-2191: Terminated +srun: error: nid002366: tasks 3776-3791: Terminated +srun: error: nid001656: tasks 472-479: Terminated +srun: error: nid003558: tasks 5376-5391: Terminated +srun: error: nid001677: tasks 768-783: Terminated +srun: error: nid001755: tasks 1104-1119: Terminated +srun: error: nid005488: tasks 6976-6991: Terminated +srun: error: nid006156: tasks 7312-7327: Terminated +srun: error: nid001955: tasks 2704-2719: Terminated +srun: error: nid001933: tasks 2368-2383: Terminated +srun: error: nid001463: tasks 160-167: Terminated +srun: error: nid001711: tasks 882,884,888-895: Terminated +srun: error: nid001660: tasks 528-535,538: Terminated +srun: error: nid001918: tasks 2128-2134,2136-2143: Terminated +srun: error: nid006280: tasks 7792-7793,7795-7799,7801-7807: Terminated +srun: error: nid006311: tasks 7984-7997,7999: Terminated +srun: error: nid002403: tasks 3904-3911,3917,3919: Terminated +srun: error: nid003497: tasks 5096-5103: Terminated +srun: error: nid003479: tasks 4960-4972,4974-4975: Terminated +srun: error: nid001858: tasks 1600-1601,1604: Aborted +srun: error: nid006346: tasks 8160-8169,8171-8175: Terminated +srun: error: nid002264: tasks 3392-3399: Terminated +srun: error: nid002357: tasks 3728-3735: Terminated +srun: error: nid003261: tasks 4800,4802-4815: Terminated +srun: error: nid001920: tasks 2160-2161,2168-2175: Terminated +srun: error: nid001954: tasks 2696-2703: Terminated +srun: error: nid001754: tasks 1096-1103: Terminated +srun: error: nid002607: tasks 4096-4103: Terminated +srun: error: nid005187: tasks 6768-6776,6778-6783: Terminated +srun: error: nid001864: tasks 1616-1617,1620,1623-1631: Terminated +srun: error: nid002406: tasks 3936-3944,3946-3951: Terminated +srun: error: nid001842: tasks 1456-1463: Terminated +srun: error: nid001660: task 540: Aborted +srun: error: nid001664: tasks 576-583: Terminated +srun: error: nid002337: tasks 3588,3596: Aborted +srun: error: nid003265: tasks 4848-4862: Terminated +srun: error: nid004750: tasks 5728-5742: Terminated +srun: error: nid002403: task 3916: Aborted +srun: error: nid001858: task 1602: Aborted (core dumped) +srun: error: nid002357: task 3740: Aborted +srun: error: nid001920: task 2164: Aborted +srun: error: nid001858: tasks 1608-1615: Terminated +srun: Force Terminated StepId=11079231.3 diff --git a/hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079233/err_run_0.log b/hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079233/err_run_0.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079233/err_run_1.log b/hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079233/err_run_1.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079233/err_run_2.log b/hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079233/err_run_2.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079233/err_run_3.log b/hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079233/err_run_3.log new file mode 100644 index 00000000..d245140e --- /dev/null +++ b/hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079233/err_run_3.log @@ -0,0 +1,561 @@ +[nid002626:145414:0:145414] ud_ep.c:255 Fatal: UD endpoint 0x55d66a1987f0 to : unhandled timeout error +==== backtrace (tid: 145414) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15163af70454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15163af6d400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15163af6d529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x15163863c133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15163af668aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15163ab01962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15163c794295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15163c7b2870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15163ca4e796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15163cae7d35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15163c54ae9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55d669dd60f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55d669e15a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55d669e19bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55d669e47258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55d669dd66c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55d669e58e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15163b69329d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55d669dc746a] +================================= +[nid004485:154374:0:154374] ud_ep.c:255 Fatal: UD endpoint 0x55577fff11a0 to : unhandled timeout error +==== backtrace (tid: 154374) ==== + 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x149322c75454] + 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x149322c72400] + 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x149322c72529] + 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x149320341133] + 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x149322c6b8aa] + 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x149322806962] + 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x149324499295] + 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1493244b7870] + 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x149324753796] + 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1493247ecd35] +10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14932424fe9a] +11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55577fc420f5] +12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55577fc81a81] +13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55577fc85bdc] +14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55577fcb3258] +15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55577fc426c3] +16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55577fcc4e63] +17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14932339829d] +18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55577fc3346a] +================================= +srun: error: nid004485: task 694: Aborted +srun: launch/slurm: _step_signal: Terminating StepId=11079233.3 +slurmstepd: error: *** STEP 11079233.3 ON nid001386 CANCELLED AT 2025-10-06T18:16:17 *** +srun: error: nid002624: tasks 534-535: Terminated +srun: error: nid001768: tasks 158-159: Terminated +srun: error: nid004763: tasks 734-735: Terminated +srun: error: nid006213: tasks 934-935: Terminated +srun: error: nid001998: tasks 358-359: Terminated +srun: error: nid002246: tasks 400-401: Terminated +srun: error: nid002651: tasks 558-559: Terminated +srun: error: nid001474: tasks 24-25: Terminated +srun: error: nid003258: tasks 600-601: Terminated +srun: error: nid004779: tasks 758-759: Terminated +srun: error: nid001892: tasks 224-225: Terminated +srun: error: nid004828: tasks 800-801: Terminated +srun: error: nid006269: tasks 958-959: Terminated +srun: error: nid002264: tasks 424-425: Terminated +srun: error: nid006312: tasks 1000-1001: Terminated +srun: error: nid003478: tasks 624-625: Terminated +srun: error: nid005132: tasks 824-825: Terminated +srun: error: nid001674: tasks 90-91: Terminated +srun: error: nid001907: tasks 248-249: Terminated +srun: error: nid001930: tasks 290-291: Terminated +srun: error: nid002337: tasks 448-449: Terminated +srun: error: nid002404: tasks 490-491: Terminated +srun: error: nid003500: tasks 648-649: Terminated +srun: error: nid001725: tasks 114-115: Terminated +srun: error: nid004483: tasks 690-691: Terminated +srun: error: nid001943: tasks 314-315: Terminated +srun: error: nid005525: tasks 890-891: Terminated +srun: error: nid001647: tasks 48-49: Terminated +srun: error: nid002611: tasks 514-515: Terminated +srun: error: nid004749: tasks 714-715: Terminated +srun: error: nid001755: tasks 138-139: Terminated +srun: error: nid001837: tasks 180-181: Terminated +srun: error: nid004778: tasks 756-757: Terminated +srun: error: nid006156: tasks 914-915: Terminated +srun: error: nid006266: tasks 956-957: Terminated +srun: error: nid002202: tasks 380-381: Terminated +srun: error: nid001391: tasks 4-5: Terminated +srun: error: nid003246: tasks 580-581: Terminated +srun: error: nid001865: tasks 204-205: Terminated +srun: error: nid004796: tasks 780-781: Terminated +srun: error: nid006286: tasks 980-981: Terminated +srun: error: nid002249: tasks 404-405: Terminated +srun: error: nid002331: tasks 446-447: Terminated +srun: error: nid003260: tasks 604-605: Terminated +srun: error: nid001663: tasks 70-71: Terminated +srun: error: nid003499: tasks 646-647: Terminated +srun: error: nid004830: tasks 804-805: Terminated +srun: error: nid001920: tasks 270-271: Terminated +srun: error: nid005187: tasks 846-847: Terminated +srun: error: nid006333: tasks 1004-1005: Terminated +srun: error: nid002359: tasks 470-471: Terminated +srun: error: nid003552: tasks 670-671: Terminated +srun: error: nid001676: tasks 94-95: Terminated +srun: error: nid001754: tasks 136-137: Terminated +srun: error: nid001932: tasks 294-295: Terminated +srun: error: nid005487: tasks 870-871: Terminated +srun: error: nid001954: tasks 336-337: Terminated +srun: error: nid002537: tasks 494-495: Terminated +srun: error: nid002625: tasks 536-537: Terminated +srun: error: nid004764: tasks 736-737: Terminated +srun: error: nid001773: tasks 160-161: Terminated +srun: error: nid006230: tasks 936-937: Terminated +srun: error: nid001999: tasks 360-361: Terminated +srun: error: nid002652: tasks 560-561: Terminated +srun: error: nid001480: tasks 26-27: Terminated +srun: error: nid004780: tasks 760-761: Terminated +srun: error: nid001845: tasks 184-185: Terminated +srun: error: nid001893: tasks 226-227: Terminated +srun: error: nid004829: tasks 802-803: Terminated +srun: error: nid006271: tasks 960-961: Terminated +srun: error: nid002265: tasks 426-427: Terminated +srun: error: nid006332: tasks 1002-1003: Terminated +srun: error: nid003479: tasks 626-627: Terminated +srun: error: nid001650: tasks 50-51: Terminated +srun: error: nid001909: tasks 250-251: Terminated +srun: error: nid005133: tasks 826-827: Terminated +srun: error: nid002340: tasks 450-451: Terminated +srun: error: nid002406: tasks 492-493: Terminated +srun: error: nid003501: tasks 650-651: Terminated +srun: error: nid004484: tasks 692-693: Terminated +srun: error: nid001728: tasks 116-117: Terminated +srun: error: nid005189: tasks 850-851: Terminated +srun: error: nid005602: tasks 892-893: Terminated +srun: error: nid001944: tasks 316-317: Terminated +srun: error: nid002614: tasks 516-517: Terminated +srun: error: nid001756: tasks 140-141: Terminated +srun: error: nid004750: tasks 716-717: Terminated +srun: error: nid001842: tasks 182-183: Terminated +srun: error: nid006157: tasks 916-917: Terminated +srun: error: nid001956: tasks 340-341: Terminated +srun: error: nid002203: tasks 382-383: Terminated +srun: error: nid002627: tasks 540-541: Terminated +srun: error: nid003247: tasks 582-583: Terminated +srun: error: nid001392: tasks 6-7: Terminated +srun: error: nid001866: tasks 206-207: Terminated +srun: error: nid004799: tasks 782-783: Terminated +srun: error: nid002250: tasks 406-407: Terminated +srun: error: nid006287: tasks 982-983: Terminated +srun: error: nid001488: tasks 30-31: Terminated +srun: error: nid003261: tasks 606-607: Terminated +srun: error: nid001664: tasks 72-73: Terminated +srun: error: nid001896: tasks 230-231: Terminated +srun: error: nid005188: tasks 848-849: Terminated +srun: error: nid004831: tasks 806-807: Terminated +srun: error: nid001921: tasks 272-273: Terminated +srun: error: nid006335: tasks 1006-1007: Terminated +srun: error: nid002366: tasks 472-473: Terminated +srun: error: nid003553: tasks 672-673: Terminated +srun: error: nid001677: tasks 96-97: Terminated +srun: error: nid005488: tasks 872-873: Terminated +srun: error: nid001933: tasks 296-297: Terminated +srun: error: nid001955: tasks 338-339: Terminated +srun: error: nid002626: task 539: Terminated +srun: error: nid002542: tasks 496-497: Terminated +srun: error: nid004486: tasks 696-697: Terminated +srun: error: nid001775: tasks 162-163: Terminated +srun: error: nid004766: tasks 738-739: Terminated +srun: error: nid005604: tasks 896-897: Terminated +srun: error: nid006246: tasks 938-939: Terminated +srun: error: nid002000: tasks 362-363: Terminated +srun: error: nid002656: tasks 562-563: Terminated +srun: error: nid001486: tasks 28-29: Terminated +srun: error: nid001846: tasks 186-187: Terminated +srun: error: nid004782: tasks 762-763: Terminated +srun: error: nid001895: tasks 228-229: Terminated +srun: error: nid006273: tasks 962-963: Terminated +srun: error: nid002216: tasks 386-387: Terminated +srun: error: nid002266: tasks 428-429: Terminated +srun: error: nid004485: task 695: Terminated +srun: error: nid003249: tasks 586-587: Terminated +srun: error: nid003481: tasks 628-629: Terminated +srun: error: nid001652: tasks 52-53: Terminated +srun: error: nid005135: tasks 828-829: Terminated +srun: error: nid001910: tasks 252-253: Terminated +srun: error: nid002341: tasks 452-453: Terminated +srun: error: nid003518: tasks 652-653: Terminated +srun: error: nid001666: tasks 76-77: Terminated +srun: error: nid001732: tasks 118-119: Terminated +srun: error: nid001945: tasks 318-319: Terminated +srun: error: nid005603: tasks 894-895: Terminated +srun: error: nid002615: tasks 518-519: Terminated +srun: error: nid001758: tasks 142-143: Terminated +srun: error: nid004751: tasks 718-719: Terminated +srun: error: nid001957: tasks 342-343: Terminated +srun: error: nid006176: tasks 918-919: Terminated +srun: error: nid002209: tasks 384-385: Terminated +srun: error: nid002628: tasks 542-543: Terminated +srun: error: nid005214: tasks 852-853: Terminated +srun: error: nid003248: tasks 584-585: Terminated +srun: error: nid001394: tasks 8-9: Terminated +srun: error: nid004768: tasks 742-743: Terminated +srun: error: nid001867: tasks 208-209: Terminated +srun: error: nid004801: tasks 784-785: Terminated +srun: error: nid006249: tasks 942-943: Terminated +srun: error: nid002252: tasks 408-409: Terminated +srun: error: nid006288: tasks 984-985: Terminated +srun: error: nid001489: tasks 32-33: Terminated +srun: error: nid003263: tasks 608-609: Terminated +srun: error: nid001665: tasks 74-75: Terminated +srun: error: nid004833: tasks 808-809: Terminated +srun: error: nid001899: tasks 232-233: Terminated +srun: error: nid001922: tasks 274-275: Terminated +srun: error: nid002276: tasks 432-433: Terminated +srun: error: nid006336: tasks 1008-1009: Terminated +srun: error: nid002367: tasks 474-475: Terminated +srun: error: nid003555: tasks 674-675: Terminated +srun: error: nid001680: tasks 98-99: Terminated +srun: error: nid005491: tasks 874-875: Terminated +srun: error: nid001934: tasks 298-299: Terminated +srun: error: nid002580: tasks 498-499: Terminated +srun: error: nid004723: tasks 698-699: Terminated +srun: error: nid001736: tasks 122-123: Terminated +srun: error: nid001812: tasks 164-165: Terminated +srun: error: nid004767: tasks 740-741: Terminated +srun: error: nid005605: tasks 898-899: Terminated +srun: error: nid006248: tasks 940-941: Terminated +srun: error: nid002002: tasks 364-365: Terminated +srun: error: nid001851: tasks 188-189: Terminated +srun: error: nid004783: tasks 764-765: Terminated +srun: error: nid006274: tasks 964-965: Terminated +srun: error: nid002217: tasks 388-389: Terminated +srun: error: nid002270: tasks 430-431: Terminated +srun: error: nid003250: tasks 588-589: Terminated +srun: error: nid003485: tasks 630-631: Terminated +srun: error: nid004809: tasks 788-789: Terminated +srun: error: nid005137: tasks 830-831: Terminated +srun: error: nid001911: tasks 254-255: Terminated +srun: error: nid003198: tasks 564-565: Terminated +srun: error: nid002344: tasks 454-455: Terminated +srun: error: nid003526: tasks 654-655: Terminated +srun: error: nid001667: tasks 78-79: Terminated +srun: error: nid001733: tasks 120-121: Terminated +srun: error: nid005215: tasks 854-855: Terminated +srun: error: nid001924: tasks 278-279: Terminated +srun: error: nid001946: tasks 320-321: Terminated +srun: error: nid001653: tasks 54-55: Terminated +srun: error: nid002384: tasks 478-479: Terminated +srun: error: nid002616: tasks 520-521: Terminated +srun: error: nid004752: tasks 720-721: Terminated +srun: error: nid001759: tasks 144-145: Terminated +srun: error: nid006177: tasks 920-921: Terminated +srun: error: nid001960: tasks 344-345: Terminated +srun: error: nid002629: tasks 544-545: Terminated +srun: error: nid001396: tasks 10-11: Terminated +srun: error: nid001820: tasks 168-169: Terminated +srun: error: nid004769: tasks 744-745: Terminated +srun: error: nid001868: tasks 210-211: Terminated +srun: error: nid004807: tasks 786-787: Terminated +srun: error: nid006254: tasks 944-945: Terminated +srun: error: nid006289: tasks 986-987: Terminated +srun: error: nid002254: tasks 410-411: Terminated +srun: error: nid001493: tasks 34-35: Terminated +srun: error: nid003264: tasks 610-611: Terminated +srun: error: nid001900: tasks 234-235: Terminated +srun: error: nid004835: tasks 810-811: Terminated +srun: error: nid001923: tasks 276-277: Terminated +srun: error: nid002308: tasks 434-435: Terminated +srun: error: nid006338: tasks 1010-1011: Terminated +srun: error: nid002383: tasks 476-477: Terminated +srun: error: nid003489: tasks 634-635: Terminated +srun: error: nid003556: tasks 676-677: Terminated +srun: error: nid001681: tasks 100-101: Terminated +srun: error: nid005139: tasks 834-835: Terminated +srun: error: nid005493: tasks 876-877: Terminated +srun: error: nid001935: tasks 300-301: Terminated +srun: error: nid002582: tasks 500-501: Terminated +srun: error: nid001737: tasks 124-125: Terminated +srun: error: nid004727: tasks 700-701: Terminated +srun: error: nid002626: task 538: Aborted (core dumped) +srun: error: nid001819: tasks 166-167: Terminated +srun: error: nid001948: tasks 324-325: Terminated +srun: error: nid006120: tasks 900-901: Terminated +srun: error: nid002004: tasks 366-367: Terminated +srun: error: nid002618: tasks 524-525: Terminated +srun: error: nid003200: tasks 566-567: Terminated +srun: error: nid004785: tasks 766-767: Terminated +srun: error: nid001852: tasks 190-191: Terminated +srun: error: nid006275: tasks 966-967: Terminated +srun: error: nid002218: tasks 390-391: Terminated +srun: error: nid003252: tasks 590-591: Terminated +srun: error: nid001409: tasks 14-15: Terminated +srun: error: nid003486: tasks 632-633: Terminated +srun: error: nid001654: tasks 56-57: Terminated +srun: error: nid001912: tasks 256-257: Terminated +srun: error: nid004815: tasks 790-791: Terminated +srun: error: nid005138: tasks 832-833: Terminated +srun: error: nid006292: tasks 990-991: Terminated +srun: error: nid002347: tasks 456-457: Terminated +srun: error: nid003539: tasks 656-657: Terminated +srun: error: nid001669: tasks 80-81: Terminated +srun: error: nid005216: tasks 856-857: Terminated +srun: error: nid001925: tasks 280-281: Terminated +srun: error: nid001947: tasks 322-323: Terminated +srun: error: nid002385: tasks 480-481: Terminated +srun: error: nid002617: tasks 522-523: Terminated +srun: error: nid004478: tasks 680-681: Terminated +srun: error: nid004753: tasks 722-723: Terminated +srun: error: nid001760: tasks 146-147: Terminated +srun: error: nid005495: tasks 880-881: Terminated +srun: error: nid006178: tasks 922-923: Terminated +srun: error: nid001961: tasks 346-347: Terminated +srun: error: nid002630: tasks 546-547: Terminated +srun: error: nid001408: tasks 12-13: Terminated +srun: error: nid001821: tasks 170-171: Terminated +srun: error: nid001869: tasks 212-213: Terminated +srun: error: nid006255: tasks 946-947: Terminated +srun: error: nid002007: tasks 370-371: Terminated +srun: error: nid002255: tasks 412-413: Terminated +srun: error: nid006290: tasks 988-989: Terminated +srun: error: nid001494: tasks 36-37: Terminated +srun: error: nid003265: tasks 612-613: Terminated +srun: error: nid004837: tasks 812-813: Terminated +srun: error: nid001901: tasks 236-237: Terminated +srun: error: nid006339: tasks 1012-1013: Terminated +srun: error: nid002309: tasks 436-437: Terminated +srun: error: nid004771: tasks 746-747: Terminated +srun: error: nid003493: tasks 636-637: Terminated +srun: error: nid001657: tasks 60-61: Terminated +srun: error: nid001688: tasks 102-103: Terminated +srun: error: nid003558: tasks 678-679: Terminated +srun: error: nid005140: tasks 836-837: Terminated +srun: error: nid005494: tasks 878-879: Terminated +srun: error: nid001936: tasks 302-303: Terminated +srun: error: nid002583: tasks 502-503: Terminated +srun: error: nid001738: tasks 126-127: Terminated +srun: error: nid004729: tasks 702-703: Terminated +srun: error: nid001949: tasks 326-327: Terminated +srun: error: nid006122: tasks 902-903: Terminated +srun: error: nid002005: tasks 368-369: Terminated +srun: error: nid002620: tasks 526-527: Terminated +srun: error: nid003231: tasks 568-569: Terminated +srun: error: nid004756: tasks 726-727: Terminated +srun: error: nid004786: tasks 768-769: Terminated +srun: error: nid001854: tasks 192-193: Terminated +srun: error: nid006276: tasks 968-969: Terminated +srun: error: nid002220: tasks 392-393: Terminated +srun: error: nid001426: tasks 16-17: Terminated +srun: error: nid003253: tasks 592-593: Terminated +srun: error: nid001656: tasks 58-59: Terminated +srun: error: nid001872: tasks 216-217: Terminated +srun: error: nid004816: tasks 792-793: Terminated +srun: error: nid001913: tasks 258-259: Terminated +srun: error: nid002259: tasks 416-417: Terminated +srun: error: nid006294: tasks 992-993: Terminated +srun: error: nid002351: tasks 458-459: Terminated +srun: error: nid003540: tasks 658-659: Terminated +srun: error: nid001670: tasks 82-83: Terminated +srun: error: nid005218: tasks 858-859: Terminated +srun: error: nid001926: tasks 282-283: Terminated +srun: error: nid002389: tasks 482-483: Terminated +srun: error: nid004479: tasks 682-683: Terminated +srun: error: nid001704: tasks 106-107: Terminated +srun: error: nid004754: tasks 724-725: Terminated +srun: error: nid001761: tasks 148-149: Terminated +srun: error: nid005503: tasks 882-883: Terminated +srun: error: nid006179: tasks 924-925: Terminated +srun: error: nid001962: tasks 348-349: Terminated +srun: error: nid001822: tasks 172-173: Terminated +srun: error: nid004773: tasks 748-749: Terminated +srun: error: nid001871: tasks 214-215: Terminated +srun: error: nid002008: tasks 372-373: Terminated +srun: error: nid006256: tasks 948-949: Terminated +srun: error: nid002256: tasks 414-415: Terminated +srun: error: nid003242: tasks 572-573: Terminated +srun: error: nid001497: tasks 38-39: Terminated +srun: error: nid003269: tasks 614-615: Terminated +srun: error: nid004789: tasks 772-773: Terminated +srun: error: nid001902: tasks 238-239: Terminated +srun: error: nid005104: tasks 814-815: Terminated +srun: error: nid002631: tasks 548-549: Terminated +srun: error: nid002310: tasks 438-439: Terminated +srun: error: nid006340: tasks 1014-1015: Terminated +srun: error: nid003495: tasks 638-639: Terminated +srun: error: nid001658: tasks 62-63: Terminated +srun: error: nid001691: tasks 104-105: Terminated +srun: error: nid005165: tasks 838-839: Terminated +srun: error: nid001915: tasks 262-263: Terminated +srun: error: nid001937: tasks 304-305: Terminated +srun: error: nid002593: tasks 504-505: Terminated +srun: error: nid001744: tasks 128-129: Terminated +srun: error: nid004736: tasks 704-705: Terminated +srun: error: nid001950: tasks 328-329: Terminated +srun: error: nid006137: tasks 904-905: Terminated +srun: error: nid002621: tasks 528-529: Terminated +srun: error: nid003241: tasks 570-571: Terminated +srun: error: nid004757: tasks 728-729: Terminated +srun: error: nid004788: tasks 770-771: Terminated +srun: error: nid001855: tasks 194-195: Terminated +srun: error: nid006188: tasks 928-929: Terminated +srun: error: nid006277: tasks 970-971: Terminated +srun: error: nid002227: tasks 394-395: Terminated +srun: error: nid001429: tasks 18-19: Terminated +srun: error: nid003255: tasks 594-595: Terminated +srun: error: nid001876: tasks 218-219: Terminated +srun: error: nid004821: tasks 794-795: Terminated +srun: error: nid001914: tasks 260-261: Terminated +srun: error: nid002260: tasks 418-419: Terminated +srun: error: nid006297: tasks 994-995: Terminated +srun: error: nid002352: tasks 460-461: Terminated +srun: error: nid003299: tasks 618-619: Terminated +srun: error: nid003541: tasks 660-661: Terminated +srun: error: nid001671: tasks 84-85: Terminated +srun: error: nid001927: tasks 284-285: Terminated +srun: error: nid005480: tasks 860-861: Terminated +srun: error: nid002400: tasks 484-485: Terminated +srun: error: nid001710: tasks 108-109: Terminated +srun: error: nid004480: tasks 684-685: Terminated +srun: error: nid001762: tasks 150-151: Terminated +srun: error: nid001939: tasks 308-309: Terminated +srun: error: nid005507: tasks 884-885: Terminated +srun: error: nid001963: tasks 350-351: Terminated +srun: error: nid006186: tasks 926-927: Terminated +srun: error: nid002632: tasks 550-551: Terminated +srun: error: nid001823: tasks 174-175: Terminated +srun: error: nid004775: tasks 750-751: Terminated +srun: error: nid006257: tasks 950-951: Terminated +srun: error: nid002009: tasks 374-375: Terminated +srun: error: nid003243: tasks 574-575: Terminated +srun: error: nid001498: tasks 40-41: Terminated +srun: error: nid003294: tasks 616-617: Terminated +srun: error: nid004790: tasks 774-775: Terminated +srun: error: nid001903: tasks 240-241: Terminated +srun: error: nid005128: tasks 816-817: Terminated +srun: error: nid006280: tasks 974-975: Terminated +srun: error: nid006341: tasks 1016-1017: Terminated +srun: error: nid002311: tasks 440-441: Terminated +srun: error: nid001659: tasks 64-65: Terminated +srun: error: nid003496: tasks 640-641: Terminated +srun: error: nid001917: tasks 264-265: Terminated +srun: error: nid005180: tasks 840-841: Terminated +srun: error: nid001938: tasks 306-307: Terminated +srun: error: nid002356: tasks 464-465: Terminated +srun: error: nid002595: tasks 506-507: Terminated +srun: error: nid003549: tasks 664-665: Terminated +srun: error: nid001747: tasks 130-131: Terminated +srun: error: nid004737: tasks 706-707: Terminated +srun: error: nid001951: tasks 330-331: Terminated +srun: error: nid006138: tasks 906-907: Terminated +srun: error: nid002622: tasks 530-531: Terminated +srun: error: nid001765: tasks 154-155: Terminated +srun: error: nid004758: tasks 730-731: Terminated +srun: error: nid001856: tasks 196-197: Terminated +srun: error: nid006189: tasks 930-931: Terminated +srun: error: nid001991: tasks 354-355: Terminated +srun: error: nid006279: tasks 972-973: Terminated +srun: error: nid002238: tasks 396-397: Terminated +srun: error: nid001463: tasks 20-21: Terminated +srun: error: nid003256: tasks 596-597: Terminated +srun: error: nid001885: tasks 220-221: Terminated +srun: error: nid004824: tasks 796-797: Terminated +srun: error: nid002261: tasks 420-421: Terminated +srun: error: nid006299: tasks 996-997: Terminated +srun: error: nid002353: tasks 462-463: Terminated +srun: error: nid003441: tasks 620-621: Terminated +srun: error: nid003542: tasks 662-663: Terminated +srun: error: nid005130: tasks 820-821: Terminated +srun: error: nid001672: tasks 86-87: Terminated +srun: error: nid005481: tasks 862-863: Terminated +srun: error: nid006346: tasks 1020-1021: Terminated +srun: error: nid001928: tasks 286-287: Terminated +srun: error: nid002402: tasks 486-487: Terminated +srun: error: nid001711: tasks 110-111: Terminated +srun: error: nid004481: tasks 686-687: Terminated +srun: error: nid001763: tasks 152-153: Terminated +srun: error: nid001940: tasks 310-311: Terminated +srun: error: nid005511: tasks 886-887: Terminated +srun: error: nid001964: tasks 352-353: Terminated +srun: error: nid002606: tasks 510-511: Terminated +srun: error: nid002635: tasks 552-553: Terminated +srun: error: nid004740: tasks 710-711: Terminated +srun: error: nid004776: tasks 752-753: Terminated +srun: error: nid001826: tasks 176-177: Terminated +srun: error: nid006258: tasks 952-953: Terminated +srun: error: nid002011: tasks 376-377: Terminated +srun: error: nid003244: tasks 576-577: Terminated +srun: error: nid001386: tasks 0-1: Terminated +srun: error: nid001504: tasks 42-43: Terminated +srun: error: nid001858: tasks 200-201: Terminated +srun: error: nid004794: tasks 776-777: Terminated +srun: error: nid005129: tasks 818-819: Terminated +srun: error: nid001904: tasks 242-243: Terminated +srun: error: nid006281: tasks 976-977: Terminated +srun: error: nid002312: tasks 442-443: Terminated +srun: error: nid006344: tasks 1018-1019: Terminated +srun: error: nid003497: tasks 642-643: Terminated +srun: error: nid001660: tasks 66-67: Terminated +srun: error: nid001918: tasks 266-267: Terminated +srun: error: nid005182: tasks 842-843: Terminated +srun: error: nid002357: tasks 466-467: Terminated +srun: error: nid002603: tasks 508-509: Terminated +srun: error: nid003550: tasks 666-667: Terminated +srun: error: nid001751: tasks 132-133: Terminated +srun: error: nid004739: tasks 708-709: Terminated +srun: error: nid005484: tasks 866-867: Terminated +srun: error: nid001952: tasks 332-333: Terminated +srun: error: nid006139: tasks 908-909: Terminated +srun: error: nid002623: tasks 532-533: Terminated +srun: error: nid001767: tasks 156-157: Terminated +srun: error: nid004761: tasks 732-733: Terminated +srun: error: nid001857: tasks 198-199: Terminated +srun: error: nid001994: tasks 356-357: Terminated +srun: error: nid006212: tasks 932-933: Terminated +srun: error: nid002241: tasks 398-399: Terminated +srun: error: nid002650: tasks 556-557: Terminated +srun: error: nid001468: tasks 22-23: Terminated +srun: error: nid003257: tasks 598-599: Terminated +srun: error: nid001888: tasks 222-223: Terminated +srun: error: nid004827: tasks 798-799: Terminated +srun: error: nid002262: tasks 422-423: Terminated +srun: error: nid006311: tasks 998-999: Terminated +srun: error: nid003476: tasks 622-623: Terminated +srun: error: nid001509: tasks 46-47: Terminated +srun: error: nid001673: tasks 88-89: Terminated +srun: error: nid005131: tasks 822-823: Terminated +srun: error: nid001906: tasks 246-247: Terminated +srun: error: nid001929: tasks 288-289: Terminated +srun: error: nid006350: tasks 1022-1023: Terminated +srun: error: nid002403: tasks 488-489: Terminated +srun: error: nid004482: tasks 688-689: Terminated +srun: error: nid001713: tasks 112-113: Terminated +srun: error: nid005519: tasks 888-889: Terminated +srun: error: nid001942: tasks 312-313: Terminated +srun: error: nid002607: tasks 512-513: Terminated +srun: error: nid002649: tasks 554-555: Terminated +srun: error: nid005482: tasks 864-865: Terminated +srun: error: nid004743: tasks 712-713: Terminated +srun: error: nid004777: tasks 754-755: Terminated +srun: error: nid001832: tasks 178-179: Terminated +srun: error: nid006151: tasks 912-913: Terminated +srun: error: nid006259: tasks 954-955: Terminated +srun: error: nid002197: tasks 378-379: Terminated +srun: error: nid001387: tasks 2-3: Terminated +srun: error: nid003245: tasks 578-579: Terminated +srun: error: nid001505: tasks 44-45: Terminated +srun: error: nid001864: tasks 202-203: Terminated +srun: error: nid004795: tasks 778-779: Terminated +srun: error: nid001905: tasks 244-245: Terminated +srun: error: nid006285: tasks 978-979: Terminated +srun: error: nid002247: tasks 402-403: Terminated +srun: error: nid002313: tasks 444-445: Terminated +srun: error: nid003259: tasks 602-603: Terminated +srun: error: nid003498: tasks 644-645: Terminated +srun: error: nid001661: tasks 68-69: Terminated +srun: error: nid005185: tasks 844-845: Terminated +srun: error: nid001919: tasks 268-269: Terminated +srun: error: nid002358: tasks 468-469: Terminated +srun: error: nid003551: tasks 668-669: Terminated +srun: error: nid001675: tasks 92-93: Terminated +srun: error: nid001752: tasks 134-135: Terminated +srun: error: nid005486: tasks 868-869: Terminated +srun: error: nid001931: tasks 292-293: Terminated +srun: error: nid001953: tasks 334-335: Terminated +srun: error: nid006149: tasks 910-911: Terminated +srun: Force Terminated StepId=11079233.3 From a54150f16d846cf2b4583baaa798778b2febb9f1 Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Mon, 20 Oct 2025 16:36:02 +0100 Subject: [PATCH 36/52] Add spherical point distribution experiments --- hpc/ijhpca/weak_scaling.py | 27 +++++++++++++-------- hpc/ijhpca/weak_scaling_ccd.json | 3 ++- hpc/ijhpca/weak_scaling_ccx.json | 3 ++- hpc/ijhpca/weak_scaling_socket.json | 3 ++- scripts/src/bin/fmm_m2l_blas_mpi_f32.rs | 20 ++++++++++++++-- scripts/src/bin/fmm_m2l_fft_mpi_f32.rs | 32 +++++++++++++++++++------ 6 files changed, 66 insertions(+), 22 deletions(-) diff --git a/hpc/ijhpca/weak_scaling.py b/hpc/ijhpca/weak_scaling.py index 422aa48b..629a9bcb 100644 --- a/hpc/ijhpca/weak_scaling.py +++ b/hpc/ijhpca/weak_scaling.py @@ -13,6 +13,11 @@ "ccx_per_ccd": 2 } +DISTRIBUTION = { + "uniform": 0, + "sphere": 1 +} + def parse_process_mapping(arch): ccd_threads_per_rank = arch["cores_per_ccd"] ccx_threads_per_rank = arch["cores_per_ccx"] @@ -47,7 +52,8 @@ def experiment_parameters( points_per_rank, local_depth=4, scaling_func=pow8, - arch=AMD_EPYC_ROME + arch=AMD_EPYC_ROME, + distribution="uniform" ): max_cpus=arch["cores_per_node"] @@ -103,12 +109,12 @@ def experiment_parameters( print(f"points per node {points_per_node}") print(f"points per leaf {points_per_leaf}") print(f"max threads per rank {max_threads_per_rank}") + print(f"distribution {distribution}") - - return n_nodes.tolist(), n_ranks.tolist(), global_depth.tolist(), max_threads_per_rank + return n_nodes.tolist(), n_ranks.tolist(), global_depth.tolist(), max_threads_per_rank, distribution -def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, points_per_rank, local_depth, contiguous=False, script_name="fmm_m2l_fft_mpi_f32"): +def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, points_per_rank, local_depth, distribution="uniform", contiguous=False, script_name="fmm_m2l_fft_mpi_f32"): expansion_order = 3 n_points = points_per_rank n_samples = 500 @@ -142,14 +148,14 @@ def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, point script_name="{script_name}" -export SCRATCH=$WORK/weak_fft_n={max_points}_p={last_nodes}_points_per_rank={n_points}_${{SLURM_JOBID}} +export SCRATCH=$WORK/weak_fft_n={max_points}_p={last_nodes}_points_per_rank={n_points}_distribution={distribution}_${{SLURM_JOBID}} mkdir -p $SCRATCH cd $SCRATCH export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK export OMP_NUM_THREADS=1 -export OUTPUT=$SCRATCH/weak_fft_n={max_points}_p={last_nodes}_points_per_rank={n_points}_${{SLURM_JOBID}}.csv +export OUTPUT=$SCRATCH/weak_fft_n={max_points}_p={last_nodes}_points_per_rank={n_points}_distribution={distribution}_${{SLURM_JOBID}}.csv touch $OUTPUT echo " experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ @@ -169,7 +175,7 @@ def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, point $WORK/$script_name --id {i} --n-points {n_points} \\ --expansion-order {expansion_order} --prune-empty \\ --global-depth {gd} --local-depth {local_depth} \\ - --n-samples {n_samples} --block-size {block_size} --n-threads {int(max_threads)} \\ + --n-samples {n_samples} --block-size {block_size} --n-threads {int(max_threads)} --distribution {DISTRIBUTION[distribution]} \\ >> $OUTPUT 2> $SCRATCH/err_run_{i}.log """ @@ -189,6 +195,7 @@ def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, point parser.add_argument("--method", type=str, default="ccx") parser.add_argument("--contiguous", type=bool, default=False) parser.add_argument("--output", type=str, default="job.slurm") + parser.add_argument("--distribution", type=str, default="uniform") parser.add_argument("--config", action='append') args = parser.parse_args() @@ -210,11 +217,11 @@ def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, point valid_methods = {"ccx", "ccd" "socket"} if any(args.method in m for m in valid_methods): - n_nodes, n_tasks, global_depths, max_threads = experiment_parameters( - args.min_nodes, args.max_nodes, ranks_per_node[args.method], args.points_per_rank, args.local_depth + n_nodes, n_tasks, global_depths, max_threads, distribution = experiment_parameters( + args.min_nodes, args.max_nodes, ranks_per_node[args.method], args.points_per_rank, args.local_depth, distribution=args.distribution ) - write_slurm(args.output, n_nodes, n_tasks, global_depths, max_threads, args.points_per_rank, args.local_depth, contiguous=args.contiguous) + write_slurm(args.output, n_nodes, n_tasks, global_depths, max_threads, args.points_per_rank, args.local_depth, contiguous=args.contiguous, distribution=distribution) else: raise ValueError("Unknown method") \ No newline at end of file diff --git a/hpc/ijhpca/weak_scaling_ccd.json b/hpc/ijhpca/weak_scaling_ccd.json index b0b689bb..d03c5d22 100644 --- a/hpc/ijhpca/weak_scaling_ccd.json +++ b/hpc/ijhpca/weak_scaling_ccd.json @@ -7,5 +7,6 @@ "output": "weak_scaling_ccd.slurm", "method": "ccd", "scaling_func": 8, - "contiguous": false + "contiguous": false, + "distribution": "uniform" } \ No newline at end of file diff --git a/hpc/ijhpca/weak_scaling_ccx.json b/hpc/ijhpca/weak_scaling_ccx.json index 77a4175d..cb5711d6 100644 --- a/hpc/ijhpca/weak_scaling_ccx.json +++ b/hpc/ijhpca/weak_scaling_ccx.json @@ -7,5 +7,6 @@ "output": "weak_scaling_ccx.slurm", "method": "ccx", "scaling_func": 8, - "contiguous": false + "contiguous": false, + "distribution": "uniform" } \ No newline at end of file diff --git a/hpc/ijhpca/weak_scaling_socket.json b/hpc/ijhpca/weak_scaling_socket.json index 6ed8c9a9..b8b8cacc 100644 --- a/hpc/ijhpca/weak_scaling_socket.json +++ b/hpc/ijhpca/weak_scaling_socket.json @@ -7,5 +7,6 @@ "output": "weak_scaling_socket.slurm", "method": "socket", "scaling_func": 8, - "contiguous": false + "contiguous": false, + "distribution": "uniform" } \ No newline at end of file diff --git a/scripts/src/bin/fmm_m2l_blas_mpi_f32.rs b/scripts/src/bin/fmm_m2l_blas_mpi_f32.rs index 502c0241..25852550 100644 --- a/scripts/src/bin/fmm_m2l_blas_mpi_f32.rs +++ b/scripts/src/bin/fmm_m2l_blas_mpi_f32.rs @@ -3,7 +3,10 @@ use clap::Parser; use green_kernels::laplace_3d::Laplace3dKernel; use kifmm::{ traits::types::{CommunicationType, FmmOperatorType, MetadataType}, - tree::{helpers::points_fixture, types::SortKind}, + tree::{ + helpers::{points_fixture, points_fixture_sphere}, + types::SortKind, + }, BlasFieldTranslationSaRcmp, DataAccessMulti, EvaluateMulti, FmmSvdMode, MultiNodeBuilder, }; use mpi::traits::*; @@ -48,6 +51,10 @@ struct Args { /// less than the number of samples and greater than 0 #[arg(long, default_value_t = 10)] n_samples: usize, + + /// Particle distribution (0 - uniform, 1 - sphere) + #[arg(long, default_value_t = 0)] + distribution: u64, } fn main() { @@ -67,6 +74,7 @@ fn main() { let n_threads = args.n_threads; let n_samples = args.n_samples; let id = args.id; + let distribution = args.distribution; assert!(n_samples > 0 && n_samples < n_points); @@ -84,7 +92,15 @@ fn main() { BlasFieldTranslationSaRcmp::::new(Some(threshold), None, FmmSvdMode::Deterministic); // Generate some random test data local to each process - let points = points_fixture::(n_points, None, None, Some(world.rank() as u64)); + let points; + if distribution == 0 { + points = points_fixture::(n_points, None, None, Some(world.rank() as u64)); + } else if distribution == 1 { + points = points_fixture_sphere::(n_points) + } else { + panic!("Unknown distribution") + } + let charges = vec![1f32; n_points]; let mut multi_fmm = MultiNodeBuilder::new(true) diff --git a/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs b/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs index 84815b98..0a5a511f 100644 --- a/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs +++ b/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs @@ -2,8 +2,14 @@ use clap::Parser; use green_kernels::laplace_3d::Laplace3dKernel; use kifmm::{ - traits::{tree::MultiFmmTree, types::{CommunicationType, FmmOperatorType, MetadataType}}, - tree::{helpers::points_fixture, types::SortKind}, + traits::{ + tree::MultiFmmTree, + types::{CommunicationType, FmmOperatorType, MetadataType}, + }, + tree::{ + helpers::{points_fixture, points_fixture_sphere}, + types::SortKind, + }, DataAccessMulti, EvaluateMulti, FftFieldTranslation, MultiNodeBuilder, }; use mpi::traits::*; @@ -48,6 +54,10 @@ struct Args { /// less than the number of samples and greater than 0 #[arg(long, default_value_t = 10)] n_samples: usize, + + /// Particle distribution (0 - uniform, 1 - sphere) + #[arg(long, default_value_t = 0)] + distribution: u64, } fn main() { @@ -66,6 +76,7 @@ fn main() { let block_size = args.block_size; let n_threads = args.n_threads; let n_samples = args.n_samples; + let distribution = args.distribution; let id = args.id; assert!(n_samples > 0 && n_samples < n_points); @@ -83,7 +94,16 @@ fn main() { let source_to_target = FftFieldTranslation::::new(Some(block_size)); // Generate some random test data local to each process - let points = points_fixture::(n_points, None, None, Some(world.rank() as u64)); + + let points; + if distribution == 0 { + points = points_fixture::(n_points, None, None, Some(world.rank() as u64)); + } else if distribution == 1 { + points = points_fixture_sphere::(n_points) + } else { + panic!("Unknown distribution") + } + let charges = vec![1f32; n_points]; let mut multi_fmm = MultiNodeBuilder::new(true) @@ -112,9 +132,8 @@ fn main() { multi_fmm.evaluate().unwrap(); let runtime = start.elapsed().as_millis(); - let mut mean_roots_per_rank_source_tree = 0.; + let mut mean_roots_per_rank_source_tree = 0.; if multi_fmm.rank() == 0 { - let mut m: HashMap = HashMap::new(); for x in multi_fmm.tree().source_tree().all_roots_ranks.iter() { *m.entry(*x).or_default() += 1.0; @@ -129,9 +148,8 @@ fn main() { mean_roots_per_rank_source_tree = tmp / (n_ranks as f64); } - let mut mean_roots_per_rank_target_tree= 0.; + let mut mean_roots_per_rank_target_tree = 0.; if multi_fmm.rank() == 0 { - let mut m: HashMap = HashMap::new(); for x in multi_fmm.tree().target_tree().all_roots_ranks.iter() { *m.entry(*x).or_default() += 1.0; From 9b89338c603b5cd71f9aac0e6efc8cd7ce40463b Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Mon, 20 Oct 2025 16:39:18 +0100 Subject: [PATCH 37/52] Configure strong scaling too --- hpc/ijhpca/Weak Scaling.ipynb | 2 +- hpc/ijhpca/strong_scaling.py | 20 ++++++++++++++------ hpc/ijhpca/strong_scaling_ccd.json | 3 ++- hpc/ijhpca/strong_scaling_ccx.json | 3 ++- hpc/ijhpca/strong_scaling_socket.json | 3 ++- 5 files changed, 21 insertions(+), 10 deletions(-) diff --git a/hpc/ijhpca/Weak Scaling.ipynb b/hpc/ijhpca/Weak Scaling.ipynb index e5c44f41..72be45ef 100644 --- a/hpc/ijhpca/Weak Scaling.ipynb +++ b/hpc/ijhpca/Weak Scaling.ipynb @@ -176,7 +176,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "kifmm (3.10.17)", "language": "python", "name": "python3" }, diff --git a/hpc/ijhpca/strong_scaling.py b/hpc/ijhpca/strong_scaling.py index 49da35c0..2790f03f 100644 --- a/hpc/ijhpca/strong_scaling.py +++ b/hpc/ijhpca/strong_scaling.py @@ -13,6 +13,11 @@ "ccx_per_ccd": 2 } +DISTRIBUTION = { + "uniform": 0, + "sphere": 1 +} + def global_depth(rank): """ will return the first power of 8 less than rank, the power corresponding to the level @@ -62,7 +67,8 @@ def experiment_parameters( min_points_per_rank, min_local_depth=4, scaling_func=pow8, - arch=AMD_EPYC_ROME + arch=AMD_EPYC_ROME, + distribution="uniform" ): max_cpus=arch["cores_per_node"] @@ -135,14 +141,15 @@ def experiment_parameters( print(f"points per node {points_per_node}") print(f"min points per leaf {min_points_per_leaf}") print(f"max threads per rank {max_threads_per_rank}") + print(f"distribution {distribution}") # Test that same number of points being used in each experiment assert(np.allclose(points_per_rank*n_ranks, points_per_node*n_nodes)) - return n_nodes.tolist(), n_ranks.tolist(), global_depth.tolist(), local_depth.tolist(), points_per_rank, max_threads_per_rank + return n_nodes.tolist(), n_ranks.tolist(), global_depth.tolist(), local_depth.tolist(), points_per_rank, max_threads_per_rank, distribution -def write_slurm(script_path, n_nodes, n_tasks, local_depths, global_depths, max_threads, points_per_rank, contiguous=False, script_name="fmm_m2l_fft_mpi_f32"): +def write_slurm(script_path, n_nodes, n_tasks, local_depths, global_depths, max_threads, points_per_rank, contiguous=False, script_name="fmm_m2l_fft_mpi_f32", distribution="uniform"): expansion_order = 3 n_samples = 500 block_size = 128 @@ -202,7 +209,7 @@ def write_slurm(script_path, n_nodes, n_tasks, local_depths, global_depths, max_ $WORK/$script_name --id {i} --n-points {ppr} \\ --expansion-order {expansion_order} --prune-empty \\ --global-depth {gd} --local-depth {ld} \\ - --n-samples {n_samples} --block-size {block_size} --n-threads {int(max_threads)} \\ + --n-samples {n_samples} --block-size {block_size} --n-threads {int(max_threads)} --distribution {DISTRIBUTION[distribution]} \\ >> $OUTPUT 2> $SCRATCH/err_run_{i}.log """ @@ -221,6 +228,7 @@ def write_slurm(script_path, n_nodes, n_tasks, local_depths, global_depths, max_ parser.add_argument("--method", type=str, default="ccx") parser.add_argument("--contiguous", type=bool, default=False) parser.add_argument("--output", type=str, default="job.slurm") + parser.add_argument("--distribution", type=str, default="uniform") parser.add_argument("--config", action='append') args = parser.parse_args() @@ -241,9 +249,9 @@ def write_slurm(script_path, n_nodes, n_tasks, local_depths, global_depths, max_ valid_methods = {"ccx", "ccd" "socket"} if any(args.method in m for m in valid_methods): - n_nodes, n_tasks, global_depths, local_depths, points_per_rank, max_threads = experiment_parameters( + n_nodes, n_tasks, global_depths, local_depths, points_per_rank, max_threads, distribution = experiment_parameters( args.min_nodes, args.max_nodes, ranks_per_node[args.method], args.min_points_per_rank, args.min_local_depth, scaling_func=scaling_func ) - write_slurm(f"{args.method}_"+args.output, n_nodes, n_tasks, local_depths, global_depths, max_threads, points_per_rank, contiguous=args.contiguous) + write_slurm(f"{args.method}_"+args.output, n_nodes, n_tasks, local_depths, global_depths, max_threads, points_per_rank, contiguous=args.contiguous, distribution=distribution) else: raise ValueError("Unknown method") \ No newline at end of file diff --git a/hpc/ijhpca/strong_scaling_ccd.json b/hpc/ijhpca/strong_scaling_ccd.json index fdb6a38a..1f0dffa2 100644 --- a/hpc/ijhpca/strong_scaling_ccd.json +++ b/hpc/ijhpca/strong_scaling_ccd.json @@ -7,5 +7,6 @@ "output": "strong_scaling_ccd.slurm", "method": "ccd", "scaling_func": 8, - "contiguous": true + "contiguous": true, + "distribution": "uniform" } \ No newline at end of file diff --git a/hpc/ijhpca/strong_scaling_ccx.json b/hpc/ijhpca/strong_scaling_ccx.json index 8ed552da..0c1675d4 100644 --- a/hpc/ijhpca/strong_scaling_ccx.json +++ b/hpc/ijhpca/strong_scaling_ccx.json @@ -7,5 +7,6 @@ "output": "strong_scaling_ccx.slurm", "method": "ccx", "scaling_func": 8, - "contiguous": false + "contiguous": false, + "distribution": "uniform" } \ No newline at end of file diff --git a/hpc/ijhpca/strong_scaling_socket.json b/hpc/ijhpca/strong_scaling_socket.json index 497d5ee9..41609757 100644 --- a/hpc/ijhpca/strong_scaling_socket.json +++ b/hpc/ijhpca/strong_scaling_socket.json @@ -7,5 +7,6 @@ "output": "strong_scaling.slurm", "method": "socket", "scaling_func": 8, - "contiguous": false + "contiguous": false, + "distribution": "uniform" } \ No newline at end of file From ffda7e2e6a5a2feca7ea689e83f8d39883aa51f3 Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Mon, 20 Oct 2025 16:40:12 +0100 Subject: [PATCH 38/52] ADd strong scaling config of point distribution too --- hpc/ijhpca/strong_scaling.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/hpc/ijhpca/strong_scaling.py b/hpc/ijhpca/strong_scaling.py index 2790f03f..13966b83 100644 --- a/hpc/ijhpca/strong_scaling.py +++ b/hpc/ijhpca/strong_scaling.py @@ -182,14 +182,14 @@ def write_slurm(script_path, n_nodes, n_tasks, local_depths, global_depths, max_ script_name="{script_name}" -export SCRATCH=$WORK/strong_fft_n={max_points}_p={last_nodes}_points_per_rank={points_per_rank[-1]}_${{SLURM_JOBID}} +export SCRATCH=$WORK/strong_fft_n={max_points}_p={last_nodes}_points_per_rank={points_per_rank[-1]}_distribution={distribution}_${{SLURM_JOBID}} mkdir -p $SCRATCH cd $SCRATCH export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK export OMP_NUM_THREADS=1 -export OUTPUT=$SCRATCH/strong_fft_n={max_points}_p={last_nodes}_points_per_rank={points_per_rank[-1]}_${{SLURM_JOBID}}.csv +export OUTPUT=$SCRATCH/strong_fft_n={max_points}_p={last_nodes}_points_per_rank={points_per_rank[-1]}_distribution={distribution}_${{SLURM_JOBID}}.csv touch $OUTPUT echo " experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ From 3cb5cdfd2853fbd49dd8ab3eb0d634b4ede84a8b Mon Sep 17 00:00:00 2001 From: srinath kailasa Date: Mon, 20 Oct 2025 16:54:30 +0100 Subject: [PATCH 39/52] Small parse issue strong scaling script generator --- hpc/ijhpca/strong_scaling.py | 2 +- hpc/ijhpca/strong_scaling_ccd.json | 4 ++-- hpc/ijhpca/strong_scaling_ccx.json | 4 ++-- hpc/ijhpca/strong_scaling_socket.json | 4 ++-- hpc/ijhpca/weak_scaling_ccd.json | 4 ++-- hpc/ijhpca/weak_scaling_ccx.json | 4 ++-- hpc/ijhpca/weak_scaling_socket.json | 4 ++-- 7 files changed, 13 insertions(+), 13 deletions(-) diff --git a/hpc/ijhpca/strong_scaling.py b/hpc/ijhpca/strong_scaling.py index 13966b83..0a1ced3c 100644 --- a/hpc/ijhpca/strong_scaling.py +++ b/hpc/ijhpca/strong_scaling.py @@ -250,7 +250,7 @@ def write_slurm(script_path, n_nodes, n_tasks, local_depths, global_depths, max_ valid_methods = {"ccx", "ccd" "socket"} if any(args.method in m for m in valid_methods): n_nodes, n_tasks, global_depths, local_depths, points_per_rank, max_threads, distribution = experiment_parameters( - args.min_nodes, args.max_nodes, ranks_per_node[args.method], args.min_points_per_rank, args.min_local_depth, scaling_func=scaling_func + args.min_nodes, args.max_nodes, ranks_per_node[args.method], args.min_points_per_rank, args.min_local_depth, scaling_func=scaling_func, distribution=args.distribution ) write_slurm(f"{args.method}_"+args.output, n_nodes, n_tasks, local_depths, global_depths, max_threads, points_per_rank, contiguous=args.contiguous, distribution=distribution) else: diff --git a/hpc/ijhpca/strong_scaling_ccd.json b/hpc/ijhpca/strong_scaling_ccd.json index 1f0dffa2..ca548ecc 100644 --- a/hpc/ijhpca/strong_scaling_ccd.json +++ b/hpc/ijhpca/strong_scaling_ccd.json @@ -4,9 +4,9 @@ "max_nodes": 128, "min_points_per_rank": 125000, "min_local_depth": 3, - "output": "strong_scaling_ccd.slurm", + "output": "strong_scaling_ccd_sphere.slurm", "method": "ccd", "scaling_func": 8, "contiguous": true, - "distribution": "uniform" + "distribution": "sphere" } \ No newline at end of file diff --git a/hpc/ijhpca/strong_scaling_ccx.json b/hpc/ijhpca/strong_scaling_ccx.json index 0c1675d4..fe80c967 100644 --- a/hpc/ijhpca/strong_scaling_ccx.json +++ b/hpc/ijhpca/strong_scaling_ccx.json @@ -4,9 +4,9 @@ "max_nodes": 128, "min_points_per_rank": 62500, "min_local_depth": 3, - "output": "strong_scaling_ccx.slurm", + "output": "strong_scaling_ccx_sphere.slurm", "method": "ccx", "scaling_func": 8, "contiguous": false, - "distribution": "uniform" + "distribution": "sphere" } \ No newline at end of file diff --git a/hpc/ijhpca/strong_scaling_socket.json b/hpc/ijhpca/strong_scaling_socket.json index 41609757..ede3e5c1 100644 --- a/hpc/ijhpca/strong_scaling_socket.json +++ b/hpc/ijhpca/strong_scaling_socket.json @@ -4,9 +4,9 @@ "max_nodes": 128, "min_points_per_rank": 1000000, "min_local_depth": 4, - "output": "strong_scaling.slurm", + "output": "strong_scaling_sphere.slurm", "method": "socket", "scaling_func": 8, "contiguous": false, - "distribution": "uniform" + "distribution": "sphere" } \ No newline at end of file diff --git a/hpc/ijhpca/weak_scaling_ccd.json b/hpc/ijhpca/weak_scaling_ccd.json index d03c5d22..ee263c58 100644 --- a/hpc/ijhpca/weak_scaling_ccd.json +++ b/hpc/ijhpca/weak_scaling_ccd.json @@ -4,9 +4,9 @@ "max_nodes": 512, "points_per_rank": 500000, "local_depth": 4, - "output": "weak_scaling_ccd.slurm", + "output": "weak_scaling_ccd_sphere.slurm", "method": "ccd", "scaling_func": 8, "contiguous": false, - "distribution": "uniform" + "distribution": "sphere" } \ No newline at end of file diff --git a/hpc/ijhpca/weak_scaling_ccx.json b/hpc/ijhpca/weak_scaling_ccx.json index cb5711d6..e084cf91 100644 --- a/hpc/ijhpca/weak_scaling_ccx.json +++ b/hpc/ijhpca/weak_scaling_ccx.json @@ -4,9 +4,9 @@ "max_nodes": 512, "points_per_rank": 250000, "local_depth": 4, - "output": "weak_scaling_ccx.slurm", + "output": "weak_scaling_ccx_sphere.slurm", "method": "ccx", "scaling_func": 8, "contiguous": false, - "distribution": "uniform" + "distribution": "sphere" } \ No newline at end of file diff --git a/hpc/ijhpca/weak_scaling_socket.json b/hpc/ijhpca/weak_scaling_socket.json index b8b8cacc..5957e13d 100644 --- a/hpc/ijhpca/weak_scaling_socket.json +++ b/hpc/ijhpca/weak_scaling_socket.json @@ -4,9 +4,9 @@ "max_nodes": 512, "points_per_rank": 4000000, "local_depth": 5, - "output": "weak_scaling_socket.slurm", + "output": "weak_scaling_socket_sphere.slurm", "method": "socket", "scaling_func": 8, "contiguous": false, - "distribution": "uniform" + "distribution": "sphere" } \ No newline at end of file From f6c1f57224fb412afefe989157ea797d1f911228 Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Tue, 21 Oct 2025 09:59:38 +0100 Subject: [PATCH 40/52] Seed random numbers to address load imbalance in results --- hpc/ijhpca/Strong Scaling.ipynb | 121 ++++++------------ hpc/ijhpca/Weak Scaling.ipynb | 70 +++++++--- .../err_run_0.log | 0 .../err_run_1.log | 0 .../err_run_2.log | 0 .../err_run_0.log | 0 .../err_run_1.log | 0 .../err_run_2.log | 0 .../err_run_0.log | 0 .../err_run_1.log | 0 .../err_run_2.log | 0 .../err_run_3.log | 0 .../err_run_0.log | 0 .../err_run_1.log | 0 .../err_run_2.log | 0 .../err_run_3.log | 0 .../err_run_0.log | 0 .../err_run_1.log | 0 .../err_run_2.log | 0 .../err_run_3.log | 0 .../err_run_0.log | 0 .../err_run_1.log | 0 .../err_run_2.log | 0 .../err_run_3.log | 0 .../err_run_0.log | 0 .../err_run_1.log | 0 .../err_run_2.log | 0 .../err_run_3.log | 0 kifmm/src/tree/helpers.rs | 5 +- scripts/src/bin/fmm_m2l_blas_mpi_f32.rs | 2 +- scripts/src/bin/fmm_m2l_fft_mpi_f32.rs | 2 +- 31 files changed, 98 insertions(+), 102 deletions(-) rename hpc/ijhpca/data/{weak_fft_n=4096000000_p=512_11079231 => sphere/strong_fft_n=128000000_p=64_points_per_rank=1000000_distribution=sphere_11248828}/err_run_0.log (100%) rename hpc/ijhpca/data/{weak_fft_n=4096000000_p=512_11079231 => sphere/strong_fft_n=128000000_p=64_points_per_rank=1000000_distribution=sphere_11248828}/err_run_1.log (100%) rename hpc/ijhpca/data/{weak_fft_n=4096000000_p=512_11079231 => sphere/strong_fft_n=128000000_p=64_points_per_rank=1000000_distribution=sphere_11248828}/err_run_2.log (100%) rename hpc/ijhpca/data/{weak_fft_n=4096000000_p=512_11079233 => sphere/strong_fft_n=128000000_p=64_points_per_rank=62500_distribution=sphere_11248825}/err_run_0.log (100%) rename hpc/ijhpca/data/{weak_fft_n=4096000000_p=512_11079233 => sphere/strong_fft_n=128000000_p=64_points_per_rank=62500_distribution=sphere_11248825}/err_run_1.log (100%) rename hpc/ijhpca/data/{weak_fft_n=4096000000_p=512_11079233 => sphere/strong_fft_n=128000000_p=64_points_per_rank=62500_distribution=sphere_11248825}/err_run_2.log (100%) create mode 100644 hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11248788/err_run_0.log create mode 100644 hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11248788/err_run_1.log create mode 100644 hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11248788/err_run_2.log create mode 100644 hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11248788/err_run_3.log create mode 100644 hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11248790/err_run_0.log create mode 100644 hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11248790/err_run_1.log create mode 100644 hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11248790/err_run_2.log create mode 100644 hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11248790/err_run_3.log create mode 100644 hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11248787/err_run_0.log create mode 100644 hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11248787/err_run_1.log create mode 100644 hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11248787/err_run_2.log create mode 100644 hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11248787/err_run_3.log create mode 100644 hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079231/err_run_0.log create mode 100644 hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079231/err_run_1.log create mode 100644 hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079231/err_run_2.log rename hpc/ijhpca/data/{ => uniform}/weak_fft_n=4096000000_p=512_11079231/err_run_3.log (100%) create mode 100644 hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079233/err_run_0.log create mode 100644 hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079233/err_run_1.log create mode 100644 hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079233/err_run_2.log rename hpc/ijhpca/data/{ => uniform}/weak_fft_n=4096000000_p=512_11079233/err_run_3.log (100%) diff --git a/hpc/ijhpca/Strong Scaling.ipynb b/hpc/ijhpca/Strong Scaling.ipynb index 9dcaff6c..89eb3ea6 100644 --- a/hpc/ijhpca/Strong Scaling.ipynb +++ b/hpc/ijhpca/Strong Scaling.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "id": "04c73ea3", "metadata": {}, "outputs": [ @@ -23,100 +23,37 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[34mstrong_fft_n=128000000_p=64_11077776\u001b[m\u001b[m \u001b[34mweak_fft_n=4096000000_p=512_11079232\u001b[m\u001b[m\n", - "\u001b[34mstrong_fft_n=128000000_p=64_11077778\u001b[m\u001b[m \u001b[34mweak_fft_n=512000000_p=64_11077796\u001b[m\u001b[m\n", - "\u001b[34mstrong_fft_n=128000000_p=64_11079093\u001b[m\u001b[m \u001b[34mweak_fft_n=512000000_p=64_11077797\u001b[m\u001b[m\n", - "\u001b[34mstrong_fft_n=128000000_p=64_11079322\u001b[m\u001b[m \u001b[34mweak_fft_n=512000000_p=64_11077800\u001b[m\u001b[m\n" + "\u001b[34mstrong_fft_n=128000000_p=64_points_per_rank=1000000_distribution=sphere_11248828\u001b[m\u001b[m\n", + "\u001b[34mstrong_fft_n=128000000_p=64_points_per_rank=62500_distribution=sphere_11248825\u001b[m\u001b[m\n", + "\u001b[34mweak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11248788\u001b[m\u001b[m\n", + "\u001b[34mweak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11248790\u001b[m\u001b[m\n", + "\u001b[34mweak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11248787\u001b[m\u001b[m\n" ] } ], "source": [ - "! ls data/" + "! ls data/sphere" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "id": "ae668ed4", "metadata": {}, "outputs": [], "source": [ - "df0 = pd.read_csv('data/strong_fft_n=128000000_p=64_11077776/strong_fft_n=128000000_p=64_11077776.csv')\n", - "df1 = pd.read_csv('data/strong_fft_n=128000000_p=64_11077778/strong_fft_n=128000000_p=64_11077778.csv')\n", - "df2 = pd.read_csv('data/strong_fft_n=128000000_p=64_11079093/strong_fft_n=128000000_p=64_11079093.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "10e3bc2c-7ab5-4be6-9b7c-f056301124e7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " n_points\n", - "experiment_id \n", - "0 4000000.0\n", - "1 500000.0\n", - "2 62500.0" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1.groupby('experiment_id')[['n_points']].mean()" + "df0 = pd.read_csv('data/uniform/strong_fft_n=128000000_p=64_11077776/strong_fft_n=128000000_p=64_11077776.csv')\n", + "df1 = pd.read_csv('data/uniform/strong_fft_n=128000000_p=64_11077778/strong_fft_n=128000000_p=64_11077778.csv')\n", + "df2 = pd.read_csv('data/uniform/strong_fft_n=128000000_p=64_11079093/strong_fft_n=128000000_p=64_11079093.csv')\n", + "\n", + "df3 = pd.read_csv('data/sphere/strong_fft_n=128000000_p=64_points_per_rank=1000000_distribution=sphere_11248828/strong_fft_n=128000000_p=64_points_per_rank=1000000_distribution=sphere_11248828.csv')\n", + "df4 = pd.read_csv('data/sphere/strong_fft_n=128000000_p=64_points_per_rank=62500_distribution=sphere_11248825/strong_fft_n=128000000_p=64_points_per_rank=62500_distribution=sphere_11248825.csv')\n", + "# df5 = pd.read_csv('data/sphere/strong_fft_n=128000000_p=64_11079093/strong_fft_n=128000000_p=64_11079093.csv')" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "30777f75", "metadata": {}, "outputs": [], @@ -257,23 +194,39 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 8, "id": "fcafbe0c-4a99-41a3-a5e2-79418587afc8", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "experiment_id\n", + "0 64000000.0\n", + "1 8000000.0\n", + "2 1000000.0\n", + "Name: n_points, dtype: float64\n", + "experiment_id\n", + "0 4000000.0\n", + "1 500000.0\n", + "2 62500.0\n", + "Name: n_points, dtype: float64\n" + ] + }, { "data": { + "image/png": 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", "text/plain": [ - "524288" + "
" ] }, - "execution_count": 39, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "2**19" + "strong_scaling([df3,df4], names=[\"Socket\", \"CCX\",\"Socket\"], title=\"Strong Scaling For 128e6 Points on 64 Nodes of ARCHER2\")" ] }, { diff --git a/hpc/ijhpca/Weak Scaling.ipynb b/hpc/ijhpca/Weak Scaling.ipynb index 72be45ef..98c8c232 100644 --- a/hpc/ijhpca/Weak Scaling.ipynb +++ b/hpc/ijhpca/Weak Scaling.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 7, + "execution_count": 1, "id": "2ae1633f-50c6-48ec-ad4b-d899118e24c8", "metadata": {}, "outputs": [], @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 2, "id": "86084f2e-5662-445c-8bac-e630a73d764f", "metadata": {}, "outputs": [ @@ -33,24 +33,51 @@ } ], "source": [ - "! ls data/" + "! ls data/uniform" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 3, + "id": "182a3aa0-aff6-45a0-a6a2-df618808793c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[34mstrong_fft_n=128000000_p=64_points_per_rank=1000000_distribution=sphere_11248828\u001b[m\u001b[m\n", + "\u001b[34mstrong_fft_n=128000000_p=64_points_per_rank=62500_distribution=sphere_11248825\u001b[m\u001b[m\n", + "\u001b[34mweak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11248788\u001b[m\u001b[m\n", + "\u001b[34mweak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11248790\u001b[m\u001b[m\n", + "\u001b[34mweak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11248787\u001b[m\u001b[m\n" + ] + } + ], + "source": [ + "! ls data/sphere" + ] + }, + { + "cell_type": "code", + "execution_count": 4, "id": "178da76c-f2f3-4e55-859b-37cccde9f575", "metadata": {}, "outputs": [], "source": [ - "df0 = pd.read_csv('data/weak_fft_n=4096000000_p=512_11079232/weak_fft_n=4096000000_p=512_11079232.csv')\n", - "df1 = pd.read_csv('data/weak_fft_n=4096000000_p=512_11079231/weak_fft_n=4096000000_p=512_11079231.csv')\n", - "df2 = pd.read_csv('data/weak_fft_n=4096000000_p=512_11079233/weak_fft_n=4096000000_p=512_11079233.csv')\n" + "df0 = pd.read_csv('data/uniform/weak_fft_n=4096000000_p=512_11079232/weak_fft_n=4096000000_p=512_11079232.csv')\n", + "df1 = pd.read_csv('data/uniform/weak_fft_n=4096000000_p=512_11079231/weak_fft_n=4096000000_p=512_11079231.csv')\n", + "df2 = pd.read_csv('data/uniform/weak_fft_n=4096000000_p=512_11079233/weak_fft_n=4096000000_p=512_11079233.csv')\n", + "\n", + "\n", + "df3 = pd.read_csv('data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11248788/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11248788.csv')\n", + "df4 = pd.read_csv('data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11248790/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11248790.csv')\n", + "df5 = pd.read_csv('data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11248787/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11248787.csv')" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 5, "id": "754504a1", "metadata": {}, "outputs": [], @@ -138,13 +165,13 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 7, "id": "67d9abc3-f9bb-41ec-9778-9912fecbc924", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -154,16 +181,29 @@ } ], "source": [ - "weak_scaling([df0, df1, df2], names=[\"CCX\", \"Socket\", \"CCD\"], title=\"Weak Scaling up to 4e9 Points on 512 nodes of ARCHER2\")" + "weak_scaling([df0, df1, df2], names=[\"CCX\", \"Socket\", \"CCD\"], title=\"Weak Scaling up to 4e9 Points on 512 nodes of ARCHER2 (Uniform)\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "447bedda-cd0e-4ff3-9201-e46d6d66c62c", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "weak_scaling([df3, df4, df5], names=[\"CCX\", \"Socket\", \"CCD\"], title=\"Weak Scaling up to 4e9 Points on 512 nodes of ARCHER2 (Sphere)\")" + ] }, { "cell_type": "code", @@ -176,7 +216,7 @@ ], "metadata": { "kernelspec": { - "display_name": "kifmm (3.10.17)", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, diff --git a/hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079231/err_run_0.log b/hpc/ijhpca/data/sphere/strong_fft_n=128000000_p=64_points_per_rank=1000000_distribution=sphere_11248828/err_run_0.log similarity index 100% rename from hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079231/err_run_0.log rename to hpc/ijhpca/data/sphere/strong_fft_n=128000000_p=64_points_per_rank=1000000_distribution=sphere_11248828/err_run_0.log diff --git a/hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079231/err_run_1.log b/hpc/ijhpca/data/sphere/strong_fft_n=128000000_p=64_points_per_rank=1000000_distribution=sphere_11248828/err_run_1.log similarity index 100% rename from hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079231/err_run_1.log rename to hpc/ijhpca/data/sphere/strong_fft_n=128000000_p=64_points_per_rank=1000000_distribution=sphere_11248828/err_run_1.log diff --git a/hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079231/err_run_2.log b/hpc/ijhpca/data/sphere/strong_fft_n=128000000_p=64_points_per_rank=1000000_distribution=sphere_11248828/err_run_2.log similarity index 100% rename from hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079231/err_run_2.log rename to hpc/ijhpca/data/sphere/strong_fft_n=128000000_p=64_points_per_rank=1000000_distribution=sphere_11248828/err_run_2.log diff --git a/hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079233/err_run_0.log b/hpc/ijhpca/data/sphere/strong_fft_n=128000000_p=64_points_per_rank=62500_distribution=sphere_11248825/err_run_0.log similarity index 100% rename from hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079233/err_run_0.log rename to hpc/ijhpca/data/sphere/strong_fft_n=128000000_p=64_points_per_rank=62500_distribution=sphere_11248825/err_run_0.log diff --git a/hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079233/err_run_1.log b/hpc/ijhpca/data/sphere/strong_fft_n=128000000_p=64_points_per_rank=62500_distribution=sphere_11248825/err_run_1.log similarity index 100% rename from hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079233/err_run_1.log rename to hpc/ijhpca/data/sphere/strong_fft_n=128000000_p=64_points_per_rank=62500_distribution=sphere_11248825/err_run_1.log diff --git a/hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079233/err_run_2.log b/hpc/ijhpca/data/sphere/strong_fft_n=128000000_p=64_points_per_rank=62500_distribution=sphere_11248825/err_run_2.log similarity index 100% rename from hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079233/err_run_2.log rename to hpc/ijhpca/data/sphere/strong_fft_n=128000000_p=64_points_per_rank=62500_distribution=sphere_11248825/err_run_2.log diff --git a/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11248788/err_run_0.log b/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11248788/err_run_0.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11248788/err_run_1.log b/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11248788/err_run_1.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11248788/err_run_2.log b/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11248788/err_run_2.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11248788/err_run_3.log b/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11248788/err_run_3.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11248790/err_run_0.log b/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11248790/err_run_0.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11248790/err_run_1.log b/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11248790/err_run_1.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11248790/err_run_2.log b/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11248790/err_run_2.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11248790/err_run_3.log b/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11248790/err_run_3.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11248787/err_run_0.log b/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11248787/err_run_0.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11248787/err_run_1.log b/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11248787/err_run_1.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11248787/err_run_2.log b/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11248787/err_run_2.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11248787/err_run_3.log b/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11248787/err_run_3.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079231/err_run_0.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079231/err_run_0.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079231/err_run_1.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079231/err_run_1.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079231/err_run_2.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079231/err_run_2.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079231/err_run_3.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079231/err_run_3.log similarity index 100% rename from hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079231/err_run_3.log rename to hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079231/err_run_3.log diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079233/err_run_0.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079233/err_run_0.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079233/err_run_1.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079233/err_run_1.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079233/err_run_2.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079233/err_run_2.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079233/err_run_3.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079233/err_run_3.log similarity index 100% rename from hpc/ijhpca/data/weak_fft_n=4096000000_p=512_11079233/err_run_3.log rename to hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079233/err_run_3.log diff --git a/kifmm/src/tree/helpers.rs b/kifmm/src/tree/helpers.rs index 287ca510..d78a67d1 100644 --- a/kifmm/src/tree/helpers.rs +++ b/kifmm/src/tree/helpers.rs @@ -52,9 +52,12 @@ pub fn points_fixture( n_points: usize, + seed: Option ) -> PointsMat { + + let seed = seed.unwrap_or(0); // Seeded random number generator for reproducibility - let mut rng = StdRng::seed_from_u64(0); + let mut rng = StdRng::seed_from_u64(seed); let pi = T::from(std::f64::consts::PI).unwrap(); let two = T::from(2.0).unwrap(); diff --git a/scripts/src/bin/fmm_m2l_blas_mpi_f32.rs b/scripts/src/bin/fmm_m2l_blas_mpi_f32.rs index 25852550..b2b6c7af 100644 --- a/scripts/src/bin/fmm_m2l_blas_mpi_f32.rs +++ b/scripts/src/bin/fmm_m2l_blas_mpi_f32.rs @@ -96,7 +96,7 @@ fn main() { if distribution == 0 { points = points_fixture::(n_points, None, None, Some(world.rank() as u64)); } else if distribution == 1 { - points = points_fixture_sphere::(n_points) + points = points_fixture_sphere::(n_points, Some(world.rank() as u64)); } else { panic!("Unknown distribution") } diff --git a/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs b/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs index 0a5a511f..f596e89a 100644 --- a/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs +++ b/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs @@ -99,7 +99,7 @@ fn main() { if distribution == 0 { points = points_fixture::(n_points, None, None, Some(world.rank() as u64)); } else if distribution == 1 { - points = points_fixture_sphere::(n_points) + points = points_fixture_sphere::(n_points, Some(world.rank() as u64)) } else { panic!("Unknown distribution") } From a88b4c2190ec401a8cbc14262acd2c2dc37778a7 Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Tue, 21 Oct 2025 11:43:41 +0100 Subject: [PATCH 41/52] Start adding weak scaling for sphere --- hpc/ijhpca/Weak Scaling.ipynb | 181 +++++++++++++++--- .../err_run_0.log | 0 .../err_run_1.log | 0 .../err_run_2.log | 0 .../err_run_3.log | 0 .../err_run_0.log | 0 .../err_run_1.log | 0 .../err_run_2.log | 0 .../err_run_3.log | 2 + .../err_run_0.log | 0 .../err_run_1.log | 0 .../err_run_2.log | 0 .../err_run_3.log | 0 .../err_run_0.log | 0 .../err_run_1.log | 0 .../err_run_2.log | 0 .../err_run_3.log | 0 .../err_run_0.log | 0 .../err_run_1.log | 0 .../err_run_2.log | 0 .../err_run_3.log | 0 .../err_run_0.log | 0 .../err_run_1.log | 0 .../err_run_2.log | 0 .../err_run_3.log | 0 .../err_run_0.log | 0 .../err_run_1.log | 0 .../err_run_2.log | 0 .../err_run_3.log | 0 .../err_run_0.log | 0 .../err_run_1.log | 0 .../err_run_2.log | 0 .../err_run_3.log | 0 .../err_run_0.log | 0 .../err_run_1.log | 0 .../err_run_2.log | 0 .../err_run_3.log | 0 .../err_run_0.log | 0 .../err_run_1.log | 0 .../err_run_2.log | 0 .../err_run_3.log | 0 .../err_run_0.log | 0 .../err_run_1.log | 0 .../err_run_2.log | 0 .../err_run_3.log | 0 45 files changed, 153 insertions(+), 30 deletions(-) create mode 100644 hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/err_run_0.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/err_run_1.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/err_run_2.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/err_run_3.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260022/err_run_0.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260022/err_run_1.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260022/err_run_2.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260022/err_run_3.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260013/err_run_0.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260013/err_run_1.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260013/err_run_2.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260013/err_run_3.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_2/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260002/err_run_0.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_2/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260002/err_run_1.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_2/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260002/err_run_2.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_2/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260002/err_run_3.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_2/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260015/err_run_0.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_2/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260015/err_run_1.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_2/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260015/err_run_2.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_2/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260015/err_run_3.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260141/err_run_0.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260141/err_run_1.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260141/err_run_2.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260141/err_run_3.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260230/err_run_0.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260230/err_run_1.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260230/err_run_2.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260230/err_run_3.log create mode 100644 hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259808/err_run_0.log create mode 100644 hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259808/err_run_1.log create mode 100644 hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259808/err_run_2.log create mode 100644 hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259808/err_run_3.log create mode 100644 hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11259810/err_run_0.log create mode 100644 hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11259810/err_run_1.log create mode 100644 hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11259810/err_run_2.log create mode 100644 hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11259810/err_run_3.log create mode 100644 hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11259807/err_run_0.log create mode 100644 hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11259807/err_run_1.log create mode 100644 hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11259807/err_run_2.log create mode 100644 hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11259807/err_run_3.log create mode 100644 hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/err_run_0.log create mode 100644 hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/err_run_1.log create mode 100644 hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/err_run_2.log create mode 100644 hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/err_run_3.log diff --git a/hpc/ijhpca/Weak Scaling.ipynb b/hpc/ijhpca/Weak Scaling.ipynb index 98c8c232..4455e340 100644 --- a/hpc/ijhpca/Weak Scaling.ipynb +++ b/hpc/ijhpca/Weak Scaling.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 21, "id": "86084f2e-5662-445c-8bac-e630a73d764f", "metadata": {}, "outputs": [ @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 106, "id": "182a3aa0-aff6-45a0-a6a2-df618808793c", "metadata": {}, "outputs": [ @@ -46,21 +46,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[34mstrong_fft_n=128000000_p=64_points_per_rank=1000000_distribution=sphere_11248828\u001b[m\u001b[m\n", - "\u001b[34mstrong_fft_n=128000000_p=64_points_per_rank=62500_distribution=sphere_11248825\u001b[m\u001b[m\n", - "\u001b[34mweak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11248788\u001b[m\u001b[m\n", - "\u001b[34mweak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11248790\u001b[m\u001b[m\n", - "\u001b[34mweak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11248787\u001b[m\u001b[m\n" + "\u001b[34mweak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260141\u001b[m\u001b[m\n", + "\u001b[34mweak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260230\u001b[m\u001b[m\n" ] } ], "source": [ - "! ls data/sphere" + "! ls data/sphere/deeper_local_3 | grep weak" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 173, "id": "178da76c-f2f3-4e55-859b-37cccde9f575", "metadata": {}, "outputs": [], @@ -70,21 +67,25 @@ "df2 = pd.read_csv('data/uniform/weak_fft_n=4096000000_p=512_11079233/weak_fft_n=4096000000_p=512_11079233.csv')\n", "\n", "\n", - "df3 = pd.read_csv('data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11248788/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11248788.csv')\n", - "df4 = pd.read_csv('data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11248790/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11248790.csv')\n", - "df5 = pd.read_csv('data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11248787/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11248787.csv')" + "df3 = pd.read_csv('data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998.csv')\n", + "df4 = pd.read_csv('data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260013/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260013.csv')\n", + "df5 = pd.read_csv('data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260022/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260022.csv')\n", + "\n", + "\n", + "df3 = pd.read_csv('data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260141/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260141.csv')\n", + "df4 = pd.read_csv('data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260230/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260230.csv')\n" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 174, "id": "754504a1", "metadata": {}, "outputs": [], "source": [ "import seaborn as sns\n", "\n", - "def weak_scaling(dfs, names=None, title=\"Weak Scaling Performance\"):\n", + "def weak_scaling(dfs, names=None, title=\"Weak Scaling Performance\", plot_err=True, stat='mean'):\n", " \"\"\"\n", " Plots weak scaling results (runtime + efficiency) for one or more DataFrames.\n", "\n", @@ -117,21 +118,37 @@ " # Aggregate per experiment\n", " stats = df.groupby('experiment_id')[['runtime', 'p2m', 'm2l', 'm2m', 'l2l',\n", " 'source_tree', 'n_points', 'source_local_trees_per_rank']]\n", - " runtime = stats.mean()['runtime']\n", + "\n", + " if stat == \"mean\":\n", + " runtime = stats.mean()['runtime']\n", + " elif stat == \"median\":\n", + " runtime = stats.median()['runtime']\n", + " else:\n", + " raise ValueError(\"Only supports 'mean' or 'median'\")\n", + " \n", " runtime_err = stats.std()['runtime']\n", " n_ranks = stats.size()\n", " n_points = stats.mean()['n_points'] * n_ranks # total DoFs = per-rank * ranks\n", "\n", - " # Reference runtime (lowest rank)\n", - " T_ref = runtime.iloc[0]\n", + " # Reference runtime\n", + " T_ref = runtime.min()\n", " efficiency = (T_ref / runtime) * 100\n", "\n", - " # Runtime plot\n", - " ax_runtime.errorbar(\n", - " n_points, runtime, yerr=runtime_err,\n", - " fmt=marker+'-', color=color, capsize=4, label=label,\n", - " linewidth=2, markersize=7\n", - " )\n", + " if plot_err:\n", + " # Runtime plot\n", + " ax_runtime.errorbar(\n", + " n_points, runtime, yerr=runtime_err,\n", + " fmt=marker+'-', color=color, capsize=4, label=label,\n", + " linewidth=2, markersize=7\n", + " )\n", + " else:\n", + " ax_runtime.plot(\n", + " n_points, runtime, marker=marker,\n", + " color=color, label=label,\n", + " linewidth=2, markersize=7\n", + " )\n", + "\n", + " \n", "\n", " # Efficiency plot\n", " ax_eff.plot(\n", @@ -165,13 +182,13 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 175, "id": "67d9abc3-f9bb-41ec-9778-9912fecbc924", "metadata": {}, "outputs": [ { "data": { - "image/png": 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Mx0EHHVS3cuXKwHdff/113Y477lg3ePDguhUrVjR6btQP5Vx55ZUNPv/qq6/U57vvvnvd9OnTA5/jGqD/4Dv0k3DXNZgXX3xR5XnmmWfCtkszefLkur59+9aNGDGibtasWQ3Ofe21125X3zvvvDNif7344ovV93/7299sy+O0004LmwbjF30Q6VBXDfo7PrvwwgvriouLA58vWrSobv/991ffQQ7B57K2RX+G46677qqrrKwMfHf33Xerz4899tgG9UFdg+eB5cuXB/oHxrSmrKys7oQTTrDdN/V1O/DAA+uWLFkS+Bxlnn/++eo7tDtU30S91q9fv12fQv8sKipq9Ny6n+l+iHF18skn1+20007qM7TDOieGItRYt8OaNWsC8wnq3RjRzh8oH3M9+vk777zToKw333xTfT506NC6VatWBT6/4IILVDm33HJLXXV1tfoM53nkkUcC50Y9NPPnz68bOHCgKuenn34KfI46QR5ID3lq3n333UB/tI7rxYsXq98mfGftA+GIdhwA/VuGNHbAfK/7t5WDDz54u3EZqm8Gg/aiT0+ZMiXQr0eNGtVg7Dz++OPqc8xBsaLPjzkJcg53aDlZr6c1P9oSDU7bHolXXnlF5dlrr70a6C7hePTRR1V65IumnsHoec86j8Uyf+r0VVVVjc4bsc7xOD755JPA51rP0f0esoNOp3nuuecC+Y477ri6jRs3Br674oor1Oe33nprSLno762/B4QQQpzDJfyEEOIy2BUXngpYcmVdggkPJHgC6B1q4c2HtPAc08tZ4dGAfEijvQawHA5eLPCiwI6w8MzRwINBe4Y+88wzgc/hrQWvCHiLWL0g4KmlvV3hjREKeBshlhy8SeCJhA0y7IK6wZsJYQbg/YmloPCiwu7K8AyC5wXaoJf6a+BNgZhyffr0UUuZrZ6jqAPkAc9CeGBgV214RcEjN3h3Y3gF6dhs4dpnB8Q+RPladqgPvL6sXofaswvL2OFlZ91ECNf1vvvuc7wxkF2wFNfq9QOPNSzZxrW0Ez4gHPBQA/A+hAeoBn3zlltuUf0P/QSePo0BLzl4A+kQA40BD1d4PmKDH+sGLDg3ljLCSwgbf+jlouhfuF6vvvqq8oi0Ai8dvcRZx7WzA+qM/mg9MKbgnYQ2wHMX3tm6z8GrEv0d1x0hDrCkWNOtWzc1DnTb7IDdyLGU2+rFDc+n4H4YDowVAM9Qq2cuNrfDuMZGXljSb7cfIL3V2xhl4pqivWh3qM2acP2scYaxPBXejvA4hKdlY2BeANjMBsviERYD1xjeVegX8GQL5dHuFHhCwqtfe3ZHs+Gd3fkDnq84D/qu9qDUYE7BZ/B2hOcagOcZYlKi78OTG97EAOdBvwy14zq8tNHn0Rbr7uioEzxfkQdec5Aj0CFl4FVt/e2ANyr6L+Y1a78OhdvjIBx6bgslOztxtvWybX2gP+lwBfA0hbcf+r517Gj5YGw6BXMSwmKEO+CtGAl46ga3wXoE/z46bXs44NUJr3KAOdKOx6MOOeLF5lpuzZ9u923ocAhBowleIQOPUqtXKn5nNFdccYXydtVofSPcBnlarnbCYBBCCLEPl/ATQogHYBktbhBwYwGjIIw8MI5iuTSMa0AbU2EUgBEKcbT08n2k0/z666/KwIplWVbjqQZGSSjiuAlGOtxUW5fza2CYRR1gyA1nSIJRA+EAUBaWg2L5V7TAWIMbC9xIYUkmjGtoF4yfMJxiCStuDnGDoTea0jdTWI4aamkzlu9H2kEYhjbc2MDYog2n0RjKggllVNKb/FjjPcL4DULtwI4bTxix9e6+XtGxY0e17DcY1AmGJsgfy3ujBX0B/Rd9K1T7sCQeIRqwzB43aTr2byhgaMSNLPomljY2FloB/Rj9HoSKtwcDIIwDMJQiHZaBYjktlm3DmIaNyrDsEkafRYsWKUMo4tNBHtZdlhsD/RV91QoeQuDGHDEXYbjBq8Y6fkMZHjDecRMNQxjCa+gYquHA0szg+up+iCXfCFsQSZaYe3DTjQcUCPNw2GGHqT6Jm3TMJ5FCeGiwVBxjCkZQqwFOA0MC4odiiSnGMYwIGsx1oYx6aAPKDI5jGwrE3UTanj17BgyGAIZc9CW9ZBtG/sZCEdgFRlNsHqXDWkRroLU7f+j+Em4pMa4XjIR6ftSvMOgGG6kwb8KghmXHVrQBJdQ4Qh48jEIelA2jmd7YDfMzDHgoE78xuP7Wvh4Jt8dBuDkCD7DQ/4MNqIh5i9AjMFDiIUK4UADaQIXfDyzLxlyJcjGX4cFkKOOeHo92Y/hGAr+PkTY4Qx0ixXLF9dD6RChC6QtO2h7uWmOsYAk75phI8YatrFixokGsVS9wOn+63bdDzYWR5g3rg6fg64HYsZH0HN2vMH8TQghxDxpQCSHEA3ATgs0AYIA68cQTAxvX6PinGtyYwoCKm1yrAdWaTt9oIFZeJAUcN+YwkmqlGze/MBjBGAojkt6VWBsoceMUymAEAytuOuA9ixtQuzfNoXaDhdEKh/b40PH2cMMGIy/ewxBg9XqyC+SGm0vc/KOtepMON3bz1XE1rWjjjY61ab05gREzFJFujt3CarCyoj1S7Wx+Ewp41kKmwd6LodoXvBFaqM118JAAHox2ZIJza0NTKKOdFesNIjynYTSENzbOh7GDm1LE2kQ/w3jQN552wLmDjfeR0LKOZBRA+yEvpG3McBSqrlaDQGMGABhXMA/hYQbmIh2XGcZHGMbgbdajRw9bbQrXx3WbQvUDGFdDjUfdhlBzUKg24JqGAkZz9HP0AcwvVi/pWIHhAxuzIT41rg+88OzE4oxl/misvwTLVacPtyt4qLGlx0ewkTFcOhhR4d0KozFiHOPANYQxCoZe/J6Fap+X4yDc/I/8MJpbN2DTwOsQcwji/8LAF4rgB3F40AIvQMTQxlyBGNLBwDgGGovfHA9wnWL9jYml7cGgb1xzzTXKGIkVD9E8cNXyC7VKw/qgBHNEpN90bcgO9fDE6fzpdt+2epCGIvh7a7uDx1xjeo7+zQ5e7UMIIcQZNKASQogHwBgKBRYeTEB7fQYbUPV7GFCx0ziMnTAiYim6Rt9wwyO0sU0rNFjeihsbeBHCWAKDLjy4YGDADUkkj0Qs8Ue94M2HnZ7h7WRnGR88IeDhitdQHpEwguCAhwqW+MNQAa8nnCuanZ0hD+zujaWsuEnGclmUCfngvDB4xWvjDm20tRpFrNgxENkhXPnBN5uhzh2Nx2Wo/JHQN6+RlmzCAwwbFiEN+rl1SaG+uYOBH0YQGP/1Zje63MZ2aA42ICN8gXXTHQ0MKY0ZAuOBHZlp3HgYgPGLByFYlgujEzzN8MABhkF4d8JTL5SHsRv9wI36NwaMmzD+wQPOKfBgh/EUhg8YDOFdHa3xNBoak60e91qujckz1Fygrw28tCMZi6webghPAQ9FPOCaOHGierD3559/qgMhAWBgC/fgxotxEGnzKBjvtGduKBC+Aw9W7BjK8LuLDergSYnwCjCEIa8VbaTXIQ8aAxvwwdgGL+3GDM+JxE7brWBsPPTQQ6oPw0CNJebRoH/zQ/22WZfFw0s9UsgIhLgIZ4iNx/zj5nwY6291pLklGt2KEEJI49CASgghHoAbWXivwWsU3nQwWmDpWLAHKW5aYNDBzqvYkRbGJsT6C+XxAuNpsNdIuBsKeIJAgX788ce38yDFTXE4sDQZu77iZhPenfCcRcw7O94oaCfqjpsd3HCHM+yhvVj+9vnnnwe8YvWyOh3PMhh4xUA2kCmWzcN4ClnCMBfsjYXlt/ECxm7EcYSnYygvuWiWz+mbq1BLQxFLNhzhZKY9l6PZPTzYGwYGalwjyDSUEV3H6IsUDxCGLdwkw7AevBze6o2E72BogAFVnxs3f4gpZ8fIgjLgjYz+gIcFweg4rXaWrceK7scIJxEOHWLCS8NcqJAHhxxyiDq0txkMO3jQgPEdyYBqp026H7jdJvRt7JaNvoOwIJHOHc4r0y7oH9idGwabkSNHKsNyY7E+nQLZYv6AbEPNH8HjS7dRj+1gQnmb4xwoH2EtojF64pzwUMaB8QvvZcRahhEVxjP8TkRql5fjAGMdDwPgdYgdz0MZz9BnEK4CdcDvGK6pHTA/4AEdPLfR92D4tBqX8RsEQyge+sBLOdJSd1y/Rx55RP0WIxSDyQZUO23XQB/AwxcY/BCPGt6n0QJZ4Dcdq1GCf1vgOYrv8duD64dwLKFAv8Qyea9DAfhhjg/n4Wt6nyOEEL/BTaQIIcQjdBxTxGnDTUKw96l1GT9uFBBDUL+3omPSwShpjZ+nmT59uopFiSXxuFHDUlYYUXHTEWr5vfaGDeX9hBsi7amDGyMYXuDBo+NRNnZjAa8VnLuxjYtw8wm00UB7rMLbKRQwZsBDETcF2qsXxtpgownOrb+P5LXpFjruJwzlwcAzCjf3dtHGmlBL7iJ5OyHUQqjNRrShHEaExgjlGQMDJjZDgxxDGd1h3NQbM4WKr6hBn4BhM9SBcA76+uO9liMMplhKjHPr8BdW0HdPP/10ZdzBwwftPQjPORgsgsGNOuqKG/Nw49ANsAkLZInrHsqQDyMd+jBkEg9PWBhIYRzFxkvBD270ctvGjPyoJ4wTmMNCefpt2bIl0M/1XOUWMKygDThmzZq13ffwqsWDG8R0tW5uFS2YQ7XxdMyYMfLEE094bjy1ygsPk8ItkbaGsYA3MeZnPETSnndWYFQMd45Q34Err7xSzaWIxwlgJMUY0eFkAM6JmMOQEcDqgUSOA/ymwvsfRtFwG/VhDkEMWaA34bILVoPg9xPnwGZ51t9K/EbqjYjwcCechx/y4OEEXjGXwaPZD0RqO8CDDBhPEVoD80osxlOgN0sK9wAQfSjS2ND9CGMWxn7rqh0vMW2OD4d+mNJYiBZCCCHRQQMqIYR4bFzTRqJIBlS9JBE3qsGb8eh4hbhpxY7WVqUdxjZ8hp1Y9a7JOgYqPJvg1arBjRBuJGEQ1Qa+SOC88EZBPuzYbWdTJr15FTxUsFmUXuKuwU0/wgLAWAbDnvYsgWEAN0AwkmAZofWmDctF//jjD5UWXri6fTC2Wm9eYeDBMkK82mmfG8DwACMPlodrAwSArGCg0nWxs5RQeye/9957DTxOYbyOtJs0ZAWvTWu/gLEQdYIn51FHHdXouXX8uOAbQuwwD2AIQExRDa4rriO8f/TuzW4DYyi4/fbbAzuxAxhVYSSFMQ+ePtpDCgYeeNug7VZDH2SJfoGHD4jxZyccRazosQo5YtMsq5ELRm6MI6B3ZHcTfQ1h0NTgAQWuEeYg61wAdJgLvZFbJHQ/QP2txnq0D+1EexE2wW0vMBgx9WY3OLc17iQewmjP+Fg2SdNgnkC4EhhiMFYQpzec97zbwPiEjX7g7R+8WRB+D2AoxPc6fik82rAUHw/cMO9b52SEZAj1oAsPGtAeeBXCk98Kfg8Q7gUP3fQGNvgdQfxGLM+2zgeYa7VBtzEvbq/HgX5AN3r06IjptNzwWxHNagA8PILxEPM2Hsjp8B9WIyO83HXoneCd0NFuPIDEbu0w5MayGWOiiNR2PHzF5mLQU/A7bderNxR4OAf0A89gdNgFrDLRD5eDHxyinjptvEjkHB8NWq6hwikRQgiJHS7hJ4QQj0BMTr3bNG4EtEdqMPgcN7i4wYOXinXnVQ2MSLhJw40EPB9wA4sbHNww48YfSrKOQYZNC/bbbz/lzYfYoPBegnEFRigs/US9cPMBr7zGgMEJhhakRziAxuKc4XxoBzYNQp1h6IKBBp5/MCbCWxD1RRw5azgCyAc37DCaIS9u6mEwhfEHRlUYUuCFCo4//ngV5xQ3c/C8hcxwM4MlplguHk37nALPF7QTXlwwNuOmENcc7cT5YVDCUj87sc3gLQXPN7QZ7YJBEGXA+xRyDbcbMwwsWEoKT0N4m8H4AVnAe/jee+9Vm0A1hl7aC48+xIBEOxDXDh7M6APPPvus8syDoRTlwaANgz7ah+sS7UYcdgg+N64zZIu24kYVHlBYZqqX96O9uBZjx45V/QiygGwwRmBERSxVGDu8BkY9GPcw/nCjjesI4y2MujB4weijDZJuAo8ueOtCBpgnzj77bHUd4S0HAyoMkZgncP0wl0COkA82omkMGOFwQw4DGvop5hTIH7LFuIbxP9KSbicglrOOv4lxgTYgzIWWJ641YirHCgyVehkwrlOoDYm0J5cTQ20o4EGPMQoDLnY+hxEU58HDL1wfyBgPL6yGaTwswQMoXAtcExg+MR4wv+N6BxukMNciDwzDkBViRsM7DueA4RS/PfAq1MuNEQPzk08+UXMIfkdQPsaY/v2A4RB9K1HjAHMrYm3jQcioUaMipsVvD+qLhweI63n55ZfbPg/mOsQDR//AbxXmIz2X4vcUv0Ew3MEbGHOLlisMapAdfufwQAebcXnlfQpvYfSRSOi40tEQru0wwuvfPTzowxEKzNeRViUAPHCBcRRzSKh5GX0Zc5MeH9APMM/gtxQ6FfojHh5iUzPMT/EkUXO8XXTIDYyRxq4DIYSQ6KABlRBCPATepPAiws1VOEMWbrJgEIWhLJyXKm5Y4DmKDTxw44zllbipxc02vKZwEwEDkgZGLexEDkMKlHrcJMPAgptjKPYwQuImFEaJSDtXwxsFnobwpkB5hx56aMSYbwDLPHFTgfri3PBcxE0ljKhYyggDDG7Ogr28UC6MhIjNCI8h3JxgeSZuRi655JLAUjTcpL755pvKOAuZIR1kiBsFtA1elygfcVJx4+U1aA+MD6g3bu5h8MWNO248YRCBATXcMlMrMBLDIwztQvux5BbXDN5LaE84AyrKhpERN5owoOGGGrEucR0au1Ya3ODDCAyjAAz0uBnUO1fDgIUbRHgUo7/AYw/XAOXDkOJljDWcG4ZQLBnFMmvIFh5y6L8wXgTHdIQhA/EZccDIi/4LAwrGB8ZJPDwLMVZhrMG1x1jFtcQ1gUEAIQf0smK3gQEffQ1LSNEP4NmujRCQAfoPrh+8h9Ff4T2Na2xnB3QYyDGnICYixh5uzgHk/7e//U0ZMELtgu0G6F/wwEYfh2EPXpQ4F+YSGIcjxW+1gzVsCDwGwwHDrdsGVACjMLz8YEyCRyPi0yLuNfo4HiAEL02GQQxe+ejj6F+Y53ANdezqUB59mL/hvf/cc8+pjQphOMXDCPRFGK/w+6SBbDHXo3x4c6NOeFiHMY/+gvR2vLi9Ggd68yhcdzt9Dl6oMGJCxvgdiQZ4GOL3BQ8JYGTGkn0NxhD6JYyIn332WSAsCeYcjAsYd9E/I8WHdooOoRIJHVc6WoLbfvXVVwfCpeAhXbh41gC/xY0Z7mCkRd/GfAWP6lC/I5Af5n/oEkgHfQLzF/ouxg08uL0MyWLaHG8XLVPoe1a9kBBCiHNS6tzaIpgQQghpYujdv3GTGmqjI3jG4aYaN9huxyKDFw4M1fBiCxc7lhBCCDERPODFQ0485NEhW4hzsAoDxm88EEpkHFZCCElGGAOVEEIIiRGEEYC3J0IbYFmxFXiJwXiKUATcyIEQQgjZBla04PcRKy+Cfz9JbCDMBuKxI4wCjaeEEOI+XMJPCCGExAiMp1i6j6WUWLKJcAhYJo4YrIgxiHACWFpPCCGEkG0g5ARiNp9yyilqSTxeiTMQrgLhJaKJ90sIIcQ+9EAlhBBCYgTxRxFb79JLL1WxCbEhBuJPIjoO4rfp+LeEEEIIaQhiGSN+MzYE3LJlS6Kr42sQEx5xou+55x5PY5MTQkhThjFQCSGEEEIIIYQQQgghJAz0QCWEEEIIIYQQQgghhJAw0IBKCCGEEEIIIYQQQgghYaABlRBCCCGEEEIIIYQQQsJAAyohhBBCCCGEEEIIIYSEgQZUQgghhBBCCCGEEEIICQMNqIQQQgghhBBCCCGEEBIGGlAJIYQQQgghhBBCCCEkDDSgEkIIIYQQQgghhBBCSBhoQCWEEEIIIYQQQgghhJAw0IBKCCGEEEIIIYQQQgghYaABlRBCCCGEEEIIIYQQQsJAAyohhBBCCCGEEEIIIYSEgQZUQgghhBBCCCGEEEIICQMNqIQQQgghhBBCCCGEEBIGGlAJIYQQQgghhBBCCCEkDDSgEkIIIYQQQgghhBBCSBhoQCWEEEIIIYQQQgghhJAw0IBKCCGEEEIIIYQQQgghYaABlRBCCCGEEEIIIYQQQsJAAyohhBBCCCGEEEIIIYSEgQZUQgghhBBCCCGEEEIICQMNqIQQQgghhBBCCCGEEBIGGlAJIYQQQgghhBBCCCEkDDSgEkIIIYQQQgghhBBCSBhoQCWEEEIIIYQQQgghhJAw0IBKCCGEEEIIIYQQQgghYaABlRBCCCGEEEIIIYQQQsJAAyohhBBCCCGEEEIIIYSEgQZUQgghhBBCCCGEEEIICQMNqIQQQgghhBBCCCGEEBIGGlAJIYQQQgghhBBCCCEkDDSgEkIIIYQQQgghhBBCSBhoQCWEEEIIIYQQQgghhJAw0IBKCCGEEEIIIYQQQgghYaABlRBCCCGEEEIIIYQQQsJAAyohhBBCCCGEEEIIIYSEgQZUQgghhBBCCCGEEEIICQMNqIQQQgghhBBCCCGEEBIGGlAJIYQQQgghhBBCCCEkDDSgEkIIIYQQQgghhBBCSBhoQCWEEEIIIYQQQgghhJAw0IBKCCGEEEIIIYQQQgghYaABlRBCCCGEEEIIIYQQQsJAAyohhBBCCCGEEEIIIYSEgQZUQgghhBBCCCGEEEIICQMNqIQQQgghhBBCCCGEEBIGGlAJIYQQQgghhBBCCCEkDDSgEkIIIYQQQgghhBBCSBhoQCWEEEIIIYQQQgghhJAw0IBKCCGEEEIIIYQQQgghYaABlRBCCCGEEEIIIYQQQsJAAyohhBBCCCGEEEIIIYSEgQZUQgghhBBCCCGEEEIICQMNqIQQQgghhBBCCCGEEBIGGlAJIYQQQgghhBBCCCEkDDSgEkIIIYQQQgghhBBCSBhoQCWEEEIIIYQQQgghhJAw0IBKCCGEEEIIIYQQQgghYaABlRBCCCGEEEIIIYQQQsJAAyohhBBCCCGEEEIIIYSEgQZUQgghhBBCCCGEEEIICQMNqIQQQgghhBBCCCGEEBIGGlAJIYQQQgghhBBCCCEkDDSgEkIIIYQQQgghhBBCSBhoQCWEEEIIIYQQQgghhJAw0IBKCCGEEEIIIYQQQgghYaABlRBCCCGEEEIIIYQQQsJAAyohhBBCCCGEEEIIIYSEgQZUQgghhBBCCCGEEEIICQMNqIQQQgghhBBCCCGEEBIGGlAJIYQQQgghhBBCCCEkDDSgEkIIIYQQQgghhBBCSBhoQCWEEEIIIYQQQgghhJAw0IBKCCGEEEIIIYQQQgghYaABlRBCCCGEEEIIIYQQQsJAAyohhBBCCCGEEEIIIYSEgQZUQkjMXH311dK3b1/58ccft/uutLRUBg0apL5/7LHHQuY/7rjjZMcdd5TNmzd7Vkec/+STT445f2VlpTz33HNywgknyC677KLaNGrUKBk3bpz89ttv4jU//fSTasPDDz8c+Gy//faTESNGSKK57bbb5OKLLw68P/3001Vdg4/+/fvLrrvuqmT4yiuvSG1tbdzquGjRogbvEym7WbNmyc477yzLli1LyPkJIYQQEn+gB4fSjwYOHCh77723/P3vf5cpU6YYUccffvhBvYeugvdXXXVVTOXZ1beQLpRsgg/oUJri4mK58sorlV4+ePBgueOOO6Sqqkpuv/122X333ZWuDpkGt8ku77zzjsr35ptvxtR2Qkjykp7oChBC/Msee+wh77//vkydOlUpLMGGPxgfMzIyZNKkSXLppZc2+B7KD5QhKDn5+fliIjACn3XWWTJjxgw55JBD5LDDDpPc3FylVEK5+vjjj+W6665TaeLJ9ddfL3V1dZJIfv75Z6VYfvTRR9t9B5m0bNky8L6mpkbJ7I033lBG19WrVysDtNf83//9n3z//ffy1VdfGSE7GJLRj2644QZ5/vnnJSUlJSH1IIQQQkj8OfHEE9WDVE11dbWsXLlSPVyeMGGCPPnkk7LPPvtIU+S+++6L+H3Hjh0D///rX/9S+id0Khig+/TpI6+99pq8/PLL6n5k9OjRKn2rVq2ka9eu6vtoGD58uKrP0KFDY24PISQ5oQGVEBIze+65p3oN9dQcRtPMzEylxLz33nuycePGBkY15IFhba+99hJTefXVV+WPP/6Qe++9V44++ugG3/3tb3+TY489Vu6//3458MADpVOnTnGr1wEHHCCJBB6kME5CJt26dQtZv86dO2/3+WmnnaaM0M8++6wyOhcWFnpaT9yMpKenGyU7PEhAHfDgIbhPEUIIISR5GTJkiBx11FHbfb7vvvuqVVkw2jVVA2oouYRDe6PC87R58+bqfzyk1w/x+/XrF0hr/d8uXbp0UQchhATDJfyEkJhp166d9OzZU37//fftvPpgQIWiuP/++yuDGzwBrfz666/q1WQDqq4jFNtg4DV7yimnKCNwopddxZvPP/9cFi5cKKeeempU+WAwPeigg9QyK/SZpkj79u3VmHjqqacS7kVMCCGEkMQzYMAA5SU5d+5cKSoqSnR1jAcr3IA2nlo/y8vLS1i9CCHJDw2ohBDHy/gRw/Svv/4KfLZ48WJZsmSJeoqOpTTwAoRB1covv/yiFB/ELtJgafdNN92kYiYhLhQMl3i6DO/VUEa8c845R3bbbTeleOL1wgsvlD///DNifbFcauzYsSq20SOPPBIxbbNmzdQrlgSFMnadccYZann/EUcc0eBzfIZzQDZY/oOn6vBmtZYBRe/pp59WHgdIg/YituqNN94o69evjyqulI7xhGsAz1AYpREa4cgjj1Tev8HMnz9f1Q/XBuc+77zz1GeIR3vttddKYyAm7A477BDTU32EQLAbpxZxX/EdwkFY43H997//lddff115N6OdWL6F0AAIC2FNh/60fPnyBnF4I8kObUc/guEf8VzxGfoelv3jc8TagucxjMfBfPjhhyrGK/JCpjAuf/311yFlgHrPmzdPvv3226jlRwghhJDkIzW1/rYcD+Y1kydPlosuukit+IKui6XlZ5555nYxPaHbnHvuuSoEAHSVYcOGyUsvvaS+W7Nmjdx5551y8MEHy0477aQOrAZ6/PHHlU4cLdHo6l7tC6AdF6wxUqGHATykxnvoguFioH722WdqVRTCKSBGP3Q+a5pwMVCnTZum7jWQB/on9DmsqrJeM7u6qgb3Bkin7wdw73DBBRfI9OnTAyGzUN4tt9yynTzWrVun+sU//vEPV+RLCGkcLuEnhDgCSh1iN0GZgVENfPfdd+oVBlQ8CYaRFJ9BSUDcx/LycqUYjBw5MrDEeunSpcqIBsMiYkRhSfzs2bNVTKOJEyeqV73kG/Ej7777bmXUuuSSS1ScVRhOYSyEovG///0v5PJwKIqIvQnjK4LPn3/++RHbhvp8+umn8s9//lMpU1ASobxC4WrduvV2y8MBFDCUi3afdNJJyksXhrRbb71VGfO0knPZZZfJN998o8IAwPBWUVGh2gllDYY7KFPRAoWrbdu26hVyfOGFF+Saa65Rn+lwCzCUol6QBRRGtAOKJLxp7WzuBEUcYQ3OPvvsqOuHc8JomJaWpuKBxgr6QklJiapzhw4d1PVEH4Qh/4EHHlDXHsvgcMOAGxIs54LyGQkYkXv16iVXXHGFMpC++OKLSkmGwRdxtHC9sCEVbkiwDP+DDz4I3OwgjAOM4TBco3/hWiI+Lm56QsXIhXKMvF9++aUymhNCCCGk6QL9EPoZdF+tv0K3ge6Bh9vQK/FQH/oh9EToLNB5rbE9oYfjezwg37Rpk9I1tmzZonRq6EfQmRAPFN8hL3Rb6OPQh+0Sja4eLRs2bAj7HUKCQa+Gngb97t///rfSyfA/dFfoVOPHj1d7Mug4/OHqAcMx2g69EHpeVlaWqjcekOO7UKvOAOLp43ogRBXSQj/E6jqE+YLsYay1xrZvTFfVIC7+22+/rQzfKB+rtOC4AR0dOifuO3DdcD+CB/qQhQZGY+jWML4SQuIDDaiEEEfAiAmDGJQHGOYAvE1hmNMeijAsYcf6mTNnqielWL4NBcG6fB9PZcvKyuTdd99VioIGS75hrIOyg6eveMqLJ+wwwMETEue2Lqt/5plnlBEVgeWtQMGAkvjFF18oZQXeo40BZQbK1M0336yeKEOR0U/0YSw+5phj1BNsqzKDtFByEeMSxlMAucCIBoMmPARghIRRFXnxFF+DOo0ZM0bJB4pktEpojx49lCFPK3DwMoACBsVMG1ChbEKhRqwofA/gLQklEspvY/z444/qNZL3KZRDqyKMaw2lG3VbsGCB8p6AMhkra9eulU8++SQQn+r4449XnhUwBGMHVii18Pp98MEHlZHbTlwtKOVW2cGDGtcI8UrRBzSrVq1SSjD6A/opvBGQDwoyrr0G1xvXGkoy+iKW7mtwE4C6w7OEEEIIIU0DbE5q1Y9giIQBEituoCth53gNdF3o0jCmWVfvIPY8dGbobFYDKsqGYRGGUw0eBq9YsULp0NCTNDB+Qi/Eg/xoDKh2dfVYsNY7GHiVom2QB3Q6GCdhQLXqd7j3gAE1XBx+AF0UOh2MkvAc1fo7VmxBPo8++mhIAyrajHsH6P44t84HPR6r2Z544gll4IRnr11dNScnR63Gg44OL1Xoi1oHRbpDDz1U9QHUFwZS9BE4IWDfBQ2uA4zYuBcjhMQHGlAJIY7AMnws4dHLaaAMYokNfuC1IgBPVChV8M6EAVXHFsVyFoB4T/BQxXIgGJesyiUMdVA+4K0HpQwGUyiNUGasxlMojvBEBcHLY/B0+qqrrlJKCzxA7RhPNVCkUE8Yu1BHKDtQdhGnCk+d8fQXhlEYb2EghuENCpU2ngLI4Z577lF1RDoYRmFQ1h6MGizd1/Gc8NQ6WgMqQglYn37juuglPgCGU7QBhmttPAWQI7wl7RhQEZ4BWBXnYGBYDgU8YeGhiSf3TsASJ2twf8gRBnXIHp4VUEqjBUqvVXYwqMKACgXWit40C0vYIAPsAqvzB3tP4DOMBdygBIcoQF5cCxj2Q3kyE0IIISS5gOEMRzBt2rRRD2GtnoTwNMUDaavxFDq21h2DdV3owDAMWoG+e/jhhzfYxBVAX4G+GVxGJKLR1WMBThHhcGvTUXiRwhEDzgVW54cWLVoo79BQYaYAPE0RogBG4mCZQdeDARVttxpQ7eiqeCAPUK5VB4WO+NZbb0mrVq0CejXuo+CcoQ2o2Ehrzpw5alWUNS8hxFt410YIcQyeGuMpKZ62IrYjDIXaOKoNeVBOYEzCsiMYIaFUaCMcjHIwcmLX9EhPoLE0GkttoPTAAImnvVhujaVPK1euDMQYDY5XCo9OeAoCnDtaAx6UUiiMOnYmlEh4suKpMIymeMWSITzZBthYK5hgj0u0AU+moZSh/fBohAFVK0F2ltMHgyfzweewlgWlDQY7eKoG07t3b1vn0AqzNXB/MFjSjrrgvFhOhqf8uCYwIruxaRhuNILRbbXGoXIiO22cD/5c37homep4qDCahwP9MxgY0iETyBOGZUIIIYQkN1iZYtWPobtAp4E+HGwEw8NVeI/C8xLL+6FLQFfU+kewngg9O9QDWegt0MMQfgl6IHRVPKQH1of9jRGtrh4teqWUl0TS0yPpwVrXe+ihh9RhR9ezo6vieoarjzXUFa4T+g1kD+MrrjW8T9Fnjj766LD1JoS4Dw2ohBDXDKhQzrB8Bj/oVgURyhvSYOkJFCuksy670Uoglt1E2tldG7Ww3Aief1B2sGkPgufj6TcUHMQaDQYKJZau4/x4eounulhKEwl4GGK5PmJPWZ8og4KCArXUHpswYdmSXoptNxg/DLBY8g2FGGEC4A2Kp8sIMg9vVsTXjIVgj9ZgsDwMaE9dK9nZ2VGdI5KhEpsX6OVT6AdYwo44r4jhheVRuM52CHcOL560h5KJnXPpvvuvf/0rsOlYMKHCFei2Wb2oCSGEEJK8QG+1ayhEGCJsRAR9Croi9GjE7YSuefHFF9vSAeE8gA1XkQc6K1aEYdk/9DR4YUbzsD5aXd1EtB4crR6pHTOw7wL2QQhFsA5o5xzR1AfeybiPgfMFdGrcB2HpfrhwBYQQb6ABlRDiGChiMMBhYyjEH4XRMXi5DTwP4TGKzXWw/N5qYNU//ghmH0qxxKZQ+sk6lv9DacDSasQDsiod8DQNBQyUWMKEOsDjU3tCNhaH86mnnlLes8EGVA28aKEw6SXjuh2hdmmHkRVxjrBMBwoQPHX/7//+bzslVC+39wIsP4e8QtUPsUntoJ+oR7PbKuQMwym8NK+++mq1IVf37t0bKNtYlhaMl7JwC33NIRcY84O9NSDXUEvC4HmK/uzWsjRCCCGEJAfwPIUOCv0aD9aty82jecgOb0ksOUcevdGrNtxBj4NDgF2i0dVNxaqnB3ucIl4slsRjRVm4fPCsDW475IvQBqE8TqOpD+6drCC2KkJv3XjjjUp3h7MIQjEgHBlWkmHVGjaKJYTEl8juSoQQYgModngiiyX6WNKOJ9zB6M8QfB3KFZ6Ea7BMGvlh3MQSeyswNiKoPp7CAyxdAXiCbjWewiAFz9JInqBQ7GC0hEKCYPCRwHIZ7JD+559/qk2CQgFvVpSlN6xCfFcYC2EkDjb+YfkUPoeCpY2PwTvDw3tXt9+uN2s0QPGCBwMUPSyttz5ZR/2iUfag3EcDbgLgMYFlYzCiWr1LsYQdmwFY40pBRrj2ToBhNpZQCNGgrz12X7VeM9ycQAnH5lzwZg4G8uvYsSPjVhFCCCFku5VK0M1gKLMaT+GAoDcztROyCLoUHBx0/HYNyoAhNJqwR9Ho6qYC71noXdiYy6qzQd6oO/RwxHcNBk4fcJiAkTU43j1W4F122WUx6aw6ninKDQ41gJiwCLmg9USslMLqPTiSwCED9cQqOEJIfDH3EREhxFfAMIcdJIHVu1SDXcixMQ+W7yOwenAMTQTPh4ciPDSxOyielMN7DwZXGD71U1YY4vAeT+ah/CFuFGIIQZmAMRPo11BgZ0scCNw+fvz47Tb3sXLnnXeqHeMR0xNLZrALKAyrMPRhN3rEIho5cmQg/iUMwwghACUSMYlOOukk5WGIoPUwWl5++eXKWIinyFBeYUjEUn7Ew4ShFvGMYPSD8S1SG5wAox7qhQP1hkEX9dObgDVm0IPnLuqI9NbNDuwAYyI2VEIf+M9//hNYggZZIQA/ZI3QCmg7rjsMvsGKajQg+D4M+jAOo98Ee4i61e9RZxjvsaQKns642YG3B5bO4fpaN+wCMKiiz0aKm0oIIYSQpgm8I2H0fO+999QqFjxwX7NmjdITsd8AwAZTjQG9FXH6oVtjp3cYZbFhKPRXGFahz+Izuw9z7erqsQCnhEjgHkJvjhoriDUKXRQ6J+qPzVfR/jfeeEM5aCBsQiigp8MBAzr0kUceqfJCn8e9AO4PoOdB34sW3C+hPFzXVatWqeuFfSReffVVdU9x7bXXNkgPvfv5559XG9hC54xl01RCiDNoQCWEuIJe0oIntOEMVVAUEPczlIEVyiGWdiNYPoybr7/+ujLuwcMPhjb99BwGSRjEsCwJCg+WfsOoCaMoFDqknzRpkoq3GQ4oQfCWRVxU1MW6S2bw03YoNVAMsbsmduiEwoo2QmmEgfXYY49toHjCoIp0aAeUHDzdh9IHpQzKqza6of4wAkOxhcEN3ogwsEJpRt3RBi8Mfqg3FDOcH0ZcKI6IoYRwCBdddFHYWKAaGDVhjIT8okXHokW8V8gHHr5YsgSDM2QI5RkyhSwQmwvX9Yorroi5rePGjVPKPtoKBdULeYI77rhDlY0+C09UGJgRogCfh4q1q2UHRZkQQgghxAp0Max+gmMCDHRvvvmmMtghFip0JoR/gidoY8ZP6M/QSWCIvfvuu9WSfXi1QvdE2C14TyL0FvRAO9jV1WMBTgWROOOMMxwbUAF0bRhSoQND94UREuXee++92z3wtoKH/VhlhusCj1Hs6QB9FbozNgcLFa7JDjgv9kDANYaOjGsET194tQZvLgUdHnXEA3rcfxBC4k9KXfB21YQQQpIWeC7AMByscMOjFN64CJB/6aWXRiwDyjwMm1gCNXz4cI9rnHzA0A8vVMTybWzjL0IIIYQQQgA8T+E5DF2cEBJ/eOdGCCFNCDzBh6dAcNwrvSmBHS9N5IenLDwPSHQgnhU2FIOnBo2nhBBCCCHEDnB2QBisSOHHCCHeQg9UQghpQmDpEWK6wnP00EMPVUY8LCn/9NNPZd9991VxoezEwsLSMYQawLL74J1MSXiuuuoqWb58uQrzQAMqIYQQQgiJBEImIAQaYtdiDwno7Ix/SkhioAGVEEKaGDB6YgMtBP5HDFnEgMXOnmeddZaKU2oXxBdduXKl8buumsKMGTNUbFfEIsPmZ4QQQgghhEQC8fWfeeYZFb8WsWz79euX6CoR0mShAZUQQgghhBBCCCGEEELCwPWDhBBCCCGEEEIIIYQQEgYaUAkhhBBCCCGEEEIIISQMNKASQgghhBBCCCGEEEJIGOzvFkJiZvr06VJVVaV2XM7Kykp0dQghhBBCPKOiokJqa2slIyNDBg0alOjqkBig7koIIYSQpkKFTd2VBtQ4AAUUe3XV1NRIaWlpoqtDCCGEEBIX/Yf4E+quhBBCCGlqVDWiu9KAGgfw9B4KaEpKirJop6dHJ/bq6mrbeeymtZMumvP6GVPa6XU93CzfSVmx5OUY8BZT2umXMeC0nGjze5Ge/d+8dvql/9spq6ysTBnfoP8Q/+uuOTk50pQxZY7wK5SfcyhDZ1B+zqD8nEH5+UOGdnVXXsk4gKVPeHoPBRQG1N69e0eVf968ebbz2E1rJ1005/UzprTT63q4Wb6TsmLJyzHgLaa00y9jwGk50eb3Ij37v3nt9Ev/t1PWrFmzlN7Dpd/Jobv2799fmjKmzBF+hfJzDmXoDMrPGZSfMyg/f8jQru5K1wBCCCGEEEIIIYQQQggJQ0od/FRJXKzZubm50rdv36iXtCGYrd08dtPaSRfNef2MKe30uh5ulu+krFjycgx4iynt9MsYcFpOtPm9SM/+b147/dL/7ZRl1XuauveiX+E1NG+O8CuUn3MoQ2dQfs6g/JxB+flDhnb1Hl7JOLN06VJP89hNayddLHX1I6a00+t6uFm+k7I4BszDlHb6ZQw4LSfa/F6k92v/xzNfKFFuHkuWLHG9TBPr4Vb5uAYm9g1CvIL93RmUn3MoQ2dQfs6g/Jxhip7p52OJAxm67S/KGKhxpLy8XCorK6Vz586yfPly9T9iS7Vp00Z1CtC6dWt1kdevX6/ed+/eXTZv3qziPiAeQ4cOHWTRokXqu1atWilL/Nq1a9X7rl27ypYtW1TazMxMdZ4FCxao71q2bKnir65Zs0a9x8YAK1eulJKSEhWQt1u3bjJ//nz1XYsWLSQ7O1s2bdqkdiHr1KmTFBUVSXFxsaSlpUmPHj1UWtQzPz9fmjVrpsoCHTt2VOlQZ2w80KtXL1UHdN7mzZur9Gg7aN++vQrWi7IB4lqgbQgSjDJR52XLlqnv2rVrp+S1ceNG9b5nz55qMkf98JQActMyhDzRvg0bNqj3qO+KFSukoqJCtQtlLV68OCBvPGmAzADksHr1anWtIG+0Z+HCheq7wsJC1X6rvNetW6fyQ7ZdunRpIG9cA5QFcC1Qdy1vXFd9zoKCAtUPrPKG/HAtcX3RVqu88/LyVHsA+gPKtMob9UX7kQ5la3lDrqgvzgOQFnLQ8kb79A9k27ZtVV2s8sa10H0WstT1h7xxfa19Fv1ByxvX2dpnrfKGDCFP9APIC223yhuyQp9FG1EOzqHljbzWPovrBXkjLa4x2mnts1Z5o89oeeMaQ9ZWees+izbgfFZ54721z1rljXpY+yzyW+WNPqr7LGRhlTeug7XPRjNHrFq1KtBnG5sjrPLWcwRkhvpb5wj0Z9Qn0hyB8wK35gjIC3Xzao7Q1zzaOQJg7Og5wjonxzJHaHnbnSPQLsjMKu9IcwT6gs4bbo7Ae9TfOkdA3sir+yzKs8o7mjnC2mejnSNQTy1v3WfRfpwP8kU+gP+RFvUDkDGuK86FzyFfO2lxoJ9Z00KuehdOa1oAuem0SIf01rRIh/SNpcV11ruca3mjr+sA9pHSom16l3S9QSVkHykt2o7yrWmD5RKpXGtatBHjD30r1ByhyyOkKezISyJD+TmHMnQG5ecMyi96oINBf4WuDj0JeiaJHX2PGAv6ngD3bLjvcAqX8McBqzswblZx8aIBN/Z289hNayddNOf1M6a00+t6uFm+k7Jiycsx4C2mtNMvY8BpOdHm9yK9n/o/jLowxmqDoNtoY2Gi8boebpaPawFlFAbuUMool3/7H15D8+ZCv0L5OYcydAbl5wzKLzrw2wmnAq234qEzl/AnXoeFI0A4vTUavYceqHFGezN5lcduWjvpYqmrHzGlnV7Xw83ynZTFMWAeprTTL2PAaTnR5vcivZ/6PxR3KKFQeOB9C29HNzFFsfW6Hm6VD+9S3BjAGwDXBt7HhCQzpsyFfoXycw5l6AzKzxmUX3RgFRX0VqxCwootrObBQRKjw0JvxWo7OGS4obcm/o6hiaGXNHqVx25aO+liqasfMaWdXtfDzfKdlMUxYB6mtNMvY8BpOdHm9yK9X/o/njjrJeQwnsLrEQqUmwcUK7fLNLEebpWPa4CwDADXhguZSLJjwlzoZyg/51CGzqD8nEH52Qc6EZbsa70VIbt0aCYeqQnRYXWYQLf0VnqgEkIIIcRIoORoRSdWz9MNm8vVEY7KikrJzNr2fWF+tjpI5GVQ1utjQggEQgghhJBEYjXO0evUHPQ9hBt6Kw2ocUZ7bXiVx25aO+liqasfMaWdXtfDzfKdlMUxYB6mtNMvY8BpOdHm9yJ9U+r/n01eJOO/mGM7/ckH9ZVTDu4n8cbt0ARelu91XQkxiWSZCxMF5eccytAZlJ8zKD9nUGdKLhmaU5MmQiybYESTx25aO+m82rDDNExpp9f1cLN8J2VxDJiHKe30yxhwWk60+b1I35T6/yF7dJddB7Rv8NktT02WouJKKcjLlJvO2TXgUQnofUoISca5MFFQfs6hDJ1B+TmD8iNkG4yBGmc2bNjgaR67ae2ki6WufsSUdnpdDzfLd1IWx4B5mNJOv4wBp+VEm9+L9E2p/8Mg2rtziwZHWmr90h28dmvXrMF3iTKgIr6TX8r3uq6EmESyzIWJgvJzDmXoDMrPGZSfGTpTbW2dlFdUq9emRrVBeicNqIQQQghpEixcUSSPjJ8iGzZXqPd4ffyt6erzeCuCL7zwghx77LEydOhQ2X333eWCCy6QH3/8sdF055xzToN0S5culWHDhsnVV1+93Xn+/PNPGTRokLz66qtxaRchhBBCCHFfdz3+uo9kzPUfq1e8j6fuakcfbSp6a0odt0/1nFmzZklpaank5ubKDjvs0GCpoF23ebt57Ka1ky6a8/oZU9rpdT3cLN9JWbHk5RjwFlPa6Zcx4LScaPN7kd4v/b+2tlbmzKmPX9q3b1+1m2asfDtlmTz06hRB3Pgay9P7VHij1omMO2WYjBzWWbymoqJCzj77bFm5cqWMHTtWKZjl5eXy9ttvy8svvyz33XefHHHEEbbTgXfeeUeuu+46efjhh+Wwww5Tn23ZskWOOeYYGTBggDz66KOubvaEvjF37tyw18Wq9/Tv39+Vc5L4wmto1lzoZyg/51CGzqD8nEH5OdNbnehf4XRXrKCqi5Pumii91YpTHdbO/YRdvYceqHFmxYoVnuaxm9ZOuljq6kdMaafX9XCz/FjL2lhWJD/PnSILNiyxdSB9tOfjGIgeU9rplzHgtJxo83uRvqn1fzylhwJaW1fXQAEFWAqFz/F9PJ7mQymEEoen61AUu3fvLv369ZN//OMfcvTRR8sdd9whJSUlYdPdcMMNDdIBPOk/5JBD5JZbbpFVq1apz66//nr1inSgqqrKtTa4WRYhppNMc2EioPycQxk6g/JzBuWXGJ0pku5aE0fd1a4++qjLequpeic3kYozsMx7mcduWjvpYqmrHzGlnV7Xw83yYy3ry/mT5K0ZH9tOf/yAw+WEgaM5BjzGlHb6ZQw4LSfa/F6kb2r9//1v56un9/A0DQe+f3/ifLn8pGGe1QMKIJ7EQ3Hs0KHDdk/HL7/8cjn55JMlIyMjbDqg02Vnb4vXetttt8mRRx6pFNWDDz5YvvnmG6XENm/ePFC+W7hZFiGmk0xzYSKg/JxDGTqD8nMG5ZcYnckE3TWS3gq81FtN1TtpQI0z1k7jRR67ae2ki6WufsSUdnpdDzfLj7WsA3vtI13T20nbtu0Cn9018THZXFEs+Vl5cv2ISxukb5lTEPX5OAaix5R2+mUMOC0n2vxepE+G/v/dH8vllc9mS1lF5MDyWPajY55GAk/zv/plqUyds8bWMqGcrHQ57ZD+stfgjrbrjLhPmzZtUrGfgsFyonbt2qljwYIFYdMBnc5KQUGB3HvvvWr5FGJNXXXVVbLTTjs1KN8t3CyLENMxfS40HcrPOZShMyg/Z1B+7ulMidRd3dZbgZd6q6l6Jw2ocSa447idx25aO+liqasfMaWdXtfDzfJjLQsG0Z17DVFPqTTpqemB156FXR2fj2Mgekxpp1/GgNNyos3vRfpk6P/vfDNPlq0pdr1cOwproA4T/opKES0qKgoojcGkp6fbSheJwYMHS9u2bWX16tUqcH+48p3iZlmEmI7pc6HpUH7OoQydQfk5g/JzT2dKtO7qpt5qxQu91VS90xxTbhNh8eLFnuaxm9ZOuljq6kdMaafX9XCzfCdlcQyYhyntNHUMIBavNTbvD7N+thW71616eJE+Gfr/cfv2kc5t86RVQXbEozA/K6pykb6xMnHg3MeO6hNd2YWF6hVP6YOprKy0lS4St99+u9oBtU+fPupJPoL3hyrfKW6WRYjpmD4Xmg7l5xzK0BmUnzMoP/d0pkTqrm7rrVa80FtN1TvNMeUSQgghBhIydu+sxmP3Em/BE3S7T9EfGT9FJkxZtl0QfivY0XTUzp09jYHapUsXad26tUyZMiWw66iV+fPny5133ql2JrWbDkon+PDDD1X8qccff1w6d+4sxx9/vFoadfPNN3vWHkIIIYQQkpy6K/XW7aEHapxBx/Iyj920dtLFUlc/Yko7va6Hm+U7KYtjwDxMaaepYwCxe+858LrAccMel6iYvQCv1u9wIL2b9fAifVPr/0eN7CV1EYLwA3x/1IhentYDMZygIL7zzjuycuXK7ZYnPf300zJ9+nTp1KlT2HTAmk57h0DhPOmkk+SAAw5Qu55edtllKhj/hAkTAuW7hUlLqQjxmmSaCxMB5eccytAZlJ8zKL/E6Ewm6K6R9Fbgpd5qqt5JAyohhBDSSOxexOfVR7f8TtvF7rUeevMzYg49OhbIuFOGSWpKinpabwXv8Tm+RzqvufDCC6V79+5yyimnyHvvvSdLliyRadOmyY033qjeYzlTbm5u2HR4em9Nh2VNV1xxhdr1FN9pzj33XBk+fLj6bN26dZ63ixBCCCGEJJfualcfvbCJ6K00oMaZWDpDNHnsprWTzuSO6yamtNPrerhZvpOyOAbMw5R2+mUMOC0n2vxepG+K/X/ksM7yyLiRaqmTlX2GdFCf4/t4kJOTIy+//LIcd9xx8tRTT8lRRx0lF1xwgQqg/9JLL8khhxwSMd2aNWsapLvvvvvkr7/+kgcffLDBTrnwGrjnnnuUonrttddKVVWVa21AvCpCmgrJNhfGG8rPOZShMyg/Z1B+idOZrLprRnq96Q6veB8v3dWuPprjst5aZ3G/NUnvNMcXlhBCCPEJVTVVDV6JP8BTesSJmjpnjdqxFEH3/37coAYKXDzAE/hLLrlEHRoEzg+uR6h0wcBzFUcoEFPqt99+C5RPCCGEEEL8p7uOPWGoVFbVSFZmmqSkNPRI9Ro7+qjbequp0AM1znTr1s3TPHbT2kkXS139iCnt9LoebpbvpCyOAfMwpZ1+GQNdu3aV0qp6YxRerU9IvaiHF+mbUv/fsLlc5i3b1ODQAfnxunRNaYPvkD4RZGZm+qZ8r+vqV9544w3p27evjB8/PmyaiooKefLJJ2X06NEyaNAgtVzttNNOk48/DtqozqV8xDnJMhcmCsrPOZShMyg/Z1B+ZuhMqakpkp2VHnfjqQlkGqR30oAaZ7BEz8s8dtPaSRdLXf2IKe30uh5ulu+krOC8djz5OAa8xZR2+mUMTJwzWWrqatT/eP1j1SxP6+FF+qbU/z+bvEiuePjbBkdRcaX6Dq/jHp3U4DukTwReL09ys3yTllKZAuJ8YelZJOAFfPbZZ8vDDz8sCxYskF69eklBQYH88ssvMm7cOLnhhhtczUfcIVnmwkRB+TmHMnQG5ecMys8Z1JmSS4Zcwh9nYllCF00eu2ntpGsqy/1MaafX9XCzfCdlWfPCcy/Yky/UUzWOAW8xpZ2N1aO2rlZqa2vVa01dw9dtn9dIbV1d/WtQ2iXrlsjmzDKptabBay1et6YNyhNIW1ujPsPrx3991aBer0//QAa372/7iXC08vYifVPq/4fs0V12HdA+7PeVFZWSmbXtyXZhfnyX82vQX/1Svtd19RuTJ0+WsWPHSklJScR0d9xxh1qa1rt3b+VN2qVLF/U5dpy9/PLL5a233pIhQ4bImDFjXMlH3CFZ5sJEQfk5hzJ0BuXnDMrPGdSZkkuGNKDGmaysLE/z2E1rJ10sdfUjprTT63q4Wb6Tsqx54bkX7Mk3pMOOTXYMaEPgNuNgvfGuttZq8Nv6fbAhcWuaeoOf9XNtCNxaXu02w6FOu27DepkzZ7HFaLitvIZ1qC83otEyKO32hsnwbcImNyl/poQwjtanxZ9j5ojrzN+4OGzfdaNfeZHexP7vFTCIRjKKIli9CUuDEDzfL+V7XVe/UFpaqgya2CyhMeV+2bJl8u6776oHLdg4QRtBwahRo9SGCTfffLM89thjagMGLeNY8xH3SJa5MB5UF62VmtItDT7LLF4jFSsb9su03OaSXtBGEg10IsQUzMxIU8tjTYV90BmUnzMoP2fwdzm5ZEgDapyf3qSnp0tNTY0sX75c3bRht7I2bdrIkiVLVJrWrVsrL7z169er9927d1fv582bpyavDh06yKJF9csLW7VqpTrT2rVr1fvN2WXy3NTX5fC1+8qOrfuoILxY6gVatmwpGRkZahc00LFjR1m5cqXylkCdENtk/vz56rsWLVqojSxQX5y3U6dOUlRUJMXFxZKWliY9evRQaVGv/Px8adasmSpLl4t0mzdvVso+lpmhDrixaN68uUqPtoP27dtLWVmZKhvAswJtg4s2ykSdceMA2rVrp+S1ceNG9b5nz56ydOlSZXBBsGLITcsQ8oSMN2zYoN6jvitWrFDxw9AulLV48eKAvJEf7QSQA5YpoO2QN9qzcOFC9V1hYaFqv5Y34iBiV0LcQEG2uKmxyhs35HrJA64F6q7ljeuqz4lleOgHVnlDflu2bFHXF221yjsvL0+1B6A/oEyrvFFftB/pULaWN+SC+m7atEm9R1rIQcsb7YNMQdu2bZVsrfLGtdB9FnLT9Ue5uL7WPov+oOWN64zrivq3LGwpmTlZMnPuLGWge2XRew3GyMt/vC3l6+HBUyd5+fmSkpoi6zesU8a3zemlsnHTRiktL5PUtFRp3aa1LF+xXBnWcnJzJDU9TX0PY9vssoVSXFIsZXhimiLqvKvXrlHGuKysTEnNSJeizUVSt6BOmuU1k7KKMlXfWqmT5vnNZVMRYiTWqDLTFqdLSWmJ1AryZklldZVUVlWqsrJzsqSktFQZ+lBXXK/yynJVJ9QRZVTXVKu0eF9VXa0MhjADwhioPR/V/3XmPFkj0ZEiKfLCr29Ilz0vVeO7sTlCz/F25wjMnZhXV61apd43NkdgDtd5w80RGPcozzpHYKxiHtJzBOpgnZOjmSOsv2t25wj9u4Z66h1fIReUCZno9HgF+Axp9e7ykCfqinPhc7zXaTF3Q1bWtJgncWjPYe1hEZwW10492NiaFvMAyoW8I6UFur46LQ60J1Raa7koE+2zpsXnegmTmosqK0Omxf/Amhb1w7mQBmVZZWhNG0mGVnnje10f9D2UadUjTFpq5RWzZ8+Wv/3tb2q8QzbwBH399dcD4ymY999/X8llp512kn79+m33/bHHHit33323mhN+/vln2X333R3l8wvTVs2S56a+IWcPPUF2at9fTAS6ILFnPF36xKVSFyIkU/CoSEnLkC4XPZYwI+rCFUXy/rfzZeLvy6Wqulbtaj1iSCc5amQvtWGLabAPOoPycwbl5wzoSCR5ZEgDahzBjRRuPnADhRtrKzAeWoEBToMbHuv3wWlxA4wbmeu/vFdWl62Xb9f9IgcP3U+dKzgtbq4Bbq6Dvwt+b82PG2IruAkPlxcGSdxca3BjHS4tjHy4udbgxjpSnXBzHS6gdXBa3OxrrB4bwWmDZYEb9kjlQt7WHxTIXnvWde7epYFnXqtObdTrlpoSSWmeLrl5zdV3SzYtl7RWWeq7jbVbZH3ZZllSvEw6duwgfxUtqvfKy603sK1ZOkVqM+rLW16yTmqLa6Umpf79vBXLGnj4TZv51zavvbIaqV2jjXR1smHJBmle0Dzg/ffF+h+2ef+tqZXaBRaPwuXbPAfVsajhkmn1MCAjvf79bO3NuDXNn9s8Bq2eiXY22llStEIeKnrWnudgpLCTixt5H4n6Zwz22BxFWqJIS0mV1NQ0SU1Jrf/f8lpbUytZmZlbP0MaGKXrX+vfp0paan1aa76GabalhWHJeo7NRZulsLBVw7Rb69KgPoFzWMpLTZGlm1bIO7M+265NMIAvL10tSytXy5BWO243RwTPJ5hz9FwG46gGxs7gtADp8eDHOhdhTsZnoebk4DkNxk/rnIyHSkgDw6X1PHpOxgOSUOUEpw2ek6P5XYs0JwMYgQHmJO0dCkMgfpeCd6rHb6om2JM0Ulr0Dyhk2nAaKS3+typvwd4Y0aS1nseaFvUIrkPwe23wbKzc4LSQC8rX8mksbaRyUWc8FMC1QN3R97RngL6Os2bNChh0kxU8MIDxdJdddlG7yfbv318ZUMMxdepU9Yr0oYDcsTkU4ppaDaGx5vMD0AvGT3tflm9epV4Htetn5OYYeOgUPEeR7YHnaSjjaSiQDukTYUD9dsoyeejVKYKupjcUhBF1wpRl8s1vy2TcKcNk5LCG9wKJhn3QGZSfMyg/Z2gHAJIcMqQBNUnA8lEsI41lOWkkxVZ7yG2/ZDf0EttolxSHjzsYZtlvUExC6xLg4LpFqre1/NKyUslckGl7WXIshkHbzBVvqXeMcweGwwnpiQhDQijjoPVzq4Fvu7SBz6wGRDvlhUjbwNhoNRimbDUO1qddu2atdOzQYVvaIINkIG1QfesNlNsbLbdvky4n8vKLUAY7N3FSvnpINfte1Q6M/WDwebSxUEl8qN6yUWqK6z1lQ1FZWSEpmduMkWl5LSW9+TZjLyGhwAOD5557Tvbcc09b6bWXdfDDAyt4gAtDqE7rJF9T1V0JaczzFMZT9Tse9FOujan4vmv75kZ6ohJCCEksNKDGGatXpFt5cGOPG3cYb3SMwAe//490zu+glh2HijdYXVMjdTPrQsYr1K/KMDhFmgalia6APwgYzkIaysJ4DAby1H9XW10jlXXVsrioPjxDKAa26yutcwtVGcgPT6bcnNywxkF9fnxWVlomzfOaR06bmialxSVSkF9g+SzIOJiSKsVbiqVli5YhjZahPCAbMxCayoaCDTHNTW7jdR2clG+90Q8F5lK7BoBo6+FFerfS+IHNU7+QTZPesJ2+xT4nSOGIEyXeWL1ATS/f67r6gR122EEddtEhLCKNK+15rUNjOMlnOsG6qwqFMvUNaZn9N8nNzJGc9GzJzsiW9NRt3t2JIlnmQiJq2b56xhnB9wHfvz9xvlx+0jAxBfZBZ1B+zqD8nEGdKblkaE5NmgjWZX5O8izauFSWFtXHHV28afl2N/YVNZURb/ZJQxpdChzJMBjC8AZjXEpEr8FgDz/E0KuWnOzskEuLQ5Xf0CAYnGZbufq7spIyyW8Ow2LD8iMbRRt+rkHcWusy5WhAfMV7f34yoidfWWW5XDTy9IAnXzTns5vWTrqilCIpaJ78HgixzEt+rEes5Yd6SBUKfG/HCzXaeniR3q00fiB/6EHSrM/wBp+tfO12qS3dLKm5+dJ2zHWSlpbewAOVELfRoSIibcahv0N8eKf5TCf4oZQKhbJltfzjizsbpMtITVeG1Jz0rIBRNTcjW7LT6z+r/y5bcjKytn6G/7ce1u/TsyQrvT4MSLQky1xoGqvfvk/S81ureTitWYGk4TW3wPJ/vqTifW5zSXHBkI4NoxDzVHuahgPff/3rUpW+fatm0rZljrRpmSttW+ZK6xY5Kl5qvGEfdAbl5wzKj5Bt0IAaZxAvK1rDU6g8z019U2at/ct2Gbixz87IChjn6mrrJDM9M7Txb+ur9vqL5PGnjXRhDYPbGenCGwaDy49kGGy4xNiaJ4RhMJxRcOt3C+YvMCKui+fLlzfNk56FDePGxrMfa36Y/2vUnnzRnM9uWjvpnLTTT5jSTq/rEWv51bXVsq50Q0TjKcD368s2qvQZaRmu1cOL9E2p/6vl+LXV2+0MrcGKDG1AVTtDx2H5/gcffCAvv/yyzJ07Vxl0ECv86KOPltNPP92V8lEONhu75557Ap9hIyK7T/CxUdQrr7wiZ511Vsjvm8ImUV7cgCJEkB2sRr5Y87mF3uASsZOj2QAVm9TpDTmDN0BFPbHxXiOOgIqq2mqpqiiWLRXFjtuC82WmZgYMrGm1qZKZlikFGPeSJqm1KZKdliUd2rSXkqJiyUzJkIJm+VJdXqXSZ6VlSud2nfGjIBUl5UoHTdQGqEBvtpfIDVCtmxtu3FB//aPZdAqHHeoyc0Wymkl2i9ZSIenqfUbzltKssJ1sLKsUycqTwg5dpCYjR4rKqkRS07bb3LB5QUsV69TW+epExUMN1Yda5mdJi7x0aZGbLq1bZEvPLm2ltrJICptnSs+ubaVZTlZg017IF+NCyxvyt27ai+tllTecDKyb9mp5ox/gGlg37cVGktbNJK2b9uKwbjiL81s37bVuOIt6WPsszmXdcBZ9VPdZjF/rZpLo99Y+69YcYd0kGTLD/3hAhP4ZaZNkyBv1Cd4kGe/RRzGWrH02mTdJdnOOQP113Z3MEV5skhy8AaqTTZLd2AAV7dHh/VAvyNa6CWg0G6DWlWyUuvLiQF61+WhNfZhElTYjUyoqKyQ1p7lktWzn6Qao7777rowfPz5wfSG7Y445Rk477bSwm5pGswEqNuVEv7ztttsCafWmpUiLV6sMgzdAxZh79dVX5Ywzzgi5AaraLHqrLoU+if4dvJG6Xd2WBlSfcvbQMcoDFd6nH8z5Imy6I/seJN1adJIuBR2ke8suURnrvDbokaYHJswvV3znmicfIfEAxtC7D7pWNpfX38AvXbZUXlrwnmyuKJb8rDy5fsSlgbQF2c0jGk+JWTtDwwt13cs3xnVn6LfeekvuvPNOueGGG2TnnXdW8+L3338v9957r7rhuuSSSyTRfPTRR2pn93AGVBI9uBnG9dVKfSj0d9aNO2PN5xa4kdK6YDQbxUXakPP3lTPVxnvh6N+6t3roX1pVLuVV5VJWjaNC/Q+DaqxA66iorZSKikrZVGHZBTL42Ur9/X5oZgZ5x/4ZwTt2S0Pv2JVLNtQbb3OyJTW9StZVbFSbjVq9Y+1ugGoN3RDLBqiR0trZ3DDU+9TCVlJv0tjGXzkZ8mGb5nLE2i3SB4ZNK+lZItXh+7WVlMpSkcpSqdhSb+SBZoieABOV9getN5/Uv0/NyZPlufmSnlsgmVs9W6ty82VUzlIpqsmU4tpsKa7LVq8ldVlSGyil8T60YXOFOuopEvnV2pfnSX6zTOW12rYQXqvl0gb/t2wmbXNypaS8OqK89eaOoTaThDEoeNNe62aSkTbtRV7rpr0wvoVLi/poA1y0m/a6NUcE91kY3yKl1Zskh+uzkJ9uU/CGnMm6SbKbc0Rw34t1joi0AWpw2lAboIZLa90ANVp5u70BKox0c+bMCfx+wvgXapPQxjZAVbrrU1fY2phP665pBW082QA1Wr01O4YNUPH7B1lZ81rlEkqG1nK/+OILeeCBB+S8884LWQd9LfQ8q/+PZQNUGlDjTPAgjTUPjKHdWnSWT+Z+HXEp9Iw1c+TUwUdvZ4SyU49Y6upHTGmn1/Vws/xYy4JnXnFNadSefNGcz25ajgHz2mnyGEBMXhygc157Gb/oI/V/emq69Czs6mk9vEjflPq/aTtD4wn5cccdJ8cff3yDGzE8EX/xxRc9M6AGK+iRaGxzxGjKIttuwnCjob1kQqG9Yqw3yLHmMxUdEiWS7lpZUyW37Dcu5APU6ppqKa+ukNJqi3G1qkLK1at+v83gajW+4v/yKuQtU68opzF9JH7esSmSDUPs1pADMCBro+z2Btr69znaWGtJm2tQ7FhI9vNWebImM1299l62URk+NZ3OuEMy2nRWD7JqSjZLTWnR1gPvi+o/3/q/ei3dLHWV9sJU1JYVq6Nqfb1Hm+aYMM8YSmozlUF1S222lNZlS15hK+nZq7MysG6qzpR1FRmyuiRVlm9OkaVFIhuKw/+mbC6pVMe8ZfUeiMHkZqercAD1htVci7G1/rMWeduHmkiW3+NEQfk5g/JzRiw6k0m6azi9dfXq1Z7qrdHI0NVNvRuBBtQ4A9fu4Kc6seZxsqmJnXrEUlc/Yko7va6Hm+XHWhaMoZcPPltyCpoFPrtr4mONevJFcz67aTkGzGunX8aAXhIVr3p4kZ79P3HgqffUqVO3i8N89tlnB5RTPGl/8skn5cMPP1RLE6GoXnzxxXLwwQcH0k+bNk0eeugh+eOPP5RXx4EHHijXXnvtdh41WJI0btw4le6ll15SN0JQerG8f9KkScrrYOjQoSovvDbeeecdue6661Tevn37KuV4t912265MEh3wtMFyP73sMBR6WafVeybWfKbidEO+9LR0ycORtU2PiBVsmFpZXakMrPXG1fJ6r9etRln1WXW5rNmwTtJzMuq/DxhuK1z2jq3bWlZ9zFunhIodq/7PyIkQOzbE9w5ix/6VmynLsuv1OLzi/Q6lDb17UhFOLL+1ioVqh9rqSk8Mrs1SK6WZVEq7tK2eycVLRP6YKnnwbsODU2viTJGUjnlSl5Un1Rl5UpaSowytRVsNrWtK02RlSaoyxobycC0tr5ZFKzerIxSZ6anbjKuF9UbVDKmQvj07qc8KC7IlLZWrs6KB+owzKD9nQGfy84PncHrr+eefrwyrXumtSA8dFHordCDkjVVvdRMaUOMM4i24kcfppiZ26hFLXf2IKe30uh5ulu+krKyajAYee/Dga8yTL5rz2U3LMWBeO00dAxvLitShWbpuqfKOBnhdsKE+XpKmZU6BOtyqhxfp2f8TB+I8XXHFFTJixAil4O2yyy6y++67S58+fQJL0KA4zpw5U2655Ra1nA9L6i+77DL517/+JQcccICK53XmmWcq5fP1119XMcWuueYaufXWWxvEPUU8q6uvvlr+/PNPeeaZZ5QSiuuKGKkDBgxQcVihGD/33HNywgknKMX3sMMOU+Xddddd8t1334WMg2s3JifZxuDBg+Wrr76SKVOmhPwey8ZwncCwYcMc5zMRtzfkcwpi4cNQiKOlFDgOaQXv2GCPV9vesYH35cZ6x8IrNjc9J6x3bEZZqVQW5EgW9lmorZMvWjWTFMQDTElRr58VNpOepZWObj7dMLjOnbtUfps6T5qnlktuSrnkpZZLc/VaIdkpNj2+ENKnvFi1pfnWI7CQGbbSbSucFVVpOVKRmqu8XGFoXV+RIVtqs+qNrHXZUlKbLVt0SIHqLFm+tkQdDamPWQnjaasWMLBqD9atXqzwYC3MkTZqoyvvPJCxtDhcTHErKqa4h6s5ooH6jDMoP2f4XWcKp7cOGjQoED7DC70VD/0RNgH975xzzpGBAwfGrLe6CQ2occYaW8JJHqebmtipRyx19SOmtNPrerhZvpOy3BoDTtNyDJjXTlPHwJfzJ8lbMz4O+R28p6/98u4Gnx0/4HA5YeBo1+rhRfpk6P/Fs36Qjd++JrWNeBjV1UTnFbbytdslZeumUpFIzcyRliNPlrz+e0RV/iGHHKLiL+EJOWJIffvtt+pzKJyIO4oYWjCY4Un+qFGj1HeXXnqpzJ49W30GRfSNN95Q6aAs6hhQd9xxh/IQsCrseCKvPU+1cfbjjz9WGzPcf//9gbyIbfXTTz+pcnEuHd/MGsPNCmNTR8+hhx6qvCdgCEV8NHhJWHn77beVBwdi/e26666O85mI2xvyxQu7cyG8Y5un5UnzLPgsuusdqwysIbxj1f9VZQ28aBsacOvf64d+Tr1jN6qooxFo0zx0GSkpsiI7Q27s3VbSa+skq65Omv3ytORkNdvqGatDF1i8YdNztnnIBj7fGl92a7gC9I9I81Eog+vOg0QK9yiS9yfOl4lTl6uNpTLSU2XE0E5y5J5dpWsL2c7DtTbIs1X/b9fDNaOmTB3oGSrKacMwgNsBr9Xi2qwGsVqL6ywG183Zsr4oWxYv2N7DFeJo2Tx7m1HVEiJAf5adle56TPFg4hFT3C6m6zOmQ/k5wzpHJVJ3dVtv7d69u9JDvdJbdexj6K0wkDrRW92EBtQ4Exx8ONY8wZuaaOrqsINbw0DooTY1sVOPWOrqR0xpp9f1cLN8J2W5NQacpuUYMK+dpo6BA3vtI7t03CniPGslkvdpLPXwIn0y9P+iye9L1frwy5pjBTfKdsBepUU/vh+1IgqGDBmiDiiLUDChjOKpOoLfQ6EECNRvZfjw4cqQBubOnas8SK0B9OENgEPz6aefqh1MsQQcCqW+AYKHAJZhobzgjYj0TqSN4eelaIkC3r9HHXWUvP/++zJ27Fj597//HdhAA9f/vvvuU/9fdNFFDa5rrPlMJJzuGgqTNuRLxFxo1zvWLuG8Y8uqy4K8ZIONr/UesW56x1anpki1pEhJ2UYRHA7lZDWoagNssLdsfbzYLMnNyAnEmh19YKEcd0gHSZMMKcjNVZ+hPOA0pIBTg2uzlAppllaxLaRAI+gYrgGDa3W2FK/Jki2rsmVBXbZMt3q41mVJs9xsaVvY0IO1jSUea15OaMO0SXEZo8F0fcZ0KD9nWHWmROquftNbNW7orW5itqaVhCxYsCDqne3D5bFuamJdZtTLRvl26hFLXf2IKe30uh5ulu+kLDfHgJO0HAPmtdPUMRC8JN/uPOtWPbxInwz9v2CPo2Xjt+NtPcW3axQFqbn5tj1QC3Y/SqIBG0X95z//kQsuuCCwC+iOO+6ojn322ScQSyrc8meteNoxlGH3WiiuWPaEJVSIRYVdSKH8YjfbJ554Yrs82GXYDpF2hCfhwQ62f/31l7oZGD16tArbAO/RxYvrY4KedNJJMmbMGNfymUgo3dV0TJ8LE+Uda40Ha/WO/Wv9Qvls3oSw+dvktkJcgEBM2ZraGkd1KakqU8d6cY7eoMtqeA0VQ1Zv4FW/qddWj9lWbSWnfddA+lAbeXlmcA2O4WrH4LolW4qLsqV4YbYU1WbLcuX1Wm9orUprJlnNCySnRaEUtGolbQrz6r1Ya4sk+LHGXzkZ8mGb5nLE2i3Sp8yecTXeJMMYTiSUnzOgM+ld4ROpu7qttx5wwAFKJ/FKb0VYAAC9Fd6u8GaNVW91ExpQCSGEEOI78ATdzlP0ipULZPmz/7BdboeTbpKsDj3FKy+EN998Uzp06KCC71vRy4+gQILffvtN9t1338D3v/76a+AGBq+I+4RYUQimD7788ksVAgBP8AGe1CN+5lVXXaW8A7CsCjEyd9hhB+XNiPPpXdvxxP/KK69Uy7QQS4pL9L0BcbnGjx+vYnd98sknanMo3IzAqwOxvI499lhX8xHitXes5Gx/w/zJ3K8lNSVFbQi2fd4UtWnoXQdeE5hnqmqqQnq+as/YbRt7aWNtWcgwBXhfUdNwk6pogXctDil3ZyOv7Q2wIUIVFORKTuuWIUMVZELWVVWyfO4c6dS6IL4GV6wgxp6Z67Z5uK6qTZMuFgsqrvDnrfJkTWa6eu29bCNs44SQJNFdI+mt+Vvjn3qltx500EEq7in01vfee88YvZUG1DjTsmVLT/PYTWsnXSx19SOmtNPrerhZvpOyOAbMw5R2+mUMOC0n2vxepGf/TwxQ/BCM/9FHH5WSkhKl+OXl5Smv5scffzwQnB8KKALrQyFEbFTEf0J8qUceeUSVc8opp6hYVDfffLOcffbZsmHDBrWUG0uhsrIaBteDd+IHH3wg//d//6d2Kj3yyCPlv//9r1oO/o9//EOdH8vCJ06cqAL+W5/oI4g/lF7tOaExfal4ovj6668bTQNZYrk9jmiINR9xDudC+/yxapbM31jvGR0KGFXxPdIN6bCj+gyhGnDAsOoUeCrBAFpaXbYt9IDF0IrvysKFKAgRsgAGYacbeSFWulNUqILlQaEK8rIlu2Wh5KZ32raxV1q6ZNeKZNbUSiZ2/q6qlKyqCsmsKFebfKWXlUh62RapUx6w0RtcJcip9q/cTFmWXW9RxSve71C6zYj9zzemSlqbjfVhAlQc1m1xWb3c6MpKbW2d5DbLV6+pqTTvxgLnQGf4WWeKpLf++9//9lRvRTxUxHl3Q291E/9eTZ8SS9ywaPLYTWsnXVOJcWZKO72uh5vlOymLY8A8TGmnX8aA03Kize9Fevb/xHH55ZerpUgIfP/KK6+opdgdO3ZUSumFF16o0mAJEw4s3caGT3j6/thjj6ndS0G7du3k2WefVQH1jz76aOWhiCfwermTFSizeJKPOJpQOHF+xK2C4nruuecqbwDEpUJ5OrYmFFp4AUCJxTmwkVFwmYQ0FTgX2gPGxtenfyApkhIxRiq+R7rB7fu7PpfAMzs3M0cdbrQHHq3bwhRs3axrq0fsNs/YbRt4WcMaBL93spGXq6EKckSym8Pg2lmFLMhOTZfsFBwpklUnklVbK1kwwFZVSVpFhaSVl0tmealkV5ZLTnWFZGETsNo6yaytk88Lm0lKXZ3aJAyvXxQ2kz6llQEv1HUrV8rMJaHNDS2bZzXY3MoagxWf5cS40ZVm4Yoief/b+TLxd8tGYUM6yVEje0mPjt7u0p1scA50ht91pnB666GHHqqW9sdDb4Xx9YEHHohZb3UTGlDjzOrVqwPL9LzIYzetnXSx1NWPmNJOr+vhZvlOyuIYMA9T2umXMeC0nGjze5G+KfX/tNzmajdgu7sGI73XQHnEYQUKqX5ijifpN954ozrCMXToUHn11VdDfofdS61AwcRSKl0+NoSAYhsOKLZQlMOBpVOENBWSZS70GhgI15VuaHSDKXy/vmyjSm/KRmGhwE28MjCmZ0kLF8rbPlSB/RAFm0qKpC5VAu9dC1XQGNhTC45dufAWbbb1CA2MqMFeqBc0/0aWVLeSqZXd5PfKbrKhdts42rilQh1zFofeSKx5bsZ2RtVthtZc9X04w9S3U5bJQ69OEXxdU1vfH2FEnTBlmXzz2zIZd8owGTmsc+PtJwrOgc6AzqSXrftVdw2lt1rxQm+FN6kGBlgnequb0IBKCCGEkOQlNV3aHvcPqS0vCXy07ounpa68RFKym0nBvmcGdqhPzW6m0hNCCIkOGEPvPuha2VzecMn60mVLpUvnhrt4F2Q3N9p46gVOQhVguax1Ex8dqkCHGlDG1wghCqwhDawbf7kRqsBKKC/Urunr1XFU7hTZktNRFmbuIH9UdZf5RRnKgBqOLaVVsqW0SBYsLwr5fU5WWsCYipAAeG3XMleqamrk0demirKbBjVLG1NhXO3avjk9UYmxpBe0kS4XPSY1pVsaTQvjKdKT+MC7hDjTuXNnT/PYTWsnXSx19SOmtNPrerhZfqxlbSwrkuq8OlmwYUngM72kCa/Wz627n3MMeIsp7fTLGHBaTrT5vUjflPr/5qlfyKZJoZ9Kw4i66dN/N/isxT4nSOGIEyXe+CWEhdtlEWI6yTIXxoPWuYXqsNIxt62n8eiaYh90O1RBJbxjGwlRgM+Li1bL+pnfyfqMNFmYm9moF2p6YUep3rAi8H3zshWyEw78jnTpJdl9d5PyDkNlbXWerNlYJms3lsoadZSp1/VF5Sp2aSjKKmpkyaot6ogWeKa+P3G+XH7SsKjzNkU4ByZGZ4JRlIZR8/ROGlDjzMaNG9UuZl7lsZvWTrpY6upHTGmn1/Vws/xYy/py/iR5a8bHIb9DkP1rv7y7wWfHDzhcThg4mmPAY0xpp1/GgNNyos3vRfqm1P/zhx4kzfoMD/t9VXWVZKRv84RKy0vMZgnV2PDDQwXRzfJRFiFNhWSZCxMF5We2DLEMPis9Ux0tbOwMvuzbT+Xxzi0DsU8jeaG2O/oKSc3KkeJZk6Vk1g9SuXphIF3lqvnqEHlV2rTvKT367yHNdt9TMlr2D6SpqalVRlSrUXXNhlJZu/X/tZvK1NL8aIEn6sSpy+WyE4f6Pj5lPOAYNlu/awpUGyRDGlDjDHYv8zKP3bR20sVSVz9iSju9roeb5cda1oG99pF2tS23W8oVDnifRns+joHoMaWdfhkDTsuJNr8X6ZtS/09v3lId4agrL5csA7yjsCTTL+V7XVdCTCJZ5sJEQfkljwyxVPivvBzlZRqOgBdqXo5027q0uOVex6qjasNKKZk9WRlUK1ctCOTB/xtwfPOKZLbrIc367yl5/feQjMIO9fFPC+t32A4G3qmbiivqjakbymT52mJ55fPZttoCw2t5RbXkRGgLMav/+RXqTMklQxpQ40x6erqneeymtZMulrr6EVPa6XU93Cw/1rJgEO2W30m6F3b17HwcA9FjSjv9MgaclhNtfi/Ss/9vwxTvE6/r4Wb5psiMkHjQVOZCr6D8kkeGafmtZcIOfSVl84qIW4XhFwLpDs5v3eBzGERb7HmsOqo2rpKSWdqYCk/UeuClimPjhO2NqcGkpqZIYX62Ovp1qzeovvHVXNteqWMfmiBHjegl+w/vKjlZZsjYREzpf36FOlNyyTClzq2o0SQss2bNktLSUrU7Wf/+25YlEEIIISTyE+c5c+ao//v27avivhHzrwv1Hv/Da0gICaaqpkou/vAGKapoPO5oi+x8eXz0HbY2C1PG1Nk/qmX+FSu3GVOt1BtT99hqTO0YtqxHxk+RCVOWBTaMskOz7HQ5aPfuMnqvHmG9XQmxA/VW/14Xu3oPHyfEmeBdFN3OYzetnXSx1NWPmNJOr+vhZvlOyuIYMA9T2umXMeC0nGjze5Ge/X8b5eXlRmxw4nU93CwfZRHSVGgqc6FXUH7JI0MYQ+8+6FrZXF7caNqC7Oa2jKeq3JbtpcUeR6sjnDF1m2fqq5LZtrsypsI7NbNVQ2PqUSN7yTe/LYt4Pjiz9enSQuYu2aTel5RXy7sT5sn7386TPQZ1VF6p/bq3NMrrLZGY0v/8iil6pp8pN0iGNKASQgghJGnZWFakjnBUVlZIZmZWg1AjOv4yIYQQQrbROrdQHV7RwJi6abVa5o+jYuW8QJrKNYvUsfHb8ZLZtpsypMKgmtmqk/ToWCDjThkmD706RRlKrZ6oaakpgrW3+H7ksM6yaOVm+WDifOWximX/SPr9tBXqgIH1yBG9ZO/BHSU9jV6EhJB6aECNMwUFBZ7msZvWTrpY6upHTGmn1/Vws3wnZXEMmIcp7fTLGHBaTrT5vUjflPr/l/MnyVszPrad/vgBh8sJA0dLvElLS/NN+V7XlRCTSJa5MFFQfs5pqjLMaNHOYkxdozagUsbUFX8F0lSuWawOqzF1j/57yCPjRsr7E+fLxKnLlXE0Iz1VRgztpLxLYWQF3Tvky9gTh8oZh+0on/24SD7+fqFs2lKhvvtr6SZ58JXf5LkPZ8jovXvIwbt3l/xmZuwCHm+aav9zC+pMySVDGlDjTE5Ojqd57Ka1ky6WuvoRU9rpdT3cLN9JWRwD5mFKO/0yBpyWE21+L9I3pf5/YK99ZJeOOzX47K6Jj8nmimLJz8qTa/f+e4NYSInyPvU6Tpab5TOmF2lKJMtcmCgoP+dQhjCmtpUWux+ljqqiNVIya+sy/7DG1K5yZr895cL995DitObSqmV+2CX5LZpnyUkH9pXj9u0tk35fLu9/u0AWrKhfubJhc7m8+Mksee3LubLvzp2VAbZLu+bSlGD/cwZ1puSSoTk1aSKsWrXK0zx209pJF0td/Ygp7fS6Hm6W76QsjgHzMKWdfhkDTsuJNr8X6ZtS/4dBtGdh1waH3j8Tr53z2jf4Lh4G1OrqannhhRfk2GOPlaFDh8ruu+8u55xzjvz4448xpdtvv/1UUHx9DBw4UEaNGiU333yzbNiwQaWpqqpyrf5ulkWI6STLXJgoKD/nUIYNySiAMfVI6XT2PdLlkiekcP8zJatjnwZpKtcskY0TX5MV/71Mil69XjZ996ZUroscGzUjPU3226Wr8l69++K9ZPeB7VUYAFVeVY18/uNiufi+r+Xm/06W32avDugSyQ77nxk607RVs+SKT29Vr/EmEXqrqXonPVAJIYQQ0mTADU9pVf0mSHiN9w1QRUWFnH322bJy5UoZO3asUjARHP+NN95Qn993331yxBFHhE339ttvN0ingYKKAyDd3Llz5f7775fTTjtNXn/9dcnIsLeZByGEEOI3YyqO6qK1UqyX+S+fG0iTUrRKNk58XR0ZbbpIXr+tMVPbdAlZJjxVB/ZqrY5V60vkw+8WyJc/LZGyimr1/ZQ5a9TRpV2eHLFPL+WZmp1JswrxDuiq46e9L8s3r1Kvg9r1i9smZ3b10QqX9dbmzc309OZIjzOdOnXyNI/dtHbSxVJXP2JKO72uh5vlOymLY8A8TGmnX8aA03Kize9F+qbc//9YNUtq6mrU/3idvXG+DM0ZGLfzP/roozJnzhz56KOPpEOHDoHPb7zxRikrK5M77rhDPZl//PHHQ6a74YYbpLi4OJCuWbNm6vPc3Fxp06ZNIF2XLl2kf//+cvjhh8vTTz8tl112mWttyMxsmnHgSNMkWefCeEH5OYcytEd6QRtpsduR6qg3pv641Zg6J5Cmau1S2bj2ddk4yZ4xtX2rZnLeUYPk1IP7yZc/L5EPJy2Q1RtK1XdLVxfLv9/6Q176ZKYcskd3OXyvHtKqIPmWu7P/JV5ngu46f+Ni9T9e8X5Ihx0lkXrrDUH6qNt66xVXXGGk3kkDahyBZX3BggXSr18/Wb58uVRWVqqYIug4S5YsUWlat26tnjCsX79eve/evbv6DoFzs7KyVGdctGiR+q5Vq1YqHsTatWvV+65du8rSpUvVZ+hknTt3VucDLVu2VN4na9asUe9xXqQrKSmR9PR06datm8yfP19916JFC8nOzpbFixerc2LSLCoqUh0f9ejRo4dKi3rm5+erQYAnDaBjx44q3ebNm9VTkV69eqk61NbWqqcISI+2g/bt26ubRZQNevfurdoG12+UiTovW1a/1KJdu3ZKXhs3blTve/bsqdoKd24MPshNyxDyrKmpCbh/o74rVqxQT0XQLpSFtml5b9q0SZ0TQA6rV69W1wptR3sWLlyovissLFTtt8p73bp1UlpaqmSLQW+VN64BygK4Fqi7ljeu67x58wKBuXE9rPKG/LZs2aKuEdpqlXdeXp5qD0B/QJlWeaO+aD/SoWwtb9QHskJ7AdLinFreaB9kCtq2batka5U3roXuswDXTssb19faZ9EftLxxna19FtdbyxsyhDxRFuqHtlvlDVmhz6Is9A+cQ8sbea19FrKDvJEWbUM7rX3WKm/IAXXS1xiytspb91ndp63yxvmtfdYqb9TD2mdRF6u80Ud1n4UsrPKGTKx9Npo5AktrdJ9tbI6wylvPEagnrpN1jkB/Rn0izRF6SY9bc4Quy6s5AueBjKKdIwDGup4jrHNyLHOElrfdOQJyQp2s8o40R6B8HJHmCNQJ/cM6R6BO6Ee6z6JPo36xzBHWPhvtHIF6anlDLigTMtHpddvwGdLqZT2QJ+qKc+FzvNdpcV0gK5Q1ftp7YuW16R/IkA4Dtkury8W1Q5noMygX1x1pIe9IaYGur06L79566y056qijAjLUafH9xRdfLMcdd5xKr9OhnyMNPtNzJ4yhxx9/vPofbdLf4fpBLgDvcc0OPPBApcxeeOGF6hyoo1WGOm1jMrTK21of9D2UaZ0jdHmEJAOYPxkDMHYoP+dQhrEaU49Qx8r5syR3/XwpRszUZWGMqa07qw2o8pQxtet25eVmZ6j4p6P37ik/z1gp709cIDMW1Os1W0qr5M2v/pJ3vpknew3uqNLt0LWlJAvsf86AzuQkhif0rdenfyCpKSlSW1enXvF+cPv+nnuhQueDBymW5FuNoprLL79cTj75ZKUH2kkHvTgSuK+C3vrxxx83MKA6laGbpNQ1leAdCWTWrFnqZhU38ehcMAJEA26i7eaxm9ZOumjO62dMaafX9XCzfCdlxZKXY8BbTGmnX8aA03Kize9Fer/0fxjy8DQbIEaSVXmavPQ3eWP6R1JWXb8c3w6VNVVSXFmy3ed5mc0kMy26Je456dly4qAjZPcuw2zngQH90EMPlUceeUS9WoHxUyuWkdKFAk/0jznmGLn00ku3+w5P8bEkavLkycqg6gbQafRDhuDrEqz3wJuA+A9eQ7PmQj9D+TmHMnRPftWb10nJ7B+3M6ZaUcbUfntIXv89lZdqOCPVvGWb5IOJ89XGU9U1DU0q/bsXypEjesoeAztIWpoZhp9YYf9zprda9btE6q5u661WvNBbp0yZEvBWtcrQ7fuJaPUeeqDGmVgs59HksZvWTjpTrPxeY0o7m8oOzBwD5mFKO/0yBpyWE21+L9InQ///YPaXsnyLOxsbhFJM7dYhGkVUe1PD8zcY6w1apHTRAq9kAM9vtwyo8Yq7RYgJmD4Xmg7l5xzK0D35pee3loJdR6ujevN6KZk9WYqxzH/Z7ECaqnXL1KZTODJadVJL/PP677WdMbV35xYy7pSd5azRA+STHxbKpz8sks0lleq7WYs2qKNNyxwZvVdPOWj3bpKX489Y5Ox/zrD2mUTrrm7qrVa80lu1AdUkvZMG1DiDZY5e5rGb1k66WOrqR0xpp9f1cLN8J2VxDJiHKe30yxhwWk60+b1Inwz9/6h+B8nr0z+0/RQ/3BN8J0/yj+x3oESDNmDqMAlWEBrATrpoQagHHR7BLax1JSTZMX0uNB3KzzmUoTfyS89v1dCYOqc+Zmr5UhhT6z1Kq9Yvl03fvaWObcZUeKZ2DRh1CvOz5bRD+suY/XeQb6csU16pi1fV//au3Vgmz300Q8Z/MVv2H95Vjtynp3Rskyd+gv3PPZ0pkbqr23qrFS/0VoQSM1HvpAE1ziA+GGLQeZXHblo76WKpqx8xpZ1e18PN8p2UxTFgHqa00y9jwGk50eb3In0y9H88Qbf7FB3Riq7/8l4prSpV8aOCQTypds1ay10HXuPpU27EwUVcXSxLOuywwxp8h+VJiEd75513ynXXXRc2nb42Ol2fPn0innPGjBkq7izin7oF6kpIU8H0udB0KD/nUIbey08ZU4cfro7qLRvUMv+SWT9EMKZ2lGZ6A6q23epjpGekyUG7dZMDd+0qf/y1VsVJ/XVWfaz78soa+fj7hcpTdZf+7eSofXrJTn1aG+VZFw72P2dYl5/7TXeNpLcG66Nu663a+9SNJfxuQn/sOBNLyNlo8thNayddUwmPa0o7va6Hm+U7KYtjwDxMaadfxoDTcqLN70X6ptb/9e6loRRQgM/1rqZeL4PD5k/vvPNOYGO14LhP06dPVxuF2U0XCWw89tVXX8kRRxzhajsIaUok01yYCCg/51CG8ZVfevNCKRh+mHQ84w7pOva/0uqgcyW7C2IibjNSVa1fIZu+f0uWP32lLHtyrGyY8KpUrF6kzgVj1pAd2srNf9tdnrx2fzlsz+6SlVn/EBNV+WXmarnxPz/I2AcnyJc/LZbKqvrNJE2F/a/p6q7UW7eHHqhxRsd08CqP3bR20sVSVz9iSju9roeb5Tspi2PAPExpp1/GgNNyos3vRfqm1P/17qUpkiJ1W71IQoHv47Gr6YUXXiiTJk2SU045RS677DIZNmyYWvL0yiuvyAcffCAPP/ywCmAfLt348ePlvffeC6TTIPD92rVrA0/qESwfwfw7d+4sZ599tqseqG6WRYjpJMtcmCgoP+dQhomTnzam4gh4ps6eLOVLZm3zTN0AY+rb6sgo7KA2oGq2417KM7VTmzy56LjBcvqh/eWLnxbLh98tlHWbylS+RSs3yz/f+F1e+GSmHLpHD2VobZlvhpedFfa/+OtMJumudvXRC13WW03VO2lAjTPWWA5e5LGb1k66WOrqR0xpp9f1cLN8J2VxDJiHKe30yxhwWk60+b1I35T6f3Vttawr3RBRAQX4fn3ZRpU+I4pYqNGSk5MjL7/8sjz77LPy1FNPyYoVK9SypB133FFeeukl2WWXXaJKp0E6HCAjI0M6dOigllGdc845ahlUTY17Hi7cUII0JZJlLkwUlJ9zKEMz5NfQmLpxW8zUJTMtxtSVsumHd9QRMKb231Oatesux+7bR44a0Ut+mL5SxUmdvXijylNUXCmvfTlH3vp6rowY2lnFSe3VuYWYAvtf/HUmk3TXROmtpuqdKXX0yfacWbNmKQs7LO7oHL17944q/7x582znsZvWTrpozutnTGmn1/Vws3wnZcWSl2PAW0xpp1/GgNNyos3vRXq/9P/a2lr1RBr07ds3ZgUKSujm8uLA+7smPiabK4olPytPrtr9fMnMrA9OX5DdXFrltpRE4HV8JzfLh06zePHisNfFqvf0749lj8Rv8BqaNRf6GcrPOZSh2fKrLt4oJbN/kpLZP0j54m3GVCvpLdurzadUzNR2PZS34JzFG+SDiQvku2krpLa2YZ6BvVrJkfv0kl0HtJe01MTGSWX/c6a3xqp/Beuu4Uik7hovnOqwdu4n7Oo99EAlhBBCSNKysazIlgIKisq3SGpKqrTMKfC8XoQQQgjxP+l5LaVgl0PU0cCYimX+dbUqTfXGVQHP1Hpj6h7Svd+ectVpO8vZRQPUBlOfTV4kxWVVKv2f89ero32rXDli755ywK5dJTfbu9UxxDxa5xaqg5gFDahxBq7JXuaxm9ZOuljq6kdMaafX9XCzfCdlcQyYhynt9MsYcFpOtPm9SN+U+v+X8yfJWzM+DvkdvFD/79uHGnx2/IDD5YSBoyXeYIWKX8r3uq6EmESyzIWJgvJzDmXoH/k1NKZuktI5P0qxXubfwJj6rjpgTG3Wb3c5adhecsL+B8qEKcvk/YkLZPna+ge/q9aXylPv/ykvfzZbDtytqzKmtm/VcHmz17D/OYM6U3LJkAbUOFNSUrJdTAc389hNayddLHX1I6a00+t6uFm+k7I4BszDlHb6ZQw4LSfa/F6kb0r9/8Be+8guHXcK+311dbWkp29ThxLlfYrlRV4GyXezfJRFSFMhWebCREH5OYcy9Kf80vNaSP7Oh6ij3pj6kxRjA6rFMxoYU4smv6eO9BbtZNf+e8io0/eQmZvzlCF16tz6TXbKKqrVcv+PJi2Q3QZ2UHFSB/Rs5emmlxr2P7P1u6ZArUEyNCcaaxNh8+bNnuaxm9ZOuljq6kdMaafX9XCzfCdlcQyYhynt9MsYcFpOtPm9SN+U+j8Moj0Lu4Y9uuZ3bPA+UQZUNzd58rp8r+tKiEkky1yYKCg/51CG/pdfvTH1YOl46i3S7bKnpfUh50t290EiKdvMMdWbVitD6srnr5E2X98ml/WZL4+d1V0O3q2rZKbXp0Oo1MnTV8p1//5ernjkW/n616VSVV2b9PLzM9SZkkuG9ECNM7E8JYomj920dtLF44mWCZjSTq/r4Wb5TsriGDAPU9rplzHgtJxo83uRnv2fEELswbnQGZSfcyjD5JJfWrMCZUzFUVNSJCVzfpKSWT9ImdUzdasxVeQ9ObJFWzl2311lSnlXefOPStmwuVKlmb+sSB4eP0We/2iGHLZXDzl0j+5SkFe/KWYyy4+QRJJSV1e3/TZxxFW4kykhhBASPVBRsGsmXnv16iWZmZmJrhIRkcrKSpk/f766qcJupsE3V9R7/A+vISGExJeAMXX2ZClb9GfAmGolraCNbGw5UD5d2UZ+WAlj6bbf34z0VBk1rLMcOaKXdO+QH+faEwB9dfbs2er/nj17SlaW+wZt4r7eGo3ewyX8cWbhwoWe5rGb1k66WOrqR0xpp9f1cLN8J2VxDJiHKe30yxhwWk60+b1I75f+DwVHG02XL1+uFCDEQXLzKCsrc71ME+vhVvm4BkuXLlXXBNeGnikk2TFhLvQzlJ9zKMOmIT/lmTrsIOlwys31y/wPu1ByegxusMy/pmit5C/6Rk6seEMe7vKJXNR1tnRPXwfTnVrK/+XPS+TSB76Rm578QX6ZuUpqsea/icjPBKAT6Xix0FuLi4tVDNlE65h+P8oc6LDQW3Et3NJbuYTfB/EbosljN62ddCbFmvASU9rZVOLfcQyYhynt9MsYcFpOtPm9SO+n/t+xY0dZsmSJlJeXq6fHbgPlKjU18c+Tva6Hm+Vj4y14VeDaEJLsmDIX+hXKzzmUYdOTnzKmDj1QHTWlm7cu84dn6vSAZ2pqyXrpJ+ulX75IWUYL+aW0s/xa2kUW17SW3/9aq46OrZupDaf2G95VcrLSm4z8Eknr1q2VzlpRUaEeOJuiZ/qZWhdkiE2o3NBbaUCNM3l5eZ7msZvWTrpY6upHTGmn1/Vws3wnZXEMmIcp7fTLGHBaTrT5vUjvp/6fnZ0tXbt2lRUrVqinyG5HHsKNgQmKrdf1cKt8PLmHEoprgmtDSLJjylzoVyg/51CGTVt+abn5QcbUn6Vk9g9StnCbMTWnapOMyNgkIwr+lCLJkyllXWVqZTdZvK5Onnx3urz02Ww5eLducvjePaRty9wmJb94gyXgCDu1du1a2bJli/KeNEHP9DM1DnRYvZoNxlM39FYaUONMQUGBp3nsprWTLpa6+hFT2ul1Pdws30lZHAPmYUo7/TIGnJYTbX4v0vut/0PhQSwpGE/dNqBCsc3JyXG1TBPr4Vb5UEThWUHjKWkqmDQX+hHKzzmUoTOSSX71xtQD1FFTukVK5lo8U2vrPUULpFj2zZmpjo01ufJ7VTeZWtFd3plQKe9NnC97DuogR43oJf26FzY5+cULPGhu3769OhBXkzpT4nRY6K1uhpuiKTzO6PgLXuWxm9ZOuljq6kdMaafX9XCzfCdlcQyYhynt9MsYcFpOtPm9SO/X/g8FCE+g3TxWrlzpepkm1sOt8nENTOwbhHgF+7szKD/nUIbOSFb5peU2l/whB0iHk2+Sbpc9I60Pv1hyeg4VSU0LpGmZVir7Zs+ScQWfyi0Fb8uR2T/Lshl/yNWPTZSrHp0oE6cuk+qabZtVVRetlYqVCxocy6f/vN1nSEfsgRVUidYx/X6sdKDDuh2rnx6ohBBCCCGEEEIIIb41pu6vjpqyLfXL/JVn6rSAZ6oypqbNUgZVeKb+sa6bvPlaN3n2wy5y+F495cABebLx+XFSV1O1ncddsAk6JS1Dulz0mKQXtIljKwlJPDSgxhm4cXuZx25aO+liqasfMaWdXtfDzfKdlMUxYB6mtNMvY8BpOdHm9yI9+7957fRL/3e7LEJMh/3dGZSfcyhDZzQ1+aXlNDSmls79RYpnIWZqQ2PqqLRZMkobUyd0kye+KZQTcxoaT8MBIytCCNCA2jhNrf8luwy5hD/OIG6Yl3nsprWTLpa6+hFT2ul1Pdws30lZHAPmYUo7/TIGnJYTbX4v0rP/m9dOv/R/t8sixHTY351B+TmHMnRGU5YfjKnNB+8nHU66Ubpd/qy0Gf13yek1TCR1mx+dMqZmz5ITc75PaF2Tlabc/5JRhjSgxplNmzZ5msduWjvpYqmrHzGlnV7Xw83ynZTFMWAeprTTL2PAaTnR5vciPfu/ee30S/93uyxCTIf93RmUn3MoQ2dQfvWk5eRtNabeIN0uf0baHHGJ5PbeuUHMVOI+7H/JJUMu4SeEEEIIIYQQQghpKsbUnfZVR015iZTO/Vk2/valVK+Yk+iqEWI0KXV1dXWJrkSyM2vWLCktLZXc3Fzp169f1DuB4RLZzWM3rZ100ZzXz5jSTq/r4Wb5TsqKJS/HgLeY0k6/jAGn5USb34v07P/mtdMv/d9OWVa9p3///q6ck8QXXkPz5gi/Qvk5hzJ0BuVnj4qVC2T5s/+wnb7TOfdLVoeentYpGWD/84cM7eo9XMIfZxYvXuxpHrtp7aSLpa5+xJR2el0PN8t3UhbHgHmY0k6/jAGn5USb34v07P/mtdMv/d/tsggxHfZ3Z1B+zqEMnUH5ecOjr0+V76etkJqa2kRXxWjY/5JLhlzCH2eqq6s9zWM3rZ10sdTVj5jSTq/r4Wb5TsriGDAPU9rplzHgtJxo83uRnv3fvHb6pf+7XRYhpsP+7gzKzzmUoTMoP29YuKJIJr3wi7RtmSOj9+4pB+3WTZrlZCS6WsbB/pdcMqQBNc40a9bM0zx209pJF0td/Ygp7fS6Hm6W76QsjgHzMKWdfhkDTsuJNr8X6dn/zWunX/q/22URYjrs786g/JxDGTqD8vOWNRvL5NkPZ8j4L2bL/sO7yhH79JSOrfMSXS1jYP9LLhlyCX+cKSws9DSP3bR20sVSVz9iSju9roeb5Tspi2PAPExpp1/GgNNyos3vRXr2f/Pa6Zf+73ZZhJgO+7szKD/nUIbOoPy84cJjd5Jd+rcLvC+rqJGPvlsoF97zldz+zE8ybd5aFbuyqcP+l1wypAE1zixdutTTPHbT2kkXS139iCnt9LoebpbvpCyOAfMwpZ1+GQNOy4k2vxfp2f/Na6df+r/bZRFiOuzvzqD8nEMZOoPys0dabnNJSbO3BB/pBvTvJjf/bXd54pr95NA9u0tWZpr6DjbTn2eukhue+EHGPjhB/vfzYqmsqpGmCvtfcsmQS/gJIYQQQgghhBBCmijpBW2ky0WPSU3plu2MV126dNnO2Ir0oHPb5nLxcYPl9EP7yxc/LpaPvlsg64rK1XeLVm6WR1//XV74eJYysuJo2Tw7jq0ixF1oQI0zbdu29TSP3bR20sVSVz9iSju9roeb5Tspi2PAPExpp1/GgNNyos3vRXr2f/Pa6Zf+73ZZhJgO+7szKD/nUIbOoPzsA6OoNoxq2jZrLVn5+Y3mbZ6bKcft10eOGtlLJk9bKe9PnC9zlmxU320qrpDxX8yRN7/6S0YO6yRH7tNLenYqkKYA+19yyZAG1DhTVVXlaR67ae2ki6WufsSUdnpdDzfLd1IWx4B5mNJOv4wBp+VEm9+L9Oz/5rXTL/3f7bIIMR32d2dQfs6hDJ1B+cVXfulpqbLP0E7qmL14g3wwcYF8P22F1NbWSXVNrXz1y1J1DOrVWo4a0VN22bG9pKWmSLLC/pdcMmQM1DizceNGT/PYTWsnXSx19SOmtNPrerhZvpOyOAbMw5R2+mUMOC0n2vxepGf/N6+dfun/bpdFiOmwvzuD8nMOZegMyi9x8uvXrVCuPn0Xefr6A+W4fXtLs5xtMVanz18ndzz3s1x0z1fywaT5UlpujpHMTdj/kkuGNKASQgghhBBCCCGEENdp0zJHzho9QJ6/6SC56LidpFObZoHvVq4vkafe+1POvv0LeeaDP2X1htKE1pWQSKTU1WGfNOIls2bNktLSUsnNzZW+fftKamp0duva2lrbeeymtZMumvP6GVPa6XU93CzfSVmx5OUY8BZT2umXMeC0nGjze5Ge/d+8dvql/9spy6r39O/f35VzkvjCa2jeHOFXKD/nUIbOoPzMkx+W80+Zs0bFSf197toG32E1/+6DOqg4qTv2KJSUFH8v72f/84cM7eo9vJJxZtmyZZ7msZvWTrpY6upHTGmn1/Vws3wnZXEMmIcp7fTLGHBaTrT5vUjP/m9eO/3S/90uqykuQ7v//vvl0EMPlUGDBsnQoUPl2GOPlWeeeUYqKyvD5quoqJAnn3xSRo8erfINHz5cTjvtNPn444/jWv+mCPu7Myg/51CGzqD8zJNfamqK7NK/ndx+wZ7yr6v2lYN26yYZ6fWmqdo6kR+mrZRrH/9Oxj3yrXzz21Kpqq4Na4gtr6hWr6bC/pdcMuQmUnEmknLsRh67ae2ki6WufsSUdnpdDzfLd1IWx4B5mNJOv4wBp+VEm9+L9Oz/5rXTL/3f7bKaEkuXLlVGz1WrVklaWpp069ZNyXLmzJkyY8YM+fTTT+X555+XvLy8BvnKy8vlnHPOkd9++03l22GHHaS4uFh++eUXdfzwww9y5513JqxdyQ77uzMoP+dQhs6g/MyWX7cO+XLpCUPkjMP6y2eTF8nH3y+UjVsq1HfzlhXJQ69Okec/miGH7dVDDtm9uxTkZcnCFUXy/rfzZeLvy5VxFcbXEUM6yVEje0mPjgViEux/ySVDGlDjCBTgmpoadSxfvlx1hJycHGnTpo0sWbJEpWndurUgqsL69evV++7du6tdx+bNmydZWVnSoUMHWbRokfquVatWypV57dp6t/euXbtKdXW1SpuZmSmdO3eWBQsWqO9atmwpGRkZsmbNGvUe/69cuVJKSkokPT1dKfHz589X37Vo0UKys7NVfVFWp06dpKioSCnrUNx79Oih0qKe+fn50qxZM1UW6Nixo0q3efNm5W7fq1cvVQe4XTdv3lylR9tB+/btpaysTJUNevfurdqGNqBM1Fk/bWjXrp2Slw4g3LNnT3UjAtnAzRpy0zKEPCHjDRs2qPeo74oVK5T3BtqFshYvXhyQN+qJdgLIYfXq1artkDfas3DhQvVdYWGhar9V3uvWrVOu3pBnly5dGsgb1wBlAVwL1F3LG9dVn7OgoED1A6u8Ib8tW7ao64u2WuWNGyu0B6A/oEyrvFFftB/pULaWN+qO+m7atEm9R1rIQcsb7YNMQdu2bZVsrfLGtdB9Fm3T9Ye8cX2tfRb9Qcsb19naZ63yhgwhT/QDlIm2W+UNWaHPQjYoD+fQ8kZea5/F9dLXDgfaae2zVnmjz2h54xpD1lZ56z6LA+ezyhvvrX3WKm/Uw9pnUWervNFHdZ+FLKzyxnWw9tlo5gjcjOs+29gcYZW3niOQF+VZ5wj0Z9Qn0hyB8wK35ggcqJtXc4S+5tHOEQBjR88Rep6NdY7Q8rY7R0COkJlV3pHmCOTXecPNEagDyrPOEZC3HjsAsrDKO5o5wtpnY5kjtLztzhG6z9qZI7S89RyBeoDgOcLaZ8PNEfhdw+HGHAFZoN95OUegvFjmiGA9Qs/J4eYI1JVsz9VXX62uBcbhY489pl4BjKCXXHKJTJ8+XRlC77777gb57rjjDmU8RX+CFyr6OpgwYYJcfvnl8tZbb8mQIUNkzJgxCWlXsoMxRmKH8nMOZegMys8f8oNh9MQD+8qx+/aR7/5Yrpb3z19Wr09t2FwhL386W974cq70616oNqBKTUmRmq2epzCiTpiyTL75bZmMO2WYjBzWWUyB/S+5ZMgYqHHAGk8ByjJuSqIBN0h289hNayddNOf1M6a00+t6uFm+k7Jiycsx4C2mtNMvY8BpOdHm9yI9+7957fRL/7dTFuNnbg+M4wcddJD6/9VXX5Wdd965wfdvvvmm3HjjjUquMKjCoA5gcD/44IOVEf69996Tfv36Ncj32muvyc0336wM8jCouhUjjNfQvDnCr1B+zqEMnUH5+VN+MFPNXLhBGVJ/+nOlWtpvBxhWHxk30hhPVPY/f8iQMVANRXuIeJXHblo76WKpqx8xpZ1e18PN8p2UxTFgHqa00y9jwGk50eb3Ij37v3nt9Ev/d7uspoL2aAbBRlCw0047BW4StAc2eP/995VHL+KehsqH+KkwtsK7+ueff/as/k0Z9ndnUH7OoQydQfn5U35YkTSgZyu5/qxd5T/XHSBHjegladhhqtF8ooyupsD+l1wypAGVEEIIIYQQD0EoCKuXQzCzZ89WrwiTgJANmqlTp6rXXXbZJWS58MiAcRXQgEoIISQZad+qmZxzxABlVG0MLOufOHW58mAlxG1oQI0ziFPmZR67ae2ki6WufsSUdnpdDzfLd1IWx4B5mNJOv4wBp+VEm9+L9Oz/5rXTL/3f7bKaCogvu/fee6v/seRex/IF06ZNk/vvv1/9f+qppzZYpqbj1eq4p6FArFprWuIu7O/OoPycQxk6g/JLDvlVVtVIdU2trbSIiVpRVSMmYIr8/Ewbg2TITaTiDDad8DKP3bR20sVSVz9iSju9roeb5Tspi2PAPExpp1/GgNNyos3vRXr2f/Pa6Zf+73ZZTYlHH31UbrjhBvn888/l8MMPVxtvQZaIj4pNzs4//3y1KZQVvRkYNk0LBzYnA3pTN+Iu7O/OoPycQxk6g/JLDvllZqRJRnqqMo42BtJlZaSJCZgiPz9Ta5AM6YEaZ7Qi7FUeu2ntpIulrn7ElHZ6XQ83y3dSFseAeZjSTr+MAaflRJvfi/Ts/+a10y/93+2ymhLY4GnHHXdUBk9sCrVgwQLlNYplhs2aNZPmzZtvd5NQXl6uXmFgDYf+rqyszOMWNE3Y351B+TmHMnQG5Zcc8ktNTZERQzo1GgcVX48Y2snWcv+mJD8/s94gGdIDlRBCCCGEEA8pLi6Ws88+Wy3X79Onj1qyj7imVVVVMmnSJLnnnnvkwQcflN9++00ef/xxSU+vV9HT0tJse154cbMIA+68efOkR48esnz5crXJVU5OjlpOpzd1aN26tTIC6xuc7t27y6pVq1ReGHc7dOgQCC/QqlUrZUheu3ZtILQB/ofxF6ELEI4AhmXQsmVLFRN2zZo1gTAGGzZskJKSEiUfePDOn1+/UQiM0thMC+cFnTp1kqKiIiV3yBD1R1rUMz8/Xxms9cZeiE+LdJs3b1Yy7NWrl6oD5A6jNozdkAFo3769qivKBr1791Ztw0ZfKBN1XrZsmfquXbt2Sl7aM7hnz56ydOlSdc2xyy/kpmUIeeI8aB9AfVesWCEVFRWqXSgLnspa3kBvNgY5YBMxLW+0R4eIgOcy2m+VN/Jhp2HIFjK1yhvXAGUBXAvUXcsb11XLoaCgQPUDq7whvy1btqjri7ZqeaMNOB/aA9AfUKZV3qgv2p+Xl6fKRl/T8ka7Nm3apN4jLeSg5Y32QaYAsYMhW6u8cS1C9Vn8j+tr7bPoD1reOK+1z6KeWt7BfRZtt8obsrL2WZxDyxt5rX0W18sqb7TT2me1vFEvyMzaZyFrq7ytfRaHVd44v7XPWuWNelj7LM5llTdkpvssZGGVN66Dtc+aOkfgPa6fV3ME0lv7bLLNEZAf+qJXcwTkh75oZ47Ya0CBfPNbfR8MR22dSL+Oqer6mTBHoI1aLl7NEZA3+kyyzhG1tbWBtnqlR6CudkipY3Rdz8FmAeiU6FRQmrVSbBdcTLt57Ka1ky6a8/oZU9rpdT3cLN9JWbHk5RjwFlPa6Zcx4LScaPN7kZ7937x2+qX/2ynLqvf079/flXP6nUceeUSeeOIJdVPz3nvvqZsdK7jhOOqoo5RSf8cdd8iYMWPU57vuuqu6qcHy/0MOOSRk2TC+Pvfcc7LXXnvJs88+60p9eQ3NmyP8CuXnHMrQGZRfcsnv2ynL5KFXpwieGWLDqFDs0r+d3HTObsprNdGYJj8/Uh0HGdrVe7iEP87opwJe5bGb1k66WOrqR0xpp9f1cLN8J2VxDJiHKe30yxhwWk60+b1Iz/5vXjv90v/dLqup8Nlnn6nX008/fTvjqfaKOO6449T/H374YeBzeE8A7e0RCu1NEylOKokd9ndnUH7OoQydQfkll/xGDussj4wbKaN27qxinQK87j24ozTLrjey/Tprtbz9zV9iAqbJz4+sNEiGNIXHGbg8e5nHblo76WKpqx8xpZ1e18PN8p2UxTFgHqa00y9jwGk50eb3Ij37v3nt9Ev/d7useIClZVgCB0Mjlr1h+ReWi+H/eKGXymG5WDiwbA7opaB6eRyWCVo/C0YvrcOSN+I+fuvvpkH5OYcydAbll3zy69GxQC4/aZiMPWGoVFbVSFZmmlpGP3XOGrn5qcmCNdYvfzpL+nUrlEG960MaJAoT5ec3KgySIQ2ocQYxM7zMYzetnXSx1NWPmNJOr+vhZvlOyuIYMA9T2umXMeC0nGjze5Ge/d+8dvql/7tdllfAWPrmm2/Kt99+K9OnT1dGVCuIk4UYpCNGjJAjjjjCc+9NxBvDDYA2pIZCx/5CzDHN4MGD5auvvpIpU6aEzINYYn/++af6f9iwYa7Xm/ijv5sM5eccytAZlJ8zTJYfluhnZ20zaQ3t21ZOPrCvvPrFHBUL9b6Xf5VHx42SwvzEtcFk+fmFbINkyBiocYAxUM3GlHYmU/w7t/NyDHiLKe30yxhgDNTkwpR2+qX/2ykrkfEzsYkD4o1iGTw2HRg0aJDssMMOaok8DJP4DMvhsTnBH3/8IX/99ZfaYADxRy+66CK1MYQXXHnllfLRRx9J37595e2331bnDDaEHnnkkWrjBmw2de2116rPscHCgQceqDZMQOxU5Lcyfvx4ueWWW9QGHV988YVr15gxUM2bI/wK5eccytAZlF/Tkh/iot761GSZOrd+k6EBPVvJnRfuKWlpiYle6Tf5mUg1Y6A2XfRubV7lsZvWTrpY6upHTGmn1/Vws3wnZXEMmIcp7fTLGHBaTrT5vUjP/m9eO/3S/90uy01eeukltdESlrvffffd8ssvv8irr76qDIznnnuunHjiiXLyyScrQ+mtt96qDJI//vijXH/99Wp318MPP1yV4QU4J3aSnTNnjlxxxRWB3WMB/r/kkkuU8RS7EcOAqsHusjDuwvA7duzYwG6xAN619913X6B83qB5g6n93S9Qfs6hDJ1B+TUt+aWlpsiVp+4srQvqvRZnLFgvL306K2H18Zv8TGSRQTKkpkUIIYQQQnzP119/rXah32mnnaJaWn/CCSeo49dff5V//vOfaqMnt0F8U3jGwhP1yy+/lG+++UZ69uypPEthvIV3RYsWLeTxxx+Xdu3aNch7ww03KE/ZmTNnyujRo9VqpvLyclm8eLH6/qSTTpIxY8a4XmdCCCHEjxTkZck1ZwyXax//Tnmkvv3NPOnfvVB2G9gh0VUjPocG1DiDjQu8zGM3rZ10sdTVj5jSTq/r4Wb5TsriGDAPU9rplzHgtJxo83uRnv3fvHb6pf+7XZabPPfcc47yIybqiy++KF6x3377yQcffCAvvPCCTJo0SRlAsekFNn8aNWqUnHXWWdKmTZvt8hUUFKil+mjfJ598ojwxYHgdMmSIMvwee+yxntWZmNvf/QLl5xzK0BmUX9OUX7/uhXLOEQPkqffr44Q//NpUeeSKfGnfqllc6+FX+ZlEK4NkSANqnIGi7GUeu2ntpIulrn7ElHZ6XQ83y3dSFseAeZjSTr+MAaflRJvfi/Ts/+a10y/93+2ymhqIxXrjjTfGtIEClunjIPGF/d0ZlJ9zKENnUH5NV35H7NNTZi7cIN9PWyElZVVyz4u/yH2X7COZGWlxq4Of5WcKKQbJkDFQ4ww2N/Ayj920dtLFUlc/Yko7va6Hm+U7KYtjwDxMaadfxoDTcqLN70V69n/z2umX/u92WfFk8uTJynh5/vnny0033aTinxKSrP3dFCg/51CGzqD8mq78YHgbe+IQ6di63ut0/rKigEdqvPCz/ExhnUEypAGVEEIIIYQkNdhM6rzzzpOVK1equKeIKYrNmvA5IYQQQpKT3OwMue6sXQNep59NXiRf/7o00dUiPiWlrq6uLtGVSHZmzZolpaWlkpubK7169ZLMzMyo8ldWVtrOYzetnXTRnNfPmNJOr+vhZvlOyoolL8eAt5jSTr+MAaflRJvfi/Ts/+a10y/9305ZVr2nf//+YgIHH3ywXHHFFXLIIYcEPsMO9ogpOmHChITWzURMvIZNfY7wK5SfcyhDZ1B+zkgW+X31yxJ55LWp6v+szDR5cOwI6dYh3/PzJov8EkllHGRoV++hB2qcWbt2rad57Ka1ky6WuvoRU9rpdT3cLN9JWRwD5mFKO/0yBpyWE21+L9Kz/5vXTr/0f7fLcpMzzzxTpkyZEvI77HIfHEML72tqauJUO+JXTO3vfoHycw5l6AzKzxnJIr/9h3eVg3brpv6vqKyRu1/4RUrLqzw/b7LIL5GsNUiG3EQqzpSVlXmax25aO+liqasfMaWdXtfDzfKdlMUxYB6mtNMvY8BpOdHm9yI9+7957fRL/3e7LDcZOHCgnHvuuTJ06FAZO3as2qVec8opp8iVV14pb7zxhhQWFsry5ctl6tSpyiuVED/2d79A+TmHMnQG5eeMZJLf+ccMknlLN8mCFUWyfG2xPPbG73L16bt4uklRMskvUZgkQ3qgxplYXI+jyWM3rZ10TcXV3JR2el0PN8t3UhbHgHmY0k6/jAGn5USb34v07P/mtdMv/d/tstzkH//4h3z11Veyww47yFlnnaWMqdOmTVPf4f/HH39c2rZtK5s2bZLu3bvLE088oTaUIsSP/d0vUH7OoQydQfk5I5nkl5WRJteeOVyaZdf7EX73xwr5+PuFnp4zmeSXKEySIWOgxgFrPAUo9Wlp9QGM7YLlZXbz2E1rJ1005/UzprTT63q4Wb6TsmLJyzHgLaa00y9jwGk50eb3Ij37v3nt9Ev/t1OWCfEzsdzrv//9r/I43XXXXZVH6qBBgxJSFz9iwjU0BVPmCL9C+TmHMnQG5eeMZJTf5Okr5a7nf1b/p6elyD1/31v6div05FzJKL94Ew8ZMgaqoSxcuNDTPHbT2kkXS139iCnt9LoebpbvpCyOAfMwpZ1+GQNOy4k2vxfp2f/Na6df+r/bZXlFmzZt5IYbbpAvvvhCunbtKqeeeqpceOGFMmPGjERXjfgMP/R3k6H8nEMZOoPyc0Yyym+PQR3kmFG91f/VNXVyz4u/yuaSSk/OlYzyizcmyZAGVEIIIYQQkhTU1tbK77//Lp999pn89NNP0qxZM7npppuUIbVdu3Zy0kknyUUXXaQ8DQghhBDSNDnjsP6yY496r9N1m8rkwVd/k9paLs4mkaEBNc5g4wIv89hNayddLHX1I6a00+t6uFm+k7I4BszDlHb6ZQw4LSfa/F6kZ/83r51+6f9ul+Um8+fPl8MOO0wZSS+//HI588wzZf/995fPP/9c2rdvL7feeqsyrLZu3VpOOOEE+fvf/y6zZ89OdLWJ4Zja3/0C5eccytAZlJ8zklV+6WmpagOpFnlZ6v2U2Wvkza/mun6eZJVfPDFJhjSgxpn09HRP89hNayddLHX1I6a00+t6uFm+k7I4BszDlHb6ZQw4LSfa/F6kZ/83r51+6f9ul+UmWLLfqlUr+fTTT2X69OkyadIkOfroo+Waa66RzZs3qzSdOnWS22+/XT755BPJz8+XMWPGJLraxHBM7e9+gfJzDmXoDMrPGcksv1YFOXLVqTtLSkr9+1c/ny1/zF3r6jmSWX7xwiQZ0oAaZ9asWeNpHrtp7aSLpa5+xJR2el0PN8t3UhbHgHmY0k6/jAGn5USb34v07P/mtdMv/d/tstwE3qTwQO3Ro4dkZGSoOKiIfVpeXi6LFy9ukLZLly5y9913K0MqIX7s736B8nMOZegMys8ZyS6/wTu0kVMP7qf+xwr+B175TdYXlblWfrLLLx6YJENzTLmEEEIIIYTEyODBg+Xpp5+WzMxM5Wm6ZcsWefnll9WS/T59+oTMA0MqIYQQQpouY/bfQWYt2iC/zV4jm4or5L6XfpU7L9pLLfMnxEpKXV0dI+V6DDYqKC0tldzcXOnZs6dkZdXH2bBLRUWF7Tx209pJF815/Ywp7fS6Hm6W76SsWPJyDHiLKe30yxhwWk60+b1Iz/5vXjv90v/tlGXVe/r37y/xYu3atXLPPfeoDaOqqqokJSVFhgwZopb2Dxw4MG71SAYSdQ1NxJQ5wq9Qfs6hDJ1B+Tmjqchvc0mlXPbQBLWhFDhmVG8554gBjsttKvLzknjI0K7eQ5N6nFm/fr2neeymtZMulrr6EVPa6XU93CzfSVkcA+ZhSjv9MgaclhNtfi/Ss/+b106/9H+3y3ITLNl/8MEH5Y8//pDvvvtOpk2bJuPHj6fxlCRlf/cLlJ9zKENnUH7OaCryy2+WKdeesYukp9UHRH13wjyZPH2F43Kbivy8xCQZ0oAaZ2DV9jKP3bR20sVSVz9iSju9roeb5Tspi2PAPExpp1/GgNNyos3vRXr2f/Pa6Zf+73ZZblJSUqJeU1NT1bJ9xEGNluLiYg9qRvyMqf3dL1B+zqEMnUH5OaMpya9vt0I598htD10feW2qrFxXr1vESlOSn1eYJEMaUONMLMp8NHnsprWTLpa6+hFT2ul1Pdws30lZHAPmYUo7/TIGnJYTbX4v0rP/m9dOv/R/t8tyk4MPPlief/55tdQrFuX8ySeflIMOOsiTuhH/Ymp/9wuUn3MoQ2dQfs5oavI7fK8ess+QTur/0vJqueeFX6Siqibm8pqa/LzAJBkyBmocsMZT6Nevn4rJFQ24RHbz2E1rJ1005/UzprTT63q4Wb6TsmLJyzHgLaa00y9jwGk50eb3Ij37v3nt9Ev/t1NWouJnzpw5U2655RZZvHixHHDAAXLggQfKnnvuqTaVCudt+ttvv8nHH38sX375pfTo0UNuu+02LvlnDFQj5wi/Qvk5hzJ0BuXnjKYov9LyKhn3yERZvrZ+VcpBu3WTS08YElNZTVF+bhMPGTIGqqHMnz/f0zx209pJF0td/Ygp7fS6Hm6W76QsjgHzMKWdfhkDTsuJNr8X6dn/zWunX/q/22W5yY477iivv/66XHfddUoRvvDCC2XnnXeWI444Qv1/1VVXyZVXXinnnnuuHHbYYbLrrruqz//66y9lOH3rrbdoPCW+6e9+gfJzDmXoDMrPGU1RfrnZGXLdWcMlKzNNvf/ip8Xyv5+XxFRWU5Sf25gkw/REV4AQQgghhBA3gIfC0UcfrY4pU6bIt99+q15nz54tmzZtUt8jPmrHjh2VYXXEiBEyYIDzXXYJIYQQkjx0a58vFx83WB4eP0W9f+KdadKrc4H06FiQ6KqRBEIDapxp0aKFp3nsprWTLpa6+hFT2ul1Pdws30lZHAPmYUo7/TIGnJYTbX4v0rP/m9dOv/R/t8vykmHDhqmDkKbQ302F8nMOZegMys8ZTVl+++3SRWYt2iCfTV4klVU1Kh7qw1eMVB6qdmnK8nMLk2TIJfxxJisry9M8dtPaSRdLXf2IKe30uh5ulu+kLI4B8zClnX4ZA07LiTa/F+nZ/81rp1/6v9tlEWI67O/OoPycQxk6g/JzRlOX33lHDVSep2DFuhL55+u/q5icdmnq8nMDk2RIA2qcWb16tad57Ka1ky6WuvoRU9rpdT3cLN9JWRwD5mFKO/0yBpyWE21+L9Kz/5vXTr/0f7fLIsR02N+dQfk5hzJ0BuXnjKYuv8yMNLn2jOHSLKfe6/T7aSvkw0kLbOdv6vJzA5NkSAMqIYQQQgghhBBCCCFBtG/VTK44aWjg/bMfzpDZizYktE4kMdCAGmc6d+7saR67ae2ki6WufsSUdnpdDzfLd1IWx4B5mNJOv4wBp+VEm9+L9Oz/5rXTL/3f7bIIMR32d2dQfs6hDJ1B+TmD8qtnt4Ed5Lh9e6v/a2rr5N4Xf5Gi4opG81F+zjFJhjSgxhnsAOtlHrtp7aSLpa5+xJR2el0PN8t3UhbHgHmY0k6/jAGn5USb34v07P/mtdMv/d/tsggxHfZ3Z1B+zqEMnUH5OYPy28bph/aXAT1bqf/XFZXLg6/8poypkaD8nGOSDB0ZUJcvXy4//fSTfPbZZ/Lll1/KlClTZNWqVe7VLgkpLi72NI/dtHbSxVJXP2JKO72uh5vlOymLY8A8TGmnX8aA03Kize9FevZ/89rpl/7vdllecv/998vs2bMTXQ3ic/zS302F8nMOZegMys8ZlN820tJS5erTd5EWzes3NZo6d6288eWciHkoP+eYJMP0aDPMnTtXXn75ZZk0aVLAWKp3IUtJSVGvXbt2lZEjR8rxxx8vO+ywg9t19jVpaWme5rGb1k66WOrqR0xpp9f1cLN8J2VxDJiHKe30yxhwWk60+b1Iz/5vXjv90v/dLstLXnrpJXn22WelZ8+eMnr0aHV06dIl0dUiPsMv/d1UKD/nUIbOoPycQfk1pDA/W64+bRe58cnvBc6n47+cI/26F8rQvm1Dpqf8nGOSDFPqtPXThuH0rrvukh9//FHat28vw4cPV8ZRKKJ5eXlSW1urXGthVP3jjz/k999/lw0bNsiee+4p48aNkwEDBkhTZdasWVJaWiq5ubnSv3//RFeHEEIIISTp9R54LHz++efyySefqBVTNTU1MnDgQDniiCPk0EMPlTZt2iSsbqZjyjUkhBBCTOTNr+bKi5/MUv/nN8uUR8eNktYtchJdLeKx3mNrCf8999wjJ554ogre+vrrr8uECRPUsqjzzjtPDjnkENl7771lxIgRcuSRR8r5558vjz/+uHz33Xfy9NNPS2FhoZxyyimqDCIyb948T/PYTWsnXSx19SOmtNPrerhZvpOyOAbMw5R2+mUMOC0n2vxepGf/N6+dfun/bpflJXjAf9xxx8kzzzwjEydOlJtuukmys7Pl3nvvlVGjRslZZ50lb731lmzZsiXRVSUG45f+biqUn3MoQ2dQfs6g/EJz3L59ZJf+7dT/m0sq1aZS1TW126Wj/JxjkgxT7T7Bx9P7O+64QwYPHmyrYCzn32uvvZSh9aOPPpKioiKndSWEEEIIISRq9AN9LOv/3//+JwcffLBaVQWjKhwBrrzySpkxY0aiq0kIIYQQH5CamiLjThkmbVvWe53OXrxRnv9oZqKrRTzGlgEVhtMOHTrEfBIs87/77rtjzp9MFBQUeJrHblo76WKpqx8xpZ1e18PN8p2UxTFgHqa00y9jwGk50eb3Ij37v3nt9Ev/d7useAEv03fffVetlILxFI4Bffr0kSuuuEIuueQSmTlzppxwwgny5ptvJrqqxDD82N9NgvJzDmXoDMrPGZRfeJrnZsq1Zw6X9LR6s9r7E+fL99NWNEhD+TnHJBlGvYlUJP766y9JTU2VXr16uVlsUoGYCl7msZvWTrpY6upHTGmn1/Vws3wnZXEMmIcp7fTLGHBaTrT5vUjP/m9eO/3S/90uy0uwggrepp9++qn88MMPUlVVJR07dpQzzjhDxUHt27dvIO2ZZ56plvs//PDDMmbMmITWm5iFX/q7qVB+zqEMnUH5OYPyi0yfLi3lvKMHyhNvT1PvH31tqvTokC8d2+Sp95Sfc0ySoS0P1GCw79R///tfue6669R7bCCFJ/qIgYodTs8991wpKSlxu65JwcqVKz3NYzetnXSx1NWPmNJOr+vhZvlOyuIYMA9T2umXMeC0nGjze5Ge/d+8dvql/7tdlpfsscceSlfF5qbHHnusvPzyy/L111/LVVdd1cB4CjIzM5UDgEk7vRIz8Et/NxXKzzmUoTMoP2dQfo1z6B7dZeTQzur/sopqufuFX6S8slq9p/ycY5IMY/JARTD+hx56SPbZZx/1Hk/2EZwfS6KwHOqpp55SG0ldffXVbteXEEIIIYSQRjnooIPUg33oq+npjau8MLa2aNEiLnUjhBBCSHKA/X/+PmawLFixSZauLpZFKzfLf96ZLpedNDTRVSMmeKAihtSBBx6oDKUAcaRycnLUrqaII4Ug/Z999pnbdU0KsHTMyzx209pJF0td/Ygp7fS6Hm6W76QsjgHzMKWdfhkDTsuJNr8X6dn/zWunX/q/22V5yYMPPijDhw9XcU2tK6PeeusttZlUeXl5g/Tt2rWTrKysBNSUmIxf+rupUH7OoQydQfk5g/KzR05Wulx35q6SnVm/kuV/vyyRL39aTPm5gEkyjMmAunTpUhkxYoT6H/GkJk+eLLvuuqtkZ2erz7AEat26de7WNEnAJgZe5rGb1k66WOrqR0xpp9f1cLN8J2VxDJiHKe30yxhwWk60+b1Iz/5vXjv90v/dLstLli9fLsccc4zcdtttsnDhwsDnU6ZMkTvvvFPFOt2wYUNC60jMxy/93VQoP+dQhs6g/JxB+dmnS7vm8vcxQwLvn3xnmsyYtyqhdUoGthjUB2Nawp+fn68C84OffvpJSktLAwZVsGTJEmndurV7tUwS4OmwatUqJRso9ZWVlcpzt02bNkpmAN8hxuz69evV++7du8uaNWtUp4FXRIcOHWTRokXqu1atWqlNu9auXaved+3aVf2PtIjl1blzZ1mwYIH6rmXLlpKRkaHKAjU1NSp2LTwysKytW7duMn/+fPUdlq/BGI66oqxOnTpJUVGRuuaIDdajRw+VFvVEX2jWrFkgLgWeDiDd5s2blSs7jOmoA87VvHlzlR5tB+3bt5eysjJVNujdu7dqW3V1tSoTdV62bFnAKwTy2rhxo3rfs2dPZciHAR9BhSE3LUPIE+3TN0Wo74oVK6SiokK1C2UtXrw4IG+k04MScli9erW6VpA32qNvugoLC1X7rfLGgwL0f8i2S5cuDeSNa4CyAK4F6q7ljes6b968wK5y6AdWeUN++B/XF221yjsvL0+1B6A/oEyrvFFftB/pULaWN+SK+m/atEm9R1rIQcsb7YNMQdu2bZVsrfLGtdB9FufUMoO8cX2tfRb9Qcsb19naZ63y1n0W/QDyQtut8oas0GdxPvRLnEPLG3mtfRbXC/JGWrQb7bT2Wau80We0vHGN8WqVt+6zaAP6rVXeOL+1z1rljXpY+yzyW+WNPqr7LGRhlTeug7XPRjNHoC26z9qZI7S89Ryh+6V1jkB/Rn0amyOAW3ME5OXlHKGvebRzBNAPBSEH65wcyxyhZWp3jkC70F6rvCPNEehzeoyFmyPwHumtcwTkjX6k+yy+h/xjmSOsfTbaOQL11PK2O0foPmtnjtDy1nME0uOaB88R1j4bbo7A/ODWHAF5Q/5ezRF4j/SxzBHBeoSek8PNEairKR6ouG7PPvusDBw4MPD5XXfdpTaMwqophKS64447ElpPYjboQxivJDYoP+dQhs6g/JxB+UXHqGGdZdbC9fLJD4uksrpW/v3uHPlXny7SLCcj0VXzLVsM6oMpddCyo+TSSy+VmTNnyjXXXCNPPvmkzJ07V7755hulTOP1hhtukAMOOEDuvvtub2rtM2bNmqVuqHBDhBs43HxFA25e7Oaxm9ZOumjO62dMaafX9XCzfCdlxZKXY8BbTGmnX8aA03Kize9FevZ/89rpl/5vpyyr3tO/f39JFHvvvbecccYZaqPTUPz73/+W1157TcXxJ2ZeQxMwZY7wK5SfcyhDZ1B+zqD8oqequkau/td3Mm9p/UPyPQZ1kOvOHK6cBIiZfdCu3hOTARVeGeeee65qCDoBNos6++yzlTfqmWeeqRqHp/3wKiFUQgkhhBDSdDBF7xk2bJiMHTtWzjrrrJDfv/jii8oD9ffff4973UzHlGtICCGE+JHVG0rl8ocmSHFZlXp/7pED5OiRvRNdLeJQ74kpBiqWf33wwQfyxhtvyIQJE5TxFPTr108pom+//TaNp2HQS+G8ymM3rZ10sdTVj5jSTq/r4Wb5TsriGDAPU9rplzHgtJxo83uRnv3fvHb6pf+7XZaX7LjjjmrjU4QuCAahEqDLQnclJBn6u6lQfs6hDJ1B+TmD8ouNdoW5Mu6UYYH3z300U2YsqA+vRPzbB2MyoALEu9ppp50axCJA3LDDDjtMxeMioUHMNC/z2E1rJ10sdfUjprTT63q4Wb6TsjgGzMOUdvplDDgtJ9r8XqRn/zevnX7p/26X5SXnnXeeCjN14oknyquvvirff/+9/PDDD2rZ/qmnnqrCUV144YWJriYxHL/0d1Oh/JxDGTqD8nMG5Rc7w3dsLwcMq99Poba2Tu576VfZtKUi0dXyHbUG9cGYNpEC7733nlJEsdlAqAZhaf8LL7zgtH5JBzab8DKP3bR20sVSVz9iSju9roeb5Tspi2PAPExpp1/GgNNyos3vRXr2f/Pa6Zf+73ZZXjJy5Eh54IEH5J577pHbbrstEHsM0auwCRk+HzVqVKKrSQzHL/3dVCg/51CGzqD8nEH5OWPMfr1kzWaRafPWyYbN5fLAK7/KrefvKWmpjIfqxz4YkwH14Ycflv/85z9qN1a9gyuxB41H5mFKO5vKzTPHgHmY0k6/jAEaUJMLU9rpl/7vdllec/jhh6vVUX/++acsX75cPfRHKKqBAwcqPZaQZOrvJkL5OYcydAbl5wzKzxktCvLlqlN3lssemiAbt1TIH3+tk/FfzJbTDmF8cT/2wZgsn4gnhZ1Nf/75ZxUD9euvvw55kO1ZsWKFp3nsprWTLpa6+hFT2ul1Pdws30lZHAPmYUo7/TIGnJYTbX4v0rP/m9dOv/R/t8uKB/A8HTRokBxyyCHKmDp06FAaT0nS9nfToPycQxk6g/JzBuXnXH4t87Pl6tN3kdStXqevfzlXfpu9OtFV8w0rDOqDMXmgFhcXy8EHH8xYp4QQQgghxFjmzZsnH330kaxbt05qampCGlfvuuuuhNSNEEIIIU2Dgb1ayxmH9pfnP56p3j/4yhR5ZNxIadsyN9FVI14bUPfZZx/58ccfZcyYMbFkb9Jg2ZiXeeymtZMulrr6EVPa6XU93CzfSVkcA+ZhSjv9MgaclhNtfi/Ss/+b106/9H+3y/KSzz77TMaNGxdx8wEaUEmy9HdTofycQxk6g/JzBuXnnvyOGdVbZi3aID/NWCVbSivlvhd/lbv/vrdkpDMkpl/6YExX6qabblK7ml555ZXy6aefqqX8v/zyy3YH2Z7S0lJP89hNayddLHX1I6a00+t6uFm+k7I4BszDlHb6ZQw4LSfa/F6kZ/83r51+6f9ul+Uljz/+uHTs2FHeeOMNmTZtmsyePXu7Y9asWYmuJjEcv/R3U6H8nEMZOoPycwbl5578sIT/8pOGSrvCeq/TOUs2ynMfzUhg7fxBqUF9MDXWGARbtmyRjz/+WD3ZP/PMM+WMM84IHKeffrp6JdtTVFTkaR67ae2ki6WufsSUdnpdDzfLd1IWx4B5mNJOv4wBp+VEm9+L9Oz/5rXTL/3f7bK8ZNGiRXLWWWfJTjvtJJmZmYmuDvEpfunvpkL5OYcydAbl5wzKz1355eVmyrVnDg94nX44aYFM+n15gmrnD4oM6oMxLeG/7bbbZPPmzXLuuedK9+7dJT09pmIIIYQQQgjxhPbt20t5eXmiq0EIIYQQEqB35xZy/tGD5PG3/lDvH3tjqvTomC+d25qz2zwJTUpdXV2dRMngwYPlkksukfPOOy/arE0SLA+D23Fubq70798/0dUhhBBCCEl6vef555+XF154Qd5++20pLCxMWD38iCnXkBBCCElGYIZ7ePwU+ea3Zep91/bN5cGxIyQ7i86JJus9qbE+0U9NZaDbWFi4cKGneeymtZMulrr6EVPa6XU93CzfSVkcA+ZhSjv9MgaclhNtfi/Ss/+b106/9H+3y/KSqqoqtUnUAQccIOeff75ce+21ct111zU4rr/++kRXkxiOX/q7qVB+zqEMnUH5OYPy80Z+0E8uPm6wMpyCJau2yL/f/kMZVom5fTAm8/bf/vY3eeyxx2TkyJHSu3dv92uVxNTU1Hiax25aO+liqasfMaWdXtfDzfKdlMUxYB6mtNMvY8BpOdHm9yI9+7957fRL/3e7LC958MEHA/9PnDgx7A3MXXfdFcdaiUyePFleeeUV+f3332XTpk3SokUL2X333eWCCy6QPn36hJX5a6+9prxp58+fL2lpadKzZ0859thj5aSTTqJjg4f4pb+bCuXnHMrQGZSfMyg/7+QHb9Przhwu4x75VsoqapQ36oCereTg3bvHtY6mU2NQH4zJgIpdS6FwHnnkkdKlSxdp3bq1UuSs4HssmyINycvL8zSP3bR20sVSVz9iSju9roeb5Tspi2PAPExpp1/GgNNyos3vRXr2f/Pa6Zf+73ZZXgJ91TQeeOABeeqpp9T/bdq0UUZQeFZ8+OGH8vnnn8u//vUv5aBgpba2Vm3a+tlnnyn9Gs4L+Gz69Onq+Prrr+WJJ56QjIyMBLUqufFLfzcVys85lKEzKD9nUH7eyg9xTy8dM1Tue/lX9f4/706XXp1bqDipxLw+GNPj6m+++UYZTLGUH8ujVq5cKcuWLWtwLF261P3aJgHwMvAyj920dtLFUlc/Yko7va6Hm+U7KYtjwDxMaadfxoDTcqLN70V69n/z2umX/u92WfECBsd169ZJZWVlwurw1ltvKeMpNl+9/fbbZdKkSfLBBx/Id999J/vtt5+q29VXXy1btmxpkO8///mPMp7C4PrOO+/IRx99JJ988om8/vrryokB5cDwSrzBj/3dJCg/51CGzqD8nEH5eS+/fYZ2ktF791D/V1XXyj0v/CLFpYnTV0yjhUF9MCYDKp502znI9sC47GUeu2ntpIulrn7ElHZ6XQ83y3dSFseAeZjSTr+MAaflRJvfi/Ts/+a10y/93+2yvGbx4sVy6aWXys477yz77LOP/Pbbb2oJ/ZgxY+TXX+u9PeJBRUWF3H///er/a665Rk444QTlTQoKCgrUd82bN1dL+r/44otAvuLiYnnuuefU/7feeqvsuOOOge+GDBki99xzj/ofq76Kiori1p6mhJ/6u4lQfs6hDJ1B+TmD8ouP/M45YqD07dpS/b96Q6k88tpUxkM1sA/aMqAuX77c8YnokUoIIYQQQuLFokWLlKH0559/VsZTDVZRLViwQM455xwVhzQewLEAxlGEvjr11FNDLk+78cYb1UZX1jio//vf/5RhFJ6m8FINBu1CmWVlZSotIYQQQvxHRnqqXH3GLtI8tz4cz08zVsm7E+YlulokFgPqcccdJ3fccYesXr1aYjGc3nTTTXL88cdHnTcZadeunad57Ka1ky6WuvoRU9rpdT3cLN9JWRwD5mFKO/0yBpyWE21+L9Kz/5vXTr/0f7fL8pKHHnpIsrOz1XL3W265JeDJseuuu6rPYJSM19L3H374Qb3CCBq8b4Dm6KOPlrPPPlt22mmnwGdTp05Vr/Cg1R6rweA7AEMxcR+/9HdTofycQxk6g/JzBuUXP/m1bZkrV56K3/v69y98Mkv+nL9OmjrtDOqDtgyo77//vqxZs0b2339/Oeuss9TOoeE8SqGczpkzR6XBE/aDDjpI1q9fr8og/9/eeYBHVaVv/EsPoSSEDqF3lSrYC1ZQURQbIPbeFXWxrK66tnUtuPa/a1/FjrjYG+BaERAFAkjvNZCQXv/Pe8IdbkIm3Jlz78y5k/f3PHmSmTnnu9955zs3Z745pXoJl5d1nJZ1Ui4cX/2IKe302g837evYYh8wD1Pa6Zc+oGsn1PpelGf8m9dOv8S/27a85KeffpKxY8dKixYt9kg+YjA+btw4mT9/fkR8wdgYYHYpxspff/213HbbbWpcfd1116n9TOvanxWzaAFmmQYjKyurRlniLn6Jd1OhfvpQQz2onx7UL7L67d+njZx1bC/1d2VllTz8+q+yPa9YGjIlBsWgowQqBpn/+te/1B5MjRs3lgceeEAlRgcPHiwnn3yyGpyOGTNGhg8frp7DN+j333+/NG/eXCVSn3nmGXXgFBG1fMvLOk7LOikXjq9+xJR2eu2Hm/Z1bLEPmIcp7fRLH9C1E2p9L8oz/s1rp1/i321bXoKEZLNmzYK+jlPrIzUoX79+feCaSJpeddVV6kAo7Mf6+eefy1133aXGz7X3+crJyVG/MzMz93q4wvbt2z1tQ0PFL/FuKtRPH2qoB/XTg/pFXr+xx/eRAT1bqr+37yyRR96YLRUVldJQ2WFQDCaGUnjo0KHqZ+PGjTJz5kyZM2eOmomKBsXHx0u7du3UMqKDDjpIDjvssHoHe4QQQgghhHhFnz591N6jde05Wl5eLh999JH07t07Ir4UFBSo3//4xz/UwVCYfXrKKadIWlqamimLrbKWLVsml112mUqsYusBgL1NQUpKSlDbtcsSQgghxL8kxMfJzecMkesfmy45ecXy+9Kt8sbni+S8E3cfJEmiQ1wVj/bynOzsbCksLFSDZAzmg+1hFQy8RU7rOC3rpFwo1/UzprTTaz/ctK9jK5y67APeYko7/dIHdO2EWt+L8ox/89rpl/h3Yss+7unbt69Ei2+//VbN9DzppJPUNlQ33nijSlRihdSLL76o9hedNGmSWkHlNfvss49UVFSov5944gkZMWJEjddXrVql/CwrK1OHSZ177rnqefiGpfk4T2D8+PF12n733XdVHawYwwQHN7DeQ0yQaNq0qXTt2lUdKotZvY0aNZJWrVrJ6tWrVVnsJYuYwJZdoEuXLmqyRXFxsUr8YoKFtb0AtlOAzS1btqjHnTp1Un8j+ZucnKy2I8ABXwDvE2bsYhsxaxsDzMhFMjoxMVE6d+6sks7WLFwkknFd0KFDB3X4FpLV2HMW/qMs/MSsZKyo27Bhgyrbvn17VS4vL0/Fdffu3ZUPlZWVqu34sWYQY0UdfIVt0KNHD9U2JORhEz5bs4jxfkAva2Zwt27d1MQTvMfoG9DN0hB6Ij6sGcfwF9fEDGm0C7YQI5beYOvW6n3xoAPOqbD0RntWrFihXsNkFrTfrjfq4b2FttDUrjfeA+vMC7wX8N3SG+/r0qXVB5qkp6erOLDrDf127typ3l+01dK7toaIB9i06w1/0X4cpgbb1gHG0BvtsmYfoSx0sPRG+6xt5Vq3bq20teuN96KumMXfeH/tMYt4sPTGde0xCz8tvWvHLNpu1xta2WMW17D0Rl17zOL9suuNdtpj1tK7rpiF1na9g8Us9Mb17TFr1xt+2GMWGtj1hmZWzEILu954H+wx21DvEShvj1neI0K7R0A/xGKs3iOgt1U21HvEio1Fct8rc6RyV8bu2tG9pFubpD3uEdAEMROr94i2bdsG4turewTqw7e9jV2ZQI0A9g8S+MEbFQoIFqd1nJZ1Ui6U6/oZU9rptR9u2texFU5d9gFvMaWdfukDunZCre9Feca/ee30S/w7sWVKAhVgNie2nsJg2Ur84jcG5EioYjl9JMAWV/ABe6BOmzatzjK33nqrTJkyRQ4++GB55ZVX1HOnnXaaLFy4UP7yl7/IxRdfXGe9119/XSWG8YED2wG4gUnvYbQx5R7hV6ifPtRQD+qnB/WLrn5Tpi+Vl/67QP3dpFGSTJowTNpkpklDYlUEYtDpuCekJfxEH2TnvazjtKyTcuH46kdMaafXfrhpX8cW+4B5mNJOv/QBXTuh1veiPOPfvHb6Jf7dtuU1o0ePVvv2f//992pWBGZfYCbMIYccomYmRArMsEECFSuRgoHkKrAf1Gr5WN/+X9ZsGm6d5Q1+incToX76UEM9qJ8e1C+6+p16ZHdZuGKb/DR/o+QXlck/Xpsl/7jmMElKTJCGQplBMcgEaoRBRtvLOk7LOikXjq9+xJR2eu2Hm/Z1bLEPmIcp7fRLH9C1E2p9L8oz/s1rp1/i321bkQBL0CKxTL8+sMwNS+uwPCwYWJIHsATNXg/JX2u5Yl1YS+swA5W4j9/i3TSonz7UUA/qpwf1i65+WDlz/ZjBsurxGbJhW4H8uWaHvPjRArlidH9pKKQZFINMoEYY7NngZR2nZZ2UC8dXP2JKO732w037OrbYB8zDlHb6pQ/o2gm1vhflGf/mtdMv8e+2LTfBwUxjxoyRAQMGBB47+WCCJf5eM3DgQPnf//4nv//+e9A9ZK09u7Cfl4XVFuzXGgwc6mptE0Dcx9R49wvUTx9qqAf104P6RV8/LN2/9fyhcvO/ZkpZeaV8/P0K6dslU44cnCUNgRYGxWB8tB1oaNiXZXlRx2lZJ+XC8dWPmNJOr/1w076OLfYB8zClnX7pA7p2Qq3vRXnGv3nt9Ev8u23LTbB/qHUggfXYyU8kGDlypEqaYhbqxx9/vMfrODjB2hvVPlv2qKOOUgdW4LCH6dOn71EPh0bh/cBhGdiqgLiPqfHuF6ifPtRQD+qnB/UzQ79uHdJrzDp96t3fZM2mndIQWGNQDGonUHGi1bx589QJX1iWhL2lCCGEEEIIiSSLFi2Sk08+ucbjvf3g0IBIgNNnzz77bPX3nXfeKV988UXgNZzce8MNN6g9UnH67UknnRR4DYnRiy66KDCj1pptCjD+tmbZnnfeeeoUXkIIIYTEJscd0EmOGdpR/V1cWiEPvvqLFJWUR9utBkXYS/hnz54t999/f2Dg+dJLL0lFRYXcfvvt6hTRE0880U0/Y4bWrVt7WsdpWSflwvHVj5jSTq/9cNO+ji32AfMwpZ1+6QO6dkKt70V5xr957fRL/Ltty2vwxT6Wzh944IGSkpKinvv6668lPj5eze6MJBgjY+LBN998I9dee620bdtWLUtbsmSJOiChXbt2MmnSpICfFldeeaVa+o92jB07Vu2LitmsS5cuVa+jHddcc01E29KQ8FO8mwj104ca6kH99KB+5uiH//2Yhbpsba6s3JAnazblyzPvzZMJ4wbXuTVQrNDaoBgMawYqBnEXXnih+qb8/PPPDzyPb74TExPl5ptvlhkzZrjpZ8xQXl7uaR2nZZ2UC8dXP2JKO732w037OrbYB8zDlHb6pQ/o2gm1vhflGf/mtdMv8e+2LS/B6fTYE/Xyyy8P7C8K/vvf/6qkJMayhYWFEfMHidFnnnlGHn30UTnooIOkqKhIJUGzsrKUjx988IH07Nlzj3o4VOr555+Xu+66S/bbbz+1DQCWs/Xp00dNWnjyySfV+Jt4g1/i3VSonz7UUA/qpwf1M0u/1OREtR9qo5Tq//vT56yVz35cKbFMuUExGFYC9YknnlCDvalTp8pll12mNsMH/fr1k48++kh9M46BHtmTnJwcT+s4LeukXDi++hFT2um1H27a17HFPmAeprTTL31A106o9b0oz/g3r51+iX+3bXnJ448/Ln/++afce++9amxq8fDDD6uf3377TSUfIwlmiGA/1FdffVV++eUXNSnhs88+kwkTJkhmZmbQekiQnnPOOfL++++rA6VQD+NwJIGTkpIi2oaGhl/i3VSonz7UUA/qpwf1M0+/Dq2ayPVnDwo8/r8P58ufa7ZLrJJjUAyGlUDFwG306NFqU/vaU4WbNGkiZ511lhqwEkIIIYQQEg2wGuqCCy6QM888U83itMDfp5xyiowfP77GXqSEEEIIIX7g0AHt5ZQjuqm/yysq5aFXZ8nOwtJouxXzhH2IlH0gWpuSkhIeJlXPIQJe1nFa1km5cHz1I6a002s/3LSvY4t9wDxMaadf+oCunVDre1Ge8W9eO/0S/27b8pK8vDy1x2gwsAcpDnAiJBbi3VSonz7UUA/qpwf1M1e/C07aV/p0bq7+3ry9SB6fPEcqK6tXh8cSXQ2KwbASqAMGDJBp06bV+Rr2knr33XfVcn6yJ+vWrfO0jtOyTsqF46sfMaWdXvvhpn0dW+wD5mFKO/3SB3TthFrfi/KMf/Pa6Zf4d9uWl2DZ/ueffx7Yaqo2OEzKpEE5MRO/xLupUD99qKEe1E8P6meufkmJ8fKXc4dKs8bVkxtnLdwk738beyvB1xkUg2ElUK+77jpZuHChWvr04YcfqmX82Ivptddek1GjRsnatWvliiuucN/bGKC0tNTTOk7LOikXjq9+xJR2eu2Hm/Z1bLEPmIcp7fRLH9C1E2p9L8oz/s1rp1/i321bXnLuuefKrFmz1JgUy/lXrlwpq1atku+++06uvfZa+fHHH+W8886LtpvEcPwS76ZC/fShhnpQPz2on9n6tWreSG46Z3+xdtb8z6fZ8sfS2FpdU2pQDIZ1ZOegQYPUIVF/+9vf5B//+Edgo37QqlUreeyxx9TpomRPGjVq5Gkdp2WdlAvHVz9iSju99sNN+zq22AfMw5R2+qUP6NoJtb4X5Rn/5rXTL/Hvti0vwZf6mzdvlqeeekpmzpy5x6FM119/vdrTn5BYiHdToX76UEM9qJ8e1M98/Qb3bi1jjustk79YLFjB//B/fpUnJgyTzGapEgs0MigG46qCrWtyAKouWLBA1qxZo/Y87dChg+y3335qUEp2k52drbY2SEtLU8vJ6ts/NljG3Wkdp2WdlAvlun7GlHZ67Yeb9nVshVOXfcBbTGmnX/qArp1Q63tRnvFvXjv9Ev9ObNnHPX379hUT9kL9/vvvZf369VJRUSHt27eXQw45pN5T7xs6pr2H0cSUe4RfoX76UEM9qJ8e1M8f+lVUVsndL/wovy3Zoh7v262F3H/FIZKQEPaxRw1Kw2yH4x4tNbF0HwnTE044QU466SQZOHAgk6d7YfXq1Z7WcVrWSblwfPUjprTTaz/ctK9ji33APExpp1/6gK6dUOt7UZ7xb147/RL/btuKBM2aNVNj1Ysvvlguu+wyGTlyJJOnJGbj3TSonz7UUA/qpwf184d+CfFxcvM5+0uL9OpZpwuWb5PXP82WWGC1QTGYGG4G+IUXXlDf5m/ZskXNPq0rufrVV1+54SMhhBBCCCH1gqX6xx9/vPTq1SvweG9gvHr11VdHwDtCCCGEEO9Ib5IiE88dKrc98z81I/X9b5dK3y6ZcuB+7aLtWswQVgL1/vvvl7ffflvatm2rlu3Hx/t/WnCkaNmypad1nJZ1Ui4cX/2IKe302g837evYYh8wD1Pa6Zc+oGsn1PpelGf8m9dOv8S/27bcBAnTzp07M4FKXMXUePcL1E8faqgH9dOD+vlLv75dM+XCk/eVf0+drx4//tZcmXRjM2nborH4lZYGxWBYCdQvv/xSLX965JFH3Pcoxglny9lQ6jgt66Scxva4vsKUdnrth5v2dWyxD5iHKe30Sx/QtRNqfS/KM/7Na6df4t9tW27y3nvvSY8ePQKPv/7666j6Q2IDU+PdL1A/faihHtRPD+rnP/1OObybLFyxTX74fYMUFJXJQ6/NkoevOVySkxLEj1QZFINhTR0tLy+XoUOHuu9NA2Dbtm2e1nFa1km5cHz1I6a002s/3LSvY4t9wDxMaadf+oCunVDre1Ge8W9eO/0S/27bcpNLL71U/vvf/wYeT5kyRQoKCtRqqfp+CPFjvPsF6qcPNdSD+ulB/fynH1bXXH/2IGnfsnrW6bK1uYEZqX5km0ExGFYCdcSIEWoWKiGEEEIIISaA01Ptg2ws4V+yZElUfSKEEEIIiTRpqUly6/lDJTmxOuX36Y8r5dvZa6Ltlu+JqwpjPiy+zccpphUVFXLsscdKixYtVJa7Nqeeeqpbfvqa7OxsNahPS0uTnj17SmJiYsgzfp3WcVrWSblQrutnTGmn1364aV/HVjh12Qe8xZR2+qUP6NoJtb4X5Rn/5rXTL/HvxJZ93NO3b1+JFOeee67MmTNH+vTpI40bN5ZffvlFunfvrsapwcD49dVXX42Yj34hWu+hiZhyj/Ar1E8faqgH9dOD+vlbv69+WS1PvD1X/Z2SnCCPXn+EdG7bTPxEeQQ0dDruCWsG6uzZs2XhwoXy22+/qX1Qb7vtNrn11ltr/OA5sicbN270tI7Tsk7KheOrHzGlnV774aZ9HVvsA+ZhSjv90gd07YRa34vyjH/z2umX+Hfblpv84x//kGOOOUby8/Nl7dq1Kjmak5Oj/g72s2YNZ2MQf8a7X6B++lBDPaifHtTP3/ode0AnOe6ATurvktIKefCVWVJYXCZ+YqNBMRhWGvfhhx9WmdlbbrlFunbtKgkJ/tyMNhoUFxd7WsdpWSflwvHVj5jSTq/9cNO+ji32AfMwpZ1+6QO6dkKt70V5xr957fRL/Ltty01mzJghEyZMkC5duqjHmIl6++23y8knnxxt14iPMTXe/QL104ca6kH99KB+/tfv8tH9ZenaHbJifZ6s25IvT787T24ev3+dq8hNpNgADbUSqKtXr1bJ03HjxrnvUYyTkpLiaR2nZZ2UC8dXP2JKO732w037OrbYB8zDlHb6pQ/o2gm1vhflGf/mtdMv8e+2LTfBF/x//etfAwnU9u3bc9khidl49wvUTx9qqAf104P6+V+/lKQEtR/qjY/PkMLicpn52zrZp2umnHRYN/EDKQZoaBHWqBKzTnfu3Om+Nw2Adu3aeVrHaVkn5cLx1Y+Y0k6v/XDTvo4t9gHzMKWdfukDunZCre9Feca/ee30S/y7bctNkpOT1SGnAwYMkEaNGsn69etl69at6nd9INFKiN/i3S9QP32ooR7UTw/qFxv6tW/ZRG4YM0geeGWWevzvj+ZLz07NpVen5mI67QzRMOw9UK+77jq14T6WSlVWVrrvVQyzcuVKT+s4LeukXDi++hFT2um1H27a17HFPmAeprTTL31A106o9b0oz/g3r51+iX+3bbnJGWecIdOnT1dL9nHIKXjggQfUvqj1/RDix3j3C9RPH2qoB/XTg/rFjn4H92svpx7ZXf1dXlElD702S/IKSsV0VhqkYVgzUN999131Lf8VV1yhptNmZGTssQ8q9lP46quv3PKTEEIIIYSQoGB7qaFDh8rixYultLRUnn76aZVI7d27d7RdI4QQQgiJOueftI8sXrVdslfmyJbtRfLYm7PlrosPkvh4f+yH6ssEakFBgdpfytpjijinRYsWntZxWtZJuXB89SOmtNNrP9y0r2OLfcA8TGmnX/qArp1Q63tRnvFvXjv9Ev9u23KbYcOGqR8wZcoUOfXUUznLlMRsvPsB6qcPNdSD+ulB/WJLv8SEeJl43hC5/rHpkptfKrMXbZZ3v1kiZx9r7pfNLQzSMKwE6uuvv+6+Jw2E+Ph4T+s4LeukXDi++hFT2um1H27a17HFPmAeprTTL31A106o9b0oz/g3r51u+5GTV6x+LPLzC2RbQfDZBZnNUtWPnzTbG9988020XSAxgF/i3VSonz7UUA/qpwf1iz39WqQ3kpvP2V/u+r8fpapK5M3PFkmfTpkyoFcrMZF4gzTk0aQRZsuWLZKenu5ZHadlnZQLx1c/Yko7vfbDTfs6ttgHzMOUdvqlD+jaCbW+F+UZ/+a1020/PvtxpUz+YrHj8mOP7y3jhvfxlWa1ueaaa+SCCy6QIUOGBJ7DXv1LliyRzp07q4Ol7Hz00UcyceJEyc7OjoK3xC+YGu9+gfrpQw31oH56UL/Y1G9gr9Zq3PfGZ4ukskrkkTdmy6QJR6rkqmlsMUhDRwlULH26/fbbA0ugnCyF4h6ohBBCCCHRYcTBXeSAfdsGHq9Zs0b+/ekaySsok2aNk+Seyw6pUd7p7FOTwbhz+PDhNZ7Lzc2V0047TV566SU5+OCDo+YbIYQQQohJnHVML8lekSNzFm+WHfkl8vDrv8r9Vx6qlvkTjQRq+/btJS0trcZjEh6dOnXytI7Tsk7KheOrHzGlnV774aZ9HVvsA+ZhSjv90gd07YRa34vyjH/z2um2H/Yl+SvW58rc5SUqeQrwe9p3y2XUkd2la/t032rmlCqsTyMkTPwW76ZB/fShhnpQPz2oX+zqh4OjJowbLDc8PkO27iiShSty5LVPsuWik/cVk+hkkIbxTvc8tX9rj8dOfkjd04+9rOO0rJNy4fjqR0xpp9d+uGlfxxb7gHmY0k6/9AFdO6HW96I849+8dnrlx4w5a+WGx2bIjLnrajw/3Xp+zlrfakZIJGC860H99KGGelA/PahfbOuX3iRFHSqVmFC9T/6U6Uvlxz/Wi0lsMUjDsObm3nbbbTJv3rygr//0009y6aWX6vgVsxQVFXlax2lZJ+XC8dWPmNJOr/1w076OLfYB8zClnX7pA7p2Qq3vRXnGv3nt9MIPzDx97M05UllVpfa3slNRieeq1Oso50fNCIkEjHc9qJ8+1FAP6qcH9Yt9/fp0zpSLTt4v8HjSW3Nlw9YCMYUigzQMK4E6ZcoUtZdWMH7++Wf1Q/YkOTnZ0zpOyzopF46vfsSUdnrth5v2dWyxD5iHKe30Sx/QtRNqfS/KM/7Na6cXfkydsUziqicUBAWvT525zJeaERIJGO96UD99qKEe1E8P6tcw9Bt5WFc5bED1Vp2FxeXy0KuzpKSsQkwg2SANHe2BimTpyJEjpbS0NPDcLbfcon6C0a9fP3c8jDGysrI8reO0rJNy4fjqR0xpp9d+uGlfxxb7gHmY0k6/9AFdO6HW96I849+8drrtR2Vllcz8bZ2aaVofeH3m3HVy/dmD1AGgftKMkEjAeNeD+ulDDfWgfnpQv4ahH8aA1541UFasz5N1W/Jl+fpc+b8pf6jnok2WQRo6moHasWNHueuuu+TUU0+VUaNGqef2339/9bj2z+jRo+WSSy6RSZMmee27L1m+fLmndZyWdVIuHF/9iCnt9NoPN+3r2GIfMA9T2umXPqBrJ9T6XpRn/JvXTrf9KC2rkLLySkdlUS6UWQamaFYXO3bskPXr1wd+Nm7cqJ7Pycmp8Tx+tm/fHm13iQ8wOd79APXThxrqQf30oH4NR7+01CS57fyhkpyUoB5/8fMq+XrW6mi7JSZp6GgGKjj99NPVD1i3bp1cddVVNQ6WIoQQQggh0Sc3v0RembbAcfmkxHhJ2TVY9jsPPPCA+qnNzTffHBV/CCGEEEL8Qud2zeTqMwbI45PnqMfPvP+7dM/KkC7tmkXbNX8lUO28/vrr7nvSQGjevLmndZyWdVIuHF/9iCnt9NoPN+3r2GIfMA9T2umXPqBrJ9T6XpRn/JvXTjf8wLL9L39ZJa9+vFB2FpY5qpMQHydHDOrgePm+SZrV5rTTTou2CyQGMTXe/QL104ca6kH99KB+DU+/o4d0lIUrtsnnP61SK5oefOUXefzGI9UM1YauYVgJVLB06VKZNm2abN26VSoq9lz2hYF4XTMAGjpJSUme1nFa1km5cHz1I6a002s/3LSvY4t9wDxMaadf+oCunVDre1Ge8W9eO3X9WLZ2hzz7/u+yePXuZempyQlqaX5VPdug4rVRR3T3pWa1efDBB6PtAolBTI13v0D99KGGelA/Pahfw9TvslP7yZ9rdsjydbmyfmuB/Oud32TiuUNC+sI9FjV0tAdqbT777DM55ZRT5LnnnpP33ntPpkyZUucP2ZPNmzd7WsdpWSflwvHVj5jSTq/9cNO+ji32AfMwpZ1+6QO6dkKt70V5xr957QzXj4KiMvm/D/+QCZNm1EieHjkoS56/7Vi5adz+Eh8XJ/Fxe848xfMTxg2Wru3TI+IrIX6E8a4H9dOHGupB/fSgfg1TP+yDiv1QG6dWz7n8ft56+e//orMXqUkahjUD9emnn5b27dvLY489Jn369JHk5GT3PSOEEEIIIXVSVVUlM+eukxc/mi/bd5YEnu/QqolceXp/6dimqeTkFUuH1k1kwjmD5dPvFsuCVfmBcvv3aS1HDM5S5Zeu3SGZzVLVDyGEEEIIIW1bNJYbxw6W+17+RT1+6aMF0qtjc+nTJVMaKnFVGIGHSL9+/WTixIkyfvx4b7yKMbKzs6WwsFDS0tKkW7dukpKSElL9kpISx3WclnVSLpTr+hlT2um1H27a17EVTl32AW8xpZ1+6QO6dkKt70V5xr957QzFjzWbdspzH/wuvy/dWmOmwJjjesmpR/ZQh0K9+fkimfzFYsfXH3t8bxk3vI8rvtrHPX379nXsAzEHvofm3SP8CvXThxrqQf30oH56xIJ+OJj0/W+Xqr9bpqfKpAnDJL1JSkxp6HTcE9YM1LZt20pxcbGOfw2WnJwcadeunWd1nJZ1Ui4cX/2IKe302g837evYYh8wD1Pa6Zc+oGsn1PpelGf8m9dOJ34Ul5bLO18tkSnTl0p5xe7vvw/ct61cemo/aZOZFnhuxMFd5IB92wYeY8/6li1bBrUdyuxTUzQjJBIw3vWgfvpQQz2onx7UT49Y0O/cE/rKolXbZcHybbI1t1gefWO2/O3Sg9V2UA1Nw7ASqOecc468+uqrMnr0aMnMbLjTd8OhoKDA0zpOyzopF46vfsSUdnrth5v2dWyxD5iHKe30Sx/QtRNqfS/KM/7Na+fe/PhlwUZ5fsrvsnl7UeC51plpcvmp/WokSi32WJJfvFV6ZGVExFdCYgnGux7UTx9qqAf104P66REL+iUkxMtfzh0i1z86XXbkl8jcJVvUF/pYvdTQNAwrgVpWVqZO3zr22GNlyJAhKola+zQuPH7ggQfc8jNmSExM9LSO07JOyoXjqx8xpZ1e++GmfR1b7APmYUo7/dIHdO2EWt+L8ox/89oZzI9NOYXywod/yM8LNu4umxAno4/qKWce01NSk9275+n6SkgswnjXg/rpQw31oH56UD89YkU/fCl/y7n7y53P/SCVVSKTv1gkfTo3l0G9WzcoDcPaAxUHR+3VcFyc2keA1NxPAdrVTjbvDbxFTus4LeukXCjX9TOmtNNrP9y0r2MrnLrsA95iSjv90gd07YRa34vyjH/z2lnbj7LyCpkyfZm8/dUSKS2rCDw/oGdLufy06kOidOy76asp+2eed955IddBO7CqitSEe6Cad4/wK9RPH2qoB/XTg/rpEWv6vfPVEnn90+o8X7PGyfLEhGHSMqOR7zV0Ou6JD8f4okWL9vrD5GndLFu2zNM6Tss6KReOr37ElHZ67Yeb9nVssQ+Yhynt9Esf0LUTan0vyjP+zWun3Y95S7bItY9MVwNUK3navGmK3DJ+f/n75YeEnDytbd9NX01i7dq1If+sWbMm2m4TwzE13v0C9dOHGupB/fSgfnrEmn5nHN1ThvRto/7OKyiVf7w2S8orKhuMhubMhSWEEEIIacDk5BXLix/Nl5lz1wWew/78Iw/rJuOG95HGjZKi6p/pfPPNN9F2gRBCCCEkZomPj5MJ4wbLDY9NV/vy43CpV6YtlEtG7ScNgbASqLfddpujcg8++GA45mOajIwMT+s4LeukXDi++hFT2um1H27a17HFPmAeprTTL31A106o9b0oz/g3q50VFZUy689C+fDlr6WwuDzwfO/OzeWq0wdItw7pMfM/IFps3rxZNmzYIN26dZOUlBS1n1Z8fFgLsUgDw4/xbhLUTx9qqAf104P66RGL+jVNS5aJ5w2ViU99J+UVVTJ15jLp2zVTDu3fPuY1DCuBOmXKlHpfb9GihTpYiuxJamqqp3WclnVSLhxf/Ygp7fTaDzft69hiHzAPU9rplz6gayfU+l6UZ/yb085Fq3Lk2fd+l+XrcwPPNU1LkvNP2leOO6CT+qY/lv4HRJrZs2fL/fffH9ha6qWXXpKKigq5/fbb5dZbb5UTTzwx2i4Sw/FTvJsI9dOHGupB/fSgfnrEqn69OjWXS0b1k+c++F09fuKtudK1XTNp36pJTGvo2h6oCxculBkzZsjEiRPVwPSRRx5x39sYYOPGjZ7WcVrWSblwfPUjprTTaz/ctK9ji33APExpp1/6gK6dUOt7UZ7xH/12Yt+op979TW7513c1kqdImj478RgZflBn15KnJv0PiCS///67XHjhhVJQUCDnn39+4Pn09HQ1A/Xmm29WY1dCYiHeTYX66UMN9aB+elA/PWJZvxMP6SJHDOqg/i4qKZcHX50lxaW7V1LFooaurV3CMqg2bdqogeoJJ5wgDz30kPiFmTNnyplnnimDBg2So48+Wp566ikpKyuLtluEEEIIiTEqK6vky59XyRUPfS2f/7Qq8Hz7Finy8DWHy3VnD5L0JilR9TFWeOKJJyQrK0umTp0ql112mTrFFfTr108++ugj6d69uzz//PPRdpMQQgghxHfExcXJNWcOlI5tqmedrtyQJ89/8IfEMp5s/rTPPvvIb7/9Jn7g+++/V4PqpKQkNRPhqKOOkmeeeUb++te/enK9Dh06eFrHaVkn5cLx1Y+Y0k6v/XDTvo4t9gHzMKWdfukDunZCre9FecZ/dNq5Yn2u3Pr0/+Rf7/wmOwtL1XONUhLVxvuPXne42j8q1v8HRJK5c+fK6NGj1dIvDPLtNGnSRM466yz5888/o+Yf8Qd+iXdToX76UEM9qJ8e1E+PWNevUUqi3HreUElJTlCPv5q1Wk0UiFUNPUmgYjlU48aNxQ/885//lM6dO8srr7wi55xzjtx5551y0UUXqdkKa9ascf16ubm5ntZxWtZJuXB89SOmtNNrP9y0r2OLfcA8TGmnX/qArp1Q63tRnvEf2XYWFpfJC1P/kBsenyHZK3MCzx8xsIM8O/FoGXVEd8nP39kg/gdEmuTk5KCvlZSUSGVlZUT9If7DT/FuItRPH2qoB/XTg/rp0RD069S2mZqJaoF9UTFpIBY1DOsQqdtuu63O50tLS2Xx4sWybNkyOe+888R0iouL1YFXhxxySI0B9pAhQ+SFF15QbenYsaOr18zPz/e0jtOyTsqF46sfMaWdXvvhpn0dW+wD5mFKO/3SB3TthFrfi/KM/8i0E0vG/zdvvfx76nzJySsOPN+hVWO5YnR/GdirdUT8cNu+X2JjwIABMm3atDrHpIWFhfLuu++q5fyExEK8mwr104ca6kH99KB+ejQU/YYNzpKFK7bJpz+slNLySrUf6uM3HCmNGyXFlIZhJVCnTJkSdB/Uli1bygUXXCA33HCDmA6WdL344ot7PI9DsUC7du1cv2ZCQoKndZyWdVIuHF/9iCnt9NoPN+3r2GIfMA9T2umXPqBrJ9T6XpRn/HvfznVb8uW593+X3/7cEnguOTFezjqul4we1kOSEhN8Gf9u2/KS6667Ts4991wZP368HHPMMWoZPw6WwrL9119/XdavXy/33HNPtN0khuOXeDcV6qcPNdSD+ulB/fRoSPpdOmo/+XPNDlm6Zods2FogT7w9V247f+ge2yj5WcO4KmtH/QYOZFi7dq3afuDhhx+WoUOH1plcDYfs7Gw10yEtLU369u3rik1CCCGEmEdJWYW8+9USef/bpVJesXt5+JC+beTy0/pJ2xb+2OIoVsY92Ov+b3/7mxrj2WnVqpXa73748OFR881kTHoPCSGEEOIPNuUUyg2PTZf8oupD2S8+ZT859cjuEivjHk/2QAXYQ9RPrFq1So499lj5+9//rvZvnThxoifXwfYGXtZxWtZJuXB89SOmtNNrP9y0r2OLfcA8TGmnX/qArp1Q63tRnvHvTTtnLdwoVz/8jbz91ZJA8rRV80Zyx4UHyF0XH1hv8tQv8e+2La859NBD5csvv5T33ntPHn/8cXn00Uflrbfekm+//ZbJUxJz8W4i1E8faqgH9dOD+unR0PRrk5kmN44bHHj8yrQFaml/rGjoeAl/eXm5fPXVVzJv3jw1WxNZ2ZEjR+4xnXbdunVy1113yQ8//CCjRo0Sv4BM8xNPPCE7d+6Uf//733L22WfLyy+/LAMH7t4M1w3CmfAbSh2nZZ2UayiTk01pp9d+uGlfxxb7gHmY0k6/9AFdO6HW96I849/ddm7eXigvfPiH/DR/Y+C5hPg4OW1YDzn72F6SmpIYM/Hvtq1IgKVj++23n/ohJNbj3TSonz7UUA/qpwf106Mh6nfAPm3lzGN6yrtf/ykVlVXy8Ou/yqQbh0lG0xTfa+gogbpt2za5+OKL1aFKlvMYjCLR+J///EfS09PVc6+++qpMmjRJioqKZP/99xc/0bp1axkxYoT6+6ijjpITTjhBteWVV15x9TrNmjXztI7Tsk7KheOrHzGlnV774aZ9HVvsA+ZhSjv90gd07YRa34vyjH932llWXilTZy6Tt75cLCWlFYHn+3VvKVeM7qdOJY2EH5G2b2pshHOAKcazGL+asF/r559/LpdeeqncfPPNe7xeUVGhZs6+//77aiYGJjB069ZNRo8eLWPGjFHnEBBvMDXe/QL104ca6kH99KB+ejRU/c4Z3kcWrdwufyzbKttyi+XRN2bL3ZcdrCYY+FlDRwnUxx57TB2sNHbsWDnttNOkUaNGMnPmTHnqqafkvvvuk/vvv19uvPFG+eabb1Tjbr/9djnzzDPFr+AgrCFDhsicOXNct43tAbys47Ssk3Lh+OpHTGmn1364aV/HFvuAeZjSTr/0AV07odb3ojzjX7+dfyzdKs9+ME/WbNp9Mii+WcdeT0cO6hDyhvl+iX+3bblJ7X1O/cKHH36okqfBqKyslAkTJshnn32m4qpHjx7quT/++EP9YPz97LPPSlKS/km3xD/x7heonz7UUA/qpwf106Oh6peQEC+3jN9frn9sumzfWaIOVX3ri8Vyzog+vtbQ0dfVP/74oxx//PFqE/7+/ftLz5491YxUfEOOZf133323fP3113L00UfLJ5984pvkKb7Bh8/YF6s22EA2NTXV9Wtu2LDB0zpOyzopF46vfsSUdnrth5v2dWyxD5iHKe30Sx/QtRNqfS/KM/7Db+f2vGJ59M3Zcvuz3weSp/gyfeRhXeXZicfIsMFZYZ026pf4d9uWmyCRGM5PNIGWmIxQH88//7xKnuLgqw8++ECmTZumxttvv/22+tL/u+++U5MaiDeYGu9+gfrpQw31oH56UD89GrJ+zZulyi3nDlHjZPD2V4tl9qJNvtbQUQJ169atcvDBB+/x/BFHHKGW6+PAKJxk+vTTT0uLFi3EL3Tq1Eny8vJk8uTJammUxYIFC+TXX39V7SOEEEIIwR5O0/63XK78x9cyffbumY69OmXIozccKZef1l+aNOIMQOIcbIt16623qv33sbqrLvLz89We/OCee+6RffbZJ/Aa9ul/6KGH1N/YhiA3NzdCnhNCCCGE7B1sa3XuidVjF+wG+ugbc9TZAX7F0RL+0tJSadKkyR7PW8+NGzdOxo8fL34DS53uuOMONXi94IIL1L6nmzZtktdff119y4/9qNymffv2ntZxWtZJuXB89SOmtNNrP9y0r2OLfcA8TGmnX/qArp1Q63tRnvEfWjuXrN4uz7w/T5at3Z2gQrL0/JP2keMP7CzxYeznFI4fptg3NTb8tgcqrvvTTz/J8OHDZfv27fLLL7/sUQYrvZAYxUxTrJqqzeGHHy4dO3aUNWvWqLKnn356hLxvOJga736B+ulDDfWgfnpQPz2on8joYT0ke0WO/LJwo+wsLJWHX/tVHrz6MElKjPedhq7sOB+JmZrvvPOO9O7dW80WrY+SkhJ57rnnZOTIkdKvXz8ZOnSoSu5+/PHHdZbHnq6PPPKIGpw+8MADajkUtivAbyRR3QYzCbys47Ssk3Lh+OpHTGmn1364aV/HFvuAeZjSTr/0AV07odb3ojzj31k7Mch7+r15cvO/ZtZInh47tJM8d+sxMuLgLq4kT/fmh2n2TY0N7IEa6g8Sj9HaRgpnDGDlFrbCCsbcuXPVbxzOGmxrCOvg1roSsCR2490vUD99qKEe1E8P6qcH9RM1Vr5x7CBpnZmmHi9evV1enrbAlxomumIk0RUzQfn9998DS5Tqo7i4WC666CKZPXu2Op20V69eSuxZs2apnx9++EEdeFWbk08+Wf1EAmwZ0Lp1a8/qOC3rpFw4vvoRU9rptR9u2texxT5gHqa00y99QNdOqPW9KM/4r7+dWFr99aw1anCXV1AaeL5z26Zy5ekDZN9uLRps/Ltty02ivZ+pU8rKyuSWW25RX/ojiZqZmRm07MqVK9VvzDINRlZWVo2yxF1MjXe/QP30oYZ6UD89qJ8e1K+aJmnJctt5Q+WWJ7+T8opK+e93y6Vvl0w5fGAH8ZOGjjOfO3bskPXr19d4ztprKScnZ4/X3JpqiwOssJS+oKBgr2WxCT+SpzidFLNQrcHm9OnT5YYbblCHRWG/qGgdcoUEL7ZDwH6r69atU39jzyvMdF29erUqgyVa+OC2bds29bhLly6q3tKlSyUlJUXatWsXGCBj1kJ8fLxs2bIlsKerVTY5OVkNqJcvX65ea968udqyYPPmzeoxTm/FZrzQFQnwzp07q9kQICMjQx2ghddgq0OHDuq9RjIaiemuXbuqsvCzWbNm6lQ0a2NfvOcohyDHTInu3bsrH3C9pk2bqvJoO2jbtq3aQ9eKI7xvaFt5ebmyCZ+tE3XbtGmj9MISN9CtWzc1awQfQtLS0pRulobQExojLgH8RXzigwraBVurVq0K6A0baCeADtjGATpCb7RnxYoV6jV8wEH77Xpjf2AcOAZtEW92vfEewBbAewHfLb3xvlrXTE9PV3Fg1xv6YU80vL9oq11vbJ1h9TfEA+rZ9Ya/aD/KwbalN56Dv+jLAGWhg6U32mfNxMENCrrY9cZ7YcUsfLH8h954f+0xi3iw9Mb7bI9ZXM+qCw2hJ+IAeqHtdr2hFWIWbYQ9XMPSG3XtMYv3C3qjLN4/tNMes3a9ETOW3niPobVdbytmcU1cz643Httj1q43/LDHLOrb9UaMWjELLex6Qxd7zIZyj9i4cWMgZvd2j7Drbd0joAXs2e8RiGf4U989AtcFbt0joBd88+oeYb3nod4jAPqOdY+w35PDuUdYeju9R6Bd0Myud333CLTNqhvsHoHnYM9+j4DeVt+xsOsdyj3CHrOh3iPgp6W303uEFbNO7hGW3tY9AuWBpdnOkkR546tVsnj1bh1SkuJlxNBWcmT/VtKzW4sa/9fw48Y9AiDuvLpH4D2HvXDuEbXHEdY9Odg9Ar76BbShvgSmF+DMAOy3f+qpp8qxxx5bb1nrPa/PR8QVsPojcZdwDoUju6F++lBDPaifHtRPD+q3mx4dM+Sy0/rJM+/NU4+ffGeudG3fTLJaNxW/aBhXhVH2XujTp09Qp1G9rtfw3MKFC8N2DB9AkAR94YUX1IcUCyxzGjt27B7l8YEEe0jhQ8qHH36ofLbz1ltvyd/+9jf1gQUJVXxgiBTZ2dmqPfhA1Ldv34hdlxBCCCHOKCwuk8lfLJaPvkNydPfQ6NAB7eWSU/aTlhl1H/JDzB73YOsnnFIPf+zjSYwXrS9V5s+fHzF/5s2bp8axSHpPmzZNJeDBueeeq5bgX3rppXLzzTcHyiPBisT3nXfeGfS8gXfffVcd5opEO9oaa+8hIYQQQmKDqqoqeWzynMCBrJ3aNpVHrztCUlO8XdXu1rjHkZfYJzSSLFq0SC655BI1CwKzGjB7FHuSWrNk6mLq1KlqxkP//v33SJ6C0aNHy4MPPqhmn2CAetBBB0k0wEwOzNTxqo7Tsk7KheOrHzGlnV774aZ9HVvsA+ZhSjv90gd07YRa34vyjP/dYNbkxvxG8sLUP2RbbnHg+XYtG8sVp/WXwX0is2TIL/Hvti0vwZfwjz76qJpNixnAmKGJGc+Y9YtZtpgBjcRlpMA1//KXv6hELvbdt5Kn9YFZ7U7xYoaGNeM+0isj6lo9FY2VEXiPrOdNWj1Ve2VEtFZPOVkZAQ1QN5zVU7VXRuisnvLTyojaq6fQLtS1x2yw1VNurowwefVUKPcItB+aeXWPMHGFpZv3CHzJCH+8ukforLD0wz0C17TmLHp1j7BWWPrlHnHaIa1k2dodsmZTvqzeuFMmTf5VLjulV6Bs7XsErmnh1TjC6eopRzNQIw1OEb366qtlyJAh6ht1ZIBx8igEDzYDFQlXfOuOPVAnTpxYp118c4+9UGEb2wJEI5uNNxBBGQroGE7rOC3rpFwo1/UzprTTaz/ctK9jK5y67APeYko7Te0D5Tu3S0X+7qWxGBjUtx9hQpPmkti0uWt+eFGe8V/N+i358vgbP8uiNbs3p8eJoGce00tOP6qHJCc5T17FavyHY8uU2YsnnHCC+qDz+uuvqw9Mxx13nHz55ZfqgwW+mP/73/8ujz/+uIwYMSIi/mAMixmx48aNU6ui7ASbgYpJDFjRhcTrxRdfXKddtA/bWOEDx+eff+6Kr6a8hybQEO6FXkL99KGGelA/PaifHtSvbtZs2ikTJs2Q4tIK9fiaMwfI8IO6RE1DV2egRhpkkV9++WU55JBDHNdxusk+EqjR3GTfyWwDnTpOyzopF46vfsSUdnrth5v2dWyxD5iHKe00tQ/kzf1Cdnz3TuAxNoAJvh5CJOPwsyTziLNd88OL8g09/kvKKuS9r/+U9775U21kb7F/n9Zy+Wn91ezTSGNq/Htty0vwxfuECRPUDApr1sqvv/6qkpJIYmLf/FdffTUiCdSZM2eq5CnGuDhAyimYPQHse+XWxppNE+m9XBsKfol3U6F++lBDPaifHtRPD+pXNx3bNJVrzxoo//zPbPX4+Sl/SI+sDOmeVb2vu6kaGplA7dWrl/oJBWt6temb7GOKupd1nJZ1Ui4cX/2IKe302g837evYYh8wD1PaaWofaDboeGncc2jgcUlJseRM+adUFuZJfFozaTfmzj1moLrphxflG3L8/5q9Sf5vyh+yYdvuwylbpqeqTe0P2q9d1DaqNzX+vbblJViehSV8FliqtXjx4sDjAw88UM1AjQSffPKJ+o2lboMGDap322x/J0UAAIDBSURBVAH8YLnjN998o5bHff/99/VuY2UtrcMMVOI+fol3U6F++lBDPaifHtRPD+oXnCMGZUn2ihyZ9v0KKSuvlIdemyWP3zhMmjRKMlbDyJ2k5DHYHwFgj4RgWK9hf4RoUd8A2I06Tss6KReOr37ElHZ67Yeb9nVssQ+YhyntNLUPYDl+SrtugZ/N5ckSl1D9/SN+21/DT33L98Pxw4vyDTH+t2wvkgdf/UXu+fdPgeRpQnycHD2whTwz8Rg5uF/7qJ7yaWr8e23LS5B8nDt3buAx9t+yHxiF/b6wF1ckQHJz8ODBQX8wQxZgXzU83m+//dTjAQMGqN/2dtRmzpw56jfqEffxS7ybCvXThxrqQf30oH56UL/6ueiUfaVXp+qJjhu3FcqkyXMCe8aaqKGRM1DDARvp2k9XrY9ofkAihBBCSOTAEv2PZi6TyV8sDuyzBPbt1kKuPL2/lOVvlkZRPvmTeAMOEL3nnntUkvTee+9V++lff/318tRTT6kDFbB8v66DR73giiuuUD/BsPZAPfnkk2vsgXrUUUepfVxx2MP06dNl2LBhe2wNgH2ZMdP2+OOP97QNhBBCCCFukpSYIBPPHSo3PD5ddhaWyc8LNsqU6ctk9FFm7hsbM58YsNkrZhLYT+iqjfUaTlmLFphZ4GUdp2WdlAvHVz9iSju99sNN+zq22AfMw5R2mtwHynO3SEXhTvV3i/hiya2oPqmxqqJcSjZUn/QIEtKaSmJ6K1f98KJ8Q4n/+cu2yrMf/K5O+LTIaJIiF568rxy1f5b6QjU/34zFOCbHv5e2vASHjuIU1zfeeEMt50eCEQlIJFABZn3ak5UmgsQoDkh95pln5LbbbpOnn346MNN03rx56jlw3nnnqT1eifv4Jd5NhfrpQw31oH56UD89qN/eaZ2ZJhPG7a9WiYFXP1kovTs3V5MdKiurJCOzlfodHx/9iZAxk0DFJvtIoJq+yT62D7CWaXlRx2lZJ+XC8dWPmNJOr/1w076OLfYB8zClnab2ASRP1zx7rVRVlO3xGvZBXffS7gNh4hKSpOOVT9abRA3VDy/Kx3r879hZIi9PWyDf/Lom8BwWn5xwcBc594S+0iQt2bh2mhr/XtvyEowJb7zxRrn22mtVAhU899xz6iApvIa9SFu0aCGmc+WVV8rvv/8u//vf/1RSGFsTIPmPU2mtWarXXHNNtN2MWfwS76ZC/fShhnpQPz2onx7UzxlD+raRs4/tJW9/tUQlSx945Wfp37OV/Dx/o9ofNSkxXo4Y2EFGHdlduraP3hfGZky7cAEMJoHpm+wjyetlHadlnZQLx1c/Yko7vfbDTfs6ttgHzMOUdpraBzDztK7kaV2gnDVT1S0/vCgfq/FfUVkln/ywQq74x9c1kqc9OmbIo9cfIVeePqBG8tSkdpoa/17b8pJTTz1Vzdi0kqcWQ4YMkWOPPdYXyVOQnJwszz//vNx1111qb9QNGzaoZfvYfuDWW2+VJ598co82Emlw8W4q1E8faqgH9dOD+ulB/Zwzdngf6d+jpfo7r6BM/vfbepU8Bfg9fc5aueGxGTJjTnVeLxrEzGgLm+x//fXXgY30a4P9r6yDA7jJPiGEEBJb/Llmuzzz/u+ydM3ulSiNGyXJeSf2leEHdVEHRpGGBVYetWpV/5YapvD666/X+zoSpOecc476IYQQQgiJNRLi4+SsY3vK70u3Bp0oAR57c450ats0KjNRY2YG6gknnKB+I4G6ePHiPV5///33pbi4WDp06CAHHHCARIsePXp4WsdpWSflwvHVj5jSTq/9cNO+ji32AfMwpZ1+6gOR9MOL8rEU//mFpfLs+/Pkpidm1kieHj2kozw38Rg58ZCu9SZPTWmnn+LfFM32xsiRI+Xdd9+VrVvrHogTEkvxbirUTx9qqAf104P66UH9QuPbX9fK3uY8YFuuqTOXSTSImQRqp06dZNSoUVJZWSnXXXedLFu2W9AZM2bIww8/HNhHKprLnFauXOlpHadlnZQLx1c/Yko7vfbDTfs6ttgHzKOh94HKshIp275RitcskvxFP0rur59KzvQ3Zcu0Z2Tj2w/IpqlPRMSPSJaPhfivqqpSy/Sv/Mc38skPK6Wq+ktp9Y30g1cdKjeOHSwZTVN8086G8j8gksTHx6t9Qo888kj1Rfv48ePVYUv2n/PPPz/abhLD8Uu8mwr104ca6kH99KB+elA/52Dv05m/rZNdE02DgpmoM+euU58FIk3MLOEHd9xxh/z555+ycOFCNeugZ8+eatbpqlWr1OtjxoyRM888M2r+wRdsJVBRUaH2asXfjRo1UsvLVq9ercq0bNlSBcK2bdsC+7Xu3LlTfQBISUmRdu3aBToh9u7Ch4MtW7YEksj5+fmqLPbLysrKkuXLlwcO2UpKSpLNmzerx/ABe2gVFBSohHLnzp0DSeeMjAxJTU1V+3XAFmbt4m/YTkhIkK5du6qy8LNZs2bqhFjYAu3bt1fl8vLy1AEH2JsWPiCx3bRpU1Xe2qcWJ9JhU2VrXxB8O4O2lZeXK5vw2dq3tk2bNkov6yCwbt26qf2/ysrKJC0tTelmaQg90b6cnBz1GP6uX79eSkpKVLtgy4oJ1IMP1kEM0GHTpk3qvYLeaM+KFSsCh4+h/Xa9MaulsLBQaduxY8caeuM9gC2A9wK+W3rjfbWuiVNzEQd2vaEf3ne8v2irXW9sQo32AMQDbNr1hr9oP8rBtqU3dIW/1kFrKAsdLL3RPmgKWrdurbS16433wopZ/Lb8h954f+0xi3iw9Mb7bI9Zu97QEHriOeiFttv1hlaIWbQR9nANS2/Utccs3i/ojbJ4/9BOe8za9UbMWHrjPYbWdr2tmMU1cT273nhsj1m73vDDHrOob9cbMWrFLLSw6433wR6zodwjcMq0FbN7u0fY9bbuEdAM9uz3CMQz/KnvHoHrArfuEdALvrlxj+japbOsWbpYynfmSEpVqTRJrJKiFUtk2S8l6nFFQa56TYp3Slx5ibiJel8LKoPeIyy9nd4j0C5oZte7vnsEdLTqBrtH4DHs2e8R0NvqOwD27HqHco+wx2yo9wj4ac0YDHaP2JBTLFN/3CKLV+cFdE9JSpCTDm4nh/RpJqmJBVJV1aLOe4Slt3WPwPsDat8j7DEb7B6B/2v4ceMeAb1xT/bqHgGfYC+ce0TtcYR1Tw52j4CvJvD9998rvwH8td4nQkLBlHj2K9RPH2qoB/XTg/rpQf2cU1pWEdjzdG+gXElZhaQmRzalGVcVjbRtGBx99NHqw8Ldd9+tTiANBj78vfzyy/LJJ5+oDyL4YNC7d28566yzZPTo0eqDWaTJzs5WH6jwgQgfnvDhJRTwIc5pHadlnZQL5bp+xpR2eu2Hm/Z1bIVTl33AW0xppxM/KkuLpCJ/u5Tn75CKgh1Sgd+Bx9sDjysK80SqnP0D3jv4v+H8X2WHi/4pKe26uaa3F+X9Gv9FJeXy1heL1bIdax8kcEj/dnLJKf2kVfNGIds0pZ2x9D/APu7p27evK9ckkYXvoXn3CL9C/fShhnpQPz2onx7UL7QZqGfcNs1REjUpMV7ef2ika/k9p+Me38xA/eabbxyVw2wWLNPHj4lYMyG8quO0rJNy4fjqR0xpp9d+uGlfxxb7gHlEu51VlRVSUZAnjUt3SOHS9dXJUCRBrQRpwQ4px+P8HVJVVuzadeNS0iSxcYYkNGkuCU0yJKFxhiQ22fV41/OJTZpLWe5mWf/yrVHT24vyfot/fNf74x8b5IUP/5CtubtjoG2LNLn8tP4ypG+bsG2b0s6G8j/ANDCLFrOrCWkI8R4NqJ8+1FAP6qcH9dOD+jknPj5OjhjYQabPWVtjokRtcLbBEYM6RGVypG8SqLEClu6FupFwKHWclnVSLhxf/Ygp7fTaDzft69hiHzAPL9qJhFdVaVEg8VmdDMVMUVti1Pq7AMuwXVoMEZ8gCY3TVeLTSoJav7fkF0tWjz6Bx/FJe98fE5TnVS+FjpbeXpT3U/xv2Fogz0/5XWYvqt5eAiQmxMuZx/SU04/uqZbu62BKOxvK/4BIM3nyZPnuu+/UrAJsu2CBbRWw/QC2bpg/f35UfSRm46d4NxHqpw811IP66UH99KB+oTHqyO7y7ezq7a6CgTX0o47oLtGACVRCSMQo37ldJGedlGxwdn6dmgHYlN/amURVRbnaP1QlQ2slQncnR3fNFi0vde268amNdydEm2TUmjmKmaK7kqKNmkhcXN3xtWXpUknN4gDGT/sgvf/tUnn36yU1lvIM7t1aLj+tn7Rv1SSq/hHzeeGFF+TRRx9V+7liD1rs2Ys9d7HvLPZ5xaqlc889N9puEkIIIYQQEenaPl0mjBssj705RzDB1D4TFTNPkTzF6ygXDZhAjTA4NMLLOk7LOikXjq9+xJR2eu2Hm/bDtZU39wuJ/+4dqT6yZu9kHH6WZB5xNvuAx+CAmorigl2JT1sy1LbPqJUcrcTeom4Rn1idDN01S7QypbGkZrSqftyk5tL6+MRk7cuF+34mpDWVuIQkqaoo22tZlEN5N/3worzp8T9n8WZ57oPf1exTixbpqXLpqH5qv1M3l+yY0s8bwv+ASPPBBx+ofaxef/11lTw97rjj5LXXXlMHg7399tvy97//XQYMGBBtN4nh+CXeTYX66UMN9aB+elA/Pahf6Bw5OEs6tW2qzjyYOXedmkiBPU+xbB8zT6OVPAVMoEYYnJjrZR2nZZ2UC8dXP2JKO732w0374dpqNuh4KW/dU9LTMwLPbXjr7yopF5/WTNqNubNGeSTQQr0e+8BukPCrTn7uXkKvHtsPW9qVLN1a4d4JkZgFqmYP11pCv3vm6K7ZoqlNaiTCcGp4ZosW4hXhvp+J6a2k45VPSkXhTvU4N3eHFH76ZJ1xi+QpyrvphxflTY3/bblF8sLU+fL9vPU19kM65fBuMvb43pKWmuT6NU3p5w3hf0CkweGjEyZMULNP8ZOeni6//vqrnHbaaTJu3DiZPXu2vPrqqzJixIhou0oMxi/xbirUTx9qqAf104P66UH9wgNJ0hvGDJbrzhokGzdtkXZtW0Vlz9PaMIEaYTADokWICYJQ6jgt66RcOL76EVPa6bUfbtoP1xaW4+clbpPWthPK4xISA7+DnVzOPlBzb9HK4nxbMnR3YrT2PqOVRfmuXVfNrLTNCLXPHLWSpHh/E9LSJS4xKeb6AJKiVmI0r2CpJDmIW7f88KK8afFfXlEp0/63XN78fJEUlVQEnu/bJVOuOmOAdGnXzLNrm9LPTY5/L215SWJiojRu3DjwuHPnzrJ48eLA4wMPPFAef/zxKHlH/IJf4t1UqJ8+1FAP6qcH9dOD+umBiRSFBXkSF9daTIAJVB/vJYlEyR4E2V+Se0kSYjaV5aU1kqD2ZfPWbFFrz1GpdHG2aFozqUhKk7TMNruXzVun0QdmjjaX+JQ0I771I7HHguXb1HL9lRt2bw/RrHGyXDhyXzl6SEc1cCIkHLp37y5z586VM888Uz3u2rVrjQOjcnNzOTOEEEIIIYQ4Iq4K05mIp2RnZ6vTX9PS0qR3794SH+/sAB0LnBpbu07OzLdlx3fvOLZh7SVZn00n141FTGmn1364aV/HVu26q/51qVTszJGEppnS+boXtK/ntGwk+kBVVaWaBVo9KxTJ0d1L52sfwFRZvHuvR13iEpNrzBat3lO0+Z4zRxunqxmU7AOh21nz1OV7jVu3/PCivAn/A3LzS+SVaQvlq1mrA88hRz/ioC5y7ol9pWma/r63TmD8u2/LPu7BHqTRYvLkyXLPPffIyJEj5d5775XvvvtOrr/+ernmmmukW7du8sADD6hZqW+88UbUfDQVU95DEzDlHuFXqJ8+1FAP6qcH9dOD+vlDQ6fjHs5AjTBr1qxRg3XdOthLsnHPoTWec7KXZCh+hOOrHzGlnV774aZ9HVtu9QHdsjp9oLKsJJD4DOwxajuBPpAsLcgVqdy9HFmPOJXwtJKgdS2hVzPNm2RIXHKjkGaLsg+ENuN/w4YNIrv2jK2qKJeSDctDmvEfqh9elI/m/4DKyir54udV8urHCyW/aPfBXN2z0uWq0wdIr06RXS3B+I+uLS8ZO3asbNy4USVIsZz/+OOPl2HDhslTTz2lXse+qDfffHO03SSG45d4NxXqpw811IP66UH99KB+saUhE6gRpqyszJU6+HBe+wO6k70kQ/EjHF/9iCnt9NoPN+3r2CrbsVlKkncnFZGAqisRZT+MJ5TrOS1bu1xVZYU6JCiwhL5gh5StWipbFyfUOoBph1SVFIpbxCWl7pkMDcwctZKj+GkmcfEJ4gXsA/WTN/eLoDP+8aXVupduqXfGv64fXpSP1v+ApWt3yLPvz5Mlq3cEnmucmijnntBXRhzSVRKisFyf8R9dW15z4403yrXXXqsSqOC5555TB0nt2LFDBg0axH3JSEzFu4lQP32ooR7UTw/qpwf1iy0NmUCNMJgSHIk6btj04romYko7vfbDTfvh2irP3SLxnz4i6+o48b12IgqHFuHkcyRRQ7le7bKVpcW20+atROh2SdqyXjb8Wrp75ihmi1ZV1qiLhQK7d2UMgbj4mrNFG1fPDA2cRG9bTh+f3EiiDftA/dSe8b9lyxZp1ao6uV8XtWf86/rhRflI/w/ATNM3Ps2WT35YIZW2jYOG7Z8lF43cV5o3S5VowfiPri2vBtpLly6V8vJy6dGjhzRqVPM+O2TIkKj5RvyH6fFuOtRPH2qoB/XTg/rpQf1iS0MmUCNMy5YtI1LHDZteXNdETGmn1364aT9cW5jhaS193htVFWWqPBKota+nZosW5O3eU1QlR6v/jtu5TdYX5gUOYKoqLQ56jaIQ/cfS+Or9RHcfsFT7sCX1d1pTz2aLegH7QP3UnvHfukWWJCcnR8wPL8pH6n8AtlmfMWetvPjfBbJjZ0ng+Y5tmsiVowdIvx7Rjz3Gf3Rtuc0rr7wiTz/9tOTn56vH6Kvjxo2Tm266KTALlZBYiXc/QP30oYZ6UD89qJ8e1C+2NORIMsKsXr1azYZwow5m86mEVAhLoUPxIxxf/Ygp7fTaDzftR0qz/AUzpWDRD7J9/WpJi6/cPVu0MG+P2aJhE5+wa7aobZbortmhW3YWS1bPvrsOXMJs0ejNkvMS9oHI2gm1vhflI/E/YM2mnfLs+7/LH8u2Bp5LSU6QMcf1llFHdJekRDM21Gf8R9eWm3z44Yfy0EMPSYcOHWTUqFHqwIGff/5ZJVUrKirk9ttvj7aLxIeYGu9+gfrpQw31oH56UD89qF9sacgEqk9B8nTNs9eqWXqhLIUmDRvM3MQPZoEi0V5VgcdlIvhtPRd4vay6PBLzu55Xz+Hxxo2Su31xdf3A65bNcnVw0m57u16vwB6joS2Iz/35v+p3XBizReNTG9uW0Nc+ib65rM/Jk659+0s8ZovG1Z3I2bJ0qaRmmXGzJsQvFJeUy9tfLZEp05dKhW29/kH7tZVLR/WT1pnmLMMhscWbb74pAwcOlFdffVVSUlICs6CxD+rbb7+tDozSmT1OCCGEEEIaLkygRpDi4uqlxJgFsW7dOiktLVX7cmEvPWTVrenJGOxv27ZNPe7SpYs6TRt7eeHDQLt27WTlypUiOeskvo7kaV0g6ZWzYY2kxqXI5s2b1XMZGRnqJOmCggK1pA2nmi1btizwWmpqamAPMczkyM3NVcvhEhISpGvXrqos/GzWrJk0bty4+lRqEWnfvr0ql5eXp/zu3r27LF++XCorK6Vp06aqPNoO2rZtK0VFRco2wLcKaBv2LIPN5s2by9q1a9Vrbdq0UXpt3159Ena3bt3UaWzwEXtiQDdLQ+gJjXNyctRj+Lt+/XopKSlR7YKtVatWBfTGe4B2AuiwadMm9V5Bb7RnxYoV6rXMzEzVfux/KFVV0jGrg2zdvEmKCvIlMSFO2rdpI6tXrlDJw2ZNG0tifJzkbNmsZkq2ymwuO3N3SHFRoSTEVUmrzEzZuGG9SGW5NEpJlqSEeKnM2SZLF34jzRqnSUlRgZQWF0lcVaU0bdxY8nZsV2VRDoesFBfk442V5MREqcCJ8GWl6rrJiQlSVlyskpbxUqWeqywvU2XjKytkRZVt80ENkG6sjtAIE58gValNJK5RM0nLbCMFFfEijZpKo+ZtJDm9heworpCyxEbSpfd+kltQWCNmrfc4PT1dxUxZyWpZsWGLtG+fJDt37lQ/mK2E2LJiNikpSQoLC1X8APQ/PLbHLOID8YbTnNF37DGLmMNBJQB9ATFqxSwOLkEMg9atW6u4t8dsKPcInDJtxWzgHiGiroE2qZgVkU6dOqm/0e+QRMjKylJthU+wh/Za94iOHTsqf+q7R+C6wK17BK4D37y6R1j3tFDvEWDr1upZlNDBfk8Oeo/YpTfqIWagLTS16433APcbgPcCvlt64321YhbloJldb+hnj1m73rjXWnURD7Bp1xv+wgfYQ3+w6404smIWbbfrjfbZYxY2aus9Z8k2+fD7TZKzszTQbVtlpMq5w7tLx0yRvJz1ktmsi4oHS29c1x6z8NPSu3bMou12vaGVPWahq6U36tpjFu+XXW+0E7oi9kDte4Q9ZoPdI6A1fty4R0BvxJ1X9wjYhb1w7hEA/c26R8AWfA92j4Cv0QDXnzBhQiB5ChBPF1xwgXz++eeqLX369ImKb8S/1LfnNdk71E8faqgH9dOD+ulB/WJLw7gqjLKJp2RnZ6sPVPhAhA9M+NAXCviAUrsOlujXPvm5Pjpc9E9JadetXptOrusFVViKXWMGpH3GY3kdMxxtMygr65vxaM2wtNmoMcOyuiwSlUnxcTWvZ7vW7sc1Xyfek3HEGGmU1VvyK+Iks30XiW/URH0YdiNuTeoD0caUdnrth1v2de2EWt+L8m7H/8ZtBfJ/H/4hsxZWJyhBYkK8nH5UDznjmJ6Smmzu97WMf/dt2cc9ffv2lUixzz77qCX8p5xySo3nkZA/7LDD5LXXXpMDDjggYv74mWi9hyZiyj3Cr1A/faihHtRPD+qnB/Xzh4ZOxz3mfqKJUcJ5890ImLLcLRKXmBhIAOasXiWN2rWtlXSsmSDM2bRR4ptn1FrGbSUjd5W1L9uunfQM/F1m+3vPhKZre1lq4P90aJzEJSapGZpxCYnqACP8FvytHidKSXm5pKY1rn6867ndr+8qH7/rsfpJqPXYej1BNm/Nkbbt2u9Z32a7+nFCjcclW9fKxv/c5bhVjXvsrxL/6zBrMK2pozpMoIaOKe30SwKJCdTdlJVXyAfTl8o7Xy6R0vLd9/KBPVvJ5aP7SVZrZ/02mjD+o2vLTTAruK4v2TAzHGAmMCGxEu9+gfrpQw31oH56UD89qF9sacgEagNh8/sP77EEu3pxYnBQpnqxYAPDloTcMyG4O1m4+3F1UrHmYyvhmFBHwnJ3UjGQYExIko2bt0i7Dlm2hKQtIWolNNVrSbWul+DoxHe1HYNLmy9vXrpUmoRhKyEvKgv/CSEe8NuSzfLcB7/Lui0Fgecym6XIJaf0k8MGtt/rbHFCCCGEEEII8QtMoEYY7FsWiTrGYJu9GEgg2hOQQWc8WglEe8LRYUKz9ozIYAnNXdevjIuTxKTk3f5E6UN/194VgVkynth3MY4iHZOhXM9pWSflfN33QsCUdnrth1v2de2EWt+L8jpltuUWyYsfLZDvfqveOxXEx8fJyYd1k3HDe0taapL4CcZ/dG25DfaUtfaktbD2pMUMhtqvWfvcEuLHePcD1E8faqgH9dOD+ulB/WJLQyZQIwwG7jjowus6tWnUY7AkNm4emPGYX1AkzTKa17uEO2fHDmnRuk2NBGSNJGQdS8DtSUyJi/fFDCQcqqGrrxu48T5Hyr7Xvupcz2lZJ+Ui3c5oYUo7/dIHdO2EWt+L8uGUqaiolGnfr5A3PlskRSW7Nz7p07m5XHXGAOnaPl38COM/urbc5oEHHlA/dXHzzTfv8RzGKQsXLoyAZ8SvmBzvfoD66UMN9aB+elA/PahfbGnIBGqEwam1kahTm8wjxtY4RGrH0qWSuZcl2JvCXKbtN9zQ1w9+uGk/XFsJaU2lCgl5B4dwYQYyyod6PadlnZQzJTa8xpR2+qUP6NoJtb4X5UMtk70iR555f56s3JAXeK5pWrJcOHIfOWZoJzUD1a8w/qNry01OO+20aLtAYhBT490vUD99qKEe1E8P6qcH9YstDZlAjTCpqakRqeOGTS+uayKmtNNrP9y0H66txPRWknzaHdI6vUnguQ1v/V0qC/MkPq2ZtBtzZ+B5JE9RPtTrOS3LPmBeO/3SB3TthFrfi/JOy+Tml8irHy+UL39ZXeO14Qd1lvNO3EeaNU4Wv8P4j64tN3nwwQej7QKJQUyNd79A/fShhnpQPz2onx7UL7Y0ZAI1wrRp0yYiddyw6cV1TcSUdnrth5v2dWy17d5XkpJ275GotnvY9ds+Szrc6zktyz5gXjv90gd07YRa34vyeytTWVklv68qlf88/7XsLCwLPN+tfbpceUZ/6dPZjJMw3YDxH11bhJgO410P6qcPNdSD+ulB/fSgfrGlIQ5aJxFk1apVrtTB7Dx1yJID7EuhQ/EjHF/9iCnt9NoPN+3r2HKrD+iWZR8wr51+6QO6dkKt70X5+sosX5crf3nqO3n2gz8CydNGKYly6an7yWM3HBFTyVPA+I+uLUJMh/GuB/XThxrqQf30oH56UL/Y0pAzUH0KljZ3vPJJqSjcGdJSaEIIIaQuCovL1AFR0/63XCqrdj9/xKAOcvEp+0lmM3OWzxBCCCGEEEJIJGECNcK0bNnStTpIitoTo06WQofiRzi++hFT2um1H27a17HlZh/QKcs+YF47/dIHdO2EWt+L8vYyVVVV8t1v6+TFj+ZLTt7uTdrbtWgkV585SAb0jO0v4Bj/0bVFiOkw3vWgfvpQQz2onx7UTw/qF1saMoFKCIkY5Tu3S/mW1VJSlBN4rqqiPPC7ZMPyGuUTmjSXxKbNI+4nIQ2FtZt3ynMf/C7z/twaeC45KUHGHNdLhg1oKa1axtZyfUIIIYQQQggJByZQI8zWrVslIyPD8zpu2PTiuiZiSju99sNN++Haypv7heR9947k1fEatp9Y99ItNZ7LOPwsyTzi7JCu57Qs+4B57fRLH9C1E2p9L8qv37BZPvphvUyZvlTKK3av1z9w37Zy6an9pE1mmixdurRBJFAZ/9G1RYjpMN71oH76UEM9qJ8e1E8P6hdbGjKBSgiJGM0GHS85qW2lY8eOjspjBiohxF1+WbBRnnp3qWzfWX1AFGjdvJFcdmo/OXC/dlH1jRBCCCGEEEJMJK4Km58RT8nOzpbCwkJJS0uTHj16SFJSUkj1y8rKHNVZ9a9LpWJnjiQ0zZTO172gbdPpdf2OKe302g837evYCqduKHWclmUfMK+dfukDunZCre9W+U05hfLCh3/Izws2Bp5LTIiT04b1kLOO7SWpyYlGxoXXmNJOv8S/E1v2cU/fvn1duSaJLHwPzbtH+BXqpw811IP66UH99KB+/tDQ6bgn3lMvyB5s2rTJlTrYSxL7Rdp/au8laf9B+VD9CMdXP2JKO732w037Orbc6gO6ZdkHzGunX/qArp1Q6+uWLyuvlHe/XiJXPfxNjeRp/x4t5V83HSXnnbjPHsnTcK7rV0xpp1/i321bhJgO410P6qcPNdSD+ulB/fSgfrGlIZfwR5ji4mJX6mAvyR3fvVNn+fr2kgzFj3B89SOmtNNrP9y0r2PLrT6gW5Z9wLx2+qUP6NoJtb5O+Xl/blGHRK3dnB94rnnTFBl5YCs5c8RgiYuLc+26fsWUdvol/t22RYjpMN71oH76UEM9qJ8e1E8P6hdbGjKBGuE3vry8XCoqKmTdunVSWloqjRo1klatWsnq1atVmZYtWwp2Vdi2bZt63KVLFzVlGYd5pKSkSLt27WTlypUizXtK+pl3SnxcvGzfXj27tG27trJ2zVpJTEyUpKREad2mjaxbu069VtE2S/Ly8mTz5s3qcUJCgmzYsEEKCgpU+c6dO8uyZcvUa9igNzU1VYqKitR1O3ToILm5uZKfn6/qde3aVZWFn82aNZPGjRsrW6B9+/aqHK6FD+bdu3eX5cuXS2VlpTRt2lSVR9uVv23bqmvANsD2BmgbNILN5s2by9q1a9Vrbdq0UXpZbe3WrZusWbNGaYNp1tDN0hB6QuOcnOqT3uHv+vXrpaSkRLULtlatWhXQG6CdADrgGw68V9Ab7VmxYoV6LTMzU7V/y5Yt6nGnTp3UhsaY6o0p5djXE20F8D05OTnwbUlWVpby3dIb76t1zfT0dBUHdr2h386dOyU+Pl611a53kyZNVHsA4gE27XrDX7Qf5WDb0huvw98dO3aoxygLHSy90T5oClq3bq20teuN98KKWbTB8h964/21xyziwdIb77OKWRFp0aJFDb2hIfRE26EX2m7XG9dBzOJ12MM1LL1R1x6zeL+gN8ri/UM77TFr1xsxY+mN9xha2/W2YhY64np2vfHYHrN2veGHPWbhs11vxKgVs9DCrjfeB3vMhnKP2LhxYyBmA/eIXXqjTfaYteuNuERb8Rj2oKt1j0A8w5/67hG4LnDrHgEd4YtX9wjrPQ/nHoG+Y90j7PfkcO4Rlt5O7xHQBprZ9a7vHoHnZs/Llo9+3CRz/qzWrvoeIDLysG5yWJ9GUlFWpK5tv0dAb6vvAPhn1zuUe4Q9ZkO9R1j3qlDuEVbMOrlHWHpb9wj4Yb8vWfcIe8wGu0fg/xp+3LhHoH2IO6/uEdAQ9sK5RwD0N+seYd2Tg90j4CshsQLuHSR8qJ8+1FAP6qcH9dOD+sWWhtwDNQLY91Po1auX+oAdCvjg5bSO07JOyoVyXT9jSju99sNN+zq2wqnLPuAtprTTL31A106o9UOL/0qZ9v1yefPzxVJYvDuJ1btTc7ny9P7SPav6BEvGv3nt9Ev8O7HF/TP9D99D8+4RfoX66UMN9aB+elA/PaifPzTkHqiGYs2c8aqO07JOyoXjqx8xpZ1e++GmfR1b7APmYUo7/dIHdO2EWt9p+UWrcmTCEzPl31MXBJKnTdOS5JozB8jD1x4eSJ46tWlKXHiNKe30S/y7bYsQ02G860H99KGGelA/PaifHtQvtjTkEn5CCCHEx+QVlMprnyyUz3+q3nbA4rgDOsn5J+0j6U3MWfZCCCGEEEIIIX6ECdQIgz3bvKzjtKyTcuH46kdMaafXfrhpX8cW+4B5mNJOv/QBXTuh1g9WvrKySr75dbW8PG2hSqJadGzdWK49a7D07Rr8Oox/89rpl/h32xYhpsN414P66UMN9aB+elA/PahfbGnIBGqECWfvhlDqOC3rpFxD2avDlHZ67Yeb9nVssQ+Yhynt9Esf0LUTav26yq9YnyvPvv+7ZK+sPlQINEpJkHNG9JXD+2VKZvPm2j6YEhdeY0o7/RL/btsixHQY73pQP32ooR7UTw/qpwf1iy0NuQdqhLFOuvWqjtOyTsqF46sfMaWdXvvhpn0dW+wD5mFKO/3SB3TthFrfXr6wuEz+PXW+3PD4jBrJ08MHdpBnJx4jo47oLjm7Tl/X9cGUuPAaU9rpl/h32xYhpsN414P66UMN9aB+elA/PahfbGnIGaiEEEKI4VRVVcn/5q1XydOcvOLA8+1bNpYrRveXQb1bR9U/QgghhBBCCIll4qrwqYx4SnZ2thQWFkpaWpp0795dkpOTQ6pfWlrquI7Tsk7KhXJdP2NKO732w037OrbCqcs+4C2mtNMvfUDXTqj1V67bLi9Oy5bfluz+9jU5MV7OOraXjD6qhyQlJngS26bEhdeY0k6/xL8TW/ZxT9++fV25JoksfA/Nu0f4FeqnDzXUg/rpQf30oH7+0NDpuIdL+CPM1q1bPa3jtKyTcuH46kdMaafXfrhpX8cW+4B5mNJOv/QBXTtO65eUVch/PsuWG5/4rkbydEjfNvL0X46Ws4/rvUfy1Kl9xr957fRL/LttixDTYbzrQf30oYZ6UD89qJ8e1C+2NOQS/giDrLaXdZyWdVIuHF/9iCnt9NoPN+3r2GIfMA9T2umXPqBrx0n9X7M3yXMf/C6bcnaXbdW8kVx2aj85cN+2EhcXp2Wf8W9eO/0S/27bIsR0GO96UD99qKEe1E8P6qcH9YstDZlAjTBJSUme1nFa1km5cHz1I6a002s/3LSvY4t9wDxMaaepfQB7jtr3Hd24vUxk7Y6g5TObpaqfcPzYvL1Q7XP64x8bAs/Fx4uMHtZTzj62l6SmJEYstk2JC68xpZ2mxr/XtggxHca7HtRPH2qoB/XTg/rpQf1iS0PugRoB7Psp9O7dW+LxaTgEKisrHddxWtZJuVCu62dMaafXfrhpX8dWOHXZB7zFlHaa2gfe/HyRTP5isePyY4/vLeOG9wnJj7LySpk6c5m89eViKSmtCDzfr3tLuey0/aRLu3TH13crtk2JC68xpZ2mxn84trh/pv/he2jePcKvUD99qKEe1E8P6qcH9fOHhtwD1VCWL1/uaR2nZZ2UC8dXP2JKO732w037OrbYB8zDlHaa2gdGHNxFHr/xyMDPhDO6SXqT6o3M8dv+Gn5QPhQ//li6Va5/7Ft59eOFgeRpRtMUuWncYLn/ykOkvGD3/qeRjG1T4sJrTGmnqfHvtS1CTIfxrgf104ca6kH99KB+elC/2NKQS/gJIYSQethjSX7xVklMqP7+Eb97ZGWEZXf7zmJ56b8LZPrstYHn4uNETjykq5xzQl9p0sic5SqEEEIIIYQQ0pBhAjXCNG/e3NM6Tss6KReOr37ElHZ67Yeb9nVssQ+Yhynt9Esf0LWTnp4hH/9vubz+abYUFJcHnu/ZMUOuOn2A9OiYoXU9t2LblLjwGlPa6Zf4d9sWIabDeNeD+ulDDfWgfnpQPz2oX2xpyARqhElOTva0jtOyTsqF46sfMaWdXvvhpn0dW+wD5mFKO/3SB3TsLFm9XZ5653dZsWFn4LnGjZLk/JP2keMP7CwJmIKqeT23YtuUuPAaU9rpl/h321ZDZOPGjfLyyy/Ld999J+vWrVPPdejQQY488ki56KKLpFWrVnvUKSkpUXWmTZsmq1atktTUVLWv/tixY+Wkk06KQisaDox3PaifPtRQD+qnB/XTg/rFlobcAzXCbNq0ydM6Tss6KReOr37ElHZ67Yeb9nVssQ+Yhynt9EsfCMdOfmGpPPPePLn5XzNrJE+PGdpRnpt4jJxwcJc6k6fhXM+t2DYlLrzGlHb6Jf7dttXQ+PXXX+Xkk0+WV155RVauXClt27ZVP/j7pZdeklNOOUXmz59fo05xcbFceOGF8vjjj6t9wLp37y7p6ekya9YsmTBhgtxxxx1Ra09DgPGuB/XThxrqQf30oH56UL/Y0pAzUAkhhBCPqKqqkm9+XSMvT1sgufmlgec7t20qV54+QPbt1iKq/hFCIkdeXp5ce+216vfhhx8uDzzwgLRu3Vq9tmbNGvnLX/4ic+bMkauvvlo+/fRTdRIsuO+++2T27NnSo0cPee6556Rjx47q+enTp8sNN9wg7733ngwcOFDOPPPMqLaPEEIIISSW4QzUCJOVleVpHadlnZQLx1c/Yko7vfbDTfs6ttgHzMOUdvqlDzi1s2pDntz2zPcy6a25geRpanKCnHdCb5k0YZjj5GmofrsV26bEhdeY0k6/xL/bthoSH3zwgeTk5Kik6aRJkwLJU4Ck6NNPP61mlmKJ/8cff6yeX7t2rUyZMkXi4uLk0UcfDSRPwbBhw+TWW29Vfz/55JNSWVkZhVbFPox3PaifPtRQD+qnB/XTg/rFloacgRpBsARr6dKl0rdvX7XnVWlpqTRq1EjtdbV69WpVpmXLlmrG0rZt29TjLl26qGVdiYmJkpKSIu3atVOPQYsWLSQ+Pl62bNmiHnfq1Enti5WQkKD2iUCgYamXtfFuUlKSbN68WT2GLdgsKChQvzt37izLli1Tr2VkZKi9tXAd/Ma+XLm5uZKfn69sd+3aVZWFn82aNZPGjRvLhg0bVN327durcphdgcE+lpnBBwzqmzZtqspb+31hyVpRUZGyDTCzAtcsLy9XNuEzPjiANm3aKL22b9+uHnfr1k3N1igrK1MzNKCbpSH0rKioUB9SAPxdv3692j8M7YEt6GTpjXLWhw7ogCnieK+gEdqzYsUK9VpmZqZqv13vrVu3SmFhodIWH2rseuM9sKab472A75beeF8RCwAflhAHdr2h386dO9X7i7ba9W7SpIlqD0A8wKZdb/iL9qMcbFt647p4bseOHeoxykIHS2+0D5oCfKiDtna98V5YMQtfoJGlN/SzxyziwdLbWppoxSyuD/8sDaEn4gB6oe12veEzYhbX6tmzp7qGpTfq2mMW75f13iGWcB17zNr1RszgOvAP7zG0tuttxSzaCZ/seuP69pi16w0/7DELDex6I0atmIUWdr3xPthjNpR7BD5sWzG7t3uEXW/rHoG6qGe/RyCe4U999whcF7h1j0B5+OTVPQIxAL9DvUcA9HXrHmH1Gwsrtqx7xNr1G+XzWVtkxh+4t1QFyh3Sv50cN6CppCaWS+6O7Y7vEZYWdr3ru0dAM7S7vnsEnkPb7PcIXAOxYMUsdIOm4dwj7DEb6j0Cflp6O71HWDHr5B5h6W3dI+DzPvvss8c9wh6zwe4R+L+GHzfuEbgmNPXqHoHroG3h3CNqjyOse3Kwe4S9f5Dd/Pzzz+r3UUcdpeKhNojpQYMGqZmlf/zxh5pROnXqVKVn//79pU+fPnvUGT16tDz44IMqtn/55Rc56KCDItKWhgTuc+g3JDyonz7UUA/qpwf104P6xZaGcVUYZRNPyc7OVh+o8IEIHz7wgSoU8KHOaR2nZZ2UC+W6fsaUdnrth5v2dWyFU5d9wFtMaadf+gDs/P2NpZKTVyKZzVLk1b+NUM/j3+kPf2yQFz78Q7blVn/BANq1bCxXnNZfBvdpHZYfXpRn/JvXTr/EvxNb9nEPvjQm1fz+++8qIY0E+IABA+osc9lll8mMGTPkjDPOkPvvv18uueQSddgUDpeaOHFinXXGjx+v9kPF0v/rrrvOFV/5Hpp3j/Ar1E8faqgH9dOD+ulB/fyhodNxD2egRhjM0vCyjtOyTsqF46sfMaWdXvvhpn0dW+wD5mFKO/3QB1asz5W3vl2vkqcAvydNniMH928nn/ywUuYsqp4NCZIS4+XMY3rJ6Uf1kOSkhLD98KI849+8dvoh/r2w1ZDALFL8BAMzejGLFPTq1Uv9tmYL25fu1wYzhZFAtcoSd2G860H99KGGelA/PaifHtQvtjTkDNQIwG/xCSHE/8yYs1Yee3OOxMWJVNiW5uNx7f+kmG2KWaeYfUpIQ4PjnvC46aabZNq0aWpriy+//FJt3YAl/dDyiSeekBEjqme71+ahhx6Sl19+WQ455BD12w34HhJCCCGkoZDtcNzDQ6QijH1POy/qOC3rpFw4vvoRU9rptR9u2texxT5gHqa00+Q+gJmnSJ5WVlXVSJ4Ce/I0o0my3Hb+ULn7koOCJk9D9cOL8ox/89ppcvx7aYtU88wzz6jkKbjiiisCB0xZ+41jD99gWK9h71riPox3PaifPtRQD+qnB/XTg/rFlobmzIUlhBBCDKO0rEI2by+Ulz6aL1VS/4INzEQd2Ku1HNK/fcT8I4T4n6eeekqefPJJ9fewYcPk8ssvD7yGg+msgy73Bg5g8+oA1EgfbljXAajRONwQh8ThEDjrw5spB6DWPtwwWgegOjncEG3A9cI5ALX24YY6B6D66XDD2gegwi9oFunDDU0+ADWUewQe4/3z6h5h4iHJbt4joB9i0at7hM4hyX64R6CN9gNnvbhHROsA1O4RukfAb6utXo0jnB6AyiX8EZ4OjOBAsIQCAsJpHadlnZQL5bp+xpR2eu2Hm/Z1bIVTl33AWxpyH8Bs0pzcYtmUUyAbtxXKphz8FOz6XVjjMCgnYN/T9x8aWW8iI9R2elGe8W9eO2PpfwCXfzsDg/V7771X3n77bfUYS/CfffZZ9eHM4oADDlAfapws4T/00EPlpZdecsU3vofm3SP8CvXThxrqQf30oH56UD9/aMhDpAwF2XYv6zgt66RcOL76EVPa6bUfbtrXscU+YB6mtNMLP/AdYV5BqUqGrlqfJ7l/7FB/b9xWnSTdsr1Qyivc+x6xrLxSSsoqJDU50bV2elGe8W9eOxvK/wBSDWaQXHfddfL999+rx8OHD5dHHnlEzZ6wg9kTSKBasz3qwppNg5ktxH0Y73pQP32ooR7UTw/qpwf1iy0NuQdqhLGmsXtVx2lZJ+XC8dWPmNJOr/1w076OLfYB8zClneH6UVxSLqs25skvCzfKf79bLi9M/UPue+lnufaRb+XsOz6R8X/7TG56Yqb869358urHC+WzH1fKb0u2yIatBfUmTzOapEjvTs3l8IHtJd7h0ljMQE1JSnC1nV6UZ/yb186G8j+AVOs3duzYQPL0wgsvlEmTJu2RPLWWxwFruWJdWEvrsOSNuA/jXQ/qpw811IP66UH99KB+saUhZ6ASQggxmoqKStmyo0g2bSuUjbWW2OO5HfklYdltlJIgbTIbS5vMNGnTIk39btti1+PmaZKasvtf5KTEOTJ9zto9DpCykxAfJ0cM6uDJPoSEkNgAe5mde+65at8v7OF1xx13yPjx44OWHzBggHz99dcyZ86cOl/HXmLz589Xfw8ePNgzvwkhhBBCGjpMoEYYbKDsZR2nZZ2UC8dXP2JKO732w037OrbYB8wj2u3EMvsdO0ukoKKxSlKqBOmu/UiRMN26o0gq60lcBiMxIU5aNa9OjOKnRbNkyWqTHnjcrHGy42TnqCO7y7ez1+6lHSKjjqieLeam3l6UZ/yb186G8j+gIYNk55VXXqmSpzjU4NFHH1VL9+vjhBNOkMcee0wlUBcvXiy9e/eu8fr777+vDlnAe4L9Uon7MN71oH76UEM9qJ8e1E8P6hdbGjKBGmFwmlyoeziEUsdpWSflwvHVj5jSTq/9cNO+ji32AfOIRDsLi8t27T26awZpYDZpoTrlvqS0Iiy7mc1Sd80cTasxm7RtZmPJTE9Vs0LtM79wemQ4dG2fLhPGDZbH3sQssCqx53NxDSRP8TrKua23F+UZ/+a1s6H8D2jIvPDCC7JgwQL195133rnX5Kl1uuyoUaNk6tSpas/UZ555JrCsf8aMGfLwww+rv5GYxYmyxH0Y73pQP32ooR7UTw/qpwf1iy0NOdKKMDt37gz5A3wodZyWdVIuHF/9iCnt9NoPN+3r2GIfMA832llWXiFbthftTpDumj1avcy+QHYWloVlt0mjpMDyeitBWp0sTZPWzdMkeS/7jbrZziMHZ0mntk3lP9N+k18W7z7QZdj+WWrmqZPkaTh+eFGe8W9eOxvK/4CGPPv01VdfVX8j0fnhhx+qn2Accsghcu2116q/scz/zz//lIULF8rIkSOlZ8+eatbpqlWr1OtjxoyRM888M0ItaXgw3vWgfvpQQz2onx7UTw/qF1saMoEaYbDflZd1nJZ1Ui4cX/2IKe302g837YdrKyevWNZtKxFJDX6acO3ZhfhhH/AWJ+3EEnq8f9V7j9acQYoE6ba8YjULM1SSE+Ol9a4l9WlJ5dKzS7vAEvs2LRqrBKpbuPF+Ikl6zrEdZemGIsnJK5HMZilyw5jBnvrhRXnGv3ntbAj/AxoyS5YskdzcXPV3eXl50D1NLdq2bRv4Oz09XSZPniwvv/yyfPLJJ7Jy5Ur1HgwcOFDOOussGT16tOf+N2QY73pQP32ooR7UTw/qpwf1iy0N46qw+RzxlOzsbCksLJS0tDTp27evKzaRyMCPU6xEFCHR5M3PF8nkLxY7Lj/2+N4ybngfT30i1eBfQX5R2e69R7fZDmpSM0qLpLyiMmS7WEHfIqORWlJvP6zJOrAJJ93H25bZ+4UL7v1ctuUWS4v0VHnlrr0vwyWkIeHFuIdEFr6HhBBCCGkoZDsc93AGaoRZtmxZYO8qnTqf/bhSKxHlxI9wfPUjprTTaz/ctB+urREHd5H26eWSlZUVeO7uF36U3PxSSW+SLHdfenCN8lbSP5TrOS3bEPtAcWm5bA4kRXf/rFq/XXYUlEthcXlYdvHe1bXEHo9bZjSSpMT4mOoDsBNJP7wo3xDj3/R2+iX+3bZFiOkw3vWgfvpQQz2onx7UTw/qF1saMoEaYcKZ8FtXHSSiDth39/Iup4moUPxoKJOTTWmn1364aT9cW4jDrJap0iMrI/BcYkJ84Lf9+XCv57RsLPaBiopK2ZpbXPMUe9uepNt3loRlNzU5IZAQ3Z0crV5ij9+NUhIbVB/QtRNqfS/Kx2L8h4sp7fRL/LttixDTYbzrQf30oYZ6UD89qJ8e1C+2NGQCNcI0a9bMlTp1Lcl3kogKxY9wfPUjprTTaz/ctK9jy60+oFvWj30A/zzwJQkSotWJUfsS+0J1iFOF/Xh4h+AU+VbNdy2ztydIdy2zb9Y4WeLivF9m75c+oGsn1PpelPdj/HuFKe30S/y7bYsQ02G860H99KGGelA/PaifHtQvtjRkAjXCNGnSJCJ13LDpxXVNxJR2eu2Hm/Z1bHndB5yWNbUPFBaX7bHE3tqPFMvvi0srwrKLw46sJfa7l9nvmkGaVClNm0a/H/ilD+jaCbW+F+VNjf9oYEo7/RL/btsixHQY73pQP32ooR7UTw/qpwf1iy0NmUCNMOvXr5cePXp4XscNm15c10RMaafXfrhpX8eW133Aadlo9YGy8krZsqMwyGFNhZJXUBqW3capidUJ0V0zSNvaltjjlPuUpISgdZcuXSpNm7IPhGInkn54UZ7/A8xrp1/i321bhJgO410P6qcPNdSD+ulB/fSgfrGlIROohBDiIpWVVbJ9Z7Ft/9HdS+zxs21HkYSxyl5tzWE/xV4lSHclTPF3k7RkL5pDCCGEEEIIIYQ0eJhAjTDt2rWLSB03bHpxXRMxpZ1e++GmfR1bXvcBp2V1+kB+UdnumaO7DmnauOvvzdsL1SzTUMEWoy3SG+1eYm+bQYrl9s2bpkp8vDf7kLIP1E9OXrH6sSiuaizlFdXvMX4vXbtjr3tU6/jhRXn+DzCvnabGv9e2CDEdxrse1E8faqgH9dOD+ulB/WJLQyZQI0xBQYE0btzY8zpu2PTiuiZiSju99sNN+zq2vO4DTsvWV660rEIlR5csXy/Flcl7LLMvKCqTcGialhyYMWo/xR6PcYhTUmLwZfZewj5QP5/9uFImf7G4ztdwqNeNj8+o8dzY43vLuOF9XPPDi/L8H2BeO02Nf69tEWI6jHc9qJ8+1FAP6qcH9dOD+sWWhkygRpi8vDxp3bq153XcsOnFdU3ElHZ67Yeb9nVs1a6Lk+Xtv3Wv56QsTqpftW6rbMqL3z171DabNCevRMIhOSlh1+FM1s/uGaT4nZaaJCbCPlA/Iw7uIgfs2zbweM2aNdKxY8eg5eubfRqOH16U5/8A89ppavx7bYsQ02G860H99KGGelA/PaifHtQvtjRkAjXCxGGdbgTquGHTi+uaiCnt9NoPN+3r2LLqrlifK1NnLAskK/F70uQ5MurI7tK1fXrY10NZJGNxGJOVFN1o7UG6a09SHOJUXoGE7Z8h+Y4l9K0ybMvsd80gtfYlzWiSYkw8hYIpPpvaB2ovyY8r2SbdszIi5ocX5fk/wLx2mhr/XtsixHQY73pQP32ooR7UTw/qpwf1iy0NmUCNIMXFxZKUlCQVFRWybt06KS0tlUaNGkmrVq1k9erVqkzLli1V8mfbtm3qcZcuXSQlJUWdko3f2P9h5cqV6rUWLVpIfHy8bNmypcYMvvLycmUvKytLli9frp5r3ry5uvbmzZvVY8ye2rBhg5oOnZiYKJ07d5Zly5ap1zIyMiQ1NVXZw3U7dOggubm5kp+fLwkJCdK1a1dVFq83a9ZMTaeGLdC+fXtVDt8SINC7d++ufKisrJSmTZuq8mg7aNu2rRQVFSnbACeroW3wHzbh89q1a9Vrbdq0UXpt375dPe7WrZuaBVZWViZpaWlKN0tD6AmNc3Jy1GP4i5PbSkpKVLtga9WqVQG9oSPaCaDDpk2b1HsFvdGeFStWqNcyMzNV+y29O3XqJFu3bpXCwkKlLTS1652cnKxsAbwX8N3SG++rdc309HQVB3a9od/OnTvV+4u22vVu0qRJ4BRwxANs2vWGv2g/ysG2XW/4u2NH9X6NKAsdLL3RPmgK8A0PtLXrjffCilm01fIfeuP9tccs4sHSG9e1xyx+3vl0tvzn67V73Ay/nbNWvpm9RsYfkyXHHthVaWXFLOzhGpberdu0l1/nLZZtO8uksCxBtu8sk7WbcmVbXqlsz8+W4tKKsPppRtMUyUiLl8xmydKuZRPJapMuCRUF0qJZsvTp0VFKSoprxKzSuyxHSgpKpSQpo0bMwme73ohRK2ahg11vvA/2mA3lHrFx48ZAzNZ3j0DM4m/0O8Sn/R4Be7XvEfCnvnsErgvcvEfAN6/uEVYfC/UeAdB3AHSw35PDvUdA71DuEdDMrnd99wjEgFU32D0CZWGv9j0CcWSPWbveodwj7DEb6j0Cflp6145ZtN2ut/0eAX3t9wjUtccs3i+73minFbPArjdixh6z0Nqutz1m8WO/J+P6e9wjdt2T4Ud99wjEnVf3CDwHe+HeI2qPI+B7sHsEfCUkVkDfJOFD/fShhnpQPz2onx7UL7Y0jKuqb90scYXs7Gz1gQofiPBhER98QgEfvpzUueDez2VbbrG0SE+VV+4arm3T6XX9jint9NoPN+3r2PrfrIXyz7eXSmU9t574uDh55PrD1Z6h2H90wZLVUhGfFphBihml2HsyHBqlJKol9U1TRbpmtaqxxL51ZpqkJicaFxteY0o7/dIHdO2EWt+L8vwfYF47/RL/TmzZxz19+/Z15ZoksvA9NO8e4Veonz7UUA/qpwf104P6+UNDp+MezkCNMJiFEok6btj04romYko7vfbDTftObVVWVklpeYU6lR4HM5WWVcrHP2FWV/3f2yC5OmHSzLB8S4iP270HaYvGu06z370nadO0JDXDDbPNMDvMD7HhNaa00y99QNdOqPW9KM//Aea10y/x77YtQkyH8a4H9dOHGupB/fSgfnpQv9jSkAnUCIMlfJGo44ZNL65rIqa00ys/MMkcCcy4xFTJySvelciskNLySikr25XYLLceV0hJWaWU4TFeU7+rE6Al+L3rubydhRL/7ZbAYys5WrNepZRXVLreHqz6x36UNfYgte1JWlq0Q9q3a7dXO+wD5rXTaz/csq9rJ9T6XpRn/JvXTr/Ev9u2CDEdxrse1E8faqgH9dOD+ulB/WJLQyZQIwz2VvOqjpPTzEOxGY6vfiQS7cR7gmSiPbloJSatBGZ+Qaks37w+kMi0kpxqBmfZrgSmbSZnzZmdu8oGEqO7H6OMXzlov7bSvmUTyWyaJB3bZqiZpK2bN5KkxOo9C+uiKMWZbfYB89rptR9u2de1E2p9L8oz/s1rp1/i321bhJgO410P6qcPNdSD+ulB/fSgfrGlIROoEQaHPuxt2XCodUI5zTwUP8Lx1WSQxKzAknJbAhJ/L1u+Stq2a787ARmYSWklIXcnOXfP2Kyd2Kw58zJQXz1X/TiWdxtOSoyXZPwkJUhSUkLg78BvPJ8YL0kJ8fLdvHWOtED52y84wLbcvrUjX5zGbUPsA6a302s/3LKvayfU+l6UZ/yb106/xL/btggxHca7HtRPH2qoB/XTg/rpQf1iS0MmUH3OjDlr5bE356hlzXamz1kr385eKxPGDZYjB2eJSVRgJmbtZGUdMykDy8Ntr+2esVm516XoNWZ7qmRnhVQGTdxVnxzsZxITkKxEIhNJzOrf1mMkMMtKiyUjvcmuxGZ1mZRdiU17sjPJqmc9t6t+tc3q59etXS29e3ZXf+O68fG1ArAeioryZc6feSqZXd8+pkcM6qCSp4QQQgghhBBCCCHRhAnUCNO2bVvX6mDmKZKn6jTzWrkoKzmF1zu1bbrHTNTWrdtIcUl5YHakfZal9XdefoJsnLeu1oxN2/Lwupai11o6XiPJuat8fYkzv4PEn5V8VInIwAzM+FqPqxOXKoG5K8kpVeXSJK2RLflpT4DG10ps7v5bzexMTFDXro/8/Hz39oBM6ShN0pLDqnv60b1l9pJZ9ZZBSI86ontY/cZpWSflwumvfsSUdnrth1v2de2EWt+L8ox/89rpl/h32xYhpsN414P66UMN9aB+elA/PahfbGnIBGqEKS4uDjmJFawOlu2rCXr15CORXJ341Hcq2WVfbl5eEbtJTOQR65pJWXNp+e4ZmBXlpdKsaWPbDEwrsWmfgVmrfo0Zm7uvkZAQH7bfW7dulZYtW4pJseeFrTYZSWpmtDVz2p5QRxIYyVO8bk/6h3I9p2WdlHNTM5MxpZ1e++GWfV07odb3ojzj37x2+iX+3bZFiOkw3vWgfvpQQz2onx7UTw/qF1saMoEaYXbs2BFykqyuOpWVVTLzt3WOZnMWlVRIUUmRRJqae2BWz5JMsc2krH6853Lz3UvL65uBWXMmp31GJpJwoSz9rt5fs4cvYyNa9nVsoe6Rg3uomdFTZy6Tr2etCbw2bP8sNfO09ozpUK7ntKyTcl6/J6ZgSjv90gd07YRa34vyjH/z2umX+HfbFiGmw3jXg/rpQw31oH56UD89qF9sacgEqk8J9XT1jCbJkpycGEhgVlqzLutIQFozMHfm7ZC2bVrVmMFpT4AG9sa0JUktO9gXk/tXkvpAkvSGMYNl7uLN6uCzzGYp6jEhhBBCCCGEEEKIScRV4Why4inZ2dlSWFgoaWlp0qdPn5ATi3iLatfBDNQzbpvmKImKBOf7D42sYaMum06uG4uY0k6v/XDTvo6t2nUvuPdz2ZZbLC3SU+WVu4ZrX89pWfYB89rplz6gayfU+l6UZ/yb106/xL8TW/ZxT9++fV25JoksfA/Nu0f4FeqnDzXUg/rpQf30oH7+0NDpuCf8DRtJWKxatcqVOjj1/IiBHfZ6cFCw08yd+BGOr37ElHZ67Yeb9nVsudUHdMuyD5jXTr/0AV07odb3ojzj37x2+iX+3bZFiOkw3vWgfvpQQz2onx7UTw/qF1saMoEaYcrLy12rM+rI7urAnVBOMw/Fj3B89SOmtNNrP9y0r2PLzT6gU5Z9wLx2+qUP6NoJtb4X5Rn/5rXTL/Hvti1CTIfxrgf104ca6kH99KB+elC/2NKQCdQI07hxY9fqYA9JnFYeHxe3x0xUPMbztU8zD8WPcHz1I6a002s/3LSvY8vNPqBTln3AvHb6pQ/o2gm1vhflGf/mtdMv8e+2LUJMh/GuB/XThxrqQf30oH56UL/Y0pCHSEWYzMxMV+scOTgrpNPMQ/EjHF/9iCnt9NoPN+3r2HK7D4Rbln3AvHb6pQ/o2gm1vhflGf/mtdMv8e+2LUJMh/GuB/XThxrqQf30oH56UL/Y0pAzUCPMmjVrXK9jnWaOU8yBdZp5sOSpUz/C8dWPmNJOr/1w076OLS/6QDhl2QfMa6df+oCunVDre1Ge8W9eO/0S/27bIsR0GO96UD99qKEe1E8P6qcH9YstDZlAjSGsg6J4yhshhBBCCCGEEEIIIe7ABGqEad26dUTquGHTi+uaiCnt9NoPN+3r2PK6Dzgtyz5gXjv90gd07YRa34vyjH/z2umX+HfbFiGmw3jXg/rpQw31oH56UD89qF9sacgEaoQpKyuLSB03bHpxXRMxpZ1e++GmfR1bXvcBp2XZB8xrp1/6gK6dUOt7UZ7xb147/RL/btsixHQY73pQP32ooR7UTw/qpwf1iy0NmUCNMNu3b49IHTdsenFdEzGlnV774aZ9HVte9wGnZdkHzGunX/qArp1Q63tRnvFvXjv9Ev9u2yLEdBjvelA/faihHtRPD+qnB/WLLQ0To+0AIaThkJNXLGu2FImk7gg8V15RGfi9dO3u50Fms1T1QwghhBBCCCGEEBIt4qqqqqqidvUGQnZ2thQWFkpaWpr07t1b4uNDm/hbWVnpqM4F934u23KLpUV6qrxy13Btm06v63dMaafXfrhpP1xbb36+SCZ/sdhx+bHH95Zxw/uEdD2nZdkHzGunX/qArp1Q63tRnvFvXjv9Ev9ObNnHPX379nXlmiSy8D007x7hV6ifPtRQD+qnB/XTg/r5Q0On4x7OQI0wa9eulU6dOnlexw2bXlzXRExpp9d+uGk/XFsjDu4inVqItG3b1lF5a/ZpKNdzWpZ9wLx2+qUP6NoJtb4X5Rn/5rXTL/Hvti1CTIfxrgf104ca6kH99KB+elC/2NKQCdQIU1paGpE6btj04romYko7vfbDTfvh2kJCtG1GovTIyvDsek7Lsg+Y106/9AFdO6HW96I849+8dvol/t22RYjpMN71oH76UEM9qJ8e1E8P6hdbGnIucYRp1KhRROq4YdOL65qIKe302g837evY8roPOC3LPmBeO/3SB3TthFrfi/KMf/Pa6Zf4d9sWIabDeNeD+ulDDfWgfnpQPz2oX2xpyARqhGnVqlVE6rhh04vrmogp7fTaDzft69jyug84Lcs+YF47/dIHdO2EWt+L8ox/89rpl/h32xYhpsN414P66UMN9aB+elA/PahfbGnIBGqEWb16dUTquGHTi+uaiCnt9NoPN+3r2PK6Dzgtyz5gXjv90gd07YRa34vyjH/z2umX+HfbFiGmw3jXg/rpQw31oH56UD89qF9sacgEKiGEEEIIIYQQQgghhASBh0hFkOLiYvW7oqJC1q1bpzbDxX4OmJJsZdVbtmwpVVVVsm3bNvW4S5cuEhcXJ0uXLpWUlBRp166drFy5Ur3WokULiY+Ply1btqjHqAfKy8uVvaysLFm+fLl6rnnz5pKUlCSbN29WjzMyMmTDhg1SUFAgiYmJ0rlzZ1m2bFngtdTUVCkrK1PX7dChg+Tm5kp+fr4kJCRI165dVVlcr1mzZtK4cWNlC7Rv316Vy8vLU353795d+VBZWSlNmzZV5dF2gJPYi4qKlG3Qo0cP1Tb4D5vwGSeugTZt2ii9tm/frh5369ZN1qxZo3xMS0tTulkaQk9onJOTox7D3/Xr10tJSYlqF2ytWrUqoDfeA7QTQIdNmzap9wp6oz0rVqxQr2VmZqr2W3rjJLitW7dKYWGh0rZjx4419E5OTla2AN4L+G7pjffVumZ6errywa439Nu5c6d6f9FWu95NmjRR7QGIB9i06w1/0X6Ug21LbzyGvzt27FCPURY6WHqjfdAUtG7dWvlj1xvvhRWzaJ/lP/TG+2uPWcSDpTfeZ3vM4v2y6kJD6Ik4gF5ou11vaIWYhS+wh2tYeqOuPWbxfkFvlMX7h3baY9auN3yw9MZ7DK3telsxi+vgena98dges3a94Yc9ZuGzXW/EqBWz0MKuN94He8yGco/YuHFjIGbru0fU1tu6R8An2LPfIxDP8Ke+ewSuC9y6R+A68M2re4T1nod6jwDoO9Y9wn5PDuceYent9B6BctDMrnd99wjca626we4R8AH27PcI6G31Havtdr1DuUfYYzbUewT8tPR2eo+wYtbJPcLS27pHwDdQ+x5hj9lg9whojR837hHQG3Hn1T0CdmEvnHtE7XGEdU8Odo+Ar4TECiYtHfQj1E8faqgH9dOD+ulB/WJLw7gqK+tGPCM7O1t9oMIHInxQxIeQUMAHVCd1Lrj3c9mWWywt0lPllbuGa9t0el2/Y0o7vfbDTfs6tsKpG0odp2XZB8xrp1/6gK6dUOt7UZ7xb147/RL/TmzZxz19+/Z15ZoksvA9NO8e4Veonz7UUA/qpwf104P6+UNDp+MeLuGPMNaMEK/ruGHTi+uaiCnt9NoPN+3r2PK6Dzgtyz5gXjv90gd07YRa34vyjH/z2umX+HfbFiGmw3jXg/rpQw31oH56UD89qF9sacgl/D4lJ69Y/dgpr6gM/F66tnpJoEVms1T1QwghhBBCCCGEEEIIcQ6X8EcA+3Tgnj17qr3CQgF7idWu8+bni2TyF4sd2xh7fG8ZN7xPvTadXDcWMaWdXvvhpn0dW+HUDaWO07LsA+a10y99QNdOqPW9KM/4N6+dfol/J7a4/Nv/8D007x7hV6ifPtRQD+qnB/XTg/r5Q0On4x6+kxEGh1LgoAvdOiMO7iIH7Nt2j7I4IAMHU9Sm9uxTJ36E46sfMaWdXvvhpn0dW271Ad2y7APmtdMvfUDXTqj1vSjP+DevnX6Jf7dtEWI6jHc9qJ8+1FAP6qcH9dOD+sWWhkygRhicWutGnaBL8ou3So+sDFf8CMdXP2JKO732w037Orbc6gO6ZdkHzGunX/qArp1Q63tRnvFvXjv9Ev9u2yLEdBjvelA/faihHtRPD+qnB/WLLQ15iFSESU1N9bSO07JOyoXjqx8xpZ1e++GmfR1b7APmYUo7/dIHdO2EWt+L8ox/89rpl/h32xYhpsN414P66UMN9aB+elA/PahfbGnIPVB9ugeqblnuf2deO2Np/zu367IPeIsp7fRLH+AeqLGFKe30S/w7scX9M/0P30Pz7hF+hfrpQw31oH56UD89qF9s7YHKGagRZuXKlZ7WcVrWSblwfPUjprTTaz/ctK9ji33APExpp1/6gK6dUOt7UZ7xb147/RL/btsixHQY73pQP32ooR7UTw/qpwf1iy0NmUAlhBBCCCGEEEIIIYSQIDCBGmFatGjhaR2nZZ2UC8dXP2JKO732w037OrbYB8zDlHb6pQ/o2gm1vhflGf/mtdMv8e+2LUJMh/GuB/XThxrqQf30oH56UL/Y0pAJ1AgTFxfnaR2nZZ2UC8dXP2JKO732w037OrbYB8zDlHb6pQ/o2gm1vhflGf/mtdMv8e+2LUJMh/GuB/XThxrqQf30oH56UL/Y0pAJ1AizdetWT+s4LeukXDi++hFT2um1H27a17HFPmAeprTTL31A106o9b0oz/g3r51+iX+3bRFnlJSUyHPPPScjR46Ufv36ydChQ2X8+PHy8ccfR9u1mIfxrgf104ca6kH99KB+elC/2NKQx4ERQgghhBBiKMXFxXLRRRfJ7NmzJSEhQXr16iX5+fkya9Ys9fPDDz/I/fffH203CSGEEEJimriqqqqqaDsR62RnZ0thYaGkpaVJ9+7dJTk5OaT6paWljus4LeukXCjX9TOmtNNrP9y0r2MrnLrsA95iSjv90gd07YRa34vyjH/z2umX+Hdiyz7u6du3ryvXbMj89a9/lXfffVd69OihZqF27NhRPT99+nS54YYbpKioSO677z4588wzXbsm30Pz7hF+hfrpQw31oH56UD89qJ8/NHQ67uES/gizZcsWT+s4LeukXDi++hFT2um1H27a17HFPmAeprTTL31A106o9b0oz/g3r51+iX+3bZH6Wbt2rUyZMkXt//Xoo48Gkqdg2LBhcuutt6q/n3zySamsrIyip7EL410P6qcPNdSD+ulB/fSgfrGlIROoEQazBLys47Ssk3Lh+OpHTGmn1364aV/HFvuAeZjSTr/0AV07odb3ojzj37x2+iX+3bZF6mfq1KlSXl6u9j3t06fPHq+PHj1aUlNTZdOmTfLLL79ExcdYh/GuB/XThxrqQf30oH56UL/Y0pAJ1AgTztTjUOo4LeukXEOZam5KO732w037OrbYB8zDlHb6pQ/o2gm1vhflGf/mtdMv8e+2LVI/c+fOVb+HDBkS9L1AchUwgeoNjHc9qJ8+1FAP6qcH9dOD+sWWhkygRpgOHTp4WsdpWSflwvHVj5jSTq/9cNO+ji32AfMwpZ1+6QO6dkKt70V5xr957fRL/Ltti9TPypUr1W/70v3aZGVl1ShL3IXxrgf104ca6kH99KB+elC/2NKQCdQIs2LFCk/rOC3rpFw4vvoRU9rptR9u2texxT5gHqa00y99QNdOqPW9KM/4N6+dfol/t22R+tm2bZv6nZmZGbRMRkaG+r19+/aI+dWQYLzrQf30oYZ6UD89qJ8e1C+2NIyrqqqqirYTsc5vv/0mFRUV6gAA/GCvqlAoLi52XMdpWSflQrmunzGlnV774aZ9HVvh1GUf8BZT2umXPqBrJ9T6XpRn/JvXTr/EvxNb2KsKw8uEhAQZOHCgK9dsqOAkWBwO9dxzz8lRRx1VZ5nHH39cvT5o0CB56623XB+7NmrUSBoyptwj/Ar104ca6kH99KB+elA/f2jodOya6KkXRGGdioo3BD+FhYUh2wiljtOyTsqF46sfMaWdXvvhpn0dW+wD5mFKO/3SB3TthFrfi/KMf/Pa6Zf4d2qLp8Lrg4G8Ux2R7PRi7GpK/4gm1EAP6qcPNdSD+ulB/fSgfv7RcG9jLiZQI0BSUpKUlZVJfHy8pKSkRNsdQgghhBDPKCkpUQNQjH+IHmlpaZKbm6s0DYb1mpszRTl2JYQQQkhDocTh2JUJ1AhgnY5KCCGEEEKIU5o3b64SqDt27Ahaxtr7tL59UkOFY1dCCCGEkJrwEClCCCGEEEIMpHv37ur3unXrgpZZu3at+t2lS5eI+UUIIYQQ0tBgApUQQgghhBADGTBggPo9Z86cOl8vLS2V+fPnq78HDx4cUd8IIYQQQhoSTKASQgghhBBiICeccEIggbp48eI9Xn///ffV6bQdOnSQAw44IAoeEkIIIYQ0DJhAJYQQQgghxEA6deoko0aNUgcbXHfddbJs2bLAazNmzJCHH35Y/X3llVdKYiKPNiCEEEII8Yq4qqqqKs+sE0IIIYQQQsIGh0hdcMEFsnDhQomPj5eePXuqWaerVq1Sr48ZM0buueeeaLtJCCGEEBLTMIFKCCGEEEKIwSBh+vLLL8snn3yiEqdIpPbu3VvOOussGT16tMTFxUXbRUIIIYSQmIYJVEIIIYQQQgghhBBCCAkC90AlhBBCCCGEEEIIIYSQIDCBSgghhBBCCCGEEEIIIUFgApUQQgghhBBCCCGEEEKCwAQqIYQQQgghhBBCCCGEBIEJVEIIIYQQQgghhBBCCAkCE6iEEEIIIYQQQgghhBASBCZQCSGEEEIIIYQQQgghJAhMoBJCCCGEEEIIIYQQQkgQmEAlWqxdu1Z69+5d7w8hDYHff/9dzj33XBkwYIAMHTpUJkyYIJs3b462W4REhB9//FHOPPNM6d+/vxx99NHy5JNPSmlpabTdIoQQ1/nqq68C97shQ4bIlVdeKcuXL4+2W77lww8/VJ8Xfv7552i74hu2bNkiEydOlAMPPFDF4KWXXirLli2Ltlu+gWP28LjgggvkrrvuqvO1lStXyhVXXKHi8eCDD5Z7771XCgoKIu6jnzXk/xY9/SL1fyXRdYukQZGZmSkPP/zwHs+vW7dOnnjiCTnyyCOj4hchkQTxjht6kyZN5IYbblADhhdffFEWLFggU6dOldTU1Gi7SIhnfP/993LZZZdJmzZt5LrrrpPc3Fx5/vnnVfw/99xz0XaPEEJcY8aMGXL11VerD7g333yz5Ofny2uvvSZjx46VKVOmSPv27aPtoq/IycmRBx98MNpu+IqdO3fK+PHjZceOHWrsmZycLC+//LJKCP73v/+VFi1aRNtFo+GYPTzwxTi+LO/UqdMer23btk3OO+88SUxMVEm/vLw8eemll2T16tXy73//Oyr++k1D/m/R0y+S/1eYQCVapKWlyahRo/Z4Hv+YkFx94IEHouIXIZHk1VdflaKiInn33Xele/fu6rmePXuqZBIGs/g2kZBY5f7775dGjRrJW2+9Ja1btw7E/y233CJffvmlHHfccdF2kRBCXAEfynr16iWTJ09WyQKAe9ypp56qEgVOZsaQmv8/OEstNPAF5Zo1a+Ttt9+Wfv36qecOO+wwOeWUU9RzV111VbRdNBqO2UMDq4kwWer1118PWgbJ0u3bt8unn34qWVlZ6jn8/utf/6oSXpiR2pBxoiH/t+jpF8n/K1zCT1znk08+UTdL/CNq2bJltN0hxHNWrFihZt9ZAzFrMAuWLFkSRc8I8X4bFywbPP300wPJU3DyySerL9Ewm4MQQmJl2TT+35900kmBD7hW8gU/v/32W1T98xszZ86Uzz77TC666KJou+Ibqqqq1P9VJFas5CnAUtWbbrpJxSGpH47ZnYMVRRjPIXF1ySWXBC2HxOmhhx4aSJ6C0047TU20wmsNGSca8n9LcJzGYCT/rzCBSlyloqJCTa/u2rUrv8EjDYaOHTvK1q1b1bIqe2IJtGrVKoqeEeItmzZtUr979OhR4/m4uDg1kM7Ozo6SZ4QQ4i7NmzdXH8zqGt9iOXVCQkJU/PIjmB109913y4UXXsjzEkIAY0vs1XnIIYeox5WVlVJYWKj+xlY6XPGxdzhmdw40wngOMyCxqqgucO/Dtgj77rtvjeeRCETfxtYIDRknGvJ/i55+kf6/wgQqcZUvvvhCbXaMTaTt36AQEsvgGzHsOYU9a5YuXSrz58+X22+/XT2Hb2AJiVUwuwDUtVQG3xpjXyxCCIkFMK7FBIHae0x+++23smHDBhk0aFDUfPMbkyZNkvj4eLnmmmui7YqvwEE9ICMjQx3Ss//++6u4w/L9hjxLLRQ4ZndO27Zt1crSww8/PGgZ6/AtzOqtDRLSGzdulIaMEw35v0VPv0j/X2GGi7gK9t7Bsv0TTzwx2q4QEjGwsTcGZNjzd/r06eo57An5yiuv8NtsEtNgCRySqDg5FHtfW2BZP/ZoI4SQWAZLLzHjBQfP4BAf4uwE9DfeeEP+7//+jwf2hIg1a/KRRx5RhyAh9rA/4LPPPqtmXb333ns1lqaTPeGY3TlOJkNZX6DX1ZdTUlICM6QbKuFOKOP/ltD0i+T/Fc5AJa6xfv16+emnn+SMM85QJ0IS0lB47LHH5L777lNLqh5//HG1eXWHDh3k4osvlj/++CPa7hHiGbjX4zTgWbNmyd/+9jeVOP3111/l+uuvVx/uGvKyI0JIbIOllUjEYIYVDkvB0mBSP2VlZXLHHXeoiRbWvpPEOUiWguLiYvnPf/6jDvLFsl8cjARtkUgl9cMxu/v78tYHZgSS0OD/FrP/r3AGKnENfIuHm+jxxx8fbVcIiRh5eXny8ssvq2VU2J8F+7SAESNGqBs5lljhpE9CYhUcGIil+m+99Zb6wbfFSKpiWdcvv/wSbfcIIcR1MDsIh1Tg0Jlrr72W+/475MUXX1T7TWKpZU5OTo0ZbJhdiedwACGpG8yUBMOHD5fGjRsHnkeCZfDgwerLTBIcjtm928qppKRkj9fwHL5MJ87h/xbz/68wgUpc47vvvlPLImpvIk1IrO9HhRkBODnRGogBDBiOOeYYmTx5spopwGVqJFZJSkpSS+GQSMUAxtrHacyYMTVOZCWEkFgAs4LOP/989f8f972rr7462i75hu+//14t6a1rqy9Lx8WLF0fBM39g7TNZe69EgATBwoULo+CVf+CY3X3atWtXYy9UO3iudevWUfDKn/B/iz/+rzCBSlwDm5cfccQR0XaDkIhibVeBk1BrU1FRoWZl7215CyF+Ztq0aWoAjRkd2OwdFBUVSXZ2towbNy7a7hFCiKsz2DA7CB9wb7rpJnXyOXHOxIkTlYZ2fv75Z3nuuefUa3369Imab36gZ8+e6ktLHNhbG5yEbiWzSN1wzO4+6enpaguERYsW1Xi+vLxczaLEAWdk7/B/i3/+rzCBSlwB3zBhejQHPqQhDmax6fwHH3ygZtxhYAu2b9+uDtbp379/YMkVIbHISy+9pJbt4xBBa0bHCy+8oGZ5cOkRISSWsPZ6xj7P/IAbOvvtt98ez2G8BLCC7cADD4yCV/4By/aPPPJI+eKLL9QMNWtvxHnz5qlDVK688spou2g0HLN7A7bve/PNN9UqJGvl0ZQpU4LOCiR7wv8t/vm/wgQqcYXVq1er39bsI0IaCjgk5/bbb5cJEybI2WefLaNHj1az77AMCN+GPfnkk9F2kRBPwTfm+LYcgz5s3j5//nyVTMUAsFu3btF2jxBCXAGz6j/55BOVgMGWVVOnTq3xetOmTeXoo4+Omn+kYXDLLbeowxrPOeccOe+889QBKtjX0zoIiQSHY3ZvwIFHH374oYrHCy64QE2qwr6URx11FL8UcQD/t/gLJlAbEO+8847ceeedcvfdd8vYsWODlsOGz/hHjGWZq1atUvvA9O7dW9XBnjHBTosD3CiaNMQ+gG9XEfvPPPOM/POf/1QnTmIzf2xmjW+zCYnl+B85cqT6AIfB8syZM9Xg76677uLyfUKIL/acw/0O+/hjCTRAIgqz/PDlED7Q2pcEWod8YFlgbbD/c0P8kBuKhkRfvy5duqiE3yOPPKLGnUgKHnrooXLrrbeqREtDI1T9GvKY3au+2rJlS3n99dfVfviPPvqoikMkqG+88UaJNbzQsCH9b9kYA/8v4qq40UeDAMs68I0QTiSr78MzNs5G8M6ePVv9Q+7Vq5fk5+fLmjVr1OtnnHGG3H///RH2nhB92AdIQ4bxTwghNcEsPix5xswz3O+s5dC432E/RBzKg+1I6loeSKqhhnpQPz2on3OolT7UUI9Y0S8+2g4Q7/nxxx/Vkg58cN4b9913n/rg3KNHD/n888/VdHzsCfP888+rPWHee+89effddyPiNyFuwT5AGjKMf0IIqQk+wF177bXq9+GHHy7Tp09X9zzrBzPSsAwVJ/hiHz+yJ9RQD+qnB/VzDrXShxrqkRdD+jGBGsMg+B577DE1m6j2yWR1gY2fseEzDgHB9HvrWwEwbNgwtTQEYH+Yuk4vJMQ02AdIQ4bxTwghdYNDZPBhrXXr1mrpLn5b4N739NNPq9Olsdzw448/jqqvpkIN9aB+elA/51ArfaihHh/EkH5MoMYoixYtUifiYdYQ9nbBZtnYX6I+sGFxeXm59OvXT/r06bPH69hoG3vhbdq0SX755RcPvSdEH/YB0pBh/BNCyN73nMMhJ3Xt34+lhIMGDVJ///HHHxH3zw9QQz2onx7UzznUSh9qqMfPMaQfD5GKUTCTCBsRDxkyRP76179K37591anI9TF37lz1G3XqIjk5WX2wnjVrlvrwfNBBB3niOyFuwD5AGjKMf0IICQ72YRs+fLg6nCMY1jER2JuN7Ak11IP66UH9nEOt9KGGelwZQ/oxgRqjdOrUSZ1wdsghhzius3LlSvXbvmyzNllZWerDs1WWEFNhHyANGcY/IYQEB6dt13fiNpYaWjPtcZge2RNqqAf104P6OYda6UMN9egfQ/oxgRqjIPBCDb5t27YFplAHIyMjQ/3evn27poeEeAv7AGnIMP4JISR87r//fikqKlLblpxwwgnRdseXUEM9qJ8e1M851Eofathw9OMeqCRAcXGx+p2SkhK0jPUaApyQWIN9gDRkGP+EECLyzDPPyLRp09TfV1xxRY3DLogzqKEe1E8P6uccaqUPNWxY+nEGKgmQkJDg+GRlnNJMSKzBPkAaMox/QkhD56mnnpInn3xS/T1s2DC5/PLLo+2S76CGelA/Paifc6iVPtSw4enHBCoJkJaWJrm5uVJSUhK0jPVao0aNIugZIZGBfYA0ZBj/hJCGSnl5udx7772Bw/awf/QTTzwh8fFcrOcUaqgH9dOD+jmHWulDDRuufkygkgDNmzdXH5537NgRtIy17119e+QR4lfYB0hDhvFPCGmI5Ofny3XXXSfff/+9eoyTgh955BFJTk6Otmu+gRrqQf30oH7OoVb6UMOGrZ/5KV4SMbp3765+r1u3LmiZtWvXqt9dunSJmF+ERAr2AdKQYfwTQhoaGzdulLFjxwY+yF144YUyadIk33yQMwFqqAf104P6OYda6UMN9YgF/TgDlQQYMGCAfP311zJnzpw6Xy8tLZX58+ervwcPHhxh7wjxHvYB0pBh/BNCGhKbNm2Sc889V1avXq2WDd5xxx0yfvz4aLvlK6ihHtRPD+rnHGqlDzXUI1b04wxUEuCEE05Qv/HhefHixXu8/v7776tTmjt06CAHHHBAFDwkxFvYB0hDhvFPCGko4AuhK6+8Un2QS0pKUjNg/PhBLppQQz2onx7UzznUSh9qqEdpDOnHBCoJ0KlTJxk1apQ6hRn7Uixbtizw2owZM+Thhx9WfyP4ExM5eZnEHuwDpCHD+CeENBReeOEFWbBggfr7zjvvVHuwkdCghnpQPz2on3OolT7UUI8XYki/uKqqqqpoO0Eiw9FHH632trv77rvV3hN1gQNELrjgAlm4cKGaWt2zZ08142jVqlXq9TFjxsg999wTYc8JcQf2AdKQYfwTQkj1TJjDDjtM3e/wZVD//v3rLY/Tga+99tqI+ecHqKEe1E8P6uccaqUPNdSjNMb04xQSUoP09HSZPHmyvPzyy/LJJ5/IypUr1YfogQMHyllnnSWjR4+OtouEeAr7AGnIMP4JIbHOkiVL1Ac5UF5eHnTfZ4u2bdtGyDP/QA31oH56UD/nUCt9qKEeS2JMP85AJYQQQgghhBBCCCGEkCBwD1RCCCGEEEIIIYQQQggJAhOohBBCCCGEEEIIIYQQEgQmUAkhhBBCCCGEEEIIISQITKASQgghhBBCCCGEEEJIEJhAJYQQQgghhBBCCCGEkCAwgUoIIYQQQgghhBBCCCFBYAKVEEIIIYQQQgghhBBCgsAEKiGEEEIIIYQQQgghhASBCVRCCCGEEEIIIYQQQggJAhOohBBCCCGEEEIIIYQQEgQmUAlpwNx6663Su3fvvf6gnFO2bdsmhYWFIfty7rnnytFHH11vmSeffFL58/DDDwctE6q/bgC/4b/JVFVVyT//+U858MADZeDAgfLGG284jom+ffvK4MGD5cwzz5QpU6aEdX0n728w8vPzJScnR9zkmmuukWeffVb9/fPPP+/R5n322UcOOOAAOeecc2Tq1KlhX2ft2rV77V/333+/Y3tr1qxR7+GmTZvC9okQQgghZmCNu4KNy6xxBMbAkSQa4+lQKS0tldtuu02NUfHzzTffBB2D1jX+wnj4uOOOkwceeECNNRu6noSQvZPooAwhJEY5++yz5eCDDw48nj17trz99tvq+f333z/wfKdOnRzZmzFjhtx8880qyZaWliZe8eqrr8qpp54qvXr18uwascb06dPl3//+twwbNkyOPfbYGu9vXWBA2rx580DyFQPLjz76SA3+tm/fLhdddFFI17/iiiukqKgoZL/nz58vV155pTzyyCMqceiWFnPmzNkjEY9BNH5AeXm5+jLgq6++kr/85S+q/D333BP2NYcMGSJnnXVWna91797dsZ2OHTvKiBEj1GD/iSeeCNsfQgghhJjDpEmTZPjw4dKyZctou+Ib3nnnHfnggw9k1KhRMnToUNlvv/3qLV973Ldjxw6VdMXniuXLl6txMiGE1AcTqIQ0YAYNGqR+LCoqKlQCFd/IYjASKr///rvk5eWJ1yC59be//U3efPNNiYuL8/x6scDixYvV7wkTJqhvwfcGkqxZWVk1njvjjDPkxBNPlKefflrGjx8vycnJjq9/6KGHhuG1yJIlS2Tz5s3iFpWVlSr5eP755++R5IcuteP+kksukYkTJ8pbb72lErhofzgg8RlOn6qLyy67TCV6f/31V5WYJYQQQoi/wfj5wQcflEcffTTarvhubHvXXXdJkyZN9lq+rnHYeeedJ5dffrmaBILPMf379/fEV0JIbMAl/IQQ34Gl4JgR+N5770XbFd9QVlamfjdu3DhsG6mpqUp7zEb9888/xY9gpsGqVavk5JNPdlQ+Pj5eJevT09PlhRdeEBPo0KGDmjn+yiuvRNsVQgghhLgAxlfTpk2TH3/8Mdqu+G5s6yR5GgxMxMCqNvDbb7+55hshJDZhApUQ4gjMdrvgggsCs1bxje2sWbMCr2Np91NPPaX+PuaYY2rsCfrpp5+qGYtYNo7lNRgkYhkN9i4Kh+uvv15atWqllnXvbW/MYHtv1n4ej/ENNJZsn3LKKdKvXz856aST1DfSSBji220sD0LiCn8XFxfvYfPdd99VbUdd7Bf63Xff7VFm7ty5cuGFFwZ0xFJ4fONtB3799a9/ldtvv119E37EEUfU2869vTewZ39vwt2LFFgzfjFb2en1g+l98cUXy8yZM2X06NFKsyOPPFLt8YVZogB/YysBAJtWfWwpgPZgqRvqHXLIIXLLLbfIhg0b9uo/Zi1jf9P27ds7bjMG5kcddZQsXLhQLeu3wFYGd999txx++OEqruHP//3f/9XQJlQmT56skrsDBgxQM16vvvrqOpPVWMaPZLCTNhNCCCHEbDDua9SokRpX7G18HGzv/drP4/G9996rxqcYo2BMefrpp6tx55YtW9R4GuM2jGMee+yxwPjLznPPPadex7gEY7HaY1bw7bffypgxY1QZjJWvvfZaWbFixR6rfLBNAbZ0wpgJY2ysKAsGxuOwCZ+x2gb1Fi1aVMOetS8//tY5iwC6W+NLC4z9MRsY4y2MNaETtmL6+uuv99if9sMPP5THH39cjdetzwA//fRTvddctmyZGufB/tatW9Vz69evV9oddthhyg5WPeHL+7reF0JIdGAClRCyVzBYwMAEyRrsR4kf/I2kmTWQwL6p1v6RSHphoAMwaLvhhhukadOman9U7CeJGXQvvviiGkiFAxJaSC5i7yIcjOQWCxYsUHaPP/545SsSZPAdS6bXrVunlr8jWYdtDmrvk4S9Ou+77z412EE5LMVCQvaHH34IlPn++++Vjjt37lSDVuiIwRIOKkIS0s7HH3+slibBHwzYMjMzw35vYMP+3uBxOGAA98svv6il+9a+nU6uX9/yfOiLASQ+OGCvXSRGkUQE8BlxBRBPlt8YzGMbAQzokczGQBUDbSSj60teYg9W+I9Ebaj07NlT/bYG77m5uWpgj1nQ+FACXaEJBts33XTTHvXxYQhJ8No/BQUFgTLYYxYfnJDgveOOO1SiHTOtrZixgwOu0Nb//e9/IbeFEEIIIWaBsfFVV10lK1euVF/GugXGR9gzHdsw4QBN7PWJJB3GGFhlgwkQOFPg+eef3+PQzM8//1xefvllNd7BF7qoiySq/Ytd7EGKsR+SkPgyG+M/TBbA2LV2EhV7jWLWKMZ8GLslJta9myAO1ML1UBZjathE4nbs2LGBBC4mYljbGOFv63NHOFgTHjD+shKpGMP/5z//UWNRjDUxxsRnAWhobR1gAX2//PJLVea6665TiVXUx+eIusDYH2XxeQaaYN9btBXbRuGzCNp75513SteuXdVkETfjgRCiSRUhhOzi/fffr+rVq5f6bVFWVlZ1xBFHVB155JFVO3fuDDyfm5tbdfjhh6uf0tJS9dy//vUvVX/NmjWBciNGjKg6++yzqyorK/ewOXLkyMBz48ePrzrqqKPq9a+2/Ysuuqiqd+/eVbNmzQqUwesTJ07cq93az+Mx6n7zzTeB5/7zn/+o584666zAc2gHfEebLGAH5aZPnx54bvv27VUHHHBA1WmnnaYeV1RUVB1zzDFVY8aMqSovLw+UKygoqDruuOOqRo0aVcNenz59qjZu3FivHrrvTV1AO5RbsGBB1bZt29TP5s2bq+bOnVt1/fXXq9ceeOCBkK8fTO+vv/468FxxcXHV0KFDa2hrxeRPP/0UeO6EE06ouuyyy2r4PXny5KpTTjmlatWqVUHb9uOPPypbn3zySY3nYRvPQ6NgvPPOO6rMtGnT1ON//vOf6vGXX35Zo9zdd99dIxagNx4H+7HH6iWXXFJ10kkn1bAHOyeeeGLVr7/+WuN5xOGAAQOq/vKXvwT1mRBCCCFmY427AMZMGAf069evauXKlTXGEfYxCsZTGEfVpvbzeIxx8qJFiwLP/eMf/1D2brjhhhpj0X333bdqwoQJgedQpm/fvjXqwqd99tmn6pprrlGPMfYbPHhw1Y033ljDD4wbMZ676qqratgbMmRIVVFRUb165OTkqPHNGWecUVVSUhJ4Hjrg+dNPP71O7erDGnNa41rrZ+nSpVXPPPOMaucFF1wQKP/bb7+p8hhb2pk5c6Z6/qWXXgr4hMcYB0NDi48//lg9//bbb9doP/zFdY8//ng1fraPyefNm6fKfPrppzXGevisw7EeIebAQ6QIIfWCZcsbN25UMzLteww1a9ZMLcvHjDvMvrQfRmUHs+ow889+2BOWQaN+YWGhlm/4RhjLnTFrD8t4kpKStOylpKSoWY0W+ObXWvZugXZglsCmTZtq1MW39/aZjRkZGcq3119/XS2TQvk1a9aob88xe9EOlodjP0uUadOmjXoOszGtv716b+rjtNNO2+M5zDzFbEhrhqXu9TFbYdiwYTX0h+bWUqZgtG3bVn7++Wf1rT2WgOGbe8yOwE99QH9Q+3CsUPbZsuIYy+cx4xSHbdnB7BFsE4DZt/Z4wHIsbFlQm9atW9doF2YpYxYu9uOCn7BR14xZKw4xy4EQQggh/gfjWIxpMYbC0nus1tIF40n74aHW2NZamQRwqGaLFi3UeNUOxsT2up07d1bL1LH6BatgMGbBUneMhexbTSUkJMhBBx2ktsHCMn1rpimW42M//frAHrD43IAZsvbDSjEmwhZbWAWGw0Xt4yenYBuu2mDMitmyODDUAlsRYCsqu69or7WU3r56CGCcZj+YtE+fPup3bT2hFWaZYibrf//73xrjUbQHYzvMBMZ5BVidhfa7EQOEEPdgApUQUi9WgsYacNnp1q1bYClKsCQZBoMYhGBjfCz9Wb16dWAfSSSAdMBADsvrsVcmlhjhbx2Q9LQvJ8IAEGBQaQfP2/dJCqYPBq0AAyVoZC0zwk9doIyVNK19TS/em/rA1ghITAIs8cIAEwlDJDnduj70hm07GCzuba8nbAOB5WIPPPCAOrF23333Vft8YQCMvXGDgS0fwj1swKrbvHnzQNvtyXYLXB9a4T2v/Ty2f6gPLFfDAQaIZ/z06NFDtQvL3KxYsoN2BFseRgghhBD/gWXp+BIbS+OxnROSeTrUNYYFtbeGqmtsa43l7GA8gi+RkTDFmB7ceOONQa+PclayM9h2VHassWVd17a2j8LYMpwEKj4rAJxj8Mknn6jPJpjYgO2kao9H8XngrbfeUls/4fBRtNU6/6C2TrXbZSV+a49nscwf18HzmGBgHz/jS3RsgYC9aJFkRUIWCV9sDXbCCScE3jdCSHRhApUQUi+1Bwl1vVbfzM+///3vag8h7Cs0cOBAGTVqlEqo4Xk3DsBB0hTf4j7zzDNqNqJT6torM9heTPbZs6Fg6WMNlgD2PoUOdWEfLDoZKOm+N/UxePDgvc7U1L1+7cGqU/DNPvblwp5VOLgAv//1r3+pgTFmJlgD7GDXC2cz/uzsbBUH1kyM+toO++HojsEz9h/D7FrMYEW7sO8V2vXSSy+pfU9rX4cDakIIISS2QCINSUp8SVx7z31TxrYYf1jjKYzpg40Z09PTA3/rjll0x7b2L7LxBTUmCmDGJ1bEYV9We9IXX15jpuuhhx6qymLsiYkfeD7c8SzOg8A+/tjT/6GHHlKzee36YKXSyJEjVaIVs3cxwxfjQRxSFUocEEK8gwlUQki9WLNEMXu0Ntbm8Ej81AVm4SF5iqRp7VmXe1um7RR8y/u3v/1NLfXBAK6uQU1dp5m6dX2L2jMOAQ4CAB07dgwMavGNcu2ZiNgQH8v697asyc33xg2icX3oiIOcMPsSWytY2ytgJgFmQODQMhyIUN8sDGs2qVOw5ArL1ZD4t2YZoO21D0ewlmuhfLt27UJum3UoAWYcWMvMZs+eLeeff77aCqJ2AhXt0J3FTQghhBCzwFgD2yMhqVfXgat1jW2xVB6rUupaseL22BaJQKzIscYg8Lf22BZfBiPBal+GH+rY0loKb2GNN90aW0JjrJLDGAtL5q1tDbAVE2bCYnst+7J/HOypA7Y6wAxjvK+XXnqp2uoKWzVYYzqMbzGBAVs44AeJXYxpMWkAY0T7dgqEkOgQ3vQfQkiDAcujsfwYJ6MjMWSBvzHAwGv77bdfjW9grW+Irb0+sRTZDr5VxQAMgz03wKAN39hiNmJt8O0ytgyw71mKZTNYjuMmODUTe4LaE7TY/xUDJQwyoRG0wiDNvncSdMTSIZziHuo386G8N14QievXnjWKBCpOgMXyfTvWErf6ZgFYg3Ls2+oUxDKuhUEsllTZ961dtmyZOt3WjnVSqn1vV6dgdjK2J7DPIMHMbcy0qN0ulEGyNpxELSGEEELM5owzzlDJtGBjW3yJay0pB5ixWlJS4qoPWAljHz8vWbJEfaGMGZmYwYrxN7Z2wuxIa694gDrYEx4nyIc609WyidU39iQxxm5YcYZ9VJ1sc+UEzM7FLF+Ms+655x7Jy8ur8UW7/fMLxoOYFAJ0P79g5imSte+8804gKYvZpvjCHO+jBSZd4IwFwBVHhJgBZ6ASQuoFgwp8U4rZfaeffroa0IH33ntPLW3B0mkruWPNzsNACoMD7BHZvn17tVwFgzp8Y4zZljjwCYOj2puw64BvaJGY3blzZ43nkVjFHkf4phf7HCGZiiRmly5dagz2dMESHCy9wUxYDHLeeOMNNcBCYrS2jqNHj1Y6QgPMmMReThhkBltm5cZ74wWRuL4VU0jSIimNg7lwkNWzzz6r9gxFjOEDBJbu41Aq+BEMJFkxGJ03b16d2z3g230sobcSlLgeEqQoj6St/TCxyy+/XL744guV/EZcIZ5++ukn9dzxxx9f58FPewPxAz0vuOACGTFihBqswx/0nXHjxtUoiw8xOGShrgMRCCGEEOJvkHjEgVIYM9ZO2GFsi1VX+GIXBythUgCScW6vSsHsUYw/MO7CF8k4vBP7vGPsY43RJkyYoJKQZ599tvIFvuJLdIxd7AczOQWTDiybGF9h3IfPCxgH4st0+1J7N0CCEuMvfFbB/v/QFZ9h8FkBYz2MbfF54dNPP1UTMDCudePzC5bxIxmNVXTY7xZfzGNP1DvuuENNysBMYsy4xecJjPVqT0YhhEQHJlAJIXsFyRwkCLHP6NNPP60SfUhG3X///WqGpQWSUkggYSCATdeRcMKMPOzz89prr6mEEAYEGDRggIX6GIy4MUsSsx2RyLOWwlhgQHLXXXep6+N6GJxgQIolO9OnTxe3QCKvX79+6rRMfHMNfbDsyt42S0ck/6AlBmE9e/ZUj+Gnl++NV3h9fQwasXk+ZmAgQYnk5HXXXacOoHr//fflH//4h0pYY5YGBr7B9j+1Pghgidavv/5a5+vYcwo/9oOzMAP08ccfV5v428H1kbTFe4ztAzBrAVs1YAYpEqDhgH21kJRGrOIQAXxQQPy88MILym87WNoPHw877LCwrkUIIYQQs8GSbXyBi33Q7SCpibEmvrBGwg9L3Z966ilVDolOt0BSFIlcayIExiKYsIDJERYY8+AAVMwYxXgJ21FhhRLGZPvvv39Y14VNHBKF9mA8hC/IsY3RNddc48kydsyWRYIUkxqQsEUC9b777lPXx2cYjHPRJoz77rzzTrU9gS7QEAeion3WQbi4HiYfYKYtvsTHZxu812g3IcQM4qrqOwmDEEIIiSEwoxQzV5Ho79y5s/iVMWPGqCV8+MBECCGEEEIIIcRbuAcqIYSQBgNmRWO5PbaR8CtYqjd37ly56KKLou0KIYQQQgghhDQImEAlhBDSYMBStJtuummPg6/8BLbFwJYP2LaAEEIIIYQQQoj3MIFKCCGkQYF9VJF8fOWVV8RvrF69Wu3TikMHCCGEEEIIIYREBu6BSgghhBBCCCGEEEIIIUHgDFRCCCGEEEIIIYQQQggJAhOohBBCCCGEEEIIIYQQEgQmUAkhhBBCCCGEEEIIISQITKASQgghhBBCCCGEEEJIEJhAJYQQQgghhBBCCCGEkCAwgUoIIYQQQgghhBBCCCFBYAKVEEIIIYQQQgghhBBCgsAEKiGEEEIIIYQQQgghhASBCVRCCCGEEEIIIYQQQgiRuvl/Dfbox+SIpKMAAAAASUVORK5CYII=", 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", 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" ] @@ -181,18 +198,18 @@ } ], "source": [ - "weak_scaling([df0, df1, df2], names=[\"CCX\", \"Socket\", \"CCD\"], title=\"Weak Scaling up to 4e9 Points on 512 nodes of ARCHER2 (Uniform)\")" + "weak_scaling([df0, df1, df2], names=[\"CCX\", \"CCD\", \"Socket\"], title=\"Weak Scaling up to 4e9 Points on 512 nodes of ARCHER2 (Uniform)\")" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 176, "id": "447bedda-cd0e-4ff3-9201-e46d6d66c62c", "metadata": {}, "outputs": [ { "data": { - "image/png": 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1+gyvkSNH+tasWeP/7ttvv/UNGDDAt9NOO/ny8/MbvDbqh3KuvvrqOp9/88036vPdd9/dN3v2bP/n6APoD76DnoTq10BeffVVlefFF18M2S7Nr7/+6uvbt69v33339c2dO7fOtW+44YZ69R0/fnxYfb3kkkvU9+edd55peZx22mkh02D8QgeRDnXVQN/x2UUXXeQrKSnxf75s2TLfgQceqL6DHAKvZWyL/gyvu+++21dRUeH/7p577lGfH3vssXXqg7oGzgOrV6/26wfGtGbbtm2+E044wbRu6n4bMWKEb8WKFf7PUeYFF1ygvkO7g+km6rVhw4Z6OgX93Lx5c4PX1nqm9RDj6uSTT/YNGjRIfYZ2GOfEYAQb62YoLCz0zyeod0NYnT9QPuZ66Pm7775bp6zJkyerz4cMGeIrKCjwf37hhReqcm677TZfVVWV+gzXefTRR/3XRj00ixcv9u24446qnN9++83/OeoEeSA95Kl57733/PpoHNfLly9Xv034zqgDobA6DoD+LUMaM2C+1/ptZNSoUfXGZTDdDATthU5Pnz7dr9fDhw+vM3aeeuop9TnmoEjR18ecBDmHemk5GfvTmB9tsYLdtofj9ddfV3n22muvOmuXUDz22GMqPfIF48knn/TXtV+/fr4TTzzR99BDD6n1k1GfQv2O4vXcc8/5P0ees846y78u0ujfILwuvfRSNTdqnnnmGfX5AQccEPQaZtdjel7AeJ45c6b/c73O0XM31ojGuf6vv/5Sayj8zixcuDBoeyFvfI81ByGEEHegByohhLgITsWFRw28HIxbMOGBBE8LfUIttnwhLTwH9XZWeGEiH9Jorwxsh4PXB7yY4G2hvXS0p4b2DDWexgpvLXgxwGPEuJ0e3hj6lG6j15EReEDAewreF/DWMOtxBFC3V199VW1rg4cTtqXB6wneZvAMgjcW2hAYswyHZsHDCV4X8HIzxjxDHSAPeBZiax9O1YZXFDwTA083hkeHjs0Wqn1mQOxDlK9lh/rA48TodQjgTQJvPniPGA8RQr/ef//9tg8GMgu24hq9fuAlCi819KWZ8AGhgIcagBcOtv1qoJu33Xab0j/oCTwlGwJelPDY0SEGGgIervBQwwE/xgNYcG14JcE7DVsi9XZR6Bf6C555iHVqBIehaO8843ZPM3WGPhpfGFPwjkMb4GENr0Ktc/CYgr6j3xHiAFuKNfAe1V6XaJsZ4FUKLyuj9xq2iQbqYSgwVgA8G42euTjcDuManqnYfmpWD5C+c+fO/s9RJvoU7UW7gx3WhP4zxhmG1xQ8neFlDk/thsC8AOCNiW3xCIuBPkZMUugFPNng0e408JK+/PLLlbcZ+tfKgXdm5w94vuI60F3tOanBnILP4O2IQ4i0pxo8UqH78OTGlmqA60Avg524/r///U/pPNpiPB0ddUJ8SeTB7gjtGau9/eDVZ/ztgHc99BfzmlGvg+H0OAiFntuCyc6MFyrabnxBn3S4AniawvMWum8cO1o+2uPbDpiTsIsi1Ksh73546ga2wfgK/H202/ZQwPNSb0fHHGnmACkdciTU4VrwqEaYCsS2xe4CrBHgQYq1Cba3IyRGuBAPWGsg9IsGOogwRNiJAI/NQM91jCWsTYwHf2KOxzjBWkJv/Y9kPabBXGuMDY+yEaIFsoAcAud6pEcYFPzOYF0VDPQdvg/c8UQIIcQ5wu+XI4QQYhtso8WiGItaGAWx6IZxFNulYVwD2pgKowCMUFhY6+37SKfBljIYWLFt1bhYN94oYCGOm2Ckw40AbpYDgWEWdYAhN5QhCUYNxG1DWb169VJbZq0CYw1umnEj9c033yjjGtoF4ycMpzjRGjeHuHnWNxP6Zgpb7YLFT0UMx3AnCMPQhpt2GFu04dSKoSyQYEYlfciPMd4jjN8g2AnsuPGEERtGPjfBVkDEFgwEdYKhCfLHzahVoAvQX+hWsPbhRhSxCrHNHjeyOnRFMHCDh5tD6CZuYgMPBQkEeqxjvwWLt4ebXBgHYChFOsTVxHZabNuGMQ0HlWHrKYw+y5YtU4ZQbFeHPBoKG2AE+gpdNYKHEDCeYGs4DDfGLeLG8RvM8IDxjhtvGMIQbkLHUA0FwlIE1lfrIQwAMCyEkyXmHhggYHxAmIdDDz1U6SRu9DGf4NUQa9asUWMKRlCjAU4DIxm28yPUBsYxDGQazHXBjHpoA8oMjGMbDGyfRdoePXr4DYYAhlzoEvoAW1xh5EffOAGMJTg8CnLTIUfcmD+0vgTGgNSgv2Ak1POj/guDbqCRCvMmDGqIGWtEG5mCjSPkwcMo5EHZMJrpg90wP8OAhzLxG4P+bygcglvjINQcgQdY0P9AAypi3iL0CAyUeIgQKhQAHoTo3w+EHMBciXIxl8EQFsy4p8ej2Ri+4cDvY7gDzlCHcLFc0R96PRGMYOsFO20P1dcYKwj3gDkmXLxhI/n5+XVirQYDxlw88MWaBSEFoKN4AIHfE3yGF6591VVX1cuL34RAcnNz/VvtYbg0zmeYEwMP7cR8Ar2HDhUXFytdjmQ9pgk2F+rxiXEXbC7HfI15To/9QLT+YJ4mhBDiDjSgEkKIy+AmBPGusFBH7Dkd51THPzUutmFAxSLaaEA1ptM3GoiVF2wBrsGNOYyk2tsLN78wGGEhDyOSjvulDZTB4jDCYISbEtwkwnsWN6Bmb5qD3azAaIUXwI0PTsyFJwVu2GDkxXsYAoxeT2aB3HBziZt/tFXHWw13gJVZ4MEbiL4RMsZW1TctMGIGI9zNsVMYDVZGtEeqmcNvggHPWsg00HsxWPtCxajTICYcHhLAg9GMTHBtbWgKZrQzYrxxhHcSjIbw/sH1MHZgzEIcUOgZxgMOLTELrh1ovA+HlnU4owDaD3khbUOGo2B1NRpUGzKgwriCeQgPMzAXaS8lGB9hGIOBArFlzbQplI7rNgXTAxhXg41H3QYzsWDRBvRpMGA0h55DBzC/GL2kIwVGPRzeMm/ePNU/8MIzE4szkvmjIX0JlKtOj7k1XPpg4yPQyBgqHYyo8G6F0RixpvFCH8KYD0Mvfs+Ctc/NcRBq/kd+GLmMB7Bp4MmHOQQHisHIFozAB3F40ILY0Yi5jLkCMTQDgeEXBDt8K9qgnyL9jYmk7YFANxDLFg9zsOPBygNXLb+Gdmlg3ob3t/YARz48uMSDWDzgQNxQPEDcb7/9TP0u6jVG4O9iqN8FPW61wTzS9RjAw6xAdHn4nQn3W4N5KRhafl7QR0IISVRoQCWEEJeBMRRGJyzwgfb6DDSg6vcwoGK7GYydWODrwwWMN9zwCG3o0AoNtrfixgZehDCWwKALDy4YGGC0COeRCI8P1AvefNhCB28nM9v44PEJD1f8DeYRCSMIXvBQwRZ/3BDAqwLXsnKyM+SBbW3YyoqbZGyXRZmQD66Lm5Bgp527gTbahjqwyuxhQQ0R7kAso4dLsGtb8bgMlj8c+qYy3JZNeO/gwCKkgZ4bt13qUA4w8MMIgptNnGxtLDeUd16oG2WEL8ArEBhSGjIERgMzMtM48TAA4xcPQrAtF0YneJrhgQMMg/DuhKdeMA9jJ/TAifo3BIybMP7BA84u8GCH8RTGFRgM4V1t1XhqhYZkq8e9lmtD8gw2F+i+gUdeOGO70eMQW5fhoYgHXDjIBg/2/v77b/VCSAAY2EIZqNwYB8HQB+fAeBfKO0+H78CDlYa83gF+d3FAHTwpEV4BxknkNaKN9IGHgYUCBj4YkuGl3ZDhOZaYabsRjA2cag8dDuUFGg79mx/stw2hQDCmIevAtQd+Iw4//HDlnY0QFDhMD7/3gQbUhn4XA783O1dFsh4Ldw1dHjxa4QVrJa8xf7ADOwkhhDgDDaiEEOIyWJzDew1eCvCmg9ECWzgDPRZw0wKDDk56xom0MDYh1l8wjxcs1gO9RoKBmHnwBMGNAk6iDfQgxU1xKLA1GadR42YT3p3wnEXMOzPeKGgn6o5YY7jhDnUDg/ZiaydufLRXrN7equNZBgKvGMgGMoX3CYynkCUMc4HeWDpWWTSAsRtxHOFFEsxLzsq2On2DFGxrKGLJhiKUzLRni5XTw43AWwYGavQRZBrMiK5j9IWLBwjDFm7yYFgP3A6vgfcMvoOhAQZUfW3cZCNeohkjC8qANzL0AQ8LAtFxWs1sW48UrccIJxEKHWLCTcNcsJAHOLUeL+1tBsMODA8Y3+EMqGbapPXA6TZBtx9//HGlOwgLEu7aobwyzQL9wAnaCCsAYwwMyw3F+rQLZIv5A7INNn8Eji/dRj22AwnmbY5roHyEtbBi9MQ14aGMF8YvvJcRaxlGVBjP8DsRrl1ujgOMdTwMgPcptnYH82KEzmD7M+qA37FAA1soMD/gAR08t6F7MHwajcv4DYIhFA994KUcbqs7+u/RRx9Vv8UIxeBlA6qZtmuwHsDDFzycQzxqeJ9aBbLAbzp2owT+tqB8PHTGGIShNBhYo+BhL9YRWHtY/V20stvFznrMbHnYjWTVCA0gv1DerYQQQpyBh0gRQkgU0HFMEacNi9xA71MNFs4wUiGGoH5vRMekg1HSGD9PM3v2bBWLElvicaOGrawwoiIGZLDt99obNpj3E26ItKcOboxgeIEHj45HGQ7cNMNrBddu6OAi3HwCbTTQHqvwdgoGbqTgoYgbZ+3VC2NtoNEE19bfh/PadAod9xOG8kDgGYWbe7NoY03gAVsNeTsh1EKww0a0oRxGhIYI5t0CAyYOQ4McgxndYdzUBzMFi6+ogU7AsBnspQ/GQP/jvZYjDKbYSoxr6/AXRqC7p59+ujLu4OGD9h6E5xwMFoHgRh11xTbNUOPQCXAIC2SJfg9myIeRDjoMmUTDExYGUhhHcfBS4IMbvd22ISM/6gnDNuawYJ5+iA2o9VzPVU4BwwragNfcuXPrfQ+vWhhP4LllPNzKKphDtfF09OjR8vTTT7tuPDXKC0agUFukjWEs4E2M+RkPkTDXBQKjYqhrBPsOXH311WouRTxOACMpxogOJwNwTcQchozCbSeO1jjAbyo87mAUDbUFHHOINr7pQ7jMgt0g+P3ENXBYnvG3Er+R+iA3PNwJtXsCefBwAn8xl8GjOR4I13aABxkwniK0BuaVSIynQHtbBjN04ncHwNM53O+4PoQO9Q0kmL5DbzHWYWyM9EGa1fWY2fLwOxesrfjtPeSQQ1RfhHto0lAoFkIIIZFDAyohhEQBbVzTRqJwBlS9JRE3qoGH8eh4hVj840Rr4w0pjG34DFve9KnJOuYWbi7g1arBYh43kjCIgsBTaAPBdeGNgnw4sdvMoUz68Cp4kCBGWeC2Mtz0IywAjGUw7OkbHxgGYNSBkQTbCI03HriJwoEPSAuvD90+GFuNN68w8MCDQ3tkNNQ+J4DhAUYebA/XBggAWcFApetiZnug9k5+//3363icwngd7jRpyApem0a9gLEQdcKN4lFHHdXgtfXhO4HGDpwwD2AIQExRDfoV/YgDYPTpzU4DYyjAScf6JHaAm0wYSWHMgxeb9pCCgQdeTWi70dAHWUIvcLOLGH9mwlFEih6rkCMOzTIauWDkxjgC+kR2J9F9CIOmBg8o0EeYg4xzAdBhLoynQodC6wHqbzTWo31oJ9qLsAnhYl5GAoyY+rAbXNsY5w8PYbRnfCSHpGkwTyBcCYynGCuI0xvKe95pYHzCQTTw9g88LAi/BzAU4nsdvxTemtiKjwdumPeNczJCMgR70IUHDWgPvArhyW8EvwcI94KHbvrgK/yOILYotmcb5wPMtdqg25Dxye1xoB/QYSt3OLTc8FthZTcAHh7BYIV5Gw/kdPgPo5ERXu469A5+f42g3XgA+eWXXypDbiSHMcaKcG3Hw1ccLoZ1Cn6nzXr1BkMbSfUDTyOnnnqq+p2HkXLMmDFBDfaY5xFbGw95dZx1I/gdNT74Q5/goDns8NBjIhKsrscaAg8fsa7B7yt+Z41jGuVgPsLcHcxAit9CrI1g1DczjxNCCIkMbuEnhJAogBhZ+rRp3HBoj9RA8DkW87jBg5eK8dABDYxIWEzDSxVePbiBxeIcN8y48YcHn97+hQM5cOACvPkQGxTeSzCuwAiF7WuoF7wW4ZXXEDA4wdCC9AgH0NAWM1wP7cCNDeoMQxcW9vD8gzER3oKoL2KbGbe/QT64YYfRDHlxUw+DKYw/MKrCkAIvVHD88cerOKe4mYOnB2SGmxhsMcV2cSvtswu2uaKd8OKCsRk3hehztBPXh0EJW0jNxCGFtxQ839BmtAsGQZQB71PINdRpzDCwYCspPA3hzQLjB2SBG8v77ruv3snCwdBbe+HRhxiQaAfi2sGDGTrw0ksvKc88GEpRHm7acAOJ9qFfzMQXtErgtdHPkC3aCiMMPKCwzVRv70d70Re44YYeQRaQDcYIjKiIpQpjh9vAqAfjHsYfbrTRjzDewqiLm2MYfbRB0kng0QUvJsgA88TZZ5+t+hHecjCgwhCJeQL9h7kEcoR8cBBNQ8DgAEMHDGjQU8wpkD9ki3EN43+4Ld12QCxnHX8T4wJtgBFEyxN9jZjKkQJDJcYcQD8FO5AIwIBhx1AbDHjQY4zCgIuTz2EExXXw8Av9AxnDqGI0TONhCR5AoS/QJzB8Yjxgfkd/BxqkMNciDwwxkBViRsPzE9eA4RS/PfAq1FvpEQPz008/VXMIfkdQPsaY/v2A4RC6FatxgLkVsbbxIGT48OFh0+K3B/WFAQpxPREz0yyY67BFHPqB3yrMR3ouxe8pfoMQIxTewJhbtFxhLIbs8DuHBzo4jMst71N4C0NHwqHjSlshVNthhNe/ezBQ4hUMzNfhdiUAPHBBCB7MIYHzMq4FQy08nmEExcNJyBfjAA/vMDagi1gXoE7BvM/xW3HZZZep+QLb5GGMxQMYrLfCxXY1g5X1WEMgH35DMRZefvllVSb0BWsZlIf2Qr+CPWyAHPDADG1y88EgIYQ0dmhAJYSQKAFvUngRYfEfypCFmywswGEoC+WlihsWeI7iAA/cOONmADe1uNmG1xRORoYBSYMFOU4ix2IcN6y4SYaBBTfHWKjDCImbUBglwp1cDW8UeBpi8Y7ysJUsXMw3gJse3DCjvrg2PCtwUwkjKrYywgCDm7NADxCUCyMhYjPCYwg33tieiRtt3AhpDwzcpE6ePFkZZyEzpIMMccOGtsHrEuUjTioME26D9sD4gHrj5h4GX9y448YTBhEYUBs6aRjgZhAeYWgX2o8tiOgzeC+hPaEMqCgbRkYYYmBAww01Yl2iHxrqKw1u2GAEhlEAN4QwdOiTq2HAgvEDHsXQF3jsoQ9QPgwpbsb1w7VhCMWWUWyNhGzh2QP9xU1wYExH3GgiPiNeMPJCf2FAwfjAOImGZyHGKow16HuMVfQl+gTGLYQcCBXTzy4w4EPXsD0aegDPdlwTBlLIAPqD/sMNOfQV3tPoYzMnoMNAjjkFMREx9mAgApD/eeedpwys2gPWaaBf8MCGjsOwBy9KXAtzCYzD4eK3msEYNgQeg6GAUcRpAyqAURhefjAmwaMR8Wlh8IGO4wGC8UBBbRCDVz50HPqFeQ59qGNXB/Pow/wNLzcYaHBQIQynMDBBF2G8wu+TBrLFXI/y4eWHOsHIgzEPfUF6M8Yat8aBPjwK/W5G5+CFCiMmZIzfESvAexa/L3hIACMztuxrMIaglzAifv755/6wJJhzMC5g3IV+hosPbRcdQiUcOq60VQLbDu9NHS4FD+lCxbMG+C1uyIAKIy10G/MVPKoDf0fwmwS5QsbQHYwLrFkgX7QJ8w7kGyr2MeZDPDhDfqxB0Cd4OIixEOnBipGuxxoC+aBHMBrDWIzfYKwHsDaDlzoeEAX77dKhP2CwJoQQ4h5JPqeOBSaEEEIaMfr0b9zQBTvoCDc+uKnGjaDTMcrg2QxDNW4gQ8WOJYQQQrwIHvDiISce8uiQLXaBJzceFsHjOpENiwipASM9DM8wZruxC4QQQkgtnGEJIYQQB0AYAXh7YrsethUbgZcYjKcIRcADHgghhJDtYEcLfh+x8yLw95OEBw9l4QkMr2oaTwkhxF24hZ8QQghxABhPsXUfWynhDYItd9hqhxisiDGIcALYWk8IIYSQ7cDwh3iiOAQK4R6CHQZF6oMYwgipgjUHjNCEEELchY+pCCGEEAdA/FHE1rv88stVbEIc+oD4k4iUg/hsOv4tIYQQQuqCWMaIV4oDAXEgEmkYxBXGYVVuHdxHCCGkLoyBSgghhBBCCCGEEEIIISGgByohhBBCCCGEEEIIIYSEgAZUQgghhBBCCCGEEEIICQENqIQQQgghhBBCCCGEEBICGlAJIYQQQgghhBBCCCEkBDSgEkIIIYQQQgghhBBCSAhoQCWEEEIIIYQQQgghhJAQ0IBKCCGEEEIIIYQQQgghIaABlRBCCCGEEEIIIYQQQkJAAyohhBBCCCGEEEIIIYSEgAZUQgghhBBCCCGEEEIICQENqIQQQgghhBBCCCGEEBICGlAJIYQQQgghhBBCCCEkBDSgEkIIIYQQQgghhBBCSAhoQCWEEEIIIYQQQgghhJAQ0IBKCCGEEEIIIYQQQgghIaABlRBCCCGEEEIIIYQQQkJAAyohhBBCCCGEEEIIIYSEgAZUQgghhBBCCCGEEEIICQENqIQQQgghhBBCCCGEEBICGlAJIYQQQgghhBBCCCEkBDSgEkIIIYQQQgghhBBCSAhoQCWEEEIIIYQQQgghhJAQ0IBKCCGEEEIIIYQQQgghIaABlRBCCCGEEEIIIYQQQkJAAyohhBBCCCGEEEIIIYSEgAZUQgghhBBCCCGEEEIICQENqIQQQgghhBBCCCGEEBICGlAJIYQQQgghhBBCCCEkBDSgEkIIIYQQQgghhBBCSAhoQCWEEEIIIYQQQgghhJAQ0IBKCCGEEEIIIYQQQgghIaABlRBCCCGEEEIIIYQQQkJAAyohhBBCCCGEEEIIIYSEgAZUQgghhBBCCCGEEEIICQENqIQQQgghhBBCCCGEEBICGlAJIYQQQgghhBBCCCEkBDSgEkIIIYQQQgghhBBCSAhoQCWEEEIIIYQQQgghhJAQ0IBKCCGEEEIIIYQQQgghIaABlRBCCCGEEEIIIYQQQkJAAyohhBBCCCGEEEIIIYSEgAZUQgghhBBCCCGEEEIICQENqIQQQgghhBBCCCGEEBICGlAJIYQQQgghhBBCCCEkBDSgEkIIIYQQQgghhBBCSAhoQCWEEEIIIYQQQgghhJAQ0IBKCCGEEEIIIYQQQgghIaABlRBCCCGEEEIIIYQQQkJAAyohhBBCCCGEEEIIIYSEgAZUQgghhBBCCCGEEEIICQENqIQQQgghhBBCCCGEEBICGlAJIYQQQgghhBBCCCEkBDSgEkIIIYQQQgghhBBCSAhoQCWEEEIIIYQQQgghhJAQ0IBKCCGEEEIIIYQQQgghIaABlRBCCCGEEEIIIYQQQkJAAyohhBBCCCGEEEIIIYSEgAZUQgghhBBCCCGEEEIICQENqIQQQgghhBBCCCGEEBICGlAJIYQQQgghhBBCCCEkBDSgEkIIIYQQQgghhBBCSAhoQCWEEEIIIYQQQgghhJAQ0IBKCCGEEEIIIYQQQgghIaABlRBCCCGEEEIIIYQQQkJAAyohhBBCCCGEEEIIIYSEgAZUQgghhBBCCCGEEEIICQENqIQQQgghhBBCCCGEEBICGlAJIYQQQgghhBBCCCEkBDSgEkIIIYQQQgghhBBCSAhoQCWEEEIIIYQQQgghhJAQ0IBKCCGEEEIIIYQQQgghIaABlRBCCCGEEEIIIYQQQkJAAyohhBBCCCGEEEIIIYSEgAZUQgghhBBCCCGEEEIICQENqIQQQgghhBBCCCGEEBICGlAJIYQQQgghhBBCCCEkBDSgEkIi5rrrrpO+ffvK1KlT6323detWGThwoPr+iSeeCJr/uOOOkwEDBsiWLVtcqyOuf/LJJ0ecv6KiQl5++WU54YQTZNiwYapNw4cPl7Fjx8q0adPEbX777TfVhkceecT/2QEHHCD77ruvxJo77rhDLrnkEv/7008/XdU18NW/f3/ZddddlQxff/11qampiVodly1bVud9LGU3d+5c2XnnnWXVqlUxuT4hhBBCog/WwcHWRzvuuKPsvffecumll8r06dM9UcdffvlFvcdaBe+vueaaiMozu95CumCyCXxhDaUpKSmRq6++Wq3Ld9ppJ7nrrruksrJS7rzzTtl9993VWh0yDWyTWd59912Vb/LkyRG1nRCSuKTGugKEkPhljz32kA8++EBmzJihFiyBhj8YH9PS0mTKlCly+eWX1/keix8shrDIad68uXgRGIHPOuss+eeff+Tggw+WQw89VJo2baoWlVhcffLJJ3LjjTeqNNHkpptuEp/PJ7Hk999/VwvLjz/+uN53kEnLli3976urq5XM3nrrLWV0Xbt2rTJAu81//vMf+fnnn+Wbb77xhOxgSIYejRs3Tl555RVJSkqKST0IIYQQEn1OPPFE9SBVU1VVJWvWrFEPl7///nt55plnZJ999pHGyP333x/2+7y8PP//Tz75pFp/Yk0FA3Tv3r3lzTfflAkTJqj7kcMPP1ylb926tXTp0kV9b4VddtlF1WfIkCERt4cQkpjQgEoIiZg999xT/Q321BxG0/T0dLWIef/996WoqKiOUQ15YFjba6+9xKu88cYbMnPmTLnvvvvk6KOPrvPdeeedJ8cee6w88MADMmLECOnYsWPU6nXQQQdJLIEHKYyTkEnXrl2D1q9Tp071Pj/ttNOUEfqll15SRudWrVq5Wk/cjKSmpnpKdniQgDrgwUOgThFCCCEkcRk8eLAcddRR9T7ff//91a4sGO0aqwE1mFxCob1R4XmanZ2t/sdDev0Qv1+/fv60xv/N0rlzZ/UihJBAuIWfEBIxubm50qNHD/nrr7/qefXBgIqF4oEHHqgMbvAENPLnn3+qv142oOo6YmEbCLxmTznlFGUEjvW2q2jzxRdfyNKlS+XUU0+1lA8G05EjR6ptVtCZxkj79u3VmHj++edj7kVMCCGEkNizww47KC/JBQsWyObNm2NdHc+DHW5AG0+Nn2VlZcWsXoSQxIcGVEKI7W38iGG6cOFC/2fLly+XFStWqKfo2EoDL0AYVI388ccfauGD2EUabO2+5ZZbVMwkxIWC4RJPl+G9GsyId84558huu+2mFp74e9FFF8nff/8dtr7YLjVmzBgV2+jRRx8Nm7ZZs2bqL7YEBTN2nXHGGWp7/xFHHFHnc3yGa0A22P6Dp+rwZjWWgYXeCy+8oDwOkAbtRWzVm2++WTZs2GAprpSO8YQ+gGcojNIIjXDkkUcq799AFi9erOqHvsG1zz//fPUZ4tHecMMN0hCICdunT5+InuojBILZOLWI+4rvEA7CGI/rueeek0mTJinvZrQT27cQGgBhIYzpoE+rV6+uE4c3nOzQdugRDP+I54rPoHvY9o/PEWsLnscwHgfy0UcfqRivyAuZwrj87bffBpUB6r1o0SL54YcfLMuPEEIIIYlHcnLtbTkezGt+/fVXufjii9WOL6x1sbX8zDPPrBfTE2ubc889V4UAwFpl6NCh8tprr6nvCgsLZfz48TJq1CgZNGiQemE30FNPPaXWxFaxslZ361wA7bhgjJGKdRjAQ2q8x1owVAzUzz//XO2KQjgFxOjHms+YJlQM1FmzZql7DeTB+hPrOeyqMvaZ2bWqBvcGSKfvB3DvcOGFF8rs2bP9IbNQ3m233VZPHuvXr1d6ce211zoiX0JIw3ALPyHEFljUIXYTFjMwqoGffvpJ/YUBFU+CYSTFZ1gkIO5jWVmZWhjst99+/i3WK1euVEY0GBYRIwpb4ufNm6diGv3444/qr97yjfiR99xzjzJqXXbZZSrOKgynMBZiofH1118H3R6OhSJib8L4iuDzF1xwQdi2oT6fffaZPP7442oxhUUiFq9YcLVp06be9nCABRjKRbtPOukk5aULQ9rtt9+ujHl6kXPFFVfId999p8IAwPBWXl6u2onFGgx3WExZBQuudu3aqb+Q4//+9z+5/vrr1Wc63AIMpagXZIEFI9qBhSS8ac0c7oSFOMIanH322Zbrh2vCaJiSkqLigUYKdKG0tFTVuUOHDqo/oYMw5D/44IOq77ENDjcMuCHBdi4sPsMBI3LPnj3lqquuUgbSV199VS2SYfBFHC30Fw6kwg0JtuF/+OGH/psdhHGAMRyGa+gX+hLxcXHTEyxGLhbHyPvVV18pozkhhBBCGi9YH2J9hrWvXr9ibYO1Bx5uY12Jh/pYH2KdiDUL1rzG2J5Yh+N7PCDftGmTWmsUFxerNTXWR1gzIR4ovkNerG2xHsd62CxW1upW2bhxY8jvEBIM62qs07C+++9//6vWZPgfa1esqSZOnKjOZNBx+EPVA4ZjtB3rQqzzMjIyVL3xgBzfBdt1BhBPH/2BEFVIi/UhdtchzBdkD2OtMbZ9Q2tVDeLiv/POO8rwjfKxSwuOG1ijY82J+w70G+5H8EAfstDAaIy1NYyvhJDoQAMqIcQWMGLCIIbFAwxzAN6mMMxpD0UYlnBi/Zw5c9STUmzfxgLBuH0fT2W3bdsm7733nlooaLDlG8Y6LHbw9BVPefGEHQY4eELi2sZt9S+++KIyoiKwvBEsMLBI/PLLL9ViBd6jDYHFDBZTt956q3qijIWMfqIPY/ExxxyjnmAbFzNIi0UuYlzCeAogFxjRYNCEhwCMkDCqIi+e4mtQp9GjRyv5YCFpdRHavXt3ZcjTCzh4GWABhoWZNqBisYkFNWJF4XsAb0ksIrH4bYipU6eqv+G8T7E4NC6E0ddYdKNuS5YsUd4TWExGyrp16+TTTz/1x6c6/vjjlWcFDME4gRWLWnj9PvTQQ8rIbSauFhblRtnBgxp9hHil0AFNQUGBWgRDH6Cn8EZAPiyQ0fca9Df6Gotk6CK27mtwE4C6w7OEEEIIIY0DHE5qXB/BEAkDJHbcYK2Ek+M1WOtiLQ1jmnH3DmLPY82MNZvRgIqyYViE4VSDh8H5+flqDY11kgbGT6wL8SDfigHV7Fo9Eoz1DgRepWgb5IE1HYyTMKAa13e494ABNVQcfoC1KNZ0MErCc1Sv37FjC/J57LHHghpQ0WbcO2Dtj2vrfFjHYzfb008/rQyc8Ow1u1Zt0qSJ2o2HNTq8VLFe1GtQpDvkkEOUDqC+MJBCR+CEgHMXNOgHGLFxL0YIiQ40oBJCbIFt+NjCo7fTYDGILTb4gdcLAXiiYlEF70wYUHVsUWxnAYj3BA9VbAeCccm4uIShDosPeOthUQaDKRaNWMwYjadYOMITFQRuj8HT6WuuuUYtWuABasZ4qsFCCvWEsQt1xGIHi13EqcJTZzz9hWEUxlsYiGF4w4JKG08B5HDvvfeqOiIdDKMwKGsPRg227ut4TnhqbdWAilACxqff6Be9xQfAcIo2wHCtjacAcoS3pBkDKsIzAOPCORAYloMBT1h4aOLJvR2wxckY3B9yhEEdsodnBRalVsGi1yg7GFRhQMUC1og+NAtb2CADnAKr8wd6T+AzjAXcoASGKEBe9AUM+8E8mQkhhBCSWMBwhlcgbdu2VQ9hjZ6E8DTFA2mj8RRrbL12DFzrYg0Mw6ARrHcPO+ywOoe4AqxXsN4MLCMcVtbqkQCniFA4degovEjhiAHnAqPzQ4sWLZR3aLAwUwCepghRACNxoMyw1oMBFW03GlDNrFXxQB6gXOMaFGvEt99+W1q3bu1fV+M+Cs4Z2oCKg7Tmz5+vdkUZ8xJC3IV3bYQQ2+CpMZ6S4mkrYjvCUKiNo9qQh8UJjEnYdgQjJBYV2ggHoxyMnDg1PdwTaGyNxlYbLHpggMTTXmy3xtanNWvW+GOMBsYrhUcnPAUBrm3VgIdFKRaMOnYmFpHwZMVTYRhN8RdbhvBkG+BgrUACPS7RBjyZxqIM7YdHIwyoehFkZjt9IHgyH3gNY1lYtMFgB0/VQHr16mXqGnrBbAzcHwi2tKMuuC62k+EpP/oERmQnDg3DjUYguq3GOFR2ZKeN84Gf6xsXLVMdDxVG81BAPwOBIR0ygTxhWCaEEEJIYoOdKcb1MdYuWNNgPRxoBMPDVXiPwvMS2/uxlsBaUa8/AteJWGcHeyCLdQvWYQi/hHUg1qp4SA+MD/sbwupa3Sp6p5SbhFunh1sH67Xeww8/rF5m1npm1qroz1D1MYa6Qj9BbyB7GF/R1/A+hc4cffTRIetNCHEeGlAJIY4ZULE4w/YZ/KAbF4hYvCENtp5gYYV0xm03ehGIbTfhTnbXRi1sN4LnHxY7OLQHwfPx9BsLHMQaDQQLSmxdx/Xx9BZPdbGVJhzwMMR2fcSeMj5RBjk5OWqrPQ5hwrYlvRXbbDB+GGCx5RsLYoQJgDconi4jyDy8WRFfMxICPVoDwfYwoD11jWRmZlq6RjhDJQ4v0NunoAfYwo44r4jhhe1R6GczhLqGG0/ag8nEzLW07j755JP+Q8cCCRauQLfN6EVNCCGEkMQF61azhkKEIcJBRFhPYa2IdTTidmKteckll5haA8J5AAeuIg/WrNgRhm3/WKfBC9PKw3qra3UvotfBVteR2jED5y7gHIRgBK4BzVzDSn3gnYz7GDhfYE2N+yBs3Q8VroAQ4g40oBJCbIOFGAxwOBgK8UdhdAzcbgPPQ3iM4nAdbL83Glj1jz+C2QdbWOJQKP1kHdv/sWjA1mrEAzIuOuBpGgwYKLGFCXWAx6f2hGwoDufzzz+vvGcDDagaeNFiwaS3jOt2BDulHUZWxDnCNh0sgOCp+5///KfeIlRvt3cDbD+HvILVD7FJzaCfqFs5bRVyhuEUXprXXXedOpCrW7dudRbb2JYWiJuycArd55ALjPmB3hqQa7AtYfA8hT47tS2NEEIIIYkBPE+xBsX6Gg/WjdvNrTxkh7cktpwjjz7oVRvusI6DQ4BZrKzVvYpxnR7ocYp4sdgSjx1lofLBszaw7ZAvQhsE8zi1Uh/cOxlBbFWE3rr55pvV2h3OIgjFgHBk2EmGXWs4KJYQEl3CuysRQogJsLDDE1ls0ceWdjzhDkR/huDrWFzhSbgG26SRH8ZNbLE3AmMjgurjKTzA1hWAJ+hG4ykMUvAsDecJioUdjJZYkCAYfDiwXQYnpP/999/qkKBgwJsVZekDqxDfFcZCGIkDjX/YPoXPscDSxsfAk+Hhvavbb9ab1QpYeMGDAQs9bK03PllH/aws9rC4twJuAuAxgW1jMKIavUuxhR2HARjjSkFG6Hs7wDAbSSgEK+i+x+mrxj7DzQkW4TicC97MgUB+eXl5jFtFCCGEkHo7lbA2g6HMaDyFA4I+zNRMyCKspeDgoOO3a1AGDKFWwh5ZWat7FXjPYt2Fg7mMazbIG3XHOhzxXQOB0wccJmBkDYx3jx14V1xxRURrVh3PFOUGhhpATFiEXNDrROyUwu49OJLAIQP1xC44Qkh08e4jIkJIXAHDHE6QBEbvUg1OIcfBPNi+j8DqgTE0ETwfHorw0MTpoHhSDu89GFxh+NRPWWGIw3s8mcfiD3GjEEMIiwkYM4H+GwycbIkXArdPnDix3uE+RsaPH69OjEdMT2yZwSmgMKzC0IfT6BGLaL/99vPHv4RhGCEEsIhETKKTTjpJeRgiaD2MlldeeaUyFuIpMhavMCRiKz/iYcJQi3hGMPrB+BauDXaAUQ/1wgv1hkEX9dOHgDVk0IPnLuqI9MbDDswAYyIOVIIOPPvss/4taJAVAvBD1gitgLaj32HwDVyoWgHB92HQh3EYehPoIeqU3qPOMN5jSxU8nXGzA28PbJ1D/xoP7AIwqEJnw8VNJYQQQkjjBN6RMHq+//77ahcLHrgXFhaqdSLOGwA4YKohsG5FnH6srXHSO4yyODAU61cYVrGexWdmH+aaXatHApwSwoF7CH04aqQg1ijWolhzov44fBXtf+utt5SDBsImBAPrdDhgYA195JFHqrxYz+NeAPcHWOdhvWcV3C+hPPRrQUGB6i+cI/HGG2+oe4obbrihTnqsu1955RV1gC3WnJEcmkoIsQcNqIQQR9BbWvCENpShCgsFxP0MZmDF4hBbuxEsH8bNSZMmKeMePPxgaNNPz2GQhEEM25Kw4MHWbxg1YRTFgg7pp0yZouJthgKLIHjLIi4q6mI8JTPwaTsWNVgY4nRNnNCJBSvaiEUjDKzHHntsnYUnDKpIh3ZgkYOn+1j0YVGGxas2uqH+MAJjYQuDG7wRYWDFohl1RxvcMPih3liY4fow4mLhiBhKCIdw8cUXh4wFqoFRE8ZIyM8qOhYt4r1CPvDwxZYlGJwhQyyeIVPIArG50K9XXXVVxG0dO3asWuyjrViguiFPcNddd6myobPwRIWBGSEK8HmwWLtadlgoE0IIIYQYwVoMu5/gmAAD3eTJk5XBDrFQsWZC+Cd4gjZk/MT6GWsSGGLvuecetWUfXq1YeyLsFrwnEXoL60AzmF2rRwKcCsJxxhln2DagAqy1YUjFGhhrXxghUe59991X74G3ETzsxy4z9As8RnGmA9arWDvjcLBg4ZrMgOviDAT0MdbI6CN4+sKrNfBwKazhUUc8oMf9ByEk+iT5Ao+rJoQQkrDAcwGG4cAFNzxK4Y2LAPmXX3552DKwmIdhE1ugdtllF5drnHjA0A8vVMTybejgL0IIIYQQQgA8T+E5jLU4IST68M6NEEIaEXiCD0+BwLhX+lACM16ayA9PWXgeEGsgnhUOFIOnBo2nhBBCCCHEDHB2QBiscOHHCCHuQg9UQghpRGDrEWK6wnP0kEMOUUY8bCn/7LPPZP/991dxoczEwsLWMYQawLb7wJNMSWiuueYaWb16tQrzQAMqIYQQQggJB0ImIAQaYtfiDAms2Rn/lJDYQAMqIYQ0MmD0xAFaCPyPGLKIAYuTPc866ywVp9QsiC+6Zs0az5+66hX++ecfFdsVschw+BkhhBBCCCHhQHz9F198UcWvRSzbfv36xbpKhDRaaEAlhBBCCCGEEEIIIYSQEHD/ICGEEEIIIYQQQgghhISABlRCCCGEEEIIIYQQQggJAQ2ohBBCCCGEEEIIIYQQEgLzp4WQiJk9e7ZUVlaqE5czMjJiXR1CCCGEENcoLy+XmpoaSUtLk4EDB8a6OiQCuHYlhBBCSGOh3OTalQbUKIAFKM7qqq6ulq1bt8a6OoQQQgghUVn/kPiEa1dCCCGENDYqG1i70oAaBfD0HgvQpKQkZdFOTbUm9qqqKkt5zKR3Kk0i4JV2ul0PJ8u3U1Ykea3kMZuWY8B77YyXMWC3HDfmdKvpqf/ea2e86L+ZsrZt26aMb1j/kPhfuzZp0kQaM16ZI+IVys8+lKE9KD97UH72oPziQ4Zm167sySiArU94eo8FKAyovXr1spR/0aJFlvKYSe9UmkTAK+10ux5Olm+nrEjyWsljNi3HgPfaGS9jwG45bszpVtNT/73XznjRfzNlzZ07V617uPU7Mdau/fv3l8aMV+aIeIXysw9laA/Kzx6Unz0ov/iQodm1K10DCCGEEEIIIYQQQgghJARJPvipkqhYs5s2bSp9+/a1vKUNwWyt5DGT3qk0iYBX2ul2PZws305ZkeS1ksdsWo4B77UzXsaA3XLcmNOtpqf+e6+d8aL/Zsoyrnsau/divMI+9N4cEa9QfvahDO1B+dmD8rMH5RcfMjS77mFPRpmVK1eG/R72bCiI8bVixYp6n4V7mUnvVJpEeAW2M1bPFBrSDS+Vb6esSPJayWM2rZl0bveJV/BKO+NlDNgtx2p+N9JT/73XznjRf6fLIsTrUN/tQfnZhzK0B+VnD8rPHo3FplITRXuNlZfTth3GQPXIqV5lZWWSn58vFRUV9Tq5vLxcBbU1i5n0TqVJBALbiQMT0tPTJS8vTzIzMxPmtGIny7dTViR5reQxm9ZMusZygrRX2hkvY8BuOVbzu5Ge+u+9dsaL/jtdFiFeh/puD8rPPpShPSg/e1B+1sEhjOvWrZPi4mIpLS1tFDYVN7Fjl3LatkMP1CgDl+BgxlNY1aEYwSzk6HArmEnvVJpEILCd6AP0BfoEfRNL3fBq+XbKiiSvlTxm05pJ53afeAWvtDNexoDdcqzmdyM99d977YwX/Xe6LEK8DvXdHpSffShDe1B+9qD8rIFt4IsXL5aioiJ1ejwOESf2sGOXctq2Qw/UKNOmTZt6n8HzFE8pYBHv2LGjpKbW7Ra4HjP+nXsEthMT3erVq/1ewT169IiZbni1fDtlRZLXSh6zac2kc7tPvIJX2hkvY8BuOVbzu5Ge+u+9dsaL/jtdFiFeh/puD8rPPpShPSg/e1B+1li/fr2y7eA093bt2ikDKo2o9rBjl3LatpP41jGPAct3oEUc2/YBjKewrkM5jC90euBn4V5m0juVJhFege1EH6AvQLCQCtHSDS+Xb6esSPJayWM2rZl0bveJV/BKO+NlDNgtx2p+N9JT/73XznjRf6fLIsTrUN/tQfnZhzK0B+VnD8rPPLAbYMs+gD0hKytLfRZre0e8v6ps2KWctu3QAzXGoAN1JwZ6nppl45Yy9dJUlFdIekZo9+RWzTOlaePYnR8xui90/yB2BiGEEEIIIYQQQkggRuMcvU4T07ZDA2qUadu2reU8DRlWP/91mUz8cr7p8k4e2VdOOLCX7esmCl5pZyS6Eavy7ZQVSV4recymNZPO7T7xCl5pZ7yMAbvlWM3vRnrqv/faGS/673RZhHgd6rs9KD/7UIb2oPzsQfklhq0hnkn1kAy9U5NGAuJhOM3Be3STXXdoX+cad770u2wuqZCcrHS57fw96nmgksahG26Vb6esSPJayWM2rZl0bveJV/BKO+NlDNgtx2p+N9JT/73XznjRf6fLIsTrUN/tQfnZhzK0B+VnD8qPkO0wBmqU2bhxo+U8iPkQDhhEe3Vq4X91zW0mKcm1bsn4a/wOL6RvqEwz100UvNLOSHQjVuXbKSuSvFbymE1rJp3bfeIVvNLOeBkDdsuxmt+N9NR/77UzXvTf6bII8TrUd3tQfvahDO1B+dmD8vOGraGmxidl5VXqb2OjyiP2GkADaoKxNH+zPPX2bNm4pVy9x99HJ05Xn0dbyf/3v//JscceK0OGDJHdd99dzjnnHJk6darldCtXrpShQ4fKddddV+86f//9twwcOFDeeOONqLSLEEIIIYQQQgghxG1gx4E95/gbP5bRN32i/kbbvkPbznaSfNE6YrwRM3fuXNm6das0bdpU+vTpIykpKf7vampqZP782vilffv2VSeFBWI20O0P01fJw29MF0mqfUKhgRcqennsKUNlv6GdTJcZaYDd8vJyOfvss2XNmjUyZswYNXjKysrknXfekQkTJsj9998vRxxxhOl04N1335Ubb7xRHnnkETn00EPVZ8XFxXLMMcfIDjvsII899pjleoZrp5l+cWN7hFE3vFy+nbIiyWslj9m0ZtK53SdewSvtjJcxYLccq/ndSE/9914740X/zZRlXPf079/fkWuS6MI+9N4cEa9QfvahDO1B+dmD8jNPMBuCnUOLtH0H2asbsO+4hRdsOz6bBz+Zse2YXffQAzXK5OfnW85TWVnZYBo8gcDgqvH56rl1Y7Dhc3yvn1SYKdNMmmBA4aGgeHKAQdCtWzfp16+fjBs3To4++mi56667pLS01HQ6gKcYBx98sNx2221SUFCgPrvpppvUX6SzQ6Tt9IJuxKp8O2VFktdKHrNpzaRzu0+8glfaGS9jwG45VvO7kZ767712Ol2PjVvKZNGqTf7X1L8W1Xkf+EL6WNWVEC9DfbcH5WcfytAelJ89KD+Jia3BaN8xGk9D2Xfcwgu2nUqP2GsAD5GKMrDMR2Ixb4gPflisnkxIGH9ifP/Bj4vlypOGmirTTJpgyo2nDBgUHTp0qPf9lVdeKSeffLKkpaWZSpeZuf3AqzvuuEOOPPJINQhHjRol3333nRqg2dnZYodI2ukV3YhV+XbKiiSvlTxm05pJ53afeAWvtDNexoDdcqzmdyM99d977XS6Hp//ukwmfln7tH0780KmP3lkXzllVL+4khkh0YD6bg/Kzz6UoT0oP3tQfhITW4NV+44beMW2U+MRew2gATXKGJWmIX6auVpe/3yebC2rDOuyDJdmHfM0HHhS8c0fK2XG/EL1viE36Mz0FDn9kAGy1055puuMmBabNm1ScS2CkZubq15Lliwxlc5ITk6O3Hfffco1HHE0rrnmGhk0aJDYJRrb853WjViXb6esSPJayWM2rZl0bveJV/BKO+NlDNgtx2p+N9JT/73XTqfrcfAe3WTXHdr73xeuLZT/frhYNpdUSE5Wutx2/h510uOAyVjVlRAvQ323B+VnH8rQHpSfPSg/52wN2r6zrTz8oUiR2Hcasu00yUiV0w7uH5e2nWSP2GsADahRJlBxwvHud4tkVWGJ43UwMxj9dfh+oaVBtnnzZv+AcCJdIDvttJO0a9dO1q5dq4ISO0Fqamrc6Uasy7dTViR5reQxm9ZMOrf7xCt4pZ3xMgbslmM1vxvpqf/ea6fT9YBB1GgU7ZrbTFI/War+T01Jll6dWsS9zAiJBtR3e1B+9qEM7UH52YPyc87WEGv7TrzadlI9Yq8B3jHlNhKWL19uOu1x+/eWTu2ypFXzDGmdkxnyhe+tgPQNlYlXx7bN5Njhva2V3aqV+osnEE6kC+TOO+9Up7v17t1bPaVAYGK7VFRUSLzpRqzLt1NWJHmt5DGb1kw6t/vEK3ilnfEyBuyWYzW/G+mp/95rZ7zov9NlEeJ1qO/2oPzsQxnag/KzB+XnnK1B23cassNEYt9pqExcN15tOxUesdcA75hyST3wdAAvKFJDrvOPTpwu309fVS/AsBGc1jZ8504qRoaZMs2kCaRz587Spk0bmT59uv9ENSOLFy+W8ePHq1PXzKbDgAIfffSRiq3x1FNPSadOneT4449Xbt+33nqrpToSQgghhBBCCCGERNu+Ywar9h03oG2nPvRAjTJQLKuYcVk+ar+e4gsTYBjg+6P27Wm6zEhcpRGfAsr/7rvvypo1a+p9/8ILL8js2bOlY8eOptPpJ18YTCeddJIcdNBB6kS3K664QgUa/v7778UOXnEJj0Q3YlW+nbIiyWslj9m0ZtK53SdewSvtjJcxYLccq/ndSE/9914740X/nS6LEK9DfbcH5WcfytAelJ89KD+Jia3Bqn3HDbxi20n1iL0G0ICaIHTPy5GxpwyV5KQk9STCCN7jc3yPdG5z0UUXSbdu3eSUU06R999/X1asWCGzZs1STxzwHq7aTZs2NZ0OLttXXXWVOtEN32nOPfdc2WWXXdRn69evd71dhBBCCCGEEEIIIY3BvkPbTl1oQI0ykSgD4kKYYb+hneTRsfvJPoM71Pkcbt34HN9bKdPsdQNp0qSJTJgwQY477jh5/vnn5aijjpILL7xQCgsL5bXXXpODDz7YUrr7779fFi5cKA899FCdkAJ4InLvvfeqQXjDDTeo0+oiIdJ2Oo3bE4WT5dspK5K8VvKYTWsmnZcnbyfxSjvjZQzYLcdqfjfSU/+918540X+nyyLE61Df7UH52YcytAflZw/Kzx52bA3avgN7TlpqrekOf4PZd9zCC7adKo/Ya4B3fGGJI+AJxKXHDZRZizao09gQUNitmBjhwNOFyy67TL3sprv55pvVKxiIlzFt2jTb9SWEEEIIIYQQQgjxkn0H9pwxJwyRispqyUhPkaSkuh6pbkPbznbogRplunbtajlPenp62O83bimTRas2+V8rC7f6gw3jr/E7vJC+oTLNXDdR8Eo7I9GNWJVvp6xI8lrJYzatmXRu94lX8Eo742UM2C3Han430lP/vdfOeNF/p8tKJN566y3p27evTJw4MWSa8vJyeeaZZ+Twww+XgQMHqu1qp512mnzyySdhy440H7EP9d0elJ99KEN7UH72oPy8YWtITk6SzIzUqBtPvUC6R+w1gAbUKLN27VrLeRpyWf7812Vy1SM/+F9jH5sim0sq1Hf4a/wOL6R3cwt/vOGVdkaiG7Eq305ZkeS1ksdsWjPp3O4Tr+CVdsbLGLBbjtX8bqSn/nuvnfGi/06XlSggzhe2noWjrKxMzj77bHnkkUdkyZIl0rNnT8nJyZE//vhDxo4dK+PGjXM0H3EG6rs9KD/7UIb2oPzsQfklhq0hnqnykAy5hT/KYBFslZqamrDfH7xHN9l1h/b+9xXlFZKeEdpK36p5ZoNlmrluouCVdkaiG7Eq305ZkeS1ksdsWjPp3O4Tr+CVdsbLGLBbjtX8bqSn/nuvnfGi/06XlQj8+uuvMmbMGCktLQ2b7q677lJb03r16qW8STt37qw+x4mzV155pbz99tsyePBgGT16tCP5iDNQ3+1B+dmHMrQH5WcPyi8xbA3xTI2HZEgDapTJyMiwnAcBdcMBgyheGgTebcjNGWnsXjdR8Eo7I9GNWJVvp6xI8lrJYzatmXRu94lX8Eo742UM2C3Han430lP/vdfOeNF/p8uKZ7Zu3aoMmjgsoaHF/apVq+S9995TW+9wcII2goLhw4erAxNuvfVWeeKJJ9QBDHptEmk+r1K1eZ1Uby1uMF1K02xJzWkrXoD6nrjyq6nxqZiC6WkpanusV/GyDOMBys8elJ89vP67HA8ke0iGNKBGmby8PMt50tLSHE/vVJpEwCvtjEQ3YlW+nbIiyWslj9m0ZtK53SdewSvtjJcxYLccq/ndSE/9b6z6P8fBsho38+bNk/POO0/WrVsnqampyhN00qRJsnr16qDpP/jgA7UFbdCgQdKvX7963x977LFyzz33qK2Sv//+u+y+++628nnVeLry6cvFV13ZYNqklDTpfPETnjCiUt8jZ1bBXHl59ltydtoJMqh9f/EKS/M3ywc/LJYf/1otlVU16lTrfQd3lKP266kObPEa1EF7UH72oPwSw9YQz6R5SIbeMeU2EpYuXWo5Dw4OcDq9U2kSAa+0MxLdiFX5dsqKJK+VPGbTmknndp94Ba+0M17GgN1yrOZ3Iz3133vtjBf9d7qseAWeoTCeDhs2TG2hv/DCC8OmnzFjhvqL9MHAziEcDgVgCLWbz4vA89SM8RQgnRlP1WhAfY8Mn88nE2d9IKuLC9RfvPcCP0xfJVc+/IN8P32VMp4C/P3+38/xvdegDtqD8rMH5ZcYtoZ4ptxDMqQHagJQVVwk1SVF/vcVFeWSlB7a1T4lq6VIWpMo1Y4QQgghJLHo0qWLvPzyy7LnnnuaSr9s2TL117gFP5BOnTqpg6F0Wjv5CIk1MwvmyuKi5ep//MX7wR0GxNzz9OE3pksNjLkB9tzqmtoP8H2X9tme9EQlhBASW2hAjTKtWrWynAdbw8KxZcaXsmnKW6bLa7HPCdJ8z+NsXzdR8Eo7I9GNWJVvp6xI8lrJYzatmXRu94lX8Eo742UM2C3Han430lP/G6v+L3awrMZNnz591MssGzZsaFB2LVq0UH+Liops5yPOQX23DrxNJ83+UJIkSXzik+SkJPV+p/b9VTzfWIFt++ryYZxh8f0HPy6WK08aKl6BOmgPys8elF9i2BrimVQPydA7NWkkpKSkOF5m8yEjpVnvXfzvq6urpHDyPVKzdYskN20uHU66pb4HKmkUuuFW+XbKiiSvlTxm05pJ53afeAWvtDNexoDdcqzmdyM99d977YwX/Xe6rMZ2inG4wzj0d9u2bbOdLxGoqfLGlj3quz3vUwCPz1h7oeLAKMQ81Z6mocD3P85YLVecOCSmxl4j1EF7UH72oPwI2Q4NqFEG8bJycqxtCcHhAeGs7qnZLbHK9MeKqqquDn+qaXZLtSBvyJLf0HXD8eGHH8qECRNkwYIFavHRo0cPGT16tJx00kniBKeffrp07NhR7r333ojyV1ZWyuuvvy5nnXWWrXbGWjdiVb6dsiLJayWP2bRm0rndJ17BK+2MlzFgtxyr+d1IT/1vnPqfaDKLtxvQmpraeIsNYTTaRJrPKbBeXLRokXTv3l0dkFVRUSFNmjSRtm3byooVK1SaNm3aKI9D7S3brVs3KSgoUHlh3O3QoUNteIGNqy0dvrDm1ZslpXVHqWreQXytOkluv52lODVLtpZVqHVb165dZfHixX4v3MzMTHVdgDXi5s2bpaSkRMkQ9Uda1LN58+bSrFkzWbNmjf+AFKTbsmWLkmHPnj1lyZIlSu7Z2dmyadMm//hp3769MlSjbNCrVy/VNqwlUWbLli1VfFyQm5ur5KU9g7EWXrlypVqDNm3aVMlNyxDyrK6ulo0bN6r3qG9+fr6K+4Z2oazly2sNksgH1q9fr/5CDjhETMsb7dExC+E5hvbr+iP0BPJt3bpVHcqB0BBoK0DdEVMXZenQEKh7aWmpkjf6FboAMP6hB0Z5Q37FxcVKhpMWb/c+NfK/P9+SXvtfo65vlDfqi/ZnZWWpsvVhbJA32oU+AEgLOWh5o32QKWjXrp2SrVHe6Auts9k5Lf0xTxsC6ebOWyjpacnSunVrVU8tb8gQ8oQeQF5ou1HekFVhYaF6D/liXGh5I69RZ9FfRnmjnUad1fKGHqBco85C1njhdGq01aizeEF/AMYfrm/UWaO8UQ+jzuJaRnlDR7XOQhZGeaMfjDpre44QUddAm4w6a5Q35GTUWcjVKG/UR+usniPwHv3k1hyB9EadTbQ5AvXXdXdijtA6a5Q3dNGosygz2nOEUWfxP/rXqLPQBy1vXNeos3qOQHt0vGfUC7LFe9QHQE5Ii3oAyBPf4Vr4HO91vE9faZH4ykr8edEfNdU1Ul1TXZs2LV3KK8oluUm2ZLTMVXLV5aq0NTVKZkiLfke5qAvqFCotQPuMafF67733ZOLEif7+heyOOeYYOe2005T8kBZlon14r8s1th11CJUWh3JCL++44w5/WtQP9UJa/DXKEOj3kBnG3BtvvCFnnHFGHRlqeeO9XktBJ6HfgesIXV5DxN5qRFw91RReqKtfurbeqaaSke1afXCYwvjx42XcuHGy8847q0Hy888/y1133aUmlssuu0xizccff6xOrYUBlRBCCCHETXAzjBvqcAch6O9wI2c3n1PgRgoGAH1jbUR/rsHNtQY31oFpy9ckS+0tr3mqN6yWJLyW/inrpr0vkpwqme26SkZeTyne1Eu65PWStDadJCk5pV6dAuWBG+tQ9YeccXOtwc2hBjf8xrS4gcfNtQY31qHK1TfXGtyshUtr3CobGPc2MK0O3RBK3kaMDzwCT9QOTAuDkAaGjHBpA+UNI85fa+bU8T41snrrWllUvEJ5oRrlDWNQqHIhb20Qsipvo87CAzUtNdmUERXp+vfrXeehhFHeMAaFqwMMQpHIG0aZYGlhtIAxKFBnIe9gOht4HeQ16mw4eaM+Rnlb0Vm7c0QonbUi72A6C/npNrkxRwSmTbQ5IlD37M4RZuSNa0Z7jmhIZ83IG0a6+fPn+38/YfyDIRX/h/LqhcHPCNIq+87zV5k6eFHbd1Jy2tYpF/8bT68P3MliNm0o2859992n1ieBtp3Athod1QLroNNiroWsjHmNcgkmQ2O5X375pTz44INy/vnnB62D7gs9z+r/jf04d+5cv0E3HDSgRpnAQWqGwEHlxKmm6dmtbV83FLD+H3fccXL88cfX+ZHB06pXX33VEwZU40mgkbbTC7oRq/LtlBVJXit5zKY1k87tPvEKXmlnvIwBu+VYze9Geup/Y9X/xQkls3gCN2G40dBeMsHQXjHGG+RI8yUCqa3ypKqoQMRnMHjVVElFwWL1KpYv1UdJaRmSnttdMjr0lIy8XpLRoZektWovSUlW/F1DQ323HvsUMU/VQU1B+N+MybJT+/9EfXt8cnKS7Du4o3w/fVXYbfwq3ZCOntm+D6iD9qD87EH52SMSW0Mk9p3UnO1Geyfxgm0nvQEZGm07buPMyoKYRrvSW8GsO7HTZUZ6XVj0Z8yY4d+6oLngggtk0qRJ/qcIjz76qBx44IEycOBAOeqoo+SLL76ok37WrFnKQ3TIkCHqlNtbb701aHwv1HPMmDEyfPhwv9s9BvRVV10lw4YNk912200uuugiv5v9u+++KzfeeKP6v2/fvvLrr79KvOpGrMq3U1Ykea3kMZvWTDq3+8QreKWd8TIG7JZjNb8b6an/3mtnvOi/02U1FrSnjd52GAy9rdPoPRNpvkQg9+irpNu1EyTvrHuk9chzJWvgcOVtKlLXsOWrLJfyVfNkyx+fyLoPHpNVz1wuyx86U/Jfv002fPualMz9VSo3F0Z8g0V9tx77NJTxFKwuLpAfl/0mseCo/XpKQ2oAT9XD967rXRhrqIP2oPzsQfnZww1bTjTxgm1n9erVpm07v/3m7u8LDahRBvEWrGI29pXTZUZ6XcSwmDNnjuy7775qYD333HNqwMDdX7vfjx07Vt5//3255ZZbVLzUgw46SK644gr5+uuv1feIVXLmmWcq130MzCeeeEK5it9+++11roVYHdddd538/fff8tprr6knZJAxYqQCxGHF5/DgOOGEE5Rh9dBDD5WbbrpJff/TTz/JoEGDJF51I1bl2ykrkrxW8phNayad233iFbzSzngZA3bLsZrfjfTUf++1M1703+myGgs77bST+jt9+vSg32PbGNYyYOjQobbzJQrJaRmS2bGP5OxyqLQ78nLpfOFj0u2aV6XDaXdIqwPPkGb995TUFtu3hGpqyrdK2bLZsvnX96Xw3Qdl5ZMXy/JHz5E1b94lG394U0oX/ilVJbWeuw1BfbfmfYrYpw3x3LQ3pLQi+nLtnpcjY08ZqjxkU5JD13Pa3NoYj16BOmgPys8elJ893LDlRBMv2HbOOecc07YdGGjdhFv4o4wxtkRDlMz9RYp+eFOqy7dJuF0kvmprTzXWvHmniiHV0M6UpLRMaTX8FMnqv4el8g8++GAVWwIu3RgYP/zwg98z4u6771bxQb755ht55pln1JMFcPnll8u8efPUZxhwb731lkqH9Dq+BWKo4umHcTLC04aZM2eqgaTj43zyyScq6PQDDzzgz4u4HXgagXJxLR27BfFpwsUV86puxLp8O2VFktdKHrNpzaRzu0+8glfaGS9jwG45VvO7kZ767712xov+O11WY+GQQw6Rhx9+WBlCER8NXhJG3nnnHeXBgbXMrrvuajufF8FBpojVZjamG9IHIzmjqTTpuoN6abB9sXzNIilfs7j2b/5iqS6pPWzFeC7AtsUz1Mtfp+zW/27776m2/uNvSpOsOvmo7+aoqqmS9Vs31js4KhiV1ZXy6K8vyo37XFonFl002G9oJ+nSPls++HGx/DhjtYqJipinA3u2kRnzC1Xt3/hingzo3loG9toeVzGWUAftQfnZg/KzhzEciLbv1FTU97y0a99JSglv2ktObyIt9zs5Lm07xcXFpm07bkMDagCwnF9//fVKQeAe7DSBwYfDsfnXD6Ryg9Vw+w2DBaTpOkz9wPIgA4MHD1YvDAQMHgw0PDFAYF8MFoAgxEZ22WUXdZMAFixYIDvssEOd4MC77767emk+++wzdTobtrcZBwuekMDFHOUZgaFUn7JmxCsxUK3oRqzLt1NWJHmt5DGb1kw6t/vEK3ilnfEyBuyWYzW/G+mp/41V/+c5WBaxArwosKXtgw8+UFvT/vvf//q352ONdP/996v/L7744jprn0jzeRHEZ8NBFzB2NgSMp1biuSF9055D1EtTVbyx1qCav8hvXK3ZVvfa1cUbZOt8vLZv+Utt2d4QT7WndMxNrNAIbpGWkib3jLxBtpSV1Pnc58Pp0rVG0nVbN8hTv70qZVVlMrNgjrw68x05a8jomHiiXnnSUBlzwhCpqKyWjPQUZeSY9NV8mfD5PEGI1Acm/CmPXT1cWmbXPYwkFnDOtQflZw/Kzx5GW0Ms7TvVjcS24zbeXmlFmY0bN6qT2d1kyZIl9U5sC0XOHkdL0Q8Tpbp8a9hA5nhCYcUomty0uUhy7UKhIQ/UnN2PEisUFBTIs88+KxdeeKH/hLMBAwaoF54+HH744aHb4fP5B5WZmwC4gGNQwqX7ySefVK7jAAMb7uRPP/10vTw4QTEQDL7Ak9pigRXdiHX5dsqKJK+VPGbTmknndp94Ba+0M17GgN1yrOZ3Iz31v3Hqf6LJLN7ACbYLFy5UNwNYD/Xu3Vt5jy5fXnti+UknnSSjR492LJ8XgVHUrYMu6l0ru5V6Neuzi3+dWbW58F+D6r+eqmuWiC/AEwgHV+FVOufn2nxJSZLeppPBS7WXpOd2leRUbzyA9xJtmrZSLyM4Ab3nv/NFj1ZdpFlaUxn/w+NS7auRTxd8K3nZuTKy174xqS8OjMrM2H7PcfyBfeTvJRvkrwXrpKi4XB5+fbrcdsEeYbf7RwPOufag/OxB+dnDaGvQ9h0zHqhW7TtmPFDj1bbTrVs35c1qxrbjNjSgGoArcGlpqXgFPB3AC4vkcAY+LP5Wv3St6XI7nHSL+FrmNWg0bOi6oZ6wTJ48WTp06KBiZBhp3ry5f3CAadOmyf777+///s8///RPzvj70UcfqTgYKSkp6rOvvvpKGbjxdALgKQRig11zzTXqycfIkSNlxx13lD59+ihPDbhy6xNp8TTj6quvVi7oiJPhpZM1CSGEEJL45OTkyMSJE+Xll1+WTz/9VB2AgJsReHUgltexxx7raD5SF6z90lrkqlfWgL383pGVG/L92/5hWK1Yu1R8VRXb8/l8UrlupXqVzPq+9sPkVElv18W/7R/equltO0tScu2alYRmx9y+ct7OJ8uzf76u3r80fZK0z2org9r3j3XVlKH06lN2lise/k42bimXvxauk7e/WSAnjqgbOoMQQuzYdxoiEvtORgfnD7/zim3n/fff94xthwbUf/nxxx/l888/l3PPPVdZ2d0CAW+t4saWLDNlRnJdKDUCDT/22GPKGA2lzsrKUk+fse0MYRFwehoGF4IGQ9m7du2qYlsgdgZObwOnnHKKCqOA09nOPvts5R2MbWpw887IyKhzTXheIFgxYmYgFtiRRx6pghtjq9u1116rro9ro48RzNj4tAIBir1yam0kuhGr8u2UFUleK3nMpjWTzu0+8QpeaWe8jAG75VjN70Z66r/32hkv+u90WYnEt99+22AaPJjGdnu8rBBpPhIebC2Hdyle2QOH+z1/Ktat9Huplq6cJzUb80VqsAHyX2qqpKJgiXoV/xvCLSk1XdJzu0tG3vZ4qmmt8/zb1xsrweaLA3vuLfnFa+Wj+V9Lja9GHv7lebnroGulU/MOEmtaZGfINacOk5uf+Vlt5fdCPFTOufag/OxB+dnD6+F1Es2206tXL1d3F8dvbzoIlOG2225TnRkYnN9pIom36YZF3UyZkV73yiuvVEZJBPV9/fXXlSdrXl6eOggB7t8A7tl4YVsaDnzCkwWcxjZixAj1fW5urrz00ksqWPDRRx+tvC/wdEG7cgfWE08pECMMgwnXR0wODEoYxPGkAzE3UJ6OG4bBiiccGKD33XefHHbYYRJr3I7F6mT5dsqKJK+VPGbTmknnlfi4buOVdsbLGLBbjtX8bqSn/nuvnfGi/06XRYjXwDbIjPbd1UuGHCSZxcXSrEmGVKxdVrv9v6A2rmrlesSx235gErxWy1fPVy9/WRlNJaN9D0NM1V4qhEFj2gkVar44ddAxsqa4UP7MnyVbK7fJfT/+V8aPuF6aZ9Q9xCsWwFh68qh+8rpH4qFyzrUH5WcPys8e8T7fe8G28+qrr8qDDz5oyraDa6BubkEDqoiyjGMr1GWXXaYs5W6ydu1a/ylhZoGLsnZ1dupU04bKNHPdcGBg4BUKPCW4+eab1SsUQ4YMkTfeeCPodziZzQgGD544GINdY9CGAoMWkwDAJOAFItGNWJVvp6xI8lrJYzatmXRu94lX8Eo742UM2C3Han430lP/G6f+J5rMCIkG0Hfl0dKxj3ppasq3SfnaJf9u/a+Nq4rYqUZ85VulbPnf6mWMVZfRHgbV7TFVU7MT18Mr1HyBe68xu58t//n2IVm2aZWsLV0vD/70jNwy/Ap1IFWsGX1gH/ln8Qa1jT/W8VA559qD8rMH5WePSGwqkdh33CTWtp3c3FzTth23afQG1FmzZilLOtyCvXCQUEQkp0q7466VmrJS/yDd/N3/xFdWKkmZzaTNyPO2J81sptITQgghhBBCIiM5o4k06bKDemmqtxX/u/W/1ksVhtXq4o118uFgkG1LZqiXJiW71fZDquCp2r6n6zfEXiAzLVOu3+cSuemr+6SobLPMW79YxUa9dNczY+61BUPp2FOHyhUPfa8MqIyHSgiJFtip0PniJ6R6a3GDafFbEa3DGUkjN6DC0Ag3Y7gP77333lG5ZqdOnRx3m98y40vZNCW4xR1G1HUfPlbnsxb7nCAt9m74tNbG4q7vlXZGohuxKt9OWZHktZLHbFoz6dzuE6/glXbGyxiwW47V/G6kp/43Vv1f7GBZhDQOrOh7SpNsadpjsHppqoqL/vVQrfVSxSvwdGUYWbfiteAP/2epLXL/3fb/r2G1fQ9ltE00+bVu2lKu2+diufXbh6SiulJ+XPabdMxuL8cMOFhiDbbsX3PaznLLM7/ENB4q51x7UH72oPxiY2uAUZSGUW/Za6SxG1BffPFFWbVqldrCj0C2Oh4qKC4uVp/pk76coqioSJ1iZoWqqqqwStN8yEhp1nsX//vKqkpJSw299SUlq2WDZZq5bqLglXZGohuxKt9OWZHktZLHbFoz6dzuE6/glXbGyxiwW47V/G6kp/43Tv1PNJkREg3s6ju256dm7yLN+tSu1X0+n1RtXuc/pKo2ruoStd3fSNWmtepVOufnfz9JkrQ2Hf0HVMG4mp7bTZJTY7+GtSu/nq26ymW7naUOkwITZ38gHbLbye6dh0qsGdSrbczjoXLOtQflZw/KLzFsDfFMlYdk2KgNqD///LNs3bpVeaAGcumll6q/8+dvDwTvBNpAa4WamhoTC7PtsZN8ZWWS0UA4AjNxPxu6bqLglXZGohuxKt9OWZHktZLHbFoz6dzuE6/glXbGyxiwW47V/G6kp/57r53xov9Ol0WI13Fa37E1Pa1FO/XK6r+H+sznq5HKjWv+3fZfa1itKFiqDqbajk8q169Sr5LZ39d+lJwi6W27GA6p6qne4yCseJMfjKUnDzxKGU/Bk7+9Im2atpJerbtJY4+HyjnXHpSfPSi/xLA1xDM1HpKhd35dY8D111+vTgkz8ttvv8kzzzyjvuvXr5/j10xNtS5yqzGAzKR3Kk0i4JV2RqIbsSrfTlmR5LWSx2xaM+nc7hOv4JV2xssYsFuO1fxupKf+e6+d8aL/TpdFiNeJhr4nJSVLeuuO6pU9cD/1ma+mWirWrfzXS7V2639F4XKRmqrtGZFm7VL1Kv7r69qyUtOVZ6oxpmpa6zx1Da/L7+j+o2R1cYHaxo/t/Pf/9LTcPeJ6ZUhtzPFQOefag/KzB+WXGLaGeCbJQzJM8mEfCfHzySefyNixY+XVV1+V3XbbzZEy586dqzxdcTpZ//7961nTtZdr37591YmUJPawXwghhCQaZ93xhWzYXCatczLllf+Mcu064dY9JD5gH3qTmqoKqVi7fPv2/zWLpHL9alhbw+ZLSs9UB1Nl5P1rVO3QU8VY9dJNqaayulLu/P4xdaAU6Naik9xxwNXqwKlYM2vROn88VDif3nXRXlGPh0oI8S60IcRvv5hd97BHo8yiRYss5zGz3d5qeqfSJAJeaWckuhGr8u2UFUleK3nMpjWTzu0+8QpeaWe8jAG75VjN70Z66r/32ul0PYq2bZYlG1f4Xz/M+llqMjZJUtPN6q/xO7yQPlZ1JcTLeEnfEe80s2NvyRl2sLQ74jLpfMGj0u2aV6XD6XdKq4POlGY77C2pLdvXy+erKJOyFf/I5qkfSuF7D8vK/14qyx85S9ZMvEM2fv+GlM7/Xaq2bPCE/NJS0uSavS+S3Ga1hsllm1bJ41Nf9sQWTh0PFeh4qEXFZY1KB+MRys8elF9i2BrimTIPyTBu/LHfeustueWWW+S2226Tk08+OWS68vJyefnll+Xjjz+W5cuXS2ZmprIyI89hhx0miQhueow3PhUV5ZKenhEyfcsmOdIkKfT3hBBCCIlvvlo8Rd7+55O6H3YTgQ8XlqE3fPVvDMV/OX6Hw+SEHQ+PbiUJIbZJTm8iTboMUC9N9bYSKS9Y/O/W/9q4qtVb1tfJV7OtRLYtmalexoNmjYdU4W9K0+am64LDsaq3Ftf9cONqKV9T12cnpWl22NOlm2dkyfX7XiI3f/2AbK3cJn/mz5LXZ70npw8+Thp7PFRCCCGxIy4MqLNmzZJ7773XlGX6nHPOkWnTpklKSor06dNHSkpK5I8//lCvX375RcaPHx+2DBhZ3TS05uTkWM6Dtli+SQoDbpKO6TvK9nUTBa+0MxLdiFX5dsqKJK+VPGbTmknndp94Ba+0M17GgN1yrOZ3Iz3133vtdLoeI3ruI8PyBvnfF20qkvt+eUkkrUKkMl3uPfTqeg9XY1VXQrxMPOp7SpMsadp9J/XSVJVs8htTaw+rWiQ1W+ueBVFdUiRbF/6hXprUnHZ1tv7jlZzRNKjxdOXTl4uvurLO5zCdrg5Im5SSJp0vfiKsEbVT8w4yds/z5e4fn5QaX418NP9rycvOlQN77i2NLR5qPOqgl6D87EH5JYatIZ5J8ZAMPW9A/fXXX2XMmDGmTn+76667lPG0V69e6iCozp07q8+///57ufLKK+Xtt9+WwYMHy+jRoyUWwMC7YcMGadWqlaxevVoqKiqUhyzC0OKF79PT01XaqqraAPEZGRlqywq+Q6yGtLQ05WVrDOi8b6ddZFCbvuq7qupqqamukQenPifFFSWSnZ4l1+5xgaSkpKo4R1VVldIis7n6H9dH2fgf19XlQkFxLdShurpafYe/eCEt6qTdqHXaysraxRLqgDKRFqB9KBftQ1q8cN2G0qJMtM+YFp8b5YLvgqXVcjGmRf1wrWBptewB2op8eOnwwIsXL1btRr+h/uvWrVOfd+nSRdavX69iZaB+0LclS5ao71q2bKnKWrt2rXrfqVMnKSoqUnqMa3br1s2/HQI/Sk2aNFG6sXnzZunYsaM63Ky4uFjVt0ePHqoOqE/z5s0lKytL8vPzVd4OHTqoMpEedezZs6csXbpUyRTpUDZ0DSAv6rtp0yb1HmnhpY22NmvWTLVv5cqV6rt27dopmaHOAHVYtWqVkhvqitgguv5t27ZVskX9Adq2Zs0a1Zfo0/bt28uyZcvUd61bt1Z103khQ8hz27ZtSl5oO+oPUB/IqrCwUNUR9cc1tLyRF3IBLVq0UP0MeSNtdna2aiceoKDPunfvXkfeqL+Wd15enpK1Ud7oR7QJdcL1jPLGe+QDmGuM8kY9ICeQm5urZGCU94oVK5RccX3Iwihv1Hvjxo3qPeqr5wjIGzJGXtCmTRulC0Z5FxQUKB2GDFBHo7zRJqPOGuUNvURbcW09v0DeAPqM+mid7dq1ax15o29xXYB+g0yM8jbqLPQLOgEgb6Qz6qxR3qib1lnoDt4b5Y22aZ3FODPKG/Iy6izkq+UNuek+hzzRZ0Z5o4+1zqIsjA0tb4CxAyAH1B/lQN5oj1FnzcwRWt5m5wjUCdc0yjvcHIHPdN5QcwTaChkY5wjIG3qkdRZyMMrbyhxh1FmrcwTqqeVtdo7QOmtmjtDy1nME5IY6Bs4RRp0NNUdgrsHLiTlC651bc0SzCuw80V5gyVKzsaLOHLFB1kmzLk2CzhEA403PEXpODjVH6N9fQhIBjLFEIDWrhaT2HibNeg9T7zFPwCu1TB1StUgqVFzVxVJTvrVOvqrNhepVOvfXfz9JUodSbfdS7aUOrYLnaaDxNBRIh/ThDKhgUPv+cs7QE+WFaRPVe/zNzWojO+Y6f9CvFVpmZ8o1p+3sj4f6xhfzZECP1jKwpzvxUBNFB2MF5WcPys8ejIOaWDL07CFSuAGBEfT555+vE/Mm1BZ+3JCMGjVK3aS8//770q9f3R/WN998U2699VZ1gwKDajQ7wRiQFjcfuKGyEtAWN7S4yTQL0l/62c1SXFEq2enN5MVjHoyoTKvXNYKbp9dff10++OADdfOIG9cBAwbIBRdcILvvvrvldAcccID/Jh9AjrhJ3G+//eSKK65QN9KREqydsQgAjZt3o254uXw7ZUWS10oes2nNpHO7T7yCV9oZL2PAbjlW87uRnvrfOPV/3NSnxJdWJkmVmTLptEdcqysPIIp/2IfemyOigc9XI5UbC5Qx1W9YLVgivqpax4OQJCWrQ6mqimofPJmh4zkPSEaHHqbSvjJjsny64Fv1f7O0JjJ+xPXKGzXWvPnVfHn983nq/1bNM+SxsftLi2znw6Q1Jh10A8rPHpSfeYLZEOzYVIzMKpgrL894S84ecoJ6uBRNYm3bKbMpw4Q/RGrevHkycuRIefbZZ1Xjxo4dq7xOwoFOQocNHDiwnvEUHHvssUro8D75/fffJZGBTXxrZa1HJf5G20YOj5ozzjhDXnnlFTn99NPlvffeU//D8+nss8+Wjz76yFI6DcIz/PTTT+r12WefqZi4v/32m5x22mnKM4gQQgghhBASGUlJyZLeOk+ydtxH2ow4WzqeOV66XTtBOp3/sLQ57BJpPnSU8jyV5IBNjL4aS8ZTq5yx03EytMOO6v/Sym1y749PSXF5iXghHurg3rVetBu3lMtDb0yTGrikEkKIg8CeM3HWB7J6S4H6G037Dm07cbCFH96k2EI2bNgwufnmm5UFeNKkSWHzzJgxQ/1FnmBgKxqMq4iFCgOq0QIeTRoyBAdDb+s3y7yixVLtq90Wj78zC+bK4A4DLJdp9bqaxx57TFn4cZAXtjNqxo0bp7ZLItQCnjo89dRTptJh6yjA0wBsU9RguyZ0AzFrX3jhBbnqqqsiqm+k7fSCbsSqfDtlRZLXSh6zac2kc7tPvIJX2hkvY8BuOVbzu5Ge+u+9dsaL/jtdFiFep7Hre1JyiqS366peMvhA9ZmvqlIqCperOKpl+YulomCRVBQi5Ig7N/VwqLlij3Pllm8elBWbV0tByTp56Ofn5Ob9xkhqSqp34qEuWCeTv10gJx7kbDzUxq6DdqH87EH52cMJWwPsOYuLasOL4W8w+45beMG2k+4Re41nPVARt+zll19W7r9mtw3p+Gk67mkwEMvLmDYWIA6dVXSMUDPgacSk2XWt+5Nmf1jvKYWZMq1cV4PYbe+8847y+DUOHA1i0SIsA9y0zaRryFUbselGjBghn3xi/hAtJ9rpFd2IVfl2yookr5U8ZtOaSed2n3gFr7QzXsaA3XKs5ncjPfXfe+2MF/13uixCvA71vT5JqWkq/mnznQ+WdkdcKp3Of0Q6nHGnq9dskpYp1+9zseRkNlfv56xbKM9Pmxj1nXah4qEmJ9W+f+PzeTJ7cW0cb6egDtqD8rMH5WcPu7aGWvvOh5KcVDvJ4G8w+44beMW2U+0Re41nPVD79OmjXlbQhyWEi4WJwxuAPvQiFsAdGXFYzfDrymny1uyPZWvlNnWohhkqqiulpKLugVt4SnHu+9dKekqa/zMMuIbKzEhJl5MHHSW7dx4qZsGBFzgQY+jQ4HnQdrxwKIWZdGaAriCEAw6y0E80rIABiUEfa6zoRqzLt1NWJHmt5DGb1kw6t/vEK3ilnfEyBuyWYzW/G+mp/41T/xNNZoREA+q7OZJTnY/9GUjbZq3lur0vktu+fVgqa6rku6W/SMfmuXJkv5ESSwb1aisnj+qn4qFiB/+DE/50NB4qddAelJ89KD97GG0N2r6zrao23GIk9p0any+ofachmqRmyokDj4hL2061R+w1njWgRoI+RR2BakOhv8PpsrHCymFEH877SlYX1566bJdAo6qVOlgZZPrkYZxi7EQ6M+AkYADX8EgMqGaN027j9kFVTpZvp6xI8lrJYzatmXReOvHPTbzSzngZA3bLsZrfjfTUf++1M1703+myCPE61Hdv0bt1d7l0tzPl0V9fVO9fn/m+tM9qJ7t2GhzzeKj/LN4gfy1c54+Hevv5e0iydk21AXXQHpSfPSg/exhtDbG278SrbSfJI/aahDKgpqSkqNO1zBDLDujRw9xpk+CofiPVdnyzTyiCeZ8ayUpvZvkpxZH9RogVtAcwnkA4kc6KV01WVlZE+cMZ3b2qG7Eu305ZkeS1ksdsWjPp3O4Tr+CVdsbLGLBbjtX8bqSn/jdS/f/FwbIIaSRQ392hdP5UyegQmWz37DJM8ovXylt/fyw+8ckTU1+W2w+4Wnq06iKJGA+VOmgPys8elJ89jLaGWNp34tm2k+ERe01CGVARhBaWb5z+FQr9XZMmTSRWLF68WJ1EZgY8HcAL3rUNxYvAlvybvrpPtlZuVW7dgSBWRm6zNnL3iOuVAdlMmWbSBIIYtG3atJHp06fLoYceGrT948ePlxtvvNF0ut69e4e95j///CPdunWLyPs00nbGWjdiXb6dsiLJayWP2bRm0rndJ17BK+2MlzFgtxyr+d1IT/1vnPqfaDIjJBpQ391h08/vSFJaprTY85iInFuOG3Co5G9ZKz+t+EPKqyvkvp/+K/ccdIO0alobsi2W8VBveeYXtZUf8VAHdG8tA3u2sVUuddAelJ89KD97GG0N2r5jBqv2HTfwim2nzCP2GpAw/tgtW7Zs0OqtY5+Gi5PqNm4F+9UnswUbXMZYGUjntov/8ccfL++++66sWbOm3vc4UW327NnqND+z6cJRUFAg33zzjRxxxBES77gdCNrJ8u2UFUleK3nMpjWTLtYHE0QLr7QzXsaA3XKs5ncjPfXfe+2MF/13uixCvA713RwpTbMlycJON1D0/euy4etXxOczt4vQCAwGF+16uvRpXesdV7RtszKillWFdqaJVjzUk0b2U//reKibiu3ViTpoD8rPHpRfbPCCfYe2nQT2QMVTkWXLlsnq1atDplm1apX6C4t2LIDlHGEGEAQX9ayoqFCWdExKeOH79PR0lbaqqqqOuzK+gwIjeK72pE1NTfWfjjZx1vuSJElqG0so8D3S9W3RXYU8wPVRHyxAcF1dLr7DtYx1Qp3xQlrUScec1WlRB4D6nXvuufLjjz/KySefrE5c23HHHZVhe/LkyfLhhx/K/fffr/Kcf/75/nSXXnqp7LbbbrJu3TqZNGmSfPTRR/Lwww+rdLgW6oI4GLoP8R5PJx5//HE1EM8880x/nbRcjDJE/dBWlIfv0XadVnvkArQV+fDSPxZ4YoI0MLyjvagj6NKli6xfv162bt2q2o0nNAigrA36KGvt2rXqfadOnZQBH8GQcU3o4KJFi/yxQuAVDfnjM7QHpx3CfR31xbYJ1AH1QUwQuLPn5+ervDjlDmUiPeqIcbB06VLVV0iHsvWYgBxQX/2QAWmXL1+u2oonPGgfAkWDdu3aKZnphw6oA2QPuaGu8PjW9W/btq2SrT7IDW3DxIn2QL/bt2+vxiZo3bq1kpXOCxlCnohLDHmh7ag/QH0gq8LCQlUWXriGljfyao8qHBCH9kHeSIf+RDuhM+iz7t2715E36q/ljdP+IGujvNGPemzgekZ5472O89KrV6868kY9tI4iUDauYZT3ihUrlFxxfcjCKG/0w8aNG9V71FfPEZA3ZIy8AE/3oAtGeePHBm2GDFBHo7zRJqPOGuUNvURbtWwhV8gbQJ9RH62zXbt2rSNv9C2uC9BvkIlR3kadhX7pH1PIG+mMOqvlDVA3rbPQHbw3yhtt0zqLcWaUN+Rl1FnIV8sbctN9Dnmiz4zyRh9rnUVZGBta3gBjB0AOqCvKgbzRHqPOmpkjtLzNzhHoQ8jMKO9wcwTaoPOGmiNQB5RnnCMgbz12QHZ2dh15W5kjjDprdY5APbW8zc4RWmfNzBFa3nqO0L9fgXOEUWdDzRGQEV5OzBEoB3rn1hyBa+kVgu/f9pqdIwDGm54j9Jwcao7Qv7+EJAI6HhsJT2pOW+l88RNSvbXugXUbizZKq5bbnVdwr1I6b6ps/uVd9X7L7x9LzdYt0vbwSyUpxdqtKbauXrv3hXLT1/fLutINsrRopTz52ysyds/zJTkpdn5CJxzUR/5Zsl5mLlzvSDxU6qA9KD97UH72wL2BVbCOmzT7Q1P2HaTbqX1/17xQL7roIpkyZYqccsopcsUVV6iDorB2nThxorz//vvyyCOPqHWr2XQarJf1+hP3H/Pnz5dHH31UrT3PPvts2zJ0iyRfnDxSOOCAA9TNwm233aYMboE8++yzyuA2bNgwef311+t9j5uMXXbZRXXOyy+/LHvuuWeUai4yd+5cpSBQGNxgGBUHN2BQFtC3b9+gQZpx4xVOaSqrK+WSj8bJ5vKGT9htkdlcnjr8LkmW5AYVsaHrhgPtfemll+Szzz5TN5a4SR4wYIBcfPHFqo+sptP9r8FNHG5U4SJ+zjnn2ApYHKydZvrFabSOxEP5dsqKJK+VPGbTmknndp94Ba+0M17GgN1yrOZ3Iz31v3Hq/9nvjhNfWpkkVWbKpNMeca2uxnVP//79I74OiR3sQ+/NEYkmvy1/fSPrP31G5F/v0yY9h0rucddIcpr1WHcrNq2WW7550B9X8Oj+o+SUQUdLLCkqLvPHQwWnHdIv4nio1EF7UH72oPzME8yGEIlNJRL7TprFHQDxZNuptmGXMmvbMbvuSRgP1EMOOUQZUBF3AcKBYIy88847yngK75Vdd901ZvWEIsEjxQrwRgmnMBgs94y8QbaUlaj3FRXl8uDU52RLeYk0z8iSm/a93J82JzNbpYcsGlLChq4bDijeZZddpl5OpPv222/FLey0M9a6Eavy7ZQVSV4recymNZPO7T7xCl5pZ7yMAbvlWM3vRnrqf+PU/0STGSHRgPrujvyaDz5QUppkSeF7j4ivulK2LZ4ua964XdqfcKOkNMm2dI0uLTrKVXueJ/dMeUp5br0/9wvJy86V4d33kESIh0odtAflZw/KL/q2hkD7Tji0fcdNYm3bqfSIvSahYqBi29lRRx2lrMtjxoypc1jCDz/8oLaNA1i/9RbvRAExf8wMLrC5rFilJ4QQQgghhJBY0azvbtL+5FskKaPWu6181XzJf+0WqdpSG4LECoM77CBnDznB//7ZP1+XOYULJdHioRJCGgdtmraSHq26NPhq3bT2LCASHRLKkjhu3DhZuHChzJkzRw4//HB1whc8LXUsu5NOOklGjx4d0zrCNdkqcGkOx1eLp8jb/3wS9Dt4od7w1T11Pjt+h8PkuP6H2L5uouCVdkaiG7Eq305ZkeS1ksdsWjPp3O4Tr+CVdsbLGLBbjtX8bqSn/nuvnfGi/06XRYjXob67K78mXXeQvNPukII375Lq0k1SuW6l5L86Ttqf/B9Jb51n6VoH9x4uq7cUyBeLfpDqmmp58Odn5e6DrpP22e0knuOhUgftQfnZg/JLDFtDPJPmIRkmlAEVsRIQpBYxTj/99FN1KAXiGwwePFhOOOEEOfbYY2NdRXXYAg7jsAK8asO5LI/ouY8Myxvkf4/DG8J52bZsktNgmWaumyh4pZ2R6EasyrdTViR5reQxm9ZMOrf7xCt4pZ3xMgbslmM1vxvpqf+NU/8TTWaERAPqu/vyy2jfXfLOHK+28FdtKpSqzeuUEbXDSTdLRoeelq531pDRUlCyTmYWzJGSilK5d8p/5a6DrpWs9Nj0YUpyklx96s7+eKh/LVgnk79dYCkeKnXQHpSfPSi/xLA1xDM1HpJh3GzhR5wExDYNdoCUEQSqxTZ9nOI+a9Ys+euvv9Sp7scdd5xrJ5NZASchWwVBc8MBg6jRjbtL87ywbt5I31CZZq6bKHilnZHoRqzKt1NWJHmt5DGb1kw6t/vEK3ilnfEyBuyWYzW/G+mp/95rZ7zov9NlEeJ1qO/RkV9ay/aSd8bdkt6uq3pfs3WL5E/4j2xbNtvS9VKSU+SqPc6TTs1rvebyi9fKI788L1U11TGPh6qdThEPdfbi9abzUwftQfnZg/JLDFtDPFPtIRnGjQE1UfCCEZc0Tt1wsnw7ZUWS10oes2nNpGss49Ur7YyXMWC3HKv53UhP/fdeO+NF/50uixCvQ32PnvxSs1tKh9PvlMzOtScg+yrKZM2bd0nJ3F8tXbNpehO5YZ9LJDsjS72fvXa+vDTtTXXAVDzGQ6UO2oPyswflR8h2aECNMj179qw3IelJCVvvQ3nVWsFMeqfSJALB2qn7wtg/0dYNL5dvp6xI8lrJYzatmXRu94lX8Eo742UM2C3Han430lP/vdfOeNF/p8sixOtQ36Mrv5TMZupgqaa9h9V+UF0lhe8+JFumf2mpnHZZbeTavS6S1OTasGZfL/lJPllg7eRnp0E81J16t1H/63ioNbCmNgB10B6Unz0oP/MY7QY4Ob4x2VTcxK4MnbTt0IAaZZYuXVrnPTowPT1d/b969WqpqKhQMR6Mr23bttX7LNzLTHqn0iTCK7Cd6AP0BUDfRMuAGqgbXi7fTlmR5LWSx2xaM+nc7hOv4JV2xssYsFuO1fxupKf+e6+d8aL/TpdFiNehvkdffslpGZJ7/HWSNWj/fz/xyfrPnpWin9625EXar21PuXiX0/3vX/vrHflz9SyJFToeasvsDPVex0NtCOqgPSg/e1B+5oHdQMeLhT2hpKRExZCNtb0j3l/bbNilnLbtJNQhUvEavyEvL09WrFghZWVlsnjx4nrfo+NxGJZZzKR3Kk0iEKqdCFSMvkmU2B5Olm+nrEjyWsljNi3jAHuvnfEyBuyWYzW/G+mp/95rZ7zov9NlEeJ1qO+xkV9Scoq0PfxSSWnaXDZP/UB9VvTDRKneullajzhbkpLM3aPs021XFQf1nTmfik988tjUl+TOA66Rbi07SSzjod7yzC9qKz/ioQ7o3loG9qz1TA0GddAelJ89KD9rtGnTRtl1ysvLZeXKlY3GpuImTsjQKdsOezLKZGXVxuIJdEnu0qWLZGRkBLWI8+bZXQLbiT5AX6BPoulyH0w3vFq+nbIiyWslj9m0ZtK53SdewSvtjJcxYLccq/ndSE/9914740X/nS6LEK9DfY+d/LAmb33gGdLqgO1epFv++FTWffC4+Kprt8eaYfSOh8kenXdW/5dXlct9P/1XirZtFu/EQ50WNh4qddAelJ89KD9rNG3aVIU9aNmypaSmpjYam4qb2JGh07YdeqBGETyJ2Lx5s7Rt29a/Xb9Jkybq/apVq1Sa3NxcZWHfuHGjet+1a1eVFnEb0PHt27eX5cuXq+9atWqlLPHr19ee4ti5c2f1P1zFUW7Hjh39LvctWrSQtLQ0WbdunXqPcuAKDZdyDGwo1JIlS9R3OTk5Srny8/P9lnqcvody8b5bt24qLbbQZGdnKzf1goIClbdDhw4qXXFxsVLWHj16qDqgTUiLF8rVbUUd9Ml+mGjQNrQVZaLO2t26Xbt2Sl6bNm1S77t3765khtgimKRat26tnvDopz4YZEVFReo96rtmzRr1FAjtQlnw+AXIh3JRXwA5FBYWqrSQFdquZQh5o/1ahkgLeW/dulXJFvLXMsSECRfxtWvXqvedOnVS9dHyRp0WLVrklzf6C7oB2aHfIBPUCf0LGcIzGfJu3ry5+hHTMoS8USbSQ96QIeqL9iMdytYyRJ1QXy3DQHmjfVqGkBFkq2WIOkDeWmfRj7r+0F/074YNG4LKG7q2bNkyv7zRJp0XMoQ8oQeQl1FnUR/ICv2B9qD+uIaWN/Jqj23oCsYH5I20eI92ap2FvhjlDZ3R8kYfQ9ZGeaMf0SbUH9czyhvvkRf06tWrjrxxXeNYhgyM8obeBdNZyBv9oMc96hs4R2idhX5DF4zyxvjD/AIZoI6B8jbqrFHe0Eu0FfWHTPGCvPV8gvponcVcZJQ3ZKPHPfpNy1PL26iz0C/oBIC8kc6os0Z5o25aZ/U8ZZQ32qZ1FjphlDfkZdRZyFfLG3LTdYQ80WajvNHHWmdRlp5nkQ/oeRZygKyhT5B3JHOElrfZOQL1R72N8g43R6BcnTfUHAFZ6fFglDfapnUWbTPK28ocYdRZq3ME6qnlbXaO0DprZo7Q8tZzhCZwjjDqbKg5IvB3zc4cgetA79yaI6AHeuOr79/2mp0jAMabniP0nBxqjggV052QeARzAomt/FrscbTyRF33ydMivhop+WeKVG8rkdzjrpHk9IZviJOTkuXSXc+QdaUbZNHGZbJha5Hc/9PTcvv+YyU9tTaUWiziof6zZL3MXLheNm4pk4ffmCa3nb+HJCfXd6ahDtqD8rMH5Wcd3A9gfYsX1oWMg2oPrEux3o0Ep8+0SfLF8jjCRsLcuXPVwMENEW4+cENlBdzkWMljJr1TaRIBr7TT7Xo4Wb6dsiLJayWP2bQcA95rZ7yMAbvluDGnW01P/W+c+j9u6lPiSyuTpMpMmXTaI67V1bju6d+/9jRtEl+wD703R8QrTsqvdMEfUvjew+Krqn0ImJHXW9qfOE5Smmabyr9p22a58ev7lAEV7N55qFy5x7nKwBoLiorL5IqHvpeif71PTz+kvzKsBkIdtAflZw/Kzx6UX3zI0Oy6h1v4CSGEEEIIIYR4mmZ9dpH2J98syRlN1fvy/IWS/9rNUrWl1uO+IVo0yZEb9rlEMlNrD3GaunK6vPX3xxIrdDxU7XT6+udz5e/FtTswCCGEeA8aUKMM3LjdzmMmvVNpEgGvtNPtejhZvp2y3B4DZtNyDHivnfEyBuyW48acbjU99d977YwX/Xe6LEK8DvXdW/Jr0mUH6XD6nZLSrIV6X7l+laz+301Ssb42PEpDdG3RSa7Y41z/ls5353wmPy77TbwSD/WBIPFQqYP2oPzsQfnZg/JLLBnSgBplEF/O7Txm0juVJhHwSjvdroeT5dspy+0xYDYtx4D32hkvY8BuOW7M6VbTU/+918540X+nyyLE61DfvSe/jNxuknfmeEltWXtTXb1lveS/erOU5dfGsm6InfMGyhk7Hed//8wfE2TeutoYzrEA2/Z36l0bc13HQ62BNfVfqIP2oPzsQfnZg/JLLBnSgBpl9GERbuYxk96pNImAV9rpdj2cLN9OWW6PAbNpOQa81854GQN2y3FjTreanvrvvXbGi/47XRYhXof67k35pbVsL3ln3CXp7bqp9zXbimXNhFtl65KZpvIf2ucAOajnPur/qpoqeeDnZ2RtSe2BetEmJTlJrj51Z2mRXRtaYMaCdfL2twv931MH7UH52YPyswfll1gypAGVEEIIIYQQQkhckZrVUvJOv0MyuwxQ732VZVIw6W4pmfNzg3mxhf+coSfKwNy+6n1xeYncN+Vp2VqxTWIWD/XUnUUfFs14qIQQ4j2SfD7f9v0BxPUTvfr16+ePuWMWdJGVPGbSO5UmEfBKO92uh5Pl2ykrkrxW8phNyzHgvXbGyxiwW44bc7rV9NT/xqn/J70+VnxpZZJUmSmTTnvEtbryBPf4h33ovTkiXomG/GqqKqTwvYdl64I//v0kSdocfJ403/ngBvOWVJTKzV8/IPnFa9X7ndr3lxv2uVRSklMkFkz8Yp688eV89X+r5pny2NjhkpOVTh20AcewPSg/e1B+8SFDs+seeqBGmeXLl7uex0x6p9IkAl5pp9v1cLJ8O2W5PQbMpuUY8F4742UM2C3HjTndanrqv/faGS/673RZhHgd6rv35Zecmi65x10r2Tsd8O8nPln/+fNS9ONb6sY7HFnpzeSGfS+V7PRm6v3MgrnyyozJEitOGNFXBvWqGw916bJlMatPIsAxbA/Kzx6UX2LJkAbUKFNVVeV6HjPpnUqTCHilnW7Xw8ny7ZTl9hgwm5ZjwHvtjJcxYLccN+Z0q+mp/95rZ7zov9NlEeJ1qO/xIb+k5BRpc9glkrPH0f7PiqZMkg1fvCA+X03YvO2z2srVe13o9zr9YtEP8tmC7yRW8VCvCYiH+sXvBTGpS6LAMWwPys8elF9iyZAG1CjTrFkz1/OYSe9UmkTAK+10ux5Olm+nLLfHgNm0HAPea2e8jAG75bgxp1tNT/33XjvjRf+dLosQr0N9jx/5YYtn6wNOl1YHnun/bMu0z6Xw/UfFV10ZNu+Adr3lwmGn+t+/8tdkmbHmb4kFLZvXjYf62R+FjIdqA45he1B+9qD8EkuGNKBGmVatWrmex0x6p9IkAl5pp9v1cLJ8O2W5PQbMpuUY8F4742UM2C3HjTndanrqv/faGS/673RZhHgd6nv8ya/F7kdK2yMuh1uqel8652cpeOseqWnggKjh3feQo/uPUv9j6/+jv7woKzatlliwU++2cvKI2gOuEIXggQnTZFNxeUzqEu9wDNuD8rMH5ZdYMqQBNcqsXLnS9Txm0juVJhHwSjvdroeT5dspy+0xYDYtx4D32hkvY8BuOW7M6VbTU/+918540X+nyyLE61Df41N+2YOGS+7o6yUpNV2937Zkpqx5/Xap3rolbL6TBh4pu3YaXJunqkzum/Jf2VwWPk8046HW1PD8Z6twDNuD8rMH5ZdYMkyNdQUaE2VlZVJRUSHV1dWyevVq9X+TJk2kbdu2smLFCpWmTZs26onnhg0b1Ptu3brJtm3bZNGiRZKRkSEdOnSQZf8GEm/durUkJyfLunXr1PsuXbqo/0tLS1V5nTp1kiVLlqjvWrZsKWlpaVJYWKjeow5r1qxRaVNTU6Vr166yePFi9V2LFi0kMzNTfYfrduzYUTZv3iwlJSWSkpIi3bt3V2lRz+bNmyuXapQF8vLyVLotW7aobTQ9e/ZUdaipqZHs7GyVHm0H7du3V21D2aBXr16qbYhxgTJR51WrVqnvcnNzlbyKiorU+x49eqiBVFlZqU5Kg9y0DCFPtG/jxo3qPeqbn58v5eXlql0oSwciRj6UgXYCyGHt2rWqryBvtGfp0qX+Jx9ov1He69evV6e1QbadO3euI+/09HRVFkBfoO5a3uhXfc2cnBylB0Z5Q37FxcWqf9FWo7yzsrJUewD0AfmM8kZ90X6kQ9la3pAr6rtp0yb1HmkhBy1vtE9PTu3atVNyMcobfaF1Fv2p6w95471RZ6EPWt7oZ6POGuWtdRZ6AHmh7UZ5Q1bQWbQR5eEaWt7Ia9RZ9BfkjbToP7TTqLNGeUNntLzRx5C1Ud5aZ3FNXM8ob7w36qxR3qiHUWeR3yhv6KjWWcjCKG/0g1FnrcwRBQUFfp01M0doees5ArJAecY5AvqM+oSbI3Bd4NQcAXmhbm7NEbrPrc4RAGNHzxHGOTmSOULL2+wcgXZBZkZ5h5sj0DadN9Qcgc9QnnGOgLz12AEozyhvK3OEUWetzhGop5a32TlC66yZOULLW88RSA8C5wijzoaaI/C7hpcTcwTkDb1za45An+vbbt+/7TU7RwSuI/ScHGqO8FKsKkJI46VZ72HS4ZT/1HqflpVKef5CyX/1Zulw8i2SmtM2aJ7kpGS5bLez5LbSh2VJ0QpZt3WjPPDTs/Kf/a+U9JS0mMRDvfT+b6R4W5WKh/r2twvlhIP6RLUehBBCaknyNXQ0IbHN3Llz1Q0Vbohw84cbXCvgxtdKHjPpnUqTCHilnW7Xw8ny7ZQVSV4recym5RjwXjvjZQzYLceNOd1qeuq/99rpdD2qioukuqTWwA1KS0vk0qkvSnVahaRUpssrB15RJ31KVktJzW7pSF2N657+/fvbaAWJFexD780R8YoX5FdRuFzWTLzTPyemZLdWhtX0Np1C5tm4bZPc9NV96i/Yq8swGbP7OeohX7T5deZyuee1v9RW/uQkkfEX7yU79qx9wEviQwfjGcrPHpRffMjQ7LqHW/ijDDxL3M5jJr1TaRIBr7TT7Xo4Wb6dstweA2bTcgx4r53xMgbsluPGnG41PfXfe+10uh5bZnwpq1+61v/aNOl2aZZUG/8Pf43f4YX0saorIV6G+h7/8ktv11XyzhwvqS3bq/fVxRsk/9VxUrZ6Qcg8rZq0kOv3uUQyUmpDAPy84k95Z86nEgv6dMryx0PFDn7GQ40/HYxnKD97UH6JJUMaUKOM3u7oZh4z6Z1Kkwh4pZ1u18PJ8u2U5fYYMJuWY8B77YyXMWC3HDfmdKvpqf/ea6fT9Wg+ZKR0POcB/6tmxBjx+Wo9p/DX+B1eSB+ruhLiZajviSG/tBa50vHMuyU9t7t6X7OtRMVE3brkr5B5urfsLJfvfrYkSe3c+dbfH8vPK/6QWMiQ8VDjXwfjFcrPHpRfYsmQBlRCCCGEkAQD2/EzOvTwv6RVxzrfG7/Dy+z2fUIIiVdSmuVI3ul3SGbXHdV7X2WZFEy6R0r++SlkHhwodepOR/vf//e3V2XB+trY0LGIh9oiO0O91/FQCSGERA/GQI0CxngKffv2VQc2WAEHVVjJYya9U2kSAa+00+16OFm+nbIiyWslj9m0HAPea2e8jAG75bgxp1tNT/1vnPp//hsXS3FasmRX1siLpz3rWl0ZPzP+YR96b46IV7wov5qqCil8/1HZOv+3fz9JktajzpWcYYcETY/b5Wf/mCDfLv1Fvc/JyJa7R1wvbZu1jroMZy5cJ7c8+wvjoca5DsYTlJ89KL/4kCFjoHoUffqum3nMpHcqTSLglXa6XQ8ny7dTlttjwGxajgHvtTNexoDdctyY062mp/57r53xov9Ol9UYt6E98MADcsghh8jAgQNlyJAhcuyxx8qLL74oFRUVIfOVl5fLM888I4cffrjKt8suu8hpp50mn3zySVTr3xihviee/JJT0yX32Ksle/BB/37ikw1fvCAbf3hTGUsDwcFR5+18suzQro96v7m8WO6d8l/ZWlkbVzqaMtypd1vGQ00AHYwnKD97UH6JJUMaUKNMuMWxU3nMpHcqTSLglXa6XQ8ny7dTlttjwGxajgHvtTNexoDdctyY062mp/57r53xov9Ol9WYWLlypRx99NHywgsvyPLly6VTp07SqlUrmTNnjtx///1yyimnSElJSb18ZWVlcvbZZ8sjjzwiS5YskZ49e0pOTo788ccfMnbsWBk3blxM2tNYoL4npvySklOkzaEXSYs9j/V/tumnybLh8+fFV1NdL31qSqpcvecF0iGrnXq/cnO+PPbrS1IdJK3bMmQ81MTQwXiB8rMH5ZdYMqQBNco0adLE9Txm0juVJhHwSjvdroeT5dspy+0xYDYtx4D32hkvY8BuOW7M6VbTU/+918540X+ny2pMXHfddVJQUKAMoB999JF89tln8s0338hrr70mLVq0kNmzZ8v48ePr5bvrrrtk2rRp0qtXL/niiy/k/fffl6+//lqeffZZ1Rdvv/22TJ48OSZtagxQ3xNXfvAsbbX/qdJ6xNn+z7ZM/0Jt7/dV1T/1OSujmVy/7yXSLL2pej9jzd/y2l/vRF2GweKhvvMd46HGow7GA5SfPSi/xJIhDahRpm3btq7nMZPeqTSJgFfa6XY9nCzfTllujwGzaTkGvNfOeBkDdstxY063mp767712xov+O11WYwEep9OnT1f/33nnncqIqsF2/GuuuUb9//HHHyuPU+O2tffee08Zeh566CHp3Lmz/7vhw4fLDTfcoP5/4oknVIww4jzU98SXX86uh0vbI8eIJKeo96Vzf5GCt+6WmvL6W/TzsnOVJ2pKUu1t9KcLv5MvF/0QdRm2bJ6pjKhJSbXvJ3w2V/5ZssHVesQr8aCDXobyswfll1gypAE1yqxYscL1PGbSO5UmEfBKO92uh5Pl2ynL7TFgNi3HgPfaGS9jwG45bszpVtNT/73XznjRf6fLaiysWbPG/3+/fv3qfT9o0CD/NrX169f7P//ggw+kqqpKxT0Nlg/xUzMzM2Xt2rXy+++/u1b/xgz1vXHIL3vgftJ+9PWSlJqu3m9bOkvWvH6bVJdurpd2x9y+Kiaq5qXpb8nMgjlRlyHioZ5kiId6/2t/yuYSxkONVx30KpSfPSi/xJIhDaiEEEIIIYS4SF5eXp2TXgOZN2+e+puWlibt2tXGWAQzZsxQf4cNGxa03PT0dGVcBTSgEmKPpr12lg6n3irJmc3U+/I1iyT/tZulavO6emkP7Lm3HNG39hCqGl+NPPzL87Jq8/YHJdHixHrxUKczHiohhLgEDahRhlv4vYdX2tlYtm9yC7/38Eo742UMcAt/YuGVdsaL/jtdVmOhS5cusvfee6v/b731Vlm6dKn/u1mzZskDDzyg/j/11FOVUVSzbNky9de4dT8QHEZlTEuchfreuOSX2amf5J1+l6RktVLvKzfky+r/3SQV61bWS3vqoGNkWMed1P/bKsvk3ilPyZay4qjKMDAe6vT5hYyHGuc66DUoP3tQfoklQxpQo0wk8ams5jGT3qk0iYBX2ul2PZws305Zbo8Bs2k5BrzXzngZA3bLcWNOt5qe+u+9dsaL/jtdVmPisccek4MPPlgWL14shx12mBxyyCEyatQoOeGEE6S4uFguuOACddCUkQ0bamMatmpVa8wJBg6gAkVFRS63oHFCfW988ktv10Xyzhwvaa06qPfVxRsl/9WbpWz1gjrpkpOTZcxuZ0m3FrUPMQpLN8iDPz8rldX1D6ByU4YqHuopjIeaSDroJSg/e1B+iSXD1FhXoDGBQwEQv6F58+ayevVqFecKJ4rBoq7jOrRp00Z8Pp9/wdytWzd1gADeZ2RkSIcOHfweBq1bt1Y/3OvWrfN7N+B/xM7CQhweCUuWLFHftWzZUm0LKywsVO+rq6tVfUpLSyU1NVW6du2qFvR6IY54WqgTrtuxY0fZvHmzlJSUSEpKinTv3l2lRT3RlmbNmvlje2GLGtJt2bJFHXiAQxJQByh9dna2v+2gffv2sm3bNlU2wOmyaBtifaFM1BltB7m5uUpe+uagR48esnLlSqmsrJSmTZsquWkZQp5o38aNG9V71Dc/P1/Ky8tVu1AWDnPQ8saJuFrekAPiiEE2kDfao71EcPOC9hvlDVlv3bpVyRbeIUZ5w4MEZQH0Bequ5Y1+XbRokfouJydH6YFR3pAf+hD9i7Ya5Z2VlaXaA6APKNMob9QX7Uc6lK3lDbni802bNqn3SAs5aHmjfZApwPZByNYob/SF1llcU8sM8kb/GnUW+qDljX426qxR3lpnoQeQF9pulDdkBZ3F9dDPyKfljbxGnUV/Qd5IizqinUadNcobZWl5o48ha6O8tc6iDSjXKG9c36izRnmjHkadRX6jvHFNrbOQhVHe6AejzlqZIyBTrbNm5ggtbz1HQGZor3GOgD6jPuHmCFwXODVHaJ1xc46A3KzOEUDHJIQcjHNyJHOElrfZOQLtQn6jvMPNEShH60eoOQLv0V7jHAF5Q4+0zqI8yD6SOcKos1bnCNRTy9vsHKF11swcoeWt5wikhy4FzhFGnQ01R+B3DS8n5gjIG/3i1hyh32vQXrNzROA6Qs/JoeYI1JXUB7IeMGCA/Pbbb2rsaNkCjDHoEvQK84hGHygF/Q2F/g79RpwHYwf6TxqX/NJatJO8M8bLmjfHS0XBYqkpK1ExUXOPu1aa9hziT5eZlinX73OJ3PTVfVJUtlnmrV8sz/7xuly625nq9yxaMtypT2081IlfzvfHQ3386uGSkxV67mgsxKsOegXKzx6UX2LJMMmHVTZxFcS6wg0VbjZw84EbKivgJsdKHjPpnUqTCHilnW7Xw8ny7ZQVSV4recym5RjwXjvjZQzYLceNOd1qeup/49T/e6Y+IMVpyZJdWSMvnvasa3U1rnv69+8f8XUSCRjrzz77bLVdv3fv3nL99deruKYwmE+ZMkXuvfdeZZwePny4PPXUU8ooDXbccUeV5plnnpH9998/aNmPPPKI+n7o0KEyceJER+qr+xBGXxh2o/1gL9jD/1g82EPb8ZBDG7W98vA/8MFerB7+m3mwhzbo9lh9+B/4YM/Ow/9IH+y1ym4mJZ89KVWra+MUS3KKpO59qlR02KHOg71VpQXy/IK3pLKm1vt0ZN7ecvqux0f0YC/w4T/qpesY7sFeVVW1PP/ZKpm3YotK169zlvzn3N2krGxbwj38tzJH4D36ya05wosOQk7OEbNnz/bX3Y05wo6DkBfmiIYe/iOPfpji1MP/UA5C0X743zNKcwRkiXa4NUcA5EfdGlq70oAaBYw3Elg060WxWaB4VvKYSe9UmkTAK+10ux5Olm+nrEjyWsljNi3HgPfaGS9jwG45bszpVtNT/xun/l/45qWOGFAbqisNqPV59NFH5emnn1Y3Ne+//7662TGCG46jjjpKLervuusuGT16tPp81113VTc1evt/MGB8ffnll2WvvfaSl156yZH6sg+9N0fEK4kgP19VpRR+8JiUzvvV/1nrkedIzi6H1Uk3deV0dZiUZuye58vunYdGVYZFW8pkzMPfy6bicvX+jEP7y+gD+0hjJhF0MJZQfvag/OJDhmbXPYyBGmX0UwE385hJ71SaRMAr7XS7Hk6Wb6cst8eA2bQcA95rZ7yMAbvluDGnW01P/W+c+o9H5r7qFPXXblnEGp9//rn6e/rpp9cznmqviOOOO079/9FHH/k/11vWtLdHMLQ3Tbg4qSRyqO/2SAT5JaWmSbtjrpLsISP9n2348iXZ+P1E5TWlgbH05IFH+d8/8dsrsmjDsqjKkPFQE1MHYwnlZw/KL7FkSANqlIHLs9t5zKR3Kk0i4JV2ul0PJ8u3U5bbY8BsWo4B77UzXsaA3XLcmNOtpqf+e6+dbtZjaf5mefmzxbJu5igpmzZC/X104nT1eTzLzCzYWobtZ9iGOGfOHLUdDlvTooneKoftYqHQYRH01kS9PS7ws0D01jpseSPOE2/67jUSRX5JySnS5pALpMXex/s/2/Tz27L+s+fEV7N9Pjm6/yjZr9vu6n8cJnX/T0/L+q21W1ijJUMdDxXoeKibSxKjHxqzDsYKys8elF9iyZC+xFEGMTPczmMmvVNpEgGvtNPtejhZvp2y3B4DZtNyDHivnfEyBuyW48acbjU99d977XSrHj9MXyUPvzEdm1BFfP8eTuRLke+nr5Lvpq2SsacMlf2GdvJEXZ0EXpmTJ0+WH374QRlOYUQ1gjhZiEG67777yhFHHOG69ybijeEGQBtSg6FjfyHmmGannXaSb775RqZPRx/WB/G6/v77b/U/YqAS54kHffcyiSQ/xDFstd/JktK0ufJABcUzvpSabcXS7qgrlKcq0lww7BQpLF0vc9ctkk1lW+S+KU/LnQdcrQ6cipYMTxzRV3mezlq0XjZuKVO/A7eet7skJztzsFU8kUg6GAsoP3tQfoklQ8ZAjQKMgeptvNLOeIn/aLcsxkD1Hl5pZ7yMAcZATSy80k436gEP0ysf/kFqwiz1kpOS5NGx+0n3vPrbyuMxBioOcUC8UWyDx2EJAwcOlD59+qgt8jBM4jNsh8fhBDNnzpSFCxeqAwYQf/Tiiy9WB0O4wdVXXy0ff/yx9O3bV9555x11zUBD6JFHHqkObsBhUzfccIP6HAcsjBgxQh2YgNipyG8Eh0bddttt6oCOL7/80jEdYgxU780R8Uqiyq/k7ylS+NETIv96n2Z2Gyjtj79ekjOaqPdbyktk3Ff3ydrS2oNlds4bKNfudZH/IJRoyJDxUBNbB6MF5WcPys8+jIHaiNGntbmZx0x6p9IkAl5pp9v1cLJ8O2W5PQbMpuUY8F4742UM2C3HjTndanrqv/fa6UY9PvhhsT8OXijw/Qc/1p5AGm8yC+S1115TBy1hu/s999wjf/zxh7zxxhvKwHjuuefKiSeeKCeffLIylN5+++3KIDl16lS56aab1Em2hx12mCrDDXBNnCQ7f/58ueqqq/ynxwL8f9lllynjKU4jhgFVg9NlYdyF4XfMmDH+02IBvGvvv/9+f/m8QXMHr+p7vJCo8svacR9pf8KNkpSWod6XLZst+RNulerS2tAozTOy5Pp9L5GmabUG1Wn5s2XCrPeiKkPGQ01sHYwWlJ89KL/EkiENqIQQQgghCUZNjU9+/Gu1VCMAXhjw/Y8zVtc5CCVe+fbbb9Up9DiR/tBDDzW15Qtb60844QR5/fXX5dlnn5WvvvrKlbohvik8Y+HZgGsMHz5chQ6AcRT/wxjaokULefrppyU3N7dO3nHjxsmAAQPUDcThhx+uPFVHjhwpF1xwgfKWOOmkk2T06NGu1JsQEpqmPYdIh1Nvk+QmtWE3KgoWS/6rN0vlpkL1vlPzDjJ2z/MlOan2lvvj+V/L14t/imodGQ+VEEKcgwbUKNO6dWvX85hJ71SaRMAr7XS7Hk6Wb6cst8eA2bQcA95rZ7yMAbvluDGnW01P/fdeO52ux8q1xVJZVWMqLdKVV1bHncwCgeF00KBBEedHTNRXX31V3OKAAw6QDz/8UE4//XTp1KmTLF++XBlFcfjTeeedp7b4ow6B5OTkqK36V155pTLEIk9hYaEMHjxY7r77buVhS9zDq/oeLyS6/DI79pG80++SlOzaOMqVG/Ml/3/jpKJwhXo/qH1/OWfoif70L06bKH+vnRdVGSIe6qBebdT/Oh4qHrI1FhJdB92G8rMH5ZdYMuRenyiDwOJu5zGT3qk0iYBX2ul2PZws305Zbo8Bs2k5BrzXzngZA3bLcWNOt5qe+u+9djpVD8S8m/T1Avn816Wm86SlJktG2r8HTMWRzOIRxGK9+eabLeeDNy226eNFogv13R6NQX7pbTtLxzPvljUT75DKDflSXbJR8l+7RdqfeKNkduonI3vtK/nFa+XTBd9Kta9GHvr5ORl/0HWS17x9VGSYkpwk15y6sz8e6vT5hfLOdwsbTTzUxqCDbkL52YPySywZ0gM1yuBwA7fzmEnvVJpEwCvtdLseTpZvpyy3x4DZtBwD3mtnvIwBu+W4MadbTU/991477dajZGuFvPrpHDn/nq/lk5+XSnWN+RvrfYd0tLQ49YrMrPLrr78q4yW2vt9yyy0q/ikhiarvXqGxyC81p63yRM3o0FO9rykrkTWv3y5bF01T78/Y6TgZ2mFH9X9p5Ta5d8p/pbi8JGoybMzxUBuLDroF5WcPyi+xZEgDKiGEEEJInFJWXiWTv1kg5939tUz+ZqGUV/x7InR6iozcraskN2AXRejTo/atveFPZHCY1Pnnny9r1qxRcU8XLlyoDmvC54QQ4gQpzXKkw6m3S5PutaFEfFUVUjD5Pime/YMkJyfLFXucK11yOqrvCkrWKU/UquqqqNWP8VAJIcQeSb5EODXA48ydO1cF+cfBAT179pT09HRL+SsqKizlMZPeqTSJgFfa6XY9nCzfTlmR5LWSx2xajgHvtTNexoDdctyY062mp/57r51W64G4pV9OXaa26xcVb78BTk1JlkP37Ka2ZrbIzpAfpq9S8e4kqfZgKaPnKVaAY08ZKvsN7eRoXY3rnv79+4sXGDVqlFx11VVy8MEH+z/DCfaffvqpfP/99zGtmxfxYh829jkiXmmM8vNVVUrhh49J6dxf/Z+1HnG25Ox6uKwr3SA3fX2/bC7boj4f3n0PuXiX08PuAnBShjg48D/P/iKzFtV6dA3t205uPW93SW7oaVsc0xh10EkoP3tQfvEhQ7PrHnqgRpl169a5nsdMeqfSJAJeaafb9XCyfDtluT0GzKblGPBeO+NlDNgtx4053Wp66r/32mm2Hrj5/fbPFXLRfd/IM+/N9htPce970C5d5NkbDpTzjx6ojKcAxtFHx+4nu/VvJck5hZIxcIr6O3zn2s+tGk+t1DXanHnmmTJ9+vSg31VVVdUzUOB9dbX5w7NI48Sr+h4vNEb5JaWmSbujr5LmQ0f5P9vw1cuy8bvXpU3TVnLd3hdJWnLtUSTfL/1VPpz3VdRkqOOh6t8IHQ81kWmMOugklJ89KL/EkiEPkYoy27Ztcz2PmfROpUkEvNJOt+vhZPl2ynJ7DJhNyzHgvXbGyxiwW44bc7rV9NR/77WzoXpgw9DUvwtkwudzZUVBcZ3v9hqUJ6ce3E8652YHzds9L0dG75Mrs/6cIDXJSdKk9zS54sTzIg7K7xWZBbLjjjvKueeeK0OGDJExY8aoU+o1p5xyilx99dXy1ltvSatWrWT16tUyY8YM5ZVKSDzqe7zQWOWXlJwirQ8+X5Kb5cimKW+pzzb98q5Ub90ivQ65QC7d7Ux59NcX1edvzHpfOmS3k107bZ+z3JShjod6y3O/qJ0IEz6fJwO6t5YdenjnpGsnaaw66BSUnz0ov8SSIT1Qo0wkrsdW85hJ71SaRMAr7XS7Hk6Wb6cst8eA2bQcA95rZ7yMAbvluDGnW01P/fdeO8PVY+bCdXLt41Pk7ld+r2M8HdKnrTxy5X5yw5m7hDSeapZtW62MpwB/ZxbMdaWuseTaa6+Vb775Rvr06SNnnXWWMqbOmjVLfYf/n3rqKWnXrp1s2rRJunXrJk8//bQ6UIqQeNT3eKExyw8PqVrte6K0Hnku3qnPiv/6Wta++5DsnjdITtjxCPWZT3zyxNSXZcnGFVGTIeKhnnjQv/FQa3zywITEjYfamHXQCSg/e1B+iSVDxkCNAsZ4CljUp6SkWMqP7WVW8phJ71SaRMAr7XS7Hk6Wb6esSPJayWM2LceA99oZL2PAbjluzOlW01P/vdfOYPVYsKJIXv10jsxcWPf00b5dW8qZhw6Qgb3amCobS70bv7pXlmxcjjt6dXJUz1bd5O4R10fkhdqQzLwQPxPbvZ577jnlcbrrrrsqj9SBAwfGpC7xiBf60Ct4ZY6IVyi/Wkr++UkKP3xCpKb20KjMrjtK7vHXyVMzJslPK/5Qn7VskiP3HHSDtGraIioyREiYW575RWYv/jcear92cuu5iRcPlTpoD8rPHpRffMjQ7LqHBtQooDsDpy/ihS1m2DqGYLhNmjSRtm3byooVtU8c27Rpo250NmzYoN7DQwL5kS4jI0M6dOggy5YtU9+1bt1aladjQnTp0kX9v379emnZsqV06tRJlixZor7D+7S0NCksLPQrYfPmzaW0tFRSU1Ola9eusnjxYvVdixYtJDMzU71v1qyZdOzYUTZv3iwlJSVKcbt3766+Qz1RBtLgVFuQl5en0m3ZskXdlOHQLNShpqZGnXqL9Gg7aN++vXLHRtmgV69eqm2IU4YyUedVq1ap73Jzc5W8ioqK1PsePXrIypUrpbKyUik55KZlCHmifRs3blTvUd/8/HwpLy9X7UJZy5cv98sbdYdsAOSwdu1aKSsrU/JGe5YuXaq+w5Y/tN8ob8gafYv8nTt3riNvPClBWQB9gbpreaNfFy1apL7LyclR/WuUN+RXXFys+hdtNco7KytLtQdAH1CmUd6oL9qPdChbyxtyRXvheQOQFnLQ8kb7IFMADx3I1ihv9IXWWVwTddPyRv8adRYy1fJGPxt1tqCgwC9vrbPQA8gLbTfKG7KCzuJ6/fr1U9fQ8kZeo86ivyBvpO3bt69qp1FnjfKGzuA92o0+hqyN8tY6izZo/dHyxvWNOmuUN+ph1FnkN8obOqp1FrIwyhv9YNRZK3MEZKp11swcoeWt5wjIDP8b5wjoM+oTbo7AdYFTcwTkBfm7NUdgLkU+q3MEwFjXc8S8efP8c3Ikc4SWt9k5Au1CWUZ5h5sjUI5eZISaI/Ae7TTOEZA39EjrLMpDvSOZI4w6a3WOQD21vM3OEVpnzcwRWt56jkD6QYMG1ZsjjDobao7A7xpeTswRkDfaCLlu2pYkn/2+Tn6fU1tfTYdWGXLobrly+PCB6ppm54h3Znwi7yz5XAIZM/QsGdiuX4NzROA6Qs/JoeYI5EfdvGB8Q5/DkDp58mTZc8895fLLL5cddtghpnWKB2hA3Q7mBoxlEhmU33a2LvlL1r79gPgqy9T79Nzu0mr09XL3H6/Igg21c233lp3l9gOulszU2hilbstw45YyueKh72XTv96nZxzaXx1CmEhQB+1B+dmD8osPGdKA6iGMnYGbD6udb1VhzKR3Kk0i4JV2ul0PJ8u3U1Ykea3kMZuWY8B77YyXMWC3HDfmdKvpqf/eayfqkd0qT974Yp58P22l1BhWZ+1bN5VTR/WTfYZ0UgeAmGXFptUy+e9P5LfVM+p9l5yUJN1bdInIC7UhmcXS+AbDNrbt44EDjL4wlsJ4jffYsv/uu+/K3nvvrTxSG7thMBw0oHpvjohXKL+6lK1eIAWTxkvNthL1PrVle2l63Fi59Y+XZV1p7YOvXTsOlrF7nS/JSclRkeFfCwrlP8/9quKhwvv07ov3Sqh4qNRBe1B+9qD8EsuAyhioUQYeM27nMZPeqTSJgFfa6XY9nCzfTllujwGzaTkGvNfOeBkDdstxY063mp767612Fm0pk0/+2CgX3fu1fPvnduNpq+YZcslxg+Tp6w+U4Tt3Nm08heH04Z+fl2u+uCuo8RTU+HyyuGh5RLFQvSCzYMAD9tBDD5WTTjpJrrzySjnzzDPlwAMPlC+++EJ5Ot9+++3y+eefKy/dE044QS699FLlUU5IPOp7vED51SWzYx/JO2O8pGTXGiirigqk9M175OodjpYmqZnqs99X/yUTZ30QNRkO7tMuoeOhUgftQfnZg/JLLBnSgBplsM3N7Txm0juVJhHwSjvdroeT5dspy+0xYDYtx4D32hkvY8BuOW7M6VbTU/+90c6SbZUqxun593wtX/+5Rqqqay2nWU3S5KzDBsizNx4kh+zZXVJTks0bTn+pNZxOXTW9wfTJkiQTp0+Wyk21YRDM4lXdGDdunAoD8dlnn8ns2bNlypQpcvTRR8v111+vwlYAhIG488475dNPP1UhL0aPHh3rahOP41V9jxcov/qkt+kkHc+6W9Jad1Tvq0uKJPndx+XS3iP9OwI+mPelfLfkl6jJ8KSRfWVgz9qwRRs2l8nDE6crY2oiQB20B+VnD8ovsWRIA2qU0bHa3MxjJr1TaRIBr7TT7Xo4Wb6dstweA2bTcgx4r53xMgbsluPGnG41PfU/tu0sK6+Syd8skPPGfyWTv1ko5RXV6vPM9BQ58aA+8vy4EXLcAb0lMz3VuuF05XbDaWZ1Tdh8NeKTpSUF8s3/xkrV5to4qGbwqm7AmxQeqIhrjJBJiA176qmnqti+Oq6xBjFz77nnHmVIJSQe9T1eoPyCk9q8jfJEzcjrrd7XlJVK289elVM67u5P89y0N2RO4YKoyBA7HK45bWdpkVUbe3X6vEJ557uFkghQB+1B+dmD8kssGXrHlEsIIYQQksBUVtXIl1OXyaSvF0hR8fbtkfAw3WNACzn/uF2kZXbtFk6zhtO353xax2gKcjKby2Edhsr3c7+SNclJ4gsT4zTJ55MvW2TK8NItkprTVuKZnXbaSV544QX/gWM4+GvChAlqy37v3rVGikBgSCWEkFiQ0jRbOpx6q6x95wHZtmSm+KoqZOAPH8kBw3aXb4sWSXVNtTz483NyYe8TJRoRFFs1z5SrTx3qj4c64fN5MqB764SKh0oIIXbgIVJRwBiQFqf34iRgK+CUXit5zKR3Kk0i4JV2ul0PJ8u3U1Ykea3kMZuWY8B77YyXMWC3HDfmdKvpqf/RbWd1jU9+mL5KHRC1duNW/+cIaXrAsC5y8si+ktMsxXQ9Vm7Ol7f/qTWc+sRXx3B6VL+RMqLnPlK5dpmM+f5BKUlNabC8rKpqeXz4NZLVsY8jMovVAUTr1q2Te++9V7788kuprKxUW2EHDx6stvbvuOOOUatHIsBDpBrfXOgWlF/D+KorpfDDJ6R0zs/qPfYkvLHjAPmnbL163z6rrTrwLyu9WVTq8/rn8+TNr+ar/1vnZMpjY4dLzr+eqfEIddAelJ89KL/4kKHZdQ89UKPMhg0bJC8vz9U8ZtI7lSYR8Eo73a6Hk+XbKcvtMWA2LceA99oZL2PAbjluzOlW01P/o9NOPKP+7Z8Cee2zubKioLjOd3sO6iCnHdxfOudmq/f5+fkN1sOM4TQjNb32w+RUuWxVkZSaiJ+aVVUjacnml4Re1Q1s2X/ooYekpqZGNm7cKDk5OWorPyF28Kq+xwuUX8MkpaRJu6OvlA1Nm8uWPz8TPPYaPWeubOzdRdZUb5OCknXqYMCb9rtcUpMbfijmRDzUf5ZskNmL1/vjod567u6SbPIgQ69BHbQH5WcPyi+xZEgDapSBVdvtPGbSO5UmEfBKO92uh5Pl2ynL7TFgNi3HgPfaGS9jwG45bszpVtNT/91v58yF6+S1T+fK/BVFdT4f0qetnH5of+nduaXpesBw+s4/n8qvgYbTjGw5qj8Mp/tuN5waaFFVo15O41XdKC0tlWbNmklycrLath8JJSUlkpWV5XjdSPziVX2PFyg/cyQlJUvrkedKStPmUvTjJMms8cnpi1fKf7vlSolUy9+F8+WlaW/K+cNO8R805XY81Cse+l42lZT746GOPtDcLgWvQR20B+VnD8ovsWRIA2qUicQTwmoeM+mdSpMIeKWdbtfDyfLtlOX2GDCblmPAe+306hjYuKVMvTQFRZUiqzaFjSGGl1P14G+AuzjdzgUripTh9K+FdQ9l6tu1pZx56AAZ2KuN6Xqs2rxG3v7nE8uGU1CxYZW4hVd1Y9SoUXLeeefJySefbHmrFxbnr776qnr98kvtydeEeFnf4wXKzzwwjLbc5wRlRF3/+QvSqqpGTl+1Tp7r2Eqqk0S+XvKT5DXPlcP7HuR6XRIpHip10B6Unz0ov8SSIWOgRgFjPIV+/fpZfmqILrKSx0x6p9IkAl5pp9v1cLJ8O2VFktdKHrNpOQa8106vjgHErZz4ZW0sMDMgnuUpo/o5Vg/+BriLU+1cUbBF3WD+OntNnc+7ts+W0w/pL7vu0D7sdYz1UIbTOZ/Kryum1TOcHomt+r32kczU+gbCmqoKKZ3zi2yZ/oWUr15gqf4dz3lAMjr0cERmsYqfOWfOHLnttttk+fLlctBBB8mIESNkzz33VIdKhfI2nTZtmnzyySfy1VdfSffu3eWOO+5gvFTGQG2Uc6FbUH6RUTLnZyn84HFM7DIjK0Mmtc9RnydJkly790UyrOOgqNQjEeKhUgftQfnZg/KLDxkyBqpHWbx4sfTq1cvVPGbSO5UmEfBKO92uh5Pl2ynL7TFgNi3HgPfa6dUxcPAe3ZTxS7Ny5Up58fNVsrmkQnKy0uW28/eokz6c92kk9eBvgLvYbScOhYKR/ftpK6XG8Ei6feumcuqofrLPkE5qO6SZemS2bRbUcNo8I0uO6jcqpOG0sqhAGU2LZ34nNdvqxlp1A6/qxoABA2TSpEnywQcfKE/Sd955R3ktdOvWTTp27Ki25mMRvmnTJlmzZo0sW7ZMvcfDbRhODzvsMLX9n5B40Pd4gfKLjKwBe0lykyxZO/l+GVJSJus2lsq3rZqp34bHpr4kdx5wjXRr2cn1eiRCPFTqoD0oP3tQfoklQxpQCSGEkDDU25Jftl5S/z2YB397dWoRu8qRmFFUXCZvfbVAPp+6TKqqtxs7WzXPkBNH9JURu3aVtFRzxrhVW9bIm0s+kdnT5tcznMLjdGSvfesZTn011bJ14TRlON225K96Zaa2bC9VRQXS2ICHwtFHH61e06dPlx9++EH9nTdvnjKc4nvER8VhBEcccYTsu+++ssMOO8S62oQQUo+m3XeSDqfdLqvfuENGbCyV9WkpMis7U8qryuW+n/4rdx90vbRsUuuZ6haJFg+VEELsQANqlGnRooXrecykdypNIuCVdrpdDyfLt1OW22PAbFqOAe+1M17GgN1y3JjTraan/kfezpJtlfLudwvlwylLpLyi2v95VpM0Of6A3nLY3t0lMz3VtOEUh0P9EsTjNJThtKqkSIr/+ka2TP9Sqos31C0wJVWy+u8pzYeOEklNk/yXrhM3iBfdGDp0qHoR0hj03atQfvbIzOslWUddJ+WfPyGjC9dLUVqKrMxMkw1bi+T+n56W2/YfGzIWtlPEezxU6qA9KD97UH6JJUMaUKOM1UMNIsljJr1TaRIBr7TT7Xo4Wb6dstweA2bTcgx4r53xMgbsluPGnG41PfXfejvLKqrkoylL5J3vFknptkr/55npKXLkvj3lmOG9lBHVDKu3FCjD6c8r/qxjOM1WW/VHyMhe+9UxnGKbednyv5W3aen830VqthtuQWqLdtJ8yEjJ3ukASWlW641UtXmdJKWkia96e11DgXQpTbPFLI1FNwgB1Hd7UH72adq+q7Q+825ZM/EOOWNNvjzZqaVsTkuRxRuXy1O//U+u3PNcSU5yN/zI4D7t5MSD+qp4qDU1Pnlgwp9xEw+VOmgPys8elF9iyZAG1Cizdu1ayc7OdjWPmfROpUkEvNJOt+vhZPl2ynJ7DJhNyzHgvXbGyxhAOdGsB38D3KWhdlZW1ciXvy2XSV/Nl6Licv/nCN9wyJ7dZPSBvaVldvi4t2YMp3u1GSqn7HaMZKZtL6u6rFRKZn+vvE0r168KKC1JmvYaKs13Plia9BwsSQE3z6k5baXzxU9I9dZif+zel2e/KqWpSdKsyif/OXScPy2Mp0hvlsaiG4QA6rs9KD9nZIj4f3mn3yXJb90tZ61ZLE93aikVyckyddV0eevvXDlp4JGu1yNe46FSB+1B+dmD8kssGdKASgghhBASQHWNT36YvkodEIWDojS4T9x/WGc5ZWQ/adeqqamy8rcUyNtzPpOfV/yhvEmNhtMj+46QUb32lVXLV/mNp+Vrlihv05J/poivcrvRFsDDNHunAyV7yAhJa9Eu7HVhFPUbRktrpCAjVYrTkiW7skYyOvSwIg5CCCExBg+7OpxyqyS/86CcUvCP/K9DjviSkuTdOZ9JXnau7NttN3evz3iohJBGDg2oUaZTp06u5zGT3qk0iYBX2ul2PZws305Zbo8Bs2k5BrzXzngZA7XlLI5aPfgb4C6B7YSB87d/CmTCZ3NleUHdE+33HNRBTh3VT7q0b+6Y4VQbTTvmtpPiWd/JlmlfSHn+wnplZXbur7xNm/XbTW25t9tOOzQW3SAEUN/tQfk5K8Pk9Expf8L1Muyjp2T9qj/l47a1XlnP/PY/adestfRr28v1eKhjTxkqtz4fP/FQqYP2oPzsQfkllgzdDZZC6oETYN3OYya9U2kSAa+00+16OFm+nbLcHgNm03IMeK+d8TIG7JbjxpxuNT31P3g7Zy5cJ9c+PkXGv/x7HePp4D5t5eEr95Ubz9zVlPEUhtMnpr4sV31+h/y0/He/8TQ7vZmcMuhoeeqwO+Wo/iOV8bRy4xrZ8PX/ZM2zl8m6j56sYzxNSm+ijKadzn9E8s64S7J22Dsi42lgO+3SWHSDEEB9twfl57wM8TvQ9qgxckjv4bLb5m3qsyrxyf3fPSYFxetcr8+Qvu3khINqvU51PNTNJXV3S3gJ6qA9KD97UH6JJUNbHqirV6+WVatWSVFRkaSkpEjr1q0lLy9P2rdv71wNE4ySkhLX85hJ71SaRMAr7XS7Hk6Wb6cst8eA2bQcA95rZ7yMAbvluDGnW01P/a/bzgUriuS1T+fKXwvr3nj27dJSzjisvwzqZS4+aH7xWhXj9KdAj9P0ZnJEvxFyMA6HSssUX021lM7/TXmbbls6s1456e26KsNp1g77SHJGk4T6DYgmDzzwgBxxxBHSr1+/WFeFxDHxou9ehfJzR4aIe91m5Lly5k/NZcOiL2RR03Qp8VXJ3Z+Nl3uOukuaZWS5WqeTR/aTOUs2+uOhPjJxuvzHo/FQqYP2oPzsQfkllgwtG1AXLFggEyZMkClTpkhBQYH6TN8kJCXVTphdunSR/fbbT44//njp04cxUYzA0Ox2HjPpnUqTCHilnW7Xw8ny7ZTl9hgwm5ZjwHvtjJcxYLccN+Z0q+mp/7WsXFssr3yxUmYu+afO513bZ8vph/SXXXdo71/bNGQ4ffefz2TKiu3epkbD6ahe+0mTtEypKi6SoqkfypYZX0t18Ya6hSSnSNaAvaT5zqMko2NfU9eNx9+AaPLaa6/JSy+9JD169JDDDz9cvTp37hzrapE4I1703atQfu7JEL8TbfY5QcZkNpU7530g69JTpcBXLve9f4vccvR4SctoGrV4qNPmFcq73y+S4w/oLV6DOmgPys8elF9iyTDJZ1zpN2A4vfvuu2Xq1KnKw3SXXXZRxlEsRLOysqSmpka51sKoOnPmTPnrr79k48aNsueee8rYsWNlhx12kMbK3LlzZevWrZKcnKxOD+vevbvy3q2oqJAmTZpI27ZtZcWKFSptmzZt1M3Xhg21N1bdunVTMi0rK5OMjAzp0KGDLFu2TH0Hj1+UuW7dOr/hGv9v27ZN0tPTVayIJUuWqO9atmwpaWlpUlhYqN6j39A/paWlkpqaKl27dpXFi2tj+rVo0UIyMzP9BvKOHTvK5s2bleUfyov6Iy3q2bx5c2nWrJmsWbNGpYUHMtJt2bJF/aj37NlT1QH6gbYjPdoOoEeoK8oGOF0SbauqqlJlos7wcAa5ublKXvB2BrgZwonClZWV0rRpUyU3LUPIs7q6WrUPoL75+flSXl6u2oWyli9f7pc3WL9+vfoLOeCUNy1vtGfp0qXqu1atWqn2G+WNfOhbyBYyNcobfaBP60ZfoO5a3ujXRYsWqe9ycnKUHhjlDfkVFxer/kVbjfLGeEN7APQBZRrljfqi/UiHso3yRru0CzzSQg5a3mgfZAratWunZGuUN/oimM7if/SvUWehD1reuK5RZ1FPLe9AnUXbjfKGrIw6i2toeSOvUWfRX0Z5/5+984COouri+H9bei+kUhJq6L13kKKIgoKC2MBe4FOxYUEFsWAXUOwIClgQFSlSpPfeewLphfSeLd+5L2zYJRBmMzPJ7OT9ztmT7Ox9d9+7c9/s7N377qNx2vqsrb3JZ2x9lmxta29bn6WHrb3p/W191tbe1A9bnyUb2NqbbGb1WbKFrb3pPNj6LL9GKPsa8dJXh5BTYESgrxteHd+YXyOc7Brh4hmIRauOY+fxDFbDzUqAtwF39GuEAZ0bIDU15YbXiCJdCdYn7cCOhP2w4Ioid50b+oZ2xvhudyA5Pgnm5DMwxO2B6eJhwGy68ob0I7RnACyNu6HRgDuReClH1mvEOztnV2wi9XL352W7RlB76huNIyYmBrUFXWvWrFmDlStXYteuXczvW7duzbJShw8fzuzGqfretbbPIYfDEcb5g//ireO/o1BXXqGvl8kVT46aCb27vJmoB06lVdRDpezTWY/3UnQ9VA6HwxFz3yMogPruu+9i6dKluOWWWzBmzBi0a9fuRk3Yzfv27duxfPly/Pvvvxg3bhxeeukl1PWTQV8+KAjgCPQl2pE2QuSlklEDShmn3P2QUr8YXdVp60gbobJ8DihvnM4yB0jPzJ/PsSVrFED94fWhsvaDfwZIR1ZeMX5Zdxqrd8TBaLpy++Pv7Yq7hzTHTV0bwqC/cXn45Lw0/H58JbbY1DclvCjjtPlgDGvaHy4mE/JpU6j9/6LsUnmQugKNFh5NOrJl+u7R7dhSzJrwf9sA6rcT5ovSVVVflRh8o2Dv6tWrsWrVKuzfv58do2QAykodOnQo+wGHo+xzWFuo8VpYk3D71ZwNDxxeg/eP/wHT5RUMo4oMGHPHm9B7+8vav0WrT2Dp2tPsf7ov+vTZ/vD1coVS4D4oDm4/cXD7OYcNhd736IX+gk+/3lMmi1Aog6RXr17sMXnyZMybN09wWw6Hw+FwOBypyC8qw7L/zuCvLedRUnolA9TL3YD+7fxx/21d4OailyRwqs1IRO6a75B/bAssxlK79jpPX3i3HwyfDjdB78uzH2sSylgeP348e1AGNNVHtWamzpgxA4MHD8bEiRPr9IopDofj3HRoOxSTTMX46vRq9ny5WykCFk/DwDHTYfCXb48SZ6qHyuFwOGIQFECdOXOmqDehZV7vvPOOKB1qgZZLyt1GiLxUMmpAKeOUux9S6hejS+45IFSWzwHljdNZ5oBYPXJc0x2Vryv+X1xqxIqtsfh9wxkWRLXi6qLDbX0bY1T/JijKz75h8DSFBU5XscCp2WK2C5yOaD4IQxr1hOXMPmQuegMlSWcqtXdr0IrVNvVs3pXtnuzM/i+1rpqCSjCsW7eOZaBSOSoqMdC0aVO2nJ9+9F+2bBnGjh2LN954g6224nCc2d+VBLdfzdpwcIfbkFyah7/jtsGi0WCRtxm+P72KDne+AtfQqDpZD5X7oDi4/cTB7acuGzq8iVRVnDlzhtXSorppnGtDKcFytxEiL5WMGlDKOOXuh5T6xeiSew4IleVzQHnjdJY5IFaPHNd0R+XV7v9lRjP+3XUBS9eeQlZeScVxvU6DYT0aYezgZvD3dmPHNGYPhwOnni4eLON0UFALGA9vQsaqyTAX2+8QqnFxh3fb/vDpOAQuwQ1U4/9S65ITWkFlDZpSWSmqL0s1be+77z4WOG3evHmF7P3334877rgDH3/8MQ+gcpzS35UKt1/N2/CeruORUpSFPanHUaLV4js/C576eTqa3vEi3BvKk2Uf4OOGZ8d3rKiHunDVCcQ0ClBEPVTug+Lg9hMHt5+6bHjjQl/XgJatffXVV3j55ZfZc9pI4ZFHHsHIkSNZLalJkyaxTQU4lbFupCJnGyHyUsmoAaWMU+5+SKlfjC6554BQWT4HlDdOZ5kDYvXIcU13VF6t/m8yW/Dfvng88f56fLnscEXwlFYRDupSH1++NBiPjmpbETy93jgpcDp31wL8b9Wb2BS3syJ4SoHTu1qPwOxmt6PHwd249PVU5Oz62y546hIShaDhj6LhlK8RNPQhQcHT6/VDjZ8BNUmPHj3YvSptbjp69GgsWrQIGzZswNSpU+2CpwRtmkUJAEra6ZWjDJzF35UKt1/N21Cr0WJy70cR5RvBnmcbdPgx0AXxS2ag4NQumXoJdGhej/1ASZjNFsxetBc5+Vd+xKwtuA+Kg9tPHNx+6rJhtTJQv/32W3z00Ufo06cPe06/7G/evJkV4aflUF9//TXmzp2LF154Qer+cjgcDofD4VT6YXfXsRQsWnUCF1Ly7F7r0SYME4a1QINQnxvqocDpsuOrsfnCrkoZp7c06omeOSUoW78cuXmZdu1oWb5ny57w6TgUrhHN2JJwTu0zZMgQ9sM+3a/q9Te+5aVgq5+fX430jcPhcOTEVe+CF/s9hWn/vovM4hxcdDPg10B33P37Bwi++TH4tB8ky/vyeqgcDkfNVCuA+scff+Cmm27C559/zp5TEX53d3e89957cHNzY9mntNMpD6BWhpaOyd1GiLxUMmpAKeOUux9S6hejS+45IFSWzwHljdNZ5kC5nnM11g/+GVA1h8+m48eVJ3DqQpbd8fbNgnHv8Bg0a+B/w3Gm5Kdj2bFVlQOnBncMrdcG3VPSYF61BMU2rxF6vxC2RN+73UDoPG4coL1RP+rCZ0BN8uGHH7Jl/L/++itbJeXp6cmO//bbbygqKmJL9em+1UpISEgt9pajVJzF35UKt1/t2TDA3Q8v9n0Sr6//ACWmUhzydkNwqRGD/5kHc2EufHvcLvkPfkqsh8p9UBzcfuLg9lOXDau1hD8+Ph59+/Zl/1M9qR07dqBr164VN6G0BCojI0PanqoE2sRA7jZC5KWSUQNKGafc/ZBSvxhdcs8BobJ8DihvnM4yB8TqkeOa7qi8Gvz/THwWXpu/Ha98sd0ueNq8gT/efrwnZjza84bBUwqcfrlnIf638g1sjNtxZam+wR23+TXDy2ml6LppBcyndgPW4KlGC4+mXRB696uo/8Qc+PW4XXTw1Jn8X2pdcpKYmIhRo0bhrbfeQmxsbMXx/fv34+2332YB1MxM+2xiDsdZ/V2pcPvVrg2j/Otjco+J0KA8ULou0AsHvVyR+d8iZK7/EZarfhiUsh6qNTZL9VCPnb+E2oL7oDi4/cTB7acuG1YrgOrj48N+0Sd27dqFwsLCioAqcfHiRQQFBUnXSxXBA6jKQynjrCtfnnkAVXkoZZzOMgd4ALV2iU/Nw6wfduPZTzbj4On0iuMNQr3xyoNdMXtyH7RtEnzDwOm83T+ywOnOlIMVgVMPvStGGOrh+bPJ6LF3K/SXrtRc0nn6wa/XnWjw5DyEjn0JHo07QKOp1m2UU/u/1LrkzkClvn733Xdo3bp1xfFZs2bhp59+Yj/2U0kqDkcN/q5UuP1q34ZdItrhnnajKp7/Vs8HF9z0yNn1F9L/nguLyQg110PlPigObj9xcPupy4bVWsLfoUMHVog/IiICX375JasrRXWmKBv1v//+w+LFizF48GDpe6sCtFqt7G2EyEslowaUMk65+yGlfjG65J4DQmX5HFDeOJ1lDojVI8c13VF5Z/T/tMxC/PzvSfy3Nx5my5XjIQEeuGdYC/TtEMmWDlZFKi3VP77abmMowkNrQL9iLbqdT4CbOd6ujVvDVqy2qWfzrqzWaV33f6l1ycnu3bsxceJEtpnU1XTq1An33nsvlixZUit94zgPzuLvSoXbTxk2vLX5YCTlpmBD7HYYtRosDPXDEwmZwJGNMBflod7o56A1uEKN9VC5D4qD208c3H7qsqHGQjsvVGMXrEmTJuH8+fOsbgrVOn3wwQdZNur999+P6Oho9mt/aGioPL12Mk6cOMGydD08PBATE1Pb3eFwOByOSB54aw37MhDo64YfXh9a291RNVl5xfhl3Wms3hEHo+nKLYu/tyvuuqk5hnRrCINeW63AqbtGhz7ZReh5KQduNlFZjasHvNv0Z/VNXYLrQy1MWvQo8gxaeJeZ8e2E+aq/7+nYsSMmT56MBx544Jqv//jjjywD9eDBgzXeN6WjlHPI4XCkw2gy4u3Nn+NY2mn2PKTUiMfjs+BmscA1sgVCx74MnbuXpO+ZmVtcUQ+VuP+WlrVaD5XD4XDE3PdUK5QbFhaGv/76C7/88gs2btzIgqdEixYt2I3o77//zoOn14GCznK3ESIvlYwaUMo45e6HlPrF6JJ7DgiV5XNAeeN0ljkgVo8c13RH5Z3B//OLyljdtEdmrcOKrbEVwVMvdwP7AvbVy4NxS6+oKoOnafkZ+HJ3eY3T/2K3VwRP3S0aDL6UjxfOpWBgenZF8NQlJApBNz+GhpO/RtDQSTUaPHUW/5dal5y0bNmSbXxaWlpa6TVaNUX3snTvyuGowd+VCrefcmyo1+nxXM9HEOZVjz1PddFjcbg/TABKEk4iedFrMOZlqq4eKvdBcXD7iYPbT1021Fe7oV6Ptm3b2h3z9fXFzTffLEW/VIvZbJa9jRB5qWTUgFLGKXc/pNQvRpfcc0CoLJ8Dyhuns8wBsXrkuKY7Kq9k/y8uNbKA6e8bzrAgqhVXFx1G9onG6AFNWRC1Kihwuuz4KpZxarLJOKVAae+sAvTKKYL75aCpRauHd6ve8Ok0FK7hTSXfkVht/i+1Ljl5+OGH8dhjj+Guu+5iG0Y1bNiQnV+q1b9s2TIcP34c8+bNq+1uchSOs/i7UuH2U5YNvVw98WLfJ/DKuvdRUFqIU+56rAwNwK0pmShNu4ikBa8gbPxrMASES14Pdena0xX1UD99tj98vaQtGXA9uA+Kg9tPHNx+6rJhtQOoy5cvx7Zt25Cenn7NAdEN6oIFC8T2T3V4e3vL3kaIvFQyakAp45S7H1LqF6NL7jkgVJbPAeWN01nmgFg9clzTHZVXov+XGc1Yu/sClq49hczcKxtN6HUaDOvRCGMHNYO/j9uNA6cnVmNT7A77wKnJjN7ZhXaBU71/KKttWhzWCvUaNkZt4yz+L7UuOenXrx8++OADvPvuu3jrrbcqguNUvSogIIAd79+/f213k6NwnMXflQq3n/JsGO4dwjJR3970Gfus3OalR72QeuiWmgZjThqSfnwVoXe9CtewaEnroVLm6dFzl2q8Hir3QXFw+4mD209dNqxWAPXjjz/G/PnzYTAYEBgYqKiirkqHB1CVh1LGWVe+PPMAqvJQyjidZQ7wAKq0mMwWbD6QgJ/XnETKpcKK4/Sdqn+n+hg/tAXbKKoq0goulWec3ihwqtHCo1ln+HQaBveottBotKzekRJwFv+XWpfc3HLLLWx11NGjR5GYmMh+9KdSVK1bt2b3sRyOmvxdiXD7KdOGrUOa4+HO4/HlnkXs+Z8+WgRrIxCdnAhTQQ6SFr2O0DEvwr1RG0nejzZ5nHpPJ0z5aCNy8kux72Qalm08WyP1ULkPioPbTxzcfuqyYbUin1RPqnfv3mx3U6qBumHDhms+OJVJSkqSvY0Qealk1IBSxil3P6TUL0aX3HNAqCyfA8obp7PMAbF65LimOyqvBP+nLMBdR5Mx5cP/8NHP++2Cpz3ahOHzqQPwzLiOVQZPKXA6f89PmPLP69hwfltF8NTVZMagzAK8eOESBmcVwsvdF36970SDp75A6JiX4BHdngVPa2KcavN/qXXVBJR52qZNGwwbNowFUzt06MCDpxzV+rvS4PZTrg0HRvfCyBY3sf+pRvhCXx2y6pcHNC2lRUheMhMFJ3dK9n6Bvu54dnynGq+Hyn1QHNx+4uD2U5cNq5WBmp+fj6FDh8Ld3V36HnE4HA6Hw1E1h8+m48eVJ3DqQpbd8fZNg3HvzTFo1sC/yvYUOP3j+GpsjN1ul3FKgdPeOUXolV0ID7MFbg1bs9qmns26QqOrdtUijhNz9uxZrFixAhkZGTCZaKuUysHVWbNm1UrfOBwOp7YZ3+Z2JOWlYW/iIRQZi/G9vyemuLaH7uxBwGRE6rIPETT8Efh0KA+0iqUj1UMd1AxL19VOPVQOh8MRQ7W+TfTp0wc7d+5kBfk5jkHLxuRuI0ReKhk1oJRxyt0PKfWL0SX3HBAqy+eA8sbpLHOgXM+5GuuHmj4DzsRnscDpwdPpdsebN/BngdN2TYOrbJ9+OXD6XxWBUy+DO7w6DWf1TV2CIm/YJ+7/tatLTlavXo1nn322ys0HeACVoxZ/Vyrcfsq2IZXim9ztAby+4UPEZScgrfASfgyMxmNufVFydDNgMSNj5ZcwFebBr+coSTZaHDekOY7F1lw9VO6D4uD2Ewe3n7psWK0A6muvvYYHH3wQzz33HAYPHszqoF7rYtqlSxcp+qgqqNaap6enrG2EyEslowaUMk65+yGlfjG65J4DQmX5HFDeOJ1lDoitmSnHNd1R+Zr2//jUPCxafQLbDyfbHW8Q6o17h8egW6vQKr+UUeB02bFV2Bi347qBU796UfAZPhReLXtD61L1ZlN11f+l1KUEm92IuXPnIjw8HB999BFatGgBFxeX2u4SxwlxFn9XKtx+yrehm8ENL/Z5AtPWvoes4hycunQeyxp2xTiPW5G7+28mk7XxJ5gKcxA4+P6KEjjVRafTVqqH+sfGs7hDpnqo3AfFwe0nDm4/ddlQX90aBHl5efjnn3+wcuXKa9Y1oy9CJ06ckKKPqiInJwfBwcGythEiL5WMGlDKOOXuh5T6xeiSew4IleVzQHnjVOocyCrKYQ8r8cnxMLtmQ+NRArNrMc5nXrST93f3ZQ+p+uHMnwFpmYX4+d+T+G9vPGgPJytU15Q2h+rXMZJtLFFl4PTQn9gYvxcmXFHgajajV3YR+uQbEdyiB3xGDoNreJNqZcbUJf+XUpcSbHYj4uLi8OKLL6Jt27a13RWOE+Ms/q5UuP2cw4aBHv54oc/jmL7hQ5SayrDlwm5EtBmJgQPvReaGhUwmd/cKGLPTyjNRtVWHEXQe3tD7Bt+wHuobX++AxQL8uOoEYqIC0DIqUPKxcR8UB7efOLj91GXDagVQ33rrLeTm5mLSpElo1KgR9HpeV4zD4XA46mTtuS347dg/9gcbAZTjWAzgpbUb7V66s9UtGNt6BOoy2Xkl+GX9aazaHgej6UrGqL+3K+66qTmGdGsIg/76GSzp+Rn4dfdibEk/DtuqldbAaX94IbTjnfBuOwA6d+XszMlRFqGhoSguplnK4XA4nBvROKAhnur2AD7a/jV7vuTIXwjr+RBa3fIEW8ZPy/kLT+9mjxuh0RlQ//HPqwyiVqqHunAvPuH1UDkcjoLRWChd1EHatWuHp556Cg8//LA8vVIZlIlLacceHh6IiYmp7e5wOBwOR0QGKvHSyg8BQylQ5oJ3b37OoQxUNVNQVIZlG8/ir83nUFx6JfTp6W7AnQObYkSvKLi5Xv9H19RLF/HbrkXYmhsPk+bqwGkxbgpqgbBON8M9qo3oJYR1kUmLHkWeQQvvMjO+nTBf9fc9P/zwAxYsWIDff/8dAQEBtdYPZ0Qp55DD4dQ8VGt88ZE/2f8GnQFvDngWYRlpbEMpmI2C9URMnA3XsOgqZUwmM16dv53VQyU6x4TgtYndZKuHyuFwOGLue7TV/UWfCk5zHCc2Nlb2NkLkpZJRA0oZp9z9kFK/GF1yzwGhsnwOKG+cSp0DFAyNDmhQ8dDkmKC5/PFJf21fo8eNgqdyXNMdlZfa/4tLjfh9wxk89PZa/LLudEXw1NVFhzGDmuKbV25iAdRrBU/pd9yEc/vw2e/T8L+1s7Ap70rwlAKng/JNmBXaGw/d8xEaj3kZHtHtJA2ecv+vXV1yUlZWxso6UL3+Rx55BC+99BJefvllu8e0adNqu5scheMs/q5UuP2cz4a3xwxFv0bd2f9lpjK8v/ULFEU2QdDQhyR/L2s9VF+v8hrVe0+ksnqoUsJ9UBzcfuLg9lOXDau19v6hhx7C559/jn79+qFJkybS90rFmEwm2dsIkZdKRg0oZZxy90NK/WJ0yT0HhMryOaC8cTrLHBCrR45ruqPyUsnQ8vx/d13A0rWnkJlbUnFcr9NgWPdGGDu4Gfx9rr2hk7msBBcPrsHyU2uxS18GE9UvvVzD1MVsRj9449bWtyCkZR9odPKVCuL+X7u65OTDDz+s+H/z5s3XlKEA66xZs2qwV8COHTvw008/4eDBg8jOzoafnx+6d++ORx99FE2bNr2uzZcsWcKyac+dOwedTofo6GiMHj0ad999N09skBFn8Xelwu3nfDak6+IjnccjrSADJ9LPIrs4F+9tmYdXWt0py/vJXQ+V+6A4uP3Ewe2nLhtW6xvJyZMn2YV15MiRqF+/PoKCgtiNnC30Oi2b4tjj5eUlexsh8lLJqAGljFPufkipX4wuueeAUFk+B5Q3TmeZA2L1yHFNd1RerAzVKtt8IAE/rTmJlEtXdnenFXf9O9XHuCHNERp47d0ySzMSELf3b/yTuA+7PfUwGShoag2cWjDQIwK3db0bgWHy7MZ7Ndz/a1eXnND9qtL44IMP8PXX5fUFaUMECoJSZsXff/+NNWvWYM6cOSxBwRaz2Yxnn30Wq1evZvfXlLxAx44cOcIeGzZswBdffAGDwVBLo1I3zuLvSoXbzzltSEv3n+v1KF5Z+x5SCzJwIScRX5xcgTHVXcJ6A+Ssh8p9UBzcfuLg9lOXDasVQP3vv/9YwJSW8tPyqOTkZOl7plIoy0DuNkLkpZJRA0oZp9z9kFK/GF1yzwGhsnwOKG+czjIHxOqR45ruqHx1ZWi5/e5jKVi0+iTiknPtXuvRJgwThrVAg1Cfyu1MRhSc3o0L+1ZiTWE89vi4w+R1JdjjagEGBbbAqB73wterZmtVcv+vXV01BQUcMzMz4ePjAxeX8qWiNc1vv/3Ggqe0+er06dMxZswYFhCl3WWpvAAFQl944QWsW7cO3t5XNkebP38+C55SwPWrr75Cy5Yt2XHKYH3yySexZcsWFnh95plnamVcascZ/V1JcPs5rw19XL3wUt8n8cq691FYVoQDmefgFeiFWy7ly/J+9OPrsdhLrB5qRk4xPllyQJJ6qNwHxcHtJw5uP3XZsFo/INENnpAHpzIJCQmytxEiL5WMGlDKOOXuh5T6xeiSew4IleVzQHnjdJY5IFaPHNd0R+WrI3PkbAZe+HwLZn6/2y542r5pMD6c0hfTHuhaKXhqzL2EzE2LcXjeY/hq83zM1Gdgp69H+XJ9CpxCi1vrd8O8UbPxwE1Tajx4SnD/r11dcnPhwgU8/fTT6NSpE/r06YN9+/axJfQUvNy7d2+N9aOkpASzZ89m/7/44osYO3YsC54Svr6+7DUKmtKS/n///beiXX5+Pr7//nv2/5tvvlkRPCXat2+Pd999l/1Pq74oEMuRHmfydyXC7efcNozwCcWzPR+G9nLt8S3+Hth9ndI8Sq2Hyn1QHNx+4uD2U5cNBWWgJiYmIiIiQtQbxcfHs+X+dZni4mKUlpayGg5kU/rf3d2dZRRcvHiRyVA5BMrwuXSpfCfCRo0aoaioCGfPnoWrqyvCwsIQFxfHXgsMDGQ1r9LT09nzBg0asP8LCgqYvsjISJw/f5695u/vz5Z2paWlsefUB8ocJlnKhGjYsCGrqWWN8Lu5ubHX6H3p3NNNOd3EU+ZxVFQUk6V+UiaHp6dnRRZyeHg4k8vNzWVfDBo3bsz6QJkf9MWA5GnsBGUw09isN/y0JI3GZjQamU7qs3WyhISEMHtlZWWx57TkjXyKMqBppzSym9WGZE8aH2WaENTfpKQk9uWFxkW66AuV1d6kg8ZJkB1SU1PZuSJ703isRYtpB18av629MzIy2G5tZFvyb1t7U4YL6SLoXFDfrfam82p9T/riRH5ga2+yX15eHju/NFZbe1MKO42HIH+gdrb2pv7S+EmOdFvtTXal/tKXM4JkyQ5We9P4yKZEvXr1mF1s7U3nwuqzdD6t/Sd703NbnyV/sNqbzrOtz9ra2+qz5AdkLxq7rb3JVuSzNEbSR+9htTe1tfVZOl9kb5Kl80fjtPVZW3uTz1jtTeeYbG1rb6vP0nvS+9nam57b+qytvakftj5L7W3tTT5q9Vmyha296TzY+qwj14iUlJQKnxVyjbDa23qNIFuQPttrBPkz9aeqawS9LyHVNYLsRX2T6xphPeeOXiMImjvWawT1yWJzbbf6ltBrhNXeQq8RNC6yma29q7pG0Nisba93jaBjpM/2GkH2ts4dgvSRvWOT87B6bwaOx9lnnEaHe2FYp0A0jfRCdKQv81Hqq5ubK3wKUpG+4y/kpJ7ERj937Al2h0lzJePUBVr0CGqHQdH90aR+NHuf1Ms+S/202lvoNcLqs0KuEVZ7W68RJG97Hq3XCFufvd41gj7X6CHFNYLsTX4n1zWCzrktNF6h14ir7yOs1+TrXSOor0qA/MoaqKTg6dq1a9lxmqc0rokTJ+LHH39kgUi5ocQCOtfkp/fcc0+l18k/Xn31VXYdsK2DStmo5E90XgcOHFipHY2LdJKvkOwdd9wh+1g4HE7dom1oDCZ2vAvf7FvMni8P9kZAmQlNisqcrh4qh8PhiEFjobvsG0CF7UeMGIGHH36Y3fA7At3Q0XIj+jV9165dqIucOHGCfaGiL0T0ZcR2WZYQ6EubI22EyEslowaUMk65+yGlfjG6qtPWkTZCZfkcUN44nWUOkJ6H/nwdFkMxNGVuWDrhY1n7UVufASfPp+CPLRex/bB9mZ4God64d3gMurUKrcigI0yFecg7/B9y96/Bpdw0bKQsFV8KnF6RcdXqMbzZQIxocRNbGqgE6pL//+/PqcgzaOFdZsa3E+bL1lfb+56YmBjUFpMnT2bL3P/44w/mqz179mTZnD169GDB9HHjxrGA+DfffCN7X1577TX88ssvuP/++zFt2jTB7WipP20eNXToUHz22WfXlKGM1uXLl+P222/He++9J0l/lXIOlYBSrhHOCrefemz47dZvsCZxH/vfzWTGkwlZyNZr8XewN25Nz0PTqwKqERNnwzUsulrvtWjVCVYPlQjydRNVD1Up9nNWuP3Ewe3nHDYUet8jaAn/n3/+yTIOBg0ahAceeIDtHGrNirgaiseeOnWKydAv7EOGDGFZEKSDU76ES+42QuSlklEDShmn3P2QUr8YXXLPAaGyfA4ob5zOMgfE6pHjmu6ofFUyaZmF+HTJAbwwb5dd8LRegAeeGdcRnz03AN1bh7GAFH3mFyeeRtpfn+PiZw8jduNC/K7Lx/uNArHDz2apvs4Ft8cMxdyR72B8u1GKCZ4S3P9rV5ec7Ny5kwVJrZnNtlBCwPjx43H06NEa6QvdGxOUXUrzZv369Xj55ZfZfTUFepcuXcoyiq/GuqKgqlVc9OO8rSxHWpzF35UKt596bDg+uj9aFJT3pVinxfdhvlgZ6IU0Fz3WBHrZrc4hCmMPiaqH2rpxedaptR4qbS7lzPZzVrj9xMHtpy4bClrCTzeZ9Kv3nj178MMPP2DWrFmYOXMmWzZGy+lo2RHdDNKyIwq00hJAulGlpUYUSO3YsaP8I3ESaPmWdUmoXG2EyEslowaUMk65+yGlfjG65J4DQmX5HFDeOJ1lDliXXNdUP2rqMyA7rwS/rD+NVdvjYDSZK477e7virpuaY0i3hjDoy393NZcWI//YVuTuW43S1Fjk6LTXzjjVuWB4swEY0XywooKmddX/1WazG0EBSSprcT2oJEFN3ZRbyzzQe1LQlIK7tqxZs4bVMaVVW9aAKGEt20AlK260uYK1hAlHWpzF35UKt596bGjw9MX49ELM0+uQ4qpHpsuVUEKCmwFnPFzQrPDKD0FZ//0EvZc/vNv2r3Y91CkfbUROfmlFPdQ7Bl4pceJs9nNWuP3Ewe2nLhsKCqBa6dKlC3tQLa3Nmzdj//79LBOVBkQ1tKj2FxXppyX/vXv3rvJmj8PhcDgcZ8CYlwVTvk1gIjMRWphhYss4zChJLq8RaUXn5Q+9tz+chYKiMval5M/N51BcSqMqx91FizGDm+PW3tFwcy2/XSjNSEDuvjXIP7IR5pLC8sBpkFflwKneFcOa9MOtFDh148uWOLVDixYtWO3Ra9UcpTqtf/31F5o3b14jfbHWoKUl9lR7l7JPR44cyZaKUTCVEhOohuwjjzyCZcuWsXrMBNWjJaiG7/W4WpbD4XDkQO8bjCaPfY4XMxMwff9C5BqvXHM00OC/Zi3Qr/045GxbhsIze2htKtL//hwWYyl8Og5x+P14PVQOh6M0HAqgWqGNJqgoPz04jkEbeMjdRoi8VDJqQCnjlLsfUuoXo0vuOSBUls8B5Y1TqXMg98C/yN7yS8VzysH0bBSIXOjgqSlC4nfP28n79RmLgL53SdYPuT4DikuN+GdrLH7bcAb5NnXLXF10GNknGqP6NYa3pysspjLkH9/GapsWXzjGZChwuokCpz7uMGqdO3Bap/x/p4S6nIBHH30UTzzxBKZOncrKUBG0+RYtn//2229x/PhxfPLJJzXSF2umK2WUfvrppxg2bFjFa/3792cbg91yyy0siPrrr7/i3nvvrdjwSihXlymQAlpVRhuO1fTmhtfaALU2Njekmmv0unWTOaVsgHr15oa1tQGqkM0NyYZXb8gpdAPUqzc3FLMBqq3POroBak1vbnj1Bqjks2Szmt7c8JoboKaTHm/c3HQwlpz4u+JaYYEFsfkp+C/lItr2vAde3gHI37+m3FdXzUdBbhYKIjs5fI1oHdUAw7qGYdWuZLaE//2Fe/G/UQ3h5a536BpB50+ua4QSN0mW8hpBkC/KdY0Qs0myM1wjbO0i1zWitjZAbVxDmySTD1vHKtd9hNANUAVtIsWRriAtPehEOQJdBBxpI0ReKhk1oJRxyt0PKfWL0VWdto60ESrL54DyxqnUOXB1BirdnMw4/mPFZjzzBr3oUAaqHNd0R+Rpef6SVQewdl86MnOvLF/W6zQY1r0Rxg5uBn8fN8QdPwiftBPIO7gOpoLyG6Vcm6X6xquW6g9t2h8jnShwWhf9/60tsyTZROpGfVXSBkSUzUmlp+hmmW55rbV76Yb8mWeeYcvpawIqZ0V9oBqoK1asuKbMSy+9xDa8ok2uqGQWMWrUKBbofeGFFzBp0qRrtlu4cCHLYKUvHFQKQAqUdA5rG6VcI5wVbj/12ZCuodPWvofzWRdZ4NSWRn6ReG9I+UZ5mf8tQs6O5RWv+fcbB79edzj8Y4/JZMYrX27HsfPlwZXOMSF4bWI3aG1+wHUm+zkb3H7i4PZzDhsKve+pVgYqp/pQdF7uNkLkpZJRA0oZp9z9kFK/GF1yzwGhsnwOKG+cSp0DFAy1DYgaC67UByUc3WFWjmu6EHnK3Nh8MBE/rz6J5Evly4kJ+h4zoFN9tmFDSIA7is4fQsqaNTCd2Yvsy7+xXgmcesBo833FmQOnVrj/164uuRk9ejTb0HTbtm0sK4KyLygTpmfPniwzoaawZpBRWYHrQcFVwnajVmsfq6pha82m4aWz5MGZ/F2JcPupz4aHUk7gXFZ5tuPVxGUnYFPcLvSP6o6AAROgNbgha/MS9lrWpsWwlBXDv/89DgVRqR7q8xOqXw9VafZzNrj9xMHtpy4b8gBqDUMRbbnbCJGXSkYNKGWccvdDSv1idMk9B4TK8jmgvHE6yxwQq0eOa3pV8pQpsud4KhauOoG45Fy713q0CcM9w1og0keDvMPrEL//XxizypdbaSoCp57Y7ecBo02WSXngtHypvq/b9TfpcQa4/9eurpqAlqANHTq0VvtAy9woe52Wrl0P63J9WoJm246Cv9blitfCurSOMlA50uNs/q40uP3UZUO6p1h65C9oNRqYr7OQdf6eRWjoG4GogPrw7zMGGoMrMtcvYK9lb/8D5rISBN70IDSa8o0p5a6HqiT7OSPcfuLg9lOXDXkAtYahmg1ytxEiL5WMGlDKOOXuh5T6xeiSew4IleVzQHnjdJY5IFaPHNf068kfOZuBH1cex8kL9rtzt2kciAduaYkG+gzk7v4BF49vZ7VOrVDgdHM9f+zyNKAMlHFrUV3g1Ar3/9rVJSW0MdPdd9+Ndu3aVTy/EZQFRUv85aZ9+/bYunUrDh8+XFFK4GqsNbuonpcV61gOHDhwXd20qau1TABHepTq784Ct5+6bFhV9qkVk8WE1zbMxkt9nkDrkBbw6z4SWoMLMlZ/zV7P3bMSlrJSBA1/BBqt8DrPHZvXw5hBzfDLutNsVc3shXvxybP94et1/U32lGY/Z4TbTxzcfuqyofCffTiSYLssS642QuSlklEDShmn3P2QUr8YXXLPAaGyfA4ob5zOMgfE6pHjmn61/Nn4bLw+fzumfbHNLnjarIEfZk7qiImNL8Bj7Swk/fAy8o9sqgieUuB0VXQjzI4OwVZP3eXgKeCiM7Cg6ZwRMzCh3WjVBE8J7v+1q0tKqH6odUMC63Mhj5pgxIgRLGhKWaj//PNPpddp4wRrbVTbbNkBAwawDStos4eNGzdWard582Z2PmizDCpVwJEepfq7s8Dtpx4bWrNPNWyNStWUmsowc9Pn2HZxD3vu02kYgm99CricdUr11dP/+hwWs8mhPowf0hytosuDKRk5xfhkyQEWTHUG+zkr3H7i4PZTlw1FZ6DSjlZ0M0g7e1FBftrNinbF4nA4HA6HU7PEp+bhhzXxOHT+mN3x+iHeeKC3H6Jz9yNv5TewlBTCdiFxvocXtkdHY0tpBsrMhdaEUxY4HdqkH25tcRP8VBQ05aiTkydPVvm8NqHdZ++66y4sWbIEr732Glumbw140s69tKEV1Uil3W9vueWWinYUGJ04cSLmzZvHMmrnzp1bkWl66NChiizb++67j+3Cy+FwOHJhNBuRUZhZaeOo62G2mPHpju+QVZSLEc0HwbvtAGj0Lkj781PAbEL+sS0wG0sRcvsz0OgNNVIPlcPhcGolgLpv3z68/fbbbLcq4rvvvoPJZMK0adPYLqI333yzqI6plXr16sneRoi8VDJqQCnjlLsfUuoXo0vuOSBUls8B5Y3TWeaAWD1yXNPTsgqxeM0pbNh7EbaJGKEBrpjUtgT1s/9DyX/HYV8BFSgOb4xtEaHYmBOHsuLy2qfWwGn/Bt1xZ9sRqg+ccv+vXV1yQ5tG0dL5bt26sR/6ifXr17Mf+ym7syahe2RKPNiwYQOefvpphIaGsmVpp0+fZhskhIWF4ZNPPqnop5XHH3+cLf2ncYwbN47VRaVs1rNnz7LXaRxPPfVUjY6lLuFM/q5EuP3UY0ODzoB3hryE3OL8GwZOV5xej+0X97LnPx78DZlF2ZjQbhS8WvZiQdTUZR8AJiMKT+1Cyq/vIeTO56E1VL0Uv6p6qFQLlWqiKtl+zgq3nzi4/dRlw2oFUOkm7sEHH2Q3evfffz8WLCgvCk2/fFMG6tSpU9kv5v369ZO6v06P0WiUvY0Qealk1IBSxil3P6TUL0aX3HNAqCyfA8obp7PMAbF6pLymZ+eV4Nf1p7FyexyMpvLl9kRD7zLc1zgN9S7thflwDkps2tAXl6LGHbEz1B/rUw6jLOuMXeB0SJN+GNniJpgLjaoPnhLc/2tXl5zQ7vSPPvoojhw5gmXLliEmJoYd//vvv7F69Wr06NGDZXTW1OYEFBilTFJawv/rr7+yJAQKgkZGRrJs1AceeAABAZUDAJStOn/+fCxdupSNg2qlUtJCixYtcPvtt2PChAns/psjD87i70qF209dNgzyCGCPGzGl+0SEe4fgt2PlJUtWnFqH7KIcPNH1Png264LQsdOQ+uu7sBhLUXT+AFKWzkLo2JegdXGvVj3U9xfuwafPDYCP55VN+JRoP2eE208c3H7qsmG11tp/+umn7Gbvzz//xCOPPMLqoRBt2rTBX3/9xX4Zpxs9TmUyMzNlbyNEXioZNaCUccrdDyn1i9El9xwQKsvngPLG6SxzQKweKa7pBUVlWLTqBB6etRZ/bTnPgqcaWNDeMxWv1N+GZ12WIOjiepgLciraGALDoR8wDpsGjcRM80WsTtyHssu1TylwOqLZIMy5ZQbua38HC5wqxS/kRinjdBb/l1qXnHz88cc4c+YM3nrrLXZvauX9999nj4MHD+Lzzz+v0T5R5ijVQ6Xkg927d7OkBArmPvvss9cMnlqhAOk999yD33//nW0oRe3oPpwSGgwGYUtfOer2d6XC7Vc3bUjXurGtR+CRzuMrNs3benEP3tkyF4VlRfCIbofQca9BczlgWnzhKJJ/ngFTcUG166F+vHj/NeuhOqP9lAS3nzi4/dRlw2oFUOnGbfTo0ayo/dW7iHp5eWHs2LHshpXD4XA4HI50lJSZsOy/MyxwunTdaRSXmuCpKcYQz+N4J3QFHnRdg3oF52inh/IGWh08W/SA+5gXsKFbP0xL2oyV5zbDaDFWLMerCJx2uBN+7ryGIkc9bNq0iWV1jhkzhmVxWqH/R44cyTI3//3331rtI4fD4aiZwY374Plej7L7DeJI6km8seEjlo3q3qAlwsZPh9bNk71WkngKyT+9AVPh1QWHqq6H6utVfn2neqjLN5WXNuFwOBw5qPZuT7Y3oldTUlLCak5xrr2JgNxthMhLJaMGlDJOufshpX4xuuSeA0Jl+RxQ3jidZQ6I1VOdazplmK7aHotHZq3D9yuOI6+wFI306bjXaytmBPyOW1z3wr00q6KNzjsA/n3vhu/DH2BNdCM8f/gn/HN6PdsVlzBoDbil2SDMrSJwqhS/kBuljNNZ/F9qXXKSm5vLaoxeD6pBShs4cThq8Helwu0nHme3YeeIdni9/xR4uZQHSuOyE/DK+tlIykuFW0RThE14C1qP8pJBpSnnkbTodRjzr9zTCKmHas3pWrDyBE7EZqrKfrUNt584uP3UZcNqBVDbtWuHFStWXPO1wsJCVteJlvNzKpOYmCh7GyHyUsmoAaWMU+5+SKlfjC6554BQWT4HlDdOZ5kDYvU40p6Woi1ffwRPvLcB834/jPzcPPRwPY0XfFbgGZ9V6OxyHjqLqULePbodtP0mwueh9/G3txZTNn7ANnKoCJzqygOnL3d4DPffIONUKX4hN0oZp7P4v9S65ISW7a9Zs6ai1NTV0GZSSrop5ygTZ/F3pcLtJx412LB5UGPMGDS1on5qesElvLZuNs5cioVrSCOE3zsDOq/y18rS45G88DUYczMcqodKWOuh5haUqsp+tQm3nzi4/dRlw2oFUCdPnozjx4+zpU/Lly9ny/ipFtOPP/6I2267DQkJCXjsscek760KKC0tlb2NEHmpZNSAUsYpdz+k1C9Gl9xzQKgsnwPKG6ezzAGxeoS0p4DP7uMpmPLRRvyw5gIs2Ym4w2MXZvj/hrs9dyJCfyUzQ+vmBd9uI1H/8c/hPup/+MuYjKdXTmcbNtgGTm9uNpAt1afAqRtcZB+ns6CUcTqL/0utS07uvfde7Nmzh92T0nL+uLg4XLhwAVu2bMHTTz+NHTt24L777qvtbnIUjrP4u1Lh9hOPWmwY4ROKmYOfR0PfCPY8r7QAb/73MfYlHYFLUCTC75sBvW8we60sMxlJP76KsqwUUfVQ6ZFXUHzN2qicuuV/tQW3n7psWK0tOzt06MA2iZo+fTree++9ikL9RHBwMD766CN0795d2p6qBHd3d9nbCJGXSkYNKGWccvdDSv1idMk9B4TK8jmgvHE6yxwQq+dG7Y+cy8DClSdwOi4dbV0u4lbv02hiSK0k5xreFD6dhsIzpidyTSVYfHIt/j27qSJoag2c3tS4D25rMQT+Ntmm3P+VN05n8X+pdckJ/aiflpaGOXPmYPPmzZU2ZZoyZQqr6c/hqMHflQq3n3jUZMMAdz+8OfA5zN72JY6lnWb3LLO3fsk2mxoY3Ytloib99AaMWSkw5qQj6cfXEHbPdBZgFVIPlX54zskvZfVQn/t0Ey6k5KHMaIZBfwp920fgtn6NERXO673XVf+rDbj91GVDjeV665oEQE2PHTuG+Ph4VvM0IiICrVu3ZjelnCucOHGClTbw8PBgy8mqqh97vYi7I22EyEslowaUMk65+yGlfjG6qtPWkTZCZfkcUN44nWUOkJ7Hf3kaeQYtvMvM+HbCfEn6cTYhmwVOY8+cR0/XM+jhegbe2mI7GY3BFV6t+sCn41C4hkUjpzgXf51cizVXB061+vLAacxQu8DpjfrgqIwaqEv+L8ZvHemr7X1PTEwMlFALddu2bUhKSoLJZEJ4eDh69uxZ5a73dR2lncPaRCnXCGeF2088arRhmakMc3ctwPb4fRXHxrYegTta3gxTfjaSF7/JlvITVB+VNpuipf43Yv+pNEz/asc1X9NpNWyPzWfHd0S/jlUHZDnq9r+ahNvPOWwo9L6n2ptIEbR0nwKmw4cPxy233IL27dvz4OkNuHjxouxthMhLJaMGlDJOufshpX4xuuSeA0Jl+RxQ3jidZQ6I1XN1+/jUPLz7w258NedndEpcgtd9/8AQ9yN2wVNDYAQCh0xCg8lfI/iWx1HsH4SFB3/HUytew9+2S/W1evSs1xGfj5iBBzqOvWbwVOgYlOIXcqOUcTqL/0utqybw8fFh96qTJk3CI488ghEjRvDgKUe1/q40uP3Eo0Yb0gqZyT0msrrsVn45ugJf7/0ZGk8fhE94Cy6h0ey4uTAXyYumozjxzA31+nu74vJ+UpUw0ZJ+iwUf/bwfsUk5ko1F7ajR/2oSbj912VBf3Qjw119/zX7NT09PZ9mn1wqurlu3Too+cjgcDoejOtKyCrFs1X6UHNuEga6nEeSdby+g1cGzeTfkhbZGZI8h7HOVMk7/PrQMa85sQonpSj0gCpwOZhmnQ5CZmMGWyHE4dQ1aqj9kyBA0a9as4vmNoHn15JNP1kDvOBwOh2OLVqNlddnpnmXhod/ZsXXntyK7OBdTekxC2D1vIGXJ2yhJPAVzcT6Sf34ToXdNg3uDltfV+eemc9BQpmkVNU81GuDPzefwv7s7yjIuDoejXqoVQH377bexdOlShIaGsmX7Wq2oRNY6RVBQkOxthMhLJaMGlDJOufshpX4xuuSeA0Jl+RxQ3jidZQ6I1aN388YvP/8DzamNGGiIg97D/kdInXcgfDoOgXe7QdB7+8M1Oxu5JXks07SqwKk1aKoV0D3u/8obp7P4v9S6pIQCpg0bNuQBVI6kKNXfnQVuP/Go3Ya3thgMPzcfzNvzI0xmE/YmHcaMjZ/ixT6PI2z8a0j55R0UXzgGS2kRUhbPQMiYl+AR3a6SHtooavPBxBtuGEWZqJsPJGLKXR3YZwCnbvuf3HD7qcuG1Qqgrl27li1/+uCDD6TvkcqpTslZR9sIkZdKRg0oZZxy90NK/WJ0yT0HhMryOaC8cTrLHKiunrycXOz86w+4x25BZ10WcFUpH9dG7eDXeRg8mnaCRqtjx3KL8/DbqVXYFL/zhoFTR/rH/V9543QW/5dal5T89ttvaNKkScXz9evX12p/OOpAqf7uLHD7iacu2LBPo67wdfPGB9vmo9hYgtOXzuP19R9iWr+nEHrXK0j9bTaKzh+AxViKlF9mIWT0VHg262Kno7TMxDaMEgLJlZSZ4ObCyw/eiLrgf3LC7acuG1YrddRoNKJLF/sLFkcYly5dkr2NEHmpZNSAUsYpdz+k1C9Gl9xzQKgsnwPKG6ezzAFH9eQnxWLXt7MR//kjaHzxL4RT8PQyZTp3eHQagfqPz0HEPa/Ds3lXFjylwOmiQ3/gyRWv4t+4zRXBUwqcDmvaH5/fMgMPdhx7zaX6/DPAMZQyTmfxf6l1ScnDDz+Mv//+u+L5H3/8gYKCArZaqqoHh+OM/u4scPuJp67YsG1oDN4c+BzLRiUS81Lw6rrZuJifjtAxL8KjebdyQZMRqb/PRv7xbXbtXQw6GPTCwhsk52oo/7GaUzV1xf/kgttPXTas1k8uw4YNY1mod911l/Q94nA4HA5HYRhz0mEqzCt/kpmI0BIjvE0aeBotKEk+X3ZFkm0AAOQqSURBVCGn8/CG3jcYFmMZck/sQPymv+GRcx7B9KLNKrFs90iE9xqBeh37QmtwrThOgVNaqr/67CaUGEsqjlPgdFDj3ri9xVAEePD6ppwbY8zLgin/SrCe/NYWW78ldF7+rGSEM0O7p9reZNMS/kaNGlUs6edwOByOsonyr4+Zg57H25s/R3JeGrKKczD9vw/xfK/H0GrUs0j/ew7yj20BzCakLf+EZaR6tx3A2mq1GvRtH4GN+xPYMv2qaNbAny/f53A4DqOxVCMfln7Np11MTSYTBg8ejMDAwGtegG6//XbHe6RCTpw4wW7qPTw80LRpU+j1eoczfh1pI0ReKhk1oJRxyt0PKfWL0VWdto60ESrL54DyxqnUOUDB0/gvnobl8k73VaLTs9ql2Ue3QVdqvylUiUWPZJ/WaDr4dtRv2cbutdySfPx9cm2lwKleq8fAqJ4Y3XK44MAp/wxwDKWMU+p+ZG5eiuwtv9gdm9UoELl6HXyMJkyLs/8136/PWAT0vUuSvtre98TExKCmuPfee7F//360aNECnp6e2L17Nxo3bszuU68H3b8uWLCgxvroLNTWOVQiSrlGOCvcfuKpizak+6L3Ns/Fmcy4ivuhp7rdjx6RHZCx6ivkHbyyWXXQsEfg02ko+z82KQf/+2gTzAJCHBOGt8DYQc14IPUG1EX/kxJuP+ewodD7nmr1Yt++fTh+/DiKiopw8ODBa8rQhYgHUCuTkpKCyMhIWdsIkZdKRg0oZZxy90NK/WJ0yT0HhMryOaC8cSp1DlDmqaDgKRM2Im//GtguCksx+SIhoAu6jhyFgQ1CkZCQYPcFYcWpdVh1ZmOlwOng6N6sxmlRZoFDWaf8M8AxlDJOqfvh02EIPJteKbeUmpaKgiNLqEocCizuiJg4u1IGam31VSree+89vPvuuzh16hSysrLYvWhmZia7X+VwqotS/d1Z4PYTT120oY+rF14b8D98suNb7E86AqPZyP7Pan8nbr75UWj0Lsjdu5LJZqz+CmZjCfy6jURUuC+eHd8RH/28HxQXtc1E1Wk1ds8XrTqJ5IwCPHlne8FL/+siddH/pITbT102rFYA9f3332eR2eeffx5RUVHQ6Xj9EKEUFxfL3kaIvFQyakAp45S7H1LqF6NL7jkgVJbPAeWN05nmwI0wWTQ4VNoQyUFdMGTkTejZKNCuH1UFTgdF98LtMUMR6FEe0Dqb5FjdH/4Z4BhKGafU/aDl+LZL8ksLzDBfLn1Pf13Dop3eZlezadMmPPvss2zZPkGZqNOmTcOtt95a213jODFK9XdngdtPPHXVhm56Vzzf61F8vfdnbIjdzo4tOPgbMouyMf6mB6B1cUX29j/Y8cx1C2ApLYFf7zvRr2MkGoR648/N57D5QCLbMIoCpH07RGBkn2jsP5WOBf8cZ+3W74lHelYRXr6/C7w8rtrdk1On/U8quP3UZcNqBVAvXrzIgqfjx4+Xvkcqx9XVVfY2QuSlklEDShmn3P2QUr8YXXLPAaGyfA4ob5zONAeqYnNRM5wN6IMxY7vgzmas+mkFFDhdn7ID2w7tY7vMVhU4rW6/+WeAYyhlnM7k/0qx2bV+4H/11VcrAqjh4eF82RxHtf7uLHD7iacu21Cn1eHRLhPYSpzfjpVnnFKt+KziXDzeZwI0BjdkbVrMjmdtXgJzWTECBkxgmaj/u7sjJo/tgPNxF9A4qmHFUv3oCD+EBnqwLFUKrh4+m4HnP9+C6Q91R2igZ62OV4nUZf+TAm4/ddmwWneVlHWal3d5Mw2OQ4SFhcneRoi8VDJqQCnjlLsfUuoXo0vuOSBUls8B5Y1TSXOAyoMbs1NRnHgahad3O/Q+rYbejvt7d7WrqZVHNU5pc6gzGysFTgdG92SB0yCPANH9FirP/V9541SS/9ekLilxcXFhm5y2a9cO7u7uSEpKQkZGBvtbFRRo5XCczd+dBW4/8dR1G9L91NjWt8LfzQ/f7F/M7tG2XtiNnOJcTO31KAIMLiwDlcjZsRyWslIEDnkQGo2WbSzVqEFkpTqnvdtFIMjXHTO/34Wc/FIkpOVj6meb8erEbmjR8Nr3Y3WVuu5/YuH2U5cNq1XsY/LkyazgPi2VMpvN0vdKxcTFxcneRoi8VDJqQCnjlLsfUuoXo0vuOSBUls8B5Y2zNueA2ViK4viTyN6xHCm/vY+Lnz6E+HlPIv3PT1FwYodD79OmSVDFjToFThcf/hNPrngVy0+sqQieUuB0SJO++OyWN/FQp3HXDZ7eqN/Vlef+r7xx1pXPADm58847sXHjRrZknzY5JWbNmoVBgwZV+eBwnNHfnQVuP/FwG5ZzU5M+LGBq0BnY8yOpJ/HGho+Atv3YRlJWqDZqxj9fwmI2VWm/Fo0C8MHkvois58WeUyD1lXnbsPVQYo2Mx1ng/icObj912bBaGai//vor+5X/scceY+m0fn5+leqg0pfHdeuu7I7H4XA4HI5SMOZeQnHiKZQknEJxwimUpMRSFFUS3aVGE8pYjdP1WHXmP7uMU1qK1jmwNe7vPrbKoCmHw3EcKi/VpUsXtolUaWkp5s6dywKpzZs3r+2ucTgcDkcCukS0w+v9p+C9LV8gv7QAsdnxeGX9bLzS72kEG55G+oq5gMWMvEPrYTGWIvjWp6rUR0v2Zz/dB+8s2MOW8pcazXjvx71IuaUQdwxoUilzlcPh1G2qFUAtKChg9aWsNaY4wgkMDJS9jRB5qWTUgFLGKXc/pNQvRpfcc0CoLJ8DyhunXP2wmIwoSY2DZ+IBpB75iwVMTbkZVbbRunrAGBiNU4X+OJpUijEeu+xeP+NuwN/B3rg1PQ9Ni8oqjhdqNfgrfgfW7j6IImOxXeB0UFR5jVNdqQb+V9U5rQr+GSAvShlnXfkMkJv+/fuzB/HHH3/g9ttv51mmHNX6uzPA7ScebkN7mgc1xluDnsOsTXOQUZiJ9IJLeG3dbLzU90mEjXoGacs/Acwm5B/bwlYYBfR/oEp9tHnUGw/3wNzfDrJNpQjaZCo5owCP39EWel21Fu2qBu5/4uD2U5cNqxVAXbhwofQ9qSNotVrZ2wiRl0pGDShlnHL3Q0r9YnTJPQeEyvI5oLxxStUPU0EOq11anHASJYmnUZJ0lmUhVIUhMAKuEc1hCGuKI3m+WH6wCGeO5rDXInWX7GQtANYEeiHNRc/+NknIQpFWgy1+Htju546S+J12gdOBUT0xKmYYgjzLM05zjOV6hcI/A+RFKeOsK58BNcmGDRtquwscFeAs/q5UuP3Ew21YmUifMMwc/Dze2TQHF3ISkVdagDf/+xjP9HwYLe54HqnLPgBMRhSe2oWyogL43T0NWsP1N6Ix6LWYclcHhAV5YtGqk+zYv7suIC2zEC/d3wWe7uVlA+oi3P/Ewe2nLhsqpyd1hPT0dNnbCJGXSkYNKGWccvdDSv1idMk9B4TK8jmgvHFWpx9U34qyS3P3rUHaX5/j4rwnceGTiUj99V22kUDxxeOVgqcagyvcGraGX687EDp2Gho+8wNcx76LlZr+ePJPE2avSMGZhCtBTg83+98az3i4IMGt/Eaa/i4J8cF7jQLxX4AnSi5/wFPg9KbGffD5zW/h4c7jK4Kn1Rkn/wyQF6WMs658BsjJU089hb1799odo1r9J0+eRFFRUSX5v/76CzExMTXYQ44zolR/dxa4/cTDbXhtAtz98ObA59CqXjP2vNRUhtlbv8QufSm7v9PoXdjxsotHkbL0bZhLKn8O2ELL9e8a3BxT7+lUkXV68Ew6nv98Cwuk1lW4/4mD209dNhSUgUpLn6ZNm1axBErIUiheA5XD4XA4UmMqLmBZpaxuaeIpFCeegaW06htivV89uEW2QK6LP+p36A2Xeg2h0erYLq6HzqRjxZLj2HM8BWZKLbUhOsIXt/aOQo/Gbkj7eiUspjKWffpvgCc0FgssVBeLdHi7VbTRWSzo36ALRre7HcGeylluwuHUBei+c+jQoXbHcnJyMGrUKHz33Xfo0aNHrfWNw+FwONLj4eKOaX2fwpxdC7Ajfh/MFjO+3LMIma1vxc13v4rUX95h94nFF44hefFbCL37VejcPKvU2a9jJIL83PH297uRV1iK+NQ8PPfZZrw2sRuaNRBegonD4agPQQHU8PBweHh42D3nVI8GDRrI3kaIvFQyakAp45S7H1LqF6NL7jkgVJbPAeWN8+p+UICz7FJiRcCUNn0qS0+4vIj+2mh0BriENYZbZDO4RbSAa2Qz6L3Kb3b9SkvZBoiFxWVYv+cC/tkWi8T0fLv2ep0GvdtF4JZeUWje0L9i84D6j38OY0Eufj65FQnpV5bo4/LrWmjQP6wtbms2CGGhTR0ap6N2kUKe+38d/QzYKaEuJ4KuJRxOXfF3pcHtJx5uw6ox6AyY0mMi/N19sfJ0eemWX47+jczGfTBh3GtIXfo2LJd/gE9eNB1h41+HzsOnSp2togPxwZQ+ePPrnUjKKEB2XglenrcNU+/piB5t6lYshPufOLj91GVDfXVqnvIaqNWjuLgYp0+fZsvFEhMT2Q6x7u7uCA4OxsWLF5lMUFAQu9G/dKm85h5t1HXu3DkYDAa4uroiLCwMcXFxFcV0qR6ENaWZHIv+z8rKgo+PDyIjI3H+/Hn2mr+/P9ORlpbGntP/FEigDcH0ej0aNmzI3ofw8/ODm5sbex/6GxERwTI48vPzodPpEBUVxWSpn/Q+np6eSE5Orgiuk1xubi4LPDRu3Jj1gZbQeXt7M3kaOxEaGsqW1JFuokmTJuw9jUYj00l9TkigYAkQEhLC7EVjI6KjoxEfH4+ysjIW3Ce7WW1I9jSZTMjMzGTPqb9JSUkoKSlh4yFdFy5cqLA32dr65YrskJqays4V2ZvGExsby14LCAhg47e1d0ZGBgoLC5k969evb2dvsi/pIuhcUN+t9qbzevbsWfaar68v8wNbe5P98vLy2Pmlsdra28vLi42HIH8gnbb2pv7S+EmOdFvtTX2nc5Cdnc2ekyzZwWpvGh/ZlKhXrx6zra296VxYfZb00/9We9P5tfVZ8gervek82/osnRerva0+S35A9qKx29qbbEU+S+ejadOm7D2s9qa2tj5L58t67siXaJy2Pmtrb/IZeh/qH51jsrWtva0+Sw86d7b2pve39Vlbe1M/bH2WbGBrb/JRq8+SLWztTefB1mcduUakpKRU+KyQa4TV3tZrBLWldrbXCPJn6k9V1wh6X0Kqa4S5tAj1dKXIOLUPmowL0GbFsxveqrC4+wCBDeEV1RoIaog8nTeKdHqEWa8RKZfg4VHE7LZl11HsOZOPvadzUFJmttPj52VAj5b+6N8+FE2jI9ncOHcuk7XLKcnDujNbsCf9MDJLr12/tJ57IMa0GsNsZfW1610jrPYWeo0gO5K8rb2rukbQ+5E/VXWNoGM0NttrBM1V6pvVZ+kck57qXCNsfdbRawT1k+zmyDXC6rNCrhFWe1uvEdSPVq1aVbpG2Prs9a4RdE2lhxTXCLIFycl1jaD3sYYR6S+NV+g14ur7COs1+XrXCKv/cThqgOYEXXs41YPbTzzchjdGq9Hi/vZ3smX9iw4tY8fWnduC7OJc3Dn4MWg3fA1zYS5KU2ORtOh1hI2bDr131dmk4UFemD25L2b9sBvHzl9CaZkJ7yzYgwdHtMLt/RpX/Miudrj/iYPbT1021Fiq8bP8yy+/jLvvvhvt2rW75us7d+7Et99+i6+//lqKPjo9J06cYF+o6AsRffmgL1SOQF9yHGkjRF4qGTWglHHK3Q8p9YvRVZ22jrQRKsvnQO2Okz56jDlp5UvxKbuU/qZdgMZiH9i0Q6OFa2gUXCObwy2iOdwim0PnE1TlDazJZMbOYyn4Z2ssjpwrD8rZ0qZxEG7pHYXurUKhs9lllYJjB1OOYf35bdiXdIQtCbsR0/o+jfZhLWvlmu6oPPf/uvkZ8MrOubAYiqEpc8PSCR/L1lfb+56arDHaokULzJ49G7feemvFMQry09L977//vtISfqqB+uKLL7L+cpRxDpWIUq4Rzgq3n3i4DR1jS9xuzNvzI0xmE3vewDMc0zrdjbxf3ocpv/yHSENAGMLueQN6n6Ab6iszmvDZLwexcV/5D6DE8B6N8OioNnb3jmqF+584uP2cw4ZC73sEZaBezR9//IFevXpdN4C6a9cu9uBUhjI65G4jRF4qGTWglHHK3Q8p9YvRJfccECrL50DNjtNsLEVp8nm2DN8aNDUVlGfeWbk6DKr18IFbRDMWKKWgqWtYkyp3ULUlK68Y/+68gNU74pCRU2z3mpuLDgM618ctPaPQMMx+CVdawSX8d347/ovdjswi+/5VhVajwdIjf6FdaMwNMxLkuKY7Ks/9X3njrCufARyOs8H9XRzcfuLhNnSMPo26wtfNGx9sm49iYwkuFiRhxoGf8fyY56BZ9gmMOekoy0xG0o+vsiCqwT+0Sn0GvQ7PjuuI8EBP/PzvKXZs1Y44pGYV4sV7O8Pj8saiaoX7nzi4/dRlQ0EBVFpCNmLEiIolu8Tzzz/PHtejTZs20vRQZdByOLnbCJGXSkYNKGWccvdDSv1idMk9B4TK8jkg7ziNuZdQnHgaJQkny/8mn6coahUtNDAE12ebPVHAlGqY6v3DHFoeRVmtpy5ksdqmWw8lwmiyX2ARHuyJEb2iMbBzfXi6X7nZLTOVYW/SYaw/tw1HUk/CclWNVX83X8QEN8H2+H3XfW+zxYJzWRdwKOXEDbNQ5bimOyrP/V9546wrnwEcjrPB/V0c3H7i4TZ0nLahMXhz4HOYtXkOcopzkZiXgjf3/oAXb38Khr+/ZAFUCqRag6guQVXbmO5Hxw1tgdAgT3y29AC7x9x/Mg0vztmK1yd1R7C/O9QK9z9xcPupy4aCAqhUV+z111/H3r172RfU5cuXo1OnTuz41VAtLapLNm7cODn66/RQLTFH048dbSNEXioZNaCUccrdDyn1i9El9xwQKsvngHTjtJiMKE2Ns8suNeZWXi5vi8bV40p2KS3Hj2iK8/FJqF+NfpSUmbDlQAJWbIvFuQT7GqVaDdClZSjbFMpTm4NmTaMrXkvITcaGc9uw6cIu5JXkV7pR7hjWGoOie6F9aCu8tv4DaKCpFFy1awNhWahyXNMdlef+Xzc/A9Rms2tBtV6tNWkJaz1aqtdqe5yw1vDlcJzV350Bbj/xcBtWjyj/+nh70POYvu4jXCrJQlZRDt7c/S2eu/lB+K9ZiLL0eJjys5C08DWEjZ8O15BGN9Q5oFN9BPm5Y9b3u5FfVIa45FxM/WwTXpvUHU0i/aBGuP+Jg9tPXTYUvIT/jjvuYA+CNi544oknKtWS4nA4HI76MRXklGeVWgOmSWdhMV5ZoXAtDIHh5YFSll3aHIagSGg04upGpVwqwKrtcVi7+wLyCsvsXvP2cMGQbg0wvGcUQgI82LGzZ3PZUq6d8ftZbdNTGeUb3thSzzMQA6N7oX+jHgjw8KvIUM0ozKwyeErQ65eKsmA0G9mOsBwOp+aZNWsWe1zN1KlTa6U/HA6Hw6k96nkF4bEWd+OX+FU4kxmHorJivLv7BzwxcCwabfoLpSnn2eZSyYteR+jdr7Ef9G8E1c+fPbkP3vpmF5IvFSAztwQvzd2KFyZ0RtdWVZcD4HA4zk21aqAuXLhQ+p7UEWgXW7nbCJGXSkYNKGWccvdDSv1idMk9B4TK8jkgbJwWswllGQksUMqCpYmn2LKnqtAYXOEa3uTyRk8t4BrRDDoPb1H9sGI2W3DwdDpWbDuPvSdScfU2iE3q+2FEryj0aR8BF4OufAwWC2KzLmJVymbsPXQERUb7mqh6rR5dI9phUOPeaFWvGdvJ1RYKhr4z5CXkFudXZLR9um0OCg1aeJSZ8frNr1TIUs2tGwVP5bimOyrP/V9546wrnwFyMmrUqNruAkeFKNXfnQVuP/FwG4ojMjgCrzX5Hz7Z/g32Jx9lP3R/vm8x7u0+Eh32GNi9rbm4AMk/v4nQu6bBvcGNNwSNrOfNgqhvf78bJ+IyUVJqwszvd+Gh21pjZJ/GUBPc/8TB7acuG1YrgGrdCWvFihXIyMiAyVS+w50ttHzxWhkAdR2DwSB7GyHyUsmoAaWMU+5+SKlfjC6554BQWT4Hrj1OU3EBShJPXw6WnmaZppbSoirb633rVWz0REFTl5CG0Gh1ovpxNbRMav2ei1i5LRZJGQX276/Tok/7cIzoHY1mDa58wBaUFmLLhd3YcH4b4rKv7JxqJdInjC3R79OoG3xcvarsW5BHAHsQufpcWEdHf6MDGkg2zpqS5/6vvHHWlc8AOXnnnXdquwscFaJUf3cWuP3Ew20o3n5uelc83/sxfLX3Z7ZRKK0a+vHon8hqOwAD9HqUXjjG7ndTFs9AyJiX4BF97c2ybfH1csXMx3ri0yUHsPlgIvtR/+vlR5GcXsACqTqduJVWSoH7nzi4/dRlw2oFUFevXo1nn30WZrP5ujI8gHpt0tLS4OPjI2sbIfJSyagBpYxT7n5IqV+MLrnngFBZPgfKMzPLMpOQtm8zSsqyWQ3TsnQKNFaxVF2nh2tY48vZpeX1S/Xe0vwqeC17U20p2hTqv33x7Nd9W6gG1c09G+Gmrg3h5+1aMaYT6WdZ0HRHwn62/N4WV50LejbozAKnTQOjHNqkyrafYpDjmu6oPPf/uvkZoDabcTg1Afd3cXD7iYfbUBr76bQ6PNZlAgLc/fD78ZXstb/P/Ies6E4YpTeg7NxBVpIq5ZdZCBk9FZ7NutxQN612eu6eTggL8sTSdafZMarJn5JZiBfu7Qx312rnqykG7n/i4PZTlw2rNaPnzp2L8PBwfPTRR2jRogVcXFyk7xmHw+FwJMVcWoySpDPl9UtpSX7iaZiL8kC/j+ddp43Oy/9Kdiktxw+JgkYv76+ARpMZO48mY8XWWBw7f6nS6+2aBrFNobq2DK34dT+7OBebYndiQ+w2JOdVDhQ1CWiENl5NcVvn4fAwqHenVA6Hw+FwOBzOtaEfzu9qcysLon6zfzH74X1r/D7khDTHvbouMJ/eA5iMSP19NurdNgVeLXvdUKdWq8GE4TEIDfTAnF8PwWS2sDJTL83Zitcf6oZAX37fyeGohWoFUOPi4vDiiy+ibdu20vdI5dSvX1/2NkLkpZJRA0oZp9z9kFK/GF1yzwGhsmqfA3RDaMxJLw+UJpxkwdLS1Dgqanr9RhotXEKiKjZ6co1sBr1PcLWyNKuDl189LP73FFbviENmrn2dUndXHQZ2bsAyThuElv8CSasgDiQfZRtC7Us8DNNVY/M0uLPl+ZRt2tAvEiUlJXA1lGeqioH5xU6R7WtZXu3+7wh16jNgp4S6OJw6Avd3cXD7iYfbUHr73dSkD6tb/+nO79hqpSNppzDPLxKPtOwB7fEdgNmEtOWfsIxU77YDBL3P4K4NEezvgXd+2I2CYiPOJ+XguU83Y/pD3REV7gtnhfufOLj91GXDagVQQ0NDUVxs/+WWI4zMzEyEhYXJ2kaIvFQyakAp45S7H1LqF6NL7jkgVFZtc8BsLGU7iVZs9pRwCqaC7CrbaN29WaC01CcMwTFd2NJ8rYsbahK25D4uE/9sjcW2w0nsV3tbIut5sU2hBnSuDw+38szX9IJL+C92B6thdakwq5JO2giKgqZdI9rDRe8i+fkkPWLbS31Nd1Rebf4vhrr0GaA2m3E4NQH3d3Fw+4mH21Ae+3WNbI/X+k3Be1vnsbr5VC//E89APNa2DzwOb2FJB+l/z4GlrAQ+nYYJeq92TYMxe3JfvPHNTqRlFuJSTjFenLMFL9zbBZ1jQuCMcP8TB7efumxYrQDqPffcgwULFmD06NEICCjfVIMjjIKCAtnbCJGXSkYNKGWccvdDSv1idMk9B4TKOvscMOZlspql5Rmmp1CScp4tObo+GrjUq89qllozTPX+YSy7lDYFdG/YpAZ7DxSXGrFpfyLbFIp+obdFqwG6tQ7DLT2j0LZpEOuj0WTEzvj9LNv0cMoJVvzfFl83HwyI6oEBUT0R5l1P1vMpVo8c13RH5Z3d/6VEKeOsK58BHI6zwf1dHNx+4uE2lM9+LYIbY8agqZi1aQ4yCjORVnAJH7uW4LEOfRF4YDOTyVj9NcxlpfDrPlLQ+9UP8caHk/ti5ve7cOpCFopKTJjx7U48MqotK0HlbHD/Ewe3n7psWK0AallZGftCO3jwYHTu3JkFUa9e4sk3kbo2er1e9jZC5KWSUQNKGafc/ZBSf3V1GfOyoMtNQUmysF0pqf4mbVDkyPsJlXWmOWAxGVGaduFydulJlCSeZsvzq0Lj6gG3iKZwi2hRXr80vAm0bp61Ps7kjAKs3B6LdbsvIr/IfoMnLzcdhveKxrAejVDP34MdS8xNYRtCbYrbidyS/EqfMx1CW2FQ497oENYaeq2uyveWapxi9chxTXdU3pn8X26UMs668BnA4Tgj3N/Fwe0nHm5Dee0X6ROGmYOex6zNc3AxJxF5Jfn4zHgeD3Xqj/r7NjKZzPULWCaqX+87BZW2os1N3368Fz5evB/bDiWBFlh9uewwuw9+8NZW0FG2gJPA/U8c3H7qsqHGQusnHYQ2jrqhYo0GJ06cqG6/VAXZobCwEB4eHsx2jtYTpFPkSBsh8lLJqAGljFPufkipv7q6MjcvRfaWXwTL+/UZi4C+dzn0fkJllTwHTIW55VmliRQwPc02fqIaTFVhCAgvD5RGNGObPRmCIqC5QUCxpsZpNluw/1Qa/tkWi30nU3H1p07zBv64uVcUerUNg6uLHiXGUuyI38cCpyczzlXSF+wZiIFRPdE/qgcCPfwF90OqcZKeh356DHkGLbzLzPh2wvxav6Y7Kq9k/69p6tJnwN0/PQuLoRiaMjcsnfCxbH21ve+JiYlBTXHfffc53IbGQauqOMo4h0pEKdcIZ4XbTzzchjVjP1rG/8G2+TiWdpo912q0mODbHC33bqqQ8e1xOwIGTBB8PugeeOGqE/htw5mKY91ahWLqPZ3g5qqcoFBVcP8TB7efc9hQ6H1PtWbtyZMnxfStTnPu3Dk0adJE1jZC5KWSUQNKGafc/ZBSf3V1+XQYgky3ULtC0MlLZsBcmAuthw/C7n6tUgaqo+8nVFYpc8BiNqEsI6E8u/TykvyyzOQq22gMrnANbwK3iOYVQVOdR/nGStVBrnHmF5Zi3Z6LWLktDsmX7JdeGPRa9GkfwZYyNWtQfp43Hd6GM6UXseXibhSV2dfZ1ml1rKYp1TZtHdKc3dTW1jhJj9j2Ul/THZVXiv8rgbr0GaA2m11NQkJCbXeBo0KU6u/OArefeLgNa8Z+ni4emNb3KczZtYD9kG+2mPFj9gnc1rk3uu/dCgrf5OxYzjJRA4dMhEbAvahWq8H9t7REaKAH5v1+mAVUdx1LwcvztuK1Sd0R4FOzew9UB+5/4uD2U5cNneNnDw6HowpoOT4CIuAaFl1xTKPTV/y1Pa5WzMUFKE46U7HRE/1vKSmsso3etx5cI5uxgCnVLnWp17DCbkokNikHK7bGYuP+BJSWmexeC/Z3x809o3BT1wbw9XJFYWkR/j27idU2jc2Kr6QrwieUBU37NuwGHzfvGhwFh8NxNjZs2FDbXeBwOByOE2PQGTClx0T4u/ti5enyz5Q/s08ju2NXDN2/GxQyzd27igVRg25+TPBqr6Hdy8tTvfvjHhQWG3E2IQfPfboZ0x/qjkZh1U+C4HA4NUu1voG//PLLguTeeeed6qhXNX5+frK3ESIvlYwaUMo45e6HlPrF6JJ7DgiVrYk5QMsNKJu0JOHk5QzT0yhLpyBhFZVTKJAc2pgFSq1BU713gOJ9o8xoxo4jSWyZ/vHYyrt9t28WjBG9otC5ZSjbJOpUxjksPLaVbQxVarKvheqqc0GPBp1Y4LRZYLRkSzakmgNi9chxTXdUnn8GKG+cdeUzoLZIS0tDcnIyoqOj4erqyuppabWOZ7Jz6h7O6O9KgttPPNyGNWs/WuV0f/s7EeDuh0WHlrFjm3LjkNu+A0YfPgSD2Yy8QxtgNpai3q1PC05q6NC8Ht5/qg/e/HYn0rOKkJFdhBc+34KX7uuCji2uvQGqEuD+Jw5uP3XZsFoB1D/++KPK1wMDA9nGUpzKuLm5yd5GiLxUMmpAKeOUux9S6hejS+45IFRWjjlgLi1GSfLZK9mliadhLsqrso3O0w9u9VvA9XJ2qWtoNDR6A2oSMefzUk4R1uy8gNU74pCVV2L3mrurHoO7NsDNPRshsp43copz8c/pday2aVJeaiVdjXwjcVOTvujVsDM8DO5Q6hwQq0eOa7qj8vwzQHnjrCufATXNvn378Pbbb1fU5f/uu+9gMpkwbdo0vPTSS7j55ptru4scheNM/q5EuP3Ew21Y8/ajH+9HtrgJ/m6+mLd7AUwWMw7kJyKvdQzGHz8FD6MRBce2IrWsFCGjnhV8794wzAcfTu6Lt77bhbPx2SgqMbKA6uOj27JNVJUI9z9xcPupy4aS1UA1m81IT0/HypUrMX/+fHzwwQdS9E91pKSkOFy/wdE2QuSlklEDShmn3P2QUr8YXXLPAaGyYucAZZcac9Ivb/RU/ihNjaOiptdXqNHCJSQKbpRZSsFSyi71Da71wuKOnhMaO2WZrth6HjuOJMNEW4va0CDUm9U27d8xEm4uOhxOPYml27Zib+IhdgNqi6fBHX0adsPA6J4wXipxijlAemqyH/wzQF7q0meA2mx2Iw4fPowHH3wQYWFhuP/++ys2i/L19WUZqFOnToWnpyf69etX213lKBhn8Xelwu0nHm7D2rNfn0Zd4evmzTaXKjaW4GxhOr5pEY37TsfBr7QUhad3I+XX9xBy5/PQGlwF6fT3ccM7T/TCRz/vZ/fRVBd17m+HkJxRwOqlUt1UJcH9TxzcfuqyoWRF9GgZVEhICLtRvXjxIt599118//33UqnncDicWsViLENJyvnLwdKTKEk8DVN+VpVttO5elzd6asGCpq5hTaB1Uc4vaI5SXGJkdU1pmX5ccq7da3Sz1711KEb0ikbrxoG4VJiFf86uwX+xO5BRWHlJf8vgphgY3QvdIzvARe/Cjp29dLbGxsLhcNTPp59+isjISCxbtoztrPrDDz+w423atMFff/2FcePGsR/9eQCVw+FwONejbWgM3hjwLN7ZMpetpkoqzsaXjcPxwIVUhBYWoej8AaQsfRuhY16G1lXYCio3Fz1evK8LflhxDMs3lW/yuGzjWaRkFuCZcR3Z6xwOR3nIMjNbtmyJ5cuXy6Ha6YmIiJC9jRB5qWTUgFLGKXc/pNQvRpfcc0Co7I3kjHlZCChIxKV1W1GccBolKecAk7GKFhoYguuzzFK3iGYsaGoICKv17FIpbJGUno9/tsdi/e6LKCi2t4GflyuGdm/Ilh35+bhgX9JhvLtlCQ4mH4flqlqvvq7e6BfVg2WbhnuHONwPsUilX6weOa7pjsrzzwDljdNZ/F9qXXJy4MABPPHEE2zpV1FRkd1rXl5eGDt2LD777LNa6x/HOXAWf1cq3H7i4TasfftFBzTAzEFTMWvTHCTnpyG7rBDz6wfi3qRsROflo/jCMSQvfguhd78KnZunIJ06rQaTRrZGWJAn5i87DFrQtf1wMjKyt+HVid3g762MpAvuf+Lg9lOXDWUJoG7atIktieJUJicnB+7u7rK2ESIvlYwaUMo45e6HlPrF6JJ7DgiVtZWzmIwoTbtweaOn8vqltDy/KjSuHnCLaFpRu9QtvCm0Am+YlMa1bEbL8vedTMU/W2Ox/1RapTYtGvrjlt7R6NU2DOlFGVh9fhU2xe5ETol9zVcKILcPbcU2hOoY3gb6KnYrdZY5QHpqsh/8M0BelDLOmvB/tdlMCC4u5Rnu16KkpISVoOJw1OLvSoTbTzzchsqwX4hXMGYMmop3t8zD2cw4FJlK8V2oN+7S69AmK4etTkteNB1h41+HzsNHsN6be0ahnr8H3l+4B0UlJpy+mI2pn27G9Ie6o0GocD1ywf1PHNx+6rJhtQKoL7/88jWPl5aW4tSpUzh37hzuu+8+sX1TJfn5+bK3ESIvlYwaUMo45e6HlPrF6JJ7DgiRNRXmIv/0HmSeyC3f8Cn5LCxl9hsgXY0hIByuVLv0csDUEBQJTRXBQGfC1mZ5haVYu+siVm6PRWpmoZ2ci16Lfh0jcXOvKNQP9cCuhAOYufkXnEg/U0lnkEcAyzTtH9WD/e9oP+RAKv1i9chxTXdUnn8GKG+czuL/UuuSk3bt2mHFihXXvCelJf2//vorW87P4ajB35UKt594uA2VYz8fN2+8PuB/+Hj7NziQfBRGiwk/B7riVn0AeqZnojQ1FkkLX0PY+Deg9/YXrLdzTAjee6oP3vpmJzJyipGWVYQXPt+Cl+/vinbNglGbcP8TB7efumxYrQDqH3/8cd06qEFBQXjggQfwv//9T2zfVIlOp5O9jRB5qWTUgFLGKXc/pNRfXV2ZucVIulQCuGVXHNOYLKBF7kaTBWcTrhwnAnzc2MOR97ta1mIxoyw9gdUtLU48zbJLyzKToAVg/25X0Ohd4BreFMVeIQhp1ZUtydd5+kKtkM3I9iu3xWLT/gSUGu0zsuoFeOCWno0wuGtDZJamYv351diyezcKy+yXxOq0OnSJaMeyTdvUa8E+Exzth5xIpV+sHjmu6Y7K888A5Y3TWfxfal1yMnnyZNx7772YMGECBg0axDLiaWOpM2fOYOHChUhKSsKbb75Z293kKBxn8Xelwu0nHm5DZdnPTe+KF3o/hq/2/oz/YrezglV/+eqRowvC0JQMlGUkIGnhqwi/5w22WaxQosJ98cGUvnjr2104n5jDymZN/3oHnryzHW7q1hC1Bfc/cXD7qcuGGgttp8yRlRMnTrBMBw8PD8TExNR2dzicWuPnNSex+N9Tdsfe9PsNftpCZJs9MD37TrvXxg1pjvFDWzj0HubiAhQnnUFJwmkUJ1LQ9AwsJfaZlFdDNzeuEc3gxjZ7ag6Xeg2h0am/eHuZ0Yxth5Pwz9bzOHmh8oZYHZvXwy29oxDT2Ac74vdiw/ltOJ91sZJchHco2xCqX6Nu7Jf5usCkRY8iz6CFd5kZ306YX9vd4XAEcdeiZ2AxFENT5oalEz6uE/c927Ztw/Tp05GQkGB3PDg4GK+++iqGDh1aa31TMko6hxwOh6NEKIyy9OjfWHZ8VcWxjsXA6IQ0lqWm9wlC2IQ3YfAPdUhvUYkRHyzah93HUyqOjRnUFBOGxbBNWzkcTu3d98gWIfjzzz9x2223yaXeaaHyBo0bN5a1jRB5qWTUgFLGKXc/pNRfXV200VB91wyE+V8JshX8XZ7tqNOY8fF9ja4Iu3ohIDyiyvejG5eyzGSUJJ4qr1+acAql6fHQXLV5kR06PVxDo8uzS1t3Y0vy9d4BivYNqcnILsLqHXFYs+sCsvPsSxd4uukxqGsDDO/RCPmaNGw4/y8+/3sfSkyldnIuOgN61u/MAqfNg6Il2SzLWeYA6anJfvDPAHlRyjhrwv/VZjMh9OrVC2vXrsWxY8cQHx/Pap7SZgStW7eGXq/+H8o4dcvflQi3n3i4DZVpP7r3vbvNSAS4++Hb/UvY95L9bkB+wxDcczENyM1A0o+vImz8dLgE1xes191Vj2kPdsV3fx3FX1vOs2O/rj+D5IwC/G9cR7gaajYbj/ufOLj91GVDwXeORqMR69atw6FDh9jFgaKyI0aMqJROm5iYiNdffx3bt2/nAdRrUJ2EX0fbCJGXSkYNKGWccvdDSv3V1eVjyUP4js+hMV/Zyd37ctzNW1MMrHir4rhGZ4DP45/TQpmK9zOXlaAk6Wx53VIKmiaehrkw1+49rg7j6Tz94EqbPLHs0mZwCY2GVu+Cs2fPwqtJE1nGqURoLEfPXcI/22Kx42gyzLTVpw2NwnxwS68odGzli11J+/Dh3mVIzLvyy7eVaP8GLGjau0EXeLi418k5IFaPHNd0R+X5Z4Dyxuks/i+1rpqAvuRSwJQeHI7a/V1pcPuJh9tQ2fYb0qQv/Nx88OnO71BmKsNpgwVfN6qH+y+mwzs/C0mLXkfYuNfhGholWKdOq8HDt7dBaKAnvvnzCOi2feuhJJYE8erEbvD1ckVNwf1PHNx+6rKhoADqpUuXMGnSJLZBlLXzdDP6zTffYNGiRfD1La8NuGDBAnzyyScoKipCp06d5O25k+Lj4yN7GyHyUsmoAaWMU+5+SKm/urpMhXl2wdOqsJjKUJJ2gdUudT1zEAmb4llhdliq2DFZo4U2MAJeDVuxgCkFTml5/rWyI+vKHKBlQP/ti2eB04speXav0TKgHm3C0LOlH3wiSrEhdi0WrDkEk9lkJ+dhcEfvhl0wKLo3ovyF/4KuljlgzMuCKf9KiQPPUvvquSXJ5dkBVnRe/lVuHCDHNd1R+bri/0JQyjiV6v9y65KS6mxgSp8PdP+qhHqta9aswcMPP4ypU6dWet1kMmHJkiX4/fffWSYGJTBER0dj9OjRuPvuux2uOc1xfn93Frj9xMNtqHz7dY1sj9f6TcF7W+ehoLQQCToLvmgYjInxGQgqzEXyT9MReverbF8FR7i1TzRCAj0we+FeFJeaWMmtqZ9txuuTuqN+SM2UzeL+Jw5uP3XZUFAA9aOPPsLJkycxbtw4jBo1Cu7u7ti8eTPmzJmDmTNn4u2338YzzzyDDRs2sMFNmzYNY8aMkb/3Toinp6fsbYTISyWjBpQyTrn7IaX+mrJZ6i/vVPm61t2LLcEvzzBtDtewJigqM/E5ACAhLQ8rt8dh/Z6LKCy2D1r7e7tiaPdG6NbBFwczDmDxuT9w6VzlGqgxwU3ZhlDdIjvAVe9SZ+dA7oF/kb3lF7tjmkaB5X81FiR+97zda359xiKg712S9YN/BsiLUsapVP+XW5eUXF3n1FlYvnw5C55eDyo78Oyzz2L16tUs4NukSRN27MiRI+xB999ffPEFDAZDjfa7rqBUf3cWuP3Ew23oHPZrEdwYMwZOxdubP8elwixkai34okEQHki4hPrFBUj++U2E3jUN7g1aOaS3a8tQvPtkb7a5FG3Im3KpEM9/vgWvPNAVbZoEQW64/4mD209dNhQUQN2xYweGDBnCivBbadq0Kdzc3PDBBx+wG7b169ezHU7feustBAaWf7HkVCY5OZnd+MrZRoi8VDJqQCnjlLsfUuq36qKMdIuxFJYyehSzJfb0v7msGBbr/8YSWEqLmVxpZrKId9XAEBzJAqYsWBrZHIaA8ErZpckXzwoapxrngMlswd7jKVixLRYHT6dXej2mUQCG92oAt8BMbLywHn9tOlZpSYSPqxf6R/XAwKieCPdxrOi9WueAT4ch8GzapeI51VEsOL0MQCkKLO6ImDi7UgaqlP3gnwHyopRx1oT/q81mV0OBRGeDbEnJCFUxf/58Fjylja+++uortGzZkh0/ePAgnnzySWzZsoUlNVAyA0d6lOrvzgK3n3i4DZ3HfpG+YXh70AuYtXkOLuYkokBjwdeRARifnI0WhcVIWTwTIWNehEd0e4f0No70w4dT+uKtb3ciNikXBUVleP2r7Xh6bHsM7NwAcsL9TxzcfuqyoaAAakZGBnr06FHpeN++fTFjxgy2YRTtZDphwgQ5+sjhcKqBXXDTWALkpqEkWcv+Z4HO0pLy4CYLdF4+VvG/fUCUtSktYX/pdU1xIWLNRva/nLiGN4V74w7I1Hghqkt/6NyU8+uTksjJL8Ha3Rexanss0rKK7F5zMejQv2MkunX0xpmCw/g5bgVyzlSuG9s+rBWrbdoprA30Or6xii20HN9uSX6BGWaUL5elv65h0bXXOQ6H49Sf0y+99BLy8vLY6i4qgXU1+fn5+P7779n/b775ZkXwlGjfvj3effddPPTQQ6wMwcSJEyvKanE4HA6ndgjw8MObA5/FB9vm41jaaZRqgB/D/DA6LRed84qR8ss7CBk9FZ7Nrvw4L4QgP3eWiTp70T7sPZEKo8mCjxcfQHJGIcYPbS7Jhq4cDqdqBH1LLi0thZeXV6Xj1mPjx4/nwVOBhIeHy95GiLxUMmqgNsbJgpumMhbEtAY0g/WlKI4/YRPIvJLJaQ1oVvzPApr0vPx/2wBoRdCTBTevZBdSuCdRov7Tx3NNlHIOGvYIC065FRZC5+Yh6flUwxw4E5+FFVtjseVgIsqM9rVhQwM9MLRnJHzDs7AtYQM+2HemUvtAD3+Wado9tAPqB0WgtpHb3lLpF6tHjmu6o/Jq8H+pUMo4ncX/pdZVl2ug0vvu3LkTQ4cORVZWFnbv3l1JhjZwzcnJQVBQEAYOHFjp9T59+qB+/fosM55k77jjjhrqfd1Bqf7uLHD7iYfb0Pns5+nigWl9n8KcXQuwI34fzBrgtxAf5Oi1GJhViNTfZ6PebVPg1bKXQ3o93Ax49cGu+Gr5EVaqi1iy9hSSMwow5e72MOjtN/iWAu5/4uD2U5cNJUkzokxUjjAok8DDw0OyNsacdLYxjy05WZnQ+QfYHdN5eLPNdBzpR3X66oxcPc6K4GalzMyrlqazv5SlWR6svDpL0z6j07Z95eCmU6DRQmNwhdbgCrNWD72bB/tfY3CB1uDG/pa/fvl/fbksHbO2M+ZnIXPdDw6/tSO+KFTWWedAmdHEduH8Z2ssTl20r1tKPzx3ahGCLh3dkWw5gX8urEJBqn1Gk06jReeIdqy2aduQGLbxSFpaGpSA3PaWSj/pqcl+yCHvrP4vB0oZZ034v9ps5sw1UGkjKNpjgMpevfHGG5gyZco15Q4cOMD+0uas18suotcogEoBWB5AlR6l+ruzwO0nHm5D57SfQWfAlB4T4e/mg5Vn/mPH1gZ6IU+vxcj0fKQt/4R9J/RuV/nHsarQ6bR4bHRbhAd74du/joKqcW06kID07EJMe6ArfL1cJR0H9z9xcPupy4aSBFD1er7cUyi5ubmoV6+eJG0oeBr/xdMs2Hc1V2caanQG1H/884ogqpB+VKevcmMf3LwSxLzyv03A0hrItMnyvLpWJ/1fXJCHYg2uBECdPLh5zUCmiyvyi0rhGxBkF9DU2AVArf/TXzdoL7dlsi6ugFZf8eXt7NmziKpGHZKrdysXiiO+KFTW2eZAelYRVu2Ixb+7LiAnv9TuNU93AwZ0CUVgo0zsS9uIH85cqNQ+3DuELdHv16gbfN18FDlOufshlX7SU5P9kEPe2fxfTuqS/6vNZs5aA7WsrAzPP/88SkpKWBA1IMD+R29b4uLKM4woy/R6REZG2slypEWp/u4scPuJh9vQee2n1Whxf4cxbFn/okN/sGM7fT2Qp9Pi7tRcpK+Yy5JxfDsPc0gvfSe7rW9j1PP3wAc/7UNpmQnHYzPZ5lJvPNSdBVelgvufOLj91GVDwZHP7OxsJCUl2R2jJUVEZmZmpdeUlmqrFKpTm+R6bSjz9FrB02tBciRvDaAK6YejfWWb0ZiMVy0lt8nMrCJL03bZuW2WZvkGRPYBUFjslyqLhUZpv0e51GigcbkqG/NyYNI2kJlXVAK/wGC7IGbF/yRv1WENatq0h+5KcPN65J87h6DGjaUZUQ3X2HHk/YTKyjEHpIbm1OGzGfhnWyx2HU2G+aq4fqNwb3Tp7IYclzPYmrgGJSftA6suOgO61+/Isk1bBDW57nhqe5w11Q+p9IvV42h7OeSdwf9rCqWM01n8X2pdtQ3dw1YVwJSDuXPn4tixY7j99tsxePDgG/aPqKqPfn5+7C+VAeBIj5r8vTbg9hMPt6Fz24/ef2SLIfBz88UXu3+EyWLGMS83fKPT4v7kHGDN1+y7rl/32xzW3aNNGN59shdmfLsLWXklbCn/1M8245UHu6FVdKAq7OfscPupy4aCA6izZs1ij2sxderUaw7y+PHj4nqnQhpXI4B1vTamQseySYx5mdD71WOByfp+7ihJibXbUOjqLE3/shJcit1aOdBpvHoDoitL06UObsqP5qrA5LWzMa/8bxMIvZzZeXVA0zYTlHQJCW4S9RToezWhS+r3EyorRK6mx2mlsLgM/+2Nxz/bYxGfar/sVqfVoGvbAIQ0ycSx7K1YkVJ5Z+0ov/oY1LgXejXowmowKXWcNd0PqfQzPTtqrh9yyCvZ/2uaOuX/OyTU5SQsXryY7VJfWFgIs/nKPYrJZEJBQQFbUXH06NEa68+hQ4fw1VdfITQ0lG3AeiOsG0u5ul5/Saabm5udLEdanMnflQi3n3i4DdVhv75sFZg3Ptz2FYqNJbjg7oIvI/0xMSkbWP8j+17t13uMw8GipvX98cHkvnjr2524kJKHvMIyvPrldky5qz36d7r+6gVns5+zwu2nLhsKCqCOGjVK/p7UEc6fP4/o6GhJ2uSf2umQntRf34FzoblqifmVIGaVgczL/19Zzu527aXtdExnQGxsLBo6eE6U4hu1pb+6uqgWL5UCgPnGOb90bpi8g+8nVFaInNzn5GriU/NYtumGvfEoKrG3kb+PCzp11qPEOxYHU9fi4EX7190NbujToCtbph8d0MCh963pcdZWP6TST3pqsh9yyCvR/2uLuuT/arPZjfj666/x4YcfwsXFhW18ShmaFLikVVUUbKTA47333ltj/aH3fOGFF1ggl5ISvL3LP+OqQqfT1WqGRnFxcXnZnqgoJCYmso1l3d3dERwcjIsXLzIZ2uCKVkxcunSJPW/UqBFSUlJYWwr8hoWFVZQXoJqvVHs7PT2dPW/QoAH7n2xD54nKEVh91d/fHwaDoaJON5UxoIxcCnxT+bCGDRuyWrLWLFw6n/S+REREBFspR3XTyIbUf5Klfvr4+MDT0xPJyckVK+ZIjpYIkg3pixr1gc4TnSPrcYL8h/pqXYXXpEkTNjaj0ch0Up+ttXhDQkKYvayZwTRnqFYtlXCgWm5kN6sNyZ4U1LdmHFN/aXUflXmgcZGuCxfKy/NQOyIjI4P9JTukpqZW2JvGQ/e21sxlGr+tvakd/aBAtiWb2tqbzgHpIuhcUN+t9qbzSr5A+Pr6Mj+wtTfZKS8vj51fGqvV3mQDamtdrUj+QDpt7U39pfHTPCXd5GtWe9O4aM4SJEt2sNqbxkc2JWh5J9nW1t50Lq7ls/Q/nV9bnyV/sNqb3tfWZ6mfVntf7bM0dlt7k61sfZbew2pvamvrs3S+bO1N47T1Wau9aVzU1tZnyda29rb1WXrY2pve39Znbe1N/bD1WbKBrb3JZlafJVvY2pvOg63PKvUaQeMnm8l1jSB5W5+t6hrR3D8ak5qOwQ9nlqHAWIg0Fz3mWYOom5ciOyMNptZD4eHp6fA14tGbI7BgbTxOxRfAaDLjw5/34/iZeDw0ujOzUXWvEfQjI51/ua4RZD/yRbVeI+g92UpdGa8RZBM6R2q9RpSUUIlFyHofQX0VgsZiPZsc2Thx4gRzSnIqOoHklI5AE+NabQrPHUTKkhmo/eBm5YAmO+7idjmQeTlYaa2reVUQs1JQ9PJydQqg1US69vXsW9PI3Q8p9YvRdfbQHtSvd2VJyalvX4OXphj5Fjc0nzTjmhufOfJ+QmWFyNWEb5hMZuw+nsICp4fOlH/w2tK8iTvCm2XiXOFRpBeWf6jY0iKoMQua9qjfCa56l2r1gc8Bx/W8snMuLIZiaMrcsHTCx7L2Qw55pfi/EqhL/i/Gbx3pq+19T0xMDGqL4cOHsy86CxcuZF+YbrrpJqxdu5Z9sVi6dClmzJiBjz/+GMOGOVZ7rrrQZlGUETt+/HhMnz7d7jUK5NImUA8//LDdyi5KYqAVXRR4nTRp0jX10vhmzpzJvnCsWbNGkr4q5RwqAaVcI5wVbj/xcBuqz36p+el4e9PnSMkvDwK5msy4LyUHjYvK4NN5OAKHTIRGo3VYLwVOv1x2GGt2XtkPYWDn+nhqTHsY9I7rU6r9nAluP+ewodD7Hr77Uw0jJNtAaBudh/1GMDfCJaQRdJ7+LHBZYrLAw9v32hmdl2t0ZhcUIrBeaKWAaPnGRC6KqkVR0+fEGfshpX4xurxDG8A1JKTiuRm6ir+uYdGi30+orBA5Oc9JTn4J2xBq5fY4ZGTbL7t0ddGgdQczLP4XcDLrFC5m2P/O5ePqhX6NurPAaYRPqOi+8DlQs3ocbS+HfG37v5JQyjidxf+l1iUnlOnw7LPPsgwKa9bK3r17WVCSgpj79u3DggULaiSAunnzZhY8pUwJ2kBKKJQ9QVizPa6FNZumpmu51hWcxd+VCrefeLgN1We/EK9gzBz0PN7ZMhfnMi+gRKfFd+F+uCslF233rmJ7hgTf8hg0WuGrEAi9Tosn72yH8CAvfL/iGDtGK9toM9qXH+gCbw8XVdjPmeD2U5cNeQC1hqEUdanaOFoDNWDAvfBo3J79TynOlDpdFVpa3nYDmbp6TpyxH1Lqr66urKIcZJpzUZB5JQ0/xVWLPI0eBRYtzmeWp/Fb8Xf3ZQ9H3k+orBA5Oc7J6YtZWLH1PLYcTGK/EttSL9SCBjHZSDCewPGSXKB8xQNDAw3ahsawDaE6h7eFXifd5ZvPgZrV42h7OeRry/+ViFLG6Sz+L7UuOaHlWbSEzwot1Tp16lTF827durEM1Jpg5cqV7C8tV+vQoUOVZQfoQcsdN2zYwJbHbdu2rWK54rWwLq2jDFSO9DiLvysVbj/xcBuq034+bt6YPuAZfLz9GxxIPgqTRoPFoT7Iy8hHr8Mb2L4j9UZOhsbBe35Kcho9oAlCAj3w0U/7UGo048i5DDz/2RZMf6g7woI8VWE/Z4HbT1025AHUGoZugB1NP75eG0droJK8NYAqpB/V6aszopRxyt0PKfVXV9fac1vw27F/7A82oCD95UD9Wvs6vXe2ugVjW49w6P2EytbkHCgtM2HLwUSs2BaLs/H2WUQarQlNWhdDG5SAi/mxOFZg3zbQwx8DonpiQFQPBHtKs5vm1fA54LiemuyHHPL8M6Bu+r/abHYjKPh44MABjBkzpqL+lu2GUVTvi2px1QQU3OzYseN1Xz99+jSra0Z11ajEANUGI9q1a8f+0jiux/79+9nfqvRz1O/vSoXbTzzchuq1n5veFc/3fgxf7f0JG2N3wKLR4O9gb+TqtRh6fBssxlKEjHoOGr3BYd292oYjyNcNM7/bjez8EiSm52PqZ5vx6oPdEBMVoAr7OQPcfuqyIQ+gOjFezbsj/8Bah+Q5nNrkpsZ9EGL2R/3IKztCvrXybRQatPAoM+P1m1+xk6fsU2cmLbMQK7fH4t9dF5FXaP8l3dOvCPVbZiEdZ5BgLALyr7ym02jRKaItyzZtF9KSFcrmcDgcjmOMHj0ab775JguSvvXWWxg4cCCmTJmCOXPmsA0VaPl+ixYtaqQvjz32GHtcD2sN1FtvvdWuBuqAAQNYHVfa7GHjxo3o379/pdIAtGEDZdoOGTJE1jFwOBwOR3r0Wh0e73IvAtx9sez4anZsk78ncnU63HF6Dyy/vouQO19g5fYcpXnDAMye3AdvfbsT8an5yC0oxStfbsMzd3dEnw4RMoyGw1E3PIBaw1BmgVRtHK2BaisvpB/V6aszopRxyt0PKfVXVxcFRDtFt2O16KzobP5eb/d4R95PqKxcc4D25Tt4Op1tCrXneArMtuVLtUaENs6GS0gi0kuTceGqzf7CvOqxuqb9orrDz63mlirwOVCzehxtL4c8/wxQ3jidxf+l1iUn48aNY7u4/vTTT2w5PwUYKQBJAVSCPotsg5VKhAKjEydOxLx58/Dyyy9j7ty5FZmmhw4dYseI++67j9V45UiPs/i7UuH2Ew+3ofrtR8vu725zGwLc/fDtvqWwwIIDPm7I12swIfYQLEveRujYl6F1dby8XmigJ95/ui/e+WE3Dp/NQJnRjPcX7UVKZgHuHNj0hvuaOIP9lAy3n7psyAOoNQzVHrUNHolpQ7uU0071FlPZDXWQHMk70o/q9NUZUco45e6HlPrF6JJyDoiRlXoOFBaXYf2eeBY4pSUyV7DA4JODsGaZyNHHIcdcCtgkoxp0BvSI7MgCpzHBTWplczY+BxzXU5P9kEOefwbUTf9Xm81uBG289Mwzz+Dpp59mAVTiyy+/ZBtJ0WtUizQwUJ7SKFLy+OOP4/Dhw9i6dSsLClNpAvqsoF1prVmqTz31VG13U7U4i78rFW4/8XAb1h37DWnSD35uvvh053coM5XhjIcrvorwwwOJx2FZ/BZC73oFOnfHx+LlbsAbD/fAF78fwtrd5XtO/LjyBJIzCvDEne3Y5lNqsJ8S4fZTlw15ALWGoXpb1rpWYtvofYNR//HPYSrMsztOS7nq17+yRJqg4CnJO9KP6vTVGVHKOOXuh5T6xeiScg6IkZVqDlxIyWVB0//2xqO41HTlBX0pfCLS4R6ehFzTJaTTMZs9oxr6RbIl+r0bdoGXi2PF3KWGzwHH9dRkP+SQ558BddP/1WazG3H77bez+qdPPvmk3fHOnTvDmXBxccH8+fOxdOlSLFu2DOfPn4fJZGLlB2iMEyZMqAgQc6THWfxdqXD7iYfbsG7Zr2tke7zWbwre2zoPBaWFSHQz4IvIAExMOgfLT28gbNxr0Hk6vuLAoNfi6bHt2SZSFDwlKJiallWIl+7vyoKsarCf0uD2U5cN+d2Wk0NBUdvAKKPADNew6NrqEoejekwmM3YeS8E/W2PZrpZXsEDrnYnA6DQUuSWgzGJCmU1M1V3vxgKmFDiN8m9QK9mmHA6HU1fIyspSzA33jVi4cGGVr1OA9J577mEPDofD4aibFsGNMWPgVLy9+XNcKsxCpkGHLyL98UBSPCyLXkfY+Deg9/Z3WC999xgzqBlCAzzx8ZL9bDn/oTMZeOHzzXh9Une23J/D4VwfjYUK9nFk5cSJEygsLISHhwdiYmJquzscjqKYtOhR5Bm08C4z49sJ86FksvKK8e/OC1i9Iw4ZOcVXXjAUwy0kGe7hyShGbqV2zYMas6Bp9/od2W6bHOfnrkXPwGIohqbMDUsnfFzb3eFwFOW3SrnveeWVV9ju9l988QWCgoJqrR/OiFLOIYfD4dRlMguzMWvzHFzMSWTPDWYLJqTkoJVrAMLveaNyIpUDnIjNxMzvd7GNpQg/L1e8OrEr23iKw6lrnBB438O3dq5h4uLiZG8jRF4qGTWglHHK3Q8p9YvRJfccECordA7Qb0wn4zLx4U/7MHHGv1i0+uTl4KkZWr80+LQ6BI8Om6AJP2UXPPV29cKIZoPw0fDXMWPQVPSP6qHY4CmfAzWrR45ruqPy/DNAeeN0Fv+XWpecaLVaVie0X79+GD58OFvqTpst2T7uv//+2u4mR+E4i78rFW4/8XAb1l37BXj44c2Bz6JlcFP2vEyrwYIwX+w0ZiPpx1dRlplcbd0xUQH4YHJfRASX15bMzi/BtHnbsO1wkmrspwS4/dRlQ76Ev4YxGo2ytxEiL5WMGlDKOOXuh5T6xeiSew4Ilb2RXEmZCduOpuOTP+JwLuFK7UCNayH0wQlwC02GUVuEq7dwaxsSg0GNe6FzeFu2QZQzwOdAzeqR45ruqDz/DFDeOJ3F/6XWJSfbtm2Dv3/5EseSkhIkJdl/KeRw1OTvSoXbTzzchnXbfp4uHpjW72nM2fUDdsbvh1mjwW8hPsi9lI/BC19D+PjpcAm23/9EKFQPdfbkPpj1w24cPXcJpUYz3l2wBw+OaIlR/cs3t3V2+9U23H7qsiEPoNYwnp6esrcRIi+VjBpQyjjl7oeU+sXoknsOCJW9nlzKpQKs2h6HtbsvIK/wcnhUY4LOPw2uoYmweJXXPLW9jAe4+2FAVE8MiOqBel7Ot0yUz4Ga1SPHNd1Ref4ZoLxxOov/S61LTjZs2FDbXeCoAGfxd6XC7ScebkNxqMF+LjoD/tdjEha4+WLVmf/YsX8DvZCbU4jbF72OiHGvwzU0qlq6vT1c8NYjPTHn14PYsDeeHft+xXEkZRTgsdFtVWG/2oTbT1025AHUGsaaCSFnGyHyUsmoAaWMU+5+SKlfjC6554BQWVs5s9mCg6fTsWLbeew9kQprZWiNex7LNjXUS4ZFWwrbgtFajRadwttgUHRvtA9tyZaKOit8DtSsHjmu6Y7K888A5Y3TWfxfal21TWZmJgICeL03Tt3w99qA20883IbiUIv96LvHAx3GsMSNnw7/wY7t9PVAnq4Y43+ajvp3vwq3iGbV0m3Qa/G/uzuwjNSfVp9kx9bsvIC0zEJMuauNpOOoa6jF/2oTfwXZ0Hm/8UvI5s2bMWbMGHTo0AEDBw7EnDlzUFZ29cJcaUhISJC9jRB5qWTUgFLGKXc/pNRfXV3GvCwkHN6FkuTzFQ9bbI/Tg+QdfT+hsiSXX1SGPzefw+Pvrcf0r3dgz/FUWDRG6IIS4NpyJ9zabIM+9AILnloJ9QrG+La348tbZ+H53o+hY3hrpw6eEnwO1KweOa7pjsrzzwDljdNZ/F9qXXKzePFiPPHEE3jggQfsap/Sbva33347+vbtW9td5CgcZ/J3JcLtJx5uQ3GoyX60pP62mCF4qtsD0GnKv38c83LDV4EGnFvyJoouHBOl++6bmuO58R2h15XrPnA6HS/P3Ya0rELJxlDXUJP/1RZKsmGdz0Cl+liPPPIIOnbsiKlTp+L8+fOYN28e4uPj8d5779V29zgcVZF74F9ot/yC8n0ky9E0Ciz/q7Eg8bvn7eT9+oxFQN+7JO9HXHIuftmUhP1nT6Kk1ATAAo1nDss21QelAFr7OisGrR7d6nfE4OheiAluym4wOBwOh6Nsvv76a3z44YdwcXGBl5cXsrKyEBoaiuzsbBQVFcHNzQ333ntvbXeTw+FwOByH6NuoG3zdvPHB1vkoMZXigrsLvqinxcRf30aLUc/Do3GHauvu36k+gv098Pb3u1g5s+TMEkz9dDNem9QNTesrJxOQw6kN6nwAdfbs2WjYsCF++OEHdoNNuLu745tvvsFTTz2F+vWrV5D5eoSEhMjeRoi8VDJqQCnjlLsfUuqvri6fDkOAiJbw9LhSx6Rg/acASlFgcUfExNl28jovf4ff73qyRpMZO44k459tsTh2/tLlNyiFLiSJBU61HvmV2kR6h2FI077o3bALvFyUU3tFavgcqFk9clzTHZXnnwHKG6ez+L/UuuRk2bJliImJwcKFC1nw9KabbsKPP/6I8PBwLF26FDNmzEC7du1qu5scheMs/q5UuP3Ew20oDrXar11oS7w58Fm8s3kOckrykeaix7wwL0xcPhvtRvwPns27Vlt3q+hAzJ7cF29+sxPJGQXIyivBS3O3Yeo9ndCjTZik41A7avW/umpD5153KpLi4mIEBgZi7NixFcFTonPnzrBYLDh16pTk71laWip7GyHyUsmoAaWMU+5+SKm/urr03v6w+IXDNSy64mG+fBmiv7bH6UHyjr7f1bJZucVY/O8pTJq5Fu8v3Itj5zOg9b4EQ/QhuHfYCJeGJ+2Cp256VwyO7o1Zg1/EtK5PYFjT/qoOnhJ8DtSsHjmu6Y7K888A5Y3TWfxfal1ykpiYiNtuu41ln9IP4r6+vti7dy90Oh3Gjx+Pm2++GQsWLKjtbnIUjrP4u1Lh9hMPt6E41Gy/6ICGmDH4BVZijMjV6/BlmDd2/PMx8o9vE6U7ItgLs5/ug2aRPux5aZkJ7yzYjeWbzrFYCUcYava/umjDOh1ApaVb3377LSZNmmR3/OTJ8sLJYWHS/7pCGRBytxEiL5WMGlDKOOXuh5T6xeiSew6QLH2oH4+9hNkL92LizH/x85qTyCzKhj7sPFzbboFrzB7og5IBrbmiXbPAaDze5V58NfJdPNLlHjQJbMSWedYF+ByoWT1yXNMdleefAcobp7P4v9S65ESv19vt3Eorjmx/HO/WrRvi4uJqqXccZ8FZ/F2pcPuJh9tQHGq3HwVPZwyaisb+DdnzYp0W34b7Yt3aecg7tEGUbl8vVzx8cwT6dYhkzylu+u1fRzH/jyMwma58j+LUXf+razas80v4rVDAhYrTbtq0idVA7d27N1q1alXb3eJwOA5QXGrEzuNZ+OzPjYhNyi3Pa/XLgAst0fdLZ3VWbfF28UTfRt0xMLon6vuG11q/ORwOhyM9jRs3xoEDB9hGoURUVBSOHj1a8XpOTo6isho4HA6Hw6kOvm4+mD7gf/ho+9c4mHIcJo0Gi0O8kbflO4wsK4Fv5+HV1k0bSj13T0eEBXliydryHyGpJFpqZiGen9AJHm4GCUfC4SgbjYXnXzMoA2Ho0KHs/4CAALakq1mzZpLoPnHiBAoLC+Hh4YHmzZs7vGO32Wx2qI0Qealk1IBSxil3P6TUL0bX1W3vWvQMLIZiaMrcsHTCx9V6P6rNs3J7LNbuvoiCojJoXAqhC05ktU01LiWV5NuEtMCAqJ7oFtkeBp1B8b4hN0oZp7PMAdIz7ufnbui3UvWDfwbIS13yfzF+60hfbe97qAZpbbF48WK8+eabGDFiBN566y1s2bIFU6ZMYTXuo6OjMWvWLJaV+tNPP9VaH5WKUs6hElDKNcJZ4fYTD7ehOOqS/YxmE77a8xM2xu2oONYvqwDj246Cf89Rou23Ye9FfP7LQRhN5SGkqHAfvD6pO4L83CUagfqoS/7nzDYUet/Dz+RlyFCffvopZs6cCR8fH9x11104ePCg5O8THx8vexsh8lLJqAGljFPufkipX4wuqeaA2WzB3hOprLj5o++uw/LNZ1DsfhEuzffArf1mGCLO2QVP/d19MbrlcMy5ZQZe6z8F9VGvyuBpdfvqjChlnM4yB8TqkeOa7qg8/wxQ3jidxf+l1iUn48aNw6OPPoqNGzey5fxDhgxB//79MWfOHDz77LOsFv7UqVNru5scheMs/q5UuP3Ew20ojrpkP71Wh8e73otRMcMqjm3y98SXx/5ExqYl1apdamu/gZ0b4K1HesLTvfw7FK34e+7TzTiXUDfKnlWHuuR/dcGGfAn/ZerVq4dhw8ovNAMGDMDw4cPxySef4IcffpD0fcrKymRvI0ReKhk1oJRxyt0PKfWL0SV2DuQXlmLdnotYuS0OyZcKoHHLh75+PHSBSdAY7HVrNVp0DG+DQdG90D60JXRanUP9UIpvyI1Sxuksc0CsHjmu6Y7Kc/9X3jidxf+l1iU3zzzzDJ5++mkWQCW+/PJLtpEU1bju0KED20yUw1GLvysRbj/xcBuKo67ZT6PRYFzb2xDo4Ydv9y0BhUwP+Ljj4/P/4onSIoQPfoDJVNd+bZoEsc2lKImFlvFn5hbjpblb8fy9ndG1ZagMI3Ju6pr/qd2GPIB6DYKCgtC5c2fs379flkxXudsIkZdKRg0oZZxy90NK/WJ0Xd2Wfgi1mHRgn+5VtDmfmMPq7Wzcn4BSUwl0ASlwiUmAzrvyL54hXsEYGNUT/aN6sMzT6o5BKb4hN0oZp7PMAbF65LimOyrP/V9543QW/5dal1w32mfPnoXRaESTJk3g7m6/tJDu8Tgctfi70uH2Ew+3oTjqqv2GNOkHPzdffLL9axgtZpzxcMUHSdvw9KoiRA1/DBqNttr2qx/ijQ+n9MXM73bh5IUsFJea8PZ3u/DQbW1wa59oGUbjvNRV/1OrDet0APXcuXN4+OGH8cQTT+DOO++0e43qH7i5uckSnJW7jRB5qWTUgFLGKXc/pNQvRpe1bWxSDv7cdA5Fh/oCFi2gMeMT/X7c1q8xosLLg55lRjN2HEnC31vO4+SFTGg8c6GPiIdbYDI0OpOdXoNWj26RHdC3QTe0DY9h2adix6AU35AbpYzTWeaAWD1yXNMdlef+r7xxOov/S61Lamjl0Ny5c5Gfn8+eu7i4YPz48XjuuecqslA5HLX4uzPA7ScebkNx1GX7dY1sj9cHPIN3N36GQnMZEt0MeD/zIJ766xO0unUKNDar8xy1n6+XK2Y+3gufLN6PrYeSYLYAXy0/wlYIThrZGjqt8CxXNVOX/U+NNqzTNVAbNGiA3NxctsmAyXQlGHPs2DG2vKtv376Sv+fFixdlbyNEXioZNaCUccrdDyn1i9FFbTftT8D/PtrEskm13plwbbOF/d14+fg/287j5zUnMWnmv5i9eCfOFB2Ea6vtcGu1A/p6CXbB0/q+4XigwxjMH/kuJveYCK8i1xsGT4WOQSm+ITdKGaezzAGxeuS4pjsqz/1feeN0Fv+XWpeULF++HO+++y6rZX/PPffg3nvvRaNGjVhQ9f3336/t7nGcFKX6u7PA7ScebkNx1HX7tQhugplDXkaAvjyLL9Ogw4cFp7D7j/dgMRlF2c/VoMPzEzpjzKCmFcco8WXW97tRVHJj3XWBuu5/arNhnf4p3mAw4JVXXsFLL72EBx54gNU9TU1NxcKFCxEcHIzJkyfXdhc5HNWRmFGMj347DjNbu2+Ba+RpaN0LYIg8jZLjVItOgy+XHYbWOwu64AS4BaRAozXb6XDTu6JXgy6stmnjgIYO1fHhcDgcjjr5+eef0b59eyxYsACurq7sGG2YQXVQly5dyjaMooxUDofD4XDqEpG+YXh7+KuYufZ9JBZno0CnxWelFzHp9xkYMPpVaPRVb6xbFVqtBvfd3BJhgZ6Y+9shmMwW7D6egpfnbcVrE7sh0Ne+jA6H48w4TQbqL7/8gubNm7Ns0aooKSlhGwSMGDECbdq0QZcuXTBhwgT8888/15QfNWoUPvjgA+Tk5GDWrFnsBpt2aaW/FESVmurodLSNEHmpZNSAUsYpdz+k1C9G167T+bDGO7W+GdB65Zb/75ULbUAy9GHn4dp2C1xjdkMflGQXPG0aGIXHukzAVyPfxaNd7kGTwEaVgqdC+8bngPLG6SxzQKweOa7pjspz/1feOJ3F/6XWJXVppltvvbUieErQZwT9SF5aWorz58/Xav84zolS/d1Z4PYTD7ehOLj9ygn08MfM4a+huVf5Rk9lWg2+Mqfiz19fg7msRLT9burWEG8+3AOebuU5eucScjD1082sbFtdhvufumzoFBmohw8fZkuybkRxcTEmTpyIffv2QafToVmzZqwG1p49e9hj+/btePvttyu1o5ttetQEtqUC5GojRF4qGTWglHHK3Q8p9VdXl9lswY6jaeyXSdo1yhBxhm0iRTFQ+uvS+HBFcNWKl4snuoa2w80xA9HAL0KyvvE5oLxxOsscEKtHjmu6o/Lc/5U3Tmfxf6l1SUlRURG8vb0rHY+MjGSZqFS2icNRi787C9x+4uE2FAe33xU8XTzw2rBp+HT9J9iTdR5mjQY/a3KQ/cs03HvHDOjcPETZr12zYLz/dB+8+c1OpGUVISOnGC/O2YIX7+uCTi1CUBfh/qcuGyo+gLpjxw62lL6goOCGsjNnzmTBU9pxlbJQ69evz45v3LgR//vf//Dbb7+xpV1jxoxBbUAB3vj4ePj6+iIxMZFlQ9DOsBRRt9Z1oAK5dJN/6dIl9pxqd5FsZmYmy6gICwtDXFwcey0wMBBarRbp6ekVNV3p/4yMDBY4pi8M1mwLf39/VrIgLS2twgkpW5fsSpsqNGzYkGVuEH5+fmwDLeorvW9ERATL0CWdFJiOiopistRPqjPm6emJ5ORk1jY8PJzJ0ZcUyvpo3Lgx64PZbGZfakiexkOEhoayLzukm6DzRmOjXXNJJ/U5ISGBvRYSEsLslZWVxZ5HR0ez/tFOu7QrG9nNakOyJ42P+k5Qf5OSkth4aVyk68KFCxX2prINVlmyAz2nc0X2pvHExsay1wICAtj4be1NtqYNx8i25G+29qZlgqSLoHNBfbfam84r7RBMkD+QH9jam+yXl5fHzi+N1dbeXl5ebDwE+QPptLU39ZfGT3JWXyPIrnQesrPLd60nWbKD1d40PuoDUa9ePWZbW3vTubD6LL2n1WZkb9Jr67PkD1Z703mm80q7M55LNcNosgCGYuhDYyuyT4mrA6emnEA81GswGhlCUVpUghD3IDZuq73J/rY+S+eL7E19I5+gcdr6rK296XWrvekck61t7W31WesYbO1N72/rs7b2pn7Y+iy1t7U3+ajVZ2n+2tqbzoOtzzpyjUhJSanwWSHXCJp35J/WawTZjHTaXiPIn6k/VV0j6H0Jqa4RZC8ar1zXCOs5d/QaQdBct14jqH/0E4AVq28JvUZY7S30GkHjIllbe1d1jSA91rFd7xpBz0mv7TWC7E1+ZPVZa8CpOtcIW58Veo2w+iz102rvq32Wxm57TSZb2fosvceNrhFWe1uvESRPuq6+Rtj67PWuEfS5Rg8prhFkb3oPua4R9Nzqt/SXxiv0GnH1fQT5FPnE9a4R1NfagM7JtUq60LxU2g04x3kgP6drBKd6cPuJh9tQHNx+9rjoDHhu8HP4dstXWJtyiB1bqS9Ezm8v44nRM2Dw8BFlvwahPvhgSl/M/G4XTl/MRlGJCW99uwuPjmqDm3tGoa7B/U9dNtRY6C5bgdAXEAqCfv311+yG2Mobb7yBcePGVZKnLyRDhw5lN8e0iUCLFi3sXl+yZAmmT5/OvrBQQJW+MNQUJ06cYOOhL0T05YO+UDkCfclxpI0Qealk1IBSxil3P6TUL1RXYXEZ9hxPxdbDCdgffwpmrzTofNOh9cy7pjwri1rmgtIT3aA3eeP3d0ewL8OO9F2oLJ8Dyhuns8wB0vPKzrmwGIqhKXPD0gkfy9oP/hkgL3XJ/8X4rSN9tb3viYmJQU1B936zZ8+utKqIAv09evTA999/z/5ybkxtnUMlopRrhLPC7ScebkNxcPtdGwoDLdu9GEvjtlQca1OqxdM9J8Hdw6/iGP2Ya01Ms6Lz8Ibet+pl1cWlRnz0837sOFL+gzRxe7/GeHBEK1Y3ta7A/c85bCj0vkeRGagnT57EQw89xLIgKKuBskepJqk1S+Za/PnnnyzjoW3btpWCp8To0aPxzjvvsOyT3bt3o3v37qgNKGtE7jZC5KWSUQNKGafc/ZBSf1W6rEHT/w6fwdG0E7B4p0Hrcwm6ZkaU5wBdH0oc0riUQudehL5NW1RkEjnSd6GyfA4ob5zOMgeYnp011w/+GSAvdcr/d0qoS6FQRq81I9iKNSOYMhiufs2aZczhOKO/OwPcfuLhNhQHt9+1oe9Zd3QbD393X3x1/G+2nP+IixmztszFfck58DBbcMbdgL+DvXHr1jw0LSq70lZnQP3HP68yiOrmosdL93XBgn+OY9nG8lU+yzedQ2pmIZ4d35G9Xhfg/qcuGypyEynKJqXgaefOndmy+0cfffSGbQ4cOMD+UptrQUvRaFMpggKotcW1btylbiNEXioZNaCUccrdDyn1X62rqMSI//ZfwMs//on75n+Czw99iuPuv0Db8Ah0AanQ6O2Xc1pMWpZtei3ouC78DEb2ia5W34XK8jmgvHE6yxwQq0eOa7qj8tz/lTdOZ/F/qXVJDW0IOmjQILsH/YhOTJ06tdJrgwcPru0ucxSOkv3dGeD2Ew+3oTi4/apmYNtbMLXjeLiwPSqAOHcXfBnhjyy9FmsCvZDmomd/bb+6WUxlMBVee0WhLZRp+uCtrfDEne0qsk4pI3XavG3Iyi1GXYD7n7psqMiwP9XgomVWPXv2FNzGWj/t6vRyW6iWF20mZZWtDajemdxthMhLJaMGlDJOufshpX7SRUHTDYdOY/2pfYgvPAd4Z0DjaoK2fGNHO9x17ugQ3godw1sjIy0dS2L/qToL1SsHuVq6UPo53HehsnwOKG+czjIHxOqR45ruqDz3f+WN01n8X2pdUjJq1Kja7gJHhSjV350Fbj/xcBuKg9vvxnRu1hcvFRbgo2PLka/XIs1Vj8/r+6Pwcg3xBDcDzni4oFlhabX0D+/RCCH+Hnj3xz3sO+SZ+Gw899lmTJ/UHQ3D7Guuqg3uf+qyoSIDqM2aNWMPR7BullBVcVnavIGwbnpRG9CGGXK3ESIvlYwaUMo45e6HFPpzC4vw94G92HxmHzKRCI17PuBCy+4ry4Z7RKJHo7boENYKTQIasbrDVGtn6rEZ0EADi93vmPbQ60uP/IV2oTFseYkjfRcqy+eA8sbpDHNACj1yXNMdlef+r7xxOov/S61LSqhUE4cjNUr1d2eB20883Ibi4PYTRtOQ5nh8TRa+C/fFJRd9efCUlgZqNLRxDv4N8ETTwlJUt3ppxxb18P7TffDmNzuRkV2E9KwivDBnC1vm36F5PagV7n/qsqEiA6jVgXYOJmiX3ethfY12l60taBMrudsIkZdKRg0oZZxy96O6+i9mpuLvQztxIPkYcpAEjc4EuFOQ0x4D3NA8oDn6NemADmEt4ePmXUmX0WxEbll+lcFTgl6/VJTF5A06g0N9FyrL54DyxqnUOSC1Hjmu6Y7Kc/9X3jidxf+l1sXhKB3u7+Lg9hMPt6E4uP2EE2g04fHELLaEP4NqlF7ej8Ki0YjOQiUahfngwyl9MePbnTibkIPCYiPe+GYnnrijLYZ2bwQ1wv1PXTZUZA3U6qC7nF4uBOvGNLXBhQsXZG8jRF4qGTWglHHK3Q+h+ktNZdgdfwRvr/ke9y5+CVPXvoFNaauRq4svD55asQA+CEHfsAGYOegFLBz7IV6/6TH0i+p2zeApQcHQR5vdjXdvernigbLL6atlLnbH37npJSbvSN8dkeVzQHnjVMockFuPHNd0R+W5/ytvnM7i/1Lr4nCUDvd3cXD7iYfbUBzcfo7habKU10O9esMKiwW/1PNG/uVaptUlwMcN7zzRG91aldd9M5stmPPrIfyw4hj7X21w/1OXDVWTgerh4cF2Wa2qPoL1NXd39xrsGYejfFLy07En/gg2nzuA+Pw4mDXGa//EYnRFiKEhWvg0wD39hsDP/dqB0qrwc/FBdECDiucaaFk+Kv21Pc7hcDgcDofD4XA4nJqDskyT3MqTWOzQaJCv1+G9RoHom12EscYSXH/tb9W4uerx8gNd8f3fx/Dn5nPs2O//nUXKpUI8M74jXA3Ck+M4nJpENQFUf39/FkDNzs6+roy19mlVdVLlJigoSPY2QuSlklEDShmn3P2w1V9qLMXx9DPYk3CEZZvmlGVeEbT5UZF+eNQWBaCRV2MMbN4JA1u1hkGnY/OsOsHTq/shRxuhsnwO1O05UJt65LimOyrP/V9543QW/5daF4ejdLi/i4PbTzzchuLg9hMOJbVQrVOqeUrL9q9FmVaL9QGe2LX7K4zMH4ZhTfvDTe94KFWn1eCh21ojLNADXy0/Ako+3XY4CRk5RXj1wW7w865ueFZZcP9Tlw1VE0Bt3Lgx4uLikJiYeF2ZhIQE9rdRo0a1Vqc1OTkZ3t7erJ+lpaUsGzY4OBgXL16scA7aaMe6KRb1NSMjgz2ohmtYWBgbJxEYGMg25klPT2fPGzRowP7Pzc1lj8jISJw/f74iwGwwGJCWlsae+/r6sr4UFBRAr9ejYcOGOHfuXMVmW1Sol16n942IiGDB6fz8fFYqISoqislSP318fODp6clkifDwcCZH70+lEui8UB/MZjMbN8lbz1FoaCirR0u6iSZNmrCxGY1GppP6bD1nVPeC7GUNgkdHRyM+Ph5lZWUs+5jsZrUh2dNkMiEzszwoSP1NSkpiGcg0LtJlTQOndmQDGidBdkhNTWXniuxN44mNja0IvNP4be1N7QoLC5lt69evb2dvFxcXpougc0F9t9qbzuvZs2crzgX5ga29yX55eXns/NJYbe3t5eXFxkOQP5BOW3tTf2n8JEe6rfYu0BZjbewWVsv0fF4CzLBZjm+DpdQV2oJ6aOrTGJ3DotAywh9NmjRm5+JCbCzrK9nG2n+yN51fW5+lsVjtTefZ1mfJXlZ7kw2tCzXoL/Xb1t5kK/JZOs/0vvQeVntTW1ufpT6RvUmW3peCvLY+a2tv8hmrvekck61t7W31WXp/Oo+29qb3t/VZW3tTP2x9lmxg/VGHzg35qNVnyRbkw0S9evWY39v6rCPXiJSUlAqfFXKNoHlH47JeI6hPNAbbawT5M/WnqmsEvS8h1TWC3ofGINc1wnrOHb1GELbXCLK97QIjq28JvUZY7S30GkFydMzW3lVdI+ivte31rhHkL/SwvUaQvemY1WfJf2ztTeOz9Vkah6296Vxcy2cdvUZQP22vEbY+S2O/1jXC6rNCrhFWe1uvEXQu6PVrXSOsPnu9awR9rtFDimsEjZ38Tq5rBPXf9npL4xV6jbj6PsJ6Tb7eNYL6yuFwOBwOx/myT6nWqRDyjcX4+fBy/HN6A0bHDMPgxr0ryq85wi29oxES6In3F+5BUYkJpy5kYepnmzH9oe6oH1K9hB0ORy40FrrLdgIGDhzIviy88cYbGDduXKXX58+fj48++gidO3fGTz/9VOl1+pLRpUsX9uXw+++/R8+ePWuo58CJEyfYFyr6QkRfPugLlSPQlxxH2giRl0pGDShlnFL3o8RYimNpp1jAlB5pBeUBiauxWDQw5/nBpSgU7cNbYVi7NmjdOJj9KihHX69ue9eiZ2AxFENT5oalEz4W/X5CZfkcUN445e6HVPpJzys7597Qb6XqB/8MkJe65P9i/NaRvtre98TExFT7fTi1Bz+HyrtGOCvcfuLhNhQHt58wipPOYdrad5Doqr9u9inDYoG7yYIivX2tt0APf9zZ8mb0i+oBvdbxJfjnE3Pw1rc7cSmnfHNwT7fyZf7tmgbDmeH+5xw2FHrfo5oM1OHDh7MA6v79+3Hq1Ck0b97c7vXff/+dBU8pe6Vr16611k8ORy7ot5CkvFQWLD2YfAwn0s+gzGy8bpapKTsYbiVh6N6oNfoPiEKr6KAqg6YcDofD4XA4HA6Hw1EfFncPZOt1VQdPCY0GOlgwq+fj+OviLuxM2M8OXyrMwvy9P+HPk/9ibOsR6Fm/M1vlIpToCF98MLkvZny7C+eTclBQbMT0r3bgqTHtMLhrQ7HD43AkQTUBVFp2dtttt+HPP//E5MmTMW/ePLZUkdi0aRPef/999v/jjz/OlprVFrTETe42QuSlklEDShlndfpRXFaMo2mncDD5OA6kHEN6QfmSzauxmDUw5/vDnB0Ej7II9GzWDH16RFQ7aCrGZnLPAaGyfA4ob5xy90Mq/UzPzprrB/8MkJc65f87JdTF4dQRuL+Lg9tPPNyG4uD2E4a7fxjeGfQ8snPLy/pYMZqM0Ovs4yd+vsEICWmCZ+u3RWxWPJYc+QsHko9WbE782c7v8cfx1birzUh0iWjHyjMJIcjPHe882QuzF+3D3hOpMJkt+HTpQSRfKsQ9Q1tA64TJPtz/1GVD1QRQiVdeeQVnzpzB8ePHMWLECDRt2pRlnVpr2d19990YM2ZMrfaR6q9RTTE52wiRl0pGDShlnEL6QVmmCbnJLGB6MOUoTqSfg/E6WabmEjeYc4JYpqm3OQxtGwVg2KhmkmSairGZ3HNAqCyfA8obp9z9kEq/tY5mTfWDfwbIS13yf7XZjMOpCbi/i4PbTzzchuLg9hNOSGgT9rCFardXZb8o//p4ue+TOJVxjgVSj6WdZsfjc5Pxwbb5iPZvgLvbjES70JaCAqkebga8+mBXfPPnUazYVl73/pd1p5GSUYApd3eAi8Hx8gC1Cfc/ddlQVQFU2vhh8eLFrMbpypUr2aYUlDbevn17jB07FqNHj67tLrKArtxthMhLJaMGlDLO6/WjqKwYR1JPsmX5B1OOI6OwfBORa2aZ5gVUBE199AHo2SYcvYeHs6Bp7PlzaNIkuNZtJvccECrL54Dyxil3P6TSL1aPHNd0R+W5/ytvnM7i/1Lr4nCUDvd3cXD7iYfbUBzcfjVjv+ZBjTF9wDPse+uSw3/iTGb5BqHnsy5i1uY5iAlugrtaj0TLek1vqEun0+LR0W0RFuzJAqm0a8/mg4lIzy7CKw92ha+XK5Mzmy0oLTOxoKpSs1O5/6nLhk4TQN2wYYMgOdrRl5bp00OJ0C7AcrcRIi+VjBpQyjit/aAs0/icpPJapinHcDL9LEwW8zXbmEvc2bJ8U04wzLkB8PPwRI82YejdPrxSpqmU4xSjS+45IFSWzwHljVPufkilX6weOa7pjspz/1feOJ3F/6XWxeEoHe7v4uD2Ew+3oTi4/WrWfm1CWqD14ObYn3yUZaReyE5gx0+kn8Ub/32EdqExLJDaJLDRDXWN7NMYIf4emP3TPpSUmnAiLhPPf7YFk0a2wo4jySyoWmY0w6DXom/7CNzWrzGiwn2hJLj/qcuGGgtFazg1tqNXs2bNoNM5lnZuMpkcaiNEXioZNaCEcRaWFrEM08OpJ1iWaWZR9jXlLGYtzHn+LMOUMk0txZ7w83K7btBUrnGK0XV127sWPXPDXaEdeT+hsnwOKG+ccvdDKv2kZ/ziqdXezVyOa7qj8tz/66b/i/FbR/rKd3B3fvg5VN41wlnh9hMPt6E4uP1qz35mixk74w/gl6N/s82Obekc0Q53t74VDfwibqjnbHw2Zny3E5m5JRXH6Cuv2SaSRd+BKbL17PiO6NdRGcu9Ce5/zmFDofc9wrdF40hCbGys7G2EyEslowZqY5z0u0VcVjwrrj19w0eYtHwqPtn5LTbEbq8UPDUXu8OY2gAlpzqieP9AlJ7qAu+C5hjWoTXefrwXfpg+FE/c2Q5tmwRXWdtUynGK0SX3HBAqy+eA8sYpdz+k0i9WjxzXdEfluf8rb5zO4v9S6+JwlA73d3Fw+4mH21Ac3H61Zz+tRoueDTrhw2Gv4Ymu9yHYM7Ditb2Jh/D8mrfxyY5vKwVXr6ZJfT98MLkfwoM8K47ZBk8J2nDKbLHgo5/3IzYpB0qB+5+6bOg0S/g5HGcnv7QAh1NOsmX5h5KPI6s45/pZprkBMOUEwZwdDEtJ+QeFn5crevy/vTMBr6I63/iXPWELEJZAIAQIEPZFREER0LKoVBRR0dqKVmutu9bWv9alFm1rXdpqXWoV27orFSxgBZWtKJuAbGELIYRAwhYCgey5/+c9cdJJyA1zc2ZuZm7eH899Qu49880573xncu4353znnE5y3uDOMqBHgsoNQwghhBBCCCGEuJWI8AgZ232knJ98tnyZuUJmb/1U8osKxCc++WrvWlmZvU7GpJwr0/pfUiPIaqZ9mzjp1bW17D98st5zYZ+qucsy5J7pwxxqDWnKMIAaZNq2bev4MVbK21UmFHCqnViygFmmWJKPfKY7j2Sq9+oCS/ErVC7Tdip4Kr6I/wVNh9kTNLWznTq2nO4DVsuyD7ivnU7Xwy77unacuKcHWp7+7752esX/7bZFiNuhv+tB/fShhnpQP/foFxkRKRNSx8jYlJGyMGOZfJz+mZwoKVTfkRdnfiXLslbJ+B6j5Yp+k6RNXM1cptgw6qtNB854DsxEXbY+R+6+ZqiEIZrayND/QktDBlCDTENyNwR6jJXydpUJBexsZ2HJSfk2rypg+m1uuhQUH6+zXJgvQiqPJ0hZfoJUFmCWabPqz1TQdKD9M03tbKeOLaf7gNWy7APua6fT9bDLvq4dJ+7pgZan/7uvnV7xf7ttEeJ26O96UD99qKEe1M99+kVHRsvkPt+Ti3qcLwt2fCn/3v65nCorkorKCvnPriVqluqkXmNlStoEaRnTQh1TWlahNoyyAsqVlFVIbHTjh7vof6GlYeN7VBOiuLhY9uzZIwMGDJCcnBwpLS2VuLg4ad++vezdu1eVadeuncqPeeTIEfV7SkqK+gzlsPtYp06dlA2QkJAg4eHhcujQIfV7cnKy+v/hw4elTZs20qVLF9m9e7f6DL9HRUXJwYMHqxPxIknuyZMnJTIyUrp16yYZGRnqs9atW0tsbKw6T/PmzSUpKUkKCgqksLBQOW/37t1VWdSzVatWqsyBA1VPgzp37qzKHT9+XD3x6dmzp6pDZWWltGzZUpVH20FiYqIUFRUp2yA1NVWds7y8XNlEnfftq9q1r2PHjkqv/Px89XuPHj0kOztbysrKVKJf6GZoCD3RvqNHj6rfUd/9+/dLSUmJahdsZWVlVeuNuhsaQoe8vDx1raA32mPk3MCTD7TfrPfBQ4dk1+FM2VWYJZmncmTX0T1qKUJdRFe0kuLDbaUsv53aCMqYZQpaNY+StKRYGd6nrYwe3ktOniyUEycKJCvrhGqrWe8WLVqo9gD4A66hWW/UF+1Hufj4+Gq9oSv0OnasKscqykIHQ2+0D5qCDh06qLJmvXEtDJ/FOQ0doDeur9lnoamhN66z2Wdzc3NraGiohZ+ot1lv+CZ8FudLS0tT54DfwpdxrNlncb1w7VC2T58+qp1mn921a5cqC03gM4Z/4xqfOHFCvdCf0FbDZ9EGnMusN85v9lmz3qiH2WdxvFlv+Kjhs9DCrDeug9lnA7lHQFPDZ63cI9DvoqOjq+8R0Az/N98junbtqupT3z0C5wV23SOgF+rl1D3CuOaB3iMA7qvGPQLXwtzLDd+q6x6B4wyfhaZmvdFW+CzA76i7oTeuq2EX7YJds97Qz+yzZr1hx6iDv3sEfkc7zfcI6A0/MnwW9mCrIfcIs88Geo9APQ29a/ss2l7XPcLwWSv3CENv4x6B8oMGDTrtHmH2WX/3CPxdw8uOewT0xv+dukfgd/P9Fu21eo+oPY4w7sn+7hGoKyGhAvoE7gukYVA/faihHtTPvfrFRcXKlf0vkYm9xsi/t30uC3YulpLyEimtKJNPti2SRbuWy6V9LpLJvS+S2KhYiYoMtxxE/eeCdLl8TKpa+t+Y0P9CS8MwH0bZJGg7euHLB75QBQK+5ARyjJXydpUJBQJt5/GSQpXDdD1ymeZuVcsO6iIqLEqalSdK/v54KT3SVnyl/5tlCuJbRMuogZ2rZ5pmZu52VG87r6eOrdrHXvPWvWfcFTqQ81ktyz7gvnY6XQ+77MPOwyv/0uDdzJ24pwdanv7fNP1fx28DqSt3cPc+vIbuu0d4FeqnDzXUg/p5Rz+s3pyTvlAW7loqZZX/exjbIrq5XJY2XjI2tJbl6/LUMn0rYIPlMcO6yNRxqdItsZU0BvQ/b2hoddzDGahBBrM7nD7GSnm7yoQCZ2onZhlhZik2f9pwYKtkHM3yO8u0bXR7iTzVQQ5ktpCiY/Fy3Bdeb9DUvDzfab3ttK9jy+k+YLUs+4D72unWPoAk93gZVLQKE59UPf3Gz91Hq2b+GSBnU+28TTr14N8AZ2lS/r/SRluENBHo73pQP32ooR7Uzzv6xce2khuGTpPJfS5SG00t3r1CKnyVajPmdzbOkRYxLSS8fVepONhVpNb3bDOREWFSXuFTgdYv12ar19n9OsqV43pJ/x51b1LlFPS/0NKQAdQgg2WJWA7o5DFWyttVJhSoq53Hio/Lxtx0WX9gs/p5orTu3f5iI2Okc2yKymW6Z3us5JyKPq1MfUHTM9XDTuy0r2PL6T5gtSz7gPva6dY+sChjuXy0ZX7NN6OMn6Xy4KLf1vhoWv9L5eoBk22rB/8GOEtT8v9Q04yQYEB/14P66UMN9aB+3tMvoVkb+cnw62RK2nj5cMt8WZ61WqUnKiwrlMhu6RKRmCkVB1Kl7FDn6kAqZptibfV91w2Twb3ay7z/7pb5KzKlsKhMfb5ma5569U1pq2akjuiXKOHhzm8yRf8LLQ0ZQA0ymBbs9DFWyttVJhRAO5GweucRY5bpFtmdX3NGmZmurTpL+8hucjw3XrZvCJMtpb4GB01r18NJ7LSvY8vpPmC1LPuA+9rp1j4wvudoGd55UPXv2fuy5fVP98mxwhK16dvjt4ysUb6+2acNqQf/BjiLW9rpVv932hYhbof+rgf104ca6kH9vKtfxxbt5Y5zZsjlaRPlg83zZOW+der9sJhiiUzZLBGJu6UsJ1XCC5LkgqFdZMoFPaV756px+PUX95UrL+wln63MkrlLd8nhgmL1fvqeo/LkrNXStWMLmTo2VcYM66pyqzoF/S+0NGQANcggB6rTx1gpb1cZL3OsqEA25G6VFZmrZdemvXKy9JTf5NYD2qdJy4okyctqLhvXn5QdZRXffeqrETQdObCznB9A0DSYettpX8eW033Aaln2Afe10619oPaS/IgTPgkvKRTfqWIJj4qVHm2TG/2eHmh5+r/72ulW/3faFiFuh/6uB/XThxrqQf28r1+X+E5y33m3SGZ+try/6RNZd2Czej8s9pRE99woXVodklEDkyWlU808p3ExkXL5mJ4y+fzusmz9Ppm9eJfszT2hPsvOK5Q/vb9B3vrPNhV4nXhuN2kWGxWS+nmdKBdpyE2kgoA5IS12B8eOt4GAHJyBHGOlvF1lvARmme44slvWH6iaZbrnWNVOyHWR0rqLDOzQV6KKEiVzZ4SsTT8kpdVBU7EtaBpMve20r2Or9rFWNpEK5HxWyzbFPuD2dnqlD8DOTTMXyZGCYkmIj5U3H53oaD34N8BZmpL/X/vO/bZsInWmunIDIu/Da+i+e4RXoX76UEM9qF/o6bf9cIa8t+kT2XJwR433e7RJlukDL5PBif0kLOz05fmVlT5Zuy1PZn+5U7ZmHq3xWfPYSLnkvO7y/dE9pE3L2JDWz2tUBkFDq+MeXskgs3v3bsePsVLerjJu5+ipY/Ll7hXy7Iq/yo/nPCCPffmczEn/7LTgafOoOBnZ9Sy5eegP5Kaed0vb3Aky5/1oeXv2IflqY26N4CmCppNGpsjMn46Svz86UW6fNljlWdEJngZDbzvt69hyug9YLdtU+oAV3NJOr/QBXTtO3NMDLU//d187veL/dtsixO3Q3/WgfvpQQz2oX+jp16ddT3ls3L3y6Ni7pVdC9+r3kYbvqWUvymNfPitbD+487TjkPEXu09/fMVqevmO0nNM/sfqzk8Xl8uEXO+XHMxfJix9ukP2HCkNWP6+x20Uacgk/CSnKKyvUEynMMv32wBbJKsjxWxZPqIZ06ifxJa2kWUSqfL0xV179JE9Ky444OtOUEEIIIYQQQgghDWdAxzSZ2aGPWtKPGalZ302S2nY4Qx5f/JwMTuwr1wy4TFITUk47tm/3tvKr7udIdt4J+dfiXbJkXbaUV/ikrLxS5U1duCpL7Wly5YWp0qtrm0ZoHXEjDKAGmTZt2th2zNHjxepVm4KSKNm179hp77dtFateVuvRkLo2BodPHZUNB7aqZfmb8rZJUfnpmoAW0c1lUGJfGZrYX9La9pYdmUWyYt1+WbM1V0rLqxJS1xk0HdRZBvR0PmjqtN522texZWcf0CkbSn1AF7e00yt9QNdOoMc7UZ7+7752esX/7bZFiNuhv+tB/fShhnpQv9DWD0v1z+o8UIZ26i+r9q2XDzbNk5wTueqzb3PT1Wt40mCZPuD7ktw66bTju3ZsKXdPHyo/mJQmnyzfLf/5OlOKSioEiS5XbNyvXoNS26kNqYb2bl9nagAv6+cF2rhIQwZQg0x0dLRtx/zn6z3y7sLtlu1cO6GPXDcxzXI9GlLXYFBWUaaeKiFgilf28QN1lguTMLW5C26mQxL7S5cWXeSbbYdkxbL98uf0r+rPaRqkoGkw9bbTvo4tO/uATlkv9wG7cUs7vdIHdO0EerwT5en/7munV/zfbltNkdzcXJk1a5YsX75ccnKqVsokJSXJmDFj5KabbpL27dufdkxJSYk6Zt68eZKVlSWxsbEqr/61114rl156aSO0oulAf9eD+ulDDfWgfk1Dv/CwcJWSb0TSEFmetVo+3DJfDp2sWlm6Nudb+SZno4xMPkuuHjBZOrfseNrx7VrHyU3f7y9Xf6+3fPpVpgqmHjtRoj7buOuwevXoHC9Tx6WqValW4wRe0c/NRLtIQwZQg0xeXp60bNnSlmOQh3OEKW8HePy1r6WgsFQFAh+/ZWSNz4zZp1br0ZC6OgVufsbmT5sObpeS8qqbWW1axrRQSaMxyxRT9qPD4mRNep78a/5+WZO+tc6gaYvYCDl/aNdGCZoGU2877evYsrMP6JT1Wh9wEre00yt9AHaCWQ8nytP/m6b/h5pmXmTt2rVy2223yfHjxyUiIkK6du2q3t+zZ49kZGTInDlz5LXXXpMBAwZUH1NcXKwCq9988406pnfv3lJYWChr1qxRr6+++kqefPLJRmxVaEN/14P66UMN9aB+TUu/iPAIGdt9pJyffLZ8mfmVzN66QPKLCsQnPvlq71pZmb1OxqScK9P6XyLtmyecdnyLuCi56qLeMuWCnvLl2mz515JdcuDwSfXZ7v0F8szb38g/P02XK8b0lItGJEtsdGRI6edG8lykIQOoQQQD4NLSUqmoqFAzDvD/uLg4NdNg7969qky7du3E5/PJkSNVT0tSUlKkqKhIdu3aJTExMdKpUyc1yAYJCQnSvmW4HDp0SP2enJwsYeJT/8fPHp1bVSfcxbTnSCmVXbuq8oKgDgcOHJCTJ09KZGSkdOvWTQ3cQevWrdXMBnyG82JWREFBgRqsY+DevXt3VRb1bNWqlTRv3lzZAp07d1bl8MUA09t79uyp6oCd0+D0KG/MtkhMTFRtg22Qmpqq2lZeXi4xcTGSV3FUVmSskR3HM+VQcc1d8gwwgT65ZZIM7zJIOvjaSFKzjhLfup2s235Inp6/VrbuPSFl5VWa1A6aDuzRSsYM6yrx0UUSG4OnGgVSWdlaDhzYr64V9EZ7MjMz1TFt27ZV7TfrffjwYbVbW1RUlPoSZNYbT0qML6xdunSR/Pz8ar1xXaEtiI+PV35g1hv6nThxQu0216NHjxp6t2jRQvbv36+OhT/gOLPeqC+uL8rBtqE3dEV9jx2rSu+AspjFgvdxDdG+7Oxs9VmHDh2krKxM1RmgDvv27av2WVxPo/7wX/xu9ln4A2bLwI9wnc0+C7vGsdDQuDr4iXqb9YZWBw8eVG2EPZzD0BvHmn0W1wt6oyyuH9pp9lmz3thdz9Ab1xham/U2fBbnxPnMeuN3s8+a9UY9oBPo2LGjOt6sN/o52o/zQwuz3rgOR49W+TnqG8g9ArOZDJ+tfY9Am8w+i/+j38E/4ZdoK7SAPegKvQH8GfWp7x6B8wK77hHQC3Wzco+ATfQzs97Qy+yz0NfQG7oZ1xx64pqZ9cY1NnwWttA3DL0B+g6ADqgT6mBg+JbVe4Sht9V7BNoFzcx613ePQNuMY/3dI/Ae7JnvEdDb6DuqT/p8NfQO5B5h9tlA7xGop6F3bZ9F2+u6Rxg+a+UeYeht3CNQ3nwdjXuE2Wf93SPwdw0vO+4R0Bt+59Q9AtfcfL9Fe63eIwD6m3GPMO7J/u4R5v5B/gf64Z133ql+jh49Wp566il1bQGu9S9+8QtZt26d3H777fLpp58qPwAzZ85UwVP40yuvvFIddF2yZIncc8898tFHH8mQIUPkqquuatT2EUIIIW4hMiJSJqReIGNTzpWFGcvk4/TP5ERJoVT6KmVx5leyLGuVjO8xWq7oN0naxMWfdnx0VISasDb+nG6yctMB+WjxTtmVXTVmyzt6Sl75eJO8s3C7TD6/h1x6Xndp1dw9sySJc4T5MMomjpKenq6+UGEgjC89+MIYCPhCa/WYGU98JkcKiiUhPlbefHSils1AzmsHeYWHqmeZbjm4Q0oqSussFx/TUs0yHdKpapYpZp0Wl5SrmaYrvsVM07w6Z5ripjZq0OnL84PdTn84XQ877evYqn3sNW/dK76oYgkri5X3r39e+3xWy7qxDzQWbmmnV/oA7Pz06aWW7rV21MOJ8vT/pun/N3z0f2e839pRV/O4p2/fvg0+T6jx5ptvym9/+1sVNEWAFEF1MwhIT5o0SQXgETRFQBQB94kTJ6ogPGanpqVVpWIyeO+99+Sxxx5TAXkEVBEQtwNeQ/fdI7wK9dOHGupB/fQIFf2Kyorl052L5ZNti+RUWVH1+9ERUTKp11iZkjZBxRX8gbAZlvFjw6l126se3hvEREfIxHO6yZQxPaVDm6qHn6GmX2NSHAQNrY57OAM1yGC2DmaoOH2MHTadOK+Z0vJS2XpoZ1XQNHeLHDhR80ZkgNlIvdt2VwFT5DNNadNV5ThB0HTttjz574b0gIOmwWynVZyuh532dWw53QeslnVDH3ALbmmnV/qAMesyWPVwojz9v2n6f6hp5jVWrVqlfo4bN+604Kkxq3ro0KEqELpp0yYVQJ07d66a0Tto0KDTgqdg6tSpKiiL2dWrV6+Wc889NyhtaUrQ3/WgfvpQQz2onx6hol9cVKxM7XexmpU6b/vnMn/HYpUWsLSiTAVVF+1aLpf2uVAm9/6eNIuOqzMmMbhXe/XanVMgsxfvlP9uyJFKn0hJaYXKmTpvRaZcMDRJrhzXS1I6tQop/RqTfBdpyABqkMFSt2AcY4dNJ86LIKna/Cm3apYpblh10Tq2ldr4CUHTQR3TpEVMc/U+gqZfbTwg/90Q+EzTYLazIThdDzvt69hyug9YLdtYfcCNuKWdXukDunYCPd6J8vR/97XTK/5vt62mBHKfYjYpViP5w1gYhhmnYP369ern8OHD6yyPVAsDBw5UuVAZQHUG+rse1E8faqgH9dMj1PRrEd1cpg+cIhf3Gidz0hfKwl1LpayyXIrKi+WjLQvkPzuXymVp49Ws1NjImDpt9EiKlweuHy4/vLivzF2aIQtX71VxicpKnyz5Zp96De/bUa4clyoxlYVBb2OocdJFPsgAapBBnjC7jkEyZLzMVMYck7BmJVIZUyy7j1blQzNAbg8jv4eVejSkrrUpKS9VgVIETdfnblHL9OsCM0r7tOuhgqaYZZrcOkm9ZwRN//ttjq1BU7vbaQdO18NO+w21BX/NLTks4Uf/lyPGJ5XVP/35bCDns1o2WH3AC7ilnV7pA7p2Aj3eifL0f/e10yv+b7etpgRmkeLlDyzhRxAUYKMoYOQHNvKe1gVy1SKAapQl9kJ/14P66UMN9aB+eoSqfvGxreSGodNkcp+LZPbWT2Xx7hVS4auUwtKT8s7GOTJ/x5dyRd+JMr7naImKiKrTRmJCc7l16iCZPqGPzPtvpsxfsVtOnKqaILY2PU+9UhKbyXWTmss5/TtJeDh2cCFe9kHmQA0CTuWR+mDzPPloy3zL5af1v1SuHjBZnATudOBEXvWy/K0Hd6onOnWBwNjQ72aZDuyYJs2j/5cvpHp5/rf71Y0H0+LrCpqOHNhJRg9OCjhoShoHN/osIQ3Bar5pQtyElZzTdsD8mQ3j/vvvl3nz5qk8X4sWLVK5UrGkH1r+6U9/UvlR6+J3v/udzJo1S0aNGqV+2gGvISGEkKYEJnp9uGW+LM9aXb0aBCQ0ayPT+l0iY7qPlMjwiHptFJWUy6JVWTJnWYYcyv9fnlWQ1L6FTB2XKuPO6iJRkfXbIcGHOVBdCna9xS6qdhyDpyHDO9ecyfDggmdFokpFyqLld5fcX+Mz8+5yVuphta7F5SWyOW979dL8gyerdv6tTURYuKS1T1UbQKlZpvFJKpdIYwdNG3JNnMDpethpv6G24LMdK9tI1y7/m0nz+Gtfy7HCEmndIkYev2VknT4byPmslrWzD3gdt7TTK33A2K09WPVwojz9v2n6f6hpFkq89NJLKngKfvrTn6rgqbFxAoiJqXsZofmzoqKaX9aIPdDf9aB++lBDPaifHk1Fv44t2ssd58yQy/tOVJN+VmavU+8fOZUvr659W+ZuWyhX9Z8s5yUP97thY1xMpFx2QU+55LzusnxDjtpwas+B4+qznEOF8sIHG+Tt/6TLZaN7ysWjUqRZbN0zW4l7fZABVA9jXpJvECbh4vvuZ4+2yY6cF09kco7nfjfLdLOkH8qQcj+zTBPi2lRv/jSgYx9pFlUzITNnmjYt4K9JzTrW8M3wknTxnSqW8KhYx3yWEEIIcSsvvviivPDCC+r/Y8eOlVtvvbX6s4iICKmsrEp1cybMD6XtAgFcfHFB3tacnBwpLS2VuLg4ad++vezdW5V2p127dmpseORI1QP0lJQUyc3NVcciuIuNH4z0AgkJCeqL56FDVSmdkpOT1f8R/EU+V6Qj2L17t/qsTZs2EhUVJQcPHqxOY4A0B8iFhuV83bp1k4yMDPVZ69at1cxdnBckJSVJQUGBFBYWKg1Rf5RFPVu1aiXNmzeXAwcOqLKdO3dW5Y4fP6407Nmzp6oDdG/ZsqXKR2s8gEhMTFR1hW2AL3RoGzb6gk3Ued++feqzjh07Kr2MDdx69Ogh2dnZUlZWpma4QDdDQ+iJ86B9APXdv3+/lJSUqHbBVlZWVrXe4PDhw+ondMAmYobeaE9mZmb1xmRov1lvHIdZNtAWmpr1xjWALYBrgbobeuO6GjrEx8crPzDrDf1OnDihri/aauiNNuB8aA+AP8CmWW/UF+3H5mqwDV8z9Ea7jh07pn5HWehg6I32QVOAhw7Q1qw3rkVdPov/4/qafRb+YOiN85p9FvU09K7ts2i7WW9oZfZZnMPQG8eafRbXy6w32mn2WUNv1AuamX0WWpv1NvssXma9cX6zz5r1Rj3MPotzmfWGZobPQguz3rgOZp916z0Cv+P6OXWPQHmzz4baPQL6wRedukdAP/iim+4RP+5/tVyUNEo+Sl8g2wuq/C238JC8sGqWfLhpnlw7aIq0K22l6ufvHtE1vkR+flUP2bDziCzecEQyDpxSnx89XiJvzt8q7y3aJpPO7Saj+raSqPByrXsENIHPhOo9orKysrqtTo0jUFcrcAl/kKcDwzngLIEAh7B6jNXleVZsmssUlRXLprxt380y3SqHT1V1hNpEhEdI33ap1UHTLq06nTagd1vQNBB9ncTpethpX8dW7WOtLIUO5HxWywbaB0IZt7TTK30Adh54eV2Dl/AHWg8nytP/m6b/3/HZU7Ys4T9TXbn82xoYrD/xxBPy/vvvq9+xBP/ll19WX4YNRowYob7UWFnCf95558kbb7xhS914Dd13j/Aq1E8faqgH9dOjqeu3/XCGvLfpE7Wvi5kebZJl+sDL1Ora+h5gGvptyzqqZqSu3HxAzBG4yIhwuejsrnLF2FS1zJ80jg9yCb9LQbQ9GMfo2lRPAMqOydfb1quZptsOZ0hF5emBTtC+WdvqgGn/Dn0kLup/A/+GBE3PH9xZBvZsF7SZpk7o68Z62Glfx5bTfcBqWSvl3OIbTuOWdnqlD+jaCfR4J8rT/93XTq/4v922miqYQXLXXXfJihUr1O8TJ06UZ555Rs2eMIPZEwigGrM96sKYTYOZNsR+6O96UD99qKEe1E+Ppq5fn3Y95bFx98rmvG3y7qZPZOeRqtm7u/P3ylPLXpS0dj1l+sAp0q9Dr3r1S+vWVh6aMUL2HTyhAqmLv9kn5RWV6vXZyixZuCpLzh3QSaZd2Et6J7cJahvdTpyLfJAB1CCDqciB5m9oyDENsXmqtEg2HdymAqbfHtgqR4qqBuS1iQyPlH7te1UHTTu37FjnUxc3B02d1teN9bDTvo4tp/uA1bJWyrnFN5zGLe30Sh8wliMFqx5OlKf/N03/DzXNvAr0u+WWW2THjqrZLDfeeKP84he/qDOnGpbHYfmnsVyxLoyldVjyRuyH/q4H9dOHGupB/fSgflUM6JgmMzv0kfUHNqsZqXuOVf3txUSzxxc/J4MT+8o1Ay6T1ISUevXr0qGl3HXNUPnBpDT59/Ld8unXe+RUcbmalfr1pgPqhbgINpw6K62DI+l5vEaui3yQAdQmDGaZZh3LURs/IWi6A7NMfXXn2erYvJ0KmOLVv0NviY2sezMDrwRNCSGEEEKCDXKZ/fCHP1R5vxAwffjhh+X666/3W37w4MHyxRdfyLp1VZtZ1Aa5xDZv3qz+P2zYMMfqTQghhDR1EMwc1nmgioms2rdePtg0T3JOVD2g/jY3Xb2GJw2W6QO+L8mtk+q1lRAfJzMm95erLuqtgqifLMuQ/BMl6rNNGYfVK6VTK7lyXKqMHpLEmIlLYAA1yCCBspPH+MIqavyszcnSU7IxL13WHtwom7f8TfKLq5IF1yYqIkrSEnrKWUlVN4hOLfw//SguLZdv0g/K8m9zPBk0bcg18WI97LSvY8vpPmC1rJVybvENp3FLO73SB6rsZAStHk6Up/+7r51e8X+7bTUlEOy87bbbVPAUmxo8++yzaul+fVx88cXy3HPPqQDq9u3bpU+fPjU+nz17ttpkAdcE+VKJ/dDf9aB++lBDPaifHtTvdMLDwmVk17NkRNIQWZ61Wj7cMl8OnazaAGltzrfyTc5GGZl8llw9YPIZ9WseF6WW7V82uocs/iZbLe/ff/ik+mzPgePy7Dvr5J+fpsvlY1Jl/IhkiY1peiG8JBf5YNNTv5HBbnKB5nCweozaDyz8u93DwjEN3Cf4tyd/n5plig2gdhzJlEo/s0wTW7SvXpbfr31vOXYkX+20FopBU91r4sV62Glfx5aTfSCQslbKucU3nMYt7fRKH4CdYNbDifL0/6bp/6Gmmdd47bXXZMuWLer/jzzyyBmDp8buslOmTJG5c+eqnKkvvfSSWtYPli5dKk8//bT6PwKz2FGW2A/9XQ/qpw811IP66UH9/IMNtMd2HynnJ58tX2Z+JbO3LpD8ogIVg/lq71pZmb1ORnQcLD8cfqW0b55Qr63oqAiZeG6KfG9EN1m1+YDMXrxTduytyn9+ML9I/jpnk7y7cLtMPr+7XHped4lvUfeK4FDkuIt8kCOtIHPixAm/QUndYzBlXMK/29It3Cczl/5Z9hbsl4Liur80RUdEqU2fEDAdkthPElt2qPe8oRQ01b0mXqyHnfZ1bDnZBwIpa6WcW3zDadzSTq/0AdgJZj2cKE//b5r+H2qaeW326d///nf1fwQ658yZo17+GDVqlNx5553q/1jmv3PnTtm6datMnjxZevXqpWadZmVlqc+nT58uV111VZBa0vSgv+tB/fShhnpQPz2o35mJjIiUCakXyNiUc2VhxnKZk/4fOV5SqCaurcxdL2sWbJTxPUbLFf0mSZu4+HptRYSHyahBnVVMZfPuIzL7y53yzbaD6rMTp0pVEPVfS3ap2aiYldqxbTMJdU64yAcZQA0iGOyWlZVJRUWF2gwAg2lE0tu3b6+Wc4F27dqpmaNHjhyp3hAAx+3atUtiYmKkU6dOajMBkJCQoPJnHTp0SB3z950fiCB++t1K+015206rQ7uYNtInvrv0apUiAxLTpLSoRA3kO7Zor84BWrduLbGxsXLq1CnZmr5D8gqjZOn6fbIpI19Ky78L0Jpo2SxKBqS0kME9W8kFZ6VKUdEp9ZRgz54TapbE7t27pbKyUlq2bCmtWrWq3gghMTFRioqK1O6yAImB0bby8nJp3ry52nnW2BgBHQZ6GTvN9ujRQ7Kzs5WezZo1U7oZGkJPaHz06FH1e/fu3WX//v1SUlKi2gVbxpcOHIfzGW3v1q2byk8GzaF3586dJTMzs3p324iICKW3MSvk8OHDSicsxevatatqK0DdsZMubIEuXbqoup88eVLpjetqnDM+Pl75AezgPUxRh364UeD6oq0ZGRnqGkO/Fi1aqPYA+ANsojxSLEBv1BftRznYNvTGNUB9jZ18URY6GHqjfdAUdOjQQWlr1hvXwvBZYNQfesO22WcPHDhQrTeus9lnzXpDQ9QV4CdeZr2h1cGDB5U2sIdzGHrjWOhi+CyuF/TG57h+aCd2OcY1gw+Y9YbPGHrjGkNrs96Gz6K9KGfWG7+bfdasN+ph9lnU2aw3fNTwWWhh1hu6mH02kHsEEmsbPuvvHmHojf+j38E/4ZdoK9oEe9AVegP4M+pj+Cz6hllvXFtjUxr4LDQx6232WfgXfAJAb5Qz+6xZb9TNqXuEcc0DvUcA9B3jHgGtUQcDw7es3iMMva3eI9AGaGbWu757BLQ0jvV3j0AdYM98j4DeRt8BKG/WO5B7hNlnA71H4LyG3rV9Fm2v6x5h+KyVe4Sht3GPgG3zdTTuEWaf9XePwN81vOy4R6Dd8Dun7hGol/EX3Pdde63eIwD6m3GPMO7J/u4R5v5BqsCGUYZfQB9/OU0N0C8M4JPvvvuuzJo1SxYsWKD6C67bkCFD5Oqrr5apU6c6Xv+mTF2bexHrUD99qKEe1E8P6med6MhomdznIvlej/Nkwc7F8sm2RXKqrEgqKivkP7uWyJeZK2RSr7EyJW2CtIxpUa8tjAsxIQ2vzP0Famn/sg05UlnpUxPZ5v03UxZ8tUdGD06SKy9Mle6d6w/MeplwF/lgmE+t+yZOkp6err5s4AtR3759HTnHhgNb5allL5z2flR4pAxK7PvdLNP+0qFFVTCgPoyZpv/9NkfWhNBMU+JOZjzxmRwpKJaE+Fh589EzL2ckxA3Qb4kXueate8UXVSxhZbHy/vXPe3rcQ5yF15AQQgjRo7D0pMzb/rnM37FYSsqrNogCcZGxcmmfC2Vy7+9Js2jrS9Pzjp6SucsyZOGqrNNiNMPSOsi0cb1kQM+qyQjEmXEPo15BxpidYecxiIG/v+kTCTOmnn4Hfu8a31l+cf5tMiF1TI3gaW2bCJqu+Ha//P4fa+T6x/4jv/vHGvnvt/trdMyWzaJl4rnd5De3jpR/PDZR7rhqiAzp3cHzwdOGXBMv1sNO+zq2nOgDDSlrpZxbfMNp3NJOr/QBXTuBHu9Eefq/+9rpFf+32xYhbof+rgf104ca6kH99KB+DadFdHM5u9kAefHSJ+TS3hepyW2gqLxYPtqyQG6f/yuZk/6ZFJuCq/WB5fo/uXygvP7weLluQh8VnzFYt+2gPPTyCrn/T8tkxcb9UlEZOvMkM1zkg1zCH2QaMuH3TMcg92lGftbpx4lPdufvVZ8P6dTvNJtWZpqiU44aFNozTd0yCdvpethpX8eWE32gIWWtlHOLbziNW9rplT6gayfQ450oT/93Xzu94v922yLE7dDf9aB++lBDPaifHtRPX7/42FZyw9Bp8v0+31MbTX25e4VU+CrlZOkpeWfjHJm/40u5ou9EGd9ztERFRJ3RJjaQunZimlwxNlUWrd4rc5buUhtNgZ3Zx+R3f18jSe2bq8/HndVVbVDlZXwu8kEGUIMM8tPZeYx59ikCprXB+/h8cGJfNZXbCJp+sTpXNu7eVm/QdEC3ZjL6rNSQDJrqXhMv1sNO+zq27O4DDS1rpZxbfMNp3NJOr/QBXTuBHu9Eefq/+9rpFf+32xYhbof+rgf104ca6kH99KB+9unXtllruWX4dXJZ2nj5cMt8WZ61WsVzsOn3m+s/lH9v/1ym9btExnQfKZHhZw56xsZEyvdH95CLR6Wo1cPYcGrPgaoNxHMOnZQXP/xW3v7PNlXmklHdpXncmYOzbqSVi3yQAdQgg00k7DzG3+xTAwRV8fm7X/1XsjNiA5ppihwQoR48beg18WI97LSvY8vuPtDQslbKucU3nMYt7fRKH9C1E+jxTpSn/7uvnV7xf7ttEeJ26O96UD99qKEe1E8P6me/ftjA+45zZsjlfSfKB5vnycrsqo0lj5zKl1fXvi1zty2Uq/pPlvOSh1vaQCkyIlzGDusiY4YmybrtB9WGUxt3VW3Imn+iRP6xIF0+/GKnXDwyRS67oIckxFvPu+oGWrjIB0M/OuYyjJ167TgGTyv+se5jOdOMZnz+r23z1FJ9fzlN//n46TlNG1JXL+KWdjpdDzvt69iysw/olLVSzi2+4TRuaadX+oCunUCPd6I8/d997fSK/9ttixC3Q3/Xg/rpQw31oH56UD/n9OvSqpPcN+oW+f2Eh2RYpwHV7+cWHpIXVs2SBz6bKav3bbC8hB0rjs9K6yhP3naePHv3BWqCnLGfVFFJufxryS65+cnP5c/vr5d9B0+IV9jvIh/kDFQPU15ZLnknjlR3Cn+oz6OLRcJ80jIuRnWk7u1EJl0wqEnMMCWEEEIIIYQQQghxG93bdJUHL7hddhzeLe9t+kQ2H9yu3s8+fkCeWfGq9GiTLNMHXiaDE/upIKkVeie3kf+7YYTkHCqUj5fski/WZEt5RaV6IW/q52v2yjn9E+XKC3tJWre2DrcwdGAANch06tTJtmMiwiKlaPO5Uh5WfEYbYeUx8uubR8ngXu1V0PTkyZNnDJ42pK5exC3tdLoedtrXsWVnH9Apa6WcW3zDadzSTq/0gSo7GUGrhxPl6f/ua6dX/N9uW4S4Hfq7HtRPH2qoB/XTg/oFT7/e7XrIo+Pukc152+TdTZ/IziOZ6n1sDP7UshclrV1PmT5wivTr0MuyzaT2LdRK4+smpsm/l++WBV9lyqnicrVKeeXmXPXq3yNBrhyXKsP7drQcoG2qPsjph0EGgUu7jiktq5CyohjxnYo/46uyNFb69UioDppaqUdD6upF3NJOp+thp30dW3b2AZ2y7APua6dX+oCunUCPd6I8/d997fSK/9ttixC3Q3/Xg/rpQw31oH56UL/g6zegY5rMvOgBeXD0zySldZfq97cdzpDHFz8nM5f8WXYd2ROQzbatYuWGS/vJrEcmyI2T+0nbVjHVn23ZfUSeeH2V3PnMYvlybdVMVTdx0kU+yABqkDl+/Lhtx0RHRUhUpLVLiHIxUREB1aMhdfUibmmn0/Ww076OLTv7gE5Z9gH3tdMrfUDXTqDHO1Ge/u++dnrF/+22RYjbob/rQf30oYZ6UD89qF/j6IeZoMM6D5TfTfg/uXfUzZLUMrH6s4156fLQ57+Xp//7iuw9lhOQ3WaxUTJ1XC/528Pj5a6rh6gZqgZZuSfk+XfXyU9++7l8sixD5U11A8dd5INcwh9kGjIl2t8x4eFhcsGQJFmybp9UVPpPLByBckOTatixUg83Tt92Are00+l62Glfx5adfUCnLPuA+9rplT6gayfQ450oT/93Xzu94v922yLE7dDf9aB++lBDPaifHtSvcfULDwuXkV3PknOShsryrNXy4ZZ5cvDkEfXZ2pxv5ZucjTIy+Sy5esBk6dyyo2W7UZERMv6cbnLR2cmyakuuzF68U7Zn5avPDuUXyWtzN8t7i7bLJed1l++f30PiW/xvxmpT9sEwn9UtvUiDSU9Pl1OnTkmzZs2kb9++ttrO3F8g9zy3VCrruYzhYWHyx/vGSPfO8baemxA7mPHEZ3KkoFgS4mPlzUcnNnZ1CLEE/ZZ4kWveuld8UcUSVhYr71//vCfHPSQ48BoSQggh7qO8oly+zPxKZm9dIPlFBTUCrWNSzpVp/S+R9s0TAraLsCCW8s9evEvWpuedtvJ5/IhkuXxMT0lMaC5NedzDJfxBJjMz09ZjEBS977phKkiKmaZm8Dvex+e1g6dW6tGQunoRt7TT6XrYaV/Hlt19oKFl2Qfc106v9AFdO4Ee70R5+r/72ukV/7fbFiFuh/6uB/XThxrqQf30oH7u0i8yIlImpF4gL1zyhPxoyDRpFVO1BL/SVymLM7+SuxY8Jq9/816N4KrVWZ4DeraTx24+V174+TgZd1aX6vgS9t6ZvyJTbv3dF/KHt9bK7pzAbIeSD3IJf5CpqKiw7Rh0Cry6poj8/Me9ZcnqQ7J602ERX7hIWKWcNaCdjB3RXpI6Vsruo3ulTVy8elmtR0Pq6kXc0k6n62GnfR1bdvYBnbLsA+5rp1f6gK6dQI93ojz9333t9Ir/222LELdDf9eD+ulDDfWgfnpQP3fqFx0ZLZP7XCTf63GeLNi5WD7ZtkhOlRVJRWWFfLZrqQqmTuo1VqakTZCW3wVZrZLSqZXcd91Zcv3FfWXusgxZuDJLiksrpLLSJ8vW56jX0N7t5coLe8mg1HaOL7F3kw8ygBpkWrRoYdsxizKWy0db5v/vjViR2OF4/BAhEl4hm8JENm3838fT+l+qcmNYrUdD6upF3NJOp+thp30dW3b2AZ2y7APua6dX+oCunUCPd6I8/d997fSK/9ttixC3Q3/Xg/rpQw31oH56UD936xcbFStT+12sZqXO2/65zN+xWErKS6S0okwFVRftWi6X9rlQJvf+njSLjgvIdoc2zeSWKQNl+vg+agbqv5fvluMnS9Vn63ccUq/Urq3lynGpMnJg59NWRIeiDzKAGmTi4+NtO2Z8z9EyvPOgGu89/trXcqywRFq3iJHHbxlZ4zNj9qnVejSkrl7ELe10uh522texZWcf0CnLPuC+dnqlD+jaCfR4J8rT/93XTq/4v922CHE79Hc9qJ8+1FAP6qcH9fOGfi2im8v0gVPk4l7jZE76Qlm4a6mUVZZLUXmxfLRlgXy6c4majYpZqbGRVRtCbcxNl1nrP5Abh14tgxL95/1s2SxaBVEvH9NTvli9Vz5emiF5R0+pz3ZlH5Pf/2OtdGrXXK4YmyoXDe+qcqaGqg8yB2qQycnJse0YBER7tE2u8QovaS2+U/HqZ+3PzAFUK/VoSF29iFva6XQ97LSvY8vOPqBTln3Afe30Sh/QtRPo8U6Up/+7r51e8X+7bRHidujvelA/faihHtRPD+rnLf3iY1vJDUOnyQuX/kZNuIsIqwr5nSw9Je9snCN3zn9UFuz4UkrLS+XdjXMl53iu+mllb/nY6Ei59Pwe8uqDF8kD158lPUz77Bw4fFJe+uhb+fGTi+TDL3ZIYVFZSPogZ6AGkeLiYiktLVU5HOAE+H9cXJy0b99e9u7dq8q0a9dOOe+RI0fU7ykpKVJUVCS7du2SmJgY6dSpk+zZs0d9lpCQIOHh4XLo0CH1e3JycnV+CPysrKyU3bt3q9/btGkjUVFRcvDgwerPDxw4ICdPnpTIyEjp1q2bZGRkqM9at24tsbGx6jOcNykpSQoKCqSwsFAiIiKke/fuqizq2apVK2nevLmyBTp37qzKHT9+XOXC6Nmzp6oD6tKyZUtV3ugAiYmJqm2wDVJTU1XbysvLlU3Ued++feqzjh07Kr3y8/PV7z169JDs7GwpKytTO6VBN0ND6In2HT16VP2O+u7fv19KSkpUu2ArKyurWm/YQDsBdMjLy1PXCnqjPUbS4rZt26r2m/U+fPiw2q0N2nbt2rWG3tHR0coW6NKli6q7oTeuq3FOPFGBH5j1hn4nTpxQ1xdtNeuNKexoD4A/4Diz3qgv2o9ysG3oDV1R32PHjqnfURY6GHqjfdAUdOjQQeli1hvXwvBZXE+j/tAbv5t9Fv5g6I3rbPZZs961fRYvs97QCj6LNsIezmHojWPNPovrBb1RFtcP7TT7rFlv+IyhN64xtDbrbfgszonzmfXG72afNeuNeph9Fseb9YaPGj4LLcx64zqYfTaQe0Rubm61z57pHoH/o9/BP+GXaCu0gD3zPQL+jPrUd4/AeYFd9wjohbo5dY8wrnmg9wiAvmPcI1An1MHA8C2r9whDb6v3CLQLmpn1ru8egbYZx/q7R+A92DPfI6C30XcA7Jn1DuQeYfbZQO8RqKehd22fRdvrukcYPmvlHmHobdwjUN58HY17hNln/d0j8HcNLzvuEdAbfufUPQLX3Bga+75rr9V7RO1xhHFP9nePMPcPQgghhBASXNo2ay23DL9OLksbr2agLstapcaHBcXH5c31H8rsrZ/KiZJCVTYjP0u+zU2XIZ36WbIdEREuFwztIqOHJKll/LO/3Ckbd1WN3Y+dKJF/LEhXQdSJ56aoWasJ8YGlDnAzYT4roWaiRXp6uvpChS9E+IIXaA4HfMGzesyMJz6TIwXFkhAfK28+OlHLZiDn9TJuaafT9bDTvo6t2sda8dlAzme1LPuA+9rplT4AO3c8t8LSvdaOejhRnv7fNP3/x3MeEV9UsYSVxcr71z/vWF3N456+ff0vCSPuhdfQffcIr0L99KGGelA/PahfaOi37/gB+WDzPFmZve60z8IkTHq0SZanxv+ywRtC7czOl9mLd8nXG/dLpSnCGBkRJmOHdZWp41Kla8eWrtXQ6riHS/iDDGb3BOMYO2w6cV434pZ2Ol0PO+3r2HK6D1gtyz7gvnZ6pQ/o2gn0eCfK0//d106v+L/dtghxO/R3PaifPtRQD+qnB/ULDf26tOok9426RX4/4SFJbdutxmc+8VXPQm0ovbq2kQd/dLa8/OBFMmlkikRFVoUayyt88vmavfKzp7+UmW+skvTMqtVUXtQQMIAaZIylek4fY4dNJ87rRtzSTqfrYad9HVtO9wGrZdkH3NdOr/QBXTuBHu9Eefq/+9rpFf+32xYhbof+rgf104ca6kH99KB+oaVfSusugjXomHVqJjwsTN7f9ImlXKj10bldC7l92mB5/Vfj5aqLeknzuKjqz1ZtyZVfvLhcfvniclm9NVcqzVNVPaIhc6ASQgghhBBCCCGEEBLCYJYpZpvWptLnCzgXan20aRkrP7qkn0y7sJd8tjJL5i7LUOnPwNbMo7L19VWSnNhSpo5NVflUjRmrtUGQtaSsUv0MD29YegE7YQ7UIGDOp5CWlhZwXglcIqvHWM2BasVmIOf1Mm5pp9P1sNO+jq3ax1rx2UDOZ7Us+4D72umVPgA7N/5mYYNzoAZaDyfK0/+bpv9Pf/s+W3KgnqmuzJ/pfXgN3XeP8CrUTx9qqAf104P6hY5+qMtDi34vmcf2qoBpbTALtXtrvVyo/igrr5Sl6/bJv5bslOy8qs2rDNrFx8qUMaky8dxuEhdTNcczc3+BzF2aIcs25KhjEWC9YEiSTBnTU7p3jhe7YQ5Ul2Ls7Oz0MXbYdOK8bsQt7XS6Hnba17HldB+wWpZ9wH3t9Eof0LUT6PFOlKf/u6+dXvF/u20R4nbo73pQP32ooR7UTw/qFzr6GbNP6wqe1p6FajdRkeHyvRHJ8uLPL5Rf3ThC+qa0rf7scEGxvP7JZrnpNwvln5+my6dfZco9zy2VJev2qeApwE/8jvcRiG0suIQ/yJSXlwflGDtsOnFeN+KWdjpdDzvt69hyug9YLcs+4L52eqUP6NoJ9HgnytP/3ddOr/i/3bYIcTv0dz2onz7UUA/qpwf1Cw39MPsUOU6R+xSbRvkDn6Pc4MS+jsycDQ8Pk3MGdFKvrZlHZPaXu1Q+VFBYVCYffL7DVOmax1Z8lzP1uXfWqeX/TsxEPROcgRpkmjdvHpRj7LDpxHndiFva6XQ97LSvY8vpPmC1LPuA+9rplT6gayfQ450oT/93Xzu94v922yLE7dDf9aB++lBDPaifHtQvNPQrryyXw6eO1hs8Bfj8SFG+Ku80/bonyCM/PkdefGCcXDi8q0RYzHGKuC5yqjYGnIEaZNq2bRuUY+yw6cR53Yhb2ul0Pey0r2PL6T5gtSz7gPva6ZU+oGsn0OOdKE//d187veL/dtsixO3Q3/WgfvpQQz2onx7ULzT0i4qIkt9OeFCOF9fMP1oX8bEtVflg0S2xldx77TC5bmKa/OSpz/2mGDDPRF22PkfuvmZo0PPLcgZqkMnOzg7KMXbYdOK8bsQt7XS6Hnbab6ito8eL5at1O2TXvmPVr/KKqrwm+Gl+Hy+UD/R8VsuyD7ivnV7pA7p2Aj3eifL0f/e10yv+b7ctQtwO/V0P6qcPNdSD+ulB/UJHv3bN2kqPtslnfCU0a9Mo9YtvHn3G4KkBcqKWlFVIsOEMVEJI0PjP13vk3YW7RQSvmhQUlsq9zy+t8d61E/qoJ1GEEEIIIYQQQggJTaKjItRmU8bGUfWBcjFRERJsGEANMh06dAjKMXbYdOK8bsQt7XS6Hnbab6itSSNTpH9KS8u5YNq2ig34fFbLsg+4r51e6QNVdjKCVg8nytP/3ddOr/i/3bYIcTv0dz2onz7UUA/qpwf104P6BbbB1AVDkmTJun3VG0bVBXKlXjA0KejL9wEDqEGmrKwsKMfYYdOJ87oRt7TT6XrYab+hthAQ9ZXFSkJCa8fOZ7Us+4D72umVPqBrJ9DjnShP/3dfO73i/3bbIsTt0N/1oH76UEM9qJ8e1E8P6hcYU8b0lMXf7Ku3DFb5T7mgpzQGzIEaZPLz84NyjB02nTivG3FLO52uh532dWw53QeslmUfcF87vdIHdO0EerwT5en/7munV/zfbluEuB36ux7UTx9qqAf104P66UH9AqN753i577phEh4WpmaamsHveB+fo1xjwBmohBBCCCGEEEIIIYSQRmXMsC6SnNhS5i7LkGXrc1ROVOQ8xbJ9zDxtrOApCPP5LG5zRRpMenq6nDp1Spo1ayZ9+vSR8PDAJv5WVlZaPmbGE5/JkYJiSYiPlTcfnahlM5Dzehm3tNPpethpX8dWQ44N5BirZdkH3NdOr/QB2Llp5iJL91o76uFEefp/0/T/a9+5X3xRxRJWFivvX/+8Y3U1j3v69u3b4POQxoPX0H33CK9C/fShhnpQPz2onx7UT4/KSp8Ul5ZJXEyUozlPrY57eCWDzL59+4JyjB02nTivG3FLO52uh532dWw53QeslmUfcF87vdIHdO0EerwT5en/7munV/zfbluEuB36ux7UTx9qqAf104P66UH99DeWOnwwt1E2jKoLBlCDTGlpaVCOscOmE+d1I25pp9P1sNO+ji2n+4DVsuwD7munV/qArp1Aj3eiPP3ffe30iv/bbYsQt0N/14P66UMN9aB+elA/PahfaGnIAGqQiYuLC8oxdth04rxuxC3tdLoedtrXseV0H7Baln3Afe30Sh/QtRPo8U6Up/+7r51e8X+7bRHidujvelA/faihHtRPD+qnB/ULLQ0ZQA0y7du3D8oxdth04rxuxC3tdLoedtrXseV0H7Baln3Afe30Sh/QtRPo8U6Up/+7r51e8X+7bRHidujvelA/faihHtRPD+qnB/ULLQ0jG7sCTYni4mKVnHbAgAGSk5OjpiIjmg6H2Lt3ryrTrl07wb5eR44cUb+npKTI9u3bVbmYmBjp1KmT7NmzR32WkJCgEhIfOnRI/Z6cnCwVFRXq//iJhMW7d+9Wv7dp00aioqLk4MGD1Z+3atVKTp48KZGRkdKtWzfJyMhQn7Vu3VpiY2PV782bN5ekpCQpKCiQwsJCiYiIkO7du6vPUE/YQJkDBw6oYzt37qzKHT9+XOWp6Nmzp6oD6tKyZUtVHm0HiYmJUlRUpGyD1NRU1bby8nJlE3U2coZ07NhR6ZWfn69+79Gjh2RnZ0tZWZlK9AvdDA2hJ9p39OhR9Tvqu3//fikpKVHtgq2srKxqvVF3aAOgQ15enrpW0BvtyczMVJ+1bdtWtd+s9+HDh1WyYRzftWvXGnpHR0crW6BLly6q7obeuK67du1Sn8XHx6vra9Yb+p04cUJdX7TVrHeLFi1UewD8ATbNeqO+aD/KwbahN3RFe48dO6Z+R1noYOiN9kFT0KFDB6WtWW9cC8NncU4jGTb0xvU1+yw0NfTGdTb7bG5ubrXe0BB6wg+gF9pu1htawWdxvrS0NHUOQ28ca/ZZXC/ojbLYrA3tNPusWW/4DH5Hu3GNobVZb8Nn0QbDfwy9cX6zz5r1Rj3MPovjzXrDRw2fhRZmvXEdzD4byD0Cmho+e6Z7hFlv+CXaCs3wf/M9Av6M+tR3j8B5gV33COgF/Z26R+D+i+MCvUcA9HXjHoF7MupgYPiW1XuEobfVewTaBVtmveu7R8AO6lHfPQK/o53mewT0hh8ZPgt7qHdD7hFmnw30HoF6GnpbvUcYPmvlHmHobdwjUH7QoEGn3SPMPuvvHoG/a3jZcY+A3mijU/cI/G7sGoqfaK/Ve0TtcYRxT/Z3jzD3D0K8DvoW+jJpGNRPH2qoB/XTg/rpQf1CS8MwH0bZxFHMO3rhy0egFx9fcqweM+OJzyztDG3FZiDn9TJuaafT9bDTvo6thhwbyDFWy7IPuK+dXukDsDPznQxL91o76uFEefp/0/T/h1f+RXxRxRJWFivvX/+8Y3XlDu7eh9fQffcIr0L99KGGelA/PaifHtTPGxpaHfdwCX+Q4RJ+9+GWdjaV5Ztcwu8+3NJOr/QBLuEPLdzSTq/4v922CHE79Hc9qJ8+1FAP6qcH9dOD+oWWhgygBhks+QvGMXbYdOK8bsQt7XS6Hnba17HldB+wWpZ9wH3t9Eof0LUT6PFOlKf/u6+dXvF/u20R4nbo73pQP32ooR7UTw/qpwf1Cy0NGUANMkZOMqePscOmE+d1I25pp9P1sNO+ji2n+4DVsuwD7munV/qArp1Aj3eiPP3ffe30iv/bbYsQt0N/14P66UMN9aB+elA/PahfaGnITaQ8zNHjxeplpryisvrnrn1VG1MYtG0Vq16EEEIIIYQQQgghhBBrcBOpIGBOSNurVy+1W20gYDfbuo5557Nt8u7C7ZbtXDuhj1w3Ma1em1bOG2q4pZ1O18NO+zq2GnJsIMdYLcs+4L52eqUPwM7NT33R4E2kAq2HE+Xp/03T/3/w3gO2bCJ1prpyAyLvw2vovnuEV6F++lBDPaifHtRPD+rnDQ2tjnt4JYPMgQMHpGvXrrYcM2lkiozon3ja+3l5edKxY8fT3jfPPrVSj4bU1Yu4pZ1O18NO+zq27OwDOmXZB9zXTq/0AdgJZj2cKE//b5r+H2qaERIM6O96UD99qKEe1E8P6qcH9QstDRlADTIlJSW2HeN3SX7xYUnt0lq7Hg2pqxdxSzudroed9nVs2dkHdMqyD7ivnV7pA7p2Aj3eifL0f/e10yv+b7ctQtwO/V0P6qcPNdSD+ulB/fSgfqGlITeRCjKxsbGOH2OlvF1lQgG3tNPpethpX8eW033Aaln2Afe10yt9QNeOE/f0QMvT/93XTq/4v922CHE79Hc9qJ8+1FAP6qcH9dOD+oWWhgygBpnExETHj7FS3q4yoYBb2ul0Pey0r2PL6T5gtSz7gPva6ZU+oGvHiXt6oOXp/+5rp1f8325bhLgd+rse1E8faqgH9dOD+ulB/UJLQwZQg8yePXscP8ZKebvKhAJuaafT9bDTvo4tp/uA1bLsA+5rp1f6gK4dJ+7pgZan/7uvnV7xf7ttEeJ26O96UD99qKEe1E8P6qcH9QstDRlAJYQQQgghhBBCCCGEED8wgBpkEhISHD/GSnm7yoQCbmmn0/Ww076OLaf7gNWy7APua6dX+oCuHSfu6YGWp/+7r51e8X+7bRHidujvelA/faihHtRPD+qnB/ULLQ0ZQA0yYWFhjh9jpbxdZUIBt7TT6XrYaV/HltN9wGpZ9gH3tdMrfUDXjhP39EDL0//d106v+L/dtghxO/R3PaifPtRQD+qnB/XTg/qFloYMoAaZw4cPO36MlfJ2lQkF3NJOp+thp30dW073Aatl2Qfc106v9AFdO07c0wMtT/93Xzu94v922yLWKCkpkVdeeUUmT54sAwcOlLPPPluuv/56mT9/fmNXLeShv+tB/fShhnpQPz2onx7UL7Q0jGzsChBCCCGEEELqpri4WG666Sb55ptvJCIiQnr37i2FhYWyZs0a9frqq6/kySefbOxqEkIIIYSENJyBGmSSk5MdP8ZKebvKhAJuaafT9bDTvo4tp/uA1bLsA+5rp1f6gK4dJ+7pgZan/7uvnV7xf7ttkTMzc+ZMFTxNTU2Vzz77TObMmSOff/65vPrqqxIXFycfffSRfPjhh41dzZCF/q4H9dOHGupB/fSgfnpQv9DSkAHUIHPo0CHHj7FS3q4yoYBb2ul0Pey0r2PL6T5gtSz7gPva6ZU+oGvHiXt6oOXp/+5rp1f8325bpH727dsnH3/8scr/9eyzz0rXrl2rPxs7dqw8+OCD6v8vvPCCVFZWNmJNQxf6ux7UTx9qqAf104P66UH9QktDBlCDTFFRkePHWClvV5lQwC3tdLoedtrXseV0H7Baln3Afe30Sh/QtePEPT3Q8vR/97XTK/5vty1SP3PnzpXy8nKV9zQtLe20z6dOnSqxsbGSl5cnq1evbpQ6hjr0dz2onz7UUA/qpwf104P6hZaGDKAGmejoaMePsVLerjKhgFva6XQ97LSvY8vpPmC1LPuA+9rplT6ga8eJe3qg5en/7munV/zfblukftavX69+Dh8+3O+1QHAVMIDqDPR3PaifPtRQD+qnB/XTg/qFloYMoAaZpKQkx4+xUt6uMqGAW9rpdD3stK9jy+k+YLUs+4D72umVPqBrx4l7eqDl6f/ua6dX/N9uW6R+9uzZo36al+7XpkuXLjXKEnuhv+tB/fShhnpQPz2onx7UL7Q0ZAA1yGRmZjp+jJXydpUJBdzSTqfrYad9HVtO9wGrZdkH3NdOr/QBXTtO3NMDLU//d187veL/dtsi9XPkyBH1s23btn7LtG7dWv3Mz88PWr2aEvR3PaifPtRQD+qnB/XTg/qFloZhPp/P19iVCHU2bNggFRUVagMAvJCrKhCKi4sDOsZKebvKhAJuaafT9bDTvo6thhwbyDFWy7IPuK+dXukDsJObXyrlFZUSGREuKZ1aOVoP/g1wlqbk//tPHsLIT8QXJj0SujpWV+SqwvAyIiJChgwZ0uDzEJG+ffuqzaFeeeUVGTduXJ1lnn/+efX50KFD5b333rN97BoXFydNGbfcI7wK9dOHGupB/fSgfnpQP29oaHXsGuloLYjC2BUVFwSvU6dOBWwj0GOslLerTCjglnY6XQ877evYcroPWC3LPuC+dnqlD3SI/9+fT7fc0wMtT/93XzudrkdiXIJt57JyPHeF1wcDeas6ItjpxNjVLf2jMaEGelA/faihHtRPD+qnB/XzjoZnGnMxgBoEoqKipKysTMLDwyUmJqaxq0MIIYQQ4hglJSVqAIrxD9GjWbNmUlBQoDT1h/GZnTNFOXYlhBBCSFOhxOLYlQHUIGDsjkoIIYQQQohV2rRpowKox44d81vGyH1aX57UQOHYlRBCCCGkJtxEihBCCCGEEBfSs2dP9TMnJ8dvmX379qmfKSkpQasXIYQQQkhTgwFUQgghhBBCXMjgwYPVz3Xr1tX5eWlpqWzevFn9f9iwYUGtGyGEEEJIU4IBVEIIIYQQQlzIxRdfXB1A3b59+2mfz549W+1Om5SUJCNGjGiEGhJCCCGENA0YQCWEEEIIIcSFJCcny5QpU9TGBnfddZdkZGRUf7Z06VJ5+umn1f9vu+02iYzk1gaEEEIIIU4R5vP5fI5ZJ4QQQgghhDQYbCI1Y8YM2bp1q4SHh0uvXr3UrNOsrCz1+fTp0+XXv/51Y1eTEEIIISSkYQCVEEIIIYQQF4OA6axZs2TBggUqcIpAap8+feTqq6+WqVOnSlhYWGNXkRBCCCEkpGEAlRBCCCGEEEIIIYQQQvzAHKiEEEIIIYQQQgghhBDiBwZQCSGEEEIIIYQQQgghxA8MoBJCCCGEEEIIIYQQQogfGEAlhBBCCCGEEEIIIYQQPzCASgghhBBCCCGEEEIIIX5gAJUQQgghhBBCCCGEEEL8wAAqIYQQQgghhBBCCCGE+IEBVEIIIYQQQgghhBBCCPEDA6hEi3379kmfPn3qfRHSFNi4caP88Ic/lMGDB8vZZ58t9913nxw8eLCxq0VIUPj666/lqquukkGDBsmFF14oL7zwgpSWljZ2tQghxHY+//zz6vvd8OHD5bbbbpPdu3c3drU8y5w5c9T3hVWrVjV2VTzDoUOH5Je//KWcc845ygdvueUWycjIaOxqeQaO2RvGjBkz5NFHH63zsz179shPf/pT5Y8jR46UJ554Qk6ePBn0OnpZQ/5t0dMvWH9XIm23SJoUbdu2laeffvq093NycuRPf/qTjBkzplHqRUgwgb/jht6iRQu555571IDh9ddfly1btsjcuXMlNja2satIiGOsWLFCfvKTn0jHjh3lrrvukoKCAnn11VeV/7/yyiuNXT1CCLGNpUuXyu23366+4P785z+XwsJC+cc//iHXXnutfPzxx9K5c+fGrqKnOHr0qPz2t79t7Gp4ihMnTsj1118vx44dU2PP6OhomTVrlgoI/vvf/5aEhITGrqKr4Zi9YeDBOB6WJycnn/bZkSNH5Ec/+pFERkaqoN/x48fljTfekL1798rf/va3Rqmv1zTk3xY9/YL5d4UBVKJFs2bNZMqUKae9jz9MCK4+9dRTjVIvQoLJ3//+dykqKpIPP/xQevbsqd7r1auXCiZhMIuniYSEKk8++aTExcXJe++9Jx06dKj2/wceeEAWLVok48ePb+wqEkKILeBLWe/eveXdd99VwQKAe9zll1+uAgVWZsaQmn8/OEstMPCAMjs7W95//30ZOHCgeu/888+Xyy67TL33s5/9rLGr6Go4Zg8MrCbCZKl//vOffssgWJqfny+ffvqpdOnSRb2Hn7/61a9UwAszUpsyVjTk3xY9/YL5d4VL+IntLFiwQN0s8YeoXbt2jV0dQhwnMzNTzb4zBmLGYBbs2LGjEWtGiPNpXLBs8Morr6wOnoLvf//76iEaZnMQQkioLJvG3/tLL720+guuEXzBa8OGDY1aP6+xbNky+c9//iM33XRTY1fFM/h8PvV3FYEVI3gKsFT1/vvvV35I6odjdutgRRHGcwhc3XzzzX7LIXB63nnnVQdPwRVXXKEmWuGzpowVDfm3xT9WfTCYf1cYQCW2UlFRoaZXd+/enU/wSJOha9eucvjwYbWsyhxYAu3bt2/EmhHiLHl5eepnampqjffDwsLUQDo9Pb2RakYIIfbSpk0b9cWsrvEtllNHREQ0Sr28CGYHPf7443LjjTdyv4QAwNgSuTpHjRqlfq+srJRTp06p/yOVDld8nBmO2a0DjTCewwxIrCqqC9z7kBahf//+Nd5HIBB9G6kRmjJWNOTfFj39gv13hQFUYisLFy5UyY6RRNr8BIWQUAZPxJBzCjlrdu3aJZs3b5aHHnpIvYcnsISEKphdAOpaKoOnxsiLRQghoQDGtZggUDvH5OLFi+XAgQMydOjQRqub1/jjH/8o4eHhcscddzR2VTwFNuoBrVu3Vpv0nHXWWcrvsHy/Kc9SCwSO2a2TmJioVpaOHj3abxlj8y3M6q0NAtK5ubnSlLGiIf+26OkX7L8rjHARW0HuHSzbv+SSSxq7KoQEDST2xoAMOX+XLFmi3kNOyDfffJNPs0lIgyVwCKJi51DkvjbAsn7kaCOEkFAGSy8x4wUbz2ATH2JtB/S3335b/vrXv3LDngAxZk0+88wzahMk+B7yA7788stq1tVHH31UY2k6OR2O2a1jZTKU8QC9rr4cExNTPUO6qdLQCWX82xKYfsH8u8IZqMQ29u/fLytXrpRp06apHSEJaSo899xzMnPmTLWk6vnnn1fJq5OSkuTHP/6xbNq0qbGrR4hj4F6P3YDXrFkjjz32mAqcrl27Vu6++2715a4pLzsihIQ2WFqJQAxmWGGzFCwNJvVTVlYmDz/8sJpoYeSdJNZBsBQUFxfLW2+9pTbyxbJfbIwEbRFIJfXDMbv9eXnrAzMCSWDwb4u7/65wBiqxDTzFw010woQJjV0VQoLG8ePHZdasWWoZFfKzIE8LmDRpkrqRY4kVdvokJFTBhoFYqv/ee++pF54WI6iKZV2rV69u7OoRQojtYHYQNqnApjN33nkn8/5b5PXXX1f5JrHU8ujRozVmsGF2Jd7DBoSkbjBTEkycOFGaN29e/T4CLMOGDVMPM4l/OGZ3LpVTSUnJaZ/hPTxMJ9bh3xb3/11hAJXYxvLly9WyiNpJpAkJ9XxUmBGAnRONgRjAgOGiiy6Sd999V80U4DI1EqpERUWppXAIpGIAY+Rxmj59eo0dWQkhJBTArKAbbrhB/f3Hfe/2229v7Cp5hhUrVqglvXWl+jJ03L59eyPUzBsYeSZr50oECBBs3bq1EWrlHThmt59OnTrVyIVqBu916NChEWrlTfi3xRt/VxhAJbaB5OUXXHBBY1eDkKBipKvATqi1qaioULOyz7S8hRAvM2/ePDWAxowOJHsHRUVFkp6eLtddd11jV48QQmydwYbZQfiCe//996udz4l1fvnLXyoNzaxatUpeeeUV9VlaWlqj1c0L9OrVSz20xIa9tcFO6EYwi9QNx+z2Ex8fr1IgbNu2rcb75eXlahYlNjgjZ4Z/W7zzd4UBVGILeMKE6dEc+JCmOJhF0vl//etfasYdBrYgPz9fbawzaNCg6iVXhIQib7zxhlq2j00EjRkdr732mprlwaVHhJBQwsj1jDzP/IIbOAMGDDjtPYyXAFawnXPOOY1QK++AZftjxoyRhQsXqhlqRm7Eb7/9Vm2icttttzV2FV0Nx+zOgPR977zzjlqFZKw8+vjjj/3OCiSnw78t3vm7wgAqsYW9e/eqn8bsI0KaCtgk56GHHpL77rtPrrnmGpk6daqafYdlQHga9sILLzR2FQlxFDwxx9NyDPqQvH3z5s0qmIoBYI8ePRq7eoQQYguYVb9gwQIVgEHKqrlz59b4vGXLlnLhhRc2Wv1I0+CBBx5QmzX+4Ac/kB/96EdqAxXk9TQ2QiL+4ZjdGbDh0Zw5c5Q/zpgxQ02qQl7KcePG8aGIBfi3xVswgNqE+OCDD+SRRx6Rxx9/XK699lq/5ZDwGX+IsSwzKytL5YHp06ePOgY5Y/ztFgeYKJo0xT6Ap6vw/Zdeekn+8Ic/qB0nkcwfyazxNJuQUPb/yZMnqy9wGCwvW7ZMDf4effRRLt8nhHgi5xzud8jjjyXQAIEozPLDwyF8oTUvCTQ2+cCywNog/3NT/JIbiIZEX7+UlBQV8HvmmWfUuBNBwfPOO08efPBBFWhpagSqX1MeszvVV9u1ayf//Oc/VT78Z599VvkhAtT33nuvhBpOaNiU/rbkhsDfizAfE300CbCsA0+EsCNZfV+ekTgbzvvNN9+oP8i9e/eWwsJCyc7OVp9PmzZNnnzyySDXnhB92AdIU4b+TwghNcEsPix5xswz3O+M5dC43yEfIjblQTqSupYHkiqooR7UTw/qZx1qpQ811CNU9Atv7AoQ5/n666/Vkg58cT4TM2fOVF+cU1NT5bPPPlPT8ZET5tVXX1U5YT766CP58MMPg1JvQuyCfYA0Zej/hBBSE3yBu/POO9XP0aNHy5IlS9Q9z3hhRhqWoWIHX+TxI6dDDfWgfnpQP+tQK32ooR7HQ0g/BlBDGDjfc889p2YT1d6ZrC6Q+BkJn7EJCKbfG08FwNixY9XSEID8MHXtXkiI22AfIE0Z+j8hhNQNNpHBl7UOHTqopbv4aYB731/+8he1uzSWG86fP79R6+pWqKEe1E8P6mcdaqUPNdTjXyGkHwOoIcq2bdvUjniYNYTcLkiWjfwS9YGExeXl5TJw4EBJS0s77XMk2kYuvLy8PFm9erWDtSdEH/YB0pSh/xNCyJlzzmGTk7ry92Mp4dChQ9X/N23aFPT6eQFqqAf104P6WYda6UMN9VgVQvpxE6kQBTOJkIh4+PDh8qtf/Ur69u2rdkWuj/Xr16ufOKYuoqOj1RfrNWvWqC/P5557riN1J8QO2AdIU4b+Twgh/kEetokTJ6rNOfxhbBOB3GzkdKihHtRPD+pnHWqlDzXU47YQ0o8B1BAlOTlZ7XA2atQoy8fs2bNH/TQv26xNly5d1JdnoywhboV9gDRl6P+EEOIf7LZd347bWGpozLTHZnrkdKihHtRPD+pnHWqlDzXUY1AI6ccAaogCxwvU+Y4cOVI9hdofrVu3Vj/z8/M1a0iIs7APkKYM/Z8QQhrOk08+KUVFRSptycUXX9zY1fEk1FAP6qcH9bMOtdKHGjYd/ZgDlVRTXFysfsbExPgtY3wGByck1GAfIE0Z+j8hhIi89NJLMm/ePPX/n/70pzU2uyDWoIZ6UD89qJ91qJU+1LBp6ccZqKSaiIgIyzsrY5dmQkIN9gHSlKH/E0KaOi+++KK88MIL6v9jx46VW2+9tbGr5DmooR7UTw/qZx1qpQ81bHr6MYBKqmnWrJkUFBRISUmJ3zLGZ3FxcUGsGSHBgX2ANGXo/4SQpkp5ebk88cQT1ZvtIX/0n/70JwkP52I9q1BDPaifHtTPOtRKH2rYdPVjAJVU06ZNG/Xl+dixY37LGHnv6suRR4hXYR8gTRn6PyGkKVJYWCh33XWXrFixQv2OnYKfeeYZiY6ObuyqeQZqqAf104P6WYda6UMNm7Z+7g/xkqDRs2dP9TMnJ8dvmX379qmfKSkpQasXIcGCfYA0Zej/hJCmRm5urlx77bXVX+RuvPFG+eMf/+iZL3JugBrqQf30oH7WoVb6UEM9QkE/zkAl1QwePFi++OILWbduXZ2fl5aWyubNm9X/hw0bFuTaEeI87AOkKUP/J4Q0JfLy8uSHP/yh7N27Vy0bfPjhh+X6669v7Gp5CmqoB/XTg/pZh1rpQw31CBX9OAOVVHPxxRern/jyvH379tM+nz17ttqlOSkpSUaMGNEINSTEWdgHSFOG/k8IaSrggdBtt92mvshFRUWpGTBe/CLXmFBDPaifHtTPOtRKH2qoR2kI6ccAKqkmOTlZpkyZonZhRl6KjIyM6s+WLl0qTz/9tPo/nD8ykpOXSejBPkCaMvR/QkhT4bXXXpMtW7ao/z/yyCMqBxsJDGqoB/XTg/pZh1rpQw31eC2E9Avz+Xy+xq4ECQ4XXnihym33+OOPq9wTdYENRGbMmCFbt25VU6t79eqlZhxlZWWpz6dPny6//vWvg1xzQuyBfYA0Zej/hBBSNRPm/PPPV/c7PAwaNGhQveWxO/Cdd94ZtPp5AWqoB/XTg/pZh1rpQw31KA0x/TiFhNQgPj5e3n33XZk1a5YsWLBA9uzZo75EDxkyRK6++mqZOnVqY1eREEdhHyBNGfo/ISTU2bFjh/oiB8rLy/3mfTZITEwMUs28AzXUg/rpQf2sQ630oYZ67Agx/TgDlRBCCCGEEEIIIYQQQvzAHKiEEEIIIYQQQgghhBDiBwZQCSGEEEIIIYQQQgghxA8MoBJCCCGEEEIIIYQQQogfGEAlhBBCCCGEEEIIIYQQPzCASgghhBBCCCGEEEIIIX5gAJUQQgghhBBCCCGEEEL8wAAqIYQQQgghhBBCCCGE+IEBVEIIIYQQQgghhBBCCPEDA6iEEEIIIYQQQgghhBDiBwZQCSGEEEIIIYQQQgghxA8MoBLShHnwwQelT58+Z3yhnFWOHDkip06dCrguP/zhD+XCCy+st8wLL7yg6vP000/7LRNofe0A9Ub93YzP55M//OEPcs4558iQIUPk7bfftuwTffv2lWHDhslVV10lH3/8cYPOb+X6+qOwsFCOHj0qdnLHHXfIyy+/rP6/atWq09rcr18/GTFihPzgBz+QuXPnNvg8+/btO2P/evLJJy3by87OVtcwLy+vwXUihBBCiDswxl3+xmXGOAJj4GDSGOPpQCktLZX/+7//U2NUvL788ku/Y9C6xl8YD48fP16eeuopNdZs6noSQs5MpIUyhJAQ5ZprrpGRI0dW//7NN9/I+++/r94/66yzqt9PTk62ZG/p0qXy85//XAXZmjVrJk7x97//XS6//HLp3bu3Y+cINZYsWSJ/+9vfZOzYsfK9732vxvWtCwxI27RpUx18xcDyk08+UYO//Px8uemmmwI6/09/+lMpKioKuN6bN2+W2267TZ555hkVOLRLi3Xr1p0WiMcgGi9QXl6uHgZ8/vnn8otf/EKV//Wvf93gcw4fPlyuvvrqOj/r2bOnZTtdu3aVSZMmqcH+n/70pwbXhxBCCCHu4Y9//KNMnDhR2rVr19hV8QwffPCB/Otf/5IpU6bI2WefLQMGDKi3fO1x37Fjx1TQFd8rdu/ercbJhBBSHwygEtKEGTp0qHoZVFRUqAAqnshiMBIoGzdulOPHj4vTILj12GOPyTvvvCNhYWGOny8U2L59u/p53333qafgZwJB1i5dutR4b9q0aXLJJZfIX/7yF7n++uslOjra8vnPO++8BtRaZMeOHXLw4EGxi8rKShV8vOGGG04L8kOX2n5/8803yy9/+Ut57733VAAX7W8ICHw2pE/VxU9+8hMV6F27dq0KzBJCCCHE22D8/Nvf/laeffbZxq6K58a2jz76qLRo0eKM5esah/3oRz+SW2+9VU0CwfeYQYMGOVJXQkhowCX8hBDPgaXgmBH40UcfNXZVPENZWZn62bx58wbbiI2NVdpjNurOnTvFi2CmQVZWlnz/+9+3VD48PFwF6+Pj4+W1114TN5CUlKRmjr/55puNXRVCCCGE2ADGV/PmzZOvv/66saviubGtleCpPzARA6vawIYNG2yrGyEkNGEAlRBiCcx2mzFjRvWsVTyxXbNmTfXnWNr94osvqv9fdNFFNXKCfvrpp2rGIpaNY3kNBolYRoPcRQ3h7rvvlvbt26tl3WfKjekv92bt9/E7nkBjyfZll10mAwcOlEsvvVQ9kUbAEE+3sTwIgSv8v7i4+DSbH374oWo7jkW+0OXLl59WZv369XLjjTdW64il8HjibQb1+tWvfiUPPfSQehJ+wQUX1NvOM10b2DNfm4bmIgXGjF/MVrZ6fn96//jHP5Zly5bJ1KlTlWZjxoxROb4wSxTg/0glAGDTOB4pBdAeLHXDcaNGjZIHHnhADhw4cMb6Y9Yy8pt27tzZcpsxMB83bpxs3bpVLes3QCqDxx9/XEaPHq38GvX561//WkObQHn33XdVcHfw4MFqxuvtt99eZ7Aay/gRDLbSZkIIIYS4G4z74uLi1LjiTONjf7n3a7+P35944gk1PsUYBWPKK6+8Uo07Dx06pMbTGLdhHPPcc89Vj7/MvPLKK+pzjEswFqs9ZgWLFy+W6dOnqzIYK995552SmZl52iofpClASieMmTDGxooyf2A8DpuoM1bb4Lht27bVsGfk5cf/dfYigO7G+NIAY3/MBsZ4C2NN6IRUTF988cVp+WnnzJkjzz//vBqvG98BVq5cWe85MzIy1DgP9g8fPqze279/v9Lu/PPPV3aw6gkP7+u6LoSQxoEBVELIGcFgAQMTBGuQjxIv/B9BM2MggbypRv5IBL0w0AEYtN1zzz3SsmVLlR8V+SQxg+71119XA6mGgIAWgovIXYSNkexiy5Ytyu6ECRNUXREgQ92xZDonJ0ctf0ewDmkOaudJQq7OmTNnqsEOymEpFgKyX331VXWZFStWKB1PnDihBq3QEYMlbFSEIKSZ+fPnq6VJqA8GbG3btm3wtYEN87XB7w0BA7jVq1erpftG3k4r569veT70xQASXxyQaxeBUQQRAeoMvwLwJ6PeGMwjjQAG9AhmY6CKgTaC0fUFL5GDFfVHoDZQevXqpX4ag/eCggI1sMcsaHwpga7QBIPt+++//7Tj8WUIQfDar5MnT1aXQY5ZfHFCgPfhhx9WgXbMtDZ8xgw2uEJb//vf/wbcFkIIIYS4C4yNf/azn8mePXvUw1i7wPgIOdORhgkbaCLXJ4J0GGNglQ0mQGBPgVdfffW0TTM/++wzmTVrlhrv4IEujkUQ1fxgFzlIMfZDEBIPszH+w2QBjF1rB1GRaxSzRjHmw9gtMrLubILYUAvnQ1mMqWETgdtrr722OoCLiRhGGiP83/je0RCMCQ8YfxmBVIzh33rrLTUWxVgTY0x8F4CGRuoAA+i7aNEiVeauu+5SgVUcj+8RdYGxP8ri+ww0Qd5btBVpo/BdBO195JFHpHv37mqyiJ3+QAjRxEcIId8xe/ZsX+/evdVPg7KyMt8FF1zgGzNmjO/EiRPV7xcUFPhGjx6tXqWlpeq9P//5z+r47Ozs6nKTJk3yXXPNNb7KysrTbE6ePLn6veuvv943bty4eutX2/5NN93k69Onj2/NmjXVZfD5L3/5yzParf0+fsexX375ZfV7b731lnrv6quvrn4P7UDd0SYD2EG5JUuWVL+Xn5/vGzFihO+KK65Qv1dUVPguuugi3/Tp033l5eXV5U6ePOkbP368b8qUKTXspaWl+XJzc+vVQ/fa1AW0Q7ktW7b4jhw5ol4HDx70rV+/3nf33Xerz5566qmAz+9P7y+++KL6veLiYt/ZZ59dQ1vDJ1euXFn93sUXX+z7yU9+UqPe7777ru+yyy7zZWVl+W3b119/rWwtWLCgxvuwjfehkT8++OADVWbevHnq9z/84Q/q90WLFtUo9/jjj9fwBeiN3/29zL568803+y699NIa9mDnkksu8a1du7bG+/DDwYMH+37xi1/4rTMhhBBC3I0x7gIYM2EcMHDgQN+ePXtqjCPMYxSMpzCOqk3t9/E7xsnbtm2rfu/3v/+9snfPPffUGIv279/fd99991W/hzJ9+/atcSzq1K9fP98dd9yhfsfYb9iwYb577723Rj0wbsR47mc/+1kNe8OHD/cVFRXVq8fRo0fV+GbatGm+kpKS6vehA96/8sor69SuPowxpzGuNV67du3yvfTSS6qdM2bMqC6/YcMGVR5jSzPLli1T77/xxhvVdcLvGAdDQ4P58+er999///0a7Ud9cd4JEyao8bN5TP7tt9+qMp9++mmNsR6+63CsR4h74CZShJB6wbLl3NxcNSPTnGOoVatWalk+Ztxh9qV5MyozmFWHmX/mzZ6wDBrHnzp1SqtueCKM5c6YtYdlPFFRUVr2YmJi1KxGAzz5NZa9G6AdmCWQl5dX41g8vTfPbGzdurWq2z//+U+1TArls7Oz1dNzzF40g+XhyGeJMh07dlTvYTam8X+nrk19XHHFFae9h5mnmA1pzLDUPT9mK4wdO7aG/tDcWMrkj8TERFm1apV6ao8lYHhyj9kReNUH9Ae1N8cKJM+W4cdYPo8Zp9hsywxmjyBNAGbfmv0By7GQsqA2HTp0qNEuzFLGLFzk40I9YaOuGbOGH2KWAyGEEEK8D8axGNNiDIWl91itpQvGk+bNQ42xrbEyCWBTzYSEBDVeNYMxsfnYbt26qWXqWP2CVTAYs2CpO8ZC5lRTERERcu6556o0WFimb8w0xXJ85NOvD+SAxfcGzJA1b1aKMRFSbGEVGDYXNY+frII0XLXBmBWzZbFhqAFSESAVlbmuaK+xlN68eghgnGbemDQtLU39rK0ntMIsU8xk/fe//11jPIr2YGyHmcDYrwCrs9B+O3yAEGIfDKASQurFCNAYAy4zPXr0qF6K4i9IhsEgBiFIjI+lP3v37q3OI4kAkA4YyGF5PXJlYokR/q8Dgp7m5UQYAAIMKs3gfXOeJH/6YNAKMFCCRsYyI7zqAmWMoGntczpxbeoDqREQmARY4oUBJgKGCHLadX7oDdtmMFg8U64npIHAcrGnnnpK7Vjbv39/lecLA2DkxvUHUj40dLMB49g2bdpUt90cbDfA+aEVrnnt95H+oT6wXA0bGMCf8UpNTVXtwjI3w5fMoB3+locRQgghxHtgWToeYmNpPNI5IZinQ11jWFA7NVRdY1tjLGcG4xE8REbAFGN6cO+99/o9P8oZwU5/6ajMGGPLus5tpI/C2LIhAVR8VwDYx2DBggXquwkmNiCdVO3xKL4PvPfeeyr1EzYfRVuN/Q9q61S7XUbgt/Z4Fsv8cR68jwkG5vEzHqIjBQJy0SLIioAsAr5IDXbxxRdXXzdCSOPCACohpF5qDxLq+qy+mZ+/+c1vVA4h5BUaMmSITJkyRQXU8L4dG+AgaIqnuC+99JKajWiVunJl+svFZJ49GwiGPsZgCSD3KXSoC/Ng0cpASffa1MewYcPOOFNT9/y1B6tWwZN95OVCzipsXICff/7zn9XAGDMTjAG2v/M1JBl/enq68gNjJkZ9bYf9huiOwTPyj2F2LWawol3Ie4V2vfHGGyrvae3zcEBNCCGEhBYIpCFIiYfEtXPuu2Vsi/GHMZ7CmN7fmDE+Pr76/7pjFt2xrflBNh5QY6IAZnxiRRzyspqDvnh4jZmu5513niqLsScmfuD9ho5nsR8E8vgjp//vfvc7NZvXrA9WKk2ePFkFWjF7FzN8MR7EJlWB+AEhxDkYQCWE1IsxSxSzR2tjJIdH4KcuMAsPwVMETWvPujzTMm2r4CnvY489ppb6YABX16Cmrt1M7Tq/Qe0ZhwAbAYCuXbtWD2rxRLn2TEQkxMey/jMta7Lz2thBY5wfOmIjJ8y+RGoFI70CZhJgBgQ2LcOGCPXNwjBmk1oFS66wXA2Bf2OWAdpee3MEY7kWynfq1CngthmbEmDGgbHM7JtvvpEbbrhBpYKoHUBFO3RncRNCCCHEXWCsgfRICOrVteFqXWNbLJXHqpS6VqzYPbZFIBArcowxCOpbe2yLh8EIsJqX4Qc6tjSWwhsY4027xpbQGKvkMMbCknkjrQFSMWEmLNJrmZf9Y2NPHZDqADOMcV1vueUWleoKqRqMMR3Gt5jAgBQOeCGwizEtJg1gjGhOp0AIaRwaNv2HENJkwPJoLD/GzugIDBng/xhg4LMBAwbUeAJrPCE2cn1iKbIZPFXFAAyDPTvAoA1PbDEbsTZ4uoyUAeacpVg2g+U4doJdM5ET1BygRf5XDJQwyIRG0AqDNHPuJOiIpUPYxT3QJ/OBXBsnCMb5a88aRQAVO8Bi+b4ZY4lbfbMAjEE58rZaBb6Mc2EQiyVV5ry1GRkZandbM8ZOqebcrlbB7GSkJzDPIMHMbcy0qN0ulEGwtiGBWkIIIYS4m2nTpqlgmr+xLR7iGkvKAWaslpSU2FoHrIQxj5937NihHihjRiZmsGL8jdROmB1p5IoHOAY54bGDfKAzXQ2bWH1jDhJj7IYVZ8ijaiXNlRUwOxezfDHO+vWvfy3Hjx+v8aDd/P0F40FMCgG6318w8xTB2g8++KA6KIvZpnhgjutogEkX2GMBcMURIe6AM1AJIfWCQQWelGJ235VXXqkGdOCjjz5SS1uwdNoI7hiz8zCQwuAAOSI7d+6slqtgUIcnxphtiQ2fMDiqnYRdBzyhRWD2xIkTNd5HYBU5jvCkF3mOEExFEDMlJaXGYE8XLMHB0hvMhMUg5+2331YDLARGa+s4depUpSM0wIxJ5HLCINPfMis7ro0TBOP8hk8hSIugNDbmwkZWL7/8ssoZCh/DFwgs3cemVKiHPxBkxWD022+/rTPdA57uYwm9EaDE+RAgRXkEbc2bid16662ycOFCFfyGX8GfVq5cqd6bMGFCnRs/nQn4D/ScMWOGTJo0SQ3WUR/0neuuu65GWXyJwSYLdW2IQAghhBBvg8AjNpTCmLF2wA5jW6y6woNdbKyESQEIxtm9KgWzRzH+wLgLD5KxeSfyvGPsY4zR7rvvPhWEvOaaa1RdUFc8RMfYxbwxk1Uw6cCwifEVxn34voBxIB6mm5fa2wEClBh/4bsK8v9DV3yHwXcFjPUwtsX3hU8//VRNwMC41o7vL1jGj2A0VtEh3y0ezCMn6sMPP6wmZWAmMWbc4vsExnq1J6MQQhoHBlAJIWcEwRwECJFn9C9/+YsK9CEY9eSTT6oZlgYISiGAhIEAkq4j4IQZecjz849//EMFhDAgwKABAywcj8GIHbMkMdsRgTxjKYwBBiSPPvqoOj/Oh8EJBqRYsrNkyRKxCwTyBg4cqHbLxJNr6INlV+a2GToi+ActMQjr1auX+h31dPLaOIXT58egEcnzMQMDAUoEJ++66y61AdXs2bPl97//vQpYY5YGBr7+8p8aXwSwRGvt2rV1fo6cU3iZN87CDNDnn39eJfE3g/MjaItrjPQBmLWAVA2YQYoAaENAXi0EpeGr2EQAXxTgP6+99pqqtxks7Ucdzz///AadixBCCCHuBku28QAXedDNIKiJsSYeWCPgh6XuL774oiqHQKddICiKQK4xEQJjEUxYwOQIA4x5sAEqZoxivIR0VFihhDHZWWed1aDzwiY2iUJ7MB7CA3KkMbrjjjscWcaO2bIIkGJSAwK2CKDOnDlTnR/fYTDORZsw7nvkkUdUegJdoCE2REX7jI1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", 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" ] @@ -202,14 +219,118 @@ } ], "source": [ - "weak_scaling([df3, df4, df5], names=[\"CCX\", \"Socket\", \"CCD\"], title=\"Weak Scaling up to 4e9 Points on 512 nodes of ARCHER2 (Sphere)\")" + "weak_scaling([df3, df4, df5], names=[\"CCX\", \"CCD\", \"Socket\"], title=\"Weak Scaling up to 4e9 Points on 512 nodes of ARCHER2 (Sphere)\", stat='mean', plot_err=False)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 195, "id": "add61ffe-1504-409b-95ba-8c06b3706c59", "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "experiment_id\n", + "0 4.0\n", + "1 5.0\n", + "2 5.0\n", + "3 5.0\n", + "Name: local_depth, dtype: float64" + ] + }, + "execution_count": 195, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df4.groupby('experiment_id')['local_depth'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "id": "1d2daf53-bfec-4778-96f1-32960de3adc3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "experiment_id\n", + "0 109.125000\n", + "1 219.585938\n", + "2 103.228516\n", + "3 41.056885\n", + "Name: m2l, dtype: float64" + ] + }, + "execution_count": 196, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df4.groupby('experiment_id')['m2l'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "id": "eb497192-9cf4-49ec-b1d4-c433db546863", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "experiment_id\n", + "0 472.625000\n", + "1 258.945312\n", + "2 473.274414\n", + "3 904.404175\n", + "Name: p2p, dtype: float64" + ] + }, + "execution_count": 197, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df4.groupby('experiment_id')['p2p'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "id": "0a669b27-d46d-4d35-98e5-46ee41a6a6f1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "experiment_id\n", + "0 581.75000\n", + "1 478.53125\n", + "2 576.50293\n", + "3 945.46106\n", + "dtype: float64" + ] + }, + "execution_count": 193, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df4.groupby('experiment_id')['p2p'].mean() + df4.groupby('experiment_id')['m2l'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1aba6245-2460-430c-b7fa-418a4e9c4ec0", + "metadata": {}, "outputs": [], "source": [] } diff --git a/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/err_run_0.log b/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/err_run_0.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/err_run_1.log b/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/err_run_1.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/err_run_2.log b/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/err_run_2.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/err_run_3.log b/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/err_run_3.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260022/err_run_0.log b/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260022/err_run_0.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260022/err_run_1.log b/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260022/err_run_1.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260022/err_run_2.log b/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260022/err_run_2.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260022/err_run_3.log b/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260022/err_run_3.log new file mode 100644 index 00000000..b6dcbf1c --- /dev/null +++ b/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260022/err_run_3.log @@ -0,0 +1,2 @@ +srun: Job 11260022 step creation temporarily disabled, retrying (Requested nodes are busy) +srun: Step created for job 11260022 diff --git a/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260013/err_run_0.log b/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260013/err_run_0.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260013/err_run_1.log b/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260013/err_run_1.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260013/err_run_2.log b/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260013/err_run_2.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260013/err_run_3.log b/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260013/err_run_3.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_2/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260002/err_run_0.log b/hpc/ijhpca/data/sphere/deeper_local_2/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260002/err_run_0.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_2/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260002/err_run_1.log b/hpc/ijhpca/data/sphere/deeper_local_2/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260002/err_run_1.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_2/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260002/err_run_2.log b/hpc/ijhpca/data/sphere/deeper_local_2/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260002/err_run_2.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_2/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260002/err_run_3.log b/hpc/ijhpca/data/sphere/deeper_local_2/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260002/err_run_3.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_2/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260015/err_run_0.log b/hpc/ijhpca/data/sphere/deeper_local_2/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260015/err_run_0.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_2/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260015/err_run_1.log b/hpc/ijhpca/data/sphere/deeper_local_2/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260015/err_run_1.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_2/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260015/err_run_2.log b/hpc/ijhpca/data/sphere/deeper_local_2/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260015/err_run_2.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_2/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260015/err_run_3.log b/hpc/ijhpca/data/sphere/deeper_local_2/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260015/err_run_3.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260141/err_run_0.log b/hpc/ijhpca/data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260141/err_run_0.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260141/err_run_1.log b/hpc/ijhpca/data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260141/err_run_1.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260141/err_run_2.log b/hpc/ijhpca/data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260141/err_run_2.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260141/err_run_3.log b/hpc/ijhpca/data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260141/err_run_3.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260230/err_run_0.log b/hpc/ijhpca/data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260230/err_run_0.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260230/err_run_1.log b/hpc/ijhpca/data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260230/err_run_1.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260230/err_run_2.log b/hpc/ijhpca/data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260230/err_run_2.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260230/err_run_3.log b/hpc/ijhpca/data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260230/err_run_3.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259808/err_run_0.log b/hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259808/err_run_0.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259808/err_run_1.log b/hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259808/err_run_1.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259808/err_run_2.log b/hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259808/err_run_2.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259808/err_run_3.log b/hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259808/err_run_3.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11259810/err_run_0.log b/hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11259810/err_run_0.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11259810/err_run_1.log b/hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11259810/err_run_1.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11259810/err_run_2.log b/hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11259810/err_run_2.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11259810/err_run_3.log b/hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11259810/err_run_3.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11259807/err_run_0.log b/hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11259807/err_run_0.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11259807/err_run_1.log b/hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11259807/err_run_1.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11259807/err_run_2.log b/hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11259807/err_run_2.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11259807/err_run_3.log b/hpc/ijhpca/data/sphere/seeded/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11259807/err_run_3.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/err_run_0.log b/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/err_run_0.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/err_run_1.log b/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/err_run_1.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/err_run_2.log b/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/err_run_2.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/err_run_3.log b/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/err_run_3.log new file mode 100644 index 00000000..e69de29b From 775a12dda68a56d901a0db5e2297a893a7acfaae Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Tue, 21 Oct 2025 13:30:32 +0100 Subject: [PATCH 42/52] Scaling --- hpc/ijhpca/Weak Scaling.ipynb | 77 ++++++++++++------- .../err_run_0.log | 0 .../err_run_1.log | 0 .../err_run_2.log | 0 .../err_run_3.log | 0 .../err_run_0.log | 0 .../err_run_1.log | 0 .../err_run_2.log | 0 .../err_run_3.log | 0 9 files changed, 51 insertions(+), 26 deletions(-) create mode 100644 hpc/ijhpca/data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260719/err_run_0.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260719/err_run_1.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260719/err_run_2.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260719/err_run_3.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260722/err_run_0.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260722/err_run_1.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260722/err_run_2.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260722/err_run_3.log diff --git a/hpc/ijhpca/Weak Scaling.ipynb b/hpc/ijhpca/Weak Scaling.ipynb index 4455e340..f6a4e7c4 100644 --- a/hpc/ijhpca/Weak Scaling.ipynb +++ b/hpc/ijhpca/Weak Scaling.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 2, "id": "86084f2e-5662-445c-8bac-e630a73d764f", "metadata": {}, "outputs": [ @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 3, "id": "182a3aa0-aff6-45a0-a6a2-df618808793c", "metadata": {}, "outputs": [ @@ -46,18 +46,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[34mweak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260141\u001b[m\u001b[m\n", - "\u001b[34mweak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260230\u001b[m\u001b[m\n" + "\u001b[34mweak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260719\u001b[m\u001b[m\n", + "\u001b[34mweak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260722\u001b[m\u001b[m\n" ] } ], "source": [ - "! ls data/sphere/deeper_local_3 | grep weak" + "! ls data/sphere/deeper_local_4 | grep weak" ] }, { "cell_type": "code", - "execution_count": 173, + "execution_count": 4, "id": "178da76c-f2f3-4e55-859b-37cccde9f575", "metadata": {}, "outputs": [], @@ -73,12 +73,37 @@ "\n", "\n", "df3 = pd.read_csv('data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260141/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260141.csv')\n", - "df4 = pd.read_csv('data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260230/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260230.csv')\n" + "df4 = pd.read_csv('data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260230/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260230.csv')\n", + "\n", + "\n", + "df3 = pd.read_csv('data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260719/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260719.csv')\n", + "df4 = pd.read_csv('data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260722/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260722.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "9e4977e5-085d-46a5-88d1-8da7dfb87b49", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "463.5" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df0.groupby('experiment_id')['runtime'].mean()[0]" ] }, { "cell_type": "code", - "execution_count": 174, + "execution_count": 27, "id": "754504a1", "metadata": {}, "outputs": [], @@ -182,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 175, + "execution_count": 28, "id": "67d9abc3-f9bb-41ec-9778-9912fecbc924", "metadata": {}, "outputs": [ @@ -203,13 +228,13 @@ }, { "cell_type": "code", - "execution_count": 176, + "execution_count": 29, "id": "447bedda-cd0e-4ff3-9201-e46d6d66c62c", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -224,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 195, + "execution_count": 30, "id": "add61ffe-1504-409b-95ba-8c06b3706c59", "metadata": {}, "outputs": [ @@ -235,11 +260,11 @@ "0 4.0\n", "1 5.0\n", "2 5.0\n", - "3 5.0\n", + "3 6.0\n", "Name: local_depth, dtype: float64" ] }, - "execution_count": 195, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -250,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 196, + "execution_count": 31, "id": "1d2daf53-bfec-4778-96f1-32960de3adc3", "metadata": {}, "outputs": [ @@ -258,14 +283,14 @@ "data": { "text/plain": [ "experiment_id\n", - "0 109.125000\n", - "1 219.585938\n", - "2 103.228516\n", - "3 41.056885\n", + "0 109.062500\n", + "1 219.562500\n", + "2 102.832031\n", + "3 156.062744\n", "Name: m2l, dtype: float64" ] }, - "execution_count": 196, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -276,7 +301,7 @@ }, { "cell_type": "code", - "execution_count": 197, + "execution_count": 32, "id": "eb497192-9cf4-49ec-b1d4-c433db546863", "metadata": {}, "outputs": [ @@ -284,14 +309,14 @@ "data": { "text/plain": [ "experiment_id\n", - "0 472.625000\n", - "1 258.945312\n", - "2 473.274414\n", - "3 904.404175\n", + "0 473.000000\n", + "1 258.664062\n", + "2 473.370117\n", + "3 258.848267\n", "Name: p2p, dtype: float64" ] }, - "execution_count": 197, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } diff --git a/hpc/ijhpca/data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260719/err_run_0.log b/hpc/ijhpca/data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260719/err_run_0.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260719/err_run_1.log b/hpc/ijhpca/data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260719/err_run_1.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260719/err_run_2.log b/hpc/ijhpca/data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260719/err_run_2.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260719/err_run_3.log b/hpc/ijhpca/data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260719/err_run_3.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260722/err_run_0.log b/hpc/ijhpca/data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260722/err_run_0.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260722/err_run_1.log b/hpc/ijhpca/data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260722/err_run_1.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260722/err_run_2.log b/hpc/ijhpca/data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260722/err_run_2.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260722/err_run_3.log b/hpc/ijhpca/data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260722/err_run_3.log new file mode 100644 index 00000000..e69de29b From 735c1a4a83f71c953f668b69ad4756394f1b71fd Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Wed, 22 Oct 2025 09:23:11 +0100 Subject: [PATCH 43/52] Add MPI call timing --- hpc/ijhpca/Weak Scaling.ipynb | 469 ++++++++++++++---- .../err_run_0.log | 0 .../err_run_1.log | 0 .../err_run_2.log | 0 .../err_run_3.log | 0 .../err_run_0.log | 0 .../err_run_1.log | 0 .../err_run_2.log | 0 .../err_run_3.log | 0 .../err_run_0.log | 0 .../err_run_1.log | 0 .../err_run_2.log | 0 .../err_run_3.log | 0 .../err_run_0.log | 0 .../err_run_1.log | 0 .../err_run_2.log | 0 .../err_run_3.log | 0 .../err_run_0.log | 0 .../err_run_1.log | 0 .../err_run_2.log | 0 .../err_run_3.log | 0 .../err_run_0.log | 0 .../err_run_1.log | 0 .../err_run_2.log | 0 .../err_run_3.log | 0 kifmm/include/kifmm_rs.h | 2 +- kifmm/src/fmm/builder/multi_node.rs | 28 +- kifmm/src/fmm/ghost_exchange.rs | 118 ++++- kifmm/src/fmm/tree/multi_node.rs | 79 ++- kifmm/src/fmm/types.rs | 8 +- kifmm/src/traits/types.rs | 43 +- kifmm/src/tree/helpers.rs | 3 +- kifmm/src/tree/multi_node.rs | 44 +- kifmm/src/tree/types.rs | 6 + scripts/src/bin/fmm_m2l_fft_mpi_f32.rs | 129 ++++- 35 files changed, 814 insertions(+), 115 deletions(-) create mode 100644 hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11261532/err_run_0.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11261532/err_run_1.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11261532/err_run_2.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11261532/err_run_3.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260024/err_run_0.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260024/err_run_1.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260024/err_run_2.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260024/err_run_3.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11261537/err_run_0.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11261537/err_run_1.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11261537/err_run_2.log create mode 100644 hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11261537/err_run_3.log create mode 100644 hpc/ijhpca/data/uniform/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11263163/err_run_0.log create mode 100644 hpc/ijhpca/data/uniform/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11263163/err_run_1.log create mode 100644 hpc/ijhpca/data/uniform/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11263163/err_run_2.log create mode 100644 hpc/ijhpca/data/uniform/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11263163/err_run_3.log create mode 100644 hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11262498/err_run_0.log create mode 100644 hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11262498/err_run_1.log create mode 100644 hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11262498/err_run_2.log create mode 100644 hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11262498/err_run_3.log create mode 100644 hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11262497/err_run_0.log create mode 100644 hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11262497/err_run_1.log create mode 100644 hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11262497/err_run_2.log create mode 100644 hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11262497/err_run_3.log diff --git a/hpc/ijhpca/Weak Scaling.ipynb b/hpc/ijhpca/Weak Scaling.ipynb index f6a4e7c4..000ba46e 100644 --- a/hpc/ijhpca/Weak Scaling.ipynb +++ b/hpc/ijhpca/Weak Scaling.ipynb @@ -16,7 +16,70 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 28, + "id": "e2cd16e0-a829-4327-b6cb-adedf3de920f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "32768000000" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "32000000*512*2" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "11130028-c403-4e46-b383-054ae92a8ea6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "32000000" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "4000000*8" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "266b423c-bdde-49cc-b380-0f79b897218e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.096" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "4000000*512*2/1e9" + ] + }, + { + "cell_type": "code", + "execution_count": 29, "id": "86084f2e-5662-445c-8bac-e630a73d764f", "metadata": {}, "outputs": [ @@ -24,11 +87,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[34mstrong_fft_n=128000000_p=64_11077776\u001b[m\u001b[m \u001b[34mweak_fft_n=4096000000_p=512_11079232\u001b[m\u001b[m\n", - "\u001b[34mstrong_fft_n=128000000_p=64_11077778\u001b[m\u001b[m \u001b[34mweak_fft_n=4096000000_p=512_11079233\u001b[m\u001b[m\n", - "\u001b[34mstrong_fft_n=128000000_p=64_11079093\u001b[m\u001b[m \u001b[34mweak_fft_n=512000000_p=64_11077796\u001b[m\u001b[m\n", - "\u001b[34mstrong_fft_n=128000000_p=64_11079322\u001b[m\u001b[m \u001b[34mweak_fft_n=512000000_p=64_11077797\u001b[m\u001b[m\n", - "\u001b[34mweak_fft_n=4096000000_p=512_11079231\u001b[m\u001b[m \u001b[34mweak_fft_n=512000000_p=64_11077800\u001b[m\u001b[m\n" + "\u001b[34mstrong_fft_n=128000000_p=64_11077776\u001b[m\u001b[m\n", + "\u001b[34mstrong_fft_n=128000000_p=64_11077778\u001b[m\u001b[m\n", + "\u001b[34mstrong_fft_n=128000000_p=64_11079093\u001b[m\u001b[m\n", + "\u001b[34mstrong_fft_n=128000000_p=64_11079322\u001b[m\u001b[m\n", + "\u001b[34mweak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11263163\u001b[m\u001b[m\n", + "\u001b[34mweak_fft_n=4096000000_p=512_11079231\u001b[m\u001b[m\n", + "\u001b[34mweak_fft_n=4096000000_p=512_11079232\u001b[m\u001b[m\n", + "\u001b[34mweak_fft_n=4096000000_p=512_11079233\u001b[m\u001b[m\n", + "\u001b[34mweak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11262498\u001b[m\u001b[m\n", + "\u001b[34mweak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11262497\u001b[m\u001b[m\n", + "\u001b[34mweak_fft_n=512000000_p=64_11077796\u001b[m\u001b[m\n", + "\u001b[34mweak_fft_n=512000000_p=64_11077797\u001b[m\u001b[m\n", + "\u001b[34mweak_fft_n=512000000_p=64_11077800\u001b[m\u001b[m\n" ] } ], @@ -46,64 +117,47 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[34mweak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260719\u001b[m\u001b[m\n", - "\u001b[34mweak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260722\u001b[m\u001b[m\n" + "\u001b[34mweak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11261532\u001b[m\u001b[m\n", + "\u001b[34mweak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260024\u001b[m\u001b[m\n", + "\u001b[34mweak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11261537\u001b[m\u001b[m\n" ] } ], "source": [ - "! ls data/sphere/deeper_local_4 | grep weak" + "! ls data/sphere/deeper_local_5 | grep weak" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 30, "id": "178da76c-f2f3-4e55-859b-37cccde9f575", "metadata": {}, "outputs": [], "source": [ "df0 = pd.read_csv('data/uniform/weak_fft_n=4096000000_p=512_11079232/weak_fft_n=4096000000_p=512_11079232.csv')\n", - "df1 = pd.read_csv('data/uniform/weak_fft_n=4096000000_p=512_11079231/weak_fft_n=4096000000_p=512_11079231.csv')\n", - "df2 = pd.read_csv('data/uniform/weak_fft_n=4096000000_p=512_11079233/weak_fft_n=4096000000_p=512_11079233.csv')\n", - "\n", + "df1 = pd.read_csv('data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11262497/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11262497.csv')\n", + "df2 = pd.read_csv('data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11262498/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11262498.csv')\n", "\n", "df3 = pd.read_csv('data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998.csv')\n", "df4 = pd.read_csv('data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260013/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260013.csv')\n", "df5 = pd.read_csv('data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260022/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260022.csv')\n", "\n", - "\n", "df3 = pd.read_csv('data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260141/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260141.csv')\n", "df4 = pd.read_csv('data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260230/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260230.csv')\n", "\n", - "\n", "df3 = pd.read_csv('data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260719/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260719.csv')\n", - "df4 = pd.read_csv('data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260722/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260722.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "9e4977e5-085d-46a5-88d1-8da7dfb87b49", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "463.5" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df0.groupby('experiment_id')['runtime'].mean()[0]" + "df4 = pd.read_csv('data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260722/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260722.csv')\n", + "\n", + "df3 = pd.read_csv('data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11261532/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11261532.csv')\n", + "df4 = pd.read_csv('data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11261537/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11261537.csv')\n", + "# df5 = pd.read_csv('data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260024/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260024.csv')\n", + "\n", + "df_big = pd.read_csv('data/uniform/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11263163/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11263163.csv')" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 16, "id": "754504a1", "metadata": {}, "outputs": [], @@ -155,8 +209,9 @@ " n_ranks = stats.size()\n", " n_points = stats.mean()['n_points'] * n_ranks # total DoFs = per-rank * ranks\n", "\n", + " print(runtime)\n", " # Reference runtime\n", - " T_ref = runtime.min()\n", + " T_ref = runtime.iloc[0]\n", " efficiency = (T_ref / runtime) * 100\n", "\n", " if plot_err:\n", @@ -207,13 +262,37 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 17, "id": "67d9abc3-f9bb-41ec-9778-9912fecbc924", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "experiment_id\n", + "0 463.500000\n", + "1 483.472656\n", + "2 572.250000\n", + "3 1262.783813\n", + "Name: runtime, dtype: float64\n", + "experiment_id\n", + "0 827.562500\n", + "1 854.195312\n", + "2 963.443359\n", + "3 1629.935425\n", + "Name: runtime, dtype: float64\n", + "experiment_id\n", + "0 5127.000000\n", + "1 5136.062500\n", + "2 5277.117188\n", + "3 5361.569336\n", + "Name: runtime, dtype: float64\n" + ] + }, { "data": { - "image/png": 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", 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" ] @@ -228,13 +307,37 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 13, "id": "447bedda-cd0e-4ff3-9201-e46d6d66c62c", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "experiment_id\n", + "0 409.062500\n", + "1 407.945312\n", + "2 756.889648\n", + "3 1081.428162\n", + "Name: runtime, dtype: float64\n", + "experiment_id\n", + "0 611.687500\n", + "1 513.593750\n", + "2 735.462891\n", + "3 1032.433472\n", + "Name: runtime, dtype: float64\n", + "experiment_id\n", + "0 1332.000000\n", + "1 1438.875000\n", + "2 1431.273438\n", + "3 1904.823242\n", + "Name: runtime, dtype: float64\n" + ] + }, { "data": { - "image/png": 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", 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", 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" ] @@ -244,111 +347,307 @@ } ], "source": [ - "weak_scaling([df3, df4, df5], names=[\"CCX\", \"CCD\", \"Socket\"], title=\"Weak Scaling up to 4e9 Points on 512 nodes of ARCHER2 (Sphere)\", stat='mean', plot_err=False)" + "weak_scaling([df3, df4, df5], names=[\"CCX\", \"CCD\", \"Socket\"], title=\"Weak Scaling up to 4e9 Points on 512 nodes of ARCHER2 (Sphere)\", plot_err=False)" ] }, { "cell_type": "code", - "execution_count": 30, - "id": "add61ffe-1504-409b-95ba-8c06b3706c59", + "execution_count": 35, + "id": "acbc24f2-9682-4719-9e7e-43ceeb63d62e", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "experiment_id\n", + "0 9035.000000\n", + "1 9191.687500\n", + "2 9307.953125\n", + "3 9746.737305\n", + "Name: runtime, dtype: float64\n" + ] + }, { "data": { + "image/png": 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runtimem2lp2pm2ml2l
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b/hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11261532/err_run_0.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11261532/err_run_1.log b/hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11261532/err_run_1.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11261532/err_run_2.log b/hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11261532/err_run_2.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11261532/err_run_3.log b/hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11261532/err_run_3.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260024/err_run_0.log b/hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260024/err_run_0.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260024/err_run_1.log b/hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260024/err_run_1.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260024/err_run_2.log b/hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260024/err_run_2.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260024/err_run_3.log b/hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260024/err_run_3.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11261537/err_run_0.log b/hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11261537/err_run_0.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11261537/err_run_1.log b/hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11261537/err_run_1.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11261537/err_run_2.log b/hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11261537/err_run_2.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11261537/err_run_3.log b/hpc/ijhpca/data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11261537/err_run_3.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11263163/err_run_0.log b/hpc/ijhpca/data/uniform/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11263163/err_run_0.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11263163/err_run_1.log b/hpc/ijhpca/data/uniform/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11263163/err_run_1.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11263163/err_run_2.log b/hpc/ijhpca/data/uniform/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11263163/err_run_2.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11263163/err_run_3.log b/hpc/ijhpca/data/uniform/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11263163/err_run_3.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11262498/err_run_0.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11262498/err_run_0.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11262498/err_run_1.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11262498/err_run_1.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11262498/err_run_2.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11262498/err_run_2.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11262498/err_run_3.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11262498/err_run_3.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11262497/err_run_0.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11262497/err_run_0.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11262497/err_run_1.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11262497/err_run_1.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11262497/err_run_2.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11262497/err_run_2.log new file mode 100644 index 00000000..e69de29b diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11262497/err_run_3.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11262497/err_run_3.log new file mode 100644 index 00000000..e69de29b diff --git a/kifmm/include/kifmm_rs.h b/kifmm/include/kifmm_rs.h index d446a301..7e8ef995 100644 --- a/kifmm/include/kifmm_rs.h +++ b/kifmm/include/kifmm_rs.h @@ -101,7 +101,7 @@ typedef enum MetadataType { */ MetadataType_GhostFmmU, /** - * Pointer and Buffer Creation + * Pointer and Buffer Creationmp */ MetadataType_MetadataCreation, /** diff --git a/kifmm/src/fmm/builder/multi_node.rs b/kifmm/src/fmm/builder/multi_node.rs index 52a29021..11cc5531 100644 --- a/kifmm/src/fmm/builder/multi_node.rs +++ b/kifmm/src/fmm/builder/multi_node.rs @@ -1,5 +1,5 @@ //! Builder for constructing FMMs on multi-node. -use std::collections::HashMap; +use std::{collections::HashMap, time::Instant}; use itertools::Itertools; use mpi::{ @@ -12,12 +12,11 @@ use rlst::{MatrixSvd, RlstScalar}; use green_kernels::{traits::Kernel as KernelTrait, types::GreenKernelEvalType}; use crate::{ - fmm::types::PinvMode, fmm::{ helpers::single_node::{level_index_pointer_single_node, ncoeffs_kifmm, optionally_time}, types::{ FmmEvalType, Isa, KiFmmMulti, Layout, MultiNodeBuilder, NeighbourhoodCommunicator, - Query, + PinvMode, Query, }, }, traits::{ @@ -27,7 +26,7 @@ use crate::{ }, fmm::{HomogenousKernel, Metadata, MetadataAccess}, general::{multi_node::GlobalFmmMetadata, single_node::Epsilon}, - types::{CommunicationType, MetadataType, OperatorTime}, + types::{CommunicationType, MPICollectiveType, MetadataType, OperatorTime}, }, tree::{ types::{Domain, SortKind}, @@ -186,6 +185,7 @@ where source_layout: Layout::default(), v_list_query: Query::default(), u_list_query: Query::default(), + mpi_times: HashMap::default(), }; // Global communication to set the source layout required @@ -201,6 +201,14 @@ where fmm_tree.set_queries(true); fmm_tree.set_queries(false); + // TODO Fix timing (adding tree setup times to fmm tree reported times) + for (&op_type, op_time) in fmm_tree.source_tree.mpi_times.iter() { + fmm_tree + .mpi_times + .entry(op_type) + .and_modify(|t| t.time += op_time.time); + } + self.communicator = Some(global_communicator.duplicate()); self.tree = Some(fmm_tree); self.communication_times = Some(communication_times); @@ -289,10 +297,14 @@ where } else { let kernel = self.kernel.unwrap(); let communicator = self.communicator.unwrap(); + + // TODO: clean operator times + let s = Instant::now(); let neighbourhood_communicator_v = NeighbourhoodCommunicator::from_comm(&communicator); let neighbourhood_communicator_u = NeighbourhoodCommunicator::from_comm(&communicator); let neighbourhood_communicator_charge = NeighbourhoodCommunicator::from_comm(&communicator); + let e = s.elapsed().as_millis() as u64; let rank = communicator.rank(); let source_to_target = self.source_to_target.unwrap(); let fmm_eval_type = self.fmm_eval_type.unwrap(); @@ -399,6 +411,7 @@ where level_index_pointer_multipoles: Vec::default(), potentials_send_pointers: Vec::default(), metadata_times: HashMap::default(), + mpi_times: HashMap::default(), ghost_requested_queries_v: Vec::default(), ghost_requested_queries_key_to_index_v: HashMap::default(), ghost_requested_queries_counts_v: Vec::default(), @@ -419,6 +432,13 @@ where ghost_received_queries_charge_displacements: Vec::default(), }; + // TODO: cleanup timing + result + .mpi_times + .entry(MPICollectiveType::DistGraphCreate) + .and_modify(|t| t.time += s.elapsed().as_millis() as u64) + .or_insert(OperatorTime { time: e }); + // Calculate required metadata let (_, duration) = optionally_time(timed, || result.source(PinvMode::svd(None, None))); diff --git a/kifmm/src/fmm/ghost_exchange.rs b/kifmm/src/fmm/ghost_exchange.rs index aa57b474..f8416f21 100644 --- a/kifmm/src/fmm/ghost_exchange.rs +++ b/kifmm/src/fmm/ghost_exchange.rs @@ -1,4 +1,7 @@ -use std::collections::{HashMap, HashSet}; +use std::{ + collections::{HashMap, HashSet}, + time::Instant, +}; use green_kernels::traits::Kernel as KernelTrait; use itertools::{izip, Itertools}; @@ -21,6 +24,7 @@ use crate::{ fmm::{DataAccess, DataAccessMulti, HomogenousKernel}, general::multi_node::{GhostExchange, GlobalFmmMetadata}, tree::{MultiFmmTree, MultiTree, SingleFmmTree, SingleTree}, + types::{MPICollectiveType, OperatorTime}, }, tree::{ types::{Domain, MortonKey}, @@ -92,7 +96,14 @@ where &displacements_buffers[..], ); + let st = Instant::now(); root_process.gather_varcount_into_root(&multipoles, &mut partition); + self.mpi_times + .entry(MPICollectiveType::GatherV) + .and_modify(|t| t.time += st.elapsed().as_millis() as u64) + .or_insert(OperatorTime { + time: st.elapsed().as_millis() as u64, + }); self.global_fmm.global_fmm_multipole_metadata( self.tree.source_tree.all_roots.clone(), @@ -110,7 +121,14 @@ where .copy_from_slice(tmp); } + let st = Instant::now(); root_process.gather_varcount_into(&multipoles); + self.mpi_times + .entry(MPICollectiveType::GatherV) + .and_modify(|t| t.time += st.elapsed().as_millis() as u64) + .or_insert(OperatorTime { + time: st.elapsed().as_millis() as u64, + }); } } @@ -165,7 +183,14 @@ where // Send back coefficient data let partition = Partition::new(&send_buffer, counts, &displacements[..]); + let st = Instant::now(); root_process.scatter_varcount_into_root(&partition, &mut receive_buffer); + self.mpi_times + .entry(MPICollectiveType::ScatterV) + .and_modify(|t| t.time += st.elapsed().as_millis() as u64) + .or_insert(OperatorTime { + time: st.elapsed().as_millis() as u64, + }); // Send back associated keys let partition = Partition::new( @@ -173,10 +198,31 @@ where &self.tree.target_tree.all_roots_counts[..], &self.tree.target_tree.all_roots_displacements[..], ); + let st = Instant::now(); root_process.scatter_varcount_into_root(&partition, &mut expected_roots); + self.mpi_times + .entry(MPICollectiveType::ScatterV) + .and_modify(|t| t.time += st.elapsed().as_millis() as u64) + .or_insert(OperatorTime { + time: st.elapsed().as_millis() as u64, + }); } else { + let st = Instant::now(); root_process.scatter_varcount_into(&mut receive_buffer); + self.mpi_times + .entry(MPICollectiveType::ScatterV) + .and_modify(|t| t.time += st.elapsed().as_millis() as u64) + .or_insert(OperatorTime { + time: st.elapsed().as_millis() as u64, + }); + let st = Instant::now(); root_process.scatter_varcount_into(&mut expected_roots); + self.mpi_times + .entry(MPICollectiveType::ScatterV) + .and_modify(|t| t.time += st.elapsed().as_millis() as u64) + .or_insert(OperatorTime { + time: st.elapsed().as_millis() as u64, + }); } // TODO might be broken if level locals is incorrectly set @@ -253,8 +299,15 @@ where neighbourhood_receive_displacements, ); + let st = Instant::now(); self.neighbourhood_communicator_u .all_to_all_varcount_into(&partition_send, &mut partition_receive); + self.mpi_times + .entry(MPICollectiveType::NeighbourAlltoAllv) + .and_modify(|t| t.time += st.elapsed().as_millis() as u64) + .or_insert(OperatorTime { + time: st.elapsed().as_millis() as u64, + }); // Filter for locally available queries to send back let receive_counts_ = partition_receive.counts().iter().cloned().collect_vec(); @@ -364,8 +417,15 @@ where ); // TODO: Investigate why all to all failing, and require all to all v + let st = Instant::now(); self.neighbourhood_communicator_u .all_to_all_varcount_into(&partition_send, &mut partition_receive); + self.mpi_times + .entry(MPICollectiveType::NeighbourAlltoAllv) + .and_modify(|t| t.time += st.elapsed().as_millis() as u64) + .or_insert(OperatorTime { + time: st.elapsed().as_millis() as u64, + }); } // Communicate expected query sizes @@ -402,8 +462,15 @@ where ); // TODO: Investigate why all to all failing, and require all to all v + let st = Instant::now(); self.neighbourhood_communicator_u .all_to_all_varcount_into(&partition_send, &mut partition_receive); + self.mpi_times + .entry(MPICollectiveType::NeighbourAlltoAllv) + .and_modify(|t| t.time += st.elapsed().as_millis() as u64) + .or_insert(OperatorTime { + time: st.elapsed().as_millis() as u64, + }); } // Create buffers to receive charge and coordinate data @@ -457,8 +524,15 @@ where &requested_queries_displacements[..], ); + let st = Instant::now(); self.neighbourhood_communicator_u .all_to_all_varcount_into(&partition_send, &mut partition_receive); + self.mpi_times + .entry(MPICollectiveType::NeighbourAlltoAllv) + .and_modify(|t| t.time += st.elapsed().as_millis() as u64) + .or_insert(OperatorTime { + time: st.elapsed().as_millis() as u64, + }); } // Communicate ghost coordinate data @@ -475,8 +549,15 @@ where &requested_coordinates_displacements[..], ); + let st = Instant::now(); self.neighbourhood_communicator_u .all_to_all_varcount_into(&partition_send, &mut partition_receive); + self.mpi_times + .entry(MPICollectiveType::NeighbourAlltoAllv) + .and_modify(|t| t.time += st.elapsed().as_millis() as u64) + .or_insert(OperatorTime { + time: st.elapsed().as_millis() as u64, + }); } // Communicate ghost charge data @@ -493,8 +574,15 @@ where requested_charges_displacements, ); + let st = Instant::now(); self.neighbourhood_communicator_u .all_to_all_varcount_into(&partition_send, &mut partition_receive); + self.mpi_times + .entry(MPICollectiveType::NeighbourAlltoAllv) + .and_modify(|t| t.time += st.elapsed().as_millis() as u64) + .or_insert(OperatorTime { + time: st.elapsed().as_millis() as u64, + }); } // Set metadata for Ghost FMM object @@ -568,8 +656,15 @@ where neighbourhood_receive_displacements, ); + let st = Instant::now(); self.neighbourhood_communicator_v .all_to_all_varcount_into(&partition_send, &mut partition_receive); + self.mpi_times + .entry(MPICollectiveType::NeighbourAlltoAllv) + .and_modify(|t| t.time += st.elapsed().as_millis() as u64) + .or_insert(OperatorTime { + time: st.elapsed().as_millis() as u64, + }); // Then, filter for queries that actually exist at this rank and send back this information let receive_counts = partition_receive.counts().iter().cloned().collect_vec(); @@ -645,8 +740,15 @@ where recv_displacements_, ); + let st = Instant::now(); self.neighbourhood_communicator_v .all_to_all_varcount_into(&partition_send, &mut partition_receive); + self.mpi_times + .entry(MPICollectiveType::NeighbourAlltoAllv) + .and_modify(|t| t.time += st.elapsed().as_millis() as u64) + .or_insert(OperatorTime { + time: st.elapsed().as_millis() as u64, + }); } // Create buffers to receive queries which have been requested from neighbours @@ -677,8 +779,15 @@ where &requested_queries_displacements[..], ); + let st = Instant::now(); self.neighbourhood_communicator_v .all_to_all_varcount_into(&partition_send, &mut partition_receive); + self.mpi_times + .entry(MPICollectiveType::NeighbourAlltoAllv) + .and_modify(|t| t.time += st.elapsed().as_millis() as u64) + .or_insert(OperatorTime { + time: st.elapsed().as_millis() as u64, + }); } // Store original ordering of received data temporarily @@ -879,8 +988,15 @@ where &requested_multipoles_displacements[..], ); + let st = Instant::now(); self.neighbourhood_communicator_v .all_to_all_varcount_into(&partition_send, &mut partition_receive); + self.mpi_times + .entry(MPICollectiveType::NeighbourAlltoAllv) + .and_modify(|t| t.time += st.elapsed().as_millis() as u64) + .or_insert(OperatorTime { + time: st.elapsed().as_millis() as u64, + }); } // Allocate ghost multipoles including sibling data, ordering dictated by tree construction diff --git a/kifmm/src/fmm/tree/multi_node.rs b/kifmm/src/fmm/tree/multi_node.rs index 8aeb1f0c..e4c8af6a 100644 --- a/kifmm/src/fmm/tree/multi_node.rs +++ b/kifmm/src/fmm/tree/multi_node.rs @@ -11,8 +11,14 @@ use num::Float; use rlst::RlstScalar; use crate::{ - fmm::types::{Layout, Query}, - traits::tree::{MultiFmmTree, MultiTree, SingleTree}, + fmm::{ + helpers::single_node::optionally_time, + types::{Layout, Query}, + }, + traits::{ + tree::{MultiFmmTree, MultiTree, SingleTree}, + types::{MPICollectiveType, OperatorTime}, + }, tree::{MortonKey, MultiNodeTree}, MultiNodeFmmTree, }; @@ -144,9 +150,22 @@ where let mut receive_counts = vec![0i32; self.source_tree().communicator.size() as usize]; let mut receive_marker = vec![0i32; self.source_tree().communicator.size() as usize]; - self.source_tree - .communicator - .all_to_all_into(&send_counts, &mut receive_counts); + let (_, duration) = optionally_time(true, { + || { + self.source_tree + .communicator + .all_to_all_into(&send_counts, &mut receive_counts); + } + }); + + if let Some(d) = duration { + self.mpi_times + .entry(MPICollectiveType::AlltoAll) + .and_modify(|t| t.time += d.as_millis() as u64) + .or_insert(OperatorTime { + time: d.as_millis() as u64, + }); + }; for (rank, &receive_count) in receive_counts.iter().enumerate() { if receive_count > 0 { @@ -184,9 +203,22 @@ where let mut counts_ = vec![0i32; size as usize]; // All gather to calculate the counts of roots on each processor - self.source_tree - .communicator - .all_gather_into(&n_roots, &mut counts_); + + // TODO cleanup timing + let (_, duration) = optionally_time(true, || { + self.source_tree + .communicator + .all_gather_into(&n_roots, &mut counts_) + }); + + if let Some(d) = duration { + self.mpi_times + .entry(MPICollectiveType::AllGather) + .and_modify(|t| t.time += d.as_millis() as u64) + .or_insert(OperatorTime { + time: d.as_millis() as u64, + }); + }; // Calculate displacements from the counts on each processor let mut displacements_ = Vec::new(); @@ -202,16 +234,31 @@ where let mut raw = vec![MortonKey::::default(); n_roots_global as usize]; // Store a copy of counts and displacements + + // TODO: tidy timing let counts; let displacements; - { - let mut partition = PartitionMut::new(&mut raw, counts_, &displacements_[..]); - self.source_tree - .communicator - .all_gather_varcount_into(&roots, &mut partition); - counts = partition.counts().to_vec(); - displacements = partition.displs().to_vec(); - } + let mut partition = PartitionMut::new(&mut raw, counts_, &displacements_[..]); + + let (_, duration) = optionally_time(true, { + || { + self.source_tree + .communicator + .all_gather_varcount_into(&roots, &mut partition); + } + }); + + counts = partition.counts().to_vec(); + displacements = partition.displs().to_vec(); + + if let Some(d) = duration { + self.mpi_times + .entry(MPICollectiveType::AllGatherV) + .and_modify(|t| t.time += d.as_millis() as u64) + .or_insert(OperatorTime { + time: d.as_millis() as u64, + }); + }; // Store as a set for easy lookup let raw_set = raw.iter().cloned().collect(); diff --git a/kifmm/src/fmm/types.rs b/kifmm/src/fmm/types.rs index 222c3e0e..b5c399bd 100644 --- a/kifmm/src/fmm/types.rs +++ b/kifmm/src/fmm/types.rs @@ -21,7 +21,7 @@ use crate::{ }; #[cfg(feature = "mpi")] -use crate::tree::MultiNodeTree; +use crate::{traits::types::MPICollectiveType, tree::MultiNodeTree}; #[cfg(feature = "mpi")] use std::collections::HashSet; @@ -521,6 +521,9 @@ pub struct MultiNodeFmmTree, } /// Stores data and metadata for FFT based acceleration scheme for field translation. @@ -1144,6 +1147,9 @@ where /// Metadata runtimes pub metadata_times: HashMap, + /// MPI Collective walltimes + pub mpi_times: HashMap, + /// Set to true if expansion order varies by level pub(crate) variable_expansion_order: bool, diff --git a/kifmm/src/traits/types.rs b/kifmm/src/traits/types.rs index 2c6e61f3..d96b9545 100644 --- a/kifmm/src/traits/types.rs +++ b/kifmm/src/traits/types.rs @@ -40,6 +40,47 @@ pub enum FmmOperatorType { P2P, } +/// Enumeration of MPI collective types for timing +#[repr(C)] +#[derive(Debug, Clone, Copy, Hash, PartialEq, Eq)] +pub enum MPICollectiveType { + /// All to All + AlltoAll, + + /// All to All V + AlltoAllV, + + /// Neighbour All to All + NeighbourAlltoAll, + + /// Neighbour All to All V + NeighbourAlltoAllv, + + /// Gather + Gather, + + /// Scatter + Scatter, + + /// Gather + GatherV, + + /// Scatter + ScatterV, + + /// All Gather + AllGather, + + /// All Gather V + AllGatherV, + + /// Cart dist-graph create + DistGraphCreate, + + /// Parallel sort + Sort, +} + /// Enumeration of communication types for timing #[repr(C)] #[derive(Debug, Clone, Copy, Hash, PartialEq, Eq)] @@ -97,7 +138,7 @@ pub enum MetadataType { /// Ghost FMM U GhostFmmU, - /// Pointer and Buffer Creation + /// Pointer and Buffer Creationmp MetadataCreation, /// Displacement Map Creation diff --git a/kifmm/src/tree/helpers.rs b/kifmm/src/tree/helpers.rs index d78a67d1..47585544 100644 --- a/kifmm/src/tree/helpers.rs +++ b/kifmm/src/tree/helpers.rs @@ -52,9 +52,8 @@ pub fn points_fixture( n_points: usize, - seed: Option + seed: Option, ) -> PointsMat { - let seed = seed.unwrap_or(0); // Seeded random number generator for reproducibility let mut rng = StdRng::seed_from_u64(seed); diff --git a/kifmm/src/tree/multi_node.rs b/kifmm/src/tree/multi_node.rs index ccf9c529..a320f170 100644 --- a/kifmm/src/tree/multi_node.rs +++ b/kifmm/src/tree/multi_node.rs @@ -1,5 +1,6 @@ //! Implementation of constructors for MPI distributed multi node trees, from distributed point data. use std::collections::{HashMap, HashSet}; +use std::time::Instant; use itertools::Itertools; use mpi::{datatype::PartitionMut, topology::SimpleCommunicator, traits::Root}; @@ -10,6 +11,7 @@ use mpi::{ use num::Float; use rlst::RlstScalar; +use crate::traits::types::{MPICollectiveType, OperatorTime}; use crate::{ sorting::{hyksort, samplesort, simplesort}, traits::tree::{MultiTree, SingleTree}, @@ -48,6 +50,7 @@ where ) -> Result, std::io::Error> { let dim = 3; let n_coords = coordinates_row_major.len() / dim; + let mut mpi_times: HashMap = HashMap::default(); let mut points = Points::default(); for i in 0..n_coords { @@ -68,7 +71,16 @@ where // Perform parallel Morton sort over encoded points match sort_kind { SortKind::Hyksort { subcomm_size } => hyksort(&mut points, subcomm_size, communicator)?, - SortKind::Samplesort { n_samples } => samplesort(&mut points, communicator, n_samples)?, + SortKind::Samplesort { n_samples } => { + let st = Instant::now(); + samplesort(&mut points, communicator, n_samples)?; + mpi_times + .entry(MPICollectiveType::Sort) + .and_modify(|t| t.time += st.elapsed().as_millis() as u64) + .or_insert(OperatorTime { + time: st.elapsed().as_millis() as u64, + }); + } SortKind::Simplesort => { let splitters = MortonKey::root().descendants(global_depth).unwrap(); let mut splitters = splitters @@ -227,7 +239,15 @@ where } let mut counts = vec![0 as Count; size as usize]; + // TODO: sort times + let st = Instant::now(); root_process.gather_into_root(&n_roots, &mut counts); + mpi_times + .entry(MPICollectiveType::Gather) + .and_modify(|t| t.time += st.elapsed().as_millis() as u64) + .or_insert(OperatorTime { + time: st.elapsed().as_millis() as u64, + }); // Calculate associated displacements from the counts let mut displacements = Vec::new(); @@ -244,7 +264,14 @@ where let mut partition = PartitionMut::new(&mut global_roots, &counts[..], &displacements[..]); + let st = Instant::now(); root_process.gather_varcount_into_root(&roots, &mut partition); + mpi_times + .entry(MPICollectiveType::GatherV) + .and_modify(|t| t.time += st.elapsed().as_millis() as u64) + .or_insert(OperatorTime { + time: st.elapsed().as_millis() as u64, + }); let mut global_roots_ranks = Vec::new(); for (rank, &count) in counts.iter().enumerate() { @@ -264,8 +291,22 @@ where roots.push(tree.root()) } + let st = Instant::now(); root_process.gather_into(&n_roots); + mpi_times + .entry(MPICollectiveType::Gather) + .and_modify(|t| t.time += st.elapsed().as_millis() as u64) + .or_insert(OperatorTime { + time: st.elapsed().as_millis() as u64, + }); + let st = Instant::now(); root_process.gather_varcount_into(&roots); + mpi_times + .entry(MPICollectiveType::GatherV) + .and_modify(|t| t.time += st.elapsed().as_millis() as u64) + .or_insert(OperatorTime { + time: st.elapsed().as_millis() as u64, + }); all_roots = Vec::default(); all_roots_counts = Vec::default(); @@ -276,6 +317,7 @@ where Ok(MultiNodeTree { domain, communicator: communicator.duplicate(), + mpi_times, rank, global_depth, local_depth, diff --git a/kifmm/src/tree/types.rs b/kifmm/src/tree/types.rs index caddf59a..13bddcb1 100644 --- a/kifmm/src/tree/types.rs +++ b/kifmm/src/tree/types.rs @@ -13,6 +13,9 @@ use mpi::{ use num::Float; use rlst::RlstScalar; +#[cfg(feature = "mpi")] +use crate::traits::types::{MPICollectiveType, OperatorTime}; + /// Represents a three-dimensional box characterized by its origin and side-length along the Cartesian axes. #[repr(C)] #[derive(Debug, Clone, Copy, Default)] @@ -148,6 +151,9 @@ where /// Roots associated with trees at this rank pub roots: Vec>, + /// MPI collective times + pub mpi_times: HashMap, + /// All global roots, only populated at nominated rank pub all_roots: Vec>, diff --git a/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs b/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs index f596e89a..f2e47e79 100644 --- a/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs +++ b/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs @@ -4,7 +4,7 @@ use green_kernels::laplace_3d::Laplace3dKernel; use kifmm::{ traits::{ tree::MultiFmmTree, - types::{CommunicationType, FmmOperatorType, MetadataType}, + types::{CommunicationType, FmmOperatorType, MPICollectiveType, MetadataType}, }, tree::{ helpers::{points_fixture, points_fixture_sphere}, @@ -167,6 +167,103 @@ fn main() { // Destructure operator times let mut operator_times = HashMap::new(); + // Destructure FMM MPI times + let mut mpi_times = HashMap::new(); + + for (&op_type, op_time) in multi_fmm.mpi_times.iter() { + match op_type { + MPICollectiveType::AlltoAll => { + mpi_times.insert("all_to_all", op_time.time); + } + MPICollectiveType::AlltoAllV => { + mpi_times.insert("all_to_all_v", op_time.time); + } + + MPICollectiveType::NeighbourAlltoAll => { + mpi_times.insert("neighbour_all_to_all", op_time.time); + } + MPICollectiveType::NeighbourAlltoAllv => { + mpi_times.insert("neighbour_all_to_all_v", op_time.time); + } + + MPICollectiveType::Gather => { + mpi_times.insert("gather", op_time.time); + } + MPICollectiveType::Scatter => { + mpi_times.insert("scatter", op_time.time); + } + + MPICollectiveType::GatherV => { + mpi_times.insert("gather_v", op_time.time); + } + MPICollectiveType::ScatterV => { + mpi_times.insert("scatter_v", op_time.time); + } + + MPICollectiveType::AllGather => { + mpi_times.insert("all_gather", op_time.time); + } + MPICollectiveType::AllGatherV => { + mpi_times.insert("all_gather_v", op_time.time); + } + + MPICollectiveType::DistGraphCreate => { + mpi_times.insert("dist_graph_create", op_time.time); + } + + MPICollectiveType::Sort => { + mpi_times.insert("sort", op_time.time); + } + } + } + + for (&op_type, op_time) in multi_fmm.tree.mpi_times.iter() { + match op_type { + MPICollectiveType::AlltoAll => { + mpi_times.insert("tree_all_to_all", op_time.time); + } + MPICollectiveType::AlltoAllV => { + mpi_times.insert("tree_all_to_all_v", op_time.time); + } + + MPICollectiveType::NeighbourAlltoAll => { + mpi_times.insert("tree_neighbour_all_to_all", op_time.time); + } + MPICollectiveType::NeighbourAlltoAllv => { + mpi_times.insert("tree_neighbour_all_to_all_v", op_time.time); + } + + MPICollectiveType::Gather => { + mpi_times.insert("tree_gather", op_time.time); + } + MPICollectiveType::Scatter => { + mpi_times.insert("tree_scatter", op_time.time); + } + + MPICollectiveType::GatherV => { + mpi_times.insert("tree_gather_v", op_time.time); + } + MPICollectiveType::ScatterV => { + mpi_times.insert("tree_scatter_v", op_time.time); + } + + MPICollectiveType::AllGather => { + mpi_times.insert("tree_all_gather", op_time.time); + } + MPICollectiveType::AllGatherV => { + mpi_times.insert("tree_all_gather_v", op_time.time); + } + + MPICollectiveType::DistGraphCreate => { + mpi_times.insert("tree_dist_graph_create", op_time.time); + } + + MPICollectiveType::Sort => { + mpi_times.insert("tree_sort", op_time.time); + } + } + } + for (&op_type, op_time) in multi_fmm.operator_times.iter() { match op_type { FmmOperatorType::P2M => { @@ -277,7 +374,9 @@ fn main() { "{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},\ {:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?}, \ {:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?}, \ - {:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?}hpc/archer2/slurm/weak_1_fft.slurm", + {:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?}, \ + {:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?}, \ + {:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?}", id, multi_fmm.rank(), runtime, @@ -314,6 +413,30 @@ fn main() { args.n_threads, args.n_samples, mean_roots_per_rank_source_tree, - mean_roots_per_rank_target_tree + mean_roots_per_rank_target_tree, + mpi_times.get("all_to_all").unwrap_or(&0), + mpi_times.get("all_to_all_v").unwrap_or(&0), + mpi_times.get("neighbour_all_to_all").unwrap_or(&0), + mpi_times.get("neighbour_all_to_all_v").unwrap_or(&0), + mpi_times.get("gather").unwrap_or(&0), + mpi_times.get("scatter").unwrap_or(&0), + mpi_times.get("gather_v").unwrap_or(&0), + mpi_times.get("scatter_v").unwrap_or(&0), + mpi_times.get("all_gather").unwrap_or(&0), + mpi_times.get("all_gather_v").unwrap_or(&0), + mpi_times.get("dist_graph_create").unwrap_or(&0), + mpi_times.get("sort").unwrap_or(&0), + mpi_times.get("tree_all_to_all").unwrap_or(&0), + mpi_times.get("tree_all_to_all_v").unwrap_or(&0), + mpi_times.get("tree_neighbour_all_to_all").unwrap_or(&0), + mpi_times.get("tree_neighbour_all_to_all_v").unwrap_or(&0), + mpi_times.get("tree_gather").unwrap_or(&0), + mpi_times.get("tree_scatter").unwrap_or(&0), + mpi_times.get("tree_gather_v").unwrap_or(&0), + mpi_times.get("tree_scatter_v").unwrap_or(&0), + mpi_times.get("tree_all_gather").unwrap_or(&0), + mpi_times.get("tree_all_gather_v").unwrap_or(&0), + mpi_times.get("tree_dist_graph_create").unwrap_or(&0), + mpi_times.get("tree_sort").unwrap_or(&0), ); } From 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a/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260022/err_run_2.log b/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260022/err_run_2.log deleted file mode 100644 index e69de29b..00000000 diff --git a/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260022/err_run_3.log b/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260022/err_run_3.log deleted file mode 100644 index b6dcbf1c..00000000 --- a/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260022/err_run_3.log +++ /dev/null @@ -1,2 +0,0 @@ -srun: Job 11260022 step creation temporarily disabled, retrying (Requested nodes are busy) -srun: Step created for job 11260022 diff --git a/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260013/err_run_0.log b/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260013/err_run_0.log deleted file mode 100644 index e69de29b..00000000 diff --git a/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260013/err_run_1.log b/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260013/err_run_1.log deleted file mode 100644 index e69de29b..00000000 diff --git a/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260013/err_run_2.log b/hpc/ijhpca/data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260013/err_run_2.log deleted file mode 100644 index e69de29b..00000000 diff --git 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a/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/err_run_1.log b/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/err_run_1.log deleted file mode 100644 index e69de29b..00000000 diff --git a/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/err_run_2.log b/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/err_run_2.log deleted file mode 100644 index e69de29b..00000000 diff --git a/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/err_run_3.log b/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/err_run_3.log deleted file mode 100644 index e69de29b..00000000 diff --git a/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11248790/err_run_0.log b/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11248790/err_run_0.log deleted file mode 100644 index e69de29b..00000000 diff --git a/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11248790/err_run_1.log b/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11248790/err_run_1.log deleted file mode 100644 index e69de29b..00000000 diff --git a/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11248790/err_run_2.log b/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11248790/err_run_2.log deleted file mode 100644 index e69de29b..00000000 diff --git a/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11248790/err_run_3.log b/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11248790/err_run_3.log deleted file mode 100644 index e69de29b..00000000 diff --git a/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11248787/err_run_0.log b/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11248787/err_run_0.log deleted file mode 100644 index e69de29b..00000000 diff --git a/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11248787/err_run_1.log b/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11248787/err_run_1.log deleted file mode 100644 index e69de29b..00000000 diff --git a/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11248787/err_run_2.log b/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11248787/err_run_2.log deleted file mode 100644 index e69de29b..00000000 diff --git a/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11248787/err_run_3.log b/hpc/ijhpca/data/sphere/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11248787/err_run_3.log deleted file mode 100644 index e69de29b..00000000 diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11263163/err_run_0.log b/hpc/ijhpca/data/uniform/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11263163/err_run_0.log deleted file mode 100644 index e69de29b..00000000 diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11263163/err_run_1.log b/hpc/ijhpca/data/uniform/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11263163/err_run_1.log deleted file mode 100644 index e69de29b..00000000 diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11263163/err_run_2.log b/hpc/ijhpca/data/uniform/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11263163/err_run_2.log deleted file mode 100644 index e69de29b..00000000 diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11263163/err_run_3.log b/hpc/ijhpca/data/uniform/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11263163/err_run_3.log deleted file mode 100644 index e69de29b..00000000 diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079231/err_run_0.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079231/err_run_0.log deleted file mode 100644 index e69de29b..00000000 diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079231/err_run_1.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079231/err_run_1.log deleted file mode 100644 index e69de29b..00000000 diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079231/err_run_2.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079231/err_run_2.log deleted file mode 100644 index e69de29b..00000000 diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079231/err_run_3.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079231/err_run_3.log deleted file mode 100644 index 2c649e1e..00000000 --- a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079231/err_run_3.log +++ /dev/null @@ -1,6552 +0,0 @@ -srun: Job 11079231 step creation temporarily disabled, retrying (Requested nodes are busy) -srun: Step created for job 11079231 -[nid001858:72021:0:72021] ud_ep.c:255 Fatal: UD endpoint 0x55c45f30b310 to : unhandled timeout error -==== backtrace (tid: 72021) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1470ef64f454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1470ef64c400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1470ef64c529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1470eb677133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1470ef6458aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1470ef1e0962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1470f0e73295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1470f0e91870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1470f112d796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1470f11c6d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1470f0c29e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55c45ebb20f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55c45ebf1a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55c45ebf5bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55c45ec23258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55c45ebb26c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55c45ec34e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1470efd7229d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55c45eba346a] -================================= -[nid001858:72026:0:72026] ud_ep.c:255 Fatal: UD endpoint 0x56170ad44290 to : unhandled timeout error -==== backtrace (tid: 72026) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x155089e6d454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x155089e6a400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x155089e6a529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x155085e95133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x155089e638aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1550899fe962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15508b691295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15508b6af870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15508b94b796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15508b9e4d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15508b447e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56170a5db0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x56170a61aa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56170a61ebdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56170a64c258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56170a5db6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56170a65de63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15508a59029d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56170a5cc46a] -================================= -[nid001858:72022:0:72022] ud_ep.c:255 Fatal: UD endpoint 0x55f4986ab190 to : unhandled timeout error -==== backtrace (tid: 72022) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1519e229c454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1519e2299400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1519e2299529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1519de2c4133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1519e22928aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1519e1e2d962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1519e3ac0295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1519e3ade870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1519e3d7a796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1519e3e13d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1519e3876e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55f497f520f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55f497f91a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55f497f95bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55f497fc3258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55f497f526c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55f497fd4e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1519e29bf29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55f497f4346a] -================================= -[nid001660:125276:0:125276] ud_ep.c:255 Fatal: UD endpoint 0x55f83740f920 to : unhandled timeout error -==== backtrace (tid: 125276) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14c1eaed8454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14c1eaed5400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14c1eaed5529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14c1e6f00133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14c1eaece8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14c1eaa69962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14c1ec6fc295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14c1ec71a870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14c1ec9b6796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14c1eca4fd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14c1ec4b2e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55f836d190f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55f836d58a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55f836d5cbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55f836d8a258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55f836d196c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55f836d9be63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14c1eb5fb29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55f836d0a46a] -================================= -[nid001409:185379:0:185379] ud_ep.c:255 Fatal: UD endpoint 0x556a0f975f90 to : unhandled timeout error -==== backtrace (tid: 185379) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14c2b1447454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14c2b1444400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14c2b1444529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14c2ad46f133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14c2b143d8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14c2b0fd8962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14c2b2c6b295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14c2b2c89870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14c2b2f25796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14c2b2fbed35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14c2b2a21e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x556a0f2eb0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x556a0f32aa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x556a0f32ebdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x556a0f35c258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x556a0f2eb6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x556a0f36de63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14c2b1b6a29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x556a0f2dc46a] -================================= -[nid005128:172740:0:172740] ud_ep.c:255 Fatal: UD endpoint 0x5568a6f71cc0 to : unhandled timeout error -==== backtrace (tid: 172740) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1479a3909454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1479a3906400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1479a3906529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14799f931133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1479a38ff8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1479a349a962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1479a512d295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1479a514b870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1479a53e7796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1479a5480d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1479a4ee3e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5568a68e00f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5568a691fa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5568a6923bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5568a6951258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5568a68e06c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5568a6962e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1479a402c29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5568a68d146a] -================================= -[nid001660:125282:0:125282] ud_ep.c:255 Fatal: UD endpoint 0x55a0ba6c6c70 to : unhandled timeout error -==== backtrace (tid: 125282) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15360340c454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x153603409400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x153603409529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1535ff434133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1536034028aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x153602f9d962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x153604c30295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x153604c4e870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x153604eea796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x153604f83d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1536049e6e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55a0b9fdb0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55a0ba01aa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55a0ba01ebdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55a0ba04c258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55a0b9fdb6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55a0ba05de63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x153603b2f29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55a0b9fcc46a] -================================= -[nid001660:125281:0:125281] ud_ep.c:255 Fatal: UD endpoint 0x55dcde4e80e0 to : unhandled timeout error -==== backtrace (tid: 125281) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15006c2bd454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15006c2ba400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15006c2ba529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1500682e5133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15006c2b38aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15006be4e962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15006dae1295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15006daff870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15006dd9b796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15006de34d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15006d897e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55dcdddff0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55dcdde3ea81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55dcdde42bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55dcdde70258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55dcdddff6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55dcdde81e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15006c9e029d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55dcdddf046a] -================================= -[nid001918:19477:0:19477] ud_ep.c:255 Fatal: UD endpoint 0x559ea497c6d0 to : unhandled timeout error -==== backtrace (tid: 19477) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x145e46fce454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x145e46fcb400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x145e46fcb529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x145e42ff6133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x145e46fc48aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x145e46b5f962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x145e487f2295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x145e48810870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x145e48aac796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x145e48b45d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x145e485a8e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559ea426f0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559ea42aea81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559ea42b2bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559ea42e0258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559ea426f6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559ea42f1e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x145e476f129d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559ea426046a] -================================= -[nid001660:125278:0:125278] ud_ep.c:255 Fatal: UD endpoint 0x5638a70e3ae0 to : unhandled timeout error -==== backtrace (tid: 125278) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x153981b87454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x153981b84400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x153981b84529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x15397dbaf133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x153981b7d8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x153981718962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1539833ab295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1539833c9870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x153983665796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1539836fed35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x153983161e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5638a69fa0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5638a6a39a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5638a6a3dbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5638a6a6b258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5638a69fa6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5638a6a7ce63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1539822aa29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5638a69eb46a] -================================= -[nid001954:89829:0:89829] ud_ep.c:255 Fatal: UD endpoint 0x555e011d0680 to : unhandled timeout error -==== backtrace (tid: 89829) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14b3a89a2454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14b3a899f400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14b3a899f529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14b3a49ca133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14b3a89988aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14b3a8533962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14b3aa1c6295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14b3aa1e4870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14b3aa480796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14b3aa519d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14b3a9f7ce9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x555e00aad0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x555e00aeca81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x555e00af0bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x555e00b1e258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x555e00aad6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x555e00b2fe63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14b3a90c529d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x555e00a9e46a] -================================= -[nid001927:149635:0:149635] ud_ep.c:255 Fatal: UD endpoint 0x5638b4c43100 to : unhandled timeout error -==== backtrace (tid: 149635) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146bc4bb3454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146bc4bb0400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146bc4bb0529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x146bc0bdb133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146bc4ba98aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146bc4744962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x146bc63d7295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146bc63f5870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146bc6691796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146bc672ad35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146bc618de9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5638b444e0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5638b448da81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5638b4491bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5638b44bf258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5638b444e6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5638b44d0e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x146bc52d629d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5638b443f46a] -================================= -[nid001409:185384:0:185384] ud_ep.c:255 Fatal: UD endpoint 0x55a15ad7c6f0 to : unhandled timeout error -[nid001463:252762:0:252762] ud_ep.c:255 Fatal: UD endpoint 0x55d45c88ab30 to : unhandled timeout error -==== backtrace (tid: 185384) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14b330bff454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14b330bfc400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14b330bfc529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14b32cc27133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14b330bf58aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14b330790962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14b332423295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14b332441870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14b3326dd796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14b332776d35] -==== backtrace (tid: 252762) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14ffb41e0454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14ffb41dd400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14ffb41dd529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14ffb0208133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14ffb41d68aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14ffb3d71962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14ffb5a04295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14ffb5a22870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14ffb5cbe796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14ffb5d57d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14b3321d9e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55a15a7810f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55a15a7c0a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55a15a7c4bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55a15a7f2258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55a15a7816c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55a15a803e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14b33132229d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55a15a77246a] -================================= -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14ffb57bae9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55d45c2290f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55d45c268a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55d45c26cbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55d45c29a258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55d45c2296c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55d45c2abe63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14ffb490329d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55d45c21a46a] -================================= -[nid001920:170321:0:170321] ud_ep.c:255 Fatal: UD endpoint 0x5562cc22a6d0 to : unhandled timeout error -==== backtrace (tid: 170321) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x149c6195f454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x149c6195c400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x149c6195c529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x149c5d987133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x149c619558aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x149c614f0962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x149c63183295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x149c631a1870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x149c6343d796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x149c634d6d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x149c62f39e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5562cb9750f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5562cb9b4a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5562cb9b8bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5562cb9e6258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5562cb9756c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5562cb9f7e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x149c6208229d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5562cb96646a] -================================= -[nid001409:185382:0:185382] ud_ep.c:255 Fatal: UD endpoint 0x5622b9cb8770 to : unhandled timeout error -==== backtrace (tid: 185382) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x153ae3e8f454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x153ae3e8c400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x153ae3e8c529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x153adfeb7133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x153ae3e858aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x153ae3a20962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x153ae56b3295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x153ae56d1870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x153ae596d796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x153ae5a06d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x153ae5469e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5622b96b00f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5622b96efa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5622b96f3bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5622b9721258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5622b96b06c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5622b9732e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x153ae45b229d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5622b96a146a] -================================= -[nid001920:170322:0:170322] ud_ep.c:255 Fatal: UD endpoint 0x55ad7805cb70 to : unhandled timeout error -==== backtrace (tid: 170322) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1466eead1454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1466eeace400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1466eeace529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1466eaaf9133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1466eeac78aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1466ee662962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1466f02f5295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1466f0313870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1466f05af796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1466f0648d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1466f00abe9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ad777990f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ad777d8a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ad777dcbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ad7780a258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ad777996c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ad7781be63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1466ef1f429d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ad7778a46a] -================================= -[nid002270:182966:0:182966] ud_ep.c:255 Fatal: UD endpoint 0x55b5a5038890 to : unhandled timeout error -==== backtrace (tid: 182966) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14ad400c8454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14ad400c5400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14ad400c5529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14ad3c0f0133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14ad400be8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14ad3fc59962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14ad418ec295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14ad4190a870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14ad41ba6796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14ad41c3fd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14ad416a2e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55b5a48630f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55b5a48a2a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55b5a48a6bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55b5a48d4258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55b5a48636c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55b5a48e5e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14ad407eb29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55b5a485446a] -================================= -[nid001954:89827:0:89827] ud_ep.c:255 Fatal: UD endpoint 0x56290402e230 to : unhandled timeout error -==== backtrace (tid: 89827) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14b047d4e454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14b047d4b400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14b047d4b529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14b043d76133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14b047d448aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14b0478df962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14b049572295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14b049590870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14b04982c796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14b0498c5d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14b049328e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56290390b0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x56290394aa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56290394ebdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56290397c258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56290390b6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56290398de63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14b04847129d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5629038fc46a] -================================= -[nid002270:182967:0:182967] ud_ep.c:255 Fatal: UD endpoint 0x55742245a890 to : unhandled timeout error -==== backtrace (tid: 182967) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14ae5f7f0454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14ae5f7ed400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14ae5f7ed529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14ae5b818133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14ae5f7e68aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14ae5f381962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14ae61014295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14ae61032870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14ae612ce796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14ae61367d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14ae60dcae9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x557421c850f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x557421cc4a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x557421cc8bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x557421cf6258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x557421c856c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x557421d07e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14ae5ff1329d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x557421c7646a] -================================= -[nid002270:182965:0:182965] ud_ep.c:255 Fatal: UD endpoint 0x563f0fd32890 to : unhandled timeout error -==== backtrace (tid: 182965) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14eaa6e07454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14eaa6e04400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14eaa6e04529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14eaa2e2f133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14eaa6dfd8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14eaa6998962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14eaa862b295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14eaa8649870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14eaa88e5796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14eaa897ed35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14eaa83e1e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x563f0f55d0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x563f0f59ca81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x563f0f5a0bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x563f0f5ce258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x563f0f55d6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x563f0f5dfe63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14eaa752a29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x563f0f54e46a] -================================= -[nid001954:89831:0:89831] ud_ep.c:255 Fatal: UD endpoint 0x55dbb0b5b3a0 to : unhandled timeout error -==== backtrace (tid: 89831) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14d35a4bc454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14d35a4b9400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14d35a4b9529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14d3564e4133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14d35a4b28aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14d35a04d962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14d35bce0295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14d35bcfe870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14d35bf9a796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14d35c033d35] -[nid002270:182963:0:182963] ud_ep.c:255 Fatal: UD endpoint 0x55eb81a64890 to : unhandled timeout error -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14d35ba96e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55dbb04380f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55dbb0477a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55dbb047bbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55dbb04a9258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55dbb04386c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55dbb04bae63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14d35abdf29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55dbb042946a] -================================= -==== backtrace (tid: 182963) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x151edd1b5454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x151edd1b2400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x151edd1b2529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x151ed91dd133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x151edd1ab8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x151edcd46962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x151ede9d9295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x151ede9f7870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x151edec93796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x151eded2cd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x151ede78fe9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55eb8128f0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55eb812cea81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55eb812d2bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55eb81300258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55eb8128f6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55eb81311e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x151edd8d829d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55eb8128046a] -================================= -[nid001920:170319:0:170319] ud_ep.c:255 Fatal: UD endpoint 0x55fd132523a0 to : unhandled timeout error -==== backtrace (tid: 170319) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1494458e8454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1494458e5400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1494458e5529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x149441910133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1494458de8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x149445479962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14944710c295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14944712a870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1494473c6796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14944745fd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x149446ec2e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55fd129a10f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55fd129e0a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55fd129e4bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55fd12a12258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55fd129a16c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55fd12a23e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14944600b29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55fd1299246a] -================================= -[nid001954:89830:0:89830] ud_ep.c:255 Fatal: UD endpoint 0x5646a5edd230 to : unhandled timeout error -[nid001864:92460:0:92460] ud_ep.c:255 Fatal: UD endpoint 0x557af3c8dd50 to : unhandled timeout error -==== backtrace (tid: 92460) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14be093a9454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14be093a6400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14be093a6529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14be053d1133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14be0939f8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14be08f3a962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14be0abcd295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14be0abeb870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14be0ae87796] -==== backtrace (tid: 89830) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1497ddcd3454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1497ddcd0400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1497ddcd0529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1497d9cfb133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1497ddcc98aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1497dd864962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1497df4f7295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1497df515870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1497df7b1796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1497df84ad35] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14be0af20d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14be0a983e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x557af34a50f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x557af34e4a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x557af34e8bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x557af3516258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x557af34a56c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x557af3527e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14be09acc29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x557af349646a] -================================= -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1497df2ade9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5646a57ba0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5646a57f9a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5646a57fdbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5646a582b258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5646a57ba6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5646a583ce63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1497de3f629d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5646a57ab46a] -================================= -[nid001927:149634:0:149634] ud_ep.c:255 Fatal: UD endpoint 0x55b083c14da0 to : unhandled timeout error -[nid001656:201192:0:201192] ud_ep.c:255 Fatal: UD endpoint 0x564a134188d0 to : unhandled timeout error -==== backtrace (tid: 149634) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14e1c8acf454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14e1c8acc400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14e1c8acc529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14e1c4af7133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14e1c8ac58aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14e1c8660962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14e1ca2f3295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14e1ca311870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14e1ca5ad796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14e1ca646d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14e1ca0a9e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55b0834210f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55b083460a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55b083464bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55b083492258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55b0834216c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55b0834a3e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14e1c91f229d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55b08341246a] -================================= -==== backtrace (tid: 201192) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x145ce7e50454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x145ce7e4d400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x145ce7e4d529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x145ce3e78133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x145ce7e468aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x145ce79e1962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x145ce9674295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x145ce9692870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x145ce992e796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x145ce99c7d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x145ce942ae9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x564a12dae0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x564a12deda81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x564a12df1bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x564a12e1f258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x564a12dae6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x564a12e30e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x145ce857329d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x564a12d9f46a] -================================= -[nid001656:201198:0:201198] ud_ep.c:255 Fatal: UD endpoint 0x5575fa5fd8f0 to : unhandled timeout error -==== backtrace (tid: 201198) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14b4bffe4454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14b4bffe1400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14b4bffe1529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14b4bc00c133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14b4bffda8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14b4bfb75962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14b4c1808295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14b4c1826870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14b4c1ac2796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14b4c1b5bd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14b4c15bee9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5575f9fe90f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5575fa028a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5575fa02cbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5575fa05a258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5575f9fe96c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5575fa06be63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14b4c070729d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5575f9fda46a] -================================= -[nid001927:149637:0:149637] ud_ep.c:255 Fatal: UD endpoint 0x55ff57d95d20 to : unhandled timeout error -[nid002270:182964:0:182964] ud_ep.c:255 Fatal: UD endpoint 0x560f9022b890 to : unhandled timeout error -==== backtrace (tid: 149637) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14bd63aad454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14bd63aaa400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14bd63aaa529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14bd5fad5133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14bd63aa38aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14bd6363e962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14bd652d1295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14bd652ef870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14bd6558b796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14bd65624d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14bd65087e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ff575b20f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ff575f1a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ff575f5bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ff57623258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ff575b26c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ff57634e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14bd641d029d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ff575a346a] -================================= -==== backtrace (tid: 182964) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15443bd40454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15443bd3d400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15443bd3d529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x154437d68133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15443bd368aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15443b8d1962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15443d564295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15443d582870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15443d81e796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15443d8b7d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15443d31ae9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x560f8fa560f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x560f8fa95a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x560f8fa99bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x560f8fac7258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x560f8fa566c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x560f8fad8e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15443c46329d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x560f8fa4746a] -================================= -[nid002270:182962:0:182962] ud_ep.c:255 Fatal: UD endpoint 0x5581e33f4890 to : unhandled timeout error -==== backtrace (tid: 182962) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14e512698454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14e512695400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14e512695529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14e50e6c0133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14e51268e8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14e512229962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14e513ebc295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14e513eda870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14e514176796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14e51420fd35] -[nid001950:100695:0:100695] ud_ep.c:255 Fatal: UD endpoint 0x5566250090e0 to : unhandled timeout error -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14e513c72e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5581e2c1f0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5581e2c5ea81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5581e2c62bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5581e2c90258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5581e2c1f6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5581e2ca1e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14e512dbb29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5581e2c1046a] -================================= -==== backtrace (tid: 100695) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1469d8cc1454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1469d8cbe400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1469d8cbe529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1469d4ce9133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1469d8cb78aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1469d8852962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1469da4e5295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1469da503870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1469da79f796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1469da838d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1469da29be9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5566249020f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x556624941a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x556624945bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x556624973258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5566249026c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x556624984e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1469d93e429d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5566248f346a] -================================= -[nid001842:138813:0:138813] ud_ep.c:255 Fatal: UD endpoint 0x55dc28d73ba0 to : unhandled timeout error -==== backtrace (tid: 138813) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14800af0a454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14800af07400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14800af07529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x148006f32133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14800af008aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14800aa9b962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14800c72e295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14800c74c870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14800c9e8796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14800ca81d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14800c4e4e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55dc2867d0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55dc286bca81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55dc286c0bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55dc286ee258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55dc2867d6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55dc286ffe63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14800b62d29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55dc2866e46a] -================================= -[nid001409:185380:0:185380] ud_ep.c:255 Fatal: UD endpoint 0x5561e56e43e0 to : unhandled timeout error -==== backtrace (tid: 185380) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x145cf7d84454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x145cf7d81400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x145cf7d81529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x145cf3dac133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x145cf7d7a8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x145cf7915962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x145cf95a8295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x145cf95c6870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x145cf9862796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x145cf98fbd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x145cf935ee9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5561e50940f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5561e50d3a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5561e50d7bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5561e5105258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5561e50946c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5561e5116e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x145cf84a729d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5561e508546a] -================================= -[nid001660:125275:0:125275] ud_ep.c:255 Fatal: UD endpoint 0x55bff19f1180 to : unhandled timeout error -==== backtrace (tid: 125275) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x151f15e8f454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x151f15e8c400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x151f15e8c529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x151f11eb7133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x151f15e858aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x151f15a20962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x151f176b3295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x151f176d1870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x151f1796d796] -[nid001656:201194:0:201194] ud_ep.c:255 Fatal: UD endpoint 0x564f10838cb0 to : unhandled timeout error - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x151f17a06d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x151f17469e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55bff13020f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55bff1341a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55bff1345bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55bff1373258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55bff13026c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55bff1384e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x151f165b229d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55bff12f346a] -================================= -==== backtrace (tid: 201194) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14c94f610454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14c94f60d400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14c94f60d529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14c94b638133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14c94f6068aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14c94f1a1962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14c950e34295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14c950e52870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14c9510ee796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14c951187d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14c950beae9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x564f1021f0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x564f1025ea81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x564f10262bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x564f10290258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x564f1021f6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x564f102a1e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14c94fd3329d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x564f1021046a] -================================= -[nid001463:252759:0:252759] ud_ep.c:255 Fatal: UD endpoint 0x55c0eb02fef0 to : unhandled timeout error -==== backtrace (tid: 252759) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14967c530454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14967c52d400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14967c52d529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x149678558133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14967c5268aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14967c0c1962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14967dd54295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14967dd72870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14967e00e796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14967e0a7d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14967db0ae9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55c0ea9ec0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55c0eaa2ba81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55c0eaa2fbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55c0eaa5d258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55c0ea9ec6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55c0eaa6ee63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14967cc5329d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55c0ea9dd46a] -================================= -[nid001920:170323:0:170323] ud_ep.c:255 Fatal: UD endpoint 0x564221bc31f0 to : unhandled timeout error -[nid001842:138809:0:138809] ud_ep.c:255 Fatal: UD endpoint 0x5567c1eeb2e0 to : unhandled timeout error -[nid001864:92461:0:92461] ud_ep.c:255 Fatal: UD endpoint 0x55d53b8f2c70 to : unhandled timeout error -==== backtrace (tid: 92461) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14ca0a06d454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14ca0a06a400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14ca0a06a529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14ca06095133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14ca0a0638aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14ca09bfe962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14ca0b891295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14ca0b8af870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14ca0bb4b796] -[nid001950:100691:0:100691] ud_ep.c:255 Fatal: UD endpoint 0x558a6188b050 to : unhandled timeout error - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14ca0bbe4d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14ca0b647e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55d53b10e0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55d53b14da81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55d53b151bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55d53b17f258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55d53b10e6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55d53b190e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14ca0a79029d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55d53b0ff46a] -================================= -[nid001954:89826:0:89826] ud_ep.c:255 Fatal: UD endpoint 0x5620e96fcde0 to : unhandled timeout error -==== backtrace (tid: 170323) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146dc8073454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146dc8070400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146dc8070529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x146dc409b133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146dc80698aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146dc7c04962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x146dc9897295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146dc98b5870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146dc9b51796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146dc9bead35] -==== backtrace (tid: 138809) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14bae75f2454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14bae75ef400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14bae75ef529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14bae361a133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14bae75e88aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14bae7183962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14bae8e16295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14bae8e34870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14bae90d0796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14bae9169d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146dc964de9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5642213140f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x564221353a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x564221357bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x564221385258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5642213146c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x564221396e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x146dc879629d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56422130546a] -================================= -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14bae8bcce9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5567c17ec0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5567c182ba81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5567c182fbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5567c185d258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5567c17ec6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5567c186ee63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14bae7d1529d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5567c17dd46a] -================================= -==== backtrace (tid: 100691) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14d1474d4454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14d1474d1400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14d1474d1529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14d1434fc133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14d1474ca8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14d147065962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14d148cf8295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14d148d16870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14d148fb2796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14d14904bd35] -==== backtrace (tid: 89826) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14ada264f454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14ada264c400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14ada264c529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14ad9e677133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14ada26458aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14ada21e0962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14ada3e73295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14ada3e91870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14ada412d796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14ada41c6d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14d148aaee9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x558a611840f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x558a611c3a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x558a611c7bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x558a611f5258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x558a611846c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x558a61206e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14d147bf729d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x558a6117546a] -================================= -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14ada3c29e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5620e8fda0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5620e9019a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5620e901dbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5620e904b258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5620e8fda6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5620e905ce63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14ada2d7229d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5620e8fcb46a] -================================= -[nid001656:201196:0:201196] ud_ep.c:255 Fatal: UD endpoint 0x55b37b4bfe30 to : unhandled timeout error -==== backtrace (tid: 201196) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1473c3023454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1473c3020400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1473c3020529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1473bf04b133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1473c30198aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1473c2bb4962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1473c4847295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1473c4865870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1473c4b01796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1473c4b9ad35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1473c45fde9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55b37ae4f0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55b37ae8ea81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55b37ae92bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55b37aec0258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55b37ae4f6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55b37aed1e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1473c374629d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55b37ae4046a] -================================= -[nid001409:185377:0:185377] ud_ep.c:255 Fatal: UD endpoint 0x55d5e5943180 to : unhandled timeout error -==== backtrace (tid: 185377) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14d26d726454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14d26d723400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14d26d723529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14d26974e133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14d26d71c8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14d26d2b7962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14d26ef4a295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14d26ef68870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14d26f204796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14d26f29dd35] -[nid001950:100694:0:100694] ud_ep.c:255 Fatal: UD endpoint 0x559268315ce0 to : unhandled timeout error -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14d26ed00e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55d5e52d80f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55d5e5317a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55d5e531bbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55d5e5349258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55d5e52d86c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55d5e535ae63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14d26de4929d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55d5e52c946a] -================================= -==== backtrace (tid: 100694) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x148a7aa2b454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x148a7aa28400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x148a7aa28529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x148a76a53133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x148a7aa218aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x148a7a5bc962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x148a7c24f295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x148a7c26d870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x148a7c509796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x148a7c5a2d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x148a7c005e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559267c0f0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559267c4ea81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559267c52bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559267c80258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559267c0f6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559267c91e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x148a7b14e29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559267c0046a] -================================= -[nid001864:92457:0:92457] ud_ep.c:255 Fatal: UD endpoint 0x55b3e54c95a0 to : unhandled timeout error -==== backtrace (tid: 92457) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x151f6c8ba454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x151f6c8b7400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x151f6c8b7529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x151f688e2133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x151f6c8b08aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x151f6c44b962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x151f6e0de295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x151f6e0fc870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x151f6e398796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x151f6e431d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x151f6de94e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55b3e4cea0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55b3e4d29a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55b3e4d2dbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55b3e4d5b258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55b3e4cea6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55b3e4d6ce63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x151f6cfdd29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55b3e4cdb46a] -================================= -[nid001660:125280:0:125280] ud_ep.c:255 Fatal: UD endpoint 0x55ce72f4bff0 to : unhandled timeout error -==== backtrace (tid: 125280) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x149023a69454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x149023a66400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x149023a66529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14901fa91133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x149023a5f8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1490235fa962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14902528d295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1490252ab870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x149025547796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1490255e0d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x149025043e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ce728530f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ce72892a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ce72896bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ce728c4258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ce728536c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ce728d5e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14902418c29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ce7284446a] -================================= -[nid002270:182968:0:182968] ud_ep.c:255 Fatal: UD endpoint 0x55be9c5bb890 to : unhandled timeout error -==== backtrace (tid: 182968) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x147f6dfc0454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x147f6dfbd400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x147f6dfbd529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x147f69fe8133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x147f6dfb68aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x147f6db51962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x147f6f7e4295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x147f6f802870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x147f6fa9e796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x147f6fb37d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x147f6f59ae9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55be9bde60f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55be9be25a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55be9be29bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55be9be57258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55be9bde66c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55be9be68e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x147f6e6e329d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55be9bdd746a] -================================= -[nid001950:100689:0:100689] ud_ep.c:255 Fatal: UD endpoint 0x56428a51dce0 to : unhandled timeout error -[nid001858:72024:0:72024] ud_ep.c:255 Fatal: UD endpoint 0x5566b514b080 to : unhandled timeout error -==== backtrace (tid: 72024) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x152d1ed6e454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x152d1ed6b400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x152d1ed6b529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x152d1ad96133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x152d1ed648aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x152d1e8ff962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x152d20592295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x152d205b0870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x152d2084c796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x152d208e5d35] -==== backtrace (tid: 100689) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x151c70512454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x151c7050f400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x151c7050f529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x151c6c53a133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x151c705088aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x151c700a3962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x151c71d36295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x151c71d54870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x151c71ff0796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x151c72089d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x152d20348e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5566b49f10f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5566b4a30a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5566b4a34bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5566b4a62258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5566b49f16c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5566b4a73e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x152d1f49129d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5566b49e246a] -================================= -[nid001842:138814:0:138814] ud_ep.c:255 Fatal: UD endpoint 0x558835b40fe0 to : unhandled timeout error -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x151c71aece9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x564289e170f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x564289e56a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x564289e5abdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x564289e88258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x564289e176c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x564289e99e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x151c70c3529d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x564289e0846a] -================================= -==== backtrace (tid: 138814) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15446f506454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15446f503400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15446f503529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x15446b52e133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15446f4fc8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15446f097962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x154470d2a295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x154470d48870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x154470fe4796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15447107dd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x154470ae0e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55883544c0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55883548ba81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55883548fbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5588354bd258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55883544c6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5588354cee63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15446fc2929d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55883543d46a] -================================= -[nid001920:170318:0:170318] ud_ep.c:255 Fatal: UD endpoint 0x559fad1f9d00 to : unhandled timeout error -==== backtrace (tid: 170318) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14d76d316454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14d76d313400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14d76d313529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14d76933e133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14d76d30c8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14d76cea7962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14d76eb3a295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14d76eb58870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14d76edf4796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14d76ee8dd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14d76e8f0e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559fac94e0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559fac98da81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559fac991bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559fac9bf258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559fac94e6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559fac9d0e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14d76da3929d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559fac93f46a] -================================= -[nid001463:252757:0:252757] ud_ep.c:255 Fatal: UD endpoint 0x563f40864c00 to : unhandled timeout error -[nid001842:138815:0:138815] ud_ep.c:255 Fatal: UD endpoint 0x5610be6e5360 to : unhandled timeout error -[nid001463:252756:0:252756] ud_ep.c:255 Fatal: UD endpoint 0x5623c255da40 to : unhandled timeout error -[nid001842:138811:0:138811] ud_ep.c:255 Fatal: UD endpoint 0x560ca7dbbef0 to : unhandled timeout error -[nid001463:252763:0:252763] ud_ep.c:255 Fatal: UD endpoint 0x55fb64a5cde0 to : unhandled timeout error -==== backtrace (tid: 138815) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x147e841e5454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x147e841e2400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x147e841e2529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x147e8020d133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x147e841db8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x147e83d76962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x147e85a09295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x147e85a27870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x147e85cc3796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x147e85d5cd35] -==== backtrace (tid: 252757) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14c4d6577454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14c4d6574400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14c4d6574529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14c4d259f133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14c4d656d8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14c4d6108962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14c4d7d9b295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14c4d7db9870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14c4d8055796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14c4d80eed35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x147e857bfe9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5610bdfee0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5610be02da81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5610be031bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5610be05f258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5610bdfee6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5610be070e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x147e8490829d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5610bdfdf46a] -================================= -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14c4d7b51e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x563f402210f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x563f40260a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x563f40264bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x563f40292258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x563f402216c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x563f402a3e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14c4d6c9a29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x563f4021246a] -================================= -==== backtrace (tid: 138811) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14a7a6c32454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14a7a6c2f400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14a7a6c2f529] -[nid001864:92458:0:92458] ud_ep.c:255 Fatal: UD endpoint 0x560ddbfff6b0 to : unhandled timeout error -==== backtrace (tid: 252756) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14602e218454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14602e215400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14602e215529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14602a240133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14602e20e8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14602dda9962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14602fa3c295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14602fa5a870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14602fcf6796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14602fd8fd35] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14a7a2c5a133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14a7a6c288aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14a7a67c3962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14a7a8456295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14a7a8474870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14a7a8710796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14a7a87a9d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14a7a820ce9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x560ca76c70f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x560ca7706a81] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14602f7f2e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5623c1f650f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5623c1fa4a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5623c1fa8bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5623c1fd6258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5623c1f656c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5623c1fe7e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14602e93b29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5623c1f5646a] -================================= -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x560ca770abdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x560ca7738258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x560ca76c76c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x560ca7749e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14a7a735529d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x560ca76b846a] -================================= -==== backtrace (tid: 92458) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x147196d8a454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x147196d87400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x147196d87529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x147192db2133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x147196d808aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14719691b962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1471985ae295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1471985cc870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x147198868796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x147198901d35] -==== backtrace (tid: 252763) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1501aad62454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1501aad5f400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1501aad5f529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1501a6d8a133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1501aad588aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1501aa8f3962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1501ac586295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1501ac5a4870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1501ac840796] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x147198364e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x560ddb82b0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x560ddb86aa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x560ddb86ebdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x560ddb89c258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x560ddb82b6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x560ddb8ade63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1471974ad29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x560ddb81c46a] -================================= - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1501ac8d9d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1501ac33ce9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55fb644140f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55fb64453a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55fb64457bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55fb64485258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55fb644146c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55fb64496e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1501ab48529d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55fb6440546a] -================================= -[nid001409:185378:0:185378] ud_ep.c:255 Fatal: UD endpoint 0x557e11c23000 to : unhandled timeout error -==== backtrace (tid: 185378) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14a571284454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14a571281400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14a571281529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14a56d2ac133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14a57127a8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14a570e15962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14a572aa8295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14a572ac6870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14a572d62796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14a572dfbd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14a57285ee9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x557e115ca0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x557e11609a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x557e1160dbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x557e1163b258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x557e115ca6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x557e1164ce63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14a5719a729d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x557e115bb46a] -================================= -[nid001842:138810:0:138810] ud_ep.c:255 Fatal: UD endpoint 0x55dd1191bd20 to : unhandled timeout error -[nid001409:185381:0:185381] ud_ep.c:255 Fatal: UD endpoint 0x55ea7007b0f0 to : unhandled timeout error -==== backtrace (tid: 138810) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x153b5ad99454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x153b5ad96400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x153b5ad96529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x153b56dc1133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x153b5ad8f8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x153b5a92a962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x153b5c5bd295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x153b5c5db870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x153b5c877796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x153b5c910d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x153b5c373e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55dd112250f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55dd11264a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55dd11268bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55dd11296258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55dd112256c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55dd112a7e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x153b5b4bc29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55dd1121646a] -================================= -[nid001664:168290:0:168290] ud_ep.c:255 Fatal: UD endpoint 0x55bbde7c4c70 to : unhandled timeout error -==== backtrace (tid: 168290) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14939de6a454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14939de67400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14939de67529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x149399e92133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14939de608aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14939d9fb962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14939f68e295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14939f6ac870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14939f948796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14939f9e1d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14939f444e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55bbde1360f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55bbde175a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55bbde179bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55bbde1a7258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55bbde1366c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55bbde1b8e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14939e58d29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55bbde12746a] -================================= -==== backtrace (tid: 185381) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x150f114e4454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x150f114e1400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x150f114e1529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x150f0d50c133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x150f114da8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x150f11075962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x150f12d08295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x150f12d26870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x150f12fc2796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x150f1305bd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x150f12abee9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ea6fa160f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ea6fa55a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ea6fa59bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ea6fa87258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ea6fa166c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ea6fa98e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x150f11c0729d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ea6fa0746a] -================================= -[nid001754:197288:0:197288] ud_ep.c:255 Fatal: UD endpoint 0x558941bdd5e0 to : unhandled timeout error -==== backtrace (tid: 197288) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14f8d72fc454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14f8d72f9400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14f8d72f9529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14f8d3324133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14f8d72f28aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14f8d6e8d962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14f8d8b20295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14f8d8b3e870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14f8d8dda796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14f8d8e73d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14f8d88d6e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55894146e0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5589414ada81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5589414b1bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5589414df258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55894146e6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5589414f0e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14f8d7a1f29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55894145f46a] -================================= -[nid001950:100690:0:100690] ud_ep.c:255 Fatal: UD endpoint 0x5585e8333ce0 to : unhandled timeout error -[nid001409:185383:0:185383] ud_ep.c:255 Fatal: UD endpoint 0x56153fe44c50 to : unhandled timeout error -==== backtrace (tid: 100690) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15081f3ab454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15081f3a8400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15081f3a8529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x15081b3d3133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15081f3a18aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15081ef3c962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x150820bcf295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x150820bed870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x150820e89796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x150820f22d35] -==== backtrace (tid: 185383) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1553fe37f454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1553fe37c400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1553fe37c529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1553fa3a7133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1553fe3758aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1553fdf10962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1553ffba3295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1553ffbc1870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1553ffe5d796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1553ffef6d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x150820985e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5585e7c2d0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5585e7c6ca81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5585e7c70bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5585e7c9e258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5585e7c2d6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5585e7cafe63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15081face29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5585e7c1e46a] -================================= -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1553ff959e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56153f7dc0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x56153f81ba81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56153f81fbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56153f84d258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56153f7dc6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56153f85ee63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1553feaa229d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56153f7cd46a] -================================= -[nid001463:252758:0:252758] ud_ep.c:255 Fatal: UD endpoint 0x55588c10b810 to : unhandled timeout error -[nid001754:197292:0:197292] ud_ep.c:255 Fatal: UD endpoint 0x55ecf007ff80 to : unhandled timeout error -==== backtrace (tid: 252758) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1527546cf454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1527546cc400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1527546cc529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1527506f7133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1527546c58aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x152754260962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x152755ef3295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x152755f11870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1527561ad796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x152756246d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x152755ca9e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55588bb170f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55588bb56a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55588bb5abdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55588bb88258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55588bb176c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55588bb99e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x152754df229d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55588bb0846a] -================================= -==== backtrace (tid: 197292) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14a0a6a66454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14a0a6a63400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14a0a6a63529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14a0a2a8e133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14a0a6a5c8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14a0a65f7962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14a0a828a295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14a0a82a8870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14a0a8544796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14a0a85ddd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14a0a8040e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ecef9230f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ecef962a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ecef966bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ecef994258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ecef9236c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ecef9a5e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14a0a718929d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ecef91446a] -================================= -[nid001754:197291:0:197291] ud_ep.c:255 Fatal: UD endpoint 0x55c537b9c7a0 to : unhandled timeout error -[nid002406:197129:0:197129] ud_ep.c:255 Fatal: UD endpoint 0x563c647a7820 to : unhandled timeout error -==== backtrace (tid: 197291) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14f4a3dd5454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14f4a3dd2400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14f4a3dd2529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14f49fdfd133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14f4a3dcb8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14f4a3966962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14f4a55f9295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14f4a5617870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14f4a58b3796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14f4a594cd35] -==== backtrace (tid: 197129) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14a08a8b1454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14a08a8ae400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14a08a8ae529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14a0868d9133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14a08a8a78aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14a08a442962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14a08c0d5295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14a08c0f3870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14a08c38f796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14a08c428d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14f4a53afe9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55c53743b0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55c53747aa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55c53747ebdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55c5374ac258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55c53743b6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55c5374bde63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14f4a44f829d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55c53742c46a] -================================= -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14a08be8be9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x563c6410c0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x563c6414ba81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x563c6414fbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x563c6417d258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x563c6410c6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x563c6418ee63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14a08afd429d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x563c640fd46a] -================================= -[nid003252:53444:0:53444] ud_ep.c:255 Fatal: UD endpoint 0x55da18e0f0f0 to : unhandled timeout error -==== backtrace (tid: 53444) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x147306dfb454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x147306df8400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x147306df8529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x147302e23133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x147306df18aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14730698c962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14730861f295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14730863d870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1473088d9796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x147308972d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1473083d5e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55da1863f0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55da1867ea81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55da18682bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55da186b0258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55da1863f6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55da186c1e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14730751e29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55da1863046a] -================================= -[nid001858:72025:0:72025] ud_ep.c:255 Fatal: UD endpoint 0x555af3fb7860 to : unhandled timeout error -==== backtrace (tid: 72025) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14612f6bd454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14612f6ba400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14612f6ba529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14612b6e5133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14612f6b38aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14612f24e962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x146130ee1295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146130eff870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14613119b796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146131234d35] -[nid001927:149632:0:149632] ud_ep.c:255 Fatal: UD endpoint 0x5595165c9960 to : unhandled timeout error -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146130c97e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x555af384f0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x555af388ea81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x555af3892bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x555af38c0258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x555af384f6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x555af38d1e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14612fde029d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x555af384046a] -================================= -==== backtrace (tid: 149632) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14ab59aef454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14ab59aec400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14ab59aec529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14ab55b17133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14ab59ae58aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14ab59680962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14ab5b313295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14ab5b331870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14ab5b5cd796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14ab5b666d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14ab5b0c9e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559515dea0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559515e29a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559515e2dbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559515e5b258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559515dea6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559515e6ce63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14ab5a21229d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559515ddb46a] -================================= -[nid001954:89824:0:89824] ud_ep.c:255 Fatal: UD endpoint 0x55eb42bdcf30 to : unhandled timeout error -[nid001754:197293:0:197293] ud_ep.c:255 Fatal: UD endpoint 0x55ad33d20730 to : unhandled timeout error -==== backtrace (tid: 89824) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x145778bee454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x145778beb400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x145778beb529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x145774c16133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x145778be48aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14577877f962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14577a412295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14577a430870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14577a6cc796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14577a765d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14577a1c8e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55eb424bb0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55eb424faa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55eb424febdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55eb4252c258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55eb424bb6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55eb4253de63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14577931129d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55eb424ac46a] -================================= -[nid001463:252761:0:252761] ud_ep.c:255 Fatal: UD endpoint 0x55d6bd026980 to : unhandled timeout error -==== backtrace (tid: 197293) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x149eef2d5454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x149eef2d2400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x149eef2d2529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x149eeb2fd133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x149eef2cb8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x149eeee66962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x149ef0af9295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x149ef0b17870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x149ef0db3796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x149ef0e4cd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x149ef08afe9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ad335bb0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ad335faa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ad335febdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ad3362c258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ad335bb6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ad3363de63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x149eef9f829d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ad335ac46a] -================================= -==== backtrace (tid: 252761) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14ac8f938454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14ac8f935400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14ac8f935529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14ac8b960133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14ac8f92e8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14ac8f4c9962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14ac9115c295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14ac9117a870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14ac91416796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14ac914afd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14ac90f12e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55d6bca1d0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55d6bca5ca81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55d6bca60bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55d6bca8e258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55d6bca1d6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55d6bca9fe63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14ac9005b29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55d6bca0e46a] -================================= -[nid001924:245144:0:245144] ud_ep.c:255 Fatal: UD endpoint 0x55981765d7d0 to : unhandled timeout error -==== backtrace (tid: 245144) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1533e840d454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1533e840a400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1533e840a529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1533e4435133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1533e84038aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1533e7f9e962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1533e9c31295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1533e9c4f870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1533e9eeb796] -[nid001950:100693:0:100693] ud_ep.c:255 Fatal: UD endpoint 0x557bab720b70 to : unhandled timeout error - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1533e9f84d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1533e99e7e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559816fd70f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559817016a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55981701abdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559817048258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559816fd76c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559817059e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1533e8b3029d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559816fc846a] -================================= -==== backtrace (tid: 100693) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1522c4baf454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1522c4bac400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1522c4bac529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1522c0bd7133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1522c4ba58aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1522c4740962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1522c63d3295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1522c63f1870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1522c668d796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1522c6726d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1522c6189e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x557bab01a0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x557bab059a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x557bab05dbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x557bab08b258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x557bab01a6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x557bab09ce63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1522c52d229d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x557bab00b46a] -================================= -[nid002580:121316:0:121316] ud_ep.c:255 Fatal: UD endpoint 0x55ce192b3940 to : unhandled timeout error -==== backtrace (tid: 121316) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x153ba4f84454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x153ba4f81400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x153ba4f81529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x153ba0fac133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x153ba4f7a8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x153ba4b15962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x153ba67a8295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x153ba67c6870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x153ba6a62796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x153ba6afbd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x153ba655ee9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ce189a80f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ce189e7a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ce189ebbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ce18a19258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ce189a86c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ce18a2ae63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x153ba56a729d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ce1899946a] -================================= -[nid002580:121318:0:121318] ud_ep.c:255 Fatal: UD endpoint 0x55c890f60950 to : unhandled timeout error -==== backtrace (tid: 121318) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14e5a7231454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14e5a722e400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14e5a722e529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14e5a3259133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14e5a72278aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14e5a6dc2962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14e5a8a55295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14e5a8a73870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14e5a8d0f796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14e5a8da8d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14e5a880be9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55c8906550f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55c890694a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55c890698bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55c8906c6258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55c8906556c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55c8906d7e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14e5a795429d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55c89064646a] -================================= -[nid001656:201197:0:201197] ud_ep.c:255 Fatal: UD endpoint 0x5609b82701c0 to : unhandled timeout error -==== backtrace (tid: 201197) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x152498dbc454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x152498db9400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x152498db9529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x152494de4133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x152498db28aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15249894d962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15249a5e0295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15249a5fe870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15249a89a796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15249a933d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15249a396e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5609b7c010f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5609b7c40a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5609b7c44bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5609b7c72258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5609b7c016c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5609b7c83e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1524994df29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5609b7bf246a] -================================= -[nid001664:168295:0:168295] ud_ep.c:255 Fatal: UD endpoint 0x55be8ba31300 to : unhandled timeout error -==== backtrace (tid: 168295) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x152345f53454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x152345f50400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x152345f50529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x152341f7b133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x152345f498aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x152345ae4962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x152347777295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x152347795870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x152347a31796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x152347acad35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15234752de9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55be8b39e0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55be8b3dda81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55be8b3e1bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55be8b40f258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55be8b39e6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55be8b420e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15234667629d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55be8b38f46a] -================================= -[nid001754:197286:0:197286] ud_ep.c:255 Fatal: UD endpoint 0x557afdbea5d0 to : unhandled timeout error -==== backtrace (tid: 197286) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146cd527a454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146cd5277400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146cd5277529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x146cd12a2133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146cd52708aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146cd4e0b962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x146cd6a9e295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146cd6abc870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146cd6d58796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146cd6df1d35] -[nid002580:121311:0:121311] ud_ep.c:255 Fatal: UD endpoint 0x55e281e5cef0 to : unhandled timeout error -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146cd6854e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x557afd4790f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x557afd4b8a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x557afd4bcbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x557afd4ea258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x557afd4796c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x557afd4fbe63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x146cd599d29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x557afd46a46a] -================================= -==== backtrace (tid: 121311) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1455f3ff9454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1455f3ff6400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1455f3ff6529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1455f0021133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1455f3fef8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1455f3b8a962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1455f581d295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1455f583b870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1455f5ad7796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1455f5b70d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1455f55d3e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55e2815510f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55e281590a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55e281594bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55e2815c2258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55e2815516c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55e2815d3e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1455f471c29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55e28154246a] -================================= -[nid003252:53448:0:53448] ud_ep.c:255 Fatal: UD endpoint 0x561240bf40f0 to : unhandled timeout error -==== backtrace (tid: 53448) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14c5db6f4454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14c5db6f1400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14c5db6f1529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14c5d771c133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14c5db6ea8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14c5db285962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14c5dcf18295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14c5dcf36870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14c5dd1d2796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14c5dd26bd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14c5dcccee9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5612404240f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x561240463a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x561240467bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x561240495258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5612404246c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5612404a6e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14c5dbe1729d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56124041546a] -================================= -[nid003252:53450:0:53450] ud_ep.c:255 Fatal: UD endpoint 0x563edf2320f0 to : unhandled timeout error -[nid001842:138808:0:138808] ud_ep.c:255 Fatal: UD endpoint 0x55865cf711a0 to : unhandled timeout error -==== backtrace (tid: 53450) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1461464af454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1461464ac400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1461464ac529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1461424d7133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1461464a58aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146146040962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x146147cd3295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146147cf1870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146147f8d796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146148026d35] -==== backtrace (tid: 138808) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15418bfeb454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15418bfe8400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15418bfe8529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x154188013133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15418bfe18aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15418bb7c962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15418d80f295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15418d82d870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15418dac9796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15418db62d35] -[nid001924:245146:0:245146] ud_ep.c:255 Fatal: UD endpoint 0x55a5debc5b10 to : unhandled timeout error -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146147a89e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x563edea620f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x563edeaa1a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x563edeaa5bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x563edead3258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x563edea626c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x563edeae4e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x146146bd229d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x563edea5346a] -================================= -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15418d5c5e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55865c8730f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55865c8b2a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55865c8b6bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55865c8e4258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55865c8736c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55865c8f5e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15418c70e29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55865c86446a] -================================= -==== backtrace (tid: 245146) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14eee6ce5454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14eee6ce2400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14eee6ce2529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14eee2d0d133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14eee6cdb8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14eee6876962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14eee8509295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14eee8527870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14eee87c3796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14eee885cd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14eee82bfe9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55a5de53e0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55a5de57da81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55a5de581bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55a5de5af258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55a5de53e6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55a5de5c0e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14eee740829d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55a5de52f46a] -================================= -[nid003252:53445:0:53445] ud_ep.c:255 Fatal: UD endpoint 0x5637c9b8b0f0 to : unhandled timeout error -[nid001954:89825:0:89825] ud_ep.c:255 Fatal: UD endpoint 0x556eb1645ce0 to : unhandled timeout error -==== backtrace (tid: 53445) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x149e64505454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x149e64502400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x149e64502529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x149e6052d133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x149e644fb8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x149e64096962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x149e65d29295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x149e65d47870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x149e65fe3796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x149e6607cd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x149e65adfe9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5637c93bb0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5637c93faa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5637c93febdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5637c942c258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5637c93bb6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5637c943de63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x149e64c2829d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5637c93ac46a] -================================= -[nid001924:245148:0:245148] ud_ep.c:255 Fatal: UD endpoint 0x561a2f144100 to : unhandled timeout error -==== backtrace (tid: 89825) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14b67a450454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14b67a44d400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14b67a44d529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14b676478133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14b67a4468aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14b679fe1962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14b67bc74295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14b67bc92870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14b67bf2e796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14b67bfc7d35] -[nid001871:180897:0:180897] ud_ep.c:255 Fatal: UD endpoint 0x562dbe632f90 to : unhandled timeout error -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14b67ba2ae9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x556eb0f250f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x556eb0f64a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x556eb0f68bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x556eb0f96258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x556eb0f256c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x556eb0fa7e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14b67ab7329d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x556eb0f1646a] -================================= -[nid002270:182961:0:182961] ud_ep.c:255 Fatal: UD endpoint 0x55dd965b9890 to : unhandled timeout error -==== backtrace (tid: 245148) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x148b366f7454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x148b366f4400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x148b366f4529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x148b3271f133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x148b366ed8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x148b36288962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x148b37f1b295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x148b37f39870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x148b381d5796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x148b3826ed35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x148b37cd1e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x561a2eabd0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x561a2eafca81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x561a2eb00bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x561a2eb2e258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x561a2eabd6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x561a2eb3fe63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x148b36e1a29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x561a2eaae46a] -================================= -==== backtrace (tid: 180897) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14a66484d454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14a66484a400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14a66484a529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14a660875133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14a6648438aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14a6643de962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14a666071295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14a66608f870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14a66632b796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14a6663c4d35] -==== backtrace (tid: 182961) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x152121157454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x152121154400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x152121154529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x15211d17f133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15212114d8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x152120ce8962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15212297b295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x152122999870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x152122c35796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x152122cced35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14a665e27e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x562dbddd80f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x562dbde17a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x562dbde1bbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x562dbde49258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x562dbddd86c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x562dbde5ae63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14a664f7029d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x562dbddc946a] -================================= -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x152122731e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55dd95de40f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55dd95e23a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55dd95e27bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55dd95e55258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55dd95de46c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55dd95e66e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15212187a29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55dd95dd546a] -================================= -[nid002580:121313:0:121313] ud_ep.c:255 Fatal: UD endpoint 0x55954ba215f0 to : unhandled timeout error -==== backtrace (tid: 121313) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14b28ba9e454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14b28ba9b400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14b28ba9b529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14b287ac6133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14b28ba948aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14b28b62f962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14b28d2c2295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14b28d2e0870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14b28d57c796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14b28d615d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14b28d078e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55954b1160f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55954b155a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55954b159bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55954b187258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55954b1166c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55954b198e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14b28c1c129d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55954b10746a] -================================= -[nid002580:121314:0:121314] ud_ep.c:255 Fatal: UD endpoint 0x557f4cfa6660 to : unhandled timeout error -==== backtrace (tid: 121314) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1497afdf0454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1497afded400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1497afded529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1497abe18133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1497afde68aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1497af981962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1497b1614295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1497b1632870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1497b18ce796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1497b1967d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1497b13cae9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x557f4c69b0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x557f4c6daa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x557f4c6debdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x557f4c70c258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x557f4c69b6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x557f4c71de63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1497b051329d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x557f4c68c46a] -================================= -[nid003252:53449:0:53449] ud_ep.c:255 Fatal: UD endpoint 0x5613b82b20f0 to : unhandled timeout error -[nid001754:197289:0:197289] ud_ep.c:255 Fatal: UD endpoint 0x561446b2dee0 to : unhandled timeout error -==== backtrace (tid: 53449) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x149c76fe6454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x149c76fe3400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x149c76fe3529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x149c7300e133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x149c76fdc8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x149c76b77962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x149c7880a295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x149c78828870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x149c78ac4796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x149c78b5dd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x149c785c0e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5613b7ae20f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5613b7b21a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5613b7b25bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5613b7b53258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5613b7ae26c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5613b7b64e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x149c7770929d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5613b7ad346a] -================================= -==== backtrace (tid: 197289) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14fb1a442454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14fb1a43f400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14fb1a43f529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14fb1646a133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14fb1a4388aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14fb19fd3962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14fb1bc66295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14fb1bc84870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14fb1bf20796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14fb1bfb9d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14fb1ba1ce9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5614463bd0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5614463fca81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x561446400bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56144642e258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5614463bd6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56144643fe63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14fb1ab6529d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5614463ae46a] -================================= -[nid003252:53446:0:53446] ud_ep.c:255 Fatal: UD endpoint 0x5565761070f0 to : unhandled timeout error -[nid002580:121317:0:121317] ud_ep.c:255 Fatal: UD endpoint 0x55d180e49280 to : unhandled timeout error -==== backtrace (tid: 53446) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x151ac5722454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x151ac571f400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x151ac571f529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x151ac174a133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x151ac57188aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x151ac52b3962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x151ac6f46295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x151ac6f64870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x151ac7200796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x151ac7299d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x151ac6cfce9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5565759370f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x556575976a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55657597abdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5565759a8258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5565759376c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5565759b9e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x151ac5e4529d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55657592846a] -================================= -==== backtrace (tid: 121317) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x152138ce3454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x152138ce0400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x152138ce0529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x152134d0b133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x152138cd98aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x152138874962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15213a507295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15213a525870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15213a7c1796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15213a85ad35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15213a2bde9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55d18053e0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55d18057da81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55d180581bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55d1805af258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55d18053e6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55d1805c0e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15213940629d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55d18052f46a] -================================= -[nid003252:53447:0:53447] ud_ep.c:255 Fatal: UD endpoint 0x5593f904b0f0 to : unhandled timeout error -==== backtrace (tid: 53447) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x152fd902e454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x152fd902b400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x152fd902b529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x152fd5056133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x152fd90248aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x152fd8bbf962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x152fda852295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x152fda870870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x152fdab0c796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x152fdaba5d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x152fda608e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5593f887b0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5593f88baa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5593f88bebdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5593f88ec258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5593f887b6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5593f88fde63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x152fd975129d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5593f886c46a] -================================= -[nid001924:245150:0:245150] ud_ep.c:255 Fatal: UD endpoint 0x5631e1cf2270 to : unhandled timeout error -==== backtrace (tid: 245150) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15009b377454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15009b374400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15009b374529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x15009739f133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15009b36d8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15009af08962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15009cb9b295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15009cbb9870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15009ce55796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15009ceeed35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15009c951e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5631e166b0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5631e16aaa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5631e16aebdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5631e16dc258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5631e166b6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5631e16ede63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15009ba9a29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5631e165c46a] -================================= -[nid001858:72019:0:72019] ud_ep.c:255 Fatal: UD endpoint 0x55b333e26c40 to : unhandled timeout error -==== backtrace (tid: 72019) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14f367ba3454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14f367ba0400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14f367ba0529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14f363bcb133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14f367b998aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14f367734962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14f3693c7295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14f3693e5870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14f369681796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14f36971ad35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14f36917de9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55b3336ce0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55b33370da81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55b333711bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55b33373f258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55b3336ce6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55b333750e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14f3682c629d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55b3336bf46a] -================================= -[nid001924:245145:0:245145] ud_ep.c:255 Fatal: UD endpoint 0x5630ab983b10 to : unhandled timeout error -==== backtrace (tid: 245145) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14cd52f7b454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14cd52f78400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14cd52f78529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14cd4efa3133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14cd52f718aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14cd52b0c962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14cd5479f295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14cd547bd870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14cd54a59796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14cd54af2d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14cd54555e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5630ab2fc0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5630ab33ba81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5630ab33fbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5630ab36d258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5630ab2fc6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5630ab37ee63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14cd5369e29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5630ab2ed46a] -================================= -[nid001754:197287:0:197287] ud_ep.c:255 Fatal: UD endpoint 0x556d100698a0 to : unhandled timeout error -==== backtrace (tid: 197287) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1493bbd8d454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1493bbd8a400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1493bbd8a529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1493b7db5133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1493bbd838aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1493bb91e962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1493bd5b1295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1493bd5cf870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1493bd86b796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1493bd904d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1493bd367e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x556d0f9160f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x556d0f955a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x556d0f959bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x556d0f987258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x556d0f9166c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x556d0f998e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1493bc4b029d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x556d0f90746a] -================================= -[nid001754:197290:0:197290] ud_ep.c:255 Fatal: UD endpoint 0x5586e07e7cd0 to : unhandled timeout error -==== backtrace (tid: 197290) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1543b035c454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1543b0359400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1543b0359529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1543ac384133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1543b03528aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1543afeed962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1543b1b80295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1543b1b9e870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1543b1e3a796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1543b1ed3d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1543b1936e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5586e00a00f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5586e00dfa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5586e00e3bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5586e0111258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5586e00a06c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5586e0122e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1543b0a7f29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5586e009146a] -================================= -[nid001842:138812:0:138812] ud_ep.c:255 Fatal: UD endpoint 0x55c84627eed0 to : unhandled timeout error -[nid001664:168293:0:168293] ud_ep.c:255 Fatal: UD endpoint 0x5611a84d77a0 to : unhandled timeout error -==== backtrace (tid: 168293) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14af21847454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14af21844400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14af21844529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14af1d86f133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14af2183d8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14af213d8962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14af2306b295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14af23089870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14af23325796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14af233bed35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14af22e21e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5611a7e450f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5611a7e84a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5611a7e88bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5611a7eb6258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5611a7e456c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5611a7ec7e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14af21f6a29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5611a7e3646a] -================================= -==== backtrace (tid: 138812) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15350dc55454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15350dc52400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15350dc52529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x153509c7d133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15350dc4b8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15350d7e6962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15350f479295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15350f497870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15350f733796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15350f7ccd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15350f22fe9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55c845b900f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55c845bcfa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55c845bd3bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55c845c01258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55c845b906c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55c845c12e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15350e37829d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55c845b8146a] -================================= -[nid002403:117137:0:117137] ud_ep.c:255 Fatal: UD endpoint 0x556c04026410 to : unhandled timeout error -==== backtrace (tid: 117137) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14952ddcb454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14952ddc8400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14952ddc8529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x149529df3133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14952ddc18aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14952d95c962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14952f5ef295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14952f60d870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14952f8a9796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14952f942d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14952f3a5e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x556c0388f0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x556c038cea81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x556c038d2bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x556c03900258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x556c0388f6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x556c03911e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14952e4ee29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x556c0388046a] -================================= -[nid002403:117136:0:117136] ud_ep.c:255 Fatal: UD endpoint 0x5618da557410 to : unhandled timeout error -==== backtrace (tid: 117136) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1496bcb93454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1496bcb90400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1496bcb90529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1496b8bbb133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1496bcb898aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1496bc724962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1496be3b7295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1496be3d5870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1496be671796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1496be70ad35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1496be16de9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5618d9dc00f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5618d9dffa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5618d9e03bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5618d9e31258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5618d9dc06c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5618d9e42e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1496bd2b629d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5618d9db146a] -================================= -[nid001911:261135:0:261135] ud_ep.c:255 Fatal: UD endpoint 0x56097b8b3920 to : unhandled timeout error -==== backtrace (tid: 261135) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146f47531454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146f4752e400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146f4752e529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x146f43559133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146f475278aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146f470c2962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x146f48d55295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146f48d73870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146f4900f796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146f490a8d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146f48b0be9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56097b14f0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x56097b18ea81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56097b192bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56097b1c0258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56097b14f6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56097b1d1e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x146f47c5429d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56097b14046a] -================================= -[nid001664:168289:0:168289] ud_ep.c:255 Fatal: UD endpoint 0x5582b2983f90 to : unhandled timeout error -==== backtrace (tid: 168289) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14b05f4a0454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14b05f49d400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14b05f49d529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14b05b4c8133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14b05f4968aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14b05f031962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14b060cc4295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14b060ce2870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14b060f7e796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14b061017d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14b060a7ae9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5582b22f50f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5582b2334a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5582b2338bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5582b2366258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5582b22f56c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5582b2377e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14b05fbc329d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5582b22e646a] -================================= -[nid001924:245143:0:245143] ud_ep.c:255 Fatal: UD endpoint 0x55ace2b69940 to : unhandled timeout error -[nid002628:135738:0:135738] ud_ep.c:255 Fatal: UD endpoint 0x55fa9a7d3f20 to : unhandled timeout error -==== backtrace (tid: 135738) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14b8a4016454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14b8a4013400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14b8a4013529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14b8a003e133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14b8a400c8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14b8a3ba7962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14b8a583a295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14b8a5858870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14b8a5af4796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14b8a5b8dd35] -==== backtrace (tid: 245143) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14cb98e9d454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14cb98e9a400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14cb98e9a529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14cb94ec5133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14cb98e938aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14cb98a2e962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14cb9a6c1295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14cb9a6df870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14cb9a97b796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14cb9aa14d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14b8a55f0e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55fa9a0a40f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55fa9a0e3a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55fa9a0e7bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55fa9a115258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55fa9a0a46c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55fa9a126e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14b8a473929d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55fa9a09546a] -================================= -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14cb9a477e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ace24e30f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ace2522a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ace2526bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ace2554258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ace24e36c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ace2565e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14cb995c029d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ace24d446a] -================================= -[nid001664:168294:0:168294] ud_ep.c:255 Fatal: UD endpoint 0x559b32c0a3a0 to : unhandled timeout error -==== backtrace (tid: 168294) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14aa13718454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14aa13715400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14aa13715529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14aa0f740133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14aa1370e8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14aa132a9962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14aa14f3c295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14aa14f5a870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14aa151f6796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14aa1528fd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14aa14cf2e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559b325790f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559b325b8a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559b325bcbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559b325ea258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559b325796c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559b325fbe63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14aa13e3b29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559b3256a46a] -================================= -[nid001871:180898:0:180898] ud_ep.c:255 Fatal: UD endpoint 0x564b02a851b0 to : unhandled timeout error -==== backtrace (tid: 180898) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14a2ff5ed454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14a2ff5ea400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14a2ff5ea529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14a2fb615133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14a2ff5e38aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14a2ff17e962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14a300e11295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14a300e2f870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14a3010cb796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14a301164d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14a300bc7e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x564b023560f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x564b02395a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x564b02399bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x564b023c7258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x564b023566c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x564b023d8e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14a2ffd1029d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x564b0234746a] -================================= -[nid001950:100688:0:100688] ud_ep.c:255 Fatal: UD endpoint 0x55eded0d0ce0 to : unhandled timeout error -[nid001911:261136:0:261136] ud_ep.c:255 Fatal: UD endpoint 0x562df997c440 to : unhandled timeout error -==== backtrace (tid: 100688) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14caf5092454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14caf508f400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14caf508f529] -[nid001924:245149:0:245149] ud_ep.c:255 Fatal: UD endpoint 0x55dd16488c40 to : unhandled timeout error - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14caf10ba133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14caf50888aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14caf4c23962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14caf68b6295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14caf68d4870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14caf6b70796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14caf6c09d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14caf666ce9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55edec9ca0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55edeca09a81] -==== backtrace (tid: 261136) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x151e8824d454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x151e8824a400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x151e8824a529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x151e84275133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x151e882438aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x151e87dde962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x151e89a71295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x151e89a8f870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x151e89d2b796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x151e89dc4d35] -[nid001871:180899:0:180899] ud_ep.c:255 Fatal: UD endpoint 0x55a80bfd3ee0 to : unhandled timeout error -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55edeca0dbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55edeca3b258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55edec9ca6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55edeca4ce63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14caf57b529d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55edec9bb46a] -================================= -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x151e89827e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x562df92080f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x562df9247a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x562df924bbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x562df9279258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x562df92086c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x562df928ae63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x151e8897029d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x562df91f946a] -================================= -==== backtrace (tid: 245149) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1487785d9454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1487785d6400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1487785d6529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x148774601133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1487785cf8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14877816a962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x148779dfd295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x148779e1b870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14877a0b7796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14877a150d35] -[nid002628:135739:0:135739] ud_ep.c:255 Fatal: UD endpoint 0x559093c35f20 to : unhandled timeout error -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x148779bb3e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55dd15e010f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55dd15e40a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55dd15e44bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55dd15e72258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55dd15e016c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55dd15e83e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x148778cfc29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55dd15df246a] -================================= -==== backtrace (tid: 180899) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15069fa89454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15069fa86400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15069fa86529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x15069bab1133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15069fa7f8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15069f61a962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1506a12ad295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1506a12cb870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1506a1567796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1506a1600d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1506a1063e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55a80b8af0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55a80b8eea81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55a80b8f2bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55a80b920258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55a80b8af6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55a80b931e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1506a01ac29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55a80b8a046a] -================================= -==== backtrace (tid: 135739) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x153495c72454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x153495c6f400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x153495c6f529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x153491c9a133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x153495c688aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x153495803962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x153497496295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1534974b4870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x153497750796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1534977e9d35] -[nid001912:205777:0:205777] ud_ep.c:255 Fatal: UD endpoint 0x55c57119bfd0 to : unhandled timeout error -==== backtrace (tid: 205777) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x151565989454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x151565986400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x151565986529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1515619b1133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15156597f8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15156551a962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1515671ad295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1515671cb870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x151567467796] -[nid002264:48356:0:48356] ud_ep.c:255 Fatal: UD endpoint 0x5591f4a7c550 to : unhandled timeout error -==== backtrace (tid: 48356) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x151c01b8e454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x151c01b8b400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x151c01b8b529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x151bfdbb6133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x151c01b848aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x151c0171f962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x151c033b2295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x151c033d0870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x151c0366c796] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15349724ce9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5590935060f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559093545a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559093549bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559093577258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5590935066c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559093588e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15349639529d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5590934f746a] -================================= - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x151c03705d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x151c03168e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5591f41650f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5591f41a4a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5591f41a8bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5591f41d6258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5591f41656c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5591f41e7e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x151c022b129d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5591f415646a] -================================= -[nid001664:168291:0:168291] ud_ep.c:255 Fatal: UD endpoint 0x556cbec67e60 to : unhandled timeout error - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x151567500d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x151566f63e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55c5709d80f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55c570a17a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55c570a1bbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55c570a49258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55c5709d86c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55c570a5ae63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1515660ac29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55c5709c946a] -================================= -[nid002264:48361:0:48361] ud_ep.c:255 Fatal: UD endpoint 0x55671df716e0 to : unhandled timeout error -==== backtrace (tid: 168291) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1551b4346454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1551b4343400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1551b4343529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1551b036e133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1551b433c8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1551b3ed7962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1551b5b6a295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1551b5b88870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1551b5e24796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1551b5ebdd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1551b5920e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x556cbe5da0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x556cbe619a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x556cbe61dbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x556cbe64b258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x556cbe5da6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x556cbe65ce63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1551b4a6929d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x556cbe5cb46a] -================================= -==== backtrace (tid: 48361) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14d265c43454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14d265c40400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14d265c40529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14d261c6b133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14d265c398aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14d2657d4962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14d267467295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14d267485870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14d267721796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14d2677bad35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14d26721de9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55671d6590f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55671d698a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55671d69cbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55671d6ca258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55671d6596c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55671d6dbe63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14d26636629d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55671d64a46a] -================================= -[nid001871:180895:0:180895] ud_ep.c:255 Fatal: UD endpoint 0x55d1d68a0fb0 to : unhandled timeout error -[nid001912:205782:0:205782] ud_ep.c:255 Fatal: UD endpoint 0x55abfa3267e0 to : unhandled timeout error -==== backtrace (tid: 180895) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14fe063ef454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14fe063ec400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14fe063ec529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14fe02417133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14fe063e58aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14fe05f80962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14fe07c13295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14fe07c31870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14fe07ecd796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14fe07f66d35] -==== backtrace (tid: 205782) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14ca92268454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14ca92265400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14ca92265529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14ca8e290133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14ca9225e8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14ca91df9962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14ca93a8c295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14ca93aaa870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14ca93d46796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14ca93ddfd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14fe079c9e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55d1d61760f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55d1d61b5a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55d1d61b9bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55d1d61e7258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55d1d61766c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55d1d61f8e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14fe06b1229d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55d1d616746a] -================================= -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14ca93842e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55abf9b620f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55abf9ba1a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55abf9ba5bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55abf9bd3258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55abf9b626c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55abf9be4e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14ca9298b29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55abf9b5346a] -================================= -[nid001871:180892:0:180892] ud_ep.c:255 Fatal: UD endpoint 0x5618c961b730 to : unhandled timeout error -==== backtrace (tid: 180892) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14a20d989454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14a20d986400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14a20d986529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14a2099b1133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14a20d97f8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14a20d51a962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14a20f1ad295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14a20f1cb870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14a20f467796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14a20f500d35] -[nid003252:53443:0:53443] ud_ep.c:255 Fatal: UD endpoint 0x55ffb70b20f0 to : unhandled timeout error -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14a20ef63e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5618c8eef0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5618c8f2ea81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5618c8f32bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5618c8f60258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5618c8eef6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5618c8f71e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14a20e0ac29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5618c8ee046a] -================================= -==== backtrace (tid: 53443) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14bc939c4454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14bc939c1400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14bc939c1529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14bc8f9ec133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14bc939ba8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14bc93555962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14bc951e8295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14bc95206870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14bc954a2796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14bc9553bd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14bc94f9ee9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ffb68e20f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ffb6921a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ffb6925bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ffb6953258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ffb68e26c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ffb6964e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14bc940e729d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ffb68d346a] -================================= -[nid002628:135743:0:135743] ud_ep.c:255 Fatal: UD endpoint 0x557d88abef20 to : unhandled timeout error -[nid002264:48357:0:48357] ud_ep.c:255 Fatal: UD endpoint 0x55c2c1ba5d00 to : unhandled timeout error -==== backtrace (tid: 135743) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x149031b0d454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x149031b0a400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x149031b0a529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14902db35133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x149031b038aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14903169e962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x149033331295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14903334f870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1490335eb796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x149033684d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1490330e7e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x557d8838f0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x557d883cea81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x557d883d2bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x557d88400258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x557d8838f6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x557d88411e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14903223029d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x557d8838046a] -================================= -[nid001858:72020:0:72020] ud_ep.c:255 Fatal: UD endpoint 0x55de6bf168d0 to : unhandled timeout error -==== backtrace (tid: 48357) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x153d599b7454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x153d599b4400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x153d599b4529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x153d559df133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x153d599ad8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x153d59548962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x153d5b1db295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x153d5b1f9870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x153d5b495796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x153d5b52ed35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x153d5af91e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55c2c128e0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55c2c12cda81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55c2c12d1bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55c2c12ff258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55c2c128e6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55c2c1310e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x153d5a0da29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55c2c127f46a] -================================= -==== backtrace (tid: 72020) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15474406a454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x154744067400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x154744067529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x154740092133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1547440608aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x154743bfb962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15474588e295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1547458ac870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x154745b48796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x154745be1d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x154745644e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55de6b7c60f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55de6b805a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55de6b809bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55de6b837258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55de6b7c66c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55de6b848e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15474478d29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55de6b7b746a] -================================= -[nid001871:180894:0:180894] ud_ep.c:255 Fatal: UD endpoint 0x5645c0904ef0 to : unhandled timeout error -==== backtrace (tid: 180894) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x153e154bf454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x153e154bc400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x153e154bc529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x153e114e7133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x153e154b58aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x153e15050962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x153e16ce3295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x153e16d01870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x153e16f9d796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x153e17036d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x153e16a99e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5645c01dc0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5645c021ba81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5645c021fbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5645c024d258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5645c01dc6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5645c025ee63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x153e15be229d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5645c01cd46a] -================================= -[nid002628:135741:0:135741] ud_ep.c:255 Fatal: UD endpoint 0x562e2594df20 to : unhandled timeout error -==== backtrace (tid: 135741) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14b2bb048454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14b2bb045400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14b2bb045529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14b2b7070133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14b2bb03e8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14b2babd9962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14b2bc86c295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14b2bc88a870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14b2bcb26796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14b2bcbbfd35] -[nid001912:205781:0:205781] ud_ep.c:255 Fatal: UD endpoint 0x55cb8a275d60 to : unhandled timeout error -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14b2bc622e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x562e2521e0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x562e2525da81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x562e25261bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x562e2528f258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x562e2521e6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x562e252a0e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14b2bb76b29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x562e2520f46a] -================================= -==== backtrace (tid: 205781) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x150b6dd20454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x150b6dd1d400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x150b6dd1d529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x150b69d48133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x150b6dd168aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x150b6d8b1962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x150b6f544295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x150b6f562870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x150b6f7fe796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x150b6f897d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x150b6f2fae9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55cb89ab20f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55cb89af1a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55cb89af5bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55cb89b23258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55cb89ab26c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55cb89b34e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x150b6e44329d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55cb89aa346a] -================================= -[nid001912:205783:0:205783] ud_ep.c:255 Fatal: UD endpoint 0x5587e9698030 to : unhandled timeout error -[nid002264:48358:0:48358] ud_ep.c:255 Fatal: UD endpoint 0x5623268df130 to : unhandled timeout error -[nid002264:48360:0:48360] ud_ep.c:255 Fatal: UD endpoint 0x564754d08ca0 to : unhandled timeout error -==== backtrace (tid: 205783) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x148358221454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14835821e400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14835821e529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x148354249133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1483582178aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x148357db2962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x148359a45295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x148359a63870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x148359cff796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x148359d98d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1483597fbe9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5587e8ee40f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5587e8f23a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5587e8f27bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5587e8f55258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5587e8ee46c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5587e8f66e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14835894429d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5587e8ed546a] -================================= -==== backtrace (tid: 48358) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1544ad7fe454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1544ad7fb400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1544ad7fb529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1544a9826133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1544ad7f48aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1544ad38f962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1544af022295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1544af040870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1544af2dc796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1544af375d35] -[nid001911:261139:0:261139] ud_ep.c:255 Fatal: UD endpoint 0x559c744e4a80 to : unhandled timeout error -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1544aedd8e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x562325fc80f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x562326007a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56232600bbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x562326039258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x562325fc86c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56232604ae63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1544adf2129d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x562325fb946a] -================================= -==== backtrace (tid: 48360) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x148e9bbe9454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x148e9bbe6400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x148e9bbe6529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x148e97c11133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x148e9bbdf8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x148e9b77a962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x148e9d40d295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x148e9d42b870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x148e9d6c7796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x148e9d760d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x148e9d1c3e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5647543f10f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x564754430a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x564754434bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x564754462258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5647543f16c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x564754473e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x148e9c30c29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5647543e246a] -================================= -==== backtrace (tid: 261139) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14c069090454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14c06908d400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14c06908d529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14c0650b8133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14c0690868aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14c068c21962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14c06a8b4295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14c06a8d2870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14c06ab6e796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14c06ac07d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14c06a66ae9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559c73d800f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559c73dbfa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559c73dc3bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559c73df1258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559c73d806c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559c73e02e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14c0697b329d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559c73d7146a] -================================= -[nid002264:48362:0:48362] ud_ep.c:255 Fatal: UD endpoint 0x562655078650 to : unhandled timeout error -==== backtrace (tid: 48362) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x154f7a6f4454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x154f7a6f1400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x154f7a6f1529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x154f7671c133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x154f7a6ea8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x154f7a285962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x154f7bf18295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x154f7bf36870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x154f7c1d2796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x154f7c26bd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x154f7bccee9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5626547630f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5626547a2a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5626547a6bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5626547d4258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5626547636c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5626547e5e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x154f7ae1729d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56265475446a] -================================= -[nid001711:229168:0:229168] ud_ep.c:255 Fatal: UD endpoint 0x55dc74e64f50 to : unhandled timeout error -==== backtrace (tid: 229168) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14bb3ad93454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14bb3ad90400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14bb3ad90529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14bb36dbb133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14bb3ad898aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14bb3a924962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14bb3c5b7295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14bb3c5d5870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14bb3c871796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14bb3c90ad35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14bb3c36de9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55dc747b40f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55dc747f3a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55dc747f7bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55dc74825258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55dc747b46c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55dc74836e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14bb3b4b629d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55dc747a546a] -================================= -[nid002403:117135:0:117135] ud_ep.c:255 Fatal: UD endpoint 0x56378064d410 to : unhandled timeout error -[nid001912:205778:0:205778] ud_ep.c:255 Fatal: UD endpoint 0x5598f1d2ee90 to : unhandled timeout error -==== backtrace (tid: 117135) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146bd5610454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146bd560d400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146bd560d529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x146bd1638133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146bd56068aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146bd51a1962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x146bd6e34295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146bd6e52870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146bd70ee796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146bd7187d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146bd6beae9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56377feb60f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x56377fef5a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56377fef9bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56377ff27258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56377feb66c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56377ff38e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x146bd5d3329d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56377fea746a] -================================= -==== backtrace (tid: 205778) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1519766f0454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1519766ed400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1519766ed529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x151972718133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1519766e68aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x151976281962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x151977f14295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x151977f32870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1519781ce796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x151978267d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x151977ccae9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5598f157c0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5598f15bba81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5598f15bfbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5598f15ed258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5598f157c6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5598f15fee63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x151976e1329d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5598f156d46a] -================================= -[nid001911:261140:0:261140] ud_ep.c:255 Fatal: UD endpoint 0x55e7d07ede70 to : unhandled timeout error -==== backtrace (tid: 261140) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x150ee619b454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x150ee6198400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x150ee6198529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x150ee21c3133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x150ee61918aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x150ee5d2c962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x150ee79bf295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x150ee79dd870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x150ee7c79796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x150ee7d12d35] -[nid001711:229167:0:229167] ud_ep.c:255 Fatal: UD endpoint 0x565130c8bac0 to : unhandled timeout error -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x150ee7775e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55e7d00790f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55e7d00b8a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55e7d00bcbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55e7d00ea258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55e7d00796c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55e7d00fbe63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x150ee68be29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55e7d006a46a] -================================= -==== backtrace (tid: 229167) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1530a67a7454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1530a67a4400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1530a67a4529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1530a27cf133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1530a679d8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1530a6338962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1530a7fcb295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1530a7fe9870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1530a8285796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1530a831ed35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1530a7d81e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5651305d40f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x565130613a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x565130617bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x565130645258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5651305d46c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x565130656e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1530a6eca29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5651305c546a] -================================= -[nid002580:121312:0:121312] ud_ep.c:255 Fatal: UD endpoint 0x559529d49650 to : unhandled timeout error -==== backtrace (tid: 121312) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1486bd25a454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1486bd257400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1486bd257529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1486b9282133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1486bd2508aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1486bcdeb962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1486bea7e295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1486bea9c870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1486bed38796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1486bedd1d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1486be834e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55952943f0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55952947ea81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559529482bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5595294b0258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55952943f6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5595294c1e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1486bd97d29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55952943046a] -================================= -[nid002607:5716 :0:5716] ud_ep.c:255 Fatal: UD endpoint 0x5645c1c4db10 to : unhandled timeout error -==== backtrace (tid: 5716) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x153d31984454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x153d31981400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x153d31981529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x153d2d9ac133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x153d3197a8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x153d31515962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x153d331a8295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x153d331c6870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x153d33462796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x153d334fbd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x153d32f5ee9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5645c14880f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5645c14c7a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5645c14cbbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5645c14f9258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5645c14886c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5645c150ae63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x153d320a729d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5645c147946a] -================================= -[nid003241:144959:0:144959] ud_ep.c:255 Fatal: UD endpoint 0x556a4919d6c0 to : unhandled timeout error -==== backtrace (tid: 144959) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x149181384454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x149181381400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x149181381529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14917d3ac133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14918137a8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x149180f15962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x149182ba8295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x149182bc6870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x149182e62796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x149182efbd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14918295ee9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x556a48bab0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x556a48beaa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x556a48beebdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x556a48c1c258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x556a48bab6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x556a48c2de63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x149181aa729d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x556a48b9c46a] -================================= -[nid001911:261137:0:261137] ud_ep.c:255 Fatal: UD endpoint 0x561674b5ef60 to : unhandled timeout error -[nid002264:48355:0:48355] ud_ep.c:255 Fatal: UD endpoint 0x5612ccbdfb10 to : unhandled timeout error -==== backtrace (tid: 261137) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14b96eed4454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14b96eed1400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14b96eed1529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14b96aefc133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14b96eeca8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14b96ea65962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14b9706f8295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14b970716870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14b9709b2796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14b970a4bd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14b9704aee9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5616743eb0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x56167442aa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56167442ebdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56167445c258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5616743eb6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56167446de63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14b96f5f729d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5616743dc46a] -================================= -==== backtrace (tid: 48355) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14fe7121d454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14fe7121a400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14fe7121a529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14fe6d245133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14fe712138aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14fe70dae962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14fe72a41295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14fe72a5f870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14fe72cfb796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14fe72d94d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14fe727f7e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5612cc2c70f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5612cc306a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5612cc30abdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5612cc338258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5612cc2c76c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5612cc349e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14fe7194029d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5612cc2b846a] -================================= -[nid001911:261141:0:261141] ud_ep.c:255 Fatal: UD endpoint 0x55570cef21a0 to : unhandled timeout error -==== backtrace (tid: 261141) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14efcb027454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14efcb024400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14efcb024529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14efc704f133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14efcb01d8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14efcabb8962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14efcc84b295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14efcc869870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14efccb05796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14efccb9ed35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14efcc601e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55570c77e0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55570c7bda81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55570c7c1bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55570c7ef258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55570c77e6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55570c800e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14efcb74a29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55570c76f46a] -================================= -[nid002628:135742:0:135742] ud_ep.c:255 Fatal: UD endpoint 0x55961d2f7f20 to : unhandled timeout error -==== backtrace (tid: 135742) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1502f8002454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1502f7fff400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1502f7fff529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1502f402a133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1502f7ff88aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1502f7b93962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1502f9826295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1502f9844870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1502f9ae0796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1502f9b79d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1502f95dce9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55961cbc80f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55961cc07a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55961cc0bbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55961cc39258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55961cbc86c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55961cc4ae63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1502f872529d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55961cbb946a] -================================= -[nid002337:209049:0:209049] ud_ep.c:255 Fatal: UD endpoint 0x563635f7c9e0 to : unhandled timeout error -==== backtrace (tid: 209049) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146b52e6a454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146b52e67400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146b52e67529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x146b4ee92133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146b52e608aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146b529fb962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x146b5468e295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146b546ac870] -[nid002357:138937:0:138937] ud_ep.c:255 Fatal: UD endpoint 0x55e13b4d1f10 to : unhandled timeout error - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146b54948796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146b549e1d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146b54444e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5636356680f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5636356a7a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5636356abbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5636356d9258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5636356686c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5636356eae63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x146b5358d29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56363565946a] -================================= -==== backtrace (tid: 138937) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14ccac8eb454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14ccac8e8400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14ccac8e8529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14cca8913133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14ccac8e18aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14ccac47c962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14ccae10f295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14ccae12d870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14ccae3c9796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14ccae462d35] -[nid002337:209046:0:209046] ud_ep.c:255 Fatal: UD endpoint 0x55669f190020 to : unhandled timeout error -[nid002607:5717 :0:5717] ud_ep.c:255 Fatal: UD endpoint 0x55d81ac0ab10 to : unhandled timeout error -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14ccadec5e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55e13acb60f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55e13acf5a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55e13acf9bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55e13ad27258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55e13acb66c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55e13ad38e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14ccad00e29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55e13aca746a] -================================= -==== backtrace (tid: 209046) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14f132f1f454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14f132f1c400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14f132f1c529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14f12ef47133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14f132f158aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14f132ab0962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14f134743295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14f134761870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14f1349fd796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14f134a96d35] -==== backtrace (tid: 5717) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x148e48b4e454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x148e48b4b400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x148e48b4b529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x148e44b76133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x148e48b448aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x148e486df962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x148e4a372295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x148e4a390870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x148e4a62c796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x148e4a6c5d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14f1344f9e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55669e87b0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55669e8baa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55669e8bebdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55669e8ec258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55669e87b6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55669e8fde63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14f13364229d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55669e86c46a] -================================= -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x148e4a128e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55d81a4450f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55d81a484a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55d81a488bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55d81a4b6258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55d81a4456c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55d81a4c7e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x148e4927129d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55d81a43646a] -================================= -[nid003264:86504:0:86504] ud_ep.c:255 Fatal: UD endpoint 0x55ff7a0eee00 to : unhandled timeout error -==== backtrace (tid: 86504) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14ee69887454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14ee69884400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14ee69884529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14ee658af133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14ee6987d8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14ee69418962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14ee6b0ab295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14ee6b0c9870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14ee6b365796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14ee6b3fed35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14ee6ae61e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ff798b50f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ff798f4a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ff798f8bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ff79926258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ff798b56c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ff79937e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14ee69faa29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ff798a646a] -================================= -[nid003264:86506:0:86506] ud_ep.c:255 Fatal: UD endpoint 0x56480dcc7e00 to : unhandled timeout error -==== backtrace (tid: 86506) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1476307b3454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1476307b0400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1476307b0529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14762c7db133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1476307a98aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x147630344962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x147631fd7295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x147631ff5870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x147632291796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14763232ad35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x147631d8de9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56480d48e0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x56480d4cda81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56480d4d1bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56480d4ff258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56480d48e6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56480d510e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x147630ed629d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56480d47f46a] -================================= -[nid002607:5713 :0:5713] ud_ep.c:255 Fatal: UD endpoint 0x564fb6370b10 to : unhandled timeout error -[nid001912:205779:0:205779] ud_ep.c:255 Fatal: UD endpoint 0x561b4a8aa770 to : unhandled timeout error -[nid002337:209059:0:209059] ud_ep.c:255 Fatal: UD endpoint 0x564274102850 to : unhandled timeout error -==== backtrace (tid: 5713) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15140d229454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15140d226400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15140d226529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x151409251133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15140d21f8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15140cdba962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15140ea4d295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15140ea6b870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15140ed07796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15140eda0d35] -==== backtrace (tid: 205779) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14ff785ed454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14ff785ea400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14ff785ea529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14ff74615133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14ff785e38aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14ff7817e962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14ff79e11295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14ff79e2f870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14ff7a0cb796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14ff7a164d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15140e803e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x564fb5bab0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x564fb5beaa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x564fb5beebdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x564fb5c1c258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x564fb5bab6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x564fb5c2de63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15140d94c29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x564fb5b9c46a] -================================= -==== backtrace (tid: 209059) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1540f88db454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1540f88d8400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1540f88d8529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1540f4903133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1540f88d18aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1540f846c962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1540fa0ff295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1540fa11d870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1540fa3b9796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1540fa452d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14ff79bc7e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x561b4a0e80f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x561b4a127a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x561b4a12bbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x561b4a159258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x561b4a0e86c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x561b4a16ae63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14ff78d1029d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x561b4a0d946a] -================================= -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1540f9eb5e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5642738280f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x564273867a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56427386bbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x564273899258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5642738286c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5642738aae63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1540f8ffe29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56427381946a] -================================= -[nid002618:161418:0:161418] ud_ep.c:255 Fatal: UD endpoint 0x559dd8860320 to : unhandled timeout error -==== backtrace (tid: 161418) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x147c61c90454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x147c61c8d400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x147c61c8d529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x147c5dcb8133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x147c61c868aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x147c61821962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x147c634b4295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x147c634d2870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x147c6376e796] -[nid002628:135737:0:135737] ud_ep.c:255 Fatal: UD endpoint 0x559bcf42af20 to : unhandled timeout error -==== backtrace (tid: 135737) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14d9dc06f454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14d9dc06c400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14d9dc06c529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14d9d8097133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14d9dc0658aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14d9dbc00962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14d9dd893295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14d9dd8b1870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14d9ddb4d796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14d9ddbe6d35] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x147c63807d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x147c6326ae9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559dd81db0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559dd821aa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559dd821ebdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559dd824c258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559dd81db6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559dd825de63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x147c623b329d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559dd81cc46a] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14d9dd649e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559bcecfb0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559bced3aa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559bced3ebdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559bced6c258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559bcecfb6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559bced7de63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14d9dc79229d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559bcecec46a] -================================= -================================= -[nid001954:89828:0:89828] ud_ep.c:255 Fatal: UD endpoint 0x55e9be5e6ec0 to : unhandled timeout error -==== backtrace (tid: 89828) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146b0e3f3454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146b0e3f0400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146b0e3f0529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x146b0a41b133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146b0e3e98aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146b0df84962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x146b0fc17295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146b0fc35870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146b0fed1796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146b0ff6ad35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146b0f9cde9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55e9bdec80f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55e9bdf07a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55e9bdf0bbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55e9bdf39258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55e9bdec86c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55e9bdf4ae63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x146b0eb1629d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55e9bdeb946a] -[nid001711:229174:0:229174] ud_ep.c:255 Fatal: UD endpoint 0x561ad2781f80 to : unhandled timeout error -================================= -[nid002357:138931:0:138931] ud_ep.c:255 Fatal: UD endpoint 0x5562410e2f10 to : unhandled timeout error -==== backtrace (tid: 229174) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14e5daa45454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14e5daa42400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14e5daa42529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14e5d6a6d133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14e5daa3b8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14e5da5d6962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14e5dc269295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14e5dc287870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14e5dc523796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14e5dc5bcd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14e5dc01fe9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x561ad20cc0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x561ad210ba81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x561ad210fbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x561ad213d258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x561ad20cc6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x561ad214ee63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14e5db16829d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x561ad20bd46a] -================================= -==== backtrace (tid: 138931) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x148502868454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x148502865400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x148502865529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1484fe890133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14850285e8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1485023f9962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14850408c295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1485040aa870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x148504346796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1485043dfd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x148503e42e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5562408c70f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x556240906a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55624090abdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x556240938258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5562408c76c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x556240949e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x148502f8b29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5562408b846a] -================================= -[nid002337:209053:0:209053] ud_ep.c:255 Fatal: UD endpoint 0x55b458081a10 to : unhandled timeout error -[nid003489:131583:0:131583] ud_ep.c:255 Fatal: UD endpoint 0x55c48a0f3680 to : unhandled timeout error -==== backtrace (tid: 131583) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14a5eab14454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14a5eab11400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14a5eab11529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14a5e6b3c133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14a5eab0a8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14a5ea6a5962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14a5ec338295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14a5ec356870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14a5ec5f2796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14a5ec68bd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14a5ec0eee9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55c489a8f0f5] -==== backtrace (tid: 209053) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146517f6a454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146517f67400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146517f67529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x146513f92133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146517f608aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146517afb962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14651978e295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1465197ac870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146519a48796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146519ae1d35] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55c489acea81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55c489ad2bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55c489b00258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55c489a8f6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55c489b11e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14a5eb23729d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55c489a8046a] -================================= -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146519544e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55b45776e0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55b4577ada81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55b4577b1bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55b4577df258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55b45776e6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55b4577f0e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14651868d29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55b45775f46a] -================================= -[nid002337:209051:0:209051] ud_ep.c:255 Fatal: UD endpoint 0x5575e4508810 to : unhandled timeout error -==== backtrace (tid: 209051) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x152de237e454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x152de237b400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x152de237b529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x152dde3a6133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x152de23748aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x152de1f0f962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x152de3ba2295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x152de3bc0870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x152de3e5c796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x152de3ef5d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x152de3958e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5575e3bf50f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5575e3c34a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5575e3c38bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5575e3c66258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5575e3bf56c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5575e3c77e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x152de2aa129d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5575e3be646a] -================================= -[nid002337:209048:0:209048] ud_ep.c:255 Fatal: UD endpoint 0x55ff9c162e60 to : unhandled timeout error -==== backtrace (tid: 209048) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x148a5b0d0454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x148a5b0cd400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x148a5b0cd529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x148a570f8133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x148a5b0c68aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x148a5ac61962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x148a5c8f4295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x148a5c912870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x148a5cbae796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x148a5cc47d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x148a5c6aae9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ff9b84f0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ff9b88ea81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ff9b892bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ff9b8c0258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ff9b84f6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ff9b8d1e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x148a5b7f329d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ff9b84046a] -================================= -[nid002337:209060:0:209060] ud_ep.c:255 Fatal: UD endpoint 0x55d48ed6c490 to : unhandled timeout error -==== backtrace (tid: 209060) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14847d413454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14847d410400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14847d410529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14847943b133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14847d4098aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14847cfa4962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14847ec37295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14847ec55870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14847eef1796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14847ef8ad35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14847e9ede9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55d48e4910f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55d48e4d0a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55d48e4d4bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55d48e502258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55d48e4916c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55d48e513e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14847db3629d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55d48e48246a] -================================= -[nid002403:117134:0:117134] ud_ep.c:255 Fatal: UD endpoint 0x5584d7d83410 to : unhandled timeout error -==== backtrace (tid: 117134) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1514cc6da454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1514cc6d7400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1514cc6d7529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1514c8702133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1514cc6d08aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1514cc26b962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1514cdefe295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1514cdf1c870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1514ce1b8796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1514ce251d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1514cdcb4e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5584d75ec0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5584d762ba81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5584d762fbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5584d765d258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5584d75ec6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5584d766ee63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1514ccdfd29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5584d75dd46a] -================================= -[nid004481:169532:0:169532] ud_ep.c:255 Fatal: UD endpoint 0x5589c4b7ad20 to : unhandled timeout error -==== backtrace (tid: 169532) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14e440ce4454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14e440ce1400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14e440ce1529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14e43cd0c133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14e440cda8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14e440875962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14e442508295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14e442526870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14e4427c2796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14e44285bd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14e4422bee9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5589c446a0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5589c44a9a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5589c44adbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5589c44db258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5589c446a6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5589c44ece63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14e44140729d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5589c445b46a] -================================= -[nid002337:209057:0:209057] ud_ep.c:255 Fatal: UD endpoint 0x561d790a94b0 to : unhandled timeout error -[nid002607:5712 :0:5712] ud_ep.c:255 Fatal: UD endpoint 0x56416bb37b10 to : unhandled timeout error -==== backtrace (tid: 209057) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x148ec9a14454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x148ec9a11400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x148ec9a11529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x148ec5a3c133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x148ec9a0a8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x148ec95a5962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x148ecb238295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x148ecb256870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x148ecb4f2796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x148ecb58bd35] -[nid002607:5714 :0:5714] ud_ep.c:255 Fatal: UD endpoint 0x555a8b9a6b10 to : unhandled timeout error -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x148ecafeee9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x561d787cf0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x561d7880ea81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x561d78812bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x561d78840258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x561d787cf6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x561d78851e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x148eca13729d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x561d787c046a] -================================= -==== backtrace (tid: 5712) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14e4d7e57454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14e4d7e54400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14e4d7e54529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14e4d3e7f133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14e4d7e4d8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14e4d79e8962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14e4d967b295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14e4d9699870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14e4d9935796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14e4d99ced35] -[nid002337:209052:0:209052] ud_ep.c:255 Fatal: UD endpoint 0x55f051daafd0 to : unhandled timeout error -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14e4d9431e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56416b3720f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x56416b3b1a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56416b3b5bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56416b3e3258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56416b3726c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56416b3f4e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14e4d857a29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56416b36346a] -================================= -==== backtrace (tid: 5714) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x147f4b4ab454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x147f4b4a8400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x147f4b4a8529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x147f474d3133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x147f4b4a18aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x147f4b03c962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x147f4cccf295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x147f4cced870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x147f4cf89796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x147f4d022d35] -==== backtrace (tid: 209052) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x147520988454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x147520985400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x147520985529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14751c9b0133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14752097e8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x147520519962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1475221ac295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1475221ca870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x147522466796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1475224ffd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x147f4ca85e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x555a8b1e10f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x555a8b220a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x555a8b224bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x555a8b252258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x555a8b1e16c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x555a8b263e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x147f4bbce29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x555a8b1d246a] -================================= -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x147521f62e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55f0514970f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55f0514d6a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55f0514dabdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55f051508258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55f0514976c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55f051519e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1475210ab29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55f05148846a] -================================= -[nid004481:169533:0:169533] ud_ep.c:255 Fatal: UD endpoint 0x5582002bed20 to : unhandled timeout error -[nid002337:209056:0:209056] ud_ep.c:255 Fatal: UD endpoint 0x55ca9d3ac030 to : unhandled timeout error -[nid002357:138933:0:138933] ud_ep.c:255 Fatal: UD endpoint 0x55b16a50bf10 to : unhandled timeout error -==== backtrace (tid: 169533) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15032c81b454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15032c818400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15032c818529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x150328843133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15032c8118aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15032c3ac962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15032e03f295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15032e05d870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15032e2f9796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15032e392d35] -[nid002357:138932:0:138932] ud_ep.c:255 Fatal: UD endpoint 0x55c3ffa9ef10 to : unhandled timeout error -==== backtrace (tid: 209056) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14c61b8c3454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14c61b8c0400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14c61b8c0529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14c6178eb133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14c61b8b98aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14c61b454962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14c61d0e7295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14c61d105870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14c61d3a1796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14c61d43ad35] -[nid001911:261134:0:261134] ud_ep.c:255 Fatal: UD endpoint 0x55727dde1230 to : unhandled timeout error -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15032ddf5e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5581ffbae0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5581ffbeda81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5581ffbf1bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5581ffc1f258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5581ffbae6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5581ffc30e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15032cf3e29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5581ffb9f46a] -================================= -==== backtrace (tid: 138933) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14c7b676a454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14c7b6767400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14c7b6767529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14c7b2792133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14c7b67608aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14c7b62fb962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14c7b7f8e295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14c7b7fac870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14c7b8248796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14c7b82e1d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14c61ce9de9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ca9cad20f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ca9cb11a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ca9cb15bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ca9cb43258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ca9cad26c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ca9cb54e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14c61bfe629d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ca9cac346a] -================================= -[nid002607:5718 :0:5718] ud_ep.c:255 Fatal: UD endpoint 0x55c8a5163b10 to : unhandled timeout error -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14c7b7d44e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55b169cf00f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55b169d2fa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55b169d33bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55b169d61258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55b169cf06c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55b169d72e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14c7b6e8d29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55b169ce146a] -================================= -[nid001871:180893:0:180893] ud_ep.c:255 Fatal: UD endpoint 0x55c4354848d0 to : unhandled timeout error -==== backtrace (tid: 180893) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14e588e37454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14e588e34400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14e588e34529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14e584e5f133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14e588e2d8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14e5889c8962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14e58a65b295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14e58a679870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14e58a915796] -==== backtrace (tid: 138932) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14dc03b7b454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14dc03b78400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14dc03b78529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14dbffba3133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14dc03b718aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14dc0370c962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14dc0539f295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14dc053bd870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14dc05659796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14dc056f2d35] -==== backtrace (tid: 261134) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14d2fe9d1454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14d2fe9ce400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14d2fe9ce529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14d2fa9f9133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14d2fe9c78aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14d2fe562962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14d3001f5295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14d300213870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14d3004af796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14d300548d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14dc05155e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55c3ff2830f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55c3ff2c2a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55c3ff2c6bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55c3ff2f4258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55c3ff2836c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55c3ff305e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14dc0429e29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55c3ff27446a] -================================= -==== backtrace (tid: 5718) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14c4970d7454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14c4970d4400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14c4970d4529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14c4930ff133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14c4970cd8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14c496c68962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14c4988fb295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14c498919870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14c498bb5796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14c498c4ed35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14d2fffabe9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55727d66d0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55727d6aca81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55727d6b0bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55727d6de258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55727d66d6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55727d6efe63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14d2ff0f429d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55727d65e46a] -================================= - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14e58a9aed35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14e58a411e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55c434d6c0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55c434daba81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55c434dafbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55c434ddd258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55c434d6c6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55c434deee63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14e58955a29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55c434d5d46a] -================================= -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14c4986b1e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55c8a499e0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55c8a49dda81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55c8a49e1bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55c8a4a0f258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55c8a499e6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55c8a4a20e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14c4977fa29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55c8a498f46a] -================================= -[nid004481:169535:0:169535] ud_ep.c:255 Fatal: UD endpoint 0x55bf56395d20 to : unhandled timeout error -[nid002337:209047:0:209047] ud_ep.c:255 Fatal: UD endpoint 0x55a51e9ceb80 to : unhandled timeout error -==== backtrace (tid: 169535) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14bbe0e00454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14bbe0dfd400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14bbe0dfd529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14bbdce28133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14bbe0df68aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14bbe0991962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14bbe2624295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14bbe2642870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14bbe28de796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14bbe2977d35] -==== backtrace (tid: 209047) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146f42cbf454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146f42cbc400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146f42cbc529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x146f3ece7133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146f42cb58aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146f42850962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x146f444e3295] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14bbe23dae9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55bf55c850f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55bf55cc4a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55bf55cc8bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55bf55cf6258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55bf55c856c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55bf55d07e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14bbe152329d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55bf55c7646a] -================================= - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146f44501870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146f4479d796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146f44836d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146f44299e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55a51e0bb0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55a51e0faa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55a51e0febdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55a51e12c258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55a51e0bb6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55a51e13de63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x146f433e229d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55a51e0ac46a] -================================= -[nid002337:209061:0:209061] ud_ep.c:255 Fatal: UD endpoint 0x56438a80a070 to : unhandled timeout error -==== backtrace (tid: 209061) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x147fb2279454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x147fb2276400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x147fb2276529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x147fae2a1133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x147fb226f8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x147fb1e0a962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x147fb3a9d295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x147fb3abb870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x147fb3d57796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x147fb3df0d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x147fb3853e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x564389f2f0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x564389f6ea81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x564389f72bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x564389fa0258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x564389f2f6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x564389fb1e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x147fb299c29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x564389f2046a] -================================= -[nid004481:169531:0:169531] ud_ep.c:255 Fatal: UD endpoint 0x562419ee8d20 to : unhandled timeout error -[nid002357:138935:0:138935] ud_ep.c:255 Fatal: UD endpoint 0x55634238bf10 to : unhandled timeout error -==== backtrace (tid: 169531) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14d8094e9454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14d8094e6400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14d8094e6529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14d805511133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14d8094df8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14d80907a962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14d80ad0d295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14d80ad2b870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14d80afc7796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14d80b060d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14d80aac3e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5624197d80f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x562419817a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56241981bbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x562419849258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5624197d86c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56241985ae63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14d809c0c29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5624197c946a] -================================= -==== backtrace (tid: 138935) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14b84ac7b454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14b84ac78400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14b84ac78529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14b846ca3133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14b84ac718aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14b84a80c962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14b84c49f295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14b84c4bd870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14b84c759796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14b84c7f2d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14b84c255e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x556341b700f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x556341bafa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x556341bb3bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x556341be1258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x556341b706c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x556341bf2e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14b84b39e29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x556341b6146a] -================================= -[nid004830:51301:0:51301] ud_ep.c:255 Fatal: UD endpoint 0x56511ab645f0 to : unhandled timeout error -==== backtrace (tid: 51301) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x147d979d0454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x147d979cd400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x147d979cd529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x147d939f8133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x147d979c68aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x147d97561962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x147d991f4295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x147d99212870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x147d994ae796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x147d99547d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x147d98faae9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56511a36c0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x56511a3aba81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56511a3afbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56511a3dd258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56511a36c6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56511a3eee63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x147d980f329d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56511a35d46a] -================================= -[nid004481:169536:0:169536] ud_ep.c:255 Fatal: UD endpoint 0x563d5ece3d20 to : unhandled timeout error -==== backtrace (tid: 169536) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14aab712d454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14aab712a400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14aab712a529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14aab3155133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14aab71238aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14aab6cbe962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14aab8951295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14aab896f870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14aab8c0b796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14aab8ca4d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14aab8707e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x563d5e5d30f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x563d5e612a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x563d5e616bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x563d5e644258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x563d5e5d36c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x563d5e655e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14aab785029d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x563d5e5c446a] -================================= -[nid002357:138936:0:138936] ud_ep.c:255 Fatal: UD endpoint 0x55c897c86f10 to : unhandled timeout error -==== backtrace (tid: 138936) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x151ad82d7454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x151ad82d4400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x151ad82d4529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x151ad42ff133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x151ad82cd8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x151ad7e68962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x151ad9afb295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x151ad9b19870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x151ad9db5796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x151ad9e4ed35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x151ad98b1e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55c89746b0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55c8974aaa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55c8974aebdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55c8974dc258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55c89746b6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55c8974ede63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x151ad89fa29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55c89745c46a] -================================= -[nid004481:169537:0:169537] ud_ep.c:255 Fatal: UD endpoint 0x5593c118fd20 to : unhandled timeout error -==== backtrace (tid: 169537) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1505f743d454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1505f743a400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1505f743a529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1505f3465133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1505f74338aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1505f6fce962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1505f8c61295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1505f8c7f870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1505f8f1b796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1505f8fb4d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1505f8a17e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5593c0a7f0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5593c0abea81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5593c0ac2bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5593c0af0258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5593c0a7f6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5593c0b01e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1505f7b6029d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5593c0a7046a] -================================= -[nid002403:117140:0:117140] ud_ep.c:255 Fatal: UD endpoint 0x56005edf6410 to : unhandled timeout error -==== backtrace (tid: 117140) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1537f9b24454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1537f9b21400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1537f9b21529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1537f5b4c133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1537f9b1a8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1537f96b5962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1537fb348295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1537fb366870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1537fb602796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1537fb69bd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1537fb0fee9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56005e65f0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x56005e69ea81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56005e6a2bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56005e6d0258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56005e65f6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56005e6e1e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1537fa24729d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56005e65046a] -================================= -[nid001871:180896:0:180896] ud_ep.c:255 Fatal: UD endpoint 0x562ee3be72f0 to : unhandled timeout error -==== backtrace (tid: 180896) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146b8a2ff454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146b8a2fc400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146b8a2fc529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x146b86327133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146b8a2f58aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146b89e90962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x146b8bb23295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146b8bb41870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146b8bddd796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146b8be76d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146b8b8d9e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x562ee34fe0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x562ee353da81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x562ee3541bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x562ee356f258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x562ee34fe6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x562ee3580e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x146b8aa2229d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x562ee34ef46a] -================================= -[nid001463:252760:0:252760] ud_ep.c:255 Fatal: UD endpoint 0x5631e67fe510 to : unhandled timeout error -==== backtrace (tid: 252760) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14dd07648454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14dd07645400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14dd07645529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14dd03670133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14dd0763e8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14dd071d9962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14dd08e6c295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14dd08e8a870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14dd09126796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14dd091bfd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14dd08c22e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5631e62100f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5631e624fa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5631e6253bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5631e6281258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5631e62106c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5631e6292e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14dd07d6b29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5631e620146a] -================================= -[nid001912:205776:0:205776] ud_ep.c:255 Fatal: UD endpoint 0x5643afbf96b0 to : unhandled timeout error -==== backtrace (tid: 205776) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14f552470454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14f55246d400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14f55246d529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14f54e498133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14f5524668aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14f552001962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14f553c94295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14f553cb2870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14f553f4e796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14f553fe7d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14f553a4ae9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5643af4370f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5643af476a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5643af47abdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5643af4a8258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5643af4376c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5643af4b9e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14f552b9329d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5643af42846a] -================================= -[nid002337:209055:0:209055] ud_ep.c:255 Fatal: UD endpoint 0x55733bca1c70 to : unhandled timeout error -[nid002628:135736:0:135736] ud_ep.c:255 Fatal: UD endpoint 0x55ac25952f20 to : unhandled timeout error -==== backtrace (tid: 135736) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146b6e7a6454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146b6e7a3400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146b6e7a3529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x146b6a7ce133] -==== backtrace (tid: 209055) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146e8012f454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146e8012c400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146e8012c529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x146e7c157133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146e801258aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146e7fcc0962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x146e81953295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146e81971870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146e81c0d796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146e81ca6d35] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146b6e79c8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146b6e337962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x146b6ffca295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146b6ffe8870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146b70284796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146b7031dd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146b6fd80e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ac252230f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ac25262a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ac25266bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ac25294258] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146e81709e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55733b3c70f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55733b406a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55733b40abdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55733b438258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55733b3c76c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55733b449e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x146e8085229d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55733b3b846a] -================================= -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ac252236c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ac252a5e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x146b6eec929d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ac2521446a] -================================= -[nid001711:229172:0:229172] ud_ep.c:255 Fatal: UD endpoint 0x55f82fe014a0 to : unhandled timeout error -==== backtrace (tid: 229172) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14d94dce7454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14d94dce4400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14d94dce4529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14d949d0f133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14d94dcdd8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14d94d878962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14d94f50b295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14d94f529870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14d94f7c5796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14d94f85ed35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14d94f2c1e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55f82f7450f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55f82f784a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55f82f788bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55f82f7b6258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55f82f7456c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55f82f7c7e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14d94e40a29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55f82f73646a] -================================= -[nid002607:5711 :0:5711] ud_ep.c:255 Fatal: UD endpoint 0x563c55dd4b10 to : unhandled timeout error -==== backtrace (tid: 5711) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14cfdce83454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14cfdce80400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14cfdce80529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14cfd8eab133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14cfdce798aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14cfdca14962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14cfde6a7295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14cfde6c5870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14cfde961796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14cfde9fad35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14cfde45de9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x563c5560f0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x563c5564ea81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x563c55652bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x563c55680258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x563c5560f6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x563c55691e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14cfdd5a629d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x563c5560046a] -================================= -[nid001950:100692:0:100692] ud_ep.c:255 Fatal: UD endpoint 0x55ba3a577340 to : unhandled timeout error -==== backtrace (tid: 100692) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x150d470c0454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x150d470bd400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x150d470bd529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x150d430e8133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x150d470b68aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x150d46c51962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x150d488e4295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x150d48902870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x150d48b9e796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x150d48c37d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x150d4869ae9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ba39e720f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ba39eb1a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ba39eb5bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ba39ee3258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ba39e726c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ba39ef4e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x150d477e329d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ba39e6346a] -================================= -[nid001664:168288:0:168288] ud_ep.c:255 Fatal: UD endpoint 0x555b7ba72a80 to : unhandled timeout error -==== backtrace (tid: 168288) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14a0e4d35454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14a0e4d32400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14a0e4d32529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14a0e0d5d133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14a0e4d2b8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14a0e48c6962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14a0e6559295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14a0e6577870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14a0e6813796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14a0e68acd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14a0e630fe9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x555b7b3dc0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x555b7b41ba81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x555b7b41fbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x555b7b44d258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x555b7b3dc6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x555b7b45ee63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14a0e545829d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x555b7b3cd46a] -================================= -[nid003264:86501:0:86501] ud_ep.c:255 Fatal: UD endpoint 0x55c7e55a1e00 to : unhandled timeout error -==== backtrace (tid: 86501) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x152efb32b454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x152efb328400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x152efb328529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x152ef7353133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x152efb3218aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x152efaebc962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x152efcb4f295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x152efcb6d870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x152efce09796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x152efcea2d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x152efc905e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55c7e4d680f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55c7e4da7a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55c7e4dabbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55c7e4dd9258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55c7e4d686c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55c7e4deae63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x152efba4e29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55c7e4d5946a] -================================= -[nid001920:170320:0:170320] ud_ep.c:255 Fatal: UD endpoint 0x559e56a0f730 to : unhandled timeout error -==== backtrace (tid: 170320) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1500b3893454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1500b3890400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1500b3890529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1500af8bb133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1500b38898aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1500b3424962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1500b50b7295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1500b50d5870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1500b5371796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1500b540ad35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1500b4e6de9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559e562dc0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559e5631ba81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559e5631fbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559e5634d258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559e562dc6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559e5635ee63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1500b3fb629d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559e562cd46a] -================================= -[nid004752:169182:0:169182] ud_ep.c:255 Fatal: UD endpoint 0x556382fe6be0 to : unhandled timeout error -==== backtrace (tid: 169182) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14eb4ef08454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14eb4ef05400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14eb4ef05529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14eb4af30133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14eb4eefe8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14eb4ea99962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14eb5072c295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14eb5074a870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14eb509e6796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14eb50a7fd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14eb504e2e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5563829470f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x556382986a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55638298abdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5563829b8258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5563829476c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5563829c9e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14eb4f62b29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55638293846a] -================================= -[nid004480:176447:0:176447] ud_ep.c:255 Fatal: UD endpoint 0x556cae00b030 to : unhandled timeout error -==== backtrace (tid: 176447) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14fcce305454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14fcce302400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14fcce302529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14fcca32d133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14fcce2fb8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14fccde96962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14fccfb29295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14fccfb47870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14fccfde3796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14fccfe7cd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14fccf8dfe9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x556cad6e80f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x556cad727a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x556cad72bbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x556cad759258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x556cad6e86c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x556cad76ae63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14fccea2829d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x556cad6d946a] -================================= -[nid004480:176452:0:176452] ud_ep.c:255 Fatal: UD endpoint 0x5642900ea030 to : unhandled timeout error -==== backtrace (tid: 176452) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1486c5e2c454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1486c5e29400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1486c5e29529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1486c1e54133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1486c5e228aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1486c59bd962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1486c7650295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1486c766e870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1486c790a796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1486c79a3d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1486c7406e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56428f7c70f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x56428f806a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56428f80abdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56428f838258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56428f7c76c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56428f849e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1486c654f29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56428f7b846a] -================================= -[nid001711:229170:0:229170] ud_ep.c:255 Fatal: UD endpoint 0x562568553c00 to : unhandled timeout error -[nid001664:168292:0:168292] ud_ep.c:255 Fatal: UD endpoint 0x559a7f9f52a0 to : unhandled timeout error -==== backtrace (tid: 229170) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x152add326454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x152add323400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x152add323529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x152ad934e133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x152add31c8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x152adceb7962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x152adeb4a295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x152adeb68870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x152adee04796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x152adee9dd35] -[nid004480:176453:0:176453] ud_ep.c:255 Fatal: UD endpoint 0x56007d903030 to : unhandled timeout error -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x152ade900e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x562567e960f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x562567ed5a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x562567ed9bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x562567f07258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x562567e966c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x562567f18e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x152adda4929d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x562567e8746a] -================================= -==== backtrace (tid: 176453) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1505c3235454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1505c3232400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1505c3232529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1505bf25d133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1505c322b8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1505c2dc6962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1505c4a59295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1505c4a77870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1505c4d13796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1505c4dacd35] -==== backtrace (tid: 168292) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15052a67b454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15052a678400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15052a678529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1505266a3133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15052a6718aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15052a20c962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15052be9f295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15052bebd870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15052c159796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15052c1f2d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1505c480fe9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56007cfe00f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x56007d01fa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56007d023bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56007d051258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56007cfe06c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56007d062e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1505c395829d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56007cfd146a] -================================= -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15052bc55e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559a7f3bc0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559a7f3fba81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559a7f3ffbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559a7f42d258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559a7f3bc6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559a7f43ee63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15052ad9e29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559a7f3ad46a] -================================= -[nid001858:72023:0:72023] ud_ep.c:255 Fatal: UD endpoint 0x55a58efa8d60 to : unhandled timeout error -==== backtrace (tid: 72023) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x148754515454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x148754512400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x148754512529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14875053d133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14875450b8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1487540a6962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x148755d39295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x148755d57870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x148755ff3796] -[nid002337:209054:0:209054] ud_ep.c:255 Fatal: UD endpoint 0x55a05b557d00 to : unhandled timeout error - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14875608cd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x148755aefe9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55a58e8620f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55a58e8a1a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55a58e8a5bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55a58e8d3258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55a58e8626c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55a58e8e4e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x148754c3829d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55a58e85346a] -================================= -==== backtrace (tid: 209054) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x145795c87454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x145795c84400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x145795c84529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x145791caf133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x145795c7d8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x145795818962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1457974ab295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1457974c9870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x145797765796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1457977fed35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x145797261e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55a05ac7d0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55a05acbca81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55a05acc0bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55a05acee258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55a05ac7d6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55a05acffe63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1457963aa29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55a05ac6e46a] -================================= -[nid004481:169530:0:169530] ud_ep.c:255 Fatal: UD endpoint 0x55cb95847d20 to : unhandled timeout error -==== backtrace (tid: 169530) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15348bb12454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15348bb0f400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15348bb0f529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x153487b3a133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15348bb088aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15348b6a3962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15348d336295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15348d354870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15348d5f0796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15348d689d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15348d0ece9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55cb951370f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55cb95176a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55cb9517abdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55cb951a8258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55cb951376c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55cb951b9e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15348c23529d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55cb9512846a] -================================= -[nid004480:176448:0:176448] ud_ep.c:255 Fatal: UD endpoint 0x55e0a5343030 to : unhandled timeout error -==== backtrace (tid: 176448) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x153ca0643454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x153ca0640400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x153ca0640529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x153c9c66b133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x153ca06398aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x153ca01d4962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x153ca1e67295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x153ca1e85870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x153ca2121796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x153ca21bad35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x153ca1c1de9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55e0a4a200f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55e0a4a5fa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55e0a4a63bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55e0a4a91258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55e0a4a206c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55e0a4aa2e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x153ca0d6629d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55e0a4a1146a] -================================= -[nid004782:158186:0:158186] ud_ep.c:255 Fatal: UD endpoint 0x5564c1be2ef0 to : unhandled timeout error -==== backtrace (tid: 158186) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14c3eb727454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14c3eb724400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14c3eb724529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14c3e774f133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14c3eb71d8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14c3eb2b8962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14c3ecf4b295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14c3ecf69870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14c3ed205796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14c3ed29ed35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14c3ecd01e9a] -[nid002264:48359:0:48359] ud_ep.c:255 Fatal: UD endpoint 0x55f5fae6efc0 to : unhandled timeout error -==== backtrace (tid: 48359) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1464779b0454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1464779ad400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1464779ad529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1464739d8133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1464779a68aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146477541962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1464791d4295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1464791f2870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14647948e796] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5564c14230f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5564c1462a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5564c1466bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5564c1494258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5564c14236c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5564c14a5e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14c3ebe4a29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5564c141446a] -================================= -[nid004480:176450:0:176450] ud_ep.c:255 Fatal: UD endpoint 0x559a722fa030 to : unhandled timeout error - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146479527d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146478f8ae9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55f5fa5720f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55f5fa5b1a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55f5fa5b5bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55f5fa5e3258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55f5fa5726c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55f5fa5f4e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1464780d329d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55f5fa56346a] -================================= -==== backtrace (tid: 176450) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14f2273a4454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14f2273a1400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14f2273a1529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14f2233cc133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14f22739a8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14f226f35962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14f228bc8295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14f228be6870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14f228e82796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14f228f1bd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14f22897ee9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559a719d70f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559a71a16a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559a71a1abdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559a71a48258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559a719d76c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559a71a59e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14f227ac729d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559a719c846a] -================================= -[nid003479:217130:0:217130] ud_ep.c:255 Fatal: UD endpoint 0x55a77756c830 to : unhandled timeout error -==== backtrace (tid: 217130) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1530d5b93454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1530d5b90400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1530d5b90529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1530d1bbb133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1530d5b898aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1530d5724962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1530d73b7295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1530d73d5870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1530d7671796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1530d770ad35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1530d716de9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55a776dbd0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55a776dfca81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55a776e00bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55a776e2e258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55a776dbd6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55a776e3fe63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1530d62b629d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55a776dae46a] -================================= -[nid004480:176449:0:176449] ud_ep.c:255 Fatal: UD endpoint 0x559b70482030 to : unhandled timeout error -==== backtrace (tid: 176449) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14b44fdef454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14b44fdec400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14b44fdec529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14b44be17133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14b44fde58aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14b44f980962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14b451613295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14b451631870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14b4518cd796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14b451966d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14b4513c9e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559b6fb5f0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559b6fb9ea81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559b6fba2bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559b6fbd0258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559b6fb5f6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559b6fbe1e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14b45051229d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559b6fb5046a] -================================= -[nid004480:176451:0:176451] ud_ep.c:255 Fatal: UD endpoint 0x5626cc694030 to : unhandled timeout error -==== backtrace (tid: 176451) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14a6f2b72454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14a6f2b6f400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14a6f2b6f529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14a6eeb9a133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14a6f2b688aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14a6f2703962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14a6f4396295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14a6f43b4870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14a6f4650796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14a6f46e9d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14a6f414ce9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5626cbd710f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5626cbdb0a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5626cbdb4bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5626cbde2258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5626cbd716c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5626cbdf3e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14a6f329529d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5626cbd6246a] -================================= -[nid002357:138930:0:138930] ud_ep.c:255 Fatal: UD endpoint 0x5566b19f3f10 to : unhandled timeout error -[nid001711:229173:0:229173] ud_ep.c:255 Fatal: UD endpoint 0x55e95bedf4c0 to : unhandled timeout error -==== backtrace (tid: 138930) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1507cdbce454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1507cdbcb400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1507cdbcb529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1507c9bf6133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1507cdbc48aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1507cd75f962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1507cf3f2295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1507cf410870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1507cf6ac796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1507cf745d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1507cf1a8e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5566b11d80f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5566b1217a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5566b121bbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5566b1249258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5566b11d86c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5566b125ae63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1507ce2f129d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5566b11c946a] -================================= -==== backtrace (tid: 229173) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1465a1168454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1465a1165400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1465a1165529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14659d190133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1465a115e8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1465a0cf9962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1465a298c295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1465a29aa870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1465a2c46796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1465a2cdfd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1465a2742e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55e95b82b0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55e95b86aa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55e95b86ebdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55e95b89c258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55e95b82b6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55e95b8ade63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1465a188b29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55e95b81c46a] -================================= -[nid004483:58045:0:58045] ud_ep.c:255 Fatal: UD endpoint 0x560d0bfb6230 to : unhandled timeout error -==== backtrace (tid: 58045) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x152688505454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x152688502400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x152688502529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x15268452d133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1526884fb8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x152688096962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x152689d29295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x152689d47870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x152689fe3796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15268a07cd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x152689adfe9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x560d0b6b90f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x560d0b6f8a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x560d0b6fcbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x560d0b72a258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x560d0b6b96c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x560d0b73be63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x152688c2829d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x560d0b6aa46a] -================================= -[nid002607:5715 :0:5715] ud_ep.c:255 Fatal: UD endpoint 0x555efc670b10 to : unhandled timeout error -==== backtrace (tid: 5715) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14bdee4d6454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14bdee4d3400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14bdee4d3529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14bdea4fe133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14bdee4cc8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14bdee067962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14bdefcfa295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14bdefd18870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14bdeffb4796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14bdf004dd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14bdefab0e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x555efbeab0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x555efbeeaa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x555efbeeebdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x555efbf1c258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x555efbeab6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x555efbf2de63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14bdeebf929d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x555efbe9c46a] -================================= -[nid004483:58050:0:58050] ud_ep.c:255 Fatal: UD endpoint 0x55849979a230 to : unhandled timeout error -==== backtrace (tid: 58050) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15198c31f454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15198c31c400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15198c31c529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x151988347133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15198c3158aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15198beb0962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15198db43295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15198db61870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15198ddfd796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15198de96d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15198d8f9e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x558498e9d0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x558498edca81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x558498ee0bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x558498f0e258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x558498e9d6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x558498f1fe63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15198ca4229d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x558498e8e46a] -================================= -[nid001656:201191:0:201191] ud_ep.c:255 Fatal: UD endpoint 0x555734192e50 to : unhandled timeout error -[nid004483:58047:0:58047] ud_ep.c:255 Fatal: UD endpoint 0x5642fa818230 to : unhandled timeout error -==== backtrace (tid: 201191) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14a9457e9454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14a9457e6400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14a9457e6529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14a941811133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14a9457df8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14a94537a962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14a94700d295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14a94702b870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14a9472c7796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14a947360d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14a946dc3e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x555733b380f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x555733b77a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x555733b7bbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x555733ba9258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x555733b386c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x555733bbae63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14a945f0c29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x555733b2946a] -================================= -==== backtrace (tid: 58047) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x150b6e83b454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x150b6e838400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x150b6e838529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x150b6a863133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x150b6e8318aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x150b6e3cc962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x150b7005f295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x150b7007d870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x150b70319796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x150b703b2d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x150b6fe15e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5642f9f1b0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5642f9f5aa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5642f9f5ebdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5642f9f8c258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5642f9f1b6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5642f9f9de63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x150b6ef5e29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5642f9f0c46a] -================================= -[nid004483:58046:0:58046] ud_ep.c:255 Fatal: UD endpoint 0x55d6dee42230 to : unhandled timeout error -==== backtrace (tid: 58046) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14e1f3b36454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14e1f3b33400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14e1f3b33529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14e1efb5e133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14e1f3b2c8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14e1f36c7962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14e1f535a295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14e1f5378870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14e1f5614796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14e1f56add35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14e1f5110e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55d6de5450f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55d6de584a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55d6de588bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55d6de5b6258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55d6de5456c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55d6de5c7e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14e1f425929d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55d6de53646a] -================================= -[nid003542:98599:0:98599] ud_ep.c:255 Fatal: UD endpoint 0x560df733ab00 to : unhandled timeout error -==== backtrace (tid: 98599) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x147835c5f454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x147835c5c400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x147835c5c529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x147831c87133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x147835c558aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1478357f0962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x147837483295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1478374a1870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14783773d796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1478377d6d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x147837239e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x560df6d610f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x560df6da0a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x560df6da4bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x560df6dd2258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x560df6d616c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x560df6de3e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14783638229d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x560df6d5246a] -================================= -[nid004481:169534:0:169534] ud_ep.c:255 Fatal: UD endpoint 0x55d376d6dd20 to : unhandled timeout error -==== backtrace (tid: 169534) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14961708b454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x149617088400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x149617088529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1496130b3133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1496170818aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x149616c1c962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1496188af295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1496188cd870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x149618b69796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x149618c02d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x149618665e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55d37665d0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55d37669ca81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55d3766a0bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55d3766ce258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55d37665d6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55d3766dfe63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1496177ae29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55d37664e46a] -================================= -[nid003497:90310:0:90310] ud_ep.c:255 Fatal: UD endpoint 0x558710e58ef0 to : unhandled timeout error -[nid006311:117413:0:117413] ud_ep.c:255 Fatal: UD endpoint 0x55c879de3c30 to : unhandled timeout error -==== backtrace (tid: 117413) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146843d78454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146843d75400] -==== backtrace (tid: 90310) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1541b03bd454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1541b03ba400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1541b03ba529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1541ac3e5133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1541b03b38aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1541aff4e962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x1541b1be1295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1541b1bff870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1541b1e9b796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1541b1f34d35] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146843d75529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14683fda0133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146843d6e8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146843909962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14684559c295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1468455ba870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146845856796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1468458efd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146845352e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55c8797e70f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55c879826a81] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1541b1997e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5587107210f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x558710760a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x558710764bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x558710792258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5587107216c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5587107a3e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x1541b0ae029d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55871071246a] -================================= -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55c87982abdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55c879858258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55c8797e76c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55c879869e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14684449b29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55c8797d846a] -================================= -[nid003497:90309:0:90309] ud_ep.c:255 Fatal: UD endpoint 0x559e475249d0 to : unhandled timeout error -==== backtrace (tid: 90309) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14fa68c23454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14fa68c20400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14fa68c20529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14fa64c4b133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14fa68c198aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14fa687b4962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14fa6a447295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14fa6a465870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14fa6a701796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14fa6a79ad35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14fa6a1fde9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559e46dec0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559e46e2ba81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559e46e2fbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559e46e5d258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559e46dec6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559e46e6ee63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14fa6934629d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559e46ddd46a] -================================= -[nid001660:125279:0:125279] ud_ep.c:255 Fatal: UD endpoint 0x5599b3a1ae30 to : unhandled timeout error -==== backtrace (tid: 125279) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x147afb2f7454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x147afb2f4400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x147afb2f4529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x147af731f133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x147afb2ed8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x147afae88962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x147afcb1b295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x147afcb39870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x147afcdd5796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x147afce6ed35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x147afc8d1e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5599b344a0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5599b3489a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5599b348dbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5599b34bb258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5599b344a6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5599b34cce63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x147afba1a29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5599b343b46a] -================================= -[nid003497:90312:0:90312] ud_ep.c:255 Fatal: UD endpoint 0x55f36bf4aef0 to : unhandled timeout error -[nid006178:72398:0:72398] ud_ep.c:255 Fatal: UD endpoint 0x55b098b30630 to : unhandled timeout error -==== backtrace (tid: 72398) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14bba308a454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14bba3087400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14bba3087529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14bb9f0b2133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14bba30808aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14bba2c1b962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14bba48ae295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14bba48cc870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14bba4b68796] -==== backtrace (tid: 90312) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146bbc6e8454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146bbc6e5400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146bbc6e5529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x146bb8710133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146bbc6de8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146bbc279962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x146bbdf0c295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146bbdf2a870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146bbe1c6796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146bbe25fd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146bbdcc2e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55f36b8130f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55f36b852a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55f36b856bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55f36b884258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55f36b8136c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55f36b895e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x146bbce0b29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55f36b80446a] -================================= - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14bba4c01d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14bba4664e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55b0983350f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55b098374a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55b098378bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55b0983a6258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55b0983356c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55b0983b7e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14bba37ad29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55b09832646a] -================================= -[nid004480:176446:0:176446] ud_ep.c:255 Fatal: UD endpoint 0x555852a78030 to : unhandled timeout error -==== backtrace (tid: 176446) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14d0c51b4454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14d0c51b1400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14d0c51b1529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14d0c11dc133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14d0c51aa8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14d0c4d45962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14d0c69d8295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14d0c69f6870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14d0c6c92796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14d0c6d2bd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14d0c678ee9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5558521550f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x555852194a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x555852198bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5558521c6258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5558521556c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5558521d7e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14d0c58d729d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55585214646a] -================================= -[nid001656:201193:0:201193] ud_ep.c:255 Fatal: UD endpoint 0x55d3802f3e40 to : unhandled timeout error -[nid001656:201195:0:201195] ud_ep.c:255 Fatal: UD endpoint 0x55b5e6e99170 to : unhandled timeout error -==== backtrace (tid: 201193) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x152e91100454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x152e910fd400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x152e910fd529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x152e8d128133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x152e910f68aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x152e90c91962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x152e92924295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x152e92942870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x152e92bde796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x152e92c77d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x152e926dae9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55d37fc8a0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55d37fcc9a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55d37fccdbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55d37fcfb258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55d37fc8a6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55d37fd0ce63] -==== backtrace (tid: 201195) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x148c7998f454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x148c7998c400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x148c7998c529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x148c759b7133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x148c799858aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x148c79520962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x148c7b1b3295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x148c7b1d1870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x148c7b46d796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x148c7b506d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x148c7af69e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55b5e68750f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55b5e68b4a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55b5e68b8bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55b5e68e6258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55b5e68756c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55b5e68f7e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x148c7a0b229d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55b5e686646a] -================================= -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x152e9182329d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55d37fc7b46a] -================================= -[nid005215:181280:0:181280] ud_ep.c:255 Fatal: UD endpoint 0x55cdaa888c00 to : unhandled timeout error -[nid003497:90314:0:90314] ud_ep.c:255 Fatal: UD endpoint 0x55b9b1e22ef0 to : unhandled timeout error -==== backtrace (tid: 181280) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x151e1a074454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x151e1a071400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x151e1a071529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x151e1609c133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x151e1a06a8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x151e19c05962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x151e1b898295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x151e1b8b6870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x151e1bb52796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x151e1bbebd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x151e1b64ee9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55cdaa1c20f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55cdaa201a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55cdaa205bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55cdaa233258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55cdaa1c26c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55cdaa244e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x151e1a79729d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55cdaa1b346a] -================================= -==== backtrace (tid: 90314) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14719c581454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14719c57e400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14719c57e529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1471985a9133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14719c5778aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14719c112962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14719dda5295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14719ddc3870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14719e05f796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14719e0f8d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14719db5be9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55b9b16eb0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55b9b172aa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55b9b172ebdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55b9b175c258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55b9b16eb6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55b9b176de63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14719cca429d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55b9b16dc46a] -================================= -[nid003497:90311:0:90311] ud_ep.c:255 Fatal: UD endpoint 0x55b26662fef0 to : unhandled timeout error -==== backtrace (tid: 90311) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x153cdcd8b454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x153cdcd88400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x153cdcd88529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x153cd8db3133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x153cdcd818aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x153cdc91c962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x153cde5af295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x153cde5cd870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x153cde869796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x153cde902d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x153cde365e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55b265ef80f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55b265f37a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55b265f3bbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55b265f69258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55b265ef86c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55b265f7ae63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x153cdd4ae29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55b265ee946a] -================================= -[nid003497:90316:0:90316] ud_ep.c:255 Fatal: UD endpoint 0x560ee5a3cef0 to : unhandled timeout error -==== backtrace (tid: 90316) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x148c707bd454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x148c707ba400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x148c707ba529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x148c6c7e5133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x148c707b38aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x148c7034e962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x148c71fe1295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x148c71fff870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x148c7229b796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x148c72334d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x148c71d97e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x560ee53050f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x560ee5344a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x560ee5348bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x560ee5376258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x560ee53056c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x560ee5387e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x148c70ee029d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x560ee52f646a] -================================= -[nid003497:90315:0:90315] ud_ep.c:255 Fatal: UD endpoint 0x5586198eeef0 to : unhandled timeout error -==== backtrace (tid: 90315) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x149bb34ad454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x149bb34aa400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x149bb34aa529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x149baf4d5133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x149bb34a38aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x149bb303e962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x149bb4cd1295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x149bb4cef870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x149bb4f8b796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x149bb5024d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x149bb4a87e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5586191b70f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5586191f6a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5586191fabdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x558619228258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5586191b76c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x558619239e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x149bb3bd029d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5586191a846a] -================================= -[nid002628:135740:0:135740] ud_ep.c:255 Fatal: UD endpoint 0x564f3341fe50 to : unhandled timeout error -==== backtrace (tid: 135740) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14b8ba99e454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14b8ba99b400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14b8ba99b529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14b8b69c6133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14b8ba9948aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14b8ba52f962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14b8bc1c2295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14b8bc1e0870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14b8bc47c796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14b8bc515d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14b8bbf78e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x564f32cf00f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x564f32d2fa81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x564f32d33bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x564f32d61258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x564f32cf06c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x564f32d72e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14b8bb0c129d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x564f32ce146a] -================================= -[nid005187:87821:0:87821] ud_ep.c:255 Fatal: UD endpoint 0x55bcff47cd90 to : unhandled timeout error -==== backtrace (tid: 87821) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14babcc7e454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14babcc7b400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14babcc7b529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14bab8ca6133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14babcc748aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14babc80f962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14babe4a2295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14babe4c0870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14babe75c796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14babe7f5d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14babe258e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55bcfeced0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55bcfed2ca81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55bcfed30bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55bcfed5e258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55bcfeced6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55bcfed6fe63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14babd3a129d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55bcfecde46a] -================================= -[nid006249:147983:0:147983] ud_ep.c:255 Fatal: UD endpoint 0x55adb6b06ea0 to : unhandled timeout error -==== backtrace (tid: 147983) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14b59b760454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14b59b75d400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14b59b75d529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14b597788133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14b59b7568aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14b59b2f1962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14b59cf84295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14b59cfa2870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14b59d23e796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14b59d2d7d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14b59cd3ae9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55adb63a10f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55adb63e0a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55adb63e4bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55adb6412258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55adb63a16c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55adb6423e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14b59be8329d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55adb639246a] -================================= -[nid003265:88961:0:88961] ud_ep.c:255 Fatal: UD endpoint 0x5628ca2f7b40 to : unhandled timeout error -==== backtrace (tid: 88961) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15229837f454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15229837c400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15229837c529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1522943a7133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1522983758aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x152297f10962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x152299ba3295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x152299bc1870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x152299e5d796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x152299ef6d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x152299959e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5628c9c710f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5628c9cb0a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5628c9cb4bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5628c9ce2258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5628c9c716c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5628c9cf3e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x152298aa229d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5628c9c6246a] -================================= -[nid001912:205780:0:205780] ud_ep.c:255 Fatal: UD endpoint 0x55861f171eb0 to : unhandled timeout error -==== backtrace (tid: 205780) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14aa899da454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14aa899d7400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14aa899d7529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14aa85a02133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14aa899d08aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14aa8956b962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14aa8b1fe295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14aa8b21c870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14aa8b4b8796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14aa8b551d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14aa8afb4e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55861e9e20f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55861ea21a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55861ea25bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55861ea53258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55861e9e26c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55861ea64e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14aa8a0fd29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55861e9d346a] -================================= -[nid002630:252991:0:252991] ud_ep.c:255 Fatal: UD endpoint 0x564ec53f0620 to : unhandled timeout error -==== backtrace (tid: 252991) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14c935702454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14c9356ff400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14c9356ff529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14c93172a133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14c9356f88aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14c935293962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14c936f26295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14c936f44870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14c9371e0796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14c937279d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14c936cdce9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x564ec4c910f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x564ec4cd0a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x564ec4cd4bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x564ec4d02258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x564ec4c916c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x564ec4d13e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14c935e2529d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x564ec4c8246a] -================================= -[nid006137:129123:0:129123] ud_ep.c:255 Fatal: UD endpoint 0x559e64d3fe60 to : unhandled timeout error -==== backtrace (tid: 129123) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14a3af8db454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14a3af8d8400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14a3af8d8529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14a3ab903133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14a3af8d18aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14a3af46c962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14a3b10ff295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14a3b111d870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14a3b13b9796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14a3b1452d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14a3b0eb5e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559e6476e0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559e647ada81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559e647b1bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559e647df258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559e6476e6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559e647f0e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14a3afffe29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559e6475f46a] -================================= -[nid002580:121315:0:121315] ud_ep.c:255 Fatal: UD endpoint 0x56257307d3a0 to : unhandled timeout error -==== backtrace (tid: 121315) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14a32e76c454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14a32e769400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14a32e769529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14a32a794133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14a32e7628aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14a32e2fd962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14a32ff90295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14a32ffae870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14a33024a796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14a3302e3d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14a32fd46e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56257278d0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5625727cca81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5625727d0bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5625727fe258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56257278d6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56257280fe63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14a32ee8f29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56257277e46a] -================================= -[nid006333:103753:0:103753] ud_ep.c:255 Fatal: UD endpoint 0x55ba22c17e20 to : unhandled timeout error -==== backtrace (tid: 103753) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14cbbada6454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14cbbada3400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14cbbada3529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14cbb6dce133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14cbbad9c8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14cbba937962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14cbbc5ca295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14cbbc5e8870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14cbbc884796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14cbbc91dd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14cbbc380e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ba225b80f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ba225f7a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ba225fbbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ba22629258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ba225b86c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ba2263ae63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14cbbb4c929d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ba225a946a] -================================= -[nid004483:58048:0:58048] ud_ep.c:255 Fatal: UD endpoint 0x55fe496fc230 to : unhandled timeout error -==== backtrace (tid: 58048) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x145dee01c454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x145dee019400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x145dee019529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x145dea044133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x145dee0128aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x145dedbad962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x145def840295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x145def85e870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x145defafa796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x145defb93d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x145def5f6e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55fe48dff0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55fe48e3ea81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55fe48e42bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55fe48e70258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55fe48dff6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55fe48e81e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x145dee73f29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55fe48df046a] -================================= -[nid005216:77378:0:77378] ud_ep.c:255 Fatal: UD endpoint 0x564fe0a0af60 to : unhandled timeout error -==== backtrace (tid: 77378) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x150274391454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15027438e400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15027438e529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1502703b9133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1502743878aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x150273f22962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x150275bb5295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x150275bd3870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x150275e6f796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x150275f08d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15027596be9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x564fe02350f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x564fe0274a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x564fe0278bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x564fe02a6258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x564fe02356c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x564fe02b7e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x150274ab429d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x564fe022646a] -================================= -[nid006179:71577:0:71577] ud_ep.c:255 Fatal: UD endpoint 0x564899de5670 to : unhandled timeout error -==== backtrace (tid: 71577) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x1487265c9454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x1487265c6400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x1487265c6529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1487225f1133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x1487265bf8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14872615a962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x148727ded295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x148727e0b870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x1487280a7796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x148728140d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x148727ba3e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5648997a90f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5648997e8a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5648997ecbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56489981a258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5648997a96c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56489982be63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x148726cec29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56489979a46a] -================================= -[nid005104:71892:0:71892] ud_ep.c:255 Fatal: UD endpoint 0x55e887b29fd0 to : unhandled timeout error -==== backtrace (tid: 71892) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14c138e31454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14c138e2e400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14c138e2e529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14c134e59133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14c138e278aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14c1389c2962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14c13a655295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14c13a673870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14c13a90f796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14c13a9a8d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14c13a40be9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55e8874690f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55e8874a8a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55e8874acbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55e8874da258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55e8874696c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55e8874ebe63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14c13955429d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55e88745a46a] -================================= -[nid006280:156831:0:156831] ud_ep.c:255 Fatal: UD endpoint 0x5562e0f56a20 to : unhandled timeout error -==== backtrace (tid: 156831) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14a9c92f3454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14a9c92f0400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14a9c92f0529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14a9c531b133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14a9c92e98aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14a9c8e84962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14a9cab17295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14a9cab35870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14a9cadd1796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14a9cae6ad35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14a9ca8cde9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5562e08c20f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5562e0901a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5562e0905bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5562e0933258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5562e08c26c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5562e0944e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14a9c9a1629d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5562e08b346a] -================================= -[nid004753:1774 :0:1774] ud_ep.c:255 Fatal: UD endpoint 0x5594c95af8b0 to : unhandled timeout error -==== backtrace (tid: 1774) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146df01d0454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146df01cd400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146df01cd529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x146dec1f8133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146df01c68aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x146defd61962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x146df19f4295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146df1a12870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146df1cae796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x146df1d47d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146df17aae9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5594c8e610f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5594c8ea0a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5594c8ea4bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5594c8ed2258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5594c8e616c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5594c8ee3e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x146df08f329d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5594c8e5246a] -================================= -[nid001924:245147:0:245147] ud_ep.c:255 Fatal: UD endpoint 0x55dd5ec27190 to : unhandled timeout error -==== backtrace (tid: 245147) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x152102017454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x152102014400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x152102014529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x1520fe03f133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15210200d8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x152101ba8962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15210383b295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x152103859870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x152103af5796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x152103b8ed35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x1521035f1e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55dd5e5150f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55dd5e554a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55dd5e558bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55dd5e586258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55dd5e5156c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55dd5e597e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15210273a29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55dd5e50646a] -================================= -[nid002337:209058:0:209058] ud_ep.c:255 Fatal: UD endpoint 0x564775ba7610 to : unhandled timeout error -==== backtrace (tid: 209058) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14f577c7b454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14f577c78400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14f577c78529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14f573ca3133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14f577c718aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14f57780c962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14f57949f295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14f5794bd870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14f579759796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14f5797f2d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14f579255e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56477548c0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5647754cba81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5647754cfbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5647754fd258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56477548c6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56477550ee63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14f57839e29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56477547d46a] -================================= -[nid006280:156837:0:156837] ud_ep.c:255 Fatal: UD endpoint 0x5565412c5380 to : unhandled timeout error -==== backtrace (tid: 156837) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14644dbf0454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14644dbed400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14644dbed529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x146449c18133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14644dbe68aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14644d781962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14644f414295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14644f432870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14644f6ce796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14644f767d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14644f1cae9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x556540b850f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x556540bc4a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x556540bc8bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x556540bf6258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x556540b856c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x556540c07e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14644e31329d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x556540b7646a] -================================= -[nid002337:209050:0:209050] ud_ep.c:255 Fatal: UD endpoint 0x565193160390 to : unhandled timeout error -[nid001911:261138:0:261138] ud_ep.c:255 Fatal: UD endpoint 0x5625e56403d0 to : unhandled timeout error -==== backtrace (tid: 209050) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14b7efbbe454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14b7efbbb400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14b7efbbb529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14b7ebbe6133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14b7efbb48aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14b7ef74f962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14b7f13e2295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14b7f1400870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14b7f169c796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14b7f1735d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14b7f1198e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x565192a480f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x565192a87a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x565192a8bbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x565192ab9258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x565192a486c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x565192acae63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14b7f02e129d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x565192a3946a] -================================= -==== backtrace (tid: 261138) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14e48d146454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14e48d143400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14e48d143529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14e48916e133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14e48d13c8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14e48ccd7962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14e48e96a295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14e48e988870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14e48ec24796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14e48ecbdd35] -srun: error: nid003264: tasks 4840,4843,4845: Aborted -srun: launch/slurm: _step_signal: Terminating StepId=11079231.3 -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14e48e720e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5625e4fa50f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5625e4fe4a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5625e4fe8bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5625e5016258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5625e4fa56c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5625e5027e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14e48d86929d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5625e4f9646a] -================================= -[nid006346:193005:0:193005] ud_ep.c:255 Fatal: UD endpoint 0x56225ff0f360 to : unhandled timeout error -==== backtrace (tid: 193005) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x152f0e0c7454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x152f0e0c4400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x152f0e0c4529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x152f0a0ef133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x152f0e0bd8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x152f0dc58962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x152f0f8eb295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x152f0f909870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x152f0fba5796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x152f0fc3ed35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x152f0f6a1e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56225f9310f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x56225f970a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56225f974bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56225f9a2258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56225f9316c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56225f9b3e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x152f0e7ea29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56225f92246a] -================================= -[nid002357:138934:0:138934] ud_ep.c:255 Fatal: UD endpoint 0x56547f661c30 to : unhandled timeout error -==== backtrace (tid: 138934) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14d857be3454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14d857be0400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14d857be0529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14d853c0b133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14d857bd98aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14d857774962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14d859407295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14d859425870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14d8596c1796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14d85975ad35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14d8591bde9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56547ee430f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x56547ee82a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56547ee86bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56547eeb4258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56547ee436c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x56547eec5e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14d85830629d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56547ee3446a] -================================= -[nid003497:90313:0:90313] ud_ep.c:255 Fatal: UD endpoint 0x55e853133ef0 to : unhandled timeout error -==== backtrace (tid: 90313) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14fe97a00454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14fe979fd400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14fe979fd529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14fe93a28133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14fe979f68aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14fe97591962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14fe99224295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14fe99242870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14fe994de796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14fe99577d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14fe98fdae9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55e8529fc0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55e852a3ba81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55e852a3fbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55e852a6d258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55e8529fc6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55e852a7ee63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14fe9812329d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55e8529ed46a] -================================= -[nid002403:117138:0:117138] ud_ep.c:255 Fatal: UD endpoint 0x560532eb9210 to : unhandled timeout error -==== backtrace (tid: 117138) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15126c96f454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15126c96c400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15126c96c529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x151268997133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15126c9658aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15126c500962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15126e193295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15126e1b1870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15126e44d796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15126e4e6d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15126df49e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x56053272a0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x560532769a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x56053276dbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x56053279b258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x56053272a6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5605327ace63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15126d09229d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x56053271b46a] -================================= -[nid003200:260344:0:260344] ud_ep.c:255 Fatal: UD endpoint 0x5611c272a790 to : unhandled timeout error -==== backtrace (tid: 260344) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x14c59d4cf454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x14c59d4cc400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x14c59d4cc529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14c5994f7133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x14c59d4c58aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x14c59d060962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14c59ecf3295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x14c59ed11870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x14c59efad796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x14c59f046d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14c59eaa9e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x5611c217c0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x5611c21bba81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x5611c21bfbdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x5611c21ed258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x5611c217c6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x5611c21fee63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14c59dbf229d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x5611c216d46a] -================================= -[nid003261:153875:0:153875] ud_ep.c:255 Fatal: UD endpoint 0x559ae47b6f00 to : unhandled timeout error -==== backtrace (tid: 153875) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x146742c57454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x146742c54400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x146742c54529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x14673ec7f133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x146742c4d8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x1467427e8962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x14674447b295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x146744499870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x146744735796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1467447ced35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x146744231e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x559ae420e0f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x559ae424da81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x559ae4251bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x559ae427f258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x559ae420e6c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x559ae4290e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14674337a29d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x559ae41ff46a] -================================= -[nid004750:243709:0:243709] ud_ep.c:255 Fatal: UD endpoint 0x55ab52d39f80 to : unhandled timeout error -==== backtrace (tid: 243709) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15401d906454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15401d903400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15401d903529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x15401992e133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15401d8fc8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15401d497962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15401f12a295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15401f148870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15401f3e4796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15401f47dd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15401eee0e9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55ab527210f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55ab52760a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55ab52764bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55ab52792258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55ab527216c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55ab527a3e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15401e02929d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55ab5271246a] -================================= -srun: error: nid004830: task 6447: Aborted -srun: error: nid004782: task 6096: Aborted -srun: error: nid003489: task 5031: Aborted -srun: error: nid006333: task 8034: Aborted -srun: error: nid003252: tasks 4680-4687: Aborted -slurmstepd: error: *** STEP 11079231.3 ON nid001386 CANCELLED AT 2025-10-06T17:55:45 *** -srun: error: nid002580: tasks 3984-3987,3989-3991: Aborted -srun: error: nid002626: tasks 4304-4319: Terminated -srun: error: nid001775: tasks 1296-1311: Terminated -srun: error: nid004766: tasks 5904-5919: Terminated -srun: error: nid006246: tasks 7504-7519: Terminated -srun: error: nid002000: tasks 2896-2911: Terminated -srun: error: nid002249: tasks 3232-3247: Terminated -srun: error: nid003264: tasks 4832-4839,4841-4842,4844,4846-4847: Terminated -srun: error: nid002656: tasks 4496-4511: Terminated -srun: error: nid001486: tasks 224-239: Terminated -srun: error: nid004782: tasks 6097-6111: Terminated -srun: error: nid004830: tasks 6432-6446: Terminated -srun: error: nid001895: tasks 1824-1839: Terminated -srun: error: nid006273: tasks 7696-7711: Terminated -srun: error: nid006333: tasks 8032-8033,8035-8047: Terminated -srun: error: nid002266: tasks 3424-3439: Terminated -srun: error: nid003489: tasks 5024-5030,5032-5039: Terminated -srun: error: nid001652: tasks 416-431: Terminated -srun: error: nid001676: tasks 752-767: Terminated -srun: error: nid005135: tasks 6624-6639: Terminated -srun: error: nid001910: tasks 2016-2031: Terminated -srun: error: nid001932: tasks 2352-2367: Terminated -srun: error: nid001911: tasks 2040-2043,2045-2047: Aborted -srun: error: nid002341: tasks 3616-3631: Terminated -srun: error: nid002537: tasks 3952-3967: Terminated -srun: error: nid003540: tasks 5216-5231: Terminated -srun: error: nid004541: tasks 5552-5567: Terminated -srun: error: nid001732: tasks 944-959: Terminated -srun: error: nid001945: tasks 2544-2559: Terminated -srun: error: nid005603: tasks 7152-7167: Terminated -srun: error: nid002615: tasks 4144-4159: Terminated -srun: error: nid002270: tasks 3448-3455: Aborted -srun: error: nid001758: tasks 1136-1151: Terminated -srun: error: nid004751: tasks 5744-5759: Terminated -srun: error: nid001845: tasks 1472-1487: Terminated -srun: error: nid004780: tasks 6080-6095: Terminated -srun: error: nid006176: tasks 7344-7359: Terminated -srun: error: nid003252: tasks 4672-4679: Terminated -srun: error: nid006271: tasks 7680-7695: Terminated -srun: error: nid002209: tasks 3072-3087: Terminated -srun: error: nid005215: task 6846: Aborted -srun: error: nid004752: task 5760: Aborted -srun: error: nid001394: tasks 64-79: Terminated -srun: error: nid001867: tasks 1664-1679: Terminated -srun: error: nid004801: tasks 6272-6287: Terminated -srun: error: nid006288: tasks 7872-7887: Terminated -srun: error: nid002252: tasks 3264-3279: Terminated -srun: error: nid002340: tasks 3600-3615: Terminated -srun: error: nid003269: tasks 4864-4879: Terminated -srun: error: nid003539: tasks 5200-5215: Terminated -srun: error: nid001665: tasks 592-607: Terminated -srun: error: nid005189: tasks 6800-6815: Terminated -srun: error: nid004833: tasks 6464-6479: Terminated -srun: error: nid001922: tasks 2192-2207: Terminated -srun: error: nid006336: tasks 8064-8079: Terminated -srun: error: nid002367: tasks 3792-3807: Terminated -srun: error: nid004478: tasks 5392-5407: Terminated -srun: error: nid001680: tasks 784-799: Terminated -srun: error: nid001756: tasks 1120-1135: Terminated -srun: error: nid005491: tasks 6992-7007: Terminated -srun: error: nid001934: tasks 2384-2399: Terminated -srun: error: nid001956: tasks 2720-2735: Terminated -srun: error: nid002627: tasks 4320-4335: Terminated -srun: error: nid004767: tasks 5920-5935: Terminated -srun: error: nid002628: tasks 4344-4351: Aborted -srun: error: nid001812: tasks 1312-1327: Terminated -srun: error: nid006248: tasks 7520-7535: Terminated -srun: error: nid002002: tasks 2912-2927: Terminated -srun: error: nid003198: tasks 4512-4527: Terminated -srun: error: nid001488: tasks 240-255: Terminated -srun: error: nid001851: tasks 1504-1519: Terminated -srun: error: nid001896: tasks 1840-1855: Terminated -srun: error: nid006274: tasks 7712-7727: Terminated -srun: error: nid004831: tasks 6448-6463: Terminated -srun: error: nid002580: task 3988: Aborted -srun: error: nid006335: tasks 8048-8063: Terminated -srun: error: nid003493: tasks 5040-5055: Terminated -srun: error: nid001653: tasks 432-447: Terminated -srun: error: nid005137: tasks 6640-6655: Terminated -srun: error: nid006249: task 7551: Aborted -srun: error: nid001912: tasks 2056-2059,2061-2063: Aborted -srun: error: nid002344: tasks 3632-3647: Terminated -srun: error: nid004783: tasks 6112-6127: Terminated -srun: error: nid002542: tasks 3968-3983: Terminated -srun: error: nid003541: tasks 5232-5247: Terminated -srun: error: nid004544: tasks 5568-5583: Terminated -srun: error: nid001733: tasks 960-975: Terminated -srun: error: nid001946: tasks 2560-2575: Terminated -srun: error: nid005604: tasks 7168-7183: Terminated -srun: error: nid002616: tasks 4160-4175: Terminated -srun: error: nid001759: tasks 1152-1167: Terminated -srun: error: nid003542: task 5259: Aborted -srun: error: nid001846: tasks 1488-1503: Terminated -srun: error: nid006177: tasks 7360-7375: Terminated -srun: error: nid001960: tasks 2752-2767: Terminated -srun: error: nid002216: tasks 3088-3103: Terminated -srun: error: nid002629: tasks 4352-4367: Terminated -srun: error: nid003253: tasks 4688-4703: Terminated -srun: error: nid001396: tasks 80-95: Terminated -srun: error: nid001868: tasks 1680-1695: Terminated -srun: error: nid004807: tasks 6288-6303: Terminated -srun: error: nid002254: tasks 3280-3295: Terminated -srun: error: nid005216: task 6862: Aborted -srun: error: nid006289: tasks 7888-7903: Terminated -srun: error: nid006178: task 7377: Aborted -srun: error: nid003294: tasks 4880-4895: Terminated -srun: error: nid001493: tasks 272-287: Terminated -srun: error: nid003200: task 4534: Aborted -srun: error: nid001924: tasks 2232-2235,2237-2239: Aborted -srun: error: nid001666: tasks 608-623: Terminated -srun: error: nid004835: tasks 6480-6495: Terminated -srun: error: nid001900: tasks 1872-1887: Terminated -srun: error: nid005214: tasks 6816-6831: Terminated -srun: error: nid006338: tasks 8080-8095: Terminated -srun: error: nid001923: tasks 2208-2223: Terminated -srun: error: nid002383: tasks 3808-3823: Terminated -srun: error: nid004753: task 5778: Aborted -srun: error: nid004479: tasks 5408-5423: Terminated -srun: error: nid001911: tasks 2032-2039: Terminated -srun: error: nid001681: tasks 800-815: Terminated -srun: error: nid005493: tasks 7008-7023: Terminated -srun: error: nid001935: tasks 2400-2415: Terminated -srun: error: nid001957: tasks 2736-2751: Terminated -srun: error: nid002582: tasks 4000-4015: Terminated -srun: error: nid002270: tasks 3440-3447: Terminated -srun: error: nid004727: tasks 5600-5615: Terminated -srun: error: nid004768: tasks 5936-5951: Terminated -srun: error: nid001819: tasks 1328-1343: Terminated -srun: error: nid004480: tasks 5432-5439: Aborted -srun: error: nid002630: task 4370: Aborted -srun: error: nid006120: tasks 7200-7215: Terminated -srun: error: nid002004: tasks 2928-2943: Terminated -srun: error: nid001656: tasks 465,467,469-471: Aborted -srun: error: nid001489: tasks 256-271: Terminated -srun: error: nid001852: tasks 1520-1535: Terminated -srun: error: nid004785: tasks 6128-6143: Terminated -srun: error: nid001899: tasks 1856-1871: Terminated -srun: error: nid002218: tasks 3120-3135: Terminated -srun: error: nid006275: tasks 7728-7743: Terminated -srun: error: nid002580: tasks 3992-3999: Terminated -srun: error: nid002276: tasks 3456-3471: Terminated -srun: error: nid001654: tasks 448-463: Terminated -srun: error: nid003495: tasks 5056-5071: Terminated -srun: error: nid003256: tasks 4720-4735: Terminated -srun: error: nid005138: tasks 6656-6671: Terminated -srun: error: nid005215: tasks 6832-6845,6847: Terminated -srun: error: nid002347: tasks 3648-3663: Terminated -srun: error: nid001669: tasks 640-655: Terminated -srun: error: nid001736: tasks 976-991: Terminated -srun: error: nid001409: tasks 112-119: Aborted -srun: error: nid004723: tasks 5584-5599: Terminated -srun: error: nid001947: tasks 2576-2591: Terminated -srun: error: nid004752: tasks 5761-5775: Terminated -srun: error: nid005605: tasks 7184-7199: Terminated -srun: error: nid002617: tasks 4176-4191: Terminated -srun: error: nid002618: task 4196: Aborted -srun: error: nid001760: tasks 1168-1183: Terminated -srun: error: nid001961: tasks 2768-2783: Terminated -srun: error: nid002217: tasks 3104-3119: Terminated -srun: error: nid001408: tasks 96-111: Terminated -srun: error: nid003255: tasks 4704-4719: Terminated -srun: error: nid001871: tasks 1712-1719: Aborted -srun: error: nid004771: tasks 5968-5983: Terminated -srun: error: nid001869: tasks 1696-1711: Terminated -srun: error: nid004809: tasks 6304-6319: Terminated -srun: error: nid006255: tasks 7568-7583: Terminated -srun: error: nid002255: tasks 3296-3311: Terminated -srun: error: nid006290: tasks 7904-7919: Terminated -srun: error: nid003299: tasks 4896-4911: Terminated -srun: error: nid001494: tasks 288-303: Terminated -srun: error: nid001667: tasks 624-639: Terminated -srun: error: nid001901: tasks 1888-1903: Terminated -srun: error: nid004837: tasks 6496-6511: Terminated -srun: error: nid006339: tasks 8096-8111: Terminated -srun: error: nid002309: tasks 3488-3503: Terminated -srun: error: nid006179: task 7403: Aborted -srun: error: nid002384: tasks 3824-3839: Terminated -srun: error: nid001688: tasks 816-831: Terminated -srun: error: nid005494: tasks 7024-7039: Terminated -srun: error: nid001936: tasks 2416-2431: Terminated -srun: error: nid001950: tasks 2632-2639: Aborted -srun: error: nid002583: tasks 4016-4031: Terminated -srun: error: nid004481: tasks 5448-5455: Aborted -srun: error: nid001738: tasks 1008-1023: Terminated -srun: error: nid001820: tasks 1344-1359: Terminated -srun: error: nid004769: tasks 5952-5967: Terminated -srun: error: nid004729: tasks 5616-5631: Terminated -srun: error: nid006122: tasks 7216-7231: Terminated -srun: error: nid002005: tasks 2944-2959: Terminated -srun: error: nid006254: tasks 7552-7567: Terminated -srun: error: nid005104: task 6526: Aborted -srun: error: nid003241: task 4574: Aborted -srun: error: nid003231: tasks 4544-4559: Terminated -srun: error: nid004786: tasks 6144-6159: Terminated -srun: error: nid001854: tasks 1536-1551: Terminated -srun: error: nid006276: tasks 7744-7759: Terminated -srun: error: nid002220: tasks 3136-3151: Terminated -srun: error: nid002308: tasks 3472-3487: Terminated -srun: error: nid001911: task 2044: Aborted -srun: error: nid001927: tasks 2273,2275-2276,2278: Aborted -srun: error: nid003496: tasks 5072-5087: Terminated -srun: error: nid003257: tasks 4736-4751: Terminated -srun: error: nid005139: tasks 6672-6687: Terminated -srun: error: nid004816: tasks 6336-6351: Terminated -srun: error: nid002628: tasks 4336-4343: Terminated -srun: error: nid006137: task 7238: Aborted -srun: error: nid001913: tasks 2064-2079: Terminated -srun: error: nid002351: tasks 3664-3679: Terminated -srun: error: nid003549: tasks 5264-5279: Terminated -srun: error: nid001670: tasks 656-671: Terminated -srun: error: nid001737: tasks 992-1007: Terminated -srun: error: nid001926: tasks 2256-2271: Terminated -srun: error: nid005218: tasks 6864-6879: Terminated -srun: error: nid001948: tasks 2592-2607: Terminated -srun: error: nid002389: tasks 3856-3871: Terminated -srun: error: nid003497: tasks 5088-5091,5093-5095: Aborted -srun: error: nid004754: tasks 5792-5807: Terminated -srun: error: nid005128: task 6537: Aborted -srun: error: nid001761: tasks 1184-1199: Terminated -srun: error: nid001962: tasks 2784-2799: Terminated -srun: error: nid002631: tasks 4384-4399: Terminated -srun: error: nid001822: tasks 1376-1391: Terminated -srun: error: nid004773: tasks 5984-5999: Terminated -srun: error: nid006249: tasks 7536-7550: Terminated -srun: error: nid004483: tasks 5481-5484,5486: Aborted -srun: error: nid006256: tasks 7584-7599: Terminated -srun: error: nid004815: tasks 6320-6335: Terminated -srun: error: nid006292: tasks 7920-7935: Terminated -srun: error: nid002256: tasks 3312-3327: Terminated -srun: error: nid001497: tasks 304-319: Terminated -srun: error: nid001902: tasks 1904-1919: Terminated -srun: error: nid001925: tasks 2240-2255: Terminated -srun: error: nid006340: tasks 8112-8127: Terminated -srun: error: nid002310: tasks 3504-3519: Terminated -srun: error: nid002385: tasks 3840-3855: Terminated -srun: error: nid003498: tasks 5104-5119: Terminated -srun: error: nid005165: tasks 6704-6719: Terminated -srun: error: nid001691: tasks 832-847: Terminated -srun: error: nid001912: tasks 2048-2055: Terminated -srun: error: nid005495: tasks 7040-7055: Terminated -srun: error: nid001937: tasks 2432-2447: Terminated -srun: error: nid003441: tasks 4912-4927: Terminated -srun: error: nid002593: tasks 4032-4047: Terminated -srun: error: nid004736: tasks 5632-5647: Terminated -srun: error: nid001744: tasks 1024-1039: Terminated -srun: error: nid001821: tasks 1360-1375: Terminated -srun: error: nid002007: tasks 2960-2975: Terminated -srun: error: nid002621: tasks 4224-4239: Terminated -srun: error: nid001463: tasks 168-175: Aborted -srun: error: nid001855: tasks 1552-1567: Terminated -srun: error: nid004788: tasks 6160-6175: Terminated -srun: error: nid006277: tasks 7760-7775: Terminated -srun: error: nid002227: tasks 3152-3167: Terminated -srun: error: nid003200: tasks 4528-4533,4535-4543: Terminated -srun: error: nid003542: tasks 5248-5258,5260-5263: Terminated -srun: error: nid001429: tasks 144-159: Terminated -srun: error: nid003258: tasks 4752-4767: Terminated -srun: error: nid001657: tasks 480-495: Terminated -srun: error: nid005140: tasks 6688-6703: Terminated -srun: error: nid001914: tasks 2080-2095: Terminated -srun: error: nid004821: tasks 6352-6367: Terminated -srun: error: nid006297: tasks 7952-7967: Terminated -srun: error: nid002352: tasks 3680-3695: Terminated -srun: error: nid003550: tasks 5280-5295: Terminated -srun: error: nid001671: tasks 672-687: Terminated -srun: error: nid005216: tasks 6848-6861,6863: Terminated -srun: error: nid005480: tasks 6880-6895: Terminated -srun: error: nid001949: tasks 2608-2623: Terminated -srun: error: nid002400: tasks 3872-3887: Terminated -srun: error: nid002620: tasks 4208-4223: Terminated -srun: error: nid004756: tasks 5808-5823: Terminated -srun: error: nid001762: tasks 1200-1215: Terminated -srun: error: nid005507: tasks 7072-7087: Terminated -srun: error: nid001963: tasks 2800-2815: Terminated -srun: error: nid001711: tasks 880-881,883,885-887: Aborted -srun: error: nid006186: tasks 7408-7423: Terminated -srun: error: nid006178: tasks 7376,7378-7391: Terminated -srun: error: nid002632: tasks 4400-4415: Terminated -srun: error: nid001426: tasks 128-143: Terminated -srun: error: nid001823: tasks 1392-1407: Terminated -srun: error: nid004775: tasks 6000-6015: Terminated -srun: error: nid001660: tasks 536-537,539,541-543: Aborted -srun: error: nid001872: tasks 1728-1743: Terminated -srun: error: nid001918: task 2135: Aborted -srun: error: nid006257: tasks 7600-7615: Terminated -srun: error: nid002009: tasks 2992-3007: Terminated -srun: error: nid004753: tasks 5776-5777,5779-5791: Terminated -srun: error: nid006294: tasks 7936-7951: Terminated -srun: error: nid002259: tasks 3328-3343: Terminated -srun: error: nid003476: tasks 4928-4943: Terminated -srun: error: nid001498: tasks 320-335: Terminated -srun: error: nid001903: tasks 1920-1935: Terminated -srun: error: nid006341: tasks 8128-8143: Terminated -srun: error: nid002311: tasks 3520-3535: Terminated -srun: error: nid003499: tasks 5120-5135: Terminated -srun: error: nid001659: tasks 512-527: Terminated -srun: error: nid001924: tasks 2224-2231: Terminated -srun: error: nid004482: tasks 5456-5471: Terminated -srun: error: nid001704: tasks 848-863: Terminated -srun: error: nid005180: tasks 6720-6735: Terminated -srun: error: nid005503: tasks 7056-7071: Terminated -srun: error: nid001938: tasks 2448-2463: Terminated -srun: error: nid002630: tasks 4368-4369,4371-4383: Terminated -srun: error: nid002595: tasks 4048-4063: Terminated -srun: error: nid001747: tasks 1040-1055: Terminated -srun: error: nid004737: tasks 5648-5663: Terminated -srun: error: nid001951: tasks 2640-2655: Terminated -srun: error: nid006138: tasks 7248-7263: Terminated -srun: error: nid002008: tasks 2976-2991: Terminated -srun: error: nid002622: tasks 4240-4255: Terminated -srun: error: nid003242: tasks 4576-4591: Terminated -srun: error: nid004758: tasks 5840-5855: Terminated -srun: error: nid004789: tasks 6176-6191: Terminated -srun: error: nid001856: tasks 1568-1583: Terminated -srun: error: nid006279: tasks 7776-7791: Terminated -srun: error: nid002238: tasks 3168-3183: Terminated -srun: error: nid004480: tasks 5424-5431: Terminated -srun: error: nid003259: tasks 4768-4783: Terminated -srun: error: nid001658: tasks 496-511: Terminated -srun: error: nid001885: tasks 1760-1775: Terminated -srun: error: nid004824: tasks 6368-6383: Terminated -srun: error: nid001915: tasks 2096-2111: Terminated -srun: error: nid006299: tasks 7968-7983: Terminated -srun: error: nid002261: tasks 3360-3375: Terminated -srun: error: nid002353: tasks 3696-3711: Terminated -srun: error: nid003551: tasks 5296-5311: Terminated -srun: error: nid001672: tasks 688-703: Terminated -srun: error: nid001928: tasks 2288-2303: Terminated -srun: error: nid005481: tasks 6896-6911: Terminated -srun: error: nid002402: tasks 3888-3903: Terminated -srun: error: nid002403: tasks 3912-3915,3918: Aborted -srun: error: nid006311: task 7998: Aborted -srun: error: nid004484: tasks 5488-5503: Terminated -srun: error: nid001763: tasks 1216-1231: Terminated -srun: error: nid004757: tasks 5824-5839: Terminated -srun: error: nid006280: tasks 7794,7800: Aborted -srun: error: nid005511: tasks 7088-7103: Terminated -srun: error: nid006188: tasks 7424-7439: Terminated -srun: error: nid001964: tasks 2816-2831: Terminated -srun: error: nid002635: tasks 4416-4431: Terminated -srun: error: nid001826: tasks 1408-1423: Terminated -srun: error: nid004776: tasks 6016-6031: Terminated -srun: error: nid001876: tasks 1744-1759: Terminated -srun: error: nid001656: tasks 464,466,468: Aborted -srun: error: nid002011: tasks 3008-3023: Terminated -srun: error: nid006258: tasks 7616-7631: Terminated -srun: error: nid002260: tasks 3344-3359: Terminated -srun: error: nid003244: tasks 4608-4623: Terminated -srun: error: nid001504: tasks 336-351: Terminated -srun: error: nid003478: tasks 4944-4959: Terminated -srun: error: nid005129: tasks 6544-6559: Terminated -srun: error: nid004794: tasks 6208-6223: Terminated -srun: error: nid001904: tasks 1936-1951: Terminated -srun: error: nid002312: tasks 3536-3551: Terminated -srun: error: nid003479: task 4973: Aborted -srun: error: nid006344: tasks 8144-8159: Terminated -srun: error: nid003500: tasks 5136-5151: Terminated -srun: error: nid001858: tasks 1603,1605-1607: Aborted -srun: error: nid001710: tasks 864-879: Terminated -srun: error: nid005182: tasks 6736-6751: Terminated -srun: error: nid001939: tasks 2464-2479: Terminated -srun: error: nid002603: tasks 4064-4079: Terminated -srun: error: nid001751: tasks 1056-1071: Terminated -srun: error: nid004739: tasks 5664-5679: Terminated -srun: error: nid006139: tasks 7264-7279: Terminated -srun: error: nid001952: tasks 2656-2671: Terminated -srun: error: nid002623: tasks 4256-4271: Terminated -srun: error: nid003243: tasks 4592-4607: Terminated -srun: error: nid004761: tasks 5856-5871: Terminated -srun: error: nid004790: tasks 6192-6207: Terminated -srun: error: nid001857: tasks 1584-1599: Terminated -srun: error: nid006212: tasks 7456-7471: Terminated -srun: error: nid002241: tasks 3184-3199: Terminated -srun: error: nid001468: tasks 176-191: Terminated -srun: error: nid001912: task 2060: Aborted -srun: error: nid003260: tasks 4784-4799: Terminated -srun: error: nid006346: task 8170: Aborted -srun: error: nid002618: tasks 4192-4195,4197-4207: Terminated -srun: error: nid001888: tasks 1776-1791: Terminated -srun: error: nid004827: tasks 6384-6399: Terminated -srun: error: nid001917: tasks 2112-2127: Terminated -srun: error: nid002356: tasks 3712-3727: Terminated -srun: error: nid001409: tasks 120-127: Terminated -srun: error: nid003481: tasks 4976-4991: Terminated -srun: error: nid002264: tasks 3400-3407: Aborted -srun: error: nid003552: tasks 5312-5327: Terminated -srun: error: nid001673: tasks 704-719: Terminated -srun: error: nid002357: tasks 3736-3739,3741-3743: Aborted -srun: error: nid005482: tasks 6912-6927: Terminated -srun: error: nid001929: tasks 2304-2319: Terminated -srun: error: nid004486: tasks 5504-5519: Terminated -srun: error: nid001713: tasks 896-911: Terminated -srun: error: nid002262: tasks 3376-3391: Terminated -srun: error: nid001765: tasks 1232-1247: Terminated -srun: error: nid001942: tasks 2496-2511: Terminated -srun: error: nid005519: tasks 7104-7119: Terminated -srun: error: nid006189: tasks 7440-7455: Terminated -srun: error: nid001991: tasks 2832-2847: Terminated -srun: error: nid002649: tasks 4432-4447: Terminated -srun: error: nid001871: tasks 1720-1727: Terminated -srun: error: nid001832: tasks 1424-1439: Terminated -srun: error: nid004777: tasks 6032-6047: Terminated -srun: error: nid002197: tasks 3024-3039: Terminated -srun: error: nid006259: tasks 7632-7647: Terminated -srun: error: nid006179: tasks 7392-7402,7404-7407: Terminated -srun: error: nid003245: tasks 4624-4639: Terminated -srun: error: nid001387: tasks 16-31: Terminated -srun: error: nid001920: tasks 2162-2163,2165-2167: Aborted -srun: error: nid001505: tasks 352-367: Terminated -srun: error: nid004795: tasks 6224-6239: Terminated -srun: error: nid005130: tasks 6560-6575: Terminated -srun: error: nid001905: tasks 1952-1967: Terminated -srun: error: nid006285: tasks 7824-7839: Terminated -srun: error: nid002313: tasks 3552-3567: Terminated -srun: error: nid003501: tasks 5152-5167: Terminated -srun: error: nid001661: tasks 544-559: Terminated -srun: error: nid003261: task 4801: Aborted -srun: error: nid005185: tasks 6752-6767: Terminated -srun: error: nid001954: tasks 2688-2695: Aborted -srun: error: nid001919: tasks 2144-2159: Terminated -srun: error: nid001940: tasks 2480-2495: Terminated -srun: error: nid002358: tasks 3744-3759: Terminated -srun: error: nid001754: tasks 1088-1095: Aborted -srun: error: nid002606: tasks 4080-4095: Terminated -srun: error: nid003555: tasks 5344-5359: Terminated -srun: error: nid001752: tasks 1072-1087: Terminated -srun: error: nid001864: tasks 1618-1619,1621-1622: Aborted -srun: error: nid004740: tasks 5680-5695: Terminated -srun: error: nid002607: tasks 4104-4111: Aborted -srun: error: nid001953: tasks 2672-2687: Terminated -srun: error: nid006149: tasks 7280-7295: Terminated -srun: error: nid002624: tasks 4272-4287: Terminated -srun: error: nid005187: task 6777: Aborted -srun: error: nid001768: tasks 1264-1279: Terminated -srun: error: nid001386: tasks 0-15: Terminated -srun: error: nid004763: tasks 5872-5887: Terminated -srun: error: nid004481: tasks 5440-5447: Terminated -srun: error: nid001998: tasks 2864-2879: Terminated -srun: error: nid006281: tasks 7808-7823: Terminated -srun: error: nid005104: tasks 6512-6525,6527: Terminated -srun: error: nid006213: tasks 7472-7487: Terminated -srun: error: nid002246: tasks 3200-3215: Terminated -srun: error: nid001474: tasks 192-207: Terminated -srun: error: nid001892: tasks 1792-1807: Terminated -srun: error: nid004828: tasks 6400-6415: Terminated -srun: error: nid001950: tasks 2624-2631: Terminated -srun: error: nid006312: tasks 8000-8015: Terminated -srun: error: nid003485: tasks 4992-5007: Terminated -srun: error: nid002406: task 3945: Aborted -srun: error: nid003553: tasks 5328-5343: Terminated -srun: error: nid001674: tasks 720-735: Terminated -srun: error: nid005132: tasks 6592-6607: Terminated -srun: error: nid001767: tasks 1248-1263: Terminated -srun: error: nid001930: tasks 2320-2335: Terminated -srun: error: nid002404: tasks 3920-3935: Terminated -srun: error: nid005484: tasks 6928-6943: Terminated -srun: error: nid001725: tasks 912-927: Terminated -srun: error: nid004487: tasks 5520-5535: Terminated -srun: error: nid001943: tasks 2512-2527: Terminated -srun: error: nid005525: tasks 7120-7135: Terminated -srun: error: nid001842: tasks 1464-1471: Aborted -srun: error: nid001994: tasks 2848-2863: Terminated -srun: error: nid002611: tasks 4112-4127: Terminated -srun: error: nid002650: tasks 4448-4463: Terminated -srun: error: nid004749: tasks 5712-5727: Terminated -srun: error: nid004778: tasks 6048-6063: Terminated -srun: error: nid006137: tasks 7232-7237,7239-7247: Terminated -srun: error: nid002202: tasks 3040-3055: Terminated -srun: error: nid006266: tasks 7648-7663: Terminated -srun: error: nid001924: task 2236: Aborted -srun: error: nid001391: tasks 32-47: Terminated -srun: error: nid003241: tasks 4560-4573,4575: Terminated -srun: error: nid003246: tasks 4640-4655: Terminated -srun: error: nid001509: tasks 368-383: Terminated -srun: error: nid004796: tasks 6240-6255: Terminated -srun: error: nid001865: tasks 1632-1647: Terminated -srun: error: nid005131: tasks 6576-6591: Terminated -srun: error: nid001906: tasks 1968-1983: Terminated -srun: error: nid006286: tasks 7840-7855: Terminated -srun: error: nid002331: tasks 3568-3583: Terminated -srun: error: nid001664: tasks 584-591: Aborted -srun: error: nid006350: tasks 8176-8191: Terminated -srun: error: nid001837: tasks 1440-1455: Terminated -srun: error: nid003518: tasks 5168-5183: Terminated -srun: error: nid001663: tasks 560-575: Terminated -srun: error: nid002337: tasks 3584-3587,3589-3595,3597-3599: Aborted -srun: error: nid002359: tasks 3760-3775: Terminated -srun: error: nid001927: tasks 2272,2274,2277,2279-2287: Terminated -srun: error: nid003556: tasks 5360-5375: Terminated -srun: error: nid004743: tasks 5696-5711: Terminated -srun: error: nid003497: task 5092: Aborted -srun: error: nid005487: tasks 6960-6975: Terminated -srun: error: nid006151: tasks 7296-7311: Terminated -srun: error: nid003265: task 4863: Aborted -srun: error: nid002625: tasks 4288-4303: Terminated -srun: error: nid001773: tasks 1280-1295: Terminated -srun: error: nid004764: tasks 5888-5903: Terminated -srun: error: nid006230: tasks 7488-7503: Terminated -srun: error: nid001999: tasks 2880-2895: Terminated -srun: error: nid002247: tasks 3216-3231: Terminated -srun: error: nid002652: tasks 4480-4495: Terminated -srun: error: nid003263: tasks 4816-4831: Terminated -srun: error: nid001480: tasks 208-223: Terminated -srun: error: nid001893: tasks 1808-1823: Terminated -srun: error: nid004829: tasks 6416-6431: Terminated -srun: error: nid002265: tasks 3408-3423: Terminated -srun: error: nid006332: tasks 8016-8031: Terminated -srun: error: nid004750: task 5743: Aborted -srun: error: nid003486: tasks 5008-5023: Terminated -srun: error: nid001650: tasks 400-415: Terminated -srun: error: nid001909: tasks 2000-2015: Terminated -srun: error: nid005133: tasks 6608-6623: Terminated -srun: error: nid001675: tasks 736-751: Terminated -srun: error: nid005486: tasks 6944-6959: Terminated -srun: error: nid001931: tasks 2336-2351: Terminated -srun: error: nid004483: tasks 5472-5480,5485,5487: Terminated -srun: error: nid004488: tasks 5536-5551: Terminated -srun: error: nid001728: tasks 928-943: Terminated -srun: error: nid005602: tasks 7136-7151: Terminated -srun: error: nid001944: tasks 2528-2543: Terminated -srun: error: nid002614: tasks 4128-4143: Terminated -srun: error: nid005128: tasks 6528-6536,6538-6543: Terminated -srun: error: nid002651: tasks 4464-4479: Terminated -srun: error: nid004779: tasks 6064-6079: Terminated -srun: error: nid006157: tasks 7328-7343: Terminated -srun: error: nid002203: tasks 3056-3071: Terminated -srun: error: nid006269: tasks 7664-7679: Terminated -srun: error: nid003247: tasks 4656-4671: Terminated -srun: error: nid001392: tasks 48-63: Terminated -srun: error: nid001647: tasks 384-399: Terminated -srun: error: nid001866: tasks 1648-1663: Terminated -srun: error: nid004799: tasks 6256-6271: Terminated -srun: error: nid006287: tasks 7856-7871: Terminated -srun: error: nid001907: tasks 1984-1999: Terminated -srun: error: nid002250: tasks 3248-3263: Terminated -srun: error: nid003526: tasks 5184-5199: Terminated -srun: error: nid005188: tasks 6784-6799: Terminated -srun: error: nid001921: tasks 2176-2191: Terminated -srun: error: nid002366: tasks 3776-3791: Terminated -srun: error: nid001656: tasks 472-479: Terminated -srun: error: nid003558: tasks 5376-5391: Terminated -srun: error: nid001677: tasks 768-783: Terminated -srun: error: nid001755: tasks 1104-1119: Terminated -srun: error: nid005488: tasks 6976-6991: Terminated -srun: error: nid006156: tasks 7312-7327: Terminated -srun: error: nid001955: tasks 2704-2719: Terminated -srun: error: nid001933: tasks 2368-2383: Terminated -srun: error: nid001463: tasks 160-167: Terminated -srun: error: nid001711: tasks 882,884,888-895: Terminated -srun: error: nid001660: tasks 528-535,538: Terminated -srun: error: nid001918: tasks 2128-2134,2136-2143: Terminated -srun: error: nid006280: tasks 7792-7793,7795-7799,7801-7807: Terminated -srun: error: nid006311: tasks 7984-7997,7999: Terminated -srun: error: nid002403: tasks 3904-3911,3917,3919: Terminated -srun: error: nid003497: tasks 5096-5103: Terminated -srun: error: nid003479: tasks 4960-4972,4974-4975: Terminated -srun: error: nid001858: tasks 1600-1601,1604: Aborted -srun: error: nid006346: tasks 8160-8169,8171-8175: Terminated -srun: error: nid002264: tasks 3392-3399: Terminated -srun: error: nid002357: tasks 3728-3735: Terminated -srun: error: nid003261: tasks 4800,4802-4815: Terminated -srun: error: nid001920: tasks 2160-2161,2168-2175: Terminated -srun: error: nid001954: tasks 2696-2703: Terminated -srun: error: nid001754: tasks 1096-1103: Terminated -srun: error: nid002607: tasks 4096-4103: Terminated -srun: error: nid005187: tasks 6768-6776,6778-6783: Terminated -srun: error: nid001864: tasks 1616-1617,1620,1623-1631: Terminated -srun: error: nid002406: tasks 3936-3944,3946-3951: Terminated -srun: error: nid001842: tasks 1456-1463: Terminated -srun: error: nid001660: task 540: Aborted -srun: error: nid001664: tasks 576-583: Terminated -srun: error: nid002337: tasks 3588,3596: Aborted -srun: error: nid003265: tasks 4848-4862: Terminated -srun: error: nid004750: tasks 5728-5742: Terminated -srun: error: nid002403: task 3916: Aborted -srun: error: nid001858: task 1602: Aborted (core dumped) -srun: error: nid002357: task 3740: Aborted -srun: error: nid001920: task 2164: Aborted -srun: error: nid001858: tasks 1608-1615: Terminated -srun: Force Terminated StepId=11079231.3 diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079233/err_run_0.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079233/err_run_0.log deleted file mode 100644 index e69de29b..00000000 diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079233/err_run_1.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079233/err_run_1.log deleted file mode 100644 index e69de29b..00000000 diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079233/err_run_2.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079233/err_run_2.log deleted file mode 100644 index e69de29b..00000000 diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079233/err_run_3.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079233/err_run_3.log deleted file mode 100644 index d245140e..00000000 --- a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_11079233/err_run_3.log +++ /dev/null @@ -1,561 +0,0 @@ -[nid002626:145414:0:145414] ud_ep.c:255 Fatal: UD endpoint 0x55d66a1987f0 to : unhandled timeout error -==== backtrace (tid: 145414) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x15163af70454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x15163af6d400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x15163af6d529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x15163863c133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x15163af668aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x15163ab01962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x15163c794295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x15163c7b2870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x15163ca4e796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x15163cae7d35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x15163c54ae9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55d669dd60f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55d669e15a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55d669e19bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55d669e47258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55d669dd66c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55d669e58e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x15163b69329d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55d669dc746a] -================================= -[nid004485:154374:0:154374] ud_ep.c:255 Fatal: UD endpoint 0x55577fff11a0 to : unhandled timeout error -==== backtrace (tid: 154374) ==== - 0 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_handle_error+0xe4) [0x149322c75454] - 1 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(ucs_fatal_error_message+0x60) [0x149322c72400] - 2 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x23529) [0x149322c72529] - 3 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/ucx/libuct_ib.so.0(+0x4a133) [0x149320341133] - 4 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucs.so.0(+0x1c8aa) [0x149322c6b8aa] - 5 /opt/cray/pe/cray-ucx/2.7.0-1/ucx/lib/libucp.so.0(ucp_worker_progress+0x22) [0x149322806962] - 6 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3b0295) [0x149324499295] - 7 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x3ce870) [0x1493244b7870] - 8 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x66a796) [0x149324753796] - 9 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(+0x703d35) [0x1493247ecd35] -10 /opt/cray/pe/mpich/8.1.27/ucx/aocc/3.0/lib/libmpi_aocc.so.12(MPI_Alltoallv+0xc3a) [0x14932424fe9a] -11 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb50f5) [0x55577fc420f5] -12 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf4a81) [0x55577fc81a81] -13 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xf8bdc) [0x55577fc85bdc] -14 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x126258) [0x55577fcb3258] -15 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xb56c3) [0x55577fc426c3] -16 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0x137e63) [0x55577fcc4e63] -17 /lib64/libc.so.6(__libc_start_main+0xef) [0x14932339829d] -18 /work/e738/e738/skailasa/fmm_m2l_fft_mpi_f32(+0xa646a) [0x55577fc3346a] -================================= -srun: error: nid004485: task 694: Aborted -srun: launch/slurm: _step_signal: Terminating StepId=11079233.3 -slurmstepd: error: *** STEP 11079233.3 ON nid001386 CANCELLED AT 2025-10-06T18:16:17 *** -srun: error: nid002624: tasks 534-535: Terminated -srun: error: nid001768: tasks 158-159: Terminated -srun: error: nid004763: tasks 734-735: Terminated -srun: error: nid006213: tasks 934-935: Terminated -srun: error: nid001998: tasks 358-359: Terminated -srun: error: nid002246: tasks 400-401: Terminated -srun: error: nid002651: tasks 558-559: Terminated -srun: error: nid001474: tasks 24-25: Terminated -srun: error: nid003258: tasks 600-601: Terminated -srun: error: nid004779: tasks 758-759: Terminated -srun: error: nid001892: tasks 224-225: Terminated -srun: error: nid004828: tasks 800-801: Terminated -srun: error: nid006269: tasks 958-959: Terminated -srun: error: nid002264: tasks 424-425: Terminated -srun: error: nid006312: tasks 1000-1001: Terminated -srun: error: nid003478: tasks 624-625: Terminated -srun: error: nid005132: tasks 824-825: Terminated -srun: error: nid001674: tasks 90-91: Terminated -srun: error: nid001907: tasks 248-249: Terminated -srun: error: nid001930: tasks 290-291: Terminated -srun: error: nid002337: tasks 448-449: Terminated -srun: error: nid002404: tasks 490-491: Terminated -srun: error: nid003500: tasks 648-649: Terminated -srun: error: nid001725: tasks 114-115: Terminated -srun: error: nid004483: tasks 690-691: Terminated -srun: error: nid001943: tasks 314-315: Terminated -srun: error: nid005525: tasks 890-891: Terminated -srun: error: nid001647: tasks 48-49: Terminated -srun: error: nid002611: tasks 514-515: Terminated -srun: error: nid004749: tasks 714-715: Terminated -srun: error: nid001755: tasks 138-139: Terminated -srun: error: nid001837: tasks 180-181: Terminated -srun: error: nid004778: tasks 756-757: Terminated -srun: error: nid006156: tasks 914-915: Terminated -srun: error: nid006266: tasks 956-957: Terminated -srun: error: nid002202: tasks 380-381: Terminated -srun: error: nid001391: tasks 4-5: Terminated -srun: error: nid003246: tasks 580-581: Terminated -srun: error: nid001865: tasks 204-205: Terminated -srun: error: nid004796: tasks 780-781: Terminated -srun: error: nid006286: tasks 980-981: Terminated -srun: error: nid002249: tasks 404-405: Terminated -srun: error: nid002331: tasks 446-447: Terminated -srun: error: nid003260: tasks 604-605: Terminated -srun: error: nid001663: tasks 70-71: Terminated -srun: error: nid003499: tasks 646-647: Terminated -srun: error: nid004830: tasks 804-805: Terminated -srun: error: nid001920: tasks 270-271: Terminated -srun: error: nid005187: tasks 846-847: Terminated -srun: error: nid006333: tasks 1004-1005: Terminated -srun: error: nid002359: tasks 470-471: Terminated -srun: error: nid003552: tasks 670-671: Terminated -srun: error: nid001676: tasks 94-95: Terminated -srun: error: nid001754: tasks 136-137: Terminated -srun: error: nid001932: tasks 294-295: Terminated -srun: error: nid005487: tasks 870-871: Terminated -srun: error: nid001954: tasks 336-337: Terminated -srun: error: nid002537: tasks 494-495: Terminated -srun: error: nid002625: tasks 536-537: Terminated -srun: error: nid004764: tasks 736-737: Terminated -srun: error: nid001773: tasks 160-161: Terminated -srun: error: nid006230: tasks 936-937: Terminated -srun: error: nid001999: tasks 360-361: Terminated -srun: error: nid002652: tasks 560-561: Terminated -srun: error: nid001480: tasks 26-27: Terminated -srun: error: nid004780: tasks 760-761: Terminated -srun: error: nid001845: tasks 184-185: Terminated -srun: error: nid001893: tasks 226-227: Terminated -srun: error: nid004829: tasks 802-803: Terminated -srun: error: nid006271: tasks 960-961: Terminated -srun: error: nid002265: tasks 426-427: Terminated -srun: error: nid006332: tasks 1002-1003: Terminated -srun: error: nid003479: tasks 626-627: Terminated -srun: error: nid001650: tasks 50-51: Terminated -srun: error: nid001909: tasks 250-251: Terminated -srun: error: nid005133: tasks 826-827: Terminated -srun: error: nid002340: tasks 450-451: Terminated -srun: error: nid002406: tasks 492-493: Terminated -srun: error: nid003501: tasks 650-651: Terminated -srun: error: nid004484: tasks 692-693: Terminated -srun: error: nid001728: tasks 116-117: Terminated -srun: error: nid005189: tasks 850-851: Terminated -srun: error: nid005602: tasks 892-893: Terminated -srun: error: nid001944: tasks 316-317: Terminated -srun: error: nid002614: tasks 516-517: Terminated -srun: error: nid001756: tasks 140-141: Terminated -srun: error: nid004750: tasks 716-717: Terminated -srun: error: nid001842: tasks 182-183: Terminated -srun: error: nid006157: tasks 916-917: Terminated -srun: error: nid001956: tasks 340-341: Terminated -srun: error: nid002203: tasks 382-383: Terminated -srun: error: nid002627: tasks 540-541: Terminated -srun: error: nid003247: tasks 582-583: Terminated -srun: error: nid001392: tasks 6-7: Terminated -srun: error: nid001866: tasks 206-207: Terminated -srun: error: nid004799: tasks 782-783: Terminated -srun: error: nid002250: tasks 406-407: Terminated -srun: error: nid006287: tasks 982-983: Terminated -srun: error: nid001488: tasks 30-31: Terminated -srun: error: nid003261: tasks 606-607: Terminated -srun: error: nid001664: tasks 72-73: Terminated -srun: error: nid001896: tasks 230-231: Terminated -srun: error: nid005188: tasks 848-849: Terminated -srun: error: nid004831: tasks 806-807: Terminated -srun: error: nid001921: tasks 272-273: Terminated -srun: error: nid006335: tasks 1006-1007: Terminated -srun: error: nid002366: tasks 472-473: Terminated -srun: error: nid003553: tasks 672-673: Terminated -srun: error: nid001677: tasks 96-97: Terminated -srun: error: nid005488: tasks 872-873: Terminated -srun: error: nid001933: tasks 296-297: Terminated -srun: error: nid001955: tasks 338-339: Terminated -srun: error: nid002626: task 539: Terminated -srun: error: nid002542: tasks 496-497: Terminated -srun: error: nid004486: tasks 696-697: Terminated -srun: error: nid001775: tasks 162-163: Terminated -srun: error: nid004766: tasks 738-739: Terminated -srun: error: nid005604: tasks 896-897: Terminated -srun: error: nid006246: tasks 938-939: Terminated -srun: error: nid002000: tasks 362-363: Terminated -srun: error: nid002656: tasks 562-563: Terminated -srun: error: nid001486: tasks 28-29: Terminated -srun: error: nid001846: tasks 186-187: Terminated -srun: error: nid004782: tasks 762-763: Terminated -srun: error: nid001895: tasks 228-229: Terminated -srun: error: nid006273: tasks 962-963: Terminated -srun: error: nid002216: tasks 386-387: Terminated -srun: error: nid002266: tasks 428-429: Terminated -srun: error: nid004485: task 695: Terminated -srun: error: nid003249: tasks 586-587: Terminated -srun: error: nid003481: tasks 628-629: Terminated -srun: error: nid001652: tasks 52-53: Terminated -srun: error: nid005135: tasks 828-829: Terminated -srun: error: nid001910: tasks 252-253: Terminated -srun: error: nid002341: tasks 452-453: Terminated -srun: error: nid003518: tasks 652-653: Terminated -srun: error: nid001666: tasks 76-77: Terminated -srun: error: nid001732: tasks 118-119: Terminated -srun: error: nid001945: tasks 318-319: Terminated -srun: error: nid005603: tasks 894-895: Terminated -srun: error: nid002615: tasks 518-519: Terminated -srun: error: nid001758: tasks 142-143: Terminated -srun: error: nid004751: tasks 718-719: Terminated -srun: error: nid001957: tasks 342-343: Terminated -srun: error: nid006176: tasks 918-919: Terminated -srun: error: nid002209: tasks 384-385: Terminated -srun: error: nid002628: tasks 542-543: Terminated -srun: error: nid005214: tasks 852-853: Terminated -srun: error: nid003248: tasks 584-585: Terminated -srun: error: nid001394: tasks 8-9: Terminated -srun: error: nid004768: tasks 742-743: Terminated -srun: error: nid001867: tasks 208-209: Terminated -srun: error: nid004801: tasks 784-785: Terminated -srun: error: nid006249: tasks 942-943: Terminated -srun: error: nid002252: tasks 408-409: Terminated -srun: error: nid006288: tasks 984-985: Terminated -srun: error: nid001489: tasks 32-33: Terminated -srun: error: nid003263: tasks 608-609: Terminated -srun: error: nid001665: tasks 74-75: Terminated -srun: error: nid004833: tasks 808-809: Terminated -srun: error: nid001899: tasks 232-233: Terminated -srun: error: nid001922: tasks 274-275: Terminated -srun: error: nid002276: tasks 432-433: Terminated -srun: error: nid006336: tasks 1008-1009: Terminated -srun: error: nid002367: tasks 474-475: Terminated -srun: error: nid003555: tasks 674-675: Terminated -srun: error: nid001680: tasks 98-99: Terminated -srun: error: nid005491: tasks 874-875: Terminated -srun: error: nid001934: tasks 298-299: Terminated -srun: error: nid002580: tasks 498-499: Terminated -srun: error: nid004723: tasks 698-699: Terminated -srun: error: nid001736: tasks 122-123: Terminated -srun: error: nid001812: tasks 164-165: Terminated -srun: error: nid004767: tasks 740-741: Terminated -srun: error: nid005605: tasks 898-899: Terminated -srun: error: nid006248: tasks 940-941: Terminated -srun: error: nid002002: tasks 364-365: Terminated -srun: error: nid001851: tasks 188-189: Terminated -srun: error: nid004783: tasks 764-765: Terminated -srun: error: nid006274: tasks 964-965: Terminated -srun: error: nid002217: tasks 388-389: Terminated -srun: error: nid002270: tasks 430-431: Terminated -srun: error: nid003250: tasks 588-589: Terminated -srun: error: nid003485: tasks 630-631: Terminated -srun: error: nid004809: tasks 788-789: Terminated -srun: error: nid005137: tasks 830-831: Terminated -srun: error: nid001911: tasks 254-255: Terminated -srun: error: nid003198: tasks 564-565: Terminated -srun: error: nid002344: tasks 454-455: Terminated -srun: error: nid003526: tasks 654-655: Terminated -srun: error: nid001667: tasks 78-79: Terminated -srun: error: nid001733: tasks 120-121: Terminated -srun: error: nid005215: tasks 854-855: Terminated -srun: error: nid001924: tasks 278-279: Terminated -srun: error: nid001946: tasks 320-321: Terminated -srun: error: nid001653: tasks 54-55: Terminated -srun: error: nid002384: tasks 478-479: Terminated -srun: error: nid002616: tasks 520-521: Terminated -srun: error: nid004752: tasks 720-721: Terminated -srun: error: nid001759: tasks 144-145: Terminated -srun: error: nid006177: tasks 920-921: Terminated -srun: error: nid001960: tasks 344-345: Terminated -srun: error: nid002629: tasks 544-545: Terminated -srun: error: nid001396: tasks 10-11: Terminated -srun: error: nid001820: tasks 168-169: Terminated -srun: error: nid004769: tasks 744-745: Terminated -srun: error: nid001868: tasks 210-211: Terminated -srun: error: nid004807: tasks 786-787: Terminated -srun: error: nid006254: tasks 944-945: Terminated -srun: error: nid006289: tasks 986-987: Terminated -srun: error: nid002254: tasks 410-411: Terminated -srun: error: nid001493: tasks 34-35: Terminated -srun: error: nid003264: tasks 610-611: Terminated -srun: error: nid001900: tasks 234-235: Terminated -srun: error: nid004835: tasks 810-811: Terminated -srun: error: nid001923: tasks 276-277: Terminated -srun: error: nid002308: tasks 434-435: Terminated -srun: error: nid006338: tasks 1010-1011: Terminated -srun: error: nid002383: tasks 476-477: Terminated -srun: error: nid003489: tasks 634-635: Terminated -srun: error: nid003556: tasks 676-677: Terminated -srun: error: nid001681: tasks 100-101: Terminated -srun: error: nid005139: tasks 834-835: Terminated -srun: error: nid005493: tasks 876-877: Terminated -srun: error: nid001935: tasks 300-301: Terminated -srun: error: nid002582: tasks 500-501: Terminated -srun: error: nid001737: tasks 124-125: Terminated -srun: error: nid004727: tasks 700-701: Terminated -srun: error: nid002626: task 538: Aborted (core dumped) -srun: error: nid001819: tasks 166-167: Terminated -srun: error: nid001948: tasks 324-325: Terminated -srun: error: nid006120: tasks 900-901: Terminated -srun: error: nid002004: tasks 366-367: Terminated -srun: error: nid002618: tasks 524-525: Terminated -srun: error: nid003200: tasks 566-567: Terminated -srun: error: nid004785: tasks 766-767: Terminated -srun: error: nid001852: tasks 190-191: Terminated -srun: error: nid006275: tasks 966-967: Terminated -srun: error: nid002218: tasks 390-391: Terminated -srun: error: nid003252: tasks 590-591: Terminated -srun: error: nid001409: tasks 14-15: Terminated -srun: error: nid003486: tasks 632-633: Terminated -srun: error: nid001654: tasks 56-57: Terminated -srun: error: nid001912: tasks 256-257: Terminated -srun: error: nid004815: tasks 790-791: Terminated -srun: error: nid005138: tasks 832-833: Terminated -srun: error: nid006292: tasks 990-991: Terminated -srun: error: nid002347: tasks 456-457: Terminated -srun: error: nid003539: tasks 656-657: Terminated -srun: error: nid001669: tasks 80-81: Terminated -srun: error: nid005216: tasks 856-857: Terminated -srun: error: nid001925: tasks 280-281: Terminated -srun: error: nid001947: tasks 322-323: Terminated -srun: error: nid002385: tasks 480-481: Terminated -srun: error: nid002617: tasks 522-523: Terminated -srun: error: nid004478: tasks 680-681: Terminated -srun: error: nid004753: tasks 722-723: Terminated -srun: error: nid001760: tasks 146-147: Terminated -srun: error: nid005495: tasks 880-881: Terminated -srun: error: nid006178: tasks 922-923: Terminated -srun: error: nid001961: tasks 346-347: Terminated -srun: error: nid002630: tasks 546-547: Terminated -srun: error: nid001408: tasks 12-13: Terminated -srun: error: nid001821: tasks 170-171: Terminated -srun: error: nid001869: tasks 212-213: Terminated -srun: error: nid006255: tasks 946-947: Terminated -srun: error: nid002007: tasks 370-371: Terminated -srun: error: nid002255: tasks 412-413: Terminated -srun: error: nid006290: tasks 988-989: Terminated -srun: error: nid001494: tasks 36-37: Terminated -srun: error: nid003265: tasks 612-613: Terminated -srun: error: nid004837: tasks 812-813: Terminated -srun: error: nid001901: tasks 236-237: Terminated -srun: error: nid006339: tasks 1012-1013: Terminated -srun: error: nid002309: tasks 436-437: Terminated -srun: error: nid004771: tasks 746-747: Terminated -srun: error: nid003493: tasks 636-637: Terminated -srun: error: nid001657: tasks 60-61: Terminated -srun: error: nid001688: tasks 102-103: Terminated -srun: error: nid003558: tasks 678-679: Terminated -srun: error: nid005140: tasks 836-837: Terminated -srun: error: nid005494: tasks 878-879: Terminated -srun: error: nid001936: tasks 302-303: Terminated -srun: error: nid002583: tasks 502-503: Terminated -srun: error: nid001738: tasks 126-127: Terminated -srun: error: nid004729: tasks 702-703: Terminated -srun: error: nid001949: tasks 326-327: Terminated -srun: error: nid006122: tasks 902-903: Terminated -srun: error: nid002005: tasks 368-369: Terminated -srun: error: nid002620: tasks 526-527: Terminated -srun: error: nid003231: tasks 568-569: Terminated -srun: error: nid004756: tasks 726-727: Terminated -srun: error: nid004786: tasks 768-769: Terminated -srun: error: nid001854: tasks 192-193: Terminated -srun: error: nid006276: tasks 968-969: Terminated -srun: error: nid002220: tasks 392-393: Terminated -srun: error: nid001426: tasks 16-17: Terminated -srun: error: nid003253: tasks 592-593: Terminated -srun: error: nid001656: tasks 58-59: Terminated -srun: error: nid001872: tasks 216-217: Terminated -srun: error: nid004816: tasks 792-793: Terminated -srun: error: nid001913: tasks 258-259: Terminated -srun: error: nid002259: tasks 416-417: Terminated -srun: error: nid006294: tasks 992-993: Terminated -srun: error: nid002351: tasks 458-459: Terminated -srun: error: nid003540: tasks 658-659: Terminated -srun: error: nid001670: tasks 82-83: Terminated -srun: error: nid005218: tasks 858-859: Terminated -srun: error: nid001926: tasks 282-283: Terminated -srun: error: nid002389: tasks 482-483: Terminated -srun: error: nid004479: tasks 682-683: Terminated -srun: error: nid001704: tasks 106-107: Terminated -srun: error: nid004754: tasks 724-725: Terminated -srun: error: nid001761: tasks 148-149: Terminated -srun: error: nid005503: tasks 882-883: Terminated -srun: error: nid006179: tasks 924-925: Terminated -srun: error: nid001962: tasks 348-349: Terminated -srun: error: nid001822: tasks 172-173: Terminated -srun: error: nid004773: tasks 748-749: Terminated -srun: error: nid001871: tasks 214-215: Terminated -srun: error: nid002008: tasks 372-373: Terminated -srun: error: nid006256: tasks 948-949: Terminated -srun: error: nid002256: tasks 414-415: Terminated -srun: error: nid003242: tasks 572-573: Terminated -srun: error: nid001497: tasks 38-39: Terminated -srun: error: nid003269: tasks 614-615: Terminated -srun: error: nid004789: tasks 772-773: Terminated -srun: error: nid001902: tasks 238-239: Terminated -srun: error: nid005104: tasks 814-815: Terminated -srun: error: nid002631: tasks 548-549: Terminated -srun: error: nid002310: tasks 438-439: Terminated -srun: error: nid006340: tasks 1014-1015: Terminated -srun: error: nid003495: tasks 638-639: Terminated -srun: error: nid001658: tasks 62-63: Terminated -srun: error: nid001691: tasks 104-105: Terminated -srun: error: nid005165: tasks 838-839: Terminated -srun: error: nid001915: tasks 262-263: Terminated -srun: error: nid001937: tasks 304-305: Terminated -srun: error: nid002593: tasks 504-505: Terminated -srun: error: nid001744: tasks 128-129: Terminated -srun: error: nid004736: tasks 704-705: Terminated -srun: error: nid001950: tasks 328-329: Terminated -srun: error: nid006137: tasks 904-905: Terminated -srun: error: nid002621: tasks 528-529: Terminated -srun: error: nid003241: tasks 570-571: Terminated -srun: error: nid004757: tasks 728-729: Terminated -srun: error: nid004788: tasks 770-771: Terminated -srun: error: nid001855: tasks 194-195: Terminated -srun: error: nid006188: tasks 928-929: Terminated -srun: error: nid006277: tasks 970-971: Terminated -srun: error: nid002227: tasks 394-395: Terminated -srun: error: nid001429: tasks 18-19: Terminated -srun: error: nid003255: tasks 594-595: Terminated -srun: error: nid001876: tasks 218-219: Terminated -srun: error: nid004821: tasks 794-795: Terminated -srun: error: nid001914: tasks 260-261: Terminated -srun: error: nid002260: tasks 418-419: Terminated -srun: error: nid006297: tasks 994-995: Terminated -srun: error: nid002352: tasks 460-461: Terminated -srun: error: nid003299: tasks 618-619: Terminated -srun: error: nid003541: tasks 660-661: Terminated -srun: error: nid001671: tasks 84-85: Terminated -srun: error: nid001927: tasks 284-285: Terminated -srun: error: nid005480: tasks 860-861: Terminated -srun: error: nid002400: tasks 484-485: Terminated -srun: error: nid001710: tasks 108-109: Terminated -srun: error: nid004480: tasks 684-685: Terminated -srun: error: nid001762: tasks 150-151: Terminated -srun: error: nid001939: tasks 308-309: Terminated -srun: error: nid005507: tasks 884-885: Terminated -srun: error: nid001963: tasks 350-351: Terminated -srun: error: nid006186: tasks 926-927: Terminated -srun: error: nid002632: tasks 550-551: Terminated -srun: error: nid001823: tasks 174-175: Terminated -srun: error: nid004775: tasks 750-751: Terminated -srun: error: nid006257: tasks 950-951: Terminated -srun: error: nid002009: tasks 374-375: Terminated -srun: error: nid003243: tasks 574-575: Terminated -srun: error: nid001498: tasks 40-41: Terminated -srun: error: nid003294: tasks 616-617: Terminated -srun: error: nid004790: tasks 774-775: Terminated -srun: error: nid001903: tasks 240-241: Terminated -srun: error: nid005128: tasks 816-817: Terminated -srun: error: nid006280: tasks 974-975: Terminated -srun: error: nid006341: tasks 1016-1017: Terminated -srun: error: nid002311: tasks 440-441: Terminated -srun: error: nid001659: tasks 64-65: Terminated -srun: error: nid003496: tasks 640-641: Terminated -srun: error: nid001917: tasks 264-265: Terminated -srun: error: nid005180: tasks 840-841: Terminated -srun: error: nid001938: tasks 306-307: Terminated -srun: error: nid002356: tasks 464-465: Terminated -srun: error: nid002595: tasks 506-507: Terminated -srun: error: nid003549: tasks 664-665: Terminated -srun: error: nid001747: tasks 130-131: Terminated -srun: error: nid004737: tasks 706-707: Terminated -srun: error: nid001951: tasks 330-331: Terminated -srun: error: nid006138: tasks 906-907: Terminated -srun: error: nid002622: tasks 530-531: Terminated -srun: error: nid001765: tasks 154-155: Terminated -srun: error: nid004758: tasks 730-731: Terminated -srun: error: nid001856: tasks 196-197: Terminated -srun: error: nid006189: tasks 930-931: Terminated -srun: error: nid001991: tasks 354-355: Terminated -srun: error: nid006279: tasks 972-973: Terminated -srun: error: nid002238: tasks 396-397: Terminated -srun: error: nid001463: tasks 20-21: Terminated -srun: error: nid003256: tasks 596-597: Terminated -srun: error: nid001885: tasks 220-221: Terminated -srun: error: nid004824: tasks 796-797: Terminated -srun: error: nid002261: tasks 420-421: Terminated -srun: error: nid006299: tasks 996-997: Terminated -srun: error: nid002353: tasks 462-463: Terminated -srun: error: nid003441: tasks 620-621: Terminated -srun: error: nid003542: tasks 662-663: Terminated -srun: error: nid005130: tasks 820-821: Terminated -srun: error: nid001672: tasks 86-87: Terminated -srun: error: nid005481: tasks 862-863: Terminated -srun: error: nid006346: tasks 1020-1021: Terminated -srun: error: nid001928: tasks 286-287: Terminated -srun: error: nid002402: tasks 486-487: Terminated -srun: error: nid001711: tasks 110-111: Terminated -srun: error: nid004481: tasks 686-687: Terminated -srun: error: nid001763: tasks 152-153: Terminated -srun: error: nid001940: tasks 310-311: Terminated -srun: error: nid005511: tasks 886-887: Terminated -srun: error: nid001964: tasks 352-353: Terminated -srun: error: nid002606: tasks 510-511: Terminated -srun: error: nid002635: tasks 552-553: Terminated -srun: error: nid004740: tasks 710-711: Terminated -srun: error: nid004776: tasks 752-753: Terminated -srun: error: nid001826: tasks 176-177: Terminated -srun: error: nid006258: tasks 952-953: Terminated -srun: error: nid002011: tasks 376-377: Terminated -srun: error: nid003244: tasks 576-577: Terminated -srun: error: nid001386: tasks 0-1: Terminated -srun: error: nid001504: tasks 42-43: Terminated -srun: error: nid001858: tasks 200-201: Terminated -srun: error: nid004794: tasks 776-777: Terminated -srun: error: nid005129: tasks 818-819: Terminated -srun: error: nid001904: tasks 242-243: Terminated -srun: error: nid006281: tasks 976-977: Terminated -srun: error: nid002312: tasks 442-443: Terminated -srun: error: nid006344: tasks 1018-1019: Terminated -srun: error: nid003497: tasks 642-643: Terminated -srun: error: nid001660: tasks 66-67: Terminated -srun: error: nid001918: tasks 266-267: Terminated -srun: error: nid005182: tasks 842-843: Terminated -srun: error: nid002357: tasks 466-467: Terminated -srun: error: nid002603: tasks 508-509: Terminated -srun: error: nid003550: tasks 666-667: Terminated -srun: error: nid001751: tasks 132-133: Terminated -srun: error: nid004739: tasks 708-709: Terminated -srun: error: nid005484: tasks 866-867: Terminated -srun: error: nid001952: tasks 332-333: Terminated -srun: error: nid006139: tasks 908-909: Terminated -srun: error: nid002623: tasks 532-533: Terminated -srun: error: nid001767: tasks 156-157: Terminated -srun: error: nid004761: tasks 732-733: Terminated -srun: error: nid001857: tasks 198-199: Terminated -srun: error: nid001994: tasks 356-357: Terminated -srun: error: nid006212: tasks 932-933: Terminated -srun: error: nid002241: tasks 398-399: Terminated -srun: error: nid002650: tasks 556-557: Terminated -srun: error: nid001468: tasks 22-23: Terminated -srun: error: nid003257: tasks 598-599: Terminated -srun: error: nid001888: tasks 222-223: Terminated -srun: error: nid004827: tasks 798-799: Terminated -srun: error: nid002262: tasks 422-423: Terminated -srun: error: nid006311: tasks 998-999: Terminated -srun: error: nid003476: tasks 622-623: Terminated -srun: error: nid001509: tasks 46-47: Terminated -srun: error: nid001673: tasks 88-89: Terminated -srun: error: nid005131: tasks 822-823: Terminated -srun: error: nid001906: tasks 246-247: Terminated -srun: error: nid001929: tasks 288-289: Terminated -srun: error: nid006350: tasks 1022-1023: Terminated -srun: error: nid002403: tasks 488-489: Terminated -srun: error: nid004482: tasks 688-689: Terminated -srun: error: nid001713: tasks 112-113: Terminated -srun: error: nid005519: tasks 888-889: Terminated -srun: error: nid001942: tasks 312-313: Terminated -srun: error: nid002607: tasks 512-513: Terminated -srun: error: nid002649: tasks 554-555: Terminated -srun: error: nid005482: tasks 864-865: Terminated -srun: error: nid004743: tasks 712-713: Terminated -srun: error: nid004777: tasks 754-755: Terminated -srun: error: nid001832: tasks 178-179: Terminated -srun: error: nid006151: tasks 912-913: Terminated -srun: error: nid006259: tasks 954-955: Terminated -srun: error: nid002197: tasks 378-379: Terminated -srun: error: nid001387: tasks 2-3: Terminated -srun: error: nid003245: tasks 578-579: Terminated -srun: error: nid001505: tasks 44-45: Terminated -srun: error: nid001864: tasks 202-203: Terminated -srun: error: nid004795: tasks 778-779: Terminated -srun: error: nid001905: tasks 244-245: Terminated -srun: error: nid006285: tasks 978-979: Terminated -srun: error: nid002247: tasks 402-403: Terminated -srun: error: nid002313: tasks 444-445: Terminated -srun: error: nid003259: tasks 602-603: Terminated -srun: error: nid003498: tasks 644-645: Terminated -srun: error: nid001661: tasks 68-69: Terminated -srun: error: nid005185: tasks 844-845: Terminated -srun: error: nid001919: tasks 268-269: Terminated -srun: error: nid002358: tasks 468-469: Terminated -srun: error: nid003551: tasks 668-669: Terminated -srun: error: nid001675: tasks 92-93: Terminated -srun: error: nid001752: tasks 134-135: Terminated -srun: error: nid005486: tasks 868-869: Terminated -srun: error: nid001931: tasks 292-293: Terminated -srun: error: nid001953: tasks 334-335: Terminated -srun: error: nid006149: tasks 910-911: Terminated -srun: Force Terminated StepId=11079233.3 diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11262498/err_run_0.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11262498/err_run_0.log deleted file mode 100644 index e69de29b..00000000 diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11262498/err_run_1.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11262498/err_run_1.log deleted file mode 100644 index e69de29b..00000000 diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11262498/err_run_2.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11262498/err_run_2.log deleted file mode 100644 index e69de29b..00000000 diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11262498/err_run_3.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11262498/err_run_3.log deleted file mode 100644 index e69de29b..00000000 diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11262497/err_run_0.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11262497/err_run_0.log deleted file mode 100644 index e69de29b..00000000 diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11262497/err_run_1.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11262497/err_run_1.log deleted file mode 100644 index e69de29b..00000000 diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11262497/err_run_2.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11262497/err_run_2.log deleted file mode 100644 index e69de29b..00000000 diff --git a/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11262497/err_run_3.log b/hpc/ijhpca/data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11262497/err_run_3.log deleted file mode 100644 index e69de29b..00000000 From 4d84d1c76e0cbd7ea2920608664442ee098e2e6e Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Wed, 22 Oct 2025 09:26:23 +0100 Subject: [PATCH 45/52] Remove log files --- .gitignore | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.gitignore b/.gitignore index d8ef49ed..3235824e 100644 --- a/.gitignore +++ b/.gitignore @@ -40,3 +40,5 @@ kifmm/python/kifmm_py/_kifmm_rs/**/*.py *.o **/build/* *.clang-format + +*.log \ No newline at end of file From 0eb781a20cbfd947c9b6ccbc13b426bd6948405d Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Wed, 22 Oct 2025 09:42:41 +0100 Subject: [PATCH 46/52] Add runtime --- kifmm/src/fmm/charge_handler/multi_node.rs | 11 ++++++- kifmm/src/fmm/ghost_exchange.rs | 14 ++++----- kifmm/src/traits/types.rs | 9 ++++++ scripts/src/bin/fmm_m2l_fft_mpi_f32.rs | 35 ++++++++++++++++++++-- 4 files changed, 59 insertions(+), 10 deletions(-) diff --git a/kifmm/src/fmm/charge_handler/multi_node.rs b/kifmm/src/fmm/charge_handler/multi_node.rs index ac3b0b34..229a4b49 100644 --- a/kifmm/src/fmm/charge_handler/multi_node.rs +++ b/kifmm/src/fmm/charge_handler/multi_node.rs @@ -1,4 +1,6 @@ //! Charge handling in multi-node setting +use std::time::Instant; + use green_kernels::traits::Kernel as KernelTrait; use itertools::izip; use mpi::{ @@ -14,7 +16,7 @@ use crate::{ fmm::{ChargeHandler, HomogenousKernel}, general::multi_node::GhostExchange, tree::{MultiFmmTree, MultiTree}, - types::FmmError, + types::{FmmError, MPICollectiveType, OperatorTime}, }, KiFmmMulti, }; @@ -171,8 +173,15 @@ where &self.charge_receive_queries_displacements[..], ); + let st = Instant::now(); self.neighbourhood_communicator_charge .all_to_all_varcount_into(&partition_send, &mut partition_receive); + self.mpi_times + .entry(MPICollectiveType::NeighbourAlltoAllv) + .and_modify(|t| t.time += st.elapsed().as_millis() as u64) + .or_insert(OperatorTime { + time: st.elapsed().as_millis() as u64, + }); } self.charges = requested_queries; diff --git a/kifmm/src/fmm/ghost_exchange.rs b/kifmm/src/fmm/ghost_exchange.rs index f8416f21..ddf65d52 100644 --- a/kifmm/src/fmm/ghost_exchange.rs +++ b/kifmm/src/fmm/ghost_exchange.rs @@ -99,7 +99,7 @@ where let st = Instant::now(); root_process.gather_varcount_into_root(&multipoles, &mut partition); self.mpi_times - .entry(MPICollectiveType::GatherV) + .entry(MPICollectiveType::GatherVRuntime) .and_modify(|t| t.time += st.elapsed().as_millis() as u64) .or_insert(OperatorTime { time: st.elapsed().as_millis() as u64, @@ -124,7 +124,7 @@ where let st = Instant::now(); root_process.gather_varcount_into(&multipoles); self.mpi_times - .entry(MPICollectiveType::GatherV) + .entry(MPICollectiveType::GatherVRuntime) .and_modify(|t| t.time += st.elapsed().as_millis() as u64) .or_insert(OperatorTime { time: st.elapsed().as_millis() as u64, @@ -186,7 +186,7 @@ where let st = Instant::now(); root_process.scatter_varcount_into_root(&partition, &mut receive_buffer); self.mpi_times - .entry(MPICollectiveType::ScatterV) + .entry(MPICollectiveType::ScatterVRuntime) .and_modify(|t| t.time += st.elapsed().as_millis() as u64) .or_insert(OperatorTime { time: st.elapsed().as_millis() as u64, @@ -201,7 +201,7 @@ where let st = Instant::now(); root_process.scatter_varcount_into_root(&partition, &mut expected_roots); self.mpi_times - .entry(MPICollectiveType::ScatterV) + .entry(MPICollectiveType::ScatterVRuntime) .and_modify(|t| t.time += st.elapsed().as_millis() as u64) .or_insert(OperatorTime { time: st.elapsed().as_millis() as u64, @@ -210,7 +210,7 @@ where let st = Instant::now(); root_process.scatter_varcount_into(&mut receive_buffer); self.mpi_times - .entry(MPICollectiveType::ScatterV) + .entry(MPICollectiveType::ScatterVRuntime) .and_modify(|t| t.time += st.elapsed().as_millis() as u64) .or_insert(OperatorTime { time: st.elapsed().as_millis() as u64, @@ -218,7 +218,7 @@ where let st = Instant::now(); root_process.scatter_varcount_into(&mut expected_roots); self.mpi_times - .entry(MPICollectiveType::ScatterV) + .entry(MPICollectiveType::ScatterVRuntime) .and_modify(|t| t.time += st.elapsed().as_millis() as u64) .or_insert(OperatorTime { time: st.elapsed().as_millis() as u64, @@ -992,7 +992,7 @@ where self.neighbourhood_communicator_v .all_to_all_varcount_into(&partition_send, &mut partition_receive); self.mpi_times - .entry(MPICollectiveType::NeighbourAlltoAllv) + .entry(MPICollectiveType::NeighbourAlltoAllvRuntime) .and_modify(|t| t.time += st.elapsed().as_millis() as u64) .or_insert(OperatorTime { time: st.elapsed().as_millis() as u64, diff --git a/kifmm/src/traits/types.rs b/kifmm/src/traits/types.rs index d96b9545..5c974e35 100644 --- a/kifmm/src/traits/types.rs +++ b/kifmm/src/traits/types.rs @@ -56,6 +56,9 @@ pub enum MPICollectiveType { /// Neighbour All to All V NeighbourAlltoAllv, + /// Neighbour All to All V + NeighbourAlltoAllvRuntime, + /// Gather Gather, @@ -68,6 +71,12 @@ pub enum MPICollectiveType { /// Scatter ScatterV, + /// Gather + GatherVRuntime, + + /// Scatter + ScatterVRuntime, + /// All Gather AllGather, diff --git a/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs b/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs index f2e47e79..041a3522 100644 --- a/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs +++ b/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs @@ -186,6 +186,10 @@ fn main() { mpi_times.insert("neighbour_all_to_all_v", op_time.time); } + MPICollectiveType::NeighbourAlltoAllvRuntime => { + mpi_times.insert("neighbour_all_to_all_v_runtime", op_time.time); + } + MPICollectiveType::Gather => { mpi_times.insert("gather", op_time.time); } @@ -200,6 +204,13 @@ fn main() { mpi_times.insert("scatter_v", op_time.time); } + MPICollectiveType::GatherVRuntime => { + mpi_times.insert("gather_v_runtime", op_time.time); + } + MPICollectiveType::ScatterVRuntime => { + mpi_times.insert("scatter_v_runtime", op_time.time); + } + MPICollectiveType::AllGather => { mpi_times.insert("all_gather", op_time.time); } @@ -232,6 +243,16 @@ fn main() { MPICollectiveType::NeighbourAlltoAllv => { mpi_times.insert("tree_neighbour_all_to_all_v", op_time.time); } + MPICollectiveType::NeighbourAlltoAllvRuntime => { + mpi_times.insert("tree_neighbour_all_to_all_v_runtime", op_time.time); + } + + MPICollectiveType::GatherVRuntime => { + mpi_times.insert("tree_gather_v_runtime", op_time.time); + } + MPICollectiveType::ScatterVRuntime => { + mpi_times.insert("tree_scatter_v_runtime", op_time.time); + } MPICollectiveType::Gather => { mpi_times.insert("tree_gather", op_time.time); @@ -375,8 +396,8 @@ fn main() { {:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?}, \ {:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?}, \ {:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?}, \ - {:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?}, \ - {:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?}", + {:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?}\ + {:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?}", id, multi_fmm.rank(), runtime, @@ -418,10 +439,15 @@ fn main() { mpi_times.get("all_to_all_v").unwrap_or(&0), mpi_times.get("neighbour_all_to_all").unwrap_or(&0), mpi_times.get("neighbour_all_to_all_v").unwrap_or(&0), + mpi_times + .get("neighbour_all_to_all_v_runtime") + .unwrap_or(&0), mpi_times.get("gather").unwrap_or(&0), mpi_times.get("scatter").unwrap_or(&0), mpi_times.get("gather_v").unwrap_or(&0), mpi_times.get("scatter_v").unwrap_or(&0), + mpi_times.get("gather_v_runtime").unwrap_or(&0), + mpi_times.get("scatter_v_runtime").unwrap_or(&0), mpi_times.get("all_gather").unwrap_or(&0), mpi_times.get("all_gather_v").unwrap_or(&0), mpi_times.get("dist_graph_create").unwrap_or(&0), @@ -430,10 +456,15 @@ fn main() { mpi_times.get("tree_all_to_all_v").unwrap_or(&0), mpi_times.get("tree_neighbour_all_to_all").unwrap_or(&0), mpi_times.get("tree_neighbour_all_to_all_v").unwrap_or(&0), + mpi_times + .get("tree_neighbour_all_to_all_v_runtime") + .unwrap_or(&0), mpi_times.get("tree_gather").unwrap_or(&0), mpi_times.get("tree_scatter").unwrap_or(&0), mpi_times.get("tree_gather_v").unwrap_or(&0), mpi_times.get("tree_scatter_v").unwrap_or(&0), + mpi_times.get("tree_gather_v_runtime").unwrap_or(&0), + mpi_times.get("tree_scatter_v_runtime").unwrap_or(&0), mpi_times.get("tree_all_gather").unwrap_or(&0), mpi_times.get("tree_all_gather_v").unwrap_or(&0), mpi_times.get("tree_dist_graph_create").unwrap_or(&0), From 04b00e5d930bb43b022fc3c28b97b5a31bd038e2 Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Wed, 22 Oct 2025 09:48:19 +0100 Subject: [PATCH 47/52] Update generation scripts --- hpc/ijhpca/strong_scaling.py | 8 ++------ hpc/ijhpca/strong_scaling_ccd.json | 1 - hpc/ijhpca/strong_scaling_ccx.json | 1 - hpc/ijhpca/strong_scaling_socket.json | 1 - hpc/ijhpca/weak_scaling.py | 9 +++------ hpc/ijhpca/weak_scaling_ccd.json | 1 - hpc/ijhpca/weak_scaling_ccx.json | 1 - hpc/ijhpca/weak_scaling_socket.json | 1 - 8 files changed, 5 insertions(+), 18 deletions(-) diff --git a/hpc/ijhpca/strong_scaling.py b/hpc/ijhpca/strong_scaling.py index 0a1ced3c..e1113929 100644 --- a/hpc/ijhpca/strong_scaling.py +++ b/hpc/ijhpca/strong_scaling.py @@ -149,7 +149,7 @@ def experiment_parameters( -def write_slurm(script_path, n_nodes, n_tasks, local_depths, global_depths, max_threads, points_per_rank, contiguous=False, script_name="fmm_m2l_fft_mpi_f32", distribution="uniform"): +def write_slurm(script_path, n_nodes, n_tasks, local_depths, global_depths, max_threads, points_per_rank, script_name="fmm_m2l_fft_mpi_f32", distribution="uniform"): expansion_order = 3 n_samples = 500 block_size = 128 @@ -169,9 +169,6 @@ def write_slurm(script_path, n_nodes, n_tasks, local_depths, global_depths, max_ #SBATCH --partition=standard #SBATCH --qos=standard """ - if contiguous: - slurm += "\n#SBATCH --contiguous\n" - slurm += f""" module load PrgEnv-aocc module load craype-network-ucx @@ -226,7 +223,6 @@ def write_slurm(script_path, n_nodes, n_tasks, local_depths, global_depths, max_ parser.add_argument("--min-local-depth", type=int, default=4) parser.add_argument("--scaling-func", type=int, default=2) parser.add_argument("--method", type=str, default="ccx") - parser.add_argument("--contiguous", type=bool, default=False) parser.add_argument("--output", type=str, default="job.slurm") parser.add_argument("--distribution", type=str, default="uniform") parser.add_argument("--config", action='append') @@ -252,6 +248,6 @@ def write_slurm(script_path, n_nodes, n_tasks, local_depths, global_depths, max_ n_nodes, n_tasks, global_depths, local_depths, points_per_rank, max_threads, distribution = experiment_parameters( args.min_nodes, args.max_nodes, ranks_per_node[args.method], args.min_points_per_rank, args.min_local_depth, scaling_func=scaling_func, distribution=args.distribution ) - write_slurm(f"{args.method}_"+args.output, n_nodes, n_tasks, local_depths, global_depths, max_threads, points_per_rank, contiguous=args.contiguous, distribution=distribution) + write_slurm(f"{args.method}_"+args.output, n_nodes, n_tasks, local_depths, global_depths, max_threads, points_per_rank, distribution=distribution) else: raise ValueError("Unknown method") \ No newline at end of file diff --git a/hpc/ijhpca/strong_scaling_ccd.json b/hpc/ijhpca/strong_scaling_ccd.json index ca548ecc..89b3d8a3 100644 --- a/hpc/ijhpca/strong_scaling_ccd.json +++ b/hpc/ijhpca/strong_scaling_ccd.json @@ -7,6 +7,5 @@ "output": "strong_scaling_ccd_sphere.slurm", "method": "ccd", "scaling_func": 8, - "contiguous": true, "distribution": "sphere" } \ No newline at end of file diff --git a/hpc/ijhpca/strong_scaling_ccx.json b/hpc/ijhpca/strong_scaling_ccx.json index fe80c967..534b9664 100644 --- a/hpc/ijhpca/strong_scaling_ccx.json +++ b/hpc/ijhpca/strong_scaling_ccx.json @@ -7,6 +7,5 @@ "output": "strong_scaling_ccx_sphere.slurm", "method": "ccx", "scaling_func": 8, - "contiguous": false, "distribution": "sphere" } \ No newline at end of file diff --git a/hpc/ijhpca/strong_scaling_socket.json b/hpc/ijhpca/strong_scaling_socket.json index ede3e5c1..f93a39e8 100644 --- a/hpc/ijhpca/strong_scaling_socket.json +++ b/hpc/ijhpca/strong_scaling_socket.json @@ -7,6 +7,5 @@ "output": "strong_scaling_sphere.slurm", "method": "socket", "scaling_func": 8, - "contiguous": false, "distribution": "sphere" } \ No newline at end of file diff --git a/hpc/ijhpca/weak_scaling.py b/hpc/ijhpca/weak_scaling.py index 629a9bcb..070a8a65 100644 --- a/hpc/ijhpca/weak_scaling.py +++ b/hpc/ijhpca/weak_scaling.py @@ -114,7 +114,7 @@ def experiment_parameters( return n_nodes.tolist(), n_ranks.tolist(), global_depth.tolist(), max_threads_per_rank, distribution -def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, points_per_rank, local_depth, distribution="uniform", contiguous=False, script_name="fmm_m2l_fft_mpi_f32"): +def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, points_per_rank, local_depth, distribution="uniform", script_name="fmm_m2l_fft_mpi_f32"): expansion_order = 3 n_points = points_per_rank n_samples = 500 @@ -135,14 +135,12 @@ def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, point #SBATCH --partition=standard #SBATCH --qos=standard """ - if contiguous: - slurm += "\n#SBATCH --contiguous\n" - slurm += f""" module load PrgEnv-aocc module load craype-network-ucx module load cray-mpich-ucx +export UCX_UD_TIMEOUT=20m export HOME="/home/e738/e738/skailasa" export WORK="/work/e738/e738/skailasa" @@ -193,7 +191,6 @@ def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, point parser.add_argument("--points-per-rank", type=int, default=250000) parser.add_argument("--local-depth", type=int, default=4) parser.add_argument("--method", type=str, default="ccx") - parser.add_argument("--contiguous", type=bool, default=False) parser.add_argument("--output", type=str, default="job.slurm") parser.add_argument("--distribution", type=str, default="uniform") parser.add_argument("--config", action='append') @@ -221,7 +218,7 @@ def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, point args.min_nodes, args.max_nodes, ranks_per_node[args.method], args.points_per_rank, args.local_depth, distribution=args.distribution ) - write_slurm(args.output, n_nodes, n_tasks, global_depths, max_threads, args.points_per_rank, args.local_depth, contiguous=args.contiguous, distribution=distribution) + write_slurm(args.output, n_nodes, n_tasks, global_depths, max_threads, args.points_per_rank, args.local_depth, distribution=distribution) else: raise ValueError("Unknown method") \ No newline at end of file diff --git a/hpc/ijhpca/weak_scaling_ccd.json b/hpc/ijhpca/weak_scaling_ccd.json index ee263c58..a09e086e 100644 --- a/hpc/ijhpca/weak_scaling_ccd.json +++ b/hpc/ijhpca/weak_scaling_ccd.json @@ -7,6 +7,5 @@ "output": "weak_scaling_ccd_sphere.slurm", "method": "ccd", "scaling_func": 8, - "contiguous": false, "distribution": "sphere" } \ No newline at end of file diff --git a/hpc/ijhpca/weak_scaling_ccx.json b/hpc/ijhpca/weak_scaling_ccx.json index e084cf91..ca1e7810 100644 --- a/hpc/ijhpca/weak_scaling_ccx.json +++ b/hpc/ijhpca/weak_scaling_ccx.json @@ -7,6 +7,5 @@ "output": "weak_scaling_ccx_sphere.slurm", "method": "ccx", "scaling_func": 8, - "contiguous": false, "distribution": "sphere" } \ No newline at end of file diff --git a/hpc/ijhpca/weak_scaling_socket.json b/hpc/ijhpca/weak_scaling_socket.json index 5957e13d..46c394b8 100644 --- a/hpc/ijhpca/weak_scaling_socket.json +++ b/hpc/ijhpca/weak_scaling_socket.json @@ -7,6 +7,5 @@ "output": "weak_scaling_socket_sphere.slurm", "method": "socket", "scaling_func": 8, - "contiguous": false, "distribution": "sphere" } \ No newline at end of file From 01e41002a208d172556303549238f58d4f4f5a38 Mon Sep 17 00:00:00 2001 From: srinath kailasa Date: Wed, 22 Oct 2025 10:36:47 +0100 Subject: [PATCH 48/52] Update scripts --- hpc/ijhpca/strong_scaling.py | 10 ++-------- hpc/ijhpca/strong_scaling_ccd.json | 4 ++-- hpc/ijhpca/strong_scaling_ccd_sphere.json | 11 +++++++++++ hpc/ijhpca/strong_scaling_ccx.json | 4 ++-- hpc/ijhpca/strong_scaling_ccx_sphere.json | 11 +++++++++++ hpc/ijhpca/strong_scaling_socket.json | 4 ++-- hpc/ijhpca/strong_scaling_socket_sphere.json | 11 +++++++++++ hpc/ijhpca/weak_scaling.py | 8 +------- hpc/ijhpca/weak_scaling_big.json | 11 +++++++++++ hpc/ijhpca/weak_scaling_big_sphere.json | 11 +++++++++++ hpc/ijhpca/weak_scaling_ccd.json | 4 ++-- hpc/ijhpca/weak_scaling_ccd_sphere.json | 11 +++++++++++ hpc/ijhpca/weak_scaling_ccx.json | 4 ++-- hpc/ijhpca/weak_scaling_ccx_sphere.json | 11 +++++++++++ hpc/ijhpca/weak_scaling_socket.json | 4 ++-- hpc/ijhpca/weak_scaling_socket_sphere.json | 11 +++++++++++ 16 files changed, 103 insertions(+), 27 deletions(-) create mode 100644 hpc/ijhpca/strong_scaling_ccd_sphere.json create mode 100644 hpc/ijhpca/strong_scaling_ccx_sphere.json create mode 100644 hpc/ijhpca/strong_scaling_socket_sphere.json create mode 100644 hpc/ijhpca/weak_scaling_big.json create mode 100644 hpc/ijhpca/weak_scaling_big_sphere.json create mode 100644 hpc/ijhpca/weak_scaling_ccd_sphere.json create mode 100644 hpc/ijhpca/weak_scaling_ccx_sphere.json create mode 100644 hpc/ijhpca/weak_scaling_socket_sphere.json diff --git a/hpc/ijhpca/strong_scaling.py b/hpc/ijhpca/strong_scaling.py index e1113929..b1204d2a 100644 --- a/hpc/ijhpca/strong_scaling.py +++ b/hpc/ijhpca/strong_scaling.py @@ -188,13 +188,7 @@ def write_slurm(script_path, n_nodes, n_tasks, local_depths, global_depths, max_ export OUTPUT=$SCRATCH/strong_fft_n={max_points}_p={last_nodes}_points_per_rank={points_per_rank[-1]}_distribution={distribution}_${{SLURM_JOBID}}.csv touch $OUTPUT -echo " -experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ -source_tree,target_tree,source_domain,target_domain,layout,\ -ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\ -source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\ -displacement_map,metadata_creation,\ -expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples,source_local_trees_per_rank,target_local_trees_per_rank" >> ${{OUTPUT}} +echo "experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,source_tree,target_tree,source_domain,target_domain,layout,ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,displacement_map,metadata_creation,expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples,source_local_trees_per_rank,target_local_trees_per_rank,all_to_all,all_to_all_v,neighbour_all_to_all,neighbour_all_to_all_v,neighbour_all_to_all_v_runtime,gather,scatter,gather_v,scatter_v,gather_v_runtime,scatter_v_runtime,all_gather,all_gather_v,dist_graph_create,sort,tree_all_to_all,tree_all_to_all_v,tree_neighbour_all_to_all,tree_neighbour_all_to_all_v,tree_neighbour_all_to_all_v_runtime,tree_gather,tree_scatter,tree_gather_v,tree_scatter_v,tree_gather_v_runtime,tree_scatter_v_runtime,tree_all_gather,tree_all_gather_v,tree_dist_graph_create,tree_sort" """ # loop of runs @@ -248,6 +242,6 @@ def write_slurm(script_path, n_nodes, n_tasks, local_depths, global_depths, max_ n_nodes, n_tasks, global_depths, local_depths, points_per_rank, max_threads, distribution = experiment_parameters( args.min_nodes, args.max_nodes, ranks_per_node[args.method], args.min_points_per_rank, args.min_local_depth, scaling_func=scaling_func, distribution=args.distribution ) - write_slurm(f"{args.method}_"+args.output, n_nodes, n_tasks, local_depths, global_depths, max_threads, points_per_rank, distribution=distribution) + write_slurm(args.output, n_nodes, n_tasks, local_depths, global_depths, max_threads, points_per_rank, distribution=distribution) else: raise ValueError("Unknown method") \ No newline at end of file diff --git a/hpc/ijhpca/strong_scaling_ccd.json b/hpc/ijhpca/strong_scaling_ccd.json index 89b3d8a3..64d1c891 100644 --- a/hpc/ijhpca/strong_scaling_ccd.json +++ b/hpc/ijhpca/strong_scaling_ccd.json @@ -4,8 +4,8 @@ "max_nodes": 128, "min_points_per_rank": 125000, "min_local_depth": 3, - "output": "strong_scaling_ccd_sphere.slurm", + "output": "strong_scaling_ccd_uniform.slurm", "method": "ccd", "scaling_func": 8, - "distribution": "sphere" + "distribution": "uniform" } \ No newline at end of file diff --git a/hpc/ijhpca/strong_scaling_ccd_sphere.json b/hpc/ijhpca/strong_scaling_ccd_sphere.json new file mode 100644 index 00000000..89b3d8a3 --- /dev/null +++ b/hpc/ijhpca/strong_scaling_ccd_sphere.json @@ -0,0 +1,11 @@ +{ + "_comment": "Strong scaling", + "min_nodes": 1, + "max_nodes": 128, + "min_points_per_rank": 125000, + "min_local_depth": 3, + "output": "strong_scaling_ccd_sphere.slurm", + "method": "ccd", + "scaling_func": 8, + "distribution": "sphere" +} \ No newline at end of file diff --git a/hpc/ijhpca/strong_scaling_ccx.json b/hpc/ijhpca/strong_scaling_ccx.json index 534b9664..f8ae1828 100644 --- a/hpc/ijhpca/strong_scaling_ccx.json +++ b/hpc/ijhpca/strong_scaling_ccx.json @@ -4,8 +4,8 @@ "max_nodes": 128, "min_points_per_rank": 62500, "min_local_depth": 3, - "output": "strong_scaling_ccx_sphere.slurm", + "output": "strong_scaling_ccx_uniform.slurm", "method": "ccx", "scaling_func": 8, - "distribution": "sphere" + "distribution": "uniform" } \ No newline at end of file diff --git a/hpc/ijhpca/strong_scaling_ccx_sphere.json b/hpc/ijhpca/strong_scaling_ccx_sphere.json new file mode 100644 index 00000000..534b9664 --- /dev/null +++ b/hpc/ijhpca/strong_scaling_ccx_sphere.json @@ -0,0 +1,11 @@ +{ + "_comment": "Strong scaling", + "min_nodes": 1, + "max_nodes": 128, + "min_points_per_rank": 62500, + "min_local_depth": 3, + "output": "strong_scaling_ccx_sphere.slurm", + "method": "ccx", + "scaling_func": 8, + "distribution": "sphere" +} \ No newline at end of file diff --git a/hpc/ijhpca/strong_scaling_socket.json b/hpc/ijhpca/strong_scaling_socket.json index f93a39e8..b4bc3fd7 100644 --- a/hpc/ijhpca/strong_scaling_socket.json +++ b/hpc/ijhpca/strong_scaling_socket.json @@ -4,8 +4,8 @@ "max_nodes": 128, "min_points_per_rank": 1000000, "min_local_depth": 4, - "output": "strong_scaling_sphere.slurm", + "output": "strong_scaling_uniform.slurm", "method": "socket", "scaling_func": 8, - "distribution": "sphere" + "distribution": "uniform" } \ No newline at end of file diff --git a/hpc/ijhpca/strong_scaling_socket_sphere.json b/hpc/ijhpca/strong_scaling_socket_sphere.json new file mode 100644 index 00000000..f93a39e8 --- /dev/null +++ b/hpc/ijhpca/strong_scaling_socket_sphere.json @@ -0,0 +1,11 @@ +{ + "_comment": "Strong scaling", + "min_nodes": 1, + "max_nodes": 128, + "min_points_per_rank": 1000000, + "min_local_depth": 4, + "output": "strong_scaling_sphere.slurm", + "method": "socket", + "scaling_func": 8, + "distribution": "sphere" +} \ No newline at end of file diff --git a/hpc/ijhpca/weak_scaling.py b/hpc/ijhpca/weak_scaling.py index 070a8a65..14d8b17b 100644 --- a/hpc/ijhpca/weak_scaling.py +++ b/hpc/ijhpca/weak_scaling.py @@ -155,13 +155,7 @@ def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, point export OUTPUT=$SCRATCH/weak_fft_n={max_points}_p={last_nodes}_points_per_rank={n_points}_distribution={distribution}_${{SLURM_JOBID}}.csv touch $OUTPUT -echo " -experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\ -source_tree,target_tree,source_domain,target_domain,layout,\ -ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\ -source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\ -displacement_map,metadata_creation,\ -expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples,source_local_trees_per_rank,target_local_trees_per_rank" >> ${{OUTPUT}} +echo "experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,source_tree,target_tree,source_domain,target_domain,layout,ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,displacement_map,metadata_creation,expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples,source_local_trees_per_rank,target_local_trees_per_rank,all_to_all,all_to_all_v,neighbour_all_to_all,neighbour_all_to_all_v,neighbour_all_to_all_v_runtime,gather,scatter,gather_v,scatter_v,gather_v_runtime,scatter_v_runtime,all_gather,all_gather_v,dist_graph_create,sort,tree_all_to_all,tree_all_to_all_v,tree_neighbour_all_to_all,tree_neighbour_all_to_all_v,tree_neighbour_all_to_all_v_runtime,tree_gather,tree_scatter,tree_gather_v,tree_scatter_v,tree_gather_v_runtime,tree_scatter_v_runtime,tree_all_gather,tree_all_gather_v,tree_dist_graph_create,tree_sort" """ # loop of runs diff --git a/hpc/ijhpca/weak_scaling_big.json b/hpc/ijhpca/weak_scaling_big.json new file mode 100644 index 00000000..5f89b254 --- /dev/null +++ b/hpc/ijhpca/weak_scaling_big.json @@ -0,0 +1,11 @@ +{ + "_comment": "Weak scaling", + "min_nodes": 1, + "max_nodes": 512, + "points_per_rank": 32000000, + "local_depth": 5, + "output": "weak_scaling_socket_32B.slurm", + "method": "socket", + "scaling_func": 8, + "distribution": "uniform" +} \ No newline at end of file diff --git a/hpc/ijhpca/weak_scaling_big_sphere.json b/hpc/ijhpca/weak_scaling_big_sphere.json new file mode 100644 index 00000000..2fa7b581 --- /dev/null +++ b/hpc/ijhpca/weak_scaling_big_sphere.json @@ -0,0 +1,11 @@ +{ + "_comment": "Weak scaling", + "min_nodes": 1, + "max_nodes": 512, + "points_per_rank": 32000000, + "local_depth": 6, + "output": "weak_scaling_socket_32B_sphere.slurm", + "method": "socket", + "scaling_func": 8, + "distribution": "sphere" +} \ No newline at end of file diff --git a/hpc/ijhpca/weak_scaling_ccd.json b/hpc/ijhpca/weak_scaling_ccd.json index a09e086e..6082da31 100644 --- a/hpc/ijhpca/weak_scaling_ccd.json +++ b/hpc/ijhpca/weak_scaling_ccd.json @@ -4,8 +4,8 @@ "max_nodes": 512, "points_per_rank": 500000, "local_depth": 4, - "output": "weak_scaling_ccd_sphere.slurm", + "output": "weak_scaling_ccd_uniform.slurm", "method": "ccd", "scaling_func": 8, - "distribution": "sphere" + "distribution": "uniform" } \ No newline at end of file diff --git a/hpc/ijhpca/weak_scaling_ccd_sphere.json b/hpc/ijhpca/weak_scaling_ccd_sphere.json new file mode 100644 index 00000000..a09e086e --- /dev/null +++ b/hpc/ijhpca/weak_scaling_ccd_sphere.json @@ -0,0 +1,11 @@ +{ + "_comment": "Weak scaling", + "min_nodes": 1, + "max_nodes": 512, + "points_per_rank": 500000, + "local_depth": 4, + "output": "weak_scaling_ccd_sphere.slurm", + "method": "ccd", + "scaling_func": 8, + "distribution": "sphere" +} \ No newline at end of file diff --git a/hpc/ijhpca/weak_scaling_ccx.json b/hpc/ijhpca/weak_scaling_ccx.json index ca1e7810..a5f6be3d 100644 --- a/hpc/ijhpca/weak_scaling_ccx.json +++ b/hpc/ijhpca/weak_scaling_ccx.json @@ -4,8 +4,8 @@ "max_nodes": 512, "points_per_rank": 250000, "local_depth": 4, - "output": "weak_scaling_ccx_sphere.slurm", + "output": "weak_scaling_ccx_uniform.slurm", "method": "ccx", "scaling_func": 8, - "distribution": "sphere" + "distribution": "uniform" } \ No newline at end of file diff --git a/hpc/ijhpca/weak_scaling_ccx_sphere.json b/hpc/ijhpca/weak_scaling_ccx_sphere.json new file mode 100644 index 00000000..ca1e7810 --- /dev/null +++ b/hpc/ijhpca/weak_scaling_ccx_sphere.json @@ -0,0 +1,11 @@ +{ + "_comment": "Weak scaling", + "min_nodes": 1, + "max_nodes": 512, + "points_per_rank": 250000, + "local_depth": 4, + "output": "weak_scaling_ccx_sphere.slurm", + "method": "ccx", + "scaling_func": 8, + "distribution": "sphere" +} \ No newline at end of file diff --git a/hpc/ijhpca/weak_scaling_socket.json b/hpc/ijhpca/weak_scaling_socket.json index 46c394b8..ae73468d 100644 --- a/hpc/ijhpca/weak_scaling_socket.json +++ b/hpc/ijhpca/weak_scaling_socket.json @@ -4,8 +4,8 @@ "max_nodes": 512, "points_per_rank": 4000000, "local_depth": 5, - "output": "weak_scaling_socket_sphere.slurm", + "output": "weak_scaling_socket_uniform.slurm", "method": "socket", "scaling_func": 8, - "distribution": "sphere" + "distribution": "uniform" } \ No newline at end of file diff --git a/hpc/ijhpca/weak_scaling_socket_sphere.json b/hpc/ijhpca/weak_scaling_socket_sphere.json new file mode 100644 index 00000000..46c394b8 --- /dev/null +++ b/hpc/ijhpca/weak_scaling_socket_sphere.json @@ -0,0 +1,11 @@ +{ + "_comment": "Weak scaling", + "min_nodes": 1, + "max_nodes": 512, + "points_per_rank": 4000000, + "local_depth": 5, + "output": "weak_scaling_socket_sphere.slurm", + "method": "socket", + "scaling_func": 8, + "distribution": "sphere" +} \ No newline at end of file From 528239cd4f21f96a56d7c0eb3d67c8a00f6e473f Mon Sep 17 00:00:00 2001 From: srinath kailasa Date: Wed, 22 Oct 2025 11:17:43 +0100 Subject: [PATCH 49/52] Fix scripts --- hpc/ijhpca/strong_scaling.py | 2 +- hpc/ijhpca/strong_scaling_socket.json | 2 +- hpc/ijhpca/strong_scaling_socket_sphere.json | 2 +- hpc/ijhpca/weak_scaling.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/hpc/ijhpca/strong_scaling.py b/hpc/ijhpca/strong_scaling.py index b1204d2a..b7208bea 100644 --- a/hpc/ijhpca/strong_scaling.py +++ b/hpc/ijhpca/strong_scaling.py @@ -188,7 +188,7 @@ def write_slurm(script_path, n_nodes, n_tasks, local_depths, global_depths, max_ export OUTPUT=$SCRATCH/strong_fft_n={max_points}_p={last_nodes}_points_per_rank={points_per_rank[-1]}_distribution={distribution}_${{SLURM_JOBID}}.csv touch $OUTPUT -echo "experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,source_tree,target_tree,source_domain,target_domain,layout,ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,displacement_map,metadata_creation,expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples,source_local_trees_per_rank,target_local_trees_per_rank,all_to_all,all_to_all_v,neighbour_all_to_all,neighbour_all_to_all_v,neighbour_all_to_all_v_runtime,gather,scatter,gather_v,scatter_v,gather_v_runtime,scatter_v_runtime,all_gather,all_gather_v,dist_graph_create,sort,tree_all_to_all,tree_all_to_all_v,tree_neighbour_all_to_all,tree_neighbour_all_to_all_v,tree_neighbour_all_to_all_v_runtime,tree_gather,tree_scatter,tree_gather_v,tree_scatter_v,tree_gather_v_runtime,tree_scatter_v_runtime,tree_all_gather,tree_all_gather_v,tree_dist_graph_create,tree_sort" +echo "experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,source_tree,target_tree,source_domain,target_domain,layout,ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,displacement_map,metadata_creation,expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples,source_local_trees_per_rank,target_local_trees_per_rank,all_to_all,all_to_all_v,neighbour_all_to_all,neighbour_all_to_all_v,neighbour_all_to_all_v_runtime,gather,scatter,gather_v,scatter_v,gather_v_runtime,scatter_v_runtime,all_gather,all_gather_v,dist_graph_create,sort,tree_all_to_all,tree_all_to_all_v,tree_neighbour_all_to_all,tree_neighbour_all_to_all_v,tree_neighbour_all_to_all_v_runtime,tree_gather,tree_scatter,tree_gather_v,tree_scatter_v,tree_gather_v_runtime,tree_scatter_v_runtime,tree_all_gather,tree_all_gather_v,tree_dist_graph_create,tree_sort" >> ${{OUTPUT}} """ # loop of runs diff --git a/hpc/ijhpca/strong_scaling_socket.json b/hpc/ijhpca/strong_scaling_socket.json index b4bc3fd7..73714274 100644 --- a/hpc/ijhpca/strong_scaling_socket.json +++ b/hpc/ijhpca/strong_scaling_socket.json @@ -4,7 +4,7 @@ "max_nodes": 128, "min_points_per_rank": 1000000, "min_local_depth": 4, - "output": "strong_scaling_uniform.slurm", + "output": "strong_scaling_socket_uniform.slurm", "method": "socket", "scaling_func": 8, "distribution": "uniform" diff --git a/hpc/ijhpca/strong_scaling_socket_sphere.json b/hpc/ijhpca/strong_scaling_socket_sphere.json index f93a39e8..a533934f 100644 --- a/hpc/ijhpca/strong_scaling_socket_sphere.json +++ b/hpc/ijhpca/strong_scaling_socket_sphere.json @@ -4,7 +4,7 @@ "max_nodes": 128, "min_points_per_rank": 1000000, "min_local_depth": 4, - "output": "strong_scaling_sphere.slurm", + "output": "strong_scaling_socket_sphere.slurm", "method": "socket", "scaling_func": 8, "distribution": "sphere" diff --git a/hpc/ijhpca/weak_scaling.py b/hpc/ijhpca/weak_scaling.py index 14d8b17b..9cb66d59 100644 --- a/hpc/ijhpca/weak_scaling.py +++ b/hpc/ijhpca/weak_scaling.py @@ -155,7 +155,7 @@ def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, point export OUTPUT=$SCRATCH/weak_fft_n={max_points}_p={last_nodes}_points_per_rank={n_points}_distribution={distribution}_${{SLURM_JOBID}}.csv touch $OUTPUT -echo "experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,source_tree,target_tree,source_domain,target_domain,layout,ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,displacement_map,metadata_creation,expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples,source_local_trees_per_rank,target_local_trees_per_rank,all_to_all,all_to_all_v,neighbour_all_to_all,neighbour_all_to_all_v,neighbour_all_to_all_v_runtime,gather,scatter,gather_v,scatter_v,gather_v_runtime,scatter_v_runtime,all_gather,all_gather_v,dist_graph_create,sort,tree_all_to_all,tree_all_to_all_v,tree_neighbour_all_to_all,tree_neighbour_all_to_all_v,tree_neighbour_all_to_all_v_runtime,tree_gather,tree_scatter,tree_gather_v,tree_scatter_v,tree_gather_v_runtime,tree_scatter_v_runtime,tree_all_gather,tree_all_gather_v,tree_dist_graph_create,tree_sort" +echo "experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,source_tree,target_tree,source_domain,target_domain,layout,ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,displacement_map,metadata_creation,expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples,source_local_trees_per_rank,target_local_trees_per_rank,all_to_all,all_to_all_v,neighbour_all_to_all,neighbour_all_to_all_v,neighbour_all_to_all_v_runtime,gather,scatter,gather_v,scatter_v,gather_v_runtime,scatter_v_runtime,all_gather,all_gather_v,dist_graph_create,sort,tree_all_to_all,tree_all_to_all_v,tree_neighbour_all_to_all,tree_neighbour_all_to_all_v,tree_neighbour_all_to_all_v_runtime,tree_gather,tree_scatter,tree_gather_v,tree_scatter_v,tree_gather_v_runtime,tree_scatter_v_runtime,tree_all_gather,tree_all_gather_v,tree_dist_graph_create,tree_sort" >> ${{OUTPUT}} """ # loop of runs From e92271886c12c0230e7eec44030f33e4d6715900 Mon Sep 17 00:00:00 2001 From: srinath kailasa Date: Wed, 22 Oct 2025 12:16:26 +0100 Subject: [PATCH 50/52] Missing comma --- scripts/src/bin/fmm_m2l_fft_mpi_f32.rs | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs b/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs index 041a3522..517da33e 100644 --- a/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs +++ b/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs @@ -396,7 +396,7 @@ fn main() { {:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?}, \ {:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?}, \ {:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?}, \ - {:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?}\ + {:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},\ {:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?}", id, multi_fmm.rank(), From dccee2fd88bbfd7fab3860b232554cd888b52c6f Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Fri, 24 Oct 2025 16:05:47 +0100 Subject: [PATCH 51/52] Add lumi config --- hpc/ijhpca/Strong Scaling.ipynb | 263 ++-- hpc/ijhpca/Weak Scaling.ipynb | 1536 +++++++++++++++++++---- hpc/ijhpca/weak_scaling.py | 43 +- hpc/ijhpca/weak_scaling_big.json | 5 +- hpc/ijhpca/weak_scaling_big_sphere.json | 5 +- hpc/ijhpca/weak_scaling_ccd.json | 5 +- hpc/ijhpca/weak_scaling_ccx.json | 5 +- hpc/ijhpca/weak_scaling_socket.json | 5 +- 8 files changed, 1507 insertions(+), 360 deletions(-) diff --git a/hpc/ijhpca/Strong Scaling.ipynb b/hpc/ijhpca/Strong Scaling.ipynb index 89eb3ea6..519199d6 100644 --- a/hpc/ijhpca/Strong Scaling.ipynb +++ b/hpc/ijhpca/Strong Scaling.ipynb @@ -2,67 +2,71 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 10, "id": "2137b1d9-7f65-410a-b79d-7ed81dcf9b46", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", - "\n", - "import matplotlib.pyplot as plt" + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" ] }, { "cell_type": "code", - "execution_count": 4, - "id": "04c73ea3", + "execution_count": 21, + "id": "b51de3af-2b62-4382-811b-1ea190101d64", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[34mstrong_fft_n=128000000_p=64_points_per_rank=1000000_distribution=sphere_11248828\u001b[m\u001b[m\n", - "\u001b[34mstrong_fft_n=128000000_p=64_points_per_rank=62500_distribution=sphere_11248825\u001b[m\u001b[m\n", - "\u001b[34mweak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11248788\u001b[m\u001b[m\n", - "\u001b[34mweak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11248790\u001b[m\u001b[m\n", - "\u001b[34mweak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11248787\u001b[m\u001b[m\n" - ] - } - ], + "outputs": [], "source": [ - "! ls data/sphere" + "setup_cols = [\n", + " 'experiment_id', \n", + " 'dist_graph_create',\n", + " 'neighbour_all_to_all_v', \n", + " 'tree_all_to_all',\n", + " 'source_tree',\n", + " 'target_tree',\n", + " 'tree_all_gather',\n", + " 'n_points'\n", + "]\n", + "\n", + "runtime_cols = [\n", + " 'experiment_id', \n", + " 'runtime', \n", + " 'p2p', \n", + " 'm2l', \n", + " 'scatter_v_runtime',\n", + " 'gather_v_runtime',\n", + " 'neighbour_all_to_all_v_runtime', \n", + " 'n_points'\n", + "]" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 22, "id": "ae668ed4", "metadata": {}, "outputs": [], "source": [ - "df0 = pd.read_csv('data/uniform/strong_fft_n=128000000_p=64_11077776/strong_fft_n=128000000_p=64_11077776.csv')\n", - "df1 = pd.read_csv('data/uniform/strong_fft_n=128000000_p=64_11077778/strong_fft_n=128000000_p=64_11077778.csv')\n", - "df2 = pd.read_csv('data/uniform/strong_fft_n=128000000_p=64_11079093/strong_fft_n=128000000_p=64_11079093.csv')\n", + "df0 = pd.read_csv('data/full/strong_fft_n=128000000_p=64_points_per_rank=62500_distribution=uniform_11277038.csv')\n", + "df1 = pd.read_csv('data/full/strong_fft_n=128000000_p=64_points_per_rank=125000_distribution=uniform_11277036.csv')\n", + "df2 = pd.read_csv('data/full/strong_fft_n=128000000_p=64_points_per_rank=1000000_distribution=uniform_11278285.csv')\n", "\n", - "df3 = pd.read_csv('data/sphere/strong_fft_n=128000000_p=64_points_per_rank=1000000_distribution=sphere_11248828/strong_fft_n=128000000_p=64_points_per_rank=1000000_distribution=sphere_11248828.csv')\n", - "df4 = pd.read_csv('data/sphere/strong_fft_n=128000000_p=64_points_per_rank=62500_distribution=sphere_11248825/strong_fft_n=128000000_p=64_points_per_rank=62500_distribution=sphere_11248825.csv')\n", - "# df5 = pd.read_csv('data/sphere/strong_fft_n=128000000_p=64_11079093/strong_fft_n=128000000_p=64_11079093.csv')" + "df3 = pd.read_csv('data/full/strong_fft_n=128000000_p=64_points_per_rank=62500_distribution=sphere_11277037.csv')\n", + "df4 = pd.read_csv('data/full/strong_fft_n=128000000_p=64_points_per_rank=125000_distribution=sphere_11277035.csv')\n", + "df5 = pd.read_csv('data/full/strong_fft_n=128000000_p=64_points_per_rank=1000000_distribution=sphere_11277039.csv')" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 115, "id": "30777f75", "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "\n", - "def strong_scaling(dfs, names=None, title=\"Strong Scaling and Parallel Efficiency\"):\n", + "def strong_scaling(dfs, names=None, title=\"Strong Scaling and Parallel Efficiency\", plot_err=True, y_scale='linear', stat='mean'):\n", " \"\"\"\n", " Plots strong scaling (runtime) and parallel efficiency for one or more DataFrames.\n", "\n", @@ -96,7 +100,16 @@ " for df, label, color, marker in zip(dfs, names, colors, markers):\n", " stats = df.groupby('experiment_id')[['runtime', 'p2m', 'm2l', 'm2m', 'l2l', 'source_tree', 'n_points', 'source_local_trees_per_rank']]\n", " n_ranks = stats.size()\n", - " runtime = stats.mean()['runtime']\n", + "\n", + " if stat == \"mean\":\n", + " runtime = stats.mean()['runtime']\n", + " elif stat == \"median\":\n", + " runtime = stats.median()['runtime']\n", + " elif stat == \"max\":\n", + " runtime = stats.max()['runtime']\n", + " else:\n", + " raise ValueError(\"Only supports 'mean' or 'median'\")\n", + " \n", " runtime_err = stats.std()['runtime']\n", "\n", " # Ideal scaling\n", @@ -108,12 +121,17 @@ "\n", " n_points = stats.mean()['n_points'] # points per rank\n", "\n", - " print(n_points)\n", " # Runtime plot\n", - " ax_runtime.errorbar(\n", - " n_points, runtime, yerr=runtime_err, fmt=marker+'-', color=color,\n", - " label=label, capsize=4, linewidth=2, markersize=7\n", - " )\n", + " if plot_err:\n", + " ax_runtime.errorbar(\n", + " n_points, runtime, yerr=runtime_err, fmt=marker+'-', color=color,\n", + " label=label, capsize=4, linewidth=2, markersize=7\n", + " )\n", + " else:\n", + " ax_runtime.plot(\n", + " n_points, runtime, marker=marker, color=color,\n", + " label=label, linewidth=2, markersize=7\n", + " )\n", " ax_runtime.plot(n_points, ideal_runtime, '--', color=color, alpha=0.7, linewidth=1.5)\n", "\n", " # Efficiency plot\n", @@ -128,7 +146,8 @@ "\n", " # Configure runtime plot\n", " ax_runtime.set_xscale(\"log\", base=2)\n", - " ax_runtime.set_yscale(\"log\")\n", + " if y_scale == \"log\":\n", + " ax_runtime.set_yscale(\"log\")\n", " ax_runtime.set_xlabel(\"Points Per Rank\", fontsize=13)\n", " ax_runtime.set_ylabel(\"Runtime (ms)\", fontsize=13)\n", " ax_runtime.set_title(\"Strong Scaling (Runtime)\", fontsize=14)\n", @@ -147,39 +166,86 @@ " fig.suptitle(title, fontsize=16, y=1.03)\n", " plt.tight_layout()\n", " plt.show()\n", - "\n" + "\n", + "\n", + "def breakdown_communication_times(df, stat='max', y_scale=\"linear\"):\n", + " \n", + " stats = df.groupby('experiment_id')[runtime_cols]\n", + " n_ranks = stats.size()\n", + " n_points = stats.mean()['n_points'] * n_ranks\n", + "\n", + " if stat == \"max\":\n", + " tmp = stats.max()\n", + " elif stat == \"mean\":\n", + " tmp = stats.mean()\n", + " elif stat == \"median\":\n", + " tmp = stats.median()\n", + " else:\n", + " return ValueError(\"invalid stat\")\n", + " \n", + " tmp = tmp.set_index(n_ranks)\n", + " \n", + " # Total communication time per experiment\n", + " tmp['comm_time'] = tmp[['scatter_v_runtime', 'gather_v_runtime', 'neighbour_all_to_all_v_runtime']].sum(axis=1)\n", + " \n", + " x = tmp.index.values.astype(float)\n", + " y = tmp['comm_time'].values\n", + " # c = y[-1]\n", + "\n", + " # # log_curve\n", + " # # log_curve = y[-1] * np.emath.logn(8, x / x[0]) + y[0]\n", + " # m = (c-y)/np.emath.logn(8,x)\n", + " # log_curve = -m*x + c\n", + " \n", + " # # linear curve\n", + " # m = (c-y)/x\n", + " # linear_curve = -m*x + c\n", + " # log_curve = y[-1] * (np.emath.logn(8, x[-1]) / np.emath.logn(8, x))\n", + " # linear_curve = y[-1] * (x[-1] / x)\n", + " \n", + " # Stacked bar for measured communication time\n", + " fig, ax = plt.subplots(figsize=(10, 6))\n", + " tmp[['scatter_v_runtime', 'gather_v_runtime', 'neighbour_all_to_all_v_runtime']].plot(\n", + " kind=\"bar\", stacked=True, ax=ax, colormap=\"tab20\"\n", + " )\n", + " \n", + " ax.set_xticks(range(len(x)))\n", + " ax.set_xticklabels([int(v) for v in x], rotation=0)\n", + " ax.set_xlabel(\"Number of MPI Ranks\")\n", + " ax.set_ylabel(\"Runtime Communication (ms)\")\n", + " \n", + " # ax.set_yscale('log')\n", + " if y_scale == \"log\":\n", + " ax_runtime.set_yscale('log')\n", + " \n", + " # Overlay both theoretical trends, starting from first value\n", + " # ax.plot(range(len(x)), log_curve, color=\"red\", marker=\"o\", linestyle=\"--\", label=\"~ log(p) scaling\")\n", + " # ax.plot(range(len(x)), linear_curve, color=\"green\", marker=\"s\", linestyle=\"--\", label=\"~ O(p) scaling\")\n", + " \n", + " # Style cleanupr\n", + " ax.spines[\"top\"].set_visible(False)\n", + " ax.spines[\"right\"].set_visible(False)\n", + " ax.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "9ff40293-c3f5-421b-9d9e-312d64651764", + "metadata": {}, + "source": [ + "# Plot Strong Scaling" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 117, "id": "bff20d11", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "experiment_id\n", - "0 8000000.0\n", - "1 1000000.0\n", - "2 125000.0\n", - "Name: n_points, dtype: float64\n", - "experiment_id\n", - "0 4000000.0\n", - "1 500000.0\n", - "2 62500.0\n", - "Name: n_points, dtype: float64\n", - "experiment_id\n", - "0 64000000.0\n", - "1 8000000.0\n", - "2 1000000.0\n", - "Name: n_points, dtype: float64\n" - ] - }, { "data": { - "image/png": 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" ] @@ -189,34 +255,18 @@ } ], "source": [ - "strong_scaling([df0,df1,df2], names=[\"CCD\", \"CCX\",\"Socket\"], title=\"Strong Scaling For 128e6 Points on 64 Nodes of ARCHER2\")" + "strong_scaling([df0,df1,df2], names=[\"CCX\", \"CCD\",\"Socket\"], title=\"Strong Scaling For 128e6 Points on 64 Nodes of ARCHER2 (Uniform)\", plot_err=True, stat='max', y_scale='log')" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 119, "id": "fcafbe0c-4a99-41a3-a5e2-79418587afc8", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "experiment_id\n", - "0 64000000.0\n", - "1 8000000.0\n", - "2 1000000.0\n", - "Name: n_points, dtype: float64\n", - "experiment_id\n", - "0 4000000.0\n", - "1 500000.0\n", - "2 62500.0\n", - "Name: n_points, dtype: float64\n" - ] - }, { "data": { - "image/png": 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", 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", 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" ] @@ -226,13 +276,58 @@ } ], "source": [ - "strong_scaling([df3,df4], names=[\"Socket\", \"CCX\",\"Socket\"], title=\"Strong Scaling For 128e6 Points on 64 Nodes of ARCHER2\")" + "strong_scaling([df3,df4,df5], names=[\"CCX\", \"CCD\",\"Socket\"], title=\"Strong Scaling For 128e6 Points on 64 Nodes of ARCHER2 (Sphere)\", stat='max', y_scale='log')" ] }, + { + "cell_type": "markdown", + "id": "9d2fd1a7-a20f-46a4-9297-f041381f2276", + "metadata": {}, + "source": [ + "# Plot Communication Breakdown" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "id": "634fd841-be60-43c1-9531-dfd9143d131f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "breakdown_communication_times(df0, stat='max')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "679b41d5-a462-43f3-a12d-b73eb19bc1f4", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e158f706-d894-4fc5-9d33-098df1f79e56", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, - "id": "05868177-af2a-4dea-8b02-6bdb74f23706", + "id": "fc6d9b48-6149-4245-8ff1-51f968bd027e", "metadata": {}, "outputs": [], "source": [] diff --git a/hpc/ijhpca/Weak Scaling.ipynb b/hpc/ijhpca/Weak Scaling.ipynb index 000ba46e..856701bf 100644 --- a/hpc/ijhpca/Weak Scaling.ipynb +++ b/hpc/ijhpca/Weak Scaling.ipynb @@ -11,160 +11,342 @@ "\n", "import pandas as pd\n", "import numpy as np\n", - "import matplotlib.pyplot as plt" + "import matplotlib.pyplot as plt\n", + "\n", + "import seaborn as sns" ] }, { "cell_type": "code", - "execution_count": 28, - "id": "e2cd16e0-a829-4327-b6cb-adedf3de920f", + "execution_count": 2, + "id": "86084f2e-5662-445c-8bac-e630a73d764f", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "32768000000" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "strong_fft_n=128000000_p=64_points_per_rank=1000000_distribution=sphere_11277039.csv\n", + "strong_fft_n=128000000_p=64_points_per_rank=1000000_distribution=uniform_11278285.csv\n", + "strong_fft_n=128000000_p=64_points_per_rank=125000_distribution=sphere_11277035.csv\n", + "strong_fft_n=128000000_p=64_points_per_rank=125000_distribution=uniform_11277036.csv\n", + "strong_fft_n=128000000_p=64_points_per_rank=62500_distribution=sphere_11277037.csv\n", + "strong_fft_n=128000000_p=64_points_per_rank=62500_distribution=uniform_11277038.csv\n", + "weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=sphere_11279395.csv\n", + "weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=sphere_11282088.csv\n", + "weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=sphere_11290286.csv\n", + "weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=sphere_11291765.csv\n", + "weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=sphere_11293393.csv\n", + "weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11279368.csv\n", + "weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11277029.csv\n", + "weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=uniform_11279473.csv\n", + "weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11277033.csv\n", + "weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11277034.csv\n", + "weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11277027.csv\n", + "weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11279480.csv\n" + ] } ], "source": [ - "32000000*512*2" + "! ls data/full" ] }, { "cell_type": "code", - "execution_count": 24, - "id": "11130028-c403-4e46-b383-054ae92a8ea6", + "execution_count": 3, + "id": "178da76c-f2f3-4e55-859b-37cccde9f575", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "32000000" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "4000000*8" + "df0 = pd.read_csv('data/full/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=uniform_11279473.csv')\n", + "df1 = pd.read_csv('data/full/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11279480.csv')\n", + "df2 = pd.read_csv('data/full/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11277034.csv')\n", + "\n", + "df3 = pd.read_csv('data/full/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11277029.csv')\n", + "df4 = pd.read_csv('data/full/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11277027.csv')\n", + "df5 = pd.read_csv('data/full/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11277033.csv')\n", + "\n", + "df_big_uniform = pd.read_csv('data/full/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11279368.csv')\n", + "df_big_sphere = pd.read_csv('data/full/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=sphere_11291765.csv')\n", + "df_big_sphere_1 = pd.read_csv('data/full/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=sphere_11293393.csv')\n", + "\n", + "# df0 = pd.read_csv('data/uniform/weak_fft_n=4096000000_p=512_11079232/weak_fft_n=4096000000_p=512_11079232.csv')\n", + "# df1 = pd.read_csv('data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11262497/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11262497.csv')\n", + "# df2 = pd.read_csv('data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11262498/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11262498.csv')\n", + "\n", + "# df3 = pd.read_csv('data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998.csv')\n", + "# df4 = pd.read_csv('data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260013/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260013.csv')\n", + "# df5 = pd.read_csv('data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260022/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260022.csv')\n", + "\n", + "# df3 = pd.read_csv('data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260141/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260141.csv')\n", + "# df4 = pd.read_csv('data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260230/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260230.csv')\n", + "\n", + "# df3 = pd.read_csv('data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260719/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260719.csv')\n", + "# df4 = pd.read_csv('data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260722/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260722.csv')\n", + "\n", + "# df3 = pd.read_csv('data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11261532/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11261532.csv')\n", + "# df4 = pd.read_csv('data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11261537/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11261537.csv')\n", + "# # df5 = pd.read_csv('data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260024/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260024.csv')\n", + "\n", + "# df_big = pd.read_csv('data/uniform/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11263163/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11263163.csv')" ] }, { "cell_type": "code", - "execution_count": 20, - "id": "266b423c-bdde-49cc-b380-0f79b897218e", + "execution_count": 4, + "id": "7d951bbf-72ad-4c6c-a696-1ee3d512d1d1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "4.096" + "Index(['experiment_id', 'rank', 'runtime', 'p2m', 'm2m', 'l2l', 'm2l', 'p2p',\n", + " 'source_tree', 'target_tree', 'source_domain', 'target_domain',\n", + " 'layout', 'ghost_exchange_v', 'ghost_exchange_v_runtime',\n", + " 'ghost_exchange_u', 'gather_global_fmm', 'scatter_global_fmm',\n", + " 'source_to_target_data', 'source_data', 'target_data', 'global_fmm',\n", + " 'ghost_fmm_v', 'ghost_fmm_u', 'displacement_map', 'metadata_creation',\n", + " 'expansion_order', 'n_points', 'local_depth', 'global_depth',\n", + " 'block_size', 'n_threads', 'n_samples', 'source_local_trees_per_rank',\n", + " 'target_local_trees_per_rank', 'all_to_all', 'all_to_all_v',\n", + " 'neighbour_all_to_all', 'neighbour_all_to_all_v',\n", + " 'neighbour_all_to_all_v_runtime', 'gather', 'scatter', 'gather_v',\n", + " 'scatter_v', 'gather_v_runtime', 'scatter_v_runtime', 'all_gather',\n", + " 'all_gather_v', 'dist_graph_create', 'sort', 'tree_all_to_all',\n", + " 'tree_all_to_all_v', 'tree_neighbour_all_to_all',\n", + " 'tree_neighbour_all_to_all_v', 'tree_neighbour_all_to_all_v_runtime',\n", + " 'tree_gather', 'tree_scatter', 'tree_gather_v', 'tree_scatter_v',\n", + " 'tree_gather_v_runtime', 'tree_scatter_v_runtime', 'tree_all_gather',\n", + " 'tree_all_gather_v', 'tree_dist_graph_create', 'tree_sort'],\n", + " dtype='object')" ] }, - "execution_count": 20, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "4000000*512*2/1e9" + "df0.columns" ] }, { "cell_type": "code", - "execution_count": 29, - "id": "86084f2e-5662-445c-8bac-e630a73d764f", + "execution_count": 5, + "id": "02ff3f27-a583-4cc7-aca2-b3a3a69494c1", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[34mstrong_fft_n=128000000_p=64_11077776\u001b[m\u001b[m\n", - "\u001b[34mstrong_fft_n=128000000_p=64_11077778\u001b[m\u001b[m\n", - "\u001b[34mstrong_fft_n=128000000_p=64_11079093\u001b[m\u001b[m\n", - "\u001b[34mstrong_fft_n=128000000_p=64_11079322\u001b[m\u001b[m\n", - "\u001b[34mweak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11263163\u001b[m\u001b[m\n", - "\u001b[34mweak_fft_n=4096000000_p=512_11079231\u001b[m\u001b[m\n", - "\u001b[34mweak_fft_n=4096000000_p=512_11079232\u001b[m\u001b[m\n", - "\u001b[34mweak_fft_n=4096000000_p=512_11079233\u001b[m\u001b[m\n", - "\u001b[34mweak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11262498\u001b[m\u001b[m\n", - "\u001b[34mweak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11262497\u001b[m\u001b[m\n", - "\u001b[34mweak_fft_n=512000000_p=64_11077796\u001b[m\u001b[m\n", - "\u001b[34mweak_fft_n=512000000_p=64_11077797\u001b[m\u001b[m\n", - "\u001b[34mweak_fft_n=512000000_p=64_11077800\u001b[m\u001b[m\n" - ] - } - ], + "outputs": [], "source": [ - "! ls data/uniform" + "setup_cols = [\n", + " 'experiment_id', \n", + " 'dist_graph_create',\n", + " 'neighbour_all_to_all_v', \n", + " 'tree_all_to_all',\n", + " 'source_tree',\n", + " 'target_tree',\n", + " 'tree_all_gather',\n", + " 'n_points'\n", + "]\n", + "\n", + "runtime_cols = [\n", + " 'experiment_id', \n", + " 'runtime', \n", + " 'p2p', \n", + " 'm2l', \n", + " 'scatter_v_runtime',\n", + " 'gather_v_runtime',\n", + " 'neighbour_all_to_all_v_runtime', \n", + " 'n_points'\n", + "]\n", + "\n", + "fmm_op_cols = [\n", + " # 'm2m',\n", + " 'm2l',\n", + " 'p2p',\n", + " # 'l2l',\n", + " # 'l2p'\n", + "]\n", + "tree_cols = [\n", + " 'source_tree',\n", + " 'source_domain',\n", + " 'layout',\n", + " 'ghost_exchange_v',\n", + " 'ghost_exchange_u',\n", + " 'tree_all_to_all',\n", + " 'tree_all_to_all_v',\n", + " 'n_points',\n", + "]" ] }, { "cell_type": "code", - "execution_count": 3, - "id": "182a3aa0-aff6-45a0-a6a2-df618808793c", + "execution_count": 6, + "id": "abf6dd51-27cc-43ba-9923-eeeb79b9d32b", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[34mweak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11261532\u001b[m\u001b[m\n", - "\u001b[34mweak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260024\u001b[m\u001b[m\n", - "\u001b[34mweak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11261537\u001b[m\u001b[m\n" - ] - } - ], + "outputs": [], "source": [ - "! ls data/sphere/deeper_local_5 | grep weak" + "stat = \"median\"\n", + "df = df_big_sphere\n", + "stats = df.groupby('experiment_id')[runtime_cols]\n", + "n_ranks = stats.size()\n", + "n_points = stats.mean()['n_points'] * n_ranks\n", + "\n", + "if stat == \"max\":\n", + " tmp = stats.max()\n", + "elif stat == \"mean\":\n", + " tmp = stats.mean()\n", + "elif stat == \"median\":\n", + " tmp = stats.median()\n", + "else: pass\n", + " # return ValueError(\"invalid stat\")\n", + " \n", + "tmp = tmp.set_index(n_ranks)\n", + " \n", + "# Total communication time per experiment\n", + "tmp['comm_time'] = tmp[['scatter_v_runtime', 'gather_v_runtime', 'neighbour_all_to_all_v_runtime']].sum(axis=1)\n", + " \n", + "x = tmp.index.values.astype(float)\n", + "y = tmp['comm_time'].values\n", + " " ] }, { "cell_type": "code", - "execution_count": 30, - "id": "178da76c-f2f3-4e55-859b-37cccde9f575", + "execution_count": 7, + "id": "53266195-0d63-4dde-804d-cf89ca5b4b80", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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experiment_idruntimep2pm2lscatter_v_runtimegather_v_runtimeneighbour_all_to_all_v_runtimen_pointscomm_time
20.010733.57245.53180.03.00.071.032000000.074.0
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10243.014782.04402.56126.5121.01584.0657.532000000.02362.5
\n", + "
" + ], + "text/plain": [ + " experiment_id runtime p2p m2l scatter_v_runtime \\\n", + "2 0.0 10733.5 7245.5 3180.0 3.0 \n", + "16 1.0 10407.5 3785.0 6183.0 18.0 \n", + "128 2.0 10487.0 6938.0 3033.5 8.0 \n", + "1024 3.0 14782.0 4402.5 6126.5 121.0 \n", + "\n", + " gather_v_runtime neighbour_all_to_all_v_runtime n_points comm_time \n", + "2 0.0 71.0 32000000.0 74.0 \n", + "16 0.0 127.0 32000000.0 145.0 \n", + "128 151.5 150.5 32000000.0 310.0 \n", + "1024 1584.0 657.5 32000000.0 2362.5 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "df0 = pd.read_csv('data/uniform/weak_fft_n=4096000000_p=512_11079232/weak_fft_n=4096000000_p=512_11079232.csv')\n", - "df1 = pd.read_csv('data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11262497/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11262497.csv')\n", - "df2 = pd.read_csv('data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11262498/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11262498.csv')\n", - "\n", - "df3 = pd.read_csv('data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998.csv')\n", - "df4 = pd.read_csv('data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260013/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260013.csv')\n", - "df5 = pd.read_csv('data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260022/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260022.csv')\n", - "\n", - "df3 = pd.read_csv('data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260141/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260141.csv')\n", - "df4 = pd.read_csv('data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260230/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260230.csv')\n", - "\n", - "df3 = pd.read_csv('data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260719/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260719.csv')\n", - "df4 = pd.read_csv('data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260722/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260722.csv')\n", - "\n", - "df3 = pd.read_csv('data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11261532/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11261532.csv')\n", - "df4 = pd.read_csv('data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11261537/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11261537.csv')\n", - "# df5 = pd.read_csv('data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260024/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260024.csv')\n", - "\n", - "df_big = pd.read_csv('data/uniform/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11263163/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11263163.csv')" + "tmp" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 8, "id": "754504a1", "metadata": {}, "outputs": [], "source": [ - "import seaborn as sns\n", + "def summarize_by_stat(stats, stat):\n", + " if stat == \"max\":\n", + " return stats.max()\n", + " elif stat == \"mean\":\n", + " return stats.mean()\n", + " elif stat == \"median\":\n", + " return stats.median()\n", + " elif stat in [\"p90\", \"p95\", \"p99\"]:\n", + " q = float(stat[1:]) / 100.0\n", + " return stats.quantile(q)\n", + " else:\n", + " raise ValueError(f\"Invalid stat: {stat}\")\n", "\n", - "def weak_scaling(dfs, names=None, title=\"Weak Scaling Performance\", plot_err=True, stat='mean'):\n", + "\n", + "\n", + "def weak_scaling(dfs, names=None, title=\"Weak Scaling Performance\", plot_err=True, stat='mean', y_scale='linear'):\n", " \"\"\"\n", " Plots weak scaling results (runtime + efficiency) for one or more DataFrames.\n", "\n", @@ -193,23 +375,29 @@ " markers = ['o', 's', '^', 'D', 'v', 'P', '*']\n", " colors = sns.color_palette(\"deep\", len(dfs))\n", "\n", + " runtimes = []\n", + " \n", " for df, label, color, marker in zip(dfs, names, colors, markers):\n", " # Aggregate per experiment\n", " stats = df.groupby('experiment_id')[['runtime', 'p2m', 'm2l', 'm2m', 'l2l',\n", " 'source_tree', 'n_points', 'source_local_trees_per_rank']]\n", "\n", - " if stat == \"mean\":\n", - " runtime = stats.mean()['runtime']\n", - " elif stat == \"median\":\n", - " runtime = stats.median()['runtime']\n", - " else:\n", - " raise ValueError(\"Only supports 'mean' or 'median'\")\n", + " # if stat == \"mean\":\n", + " # runtime = stats.mean()['runtime']\n", + " # elif stat == \"median\":\n", + " # runtime = stats.median()['runtime']\n", + " # elif stat == \"max\":\n", + " # runtime = stats.max()['runtime']\n", + " # else:\n", + " # raise ValueError(\"Only supports 'mean' or 'median'\")\n", + " runtime = summarize_by_stat(stats, stat)['runtime']\n", + "\n", " \n", " runtime_err = stats.std()['runtime']\n", " n_ranks = stats.size()\n", " n_points = stats.mean()['n_points'] * n_ranks # total DoFs = per-rank * ranks\n", "\n", - " print(runtime)\n", + " # print(runtime)\n", " # Reference runtime\n", " T_ref = runtime.iloc[0]\n", " efficiency = (T_ref / runtime) * 100\n", @@ -229,6 +417,7 @@ " )\n", "\n", " \n", + " runtimes.append(runtime)\n", "\n", " # Efficiency plot\n", " ax_eff.plot(\n", @@ -236,18 +425,33 @@ " label=label, linewidth=2, markersize=7\n", " )\n", "\n", + " # print(\"efficiency\", efficiency)\n", + " \n", " # --- Runtime plot formatting ---\n", + " if y_scale == \"log\":\n", + " ax_runtime.set_yscale('log')\n", + "\n", + " min_runtime = runtime.min()\n", + " for runtime in runtimes:\n", + " if runtime.min() < min_runtime:\n", + " min_runtime = runtime.min()\n", + "\n", + " max_runtime = runtime.max()\n", + " for runtime in runtimes:\n", + " if runtime.max() > max_runtime:\n", + " max_runtime = runtime.max()\n", + "\n", " ax_runtime.set_xscale('log')\n", - " ax_runtime.set_yscale('log')\n", " ax_runtime.set_xlabel('Total Number of Points (DoFs)', fontsize=13)\n", " ax_runtime.set_ylabel('Runtime (ms)', fontsize=13)\n", " ax_runtime.set_title('Weak Scaling (Runtime)', fontsize=14)\n", " ax_runtime.grid(True, which=\"both\", ls=\"--\", lw=0.5)\n", " ax_runtime.legend(fontsize=11)\n", + " ax_runtime.set_ylim(min_runtime-max_runtime*0.15, max_runtime+max_runtime*0.15)\n", "\n", " # --- Efficiency plot formatting ---\n", " ax_eff.set_xscale('log', base=2)\n", - " ax_eff.set_ylim(0, 110)\n", + " ax_eff.set_ylim(0, 120)\n", " ax_eff.set_xlabel('Number of Ranks', fontsize=13)\n", " ax_eff.set_ylabel('Parallel Efficiency (%)', fontsize=13)\n", " ax_eff.set_title('Parallel Efficiency', fontsize=14)\n", @@ -257,42 +461,311 @@ " # --- Global figure adjustments ---\n", " fig.suptitle(title, fontsize=16, y=1.03)\n", " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "\n", + "\n", + "\n", + "def breakdown_communication_times(df, stat='max', y_scale=\"linear\"):\n", + " \n", + " stats = df.groupby('experiment_id')[runtime_cols]\n", + " n_ranks = stats.size()\n", + " n_points = stats.mean()['n_points'] * n_ranks\n", + "\n", + " tmp = summarize_by_stat(stats, stat)\n", + " tmp = tmp.set_index(n_ranks)\n", + " \n", + " # Total communication time per experiment\n", + " tmp['comm_time'] = tmp[['scatter_v_runtime', 'gather_v_runtime', 'neighbour_all_to_all_v_runtime']].sum(axis=1)\n", + " \n", + " x = tmp.index.values.astype(float)\n", + " y = tmp['comm_time'].values\n", + " \n", + " # log_curve\n", + " log_curve = y[0] * np.emath.logn(8, x / x[0]) + y[0]\n", + "\n", + " # linear curve\n", + " linear_curve = y[0] * (x / x[0])\n", + "\n", + " # log_curve = y[-1] * (np.emath.logn(8, x) / np.emath.logn(8, x[-1]))\n", + " # linear_curve = y[-1] * (x / x[-1])\n", + "\n", + " \n", + " # Stacked bar for measured communication time\n", + " fig, ax = plt.subplots(figsize=(10, 6))\n", + " tmp[['scatter_v_runtime', 'gather_v_runtime', 'neighbour_all_to_all_v_runtime']].rename(columns={\n", + " 'scatter_v_runtime': 'ScatterV',\n", + " 'gather_v_runtime': 'GatherV',\n", + " 'neighbour_all_to_all_v_runtime': 'Neighbor AllToAllV'\n", + "\n", + " }).plot(\n", + " kind=\"bar\", stacked=True, ax=ax, colormap=\"tab20\"\n", + " )\n", + " \n", + " ax.set_xticks(range(len(x)))\n", + " ax.set_xticklabels([int(v) for v in x], rotation=0)\n", + " ax.set_xlabel(\"Number of MPI Ranks\")\n", + " ax.set_ylabel(\"Wall Time (ms)\")\n", + " \n", + " # ax.set_yscale('log')\n", + " if y_scale == \"log\":\n", + " ax.set_yscale('log')\n", + "\n", + " # Overlay both theoretical trends, starting from first value\n", + " # ax.plot(range(len(x)), log_curve, color=\"red\", marker=\"o\", linestyle=\"--\", label=\"~ log(p) scaling\")\n", + " # ax.plot(range(len(x)), linear_curve, color=\"green\", marker=\"s\", linestyle=\"--\", label=\"~ O(p) scaling\")\n", + " \n", + " # Style cleanupr\n", + " ax.spines[\"top\"].set_visible(False)\n", + " ax.spines[\"right\"].set_visible(False)\n", + " ax.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", + " plt.show()\n", + "\n", + "\n", + "def communication_trend(df, stat='max', y_scale=\"linear\"):\n", + " \n", + " stats = df.groupby('experiment_id')[runtime_cols]\n", + " n_ranks = stats.size()\n", + " n_points = stats.mean()['n_points'] * n_ranks\n", + " tmp = summarize_by_stat(stats, stat)\n", + " \n", + " tmp = tmp.set_index(n_ranks)\n", + " \n", + " # Total communication time per experiment\n", + " tmp['comm_time'] = tmp[['scatter_v_runtime', 'gather_v_runtime', 'neighbour_all_to_all_v_runtime']].sum(axis=1)\n", + " tmp['comp_time'] = tmp['runtime']-tmp['comm_time']\n", + "\n", + " x = tmp.index.values.astype(float)\n", + " y = tmp['comm_time'].values\n", + "\n", + " print(tmp['comm_time'], tmp['comp_time'])\n", + " # log_curve\n", + " log_curve = y[0] * np.emath.logn(8, x / x[0]) + y[0]\n", + "\n", + " # linear curve\n", + " linear_curve = y[0] * (x / x[0])\n", + "\n", + " # log_curve = y[-1] * (np.emath.logn(8, x) / np.emath.logn(8, x[-1]))\n", + " # linear_curve = y[-1] * (x / x[-1])\n", + "\n", + " \n", + " # Stacked bar for measured communication time\n", + " fig, ax = plt.subplots(figsize=(10, 6))\n", + " tmp[['comm_time', 'comp_time']] \\\n", + " .rename(columns={\n", + " 'comm_time': 'Communication Time',\n", + " 'comp_time': 'Computation Time'\n", + " }) \\\n", + " .plot(kind='bar', stacked=True, ax=ax, colormap='tab20')\n", + "\n", + " ax.set_xticks(range(len(x)))\n", + " ax.set_xticklabels([int(v) for v in x], rotation=0)\n", + " ax.set_xlabel(\"Number of MPI Ranks\")\n", + " ax.set_ylabel(\"Wall Time (ms)\")\n", + " \n", + " # ax.set_yscale('log')\n", + " if y_scale == \"log\":\n", + " ax.set_yscale('log')\n", + "\n", + " # Overlay both theoretical trends, starting from first value\n", + " ax.plot(range(len(x)), log_curve, color=\"red\", marker=\"o\", linestyle=\"--\", label=\"~ log(p) scaling\")\n", + " ax.plot(range(len(x)), linear_curve, color=\"green\", marker=\"s\", linestyle=\"--\", label=\"~ O(p) scaling\")\n", + " \n", + " # Style cleanupr\n", + " ax.spines[\"top\"].set_visible(False)\n", + " ax.spines[\"right\"].set_visible(False)\n", + " ax.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", + " plt.show()\n", + "\n", + "def summarize_by_stat(stats, stat):\n", + " \"\"\"Return aggregation by requested statistic.\"\"\"\n", + " if stat == \"max\":\n", + " return stats.max()\n", + " elif stat == \"mean\":\n", + " return stats.mean()\n", + " elif stat == \"median\":\n", + " return stats.median()\n", + " elif stat.startswith(\"p\") and stat[1:].isdigit():\n", + " q = float(stat[1:]) / 100.0\n", + " return stats.quantile(q)\n", + " else:\n", + " raise ValueError(f\"Invalid stat: {stat}\")\n", + "\n", + "\n", + "def breakdown_communication_times(df, stat='max', y_scale=\"linear\"):\n", + " stats = df.groupby('experiment_id')[runtime_cols]\n", + " n_ranks = stats.size()\n", + " n_points = stats.mean()['n_points'] * n_ranks\n", + "\n", + " tmp = summarize_by_stat(stats, stat)\n", + " tmp = tmp.set_index(n_ranks).sort_index()\n", + "\n", + " # Total communication time per experiment\n", + " tmp['comm_time'] = tmp[['scatter_v_runtime',\n", + " 'gather_v_runtime',\n", + " 'neighbour_all_to_all_v_runtime']].sum(axis=1)\n", + "\n", + " x = tmp.index.values.astype(float)\n", + " y = tmp['comm_time'].values\n", + "\n", + " # Theoretical scaling curves (normalized to first point)\n", + " log_curve = y[0] * np.log2(x / x[0] + 1)\n", + " linear_curve = y[0] * (x / x[0])\n", + "\n", + " # --- Plot ---\n", + " fig, ax = plt.subplots(figsize=(10, 6))\n", + " tmp[['scatter_v_runtime', 'gather_v_runtime', 'neighbour_all_to_all_v_runtime']] \\\n", + " .rename(columns={\n", + " 'scatter_v_runtime': 'ScatterV',\n", + " 'gather_v_runtime': 'GatherV',\n", + " 'neighbour_all_to_all_v_runtime': 'Neighbor AllToAllV'\n", + " }).plot(kind=\"bar\", stacked=True, ax=ax, colormap=\"tab20\")\n", + "\n", + " ax.set_xticks(range(len(x)))\n", + " ax.set_xticklabels([int(v) for v in x], rotation=0)\n", + " ax.set_xlabel(\"Number of MPI Ranks\")\n", + " ax.set_ylabel(\"Wall Time (ms)\")\n", + " if y_scale == \"log\":\n", + " ax.set_yscale('log')\n", + "\n", + " # Overlay theoretical curves\n", + " ax.plot(range(len(x)), log_curve, color=\"red\", marker=\"o\", linestyle=\"--\", label=\"~ log(p) scaling\")\n", + " ax.plot(range(len(x)), linear_curve, color=\"green\", marker=\"s\", linestyle=\"--\", label=\"~ O(p) scaling\")\n", + "\n", + " ax.spines[\"top\"].set_visible(False)\n", + " ax.spines[\"right\"].set_visible(False)\n", + " ax.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "def summarize_by_stat(stats, stat):\n", + " \"\"\"Return aggregation by requested statistic.\"\"\"\n", + " if stat == \"max\":\n", + " return stats.max()\n", + " elif stat == \"mean\":\n", + " return stats.mean()\n", + " elif stat == \"median\":\n", + " return stats.median()\n", + " elif stat.startswith(\"p\") and stat[1:].isdigit():\n", + " q = float(stat[1:]) / 100.0\n", + " return stats.quantile(q)\n", + " else:\n", + " raise ValueError(f\"Invalid stat: {stat}\")\n", + "\n", + "\n", + "def breakdown_communication_times(df, stat='max', y_scale=\"linear\"):\n", + " stats = df.groupby('experiment_id')[runtime_cols]\n", + " n_ranks = stats.size()\n", + " n_points = stats.mean()['n_points'] * n_ranks\n", + "\n", + " tmp = summarize_by_stat(stats, stat)\n", + " tmp = tmp.set_index(n_ranks).sort_index()\n", + "\n", + " # Total communication time per experiment\n", + " tmp['comm_time'] = tmp[['scatter_v_runtime',\n", + " 'gather_v_runtime',\n", + " 'neighbour_all_to_all_v_runtime']].sum(axis=1)\n", + "\n", + " x = tmp.index.values.astype(float)\n", + " y = tmp['comm_time'].values\n", + "\n", + " # Theoretical scaling curves (normalized to first point)\n", + " log_curve = y[0] * np.log2(x / x[0] + 1)\n", + " linear_curve = y[0] * (x / x[0])\n", + "\n", + " # --- Plot ---\n", + " fig, ax = plt.subplots(figsize=(10, 6))\n", + " tmp[['scatter_v_runtime', 'gather_v_runtime', 'neighbour_all_to_all_v_runtime']] \\\n", + " .rename(columns={\n", + " 'scatter_v_runtime': 'ScatterV',\n", + " 'gather_v_runtime': 'GatherV',\n", + " 'neighbour_all_to_all_v_runtime': 'Neighbor AllToAllV'\n", + " }).plot(kind=\"bar\", stacked=True, ax=ax, colormap=\"tab20\")\n", + "\n", + " ax.set_xticks(range(len(x)))\n", + " ax.set_xticklabels([int(v) for v in x], rotation=0)\n", + " ax.set_xlabel(\"Number of MPI Ranks\")\n", + " ax.set_ylabel(\"Wall Time (ms)\")\n", + " if y_scale == \"log\":\n", + " ax.set_yscale('log')\n", + "\n", + " # Overlay theoretical curves\n", + " # ax.plot(range(len(x)), log_curve, color=\"red\", marker=\"o\", linestyle=\"--\", label=\"~ log(p) scaling\")\n", + " # ax.plot(range(len(x)), linear_curve, color=\"green\", marker=\"s\", linestyle=\"--\", label=\"~ O(p) scaling\")\n", + "\n", + " ax.spines[\"top\"].set_visible(False)\n", + " ax.spines[\"right\"].set_visible(False)\n", + " ax.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", + " # plt.tight_layout()\n", + " plt.show()\n", + "\n", + "\n", + "def communication_trend(df, stat='max', y_scale=\"linear\"):\n", + " stats = df.groupby('experiment_id')[runtime_cols]\n", + " n_ranks = stats.size()\n", + " n_points = stats.mean()['n_points'] * n_ranks\n", + "\n", + " tmp = summarize_by_stat(stats, stat)\n", + " tmp = tmp.set_index(n_ranks).sort_index()\n", + "\n", + " # Total communication and computation times\n", + " tmp['comm_time'] = tmp[['scatter_v_runtime',\n", + " 'gather_v_runtime',\n", + " 'neighbour_all_to_all_v_runtime']].sum(axis=1)\n", + " tmp['comp_time'] = tmp['runtime'] - tmp['comm_time']\n", + "\n", + " x = tmp.index.values.astype(float)\n", + " y = tmp['comm_time'].values\n", + "\n", + " # Theoretical scaling curves (normalized)\n", + " log_curve = y[0] * np.log2(x / x[0] + 1)\n", + " linear_curve = y[0] * (x / x[0])\n", + "\n", + " print(tmp[['comm_time', 'comp_time']])\n", + "\n", + " # --- Plot ---\n", + " fig, ax = plt.subplots(figsize=(10, 6))\n", + " tmp[['comm_time', 'comp_time']] \\\n", + " .rename(columns={\n", + " 'comm_time': 'Communication Time',\n", + " 'comp_time': 'Computation Time'\n", + " }).plot(kind='bar', stacked=True, ax=ax, colormap='tab20')\n", + "\n", + " ax.set_xticks(range(len(x)))\n", + " ax.set_xticklabels([int(v) for v in x], rotation=0)\n", + " ax.set_xlabel(\"Number of MPI Ranks\")\n", + " ax.set_ylabel(\"Wall Time (ms)\")\n", + " if y_scale == \"log\":\n", + " ax.set_yscale('log')\n", + "\n", + " # Overlay theoretical trends\n", + " ax.plot(range(len(x)), log_curve, color=\"red\", marker=\"o\", linestyle=\"--\", label=\"~ log(p) scaling\")\n", + " ax.plot(range(len(x)), linear_curve, color=\"green\", marker=\"s\", linestyle=\"--\", label=\"~ O(p) scaling\")\n", + "\n", + " ax.spines[\"top\"].set_visible(False)\n", + " ax.spines[\"right\"].set_visible(False)\n", + " ax.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", + " # plt.tight_layout()\n", " plt.show()\n" ] }, + { + "cell_type": "markdown", + "id": "3a787dad-8eff-4e58-a301-84ada4b956f2", + "metadata": {}, + "source": [ + "# Plot Weak Scaling" + ] + }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 158, "id": "67d9abc3-f9bb-41ec-9778-9912fecbc924", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "experiment_id\n", - "0 463.500000\n", - "1 483.472656\n", - "2 572.250000\n", - "3 1262.783813\n", - "Name: runtime, dtype: float64\n", - "experiment_id\n", - "0 827.562500\n", - "1 854.195312\n", - "2 963.443359\n", - "3 1629.935425\n", - "Name: runtime, dtype: float64\n", - "experiment_id\n", - "0 5127.000000\n", - "1 5136.062500\n", - "2 5277.117188\n", - "3 5361.569336\n", - "Name: runtime, dtype: float64\n" - ] - }, { "data": { - "image/png": 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sRx55ZPXGjRsDn33zzTfVAwYMqB46dGj1hg0b6v1utA/1XHfddbXe//rrr9X7o0ePrp43b17gfRwD+A8+g5+EO67BvPLKK2qf//znP2H7ZTB9+vTqvn37Vo8ZM6Z60aJFtb77pptuqtPe++67L6K/XnbZZerzCy64wLI9zjzzzLBlcP7CB1EObTWAv+O9Sy65pLqgoCDw/urVq6sPO+ww9RnsEPxd5r4Y72G7//77q8vKygKfPfDAA+r9SZMm1WoP2hp8HcjJyQn4B85pg+Li4upTTjnFsm8ax+2II46oXrt2beB91HnRRRepz9DvUL6Jdm3btq2OT8E/8/Ly6v1uw88MP8R5dfrpp1cPGTJEvYd+mK+JoQh1rlth8+bNgesJ2l0fdq8fqB/Xevj5e++9V6uud955R70/fPjw6tzc3MD7F198sarnzjvvrK6oqFDv4XsmT54c+G60w2DFihXVgwYNUvX8+uuvgffRJtgD5WFPg/fffz/gj+bzes2aNeq3CZ+ZfSAcds8DYPyWoYwVcL03/NvMUUcdVee8DOWbwaC/8OnZs2cH/Prggw+ude489dRT6n1cg5xifD+uSbBzuM2wk/l4mvdHX+wQbd8j8frrr6t9DjjggFpjl3A8/vjjqjz2s9POYIzrnvk65uT6aZQvLy+v97rh9BqP7ZNPPgm8b4xzDL+H7TCmM5gyZUpgvxNPPLF6x44dgc+uueYa9f5dd90V0i7G5+bfA0IIIdHDKfyEEOIyWBUXkQqYcmWegokIJEQCGCvUIpoPZRE5ZkxnRUQD9kMZI2oA0+EQxYIoCqwIi8gcA0QwGJGh//nPfwLvI1oLURGIFjFHQSBSy4h2RTRGKBBthFxyiCZBJBIWyLAK2oZoJqQZQPQnpoIiigqrKyMyCJEX6IMx1d8A0RTIKde7d281ldkcOYo2wB6ILEQEBlbVRlQUInKDVzdGVJCRmy1c/6yA3Ieo37Ad2oOoL3PUoRHZhWnsiLIzLyKE4/rwww9HvTCQVTAV1xz1g4g1TNnGsbSSPiAciFADiD5EBKgBfPPOO+9U/gc/QaRPfSBKDtFARoqB+kCEKyIfscCPeQEWfDemMiJKCAt/GNNF4V84Xm+88YaKiDSDKB1jirOR184KaDP80bzhnEJ0EvqAyF1EZxs+h6hK+DuOO1IcYEqxQbdu3dR5YPTNCliNHFO5zVHciHwK9sNw4FwBiAw1R+ZicTuc11jIC1P6rfoBypujjVEnjin6i36HWqwJx8+cZxjTUxHtiIhDRFrWB64LAIvZYFo80mLgGCO6Cn6BSLZQEe3RgkhIRPUbkd12Fryzev1A5Cu+B75rRFAa4JqC9xDtiMg1gMgz5KSE7yOSG9HEAN8Dvwy14jqitOHz6It5dXS0CZGv2AdRc7AjMFLKIKra/NuBaFT4L65rZr8OhdvnQTiMa1so21nJs21M2zY2+JORrgCRpoj2g++bzx3DPjg3owXXJKTFCLchWjESiNQN7oN5C/59jLbv4UBUJ6LKAa6RViIejZQjXiyu5db1023fxhgOKWgMgmfIIKLUHJWK3xmDa665RkW7GhjjjXAL5Bl2tZIGgxBCiHU4hZ8QQjwA02hxg4AbC4iCEHkgjmK6NMQ1YIipEAUgQiGPljF9H+UMfv/9dyWwYlqWWTw1gCiJgThuglEON9Xm6fwGEGbRBgi54YQkiBpIB4C6MB0U07/sArEGNxa4kcKUTIhr6BfETwinmMKKm0PcYBgLTRk3U5iOGmpqM6bvR1pBGEIbbmwgthjCqR2hLJhQopKxyI853yPEbxBqBXbceELENlb39YpOnTqpab/BoE0QmmB/TO+1C3wB/gvfCtU/TIlHigZMs8dNmpH7NxQQGnEjC9/E1Mb6UivAj+H3IFS+PQiAEAcglKIcpoFiOi2mbUNMw0JlmHYJ0Wf16tVKCEV+OtjDvMpyfcBf4atm8BACN+bIuQjhBn8NzOdvKOEB5ztuoiGEIb2GkUM1HJiaGdxeww8x5RtpCyLZEtce3HTjAQXSPBx99NHKJ3GTjutJpBQeBpgqjnMKIqhZgDOAkID8oZhiivMYIoIBrnWhRD30AXUG57ENBfJuomyPHj0CgiGAkAtfMqZsQ+SvLxWBVSCaYvEoI62FXYHW6vXD8JdwU4lxvCASGtdH4y8E3WCRCtdNCGqYdmzGEFBCnUfYBw+jsA/qhmhmLOyG6zMEPNSJ3xgcf7OvR8Lt8yDcNQIPsOD/wQIqct4i9QgESjxECJcKwBCo8PuBadm4VqJeXMvwYDKUuGecj1Zz+EYCv4+RFjhDGyLlcsXxMMYToQg1Xoim7+GONc4VTGHHNSZSvmEzGzZsqJVr1QuivX667duhroWRrhvmB0/BxwO5YyONcwy/wvWbEEKIe1BAJYQQD8BNCBYDgAB16qmnBhauMfKfGuDGFAIqbnLNAqq5nHGjgVx5kQbguDGHSGoMunHzC8EIYihEJGNVYkOgxI1TKMEIAituOhA9ixtQqzfNoVaDhWiFzYj4MPLt4YYNIi9eQwgwRz1ZBXbDzSVu/tFXY5EON1bzNfJqmjHEGyPXpvnmBCJmKCLdHLuFWbAyY0SkWln8JhSIrIVNg6MXQ/UveCG0UIvr4CEBIhit2ATfbQhNoUQ7M+YbREROQzRENDa+D+cObkqRaxN+hvPBuPG0Ar47WLyPhGHrSKIA+g97oWx9wlGotpoFgfoEAIgruA7hYQauRUZeZoiPEMYQbbbXXntZ6lM4Hzf6FMoPIK6GOh+NPoS6BoXqA45pKCCaw8/hA7i+mKOknQLhAwuzIT81jg+i8Kzk4nRy/ajPX4LtapQPtyp4qHPLOD+CRcZw5SCiIroVojFyHGPDMYQYBaEXv2eh+ufleRDu+o/9IZqbF2AzQNQhriHI/wuBLxTBD+LwoAVRgMihjWsFckgHA3EM1Je/uSHAcXL6G+Ok78HAN2688UYlRmLGg50Hrob9Qs3SMD8owTUi0m+6IWSHengS7fXTbd82R5CGIvhzc7+Dz7n6xjnGb3bwbB9CCCHRQQGVEEI8AGIoBrCIYAJG1GewgGq8hoCKlcYhdkJExFR0A+OGGxGh9S1aYYDprbixQRQhxBIIuojggsCAG5JIEYmY4o92IZoPKz0j2snKND5EQiDCFX9DRURCBMGGCBVM8YdQgagnfJedlZ1hD6zujamsuEnGdFnUCfvgeyF4NdTCHYZoaxZFzFgRiKwQrv7gm81Q320n4jLU/pEwbl4jTdlEBBgWLEIZ+Ll5SqFxcweBHyIIxH9jsRuj3vpWaA4WkJG+wLzojgGElPqEwIbAis0M3HgYgPMXD0IwLReiEyLN8MABwiCiOxGpFyrC2A0/cKP99QFxE+IfIuCiBRHsEE8hfEAwRHS1XfHUDvXZ1jjvDbvWZ89Q1wLj2CBKO5JYZI5wQ3oKRCjiAdcPP/ygHuzNnz9fbUgJAIEt3IMbL86DSItHQbwzInNDgfQdeLBiRSjD7y4WqEMkJdIrQAjDvmYMkd5IeVAfWIAPYhuitOsTnhsTK303g3PjscceUz4MgRpTzO1g/OaH+m0zT4tHlHqklBFIcRFOiG2I64+b10Onv9WRri12xlaEEELqhwIqIYR4AG5kEb2GqFFE00G0wNSx4AhS3LRA0MHKq1iRFmITcv2FiniBeBocNRLuhgKRIBhAP/XUU3UiSHFTHA5MTcaqr7jZRHQnImeR885KNAr6ibbjZgc33OGEPfQX098+//zzQFSsMa3OyGcZDKJiYBvYFNPmIZ7ClhDmgqOxMP22oYDYjTyOiHQMFSVnZ/qccXMVamoocsmGI5zNjMhlO6uHB0fDQKDGMYJNQ4noRo6+SPkAIWzhJhnCevB0eHM0Ej6D0AAB1fhu3Pwhp5wVkQV1IBoZ/oCHBcEYeVqtTFt3iuHHSCcRDiPFhJfCXKiUB+PGjVObEW0GYQcPGnB+RxJQrfTJ8AO3+wTfxmrZ8B2kBYn03eGiMq0C/8Dq3BBsxo4dq4Tl+nJ9Rgtsi+sHbBvq+hF8fhl9NM7tYEJFm+M7UD/SWtgRPfGdiFDGhvMX0cvItQwRFeIZfici9cvL8wDnOh4GIOoQK56HEs/gM0hXgTbgdwzH1Aq4PuABHSK34XsQPs3iMn6DIITioQ+ilCNNdcfxmzx5svotRioGnQVUK303wHgAD18g+CEfNaJP7QJb4Dcds1GCf1sQOYrP8duD44d0LKGAX2KavNepAPxwjQ8X4au7zxFCiN/gIlKEEOIRRh5T5GnDTUJw9Kl5Gj9uFJBD0HhtxshJB1HSnD/PYN68eSoXJabE40YNU1khouKmI9T0eyMaNlT0E26IjEgd3BhBeEEEj5GPsr4bC0St4LvrW7gIN5/AEA2MiFVEO4UCYgYiFHFTYET1QqwNFk3w3cbnkaI23cLI+wmhPBhERuHm3iqGWBNqyl2kaCekWgi12IghlENEqI9QkTEQMLEYGuwYSnSHuGkszBQqv6IBfALCZqgN6RyM44/Xhh0hmGIqMb7bSH9hBr571llnKXEHDx+M6EFEzkGwCAY36mgrbszDnYdugEVYYEsc91BCPkQ6+DBs0hCRsBBIIY5i4aXgBzfGdNv6RH60E+IErmGhIv3y8/MDfm5cq9wCwgr6gG3RokV1PkdULR7cIKereXEru+AaaoinJ598sjzzzDOei6dme+FhUrgp0uY0FogmxvUZD5GMyDszEBXDfUeoz8B1112nrqXIxwkgkuIcMdLJAHwncg7DRgCzBxrzPMBvKqL/IYqGW6gP1xDkkAXGIlxWwWwQ/H7iO7BYnvm3Er+RxkJEeLgTLsIP++DhBP7iWoaIZj8Qqe8ADzIgniK1Bq4rTsRTYCyWFO4BIHwo0rlh+BHOWYj95lk7XqLbNT4cxsOU+lK0EEIIsQcFVEII8VhcM0SiSAKqMSURN6rBi/EY+Qpx04oVrc2DdohteA8rsRqrJhs5UBHZhKhWA9wI4UYSgqgh8EUC34toFOyHFbutLMpkLF6FCBUsFmVMcTfATT/SAkAsg7BnRJZAGMANEEQSTCM037Rhuuiff/6pyiIK1+gfxFbzzSsEHkwjxF8r/XMDCA8QeTA93BAgAGwFgcpoi5WphEZ08tSpU2tFnEK8jrSaNGyFqE2zX0AsRJsQyTlx4sR6v9vIHxd8Q4gV5gGEAOQUNcBxxXFE9I+xerPbQAwF99xzT2AldgBRFSIpxDxE+hgRUhB4EG2DvpuFPtgSfoGHD8jxZyUdhVOMcxV2xKJZZpELIjfOI2CsyO4mxjGEoGmABxQ4RrgGma8FwEhzYSzkFgnDD9B+s1iP/qGf6C/SJrgdBQYR01jsBt9tzjuJhzBGZLyTRdIMcJ1AuhIIMThXkKc3XPS820B8wkI/iPYPXiwIvwcQCvG5kb8UEW2Yio8Hbrjum6/JSMkQ6kEXHjSgP4gqRCS/GfweIN0LHroZC9jgdwT5GzE923w9wLXWEHTri+L2+jwwHtBNmDAhYjnDbvitsDMbAA+PIB7iuo0Hckb6D7PIiCh3I/VO8Ero6DceQGK1dgi5ThZjbCwi9R0PX7G4GMYp+J22GtUbCjycA8YDz2CMtAuYZWI8XA5+cIh2GmUbisa8xtvBsGuodEqEEEKcwyn8hBDiEcjJaaw2jRsBIyI1GLyPG1zc4CFKxbzyqgFEJNyk4UYCkQ+4gcUNDm6YceOPQbKRgwyLFhx66KEqmg+5QRG9BHEFIhSmfqJduPlAVF59QHCC0ILySAdQX54zfB/6gUWD0GYIXRBoEPkHMRHRgmgv8siZ0xHAPrhhh2iGfXFTD8EU4g9EVQgpiEIFJ510kspzips5RN7CZriZwRRTTBe3079oQeQL+okoLojNuCnEMUc/8f0QlDDVz0puM0RLIfINfUa/IAiiDkSfwq7hVmOGwIKppIg0RLQZxA/YAtHDDz30kFoEqj6Mqb2I6EMOSPQDee0QwQwfePHFF1VkHoRS1AdBG4I++ofjYnchDisEfzeOM2yLvuJGFRFQmGZqTO9Hf3EsrrzySuVHsAVsg3MEIipyqULs8BqIehD3cP7hRhvHEeItRF0IXhB9DEHSTRDRhWhd2ADXiXPPPVcdR0TLQUCFEInrBI4friWwI+yDhWjqAyIcbsghoMFPcU2B/WFbnNcQ/yNN6Y4G5HI28m/ivEAfkObCsCeONXIqOwVCpTENGMcp1IJERiRXNEJtKBBBj3MUAi5WPocIiu/Bwy8cH9gYDy/MwjQeluABFI4FjgmET5wPuL7jeAcLUrjWYh8Iw7AVckYjOg7fAeEUvz2IKjSmGyMH5ieffKKuIfgdQf04x4zfDwiH8K3GOg9wbUWubTwIOfjggyOWxW8P2ouHB8jrefXVV1v+HlzrkA8c/oHfKlyPjGspfk/xGwThDtHAuLYYdoWgBtvhdw4PdLAYl1fRp4gWho9EwsgrbYdwfYcIb/zu4UEftlDgeh1pVgLAAxeIo7iGhLouw5dxbTLOD4wPcJ3BbynGVPBHPDzEoma4PjUkjXWNt4qRcgPnSH3HgRBCiD0ooBJCiIcgmhRRRLi5Cidk4SYLgiiEsnBRqrhhQeQoFvDAjTOmV+KmFjfbiJrCTQQEJAOIWliJHEIKBvW4SYbAgptjDOwhQuImFKJEpJWrEY2CSENEU6C+8ePHR8z5BjDNEzcVaC++G5GLuKmEiIqpjBBgcHMWHOWFeiESIjcjIoZwc4LpmbgZufzyywNT0XCT+s477yhxFjZDOdgQNwroG6IuUT/ypOLGy2vQH4gPaDdu7iH44sYdN54QRCCghptmagYiMSLC0C/0H1NuccwQvYT+hBNQUTdERtxoQkDDDTVyXeI41HesDHCDDxEYogAEetwMGitXQ8DCDSIiiuEviNjDMUD9EFK8zLGG74YQiimjmGYN2yJCDv4L8SI4pyOEDORnxAaRF/4LAQXnB86ThogsxLkKsQbHHucqjiWOCQQBpBwwphW7DQR8+BqmkMIPENluiBCwAfwHxw/Rw/BXRE/jGFtZAR0COa4pyImIcw835wD2v+CCC5SAEWoVbDeAfyECGz4OYQ9RlPguXEsgDkfK32oFc9oQRAyGA8Kt2wIqgCiMKD+ISYhoRH5a5L2Gj+MBQvDUZAhiiMqHj8O/cJ3DMTRyV4eK6MP1G9H7U6ZMUQsVQjjFwwj4IsQr/D4ZwLa41qN+RHOjTXhYh3Me/oLyVqK4vToPjMWjcNyt+ByiUCFiwsb4HbEDIgzx+4KHBBCZMWXfAOcQ/BIi4meffRZIS4JrDs4LiLvwz0j5oaPFSKESCSOvtF2C+37DDTcE0qXgIV24fNYAv8X1CXcQaeHbuF4hojrU7wjsh+s/xhIoh/EErl/wXZw3iOD2MiWLbtd4qxg2xXjPPC4khBASPTHVbi0RTAghhDQzjNW/cZMaaqEjRMbhpho32G7nIkMUDoRqRLGFyx1LCCGE6Age8OIhJx7yGClbSPRgFgbEbzwQasw8rIQQ0hRhDlRCCCHEIUgjgGhPpDbAtGIziBKDeIpUBFzIgRBCCNkDZrTg9xEzL4J/P4kzkGYD+diRRoHiKSGEuA+n8BNCCCEOgXiKqfuYSokpm0iHgGniyMGKHINIJ4Cp9YQQQgjZA1JOIGfzGWecoabE4y+JDqSrQHoJO/l+CSGEWIcRqIQQQohDkH8UufWuuOIKlZsQC2Ig/ySy4yB/m5H/lhBCCCG1QS5j5G/GgoD5+fmN3Rxfg5zwyBP94IMPepqbnBBCmjPMgUoIIYQQQgghhBBCCCFhYAQqIYQQQgghhBBCCCGEhIECKiGEEEIIIYQQQgghhISBAiohhBBCCCGEEEIIIYSEgQIqIYQQQgghhBBCCCGEhIECKiGEEEIIIYQQQgghhISBAiohhBBCCCGEEEIIIYSEgQIqIYQQQgghhBBCCCGEhIECKiGEEEIIIYQQQgghhISBAiohhBBCCCGEEEIIIYSEgQIqIYQQQgghhBBCCCGEhIECKiGEEEIIIYQQQgghhISBAiohhBBCCCGEEEIIIYSEgQIqIYQQQgghhBBCCCGEhIECKiGEEEIIIYQQQgghhISBAiohhBBCCCGEEEIIIYSEgQIqIYQQQgghhBBCCCGEhIECKiGEEEIIIYQQQgghhISBAiohhBBCCCGEEEIIIYSEgQIqIYQQQgghhBBCCCGEhIECKiGEEEIIIYQQQgghhISBAiohhBBCCCGEEEIIIYSEgQIqIYQQQgghhBBCCCGEhIECKiGEEEIIIYQQQgghhISBAiohhBBCCCGEEEIIIYSEgQIqIYQQQgghhBBCCCGEhIECKiGEEEIIIYQQQgghhISBAiohhBBCCCGEEEIIIYSEgQIqIYQQQgghhBBCCCGEhIECKiGEEEIIIYQQQgghhISBAiohhBBCCCGEEEIIIYSEgQIqIYQQQgghhBBCCCGEhIECKiGEEEIIIYQQQgghhISBAiohhBBCCCGEEEIIIYSEgQIqIYQQQgghhBBCCCGEhIECKiGEEEIIIYQQQgghhISBAiohhBBCCCGEEEIIIYSEgQIqIYQQQgghhBBCCCGEhIECKiGEEEIIIYQQQgghhISBAiohhBBCCCGEEEIIIYSEgQIqIYQQQgghhBBCCCGEhIECKiGEEEIIIYQQQgghhISBAiohhBBCCCGEEEIIIYSEgQIqIYQQQgghhBBCCCGEhIECKiGEEEIIIYQQQgghhISBAiohhBBCCCGEEEIIIYSEgQIqIYQQQgghhBBCCCGEhIECKiGEEEIIIYQQQgghhISBAiohhBBCCCGEEEIIIYSEgQIqIYQQQgghhBBCCCGEhIECKiGEEEIIIYQQQgghhISBAiohhBBCCCGEEEIIIYSEgQIqIYQQQgghhBBCCCGEhIECKiGEEEIIIYQQQgghhISBAiohhBBCCCGEEEIIIYSEgQIqIYQQQgghhBBCCCGEhIECKiGEEEIIIYQQQgghhISBAiohhBBCCCGEEEIIIYSEgQIqIYQQQgghhBBCCCGEhIECKiGEEEIIIYQQQgghhISBAiohhBBCCCGEEEIIIYSEgQIqIYQQQgghhBBCCCGEhIECKiGEEEIIIYQQQgghhISBAiohhBBCCCGEEEIIIYSEgQIqIYQQQgghhBBCCCGEhIECKiGEEEIIIYQQQgghhISBAiohhBBCCCGEEEIIIYSEgQIqIYQQQgghhBBCCCGEhIECKiGEEEIIIYQQQgghhISBAiohhBBCCCGEEEIIIYSEgQIqIYQQQgghhBBCCCGEhIECKiGEEEIIIYQQQgghhISBAiohhBBCCCGEEEIIIYSEgQIqIYQQQgghhBBCCCGEhIECKiGEEEIIIYQQQgghhISBAiohhBBCCCGEEEIIIYSEgQIqIYQQQgghhBBCCCGEhIECKiGkQbnhhhukb9++MmPGjDqfFRUVyeDBg9XnTzzxRMj9TzzxRBkwYIDs2rXLszbi+08//XTH+5eVlcmUKVPklFNOkZEjR6o+HXzwwXLttdfKrFmzxGt+/fVX1Yd//etfgfcOPfRQGTNmjDQ2d999t1x22WWB12eddZZqa/DWv39/GTVqlLLh66+/LlVVVQ3WxtWrV9d63Zi2W7Rokey9996yfv36Rvl+QgghhLgLxrihxj6DBg2SAw88UP72t7/J7NmztWjjL7/8ol5jHILXf//73x3VZ3UshXKhbBO8YXxkUFBQINddd50acw8dOlTuvfdeKS8vl3vuuUdGjx6txuGwaXCfrPLee++p/d555x1HfSeENB3iG7sBhJDmxX777SfTpk2TOXPmqEFNsPAH8TEhIUF+/PFHueKKK2p9jgESBkwYCLVs2VJ0BCLwOeecIwsWLJBx48bJ0UcfLampqWrgiQHYxx9/LDfffLMq05DccsstUl1dLY3JzJkz1eDzo48+qvMZbNK6devA68rKSmWzt99+W4mumzZtUgK01/zjH/+Qn3/+Wb7++mstbAchGX506623yksvvSQxMTGN0g5CCCGEuMupp56qHpIaVFRUyMaNG9WD4++++07+/e9/y0EHHSTNkYcffjji5506dQr8/+STT6qxJcZLEKB79+4t//3vf+W1115T9xoTJkxQ5du2bStdu3ZVn9thn332Ue0ZPny44/4QQpoGFFAJIQ3K/vvvr/6GerIO0TQxMVENdKZOnSo7duyoJaphHwhrBxxwgOjKG2+8IX/++ac89NBDcvzxx9f67IILLpBJkybJP//5TzniiCOkc+fODdauww8/XBoTRJBCnIRNunXrFrJ92dnZdd4/88wzlQj94osvKtG5TZs2nrYTNyzx8fFa2Q4PEtAGPHgI9ilCCCGE+JNhw4bJxIkT67x/yCGHqBlXEO2aq4Aayi7hMKJREXmanp6u/scDeOMBfb9+/QJlzf9bpUuXLmojhBBO4SeENCgdO3aUHj16yB9//FEnqg8CKgaThx12mBLcEAlo5vfff1d/dRZQjTZi8BsMombPOOMMJQI39tSshubzzz+XVatWyV/+8hdb+0EwPfLII9VULPhMcyQzM1OdE88//3yjRxETQgghxFsGDhyooiSXLl0qeXl5jd0c7cHsNWCIp+b30tLSGq1dhJCmBwVUQkijTONHDtNly5YF3luzZo2sXbtWPWnHdBtEAUJQNfPbb7+pwRHyGxlgavftt9+u8iohdxSESzyBRvRqKBHvvPPOk3333VcNTvH3kksukfnz50dsL6ZUXXnllSr/0eTJkyOWbdGihfqLaUOhxK6zzz5bTe8/9thja72P9/AdsA2mCOHJO6JZzXVgMPjCCy+oqASUQX+RW/W2226Tbdu22co9ZeSBwjFAZChEaaRGOO6441T0bzArVqxQ7cOxwXdfeOGF6j3ko73pppukPpATtk+fPo6e/CMFgtU8tcj7is+QDsKcs+u5556Tt956S0U3o5+Y4oXUAEgLYS4Hf8rJyamVhzeS7dB3+BGEf+RzxXvwPUz7x/vIx4XIY4jHwXz44Ycqxyv2hU0hLn/zzTchbYB2L1++XL7//nvb9iOEEEKIv4iNrblNx0N3g+nTp8ull16qZnNhHIup5X/961/r5PTEuOX8889XKQAwDhkxYoS8+uqr6rPNmzfLfffdJ0cddZQMGTJEbZjp89RTT6nxrl3sjMO9yvlvBCWYc6RijAXwABqvMc4LlwP1s88+UzOekE4B+fcxnjOXCZcDde7cueo+AvtgbImxGmZMmY+Z1XGoAcb9KGeM9XFfcPHFF8u8efMC6bBQ35133lnHHlu3blV+cf3117tiX0JIXTiFnxDS4GDgh/xOGPBAVAM//fST+gsBFU+LIZLiPQwkkPexpKREDR7Gjh0bmGK9bt06JaJBWEQeKUyJX7x4scp79MMPP6i/xpRv5I984IEHlKh1+eWXqzyrEE4hFmIw8tVXX4WcHo7BJHJvQnxFgvqLLrooYt/Qnk8//VT+7//+Tw24MJDEABeDsnbt2tWZHg4wSEO96Pdpp52monQhpN11111KzDMGQldddZV8++23Kg0AhLfS0lLVTwzoINxhwGUXDMo6dOig/sKOL7/8stx4443qPSPdAoRStAu2wKAS/cBgE9G0VhZ3wmAdaQ3OPfdc2+3Dd0I0jIuLU/lAnQJfKCwsVG3OyspSxxM+CCH/kUceUcceU+VwU4GbFkz5wgA1EhCRe/bsKddcc40SSF955RU1kIbgi1xbOF5YkAo3LZiG/8EHHwRuiJDGAWI4hGv4F44l8uPixihUjlwMoLHvl19+qURzQgghhDRNMPbD2AvjWmNsinELxhV4cI0xIx7YY+yHMSDGIxjPmnN7YoyNz/Hwe+fOnWockZ+fr8bLGPtgPIR8oPgM+2LcirE2xrpWsTMOt8v27dvDfoZ0XxgzYwyGsdvTTz+txlv4H+NSjJfefPNNtd6CkWM/XDsgHKPvGPNhDJeUlKTajYff+CzUjDKAXPk4Hkg/hbIY+2HmHFJ4wfYQa8156+sbhxog5/27776rhG/UjxlYCMrA+BvjSdxT4LjhXgMP62ELA4jGGDdDfCWEeAMFVEJIgwMRE4IYBhgQ5gCiTSHMGRGKEJawYv3ChQvV01RM38Ygwjx9H09ui4uL5f3331eDCQNM+YZYhwERntDiSTCewkOAQyQkvts8rf4///mPElGRfN4MBiEYSH7xxRdqQIPo0frAgAcDrjvuuEM9dcZgx3jqD7H4hBNOUE+5zQMelMVAGDkuIZ4C2AUiGgRNRBFAhISoin3xpN8AbTr55JOVfTDYtDtQ3WuvvZSQZwzyEImAQRoGb4aAigEpBt3IJ4XPAaIlMdDEALk+ZsyYof5Gij7FANI8WMaxxsAcbVu5cqWKsMCA0ylbtmyRTz75JJDD6qSTTlLRFxCCsUorBr6I+n300UeVyG0l9xYG7mbbIYIaxwj5SuEDBrm5uWqgDH+AnyJiAfthEI1jb4DjjWONgTR8EVP3DXCjgLYj+oQQQggh/gcLj5rHPhAiIUBiNg3GQVg53gDjWIyTIaaZZ+YgrzzGwxiPmQVU1A1hEcKpAR70btiwQY2PMQYygPiJMR8e0tsRUK2Ow51gbncwiCpF32APjNcgTkJANY/dcF8BATVcjn2AcSbGaxAlETlqjM0xGwv2efzxx0MKqOgz7gswrsd3G/thjI6Zas8884wSOBHZa3UcmpKSombaYfyNKFWMBY3xJcqNHz9e+QDaC4EUPoIAA6ypYIDjABEb91mEEG+ggEoIaXAwDR/TfIwpNxgwYhoOBgHGYAGRqBh4IToTAqqRWxRTXgByQiFCFVOGIC6ZB6AQ6jBAQbQeBm4QTDGwxIDHLJ5icIlIVBA8hQZPsP/+97+rgQ0iQK2IpwYYbKGdELvQRgyIMCBGLis8mcYTYgijEG8hEEN4w6DLEE8B7PDggw+qNqIchFEIykYEowGm7hs5n/Bk266AilQC5ifkOC7GNCAA4RR9gHBtiKcAdkS0pBUBFekZgHlwHQyE5VAgEhYRmni6Hw2YBmVeAAB2hKAO2yP6AgNXu2BgbLYdBFUIqBjkmjEWzcI0N9gAK8Ua+wdHWOA9nAu4iQlOUYB9cSwg7IeKZCaEEEKIf4Bwhi2Y9u3bqwes5khCRJriYbNZPMX42RgXBo9jMb6FMGgGY9ljjjmm1gKtAGMRjCWD64iEnXG4ExDwEA63FhRFFCmCLBA4YA5saNWqlYoODZVCCiDSFCkKIBIH2wzjOAio6LtZQLUyDsXDdoB6zeNLjP/+97//Sdu2bQNjZtwjIfDCEFCxkNaSJUvUjCfzvoQQd+EdGCGkUcCTZTxJxRNZ5HaEUGiIo4aQhwEMxCRMTYIIiYGHIcJBlIPIiVXTIz2lxtRoTMfBwAgCJJ4IY7o1pkdt3LgxkGM0OF8pIjoRKQjw3XYFPAxcMag0cmdioIlIVjw5hmiKv5hWhKffAAtrBRMccYk+4Ok1Bm7oPyIaIaAaAyUr0+mDwdP74O8w14WBHQQ7RKoG06tXL0vfYQyqzcn9g8GUdrQF34spZ4gEwDGBiOzGomG4GQnG6Ks5V1U0tjPE+eD3jZsbw6ZGPlSI5uGAfwYDIR02gT0hLBNCCCHEv2DWiXnsi3EJxisY6waLYHhwiuhRRF5iej/GCRgHGmOL4DEgxtChHrZiTIIxFlIrYYyHcSgewAPzg/z6sDsOt4sxC8pLIo3BI41xjXHcY489pjYr4zgr41Acz3DtMaexwnGC38D2EF9xrBF9Cp85/vjjw7abEBI9FFAJIY0qoGIAhyk2+NE3DyIxwEMZTE/B4AvlzFNzjIEipuZEWtndELUwJQmRfxgQYdEeJNjHE3IMgpBrNBgMOjF1Hd+PJ7x48ovpNpFAhCGm6yM/lfmpM8jIyFBT7bEIE6Y2GVOxrSbshwCLKd8YNCNNAKJB8QQaiegRzYr8mk4IjmgNBlPIgBGpayY5OdnWd0QSKrHAgTHFCn6AKezI84o8X5hCheNshXDf4cXT+FA2sfJdhu8++eSTgUXHggmVrsDomzmKmhBCCCH+BGNSq0IhUgxhISKMlTAOxBgZeTsxjrzsssssje8QGIDFVLEPxqOY7YVp/xiDIQrTzoN4u+NwHTHGuHbHiEbQBdZUwBoHoQge31n5DjvtQXQy7lEQWIHxMu5xMHU/XLoCQog7UEAlhDQKGKxBgMPCUMg/CtExeEoOIg8RMYrFdTD93iywGgMEJLwPNfjEolDG03dM/8fAAlOrkTPIPDBBpGkoIFBimhPagIhPIxKyvjyczz//vIqeDRZQDRBFi0GVMWXc6EeoVdohsiIXEqbyYJCESN1//OMfdQaqxnR7L8D0c9grVPuQm9QKxlN3Oyuyws4QThGlecMNN6gFubp3715rQI6pa8F4aQu3MI457AIxPziiA3YNNW0MkafwZ7emrhFCCCFEfxB5ivElxs54aG6ebm7nATqiJTHlHPsYi7gawh3GaHjYbxU743BdMY/BgyNOkS8WU+IxWyzcfoisDe477IvUBqEiTu20B/dFZpBbFWm1brvtNjUuRyAIUjEg1RhmiWFGGhaBJYR4S+TQI0II8QgM/vDUFlP0MaUdT8GDMd5DgnYMwPC03ADTpLE/xE1MsTcDsRGJ9/GkHmB6C8BTdrN4CkEKkaWRIkEx+INoiUELEsZHAlNqsEL6/Pnz1SJBoUA0K+oyFqxCfleIhRCJg8U/TLHC+xiEGeJj8MrwiN41+m81mtUOGJwhygGDQUytNz99R/vsDAhxA2AH3CggqgJTyyCimqNLMYUdCwaYc0/BRjj20QBh1kkqBDsYxx4rtJqPGW5gMFDH4lyIZg4G9uvUqRNzWxFCCCHNCMxCwrgLQplZPEVwgbFQqZV0RBgnIXjByM1ugDoghNpJaWRnHK4riJ7FmAoLc5nHY7A32o4xNvK7BoOADgRDQGQNzmWP2XVXXXWVo/Gokc8U9QanGkBOWKRcMMaAmAWFmXkIEkGwBdqJGW6EEG/R95EQIaTJA2EOq0wCc3SpAVYhx8I8mL6P5OvBOTSRYB8RiojQxAqieJqO6D0IrhA+jSexEOLwGk/vMUBEbinkGcKAA2ImMP6GAqtfYkNy9zfffLPO4j5m7rvvPrViPHJ6YloNVgqFsAqhD6vRI1/R2LFjA/kvIQwjhQAGmshbdNppp6kIQyS2h2h59dVXK7EQT5oxwIWQiKn8yIcJoRY5jyD6QXyL1IdogKiHdmFDuyHoon3GImD1CXqI3EUbUd68IIIVICZiQSX4wLPPPhuYpgZbIUk/bI3UCug7jjsE3+DBrB2QoB+CPsRh+E1whKhbfo82Q7zHtCtEOuOGCBEhmF6H42tesAtAUIXPRsqbSgghhJCmB6IjIXpOnTpVzVDBw/TNmzerMSDWEgBYYKo+MCZFDn6Mm7HSO0RZLAaKsSmEVYxV8Z7VB7VWx+FOQMBBJHB/YCx86hTkGsU4E+NJtB8Lq6L/b7/9tgq+QNqEUGAMjuAKjI+PO+44tS/G6hjnY+yPMRzGcnbBvRDqw3HNzc1VxwtrRLzxxhvqfuGmm26qVR5j6pdeekktTovxpJMFUQkh9qCASghpNIxpL3iKG06owmACeT9DCawYQGJqNxLqQ9x86623lLiHCD8IbcYTdgiSEMQwdQmDIkz9hqgJURSDPpT/8ccfVb7NcGCghGhZ5EVFW8wraQY/kcfAB4NHrMCJVTwxqEUfMbCEwDpp0qRag1MIqiiHfmAghAgADAwxcMMA1xDd0H6IwBj8QnBDNCIEVgys0Xb0wQvBD+3G4A3fDxEXg0vkWUI6hEsvvTRsLlADiJoQI2E/uxi5aJHvFfZBhC+mNUFwhg0xwIZNYQvk78Jxveaaaxz39dprr1U3BOgrBrFe2BPce++9qm74LCJRITAjRQHeD5Vr17AdBtOEEEIIaT5gnIWZTQg6gED3zjvvKMEOuVAxHkJqJ0SC1id+YmyM8QaE2AceeEBN2UdUK8aVSKmF6Emk1cIYzwpWx+FOQMBAJM4+++yoBVSAcTSEVIxvMa6FCIl6H3rooToPs83gQT5mkOG4IGIU6zVgLIpxMRYHC5WKyQr4XqxvgGOM8S+OESJ9EdUavLgUxudoIx6+496CEOI9MdXBS08TQgghJhDdAGE4eFCOiFJE4yKJ/hVXXBGxDgz4IWximtQ+++zjcYubHhD6EYWKXL71LfxFCNETPMC7/fbb5c477ww7kwFRR5iqiQdixirOnTt3Vg/asPhLuLx6uHnHfrhGIJcyoskgbuB7EOVOCCGk6YHIU0QOY5xNCPEe3oURQgip9yk/ogmCc2MZCxdYidLE/oiURXQCsQdyXmFBMURzUDwlxJ8gQgiLEUYCuewwhRQzEZDjGWlssOF/zKJAVDxStwSD1DR4yILoKUyfxQwGRC0hLyGi6uvL300IIcR/IJABKa4ipRYjhLgL78QIIYREBDmWcAOPfKNINYA8sJjyhL+HHHJIyPQKwUD4u+WWW+TTTz+V5cuXN0i7mwr/93//p3IAH3300Y3dFEKIA/AABFM6sSBeOJDqBZH8+IsFFJGTEFNijQ1pUJDfGdN1kRPPDFJ/zJo1Sz2kQllMz8UK2MgbjemoyLeM6aCEEEL8D1ImXHfddXLhhReqNAKhUj8RQryBAiohhJCIXHDBBSoPE1Yoffzxx1V+JuSlvf766+XJJ5+0vNgAFpPCIA91EWssWLBAvvnmG2VzRp8S4i8gdCKfMqbe17fAC/IIQiBFXsPJkyervwbIuY0chYgqxRT/jz/+OPAZFpdD3m1ch5E325yfGzmjjUVHkGu5qqrKk34SQghpODAjDIu5YlFc5K3l4lGENBxcRIoQQki9TJw4UW3Rctddd7nSnubCwIED1RQtQoi/WLx4sXr4hBzSWAwPUftIYWLkNQ23UByi+tPS0up8jsUQEYmOyFQs9nLyySer97GQHh5uYSGRfv361dkPC4tgsRjkUMbiMKNHj3a9r4QQQhoOzFaob+0BQog3MJyFEEIIIYQQF0FkKMRTrJKNKfQXX3xxxPJYuRmR5kiZEg5j3VdzPuo5c+aov/ieUCQmJqoVnQEEVEIIIYQQ4gxGoBJCCCGEEOIimFo5ZcoU2X///S2VRwQptnBger8hgPbp0yfwPvJTA/PU/WCys7PVglJGWUIIIYQQYh8KqIQQQgghhLgIRE6z0Bkt9913nxQXF0tycrKMHz8+8P62bdsCU/zD0apVK/V3x44drrWHEEIIIaS5QQHVJyDfVXl5uVpEJCkpqbGbQwghhBDimNLSUrWoUUJCQmCKOQm/4vJHH32k/r/kkktqLTBVUlKi/kYaGxqfQYB1C45LCSGEENLcxqUUUH0CBqnIfYW8V1jVlRBCCCGkKYxvSHiefPJJeeKJJ9T/Bx98cJ1cqnFxcWrAb4WYmBjX2sVxKSGEEEKa27iUAqpPwBN+DFIx+IUqjhVd7YAVWq3uY6WsW2X8ji599LIdbtYdTV1293Xb562W08UnvESHPnrdBvq9vXI6+ITX6NBHv/i9lXoQDQkBDuMbEtqGd999t7z11lvqNXKpPv7443XslZqaKnl5eSpyIhzGZykpKZ6MS92s12/ocF3wK7RddNB+zqHtnEPbOYe209t2VselPII+AdOj8IQfg1QIqL169bK1//Llyy3vY6WsW2X8ji599LIdbtYdTV1293Xb562W08UnvESHPnrdBvq9vXI6+ITX6NBHv/i9lXoWLVqkxjWc/l2XgoICufLKK+Xnn39Wr4866ih55JFHJDExsU7Z1q1bKwF1586dYeszcp9GypMazbi0f//+0lzR4brgV2i76KD9nEPbOYe2cw5tp7ftrI5L+difEEIIIYQQDcjNzZXTTz89IJ6ee+65Mnny5JDiKejZs6f6m5OTE7bO9evXq7/du3f3pM2EEEIIIc0BCqg+pEePHp7uY6WsW2X8ji599LIdbtYdTV1293Xb562W08UnvESHPnrdBvq9vXI6+ITX6NBHv/i9DrbyI5s2bZKzzjpLli5dqqaQ3X777XLTTTdFnE42dOhQ9Xf27NkhPy8rK5P58+er/0eMGOFRy5sv9HXn0HbRQfs5h7ZzDm3nHNquadiOAqoPWbduXcTPkbsBCwqYt7Vr19Z5L9xmpaxbZfy+heoj7K+bT+hSdzR12d3XTnmrZa2U8/JY6IIOffS6DfR7e+V08Amv0aGPfvF7HWzlNyB0XnrppWpcgVRNiDo988wz691v/PjxAQF1yZIldT5/9913paSkRDp37iyjRo3ypO3NGfq6c2i76KD9nEPbOYe2s4+hzTQHbaSqATUXq5vb2gxzoDahlcEwQN6wYYMahAc7ChYQQGJcK1gp61YZvxOqj1hQAVPtOnXqJMnJyb5fxdjNuqOpy+6+dspbLWulXHNYUVqHPnrdBvq9vXI6+ITX6NBHv/i9DrbyG88//7wsWLBA/Y/IU+Q9tULXrl1l4sSJMm3aNJU39emnnw5M6//+++/l4YcfVv9DnOXiFe5DX3cObRcdtJ9zaDvn0HbWwCKLW7Zskfz8fLUAUnPRRrwiGtu5rc1wJOVDsOJqKPEUyjxO1lCEy53ltKxbZfxOqD5CvMZJjuOBG5uGEFFD+YSOdUdTl9197ZS3WtZKOS+PhS7o0Eev20C/t1dOB5/wGh366Be/18FWfgIPvl9++WX1P0TOqVOnqi0c+++/v1xxxRWB17feeqssW7ZMFi5cKBMmTJDevXurceGaNWvU56eddpqcfPLJDdCT5gd93Tm0XXTQfs6h7ZxD29UPFiJC7vFgXaY5aCNeEY3t3NZmKKD6kHbt2tV5D5GnOEnhEJimFRxlgPDlSDm07JZ1q4zfCdVHPGXCYg5GRHBD5OwI5RM61h1NXXb3tVPealkr5bw8FrqgQx+9bgP93l45HXzCa3Too1/8Xgdb+QnkPM3LywuMIcLlMzXIzMys9TojI0PefPNNmTJlinzyySeyevVqNTYZNmyYnHLKKTJp0iRP29+coa87h7aLDtrPObSdc2i7+tm6davSZbCae4cOHdTfuLi4ZqGNeEU0tnNbm6GA6kOgnvfq1auWqo7oBQDxNJRCj8+tqu1WyrpVxu+E6iPsj+OwYsWKQDoFhI43pE/oWnc0ddnd1055q2WtlPPyWOiCDn30ug30e3vldPAJr9Ghj37xex1spSPffPNNyPcHDRoUMn+pHTAWwTR9bKThoK87h7aLDtrPObSdc2i7yOC+v7CwUP0PPQDiaXPSRrwiGtu5rc1QAm8CwAmMnKfMb6UHxnEwHxtCCCGEEEIIIYQ0Pcz3/VgQkjQ9bYYCqg9p37697X3sCKtWyrpVxu/o0kcnPtEYdUdTl9197ZS3WtZKOS+PhS7o0Eev20C/t1dOB5/wGh366Be/18FWhDQE9HXn0HbRQfs5h7ZzDm3nf93Aj8RrZDsKqD4k3EJRpPnipU+4WXc0ddnd1055q2WtlGsO56cOffS6DfR7e+V08Amv0aGPfvF7HWxFSENAX3cObRcdtJ9zaDvn0HakuUMB1Yds377dUfJcu2WrqqqlpLRC/XVSn53v9Cu69NGJTzRG3dHUZXdfO+WtlrVSzstjoQs69NHrNtDv7ZXTwSe8Roc++sXvdbAVIQ0Bfd05tF100H7Ooe2cQ9s1nm4QSZtp6lRoorkAfWJhiTas3rhLPpuxUH74I0fKK6okIT5WxgzrLBPH9pS9OmU02Eny+uuvy7Rp02TVqlUqAfOAAQPkoosuktGjR9sqt27dOpk4caIcfvjh8vDDD9f6nvnz58vpp58uN998s5xxxhkN0jdCCCGEEEIIIYSQSKzakCfTvl9BbUYTYqq5wo0vWLRokRQVFUlqaqr06dNH4uLiAp9VVVUFVm/t27evxMbWDSy2utrY97PXy2NvzBYUrTQ93YiLjRF4yrVnjJCxI7It1ed0hbPS0lI599xzZePGjXLllVfK8OHDpaSkRN5991157bXX1Il27LHHWi4H3nvvPXUi/utf/5Kjjz5avZefny8nnHCCDBw4UB5//HHb7YzURyvHxO3pFGaf0LXuaOqyu6+d8lbLWinn5bHQBR366HUb6Pf2yungE16jQx/94vdW6jGPa/r37x/1d5KGhcdPn+uCX6HtooP2cw5t5xzaLjKRNAAn2ohVbcZLdNBmqh3qSna0GavjGk7h9yEbNmywvU95ebmlpxs4Qauqq2udoACv8T4+Rzkr9VkpEwqcMHDwN954Q51E3bt3l379+smtt94qxx9/vNx7771SWFhouRyYNGmSjBs3Tu68807Jzc1V791yyy3qL8o5xWkfdfCJxqg7mrrs7munvNWyVsp5eSx0QYc+et0G+r29cjr4hNfo0Ee/+L0OtiKkIaCvO4e2iw7azzm0nXNoO2kw3cCONuMlOmgz5ZpoLoACqg+Buu9Eda8PhIbXJ+zj82k/rLBUn5UyoU4OPKXASZWVlVXn86uvvlqef/55SUhIsFQuOTk58N7dd98tKSkp6iR+++235dtvv5XHHntM0tPTxSlO+qiLTzRG3dHUZXdfO+WtlrVSzstjoQs69NHrNtDv7ZXTwSe8Roc++sXvdbAVIQ0Bfd05tF100H7Ooe2cQ9tJg+kGdrQZr9BFm6nSRHMBzIHqQ8yOVx8//Zkjr3+2WIpKyiOGPSMsevuu+i+IeNrx9W/rZPbiTfVOS09OjJOzxg+QA4Z2stxe5MTYuXOnjBgxIuTnHTt2VNvKlSstlTOTkZEhDz30kAotnzFjhvz973+XIUOGSDR4PTXfC59ozLqjqcvuvnbKWy1rpZyXx0IXdOij122g39srp4NPeI0OffSL3+tgK0IaAvq6c2i76KD9nEPbOYe2i043MLSZ4tLIiyLZ1WbmLNlc7xT3lKR4OXNcf19qM7GaaC6AAqoPCXa+SLz37XJZv7nA9TbsyC+z9v3fLbN1kubl5QVOKDfKBTN06FDp0KGDbNq0qVbCY6fEx8f7zicas+5o6rK7r53yVstaKeflsdAFHfrodRvo9/bK6eATXqNDH/3i9zrYipCGgL7uHNouOmg/59B2zqHtotMNvNJmrIitftZm4jXRXIA+Ui6xzJo1ayyXPfGQ3pLdIU3atEySthnJYTd8bofW6YkR68PWuX0LmXRwb1v1tmnTRv3FEww3ygVzzz33qNXhevfurZ5yILFxNJSVWROSdfKJxqw7mrrs7munvNWyVsp5eSx0QYc+et0G+r29cjr4hNfo0Ee/+L0OtiKkIaCvO4e2iw7azzm0nXNou+h0A0ObqU9HsavN1Kf1YMP3+lWbKdNEcwH6SLnEE/CEARucsb6Q+8lvzpbvZq+vk6TYDFZ8O3jvbLnk+AH11mflO4Pp0qWLtGvXTmbPnh1Ykc3MihUr5L777lOrtlkthxMSfPjhhyo3x1NPPSXZ2dly0kknqbDxO+64w1YbCSGEEEIIIYQQQuxqM1awo81cfVroqfPRQm2mLoxA9SFwTrtYCXueOLanVIc/PxX4fOKYnpbqcxJqjfwWOHnee+892bhxY53PX3jhBZk3b5507tzZcjnjaRlOxtNOO00OP/xwtSLcVVddpVaJ++6778QpuoSTO/GJxqg7mrrs7munvNWyVsp5eSx0QYc+et0G+r29cjr4hNfo0Ee/+L0OtiKkIaCvO4e2iw7azzm0nXNoO2kw3cCONuMVumgz8ZpoLoACKgmwV6cMufaMERIbU/M0wwxex8bEqM9RzksuueQS6d69u5xxxhkydepUWbt2rcydO1c9scBrhHqnpqZaLoeQ72uuuUatCIfPDM4//3zZZ5991Htbt271tE+EEEIIIYQQQggh1rWZGGozGkEB1Yc4cSjklrDC2BHZ8tDf9lOh4AnxNe6Bv3g9+dqx6nOr9Vn9zmBSUlLktddekxNPPFGef/55mThxolx88cWyefNmefXVV2XcuHG2yj388MOybNkyefTRR2ulFMATlQcffFCdxDfddJNa7c4uTvvoNl5eZNysO5q67O5rp7zVslbK6XzBdwsd+uh1G+j39srp4BNeo0Mf/eL3OtiKkIaAvu4c2i46aD/n0HbOoe2c40Q3gPYCDaY+bcZLdNBmKjTRXIA+sbBEG7pntVR5NK48ZbiUlVdKUmKcxMTUfurhNXg6cfnll6st2nK33Xab2kKBfBuzZs2Kur2EEEIIIYQQQgghboEIU2oz+kAB1Yd069bN9j6JiYm2y8bGxkhyUrzj+ux8p1/RpY9OfKIx6o6mLrv72ilvtayVcl4eC13QoY9et4F+b6+cDj7hNTr00S9+r4OtCGkI6OvOoe2ig/ZzDm3nHNqu8XSDSNpMUydRE80FcAq/D9m0aZPtfeyEPbs1PV+nUGuv0KWPTnyiMeqOpi67+9opb7WslXJeHgtd0KGPXreBfm+vnA4+4TU69NEvfq+DrQhpCOjrzqHtooP2cw5t5xzazv+6gR+p0Mh2FFB9SElJie19qqqqXC3rVhm/o0sfnfhEY9QdTV1297VT3mpZK+W8PBa6oEMfvW4D/d5eOR18wmt06KNf/F4HWxHSENDXnUPbRQft5xzazjm0nf91Az9SpZHtKKD6kKSkJNv7ICmvm2XdKuN3dOmjE59ojLqjqcvuvnbKWy1rpZyXx0IXdOij122g39srp4NPeI0OffSL3+tgK0IaAvq6c2i76KD9nEPbOYe2879u4EdiNbKdPi0hlunUqZPtfRISElwt61YZv6NLH534RGPUHU1ddve1U95qWSvlvDwWuqBDH71uA/3eXjkdfMJrdOijX/xeB1sR0hDQ151D20UH7ecc2s45tJ3/dQM/kqCR7Sig+pBVq1bZ3qe0tNTVsm6V8Tu69NGJTzRG3dHUZXdfO+WtlrVSzstjoQs69NHrNtDv7ZXTwSe8Roc++sXvdbAVIQ0Bfd05tF100H7Ooe2cQ9v5XzfwI6Ua2Y4CKiGEEEIIIYQQQgghhISBAqoPadOmje194uPjXS3rVhm/o0sfnfhEY9QdTV1297VT3mpZK+W8PBa6oEMfvW4D/d5eOR18wmt06KNf/F4HWxHSENDXnUPbRQft5xzazjm0nf91Az8Sr5HtKKD6kLi4uMZuAmlGPuFm3dHUZXdfO+WtlrVSrjmcnzr00es20O/tldPBJ7xGhz76xe91sBUhDQF93Tm0XXTQfs6h7ZxD25Hmjj5SLrHMli1bJCMjw9Y+FRUV9Sr3FXlbpLIoX0rLSiUpMfwKe3Gp6VKRlF5/fRa+M9K+r7/+ukybNk3lWsGKfwMGDJCLLrpIRo8ebbvcoYceKjk5ObUSEbdr107Gjh0rV111leOnadH0sbF9ojHqjqYuu/vaKW+1rJVyXh4LXdChj163gX5vr5wOPuE1OvTRL36vg60IaQjo686h7aKD9nMObecc2s45dnUDQ5upD2gz8Rnto2yd3tpMhSaaC9CjFaTRwQm67pkrpLqyvN6yMXEJ0v68R0SSsz1LEnzuuefKxo0b5corr5Thw4dLSUmJvPvuu+r9hx9+WI499ljL5QzOO+88tQGUW7p0qfzzn/+UM888U9566y1JT0/3pD+EEEIIIYQQQgghbmszXS59wjMRldpMbSig+pCuXbva3icxMTHi53i6YeUEBSgXV1ES9XeG4/HHH5clS5bIRx99JFlZWYH3b731VikoKJB7771XPbV46qmnLJVr0aKFej81NVXat99zYenSpYv0799fjjnmGHnhhRfkmmuusd1Wp33UwScao+5o6rK7r53yVstaKeflsdAFHfrodRvo9/bK6eATXqNDH/3i9zrYipCGgL7uHNouOmg/59B2zqHtnGNHN7CrzaC8VwKqDtpMoiaaC2AOVB+ydetW2/sg7NlNKisrPfnO8vJy9ZRi0qRJtU48g6uvvlqef/55FeZtpVxycnLE7+vUqZMcccQR8vHHH4sT3LZrQ/pEY9QdTV1297VT3mpZK+W8PBa6oEMfvW4D/d5eOR18wmt06KNf/F4HWxHSENDXnUPbRQft5xzazjm0nXN00Q38qM1UaGQ7Cqg+pKioyPY+VVVVrrbBSn1OvnPdunWyc+dOGTFiRMjPO3bsKEOGDJH169dbKmcl0XWfPn3U9xYWFja6XRvSJxqj7mjqsruvnfJWy1op5+Wx0AUd+uh1G+j39srp4BNeo0Mf/eL3OtiKkIaAvu4c2i46aD/n0HbOoe2co4tu4Edtpkoj23EKvw+Bwm+VgkW/yI7v/yuVpcUSExO+XHWlPVV/27sPy456EvnGJCRLm4PPkLT++1muNy8vT/2tLzm11XJWaNmypfqL0HIjpNwqMZGMqqlPNGbd0dRld1875a2WtVLOy2OhCzr00es20O/tldPBJ7xGhz76xe91sBUhDQF93Tm0XXTQfs6h7ZxD2zkHuoGhzVSVFUcsa1eb2fjfeyQmLrI2E5uYIq3Hnu5LbSZGE80FUED1IcgPYZW86dOkfNueFc7corp4l9Q/iV8kb8Y0WyepseIanmC4Uc4K+fk1q9ulpaXZ3leXfBx2fKIx646mLrv72ilvtayVcl4eC13QoY9et4F+b6+cDj7hNTr00S9+r4OtCGkI6OvOoe2ig/ZzDm3nHNrOOdANtnqkzVQV7aq3TKWPtZlETTQXwCn8PmTlypWWy2bsd7wktO0ssWmtJS69TdgtNrVG6bdKTEp6xPqwxbfpJBmjJ9q+KLdr105mz54d8vMVK1ao1drKysoslVu2bFm937lgwQLp3r277ehTgNXm/OYTjVl3NHXZ3ddOeatlrZTz8ljogg599LoN9Ht75XTwCa/RoY9+8XsdbEVIQ0Bfdw5tFx20n3NoO+fQds6BbmBoM/XpKHa1GZSvr058r1+1mVJNNBfACNQmDp4wYCspKYmYtLd040rJefF6y/W2PfFGadmtf8Qy9X1nKGJjY+Wkk06SV199Vc4///w6SYixItu8efOkc+fOlstFIjc3V77++mu58MILbbWTEEIIIYQQQgghxI42Ux92tZms026XpKwe4jbUZupCAdWHtG7d2vY+8fXkK7Vdn4UEwE6/85JLLpEff/xRzjjjDLnqqqtUMmKEg7/55psydepU+de//iWpqamWy5mTXm/ZsiUg7i5ZskQmT54s2dnZcu655zpqq9t2bUifaIy6o6nL7r52ylsta6Wcl8dCF3Too9dtoN/bK6eDT3hNc/H7ubmLZMqct+Xc4afIkMz+jushpDlAX3cObRcdtJ9zaDvn0HbO0UU38KM2E6+R7fRpCbGMkxwQrifetVCf0+9MSUmR1157TV588UV5/vnnZcOGDSqSdcCAAeqpxsiRI22VM0A5bEYCbDwZOfroo1U4uZPp+9H00W28zAviZt3R1GV3XzvlrZa1Uk6nHC1eoUMfvW4D/d5eOR18wmt06KPXbcBv45uzpknOrlx5c+40Gdyxn6PfOR1sRUhDQF93Dm0XHbSfc2g759B2ztFFN/CjNhOjke0ooPqQTZs2SXp6uq19ysvLJS5C1GhcarrExCVIdWV5vXWhXFVCStTfGQk8nbj88svV5ka5b775Rrwgmj42tk80Rt3R1GV3XzvlrZa1Us7LY6ELOvTR6zbQ7+2V08EnvEaHPnrdhp+WzZQVO9ao//H3z9xFMixrgC9tRUhDQF93Dm0XHbSfc2g759B2DaMb2NVmUN5LGlubKddEcwEUUIkiPqO9dLn0CaksypfSslJJSkwKWxYnaEUSL5yEEEKIX6iorJCSilIprihRf2tt5aVSXF4i76/5QvCMvxp5r2Ji5K15H8jQzP5aPfknhBBCCGku2kx9QJtBedIwUED1IcgL4UW4PU48bAlVVSphcCRiq6pc+U6/o0sfnfhEY9QdTV1297VT3mpZK+W8PBa6oEMfvW4D/d5eOR18wmsaqo/V1dVSXlVhEjf3CJ75cQWyYfWvUmIWQctri6EhBdKKUqmsqrTVjqrqasdRqM3BHwgB9HXn0HbRQfs5h7ZzDm3XcLqBoc0Q0UZzARRQfciOHTvqrGxWHxUVFZYdz0pZt8r4HV366MQnGqPuaOqyu6+d8lbLWinn5bHQBR366HUb6Pf2yungE14Tqo9K7KwsDylYKiEzSNzcs5VIcUWplNYRQGvEz8rq+h9SNgROo1Cbgz8QAujrzqHtooP2cw5t5xzazv+6gR+p0Mh2FFB9SGFhoe19qixEjNop61YZv6NLH534RGPUHU1ddve1U95qWSvlvDwWuqBDH71uA/3eXjkdfKI+IHaWKbGzdoRmsRIwQ0dtmqM/d+TvlJiE2DplqjQRO83ESIwkxyfV3hKS6763e9tWtEO+WPGDa1GofvAHQtyAvu4c2i46aD/n0HbOoe38rxv4kSqNbEcB1YfEx9s/bHYiR6yUdauM39Glj058ojHqjqYuu/vaKW+1rJVyXh4LXdChj163gX5vr5zbxwNiZ2llWdA09ZLw0ZwhIz13lzd9Vq2ye+r3OxIsaKbEhxA7E5KkOL9Ystpnmt43lUvAfjXvJcYlWP59gq1v+fIhFW0KwdSNKFQdrhGENAT0defQdtFB+zmHtnMObed/3cCPxGhkO54BPqR79+6290lKSnK1rFtl/I4ufXTiE41RdzR12d3XTnmrZa2U8/JY6IIOffS6DW7Wvyu5WK759C45d/gpMiSzf5Pze0RgZnbOlJ3FeWpaei3x0iRghsrNWWpexMj0eWlFmbZip1ncVP8nJElSiKjOWiJoQvjPEmyInV6A6FJEmYbDSRSqDtcIQhoC+rpzaLvooP2cQ9s5h7bzv27gR5I0sh0FVB+yfPly6dWrl619SkpKJDk52bWybpXxO7r00YlPNEbd0dRld1875a2WtVLOy2OhCzr00es2uFU/ovum/PaW5BRtkjfnTpPBHfvZEsvc9nuInYZIuXTFUunQKTN85OZuYXPzji2SmJIUyNsZSgTVkdiY2EBEZrCAaY7eTKn1WajIz5r3ctasl369+zaq2Om238M/EV2KKf+RBGt8bicKVYdrBCENAX3dObRddNB+zqHtnEPb+V838CMlGtmOAiohhBDiEYjag3gK7EbxId9PSWWpbC/aGXL6erEpP6exbdmxVRI2JdaUDzGtHdPia7FAtCAuJlYJlSGnrlvM45kS9Fl8bLyrYudml+vTgYqqCtlatL3eaF98vq14hyqPiFlCCCGEEEKaGxRQfUhGRobtfeLi4lwt61YZv6NLH534RGPUHU1ddve1U95qWSvlvDwWuqBDH71ug5X6Ec1ZXlkhZZVlanGimm3P/xAwX5rzdiC6D3+f+e0VObDrKCmtrD29PVT0J+pQ/CFaAWGyZhr6HrEytjpW0lPTTVPUQ+foDJffMz5O/+FIU/R7iKEPHHmT7CopUK937NwhrVu1Dv3dyemWxVMdbEVIQ0Bfdw5tFx20n3NoO+fQdv7XDfxInEa20/+OhdQhJSXF9j6xsbGulnWrjN/RpY9OfKIx6o6mLrv72ilvtayVcl4eC13QoY+h2gBBs46QWWH8bxY5awud5rKI0MTrotJiqYqpDl22ouZ1eVWFrTZDRN1RnCcfLvlSGooEiJ1hojbjJU7Sk9PCTG/fI35WlVVKm4zWNe/FhRY7CwoKJC0tTZoyuvp9tLRLbaM2UJDYxpXjqIOtdOTtt9+W22+/Xe688045/fTTQ5YpLS2VKVOmyEcffSRr1qxRU9b69u2ryh9zzDFh63a6H4kO+rpzaLvooP2cQ9s5h7bzv27gR2I1sh0FVB+Sm5trO/dIeXm5ZeXeKDs3d5FMmfN2yIVPrNRn5zuD+eCDD+S1116TpUuXqimTPXr0kJNPPllOO+00cYOzzjpLOnfuLA8++KCj/dG3119/XbVHhyciTnyiMeqOpi67+9opb7WslXJeHgtdsNpHTEEPJVSW1hEkQ4uatcuVS7lJ6MwvLpCYuJhaZTC92M9g5fSQ4mXL1iaRMzni9PVNG3Klb48+6n8sbBQfG+dK7t92WTUCWzjo902jDW7Vr4OtdGPu3Ln1jjmQ4+u8886TWbNmqbFFnz591MOJ3377TW2//PKL3Hfffa7tR6KHvu4c2i46aD/n0HbOoe2cE402AiJpM17T2NpMeT22M7SZc845R7yGAioJu7AEFjzJ2ZXraOGTaPjf//6nBvq33nqr7L333qotP//8s9x7772ydetWufzyy6WxQYTHAw884NpFg/jrR8SvVFZVhhAqy9WK5+Gmoe+Jztzz3ra87ZK0MSmM6LnnPXxfUwHiZmJcoiTFJe7+f/cWX/M6Yffnxvt5xbtk+vrZYes7b7ffGoJoUnyixIUQO+0m66/eXi5tU0NPwSaENA7Tp0+XK6+8UgoLCyOWwzgHIijO+X//+9/SpUsX9f53330nV199tRofDRs2TN20uLEfIYQQQvSH2ow1bYYCKgkJ1Hm7JCYm2iqLhU6w4Em4hU+s1GfnO8288cYbcuKJJ8pJJ50UeA9POTZt2iSvvPKKFicpLhzR9FEHn2iMuqOpy7yvlR8RO99ltayVcna+t0IJmqGnh4cVMgOCZ7kt0RPvVVZXiWvskEYDuUQhWEJ03CNmhv8/ySR0hi9rei8+UarKK6Vli3T1PqbB2xmkwD9v+fIhiY2Jkard1wozeP/71b/KUb0Prrdeu+eMH/zer+jQR6/b4Fb9OthKB4qKipSg+fzzz6uI/EisX79e3n//fXVNePTRRwMiKDj44IPlpptukjvuuEOeeOIJNUYyprM53Y+4A33dGhV5W6SyKL/Wex3iy6R048pa78Wlpkt8RntpbKqqqqWsvFISE+IkNlbPxQPpe86h7ZxD2zknGt2gPm3GS3TQZhLrsZ2hzTQEFFB9yK5du2znH6msrLQ8aK6oqJC35n0QuPnHX7wemtk/cLNvpT4732kG+8yZM0fy8vJqJaq+6KKL1MlrTFfDTcmHH34omzdvVifxZZddJkcddVSt6XKPPfaY/Pnnn8peRxxxhLqRCLYd+nvttdeq8rgIdO3aVV0QEEL+448/qnDx4cOHq327d+8u7733ntx8881q3/79+6t99t13X/GbTzRG3dHUZd7Xyo+Ine9CWeSLC47Q3DM9vObv5m1bJDU91TTlfE/OTOP1roJdEpsYt2fK+e6ywXVhQ87OpgCuC3siMyMIlfH1fL77/z3CaOjPsYgRzvuOHTt61idcA9Jat3C0r9k/Q4HrqtXBj91zxq7fWylrpZyX1yBd0KGPXrfBrfp1sFVjs3jxYrngggtky5YtEh8fryJB33rrLcnJyQlZftq0aWo8MmTIEOnXr1+dzydNmqSiK3BtmjlzpowePTqq/fyC7rNN6OvWxNN1z1wh1cbCiBGIiUuQLpc+0Wgi6qoNeTLt+xXywx85Ul5RJQnxsTJmWGeZOLan7NVJr8Vz6HvOoe2cQ9s5x6k2AnGwPm3GS3TQZjZs2KAeEtenzSD3u9faDAVUH5Kfn29ZOJi+bpa8Pe8jKSovtnyCIWqtoLyozs3++VOvV+KFcSLXVx8EldOHTJTRXUaIHXDDcc0118iYMWOU848cOVIN+AcPHiwtW7ZUZXBSLVy4UC3E0K1bNxW2fdVVV8mTTz4phx9+uKxbt07++te/qhMTNyyw2Y033ih33XVXrdwauJDdcMMNMn/+fHn11VdV5AYiRpCHY+DAgSrXBy4aWJjhlFNOUReFo48+WtV3//33y9dffy0dOnQQP/lEY9YdTV3GvsaPiHll8+d/f0Mm9D1MLepTS+xcmxo6gjNI9CwtL5Xy6ooGfXrlJXExseGFynhMNw+eim5PyNywfoP06dEr8D6mnjfUNJKG8Plo6g/2z3DgcyuDH7vtsFPealkr5bw+HjqgQx919Xuv6vEziAyFeIoxzG233aYeuGI8Eg7cnACUDxd9gXEQcpqahVCn+/mBxpyyaBX6ev0g8tSKeApQDuUbQ0D9fvZ6eeyN2QIXq6yq+f2GiPrd7PXy7az1cu0ZI2TsiGzRBfqec2g759B2zoHu8HvuXKXNFFeUWN4P96oFZYURtRkrpMQny6mDj/WlNnPuuefKoEGD6tVmfvrpp1oirxdQQPUhdp5cfLD4S8nJz3Xle80nrp3vt3uSjhs3TjIzM9XTA+TX+P7779X7eMKAE6NVq1ZKuMRTDkxPA1dccYWK9sB7OEmx0i3KoTwiPwDydBg3GgDT6fC0Ak9BcIIaUxI+/vhj9XTtn//8Z2Bf5P349ddfVb34rvT0dPV++/bttZjG7+WUPDfrjqYu3DQt37ZaPlz8Za3oPohUW4q2qQiVOmwWLYDAWFeIDJVTs7bQWVfYDFE2RLlICwe5Qdm2YmmV0riRGF5PQ3VaPxay2lq0PaJ4CvD5tuIdqjzSEbjVDjvlrZa1Uq45TAvWoY+6+r1X9fgZRExggL///vtbKr969Wr11zwFP5js7GwlhBplo9nPDzTmlEWr0NebBog8hXiq0u4E/XwbYio+75qZrk0kKn3PObSdc2g7iepeltrM/Y60GQikVrUZr6GA6kMQEm2Vif2OlLfmfWj5KUfwE45g0hJbWH7Sgaccx/U7QpyAxQ6w4UTCyYcTFU8cLrzwQnWyASQxNrPPPvuosHCAFeIQQWqcZABPSsyRF59++qlasa1nz561TjY8PUGIOuozU1paKitWrKj1XlJSkvjNJxqzbrt1qeO/dYXMXD9Hfs35Q7YVuZN4E1PAg4VMc8QlIjRDfV5H9IwwJd0oA3Es1OJAfsZLf9OlDU7rx/F+4MibZFdJQb1lM5LTI4qnTtphp7zVslbK6eATXqNDH3X1e6/q8TN9+vRRm1W2bdum/rZp0yZsGdx8gB07dkS9nx+iT9+YO9V21H5DQ193n5L1i9VjxtjUdIlLzZDYBO/H2pi2r9yqOvKDz6f+96ccd1APSUtJlLTUBGmRkiBpKTV/4+MaVlii7zmHtnMObecc6AbUZuI91WYaAgqoPgSOAseyAp4wYENeCuR4tLLwSVFZkVSFGEEg30bHFu3k/iNuVA5bX31WvjOY3NxcefbZZ+Xiiy9WTzrwlGvAgAFqw9OLCRMmRGy/cVKaT85wYOo9TurzzjtPhZcj9BzgwrDXXnvJM888U2ef1NTUqPvY2D7RmHVbqauiskLmb16qRNPfcv6UvNLaiw5EYlL/cdKnXQ/1Q7I5d4v06NrdtFL6bnEzNqHO01OrfbRSDmU6e3QsdMFLf9OlDdHU3y61jdrcqMvuvnbKu+33je0TXqNDH3X2ey/qaU5gPFHfg1njs+Li4qj30x1Em67eua6WeIUo1K9X/iSH9zxIdIG+7j7bvvhPrdcxCUkSl5IusakZEpfaUi00hb81r2v+V0Lr7s9ik1tITEysrQWjkPPUiDQNB4JTl6zZIf9cMyvk5ylJcdICwupuQRV/IbIaYmut903v4W9CvP0H7fQ959B2zqHtnIPfa0ObsUJAmykvCrsoraHNePVgURdtpnv37iqatT5tpiGggOpDvMrTaGfhk36tvXn6hOnw77zzjmRlZanExGaMHBtGztFZs2bJIYccEvj8999/l169eqn/8Rc5MZBHA4mGwZdffqkWUsDTDYCnGEOHDpW///3v6snJkUceqXJrIGIEizIgFNyI6MDTkOuuu06FsCPPhk7RD8DL3J1u1h2uLuTd/SN3ocxc/4fM2jBXCsvr3uTFSowkJSRJSXlpyOnR+BGBb546+Lia6f758dK9dZeo2uWkXFPJo6p7H71uQ0P4vRf72ilPv7eHDn30i9/rYCu/gbEKbhKsYB6DON3PzRvC5cuXqwfPWCCrrKxMLQiB6JG1a9eqMu3atVM+YUTL4kYIN2XYF+IuxnxGeoG2bduqdr78+9shc0k/9/sb8tOymXJIp9EyqHM/SUhIUAtWGGkMtm/fLoWFhepmDXnYjOgUROHigTe+F2BqICJaCgoKlA3RfpRFOzHebNGihWzcuFGV7dSpkyqH9E5oG8SDlStXKrsjoADCtLE4GG4w8Rp1G+NR9A2LYqDO1q1bq/y4AHkEYS8jMhiRXcgThzEnbgphN8OGsCfGtOgfQHuxqIYR0IC61qypGcNjP7B161b1F3bAImKGvdGfVatWqc8wzkX/ka/XSD2B/ZBzDraFTdFXgLZjnI66jNQQaLthbxxX+AJADjr4gbL39hyJJjazurxUKspLRXbV9KdeYmKlOilVYjHLI721lEqcSGILSW3TQb1fVBkjkpQm2T37yqadBVJQGadynUZLcWmlFJcWy9ad9h9UJMTFSEpynGSkpUh8TKWkJMVKq/QUSU9NkurKUklNipPOWe1EqsokpqpMCa9JCTGyYNFSSYyPUf4NnzH7LKa8YoPgAd8yfBb3N9jgPwDnH4632WfhH/C3tLQ0VbfZZ+FzO3fuVK9xLsBHDZ/F+QsfNu7X4Pdmn3XrGoE+mX0W/+O8g3/CL80+G+oagfMZ9TXENQK2Rvmmco1An1Cvq9eI3faG/cw+a7Y3fNHss6jTbG+zz6Jus73RL7PPwg6GvdE/s8/CtmZ741iE8ln8D1uYfRb+gO9CedgB/6MvhniI8ngPn+H78Rrtx2scJ4CyeA/tmLt5sSVt5rd1f8qQDv1UPbABNrXAb1JS4CErjiHagnrVNSchQX2/uSzaAHuby+I1psnDf8455xy1L/wJZY00hoY/TZ8+XYmqRr2YYg//RlnY55NPPlF14jOAKFbkP506daraB7lVsRgmcqc+9NBDKl9q7969lS+jDHzemKqP9kFgRZnx48cHbIL+GvaGjQHaiWOC7zDsHuoaYdiqPiig+hBDSLSDISK6tfDJXQdfG/V3hgIXMSQqfvzxx9WFEYIlLoS42D799NOBxMUQTpF0GCc8LvrIjYHcG5MnT1b1nHHGGSpPxx133KGSDuPH5OGHH1Zh4sFRGqeddpp88MEHKufGu+++K8cdd5w899xzcuWVV8r111+vvh/f/cMPP6gT2vy0AyHsONEbOwrViU80Rt3murCw2ewN85VoOmfjfLWwUzCIGB2WNVD27TxciguL5IX5b1le2dxOu62WtVLOy2OhCzr00es2eOX3Xu9Lv/cOHfroF7/XwVZ+A+MK3FAbN1GhMD4zr1jrdD+3wPjHeHgNIcOM8b4BblQMcGMdruwfGxdKTlHNDXgoFuYtV9vwnQPlhP7jpZ9pX9xYR2qD+XWwPYKjqsxlYWfzoqHGNFbcdKEec1mMG83TD3HjGKlNEIQMMKaNVNacqiE4721wWSN1Q332BuZFNyCeRCpr3MBatXfpxlipkTKskTbscImNjZPKol1SWbxL/a3C/0X5ItUWhM7qKokpKZDqkgIp27lRjMcGxbtnehpi7obvav7CCx5snSAFVclSUJ2k/hZWJ0tBVZIUqL94XfN+cUyyTDxyqOSXxkphSYUUFJfXbEVlpv/LpaLSniBbXlkt5YUVsqvQPOsqOBVQ6LyJ8XExKqI1kE5ARbbu3B3dWhMRu3zLGklLSVbRrrEViRJTItIpu5ukJNWINRCSzD4LwSLcccXxNwQTuz7r1jUi2GeNXInhypp/kzp2zJSKSpHOnTJV372+RoQq6+drBK55wQsoR3uNMNvbvEBVJHvDZ83tiOSzsLfZZ+3Y247Pwt4Q55YsWRL4fTTPeoTeArEOBK+hEqwlYL/3F39mSZtBuX26DFX+jP2M7whVr1mfwf/mssEaCT7H8cM0/SeeeEIJ/+NCaDOjRo1S2gwC1VCHoc189913SpvBe2effba88cYbSr8xtJlHHnlE9ttvP+VbaLch5GIxbwS83XLLLUqbOfHEE+XFF19UC1mZtRksGIX3YEv4A0C7cIzQb3PUK8rgO4y/oa4RixYtUg8G6oMCqg+B07id8NnuwidVUuVZkumrr75aXdjwtOP1119XTwMwmMPTBYSPA4R3Y7v11lvV0ydEjeLExlMIgIsvTjQkGz7++OPViYnIUSMU3AxOVkSgTpw4UZ2M+H7k9IDgev7556unJMjZgfqMCzmEWESvnnnmmeo7zE8+/OITjVJ3Yox8u/IX+XX9HJm7abHyu2BSEpJl705DZN/sYTIsc6DKS4ofnJs+f8DWyuZ22m21rJVyXh4LXdChj163wc36o6nL7r70e+/QoY9+8XsdbOU3cBMGIdSIkgmFERVjvkF2up+uGA/0Mask1JRFM3M2LlDbgPa9ZdKA8TK4Y78GnyFEX3efjBFHSVJW3Zlu1dVVUlVSaBJU92y1X+dLVVGe+r+6ou7D+VCkxJRLSly5tBcLaaNmvCcxcQm7Uwbs3tq1rPW6KilNSmNTpDgmRYmxhZWJUlBSKQXFZVJYVF5LbC0sqS3AlpbVRGdZpaKyWnYWlKrNLrExslt4TdwtvAalGtgtwNZOQVCTfiA1GTn+9ZqRV99CYch1i3QNiDhOiI+VMcM6y8SxPbVZGMwP8JrnHDvaiNuL0kaDDtrMq6++qgTX+rQZBMZ5rc1QQPUhCF8PVs3rA+HSkSJCzQuflJUhJDsp4sInFvTTer8zEjixsIUDT/luu+02tYVj+PDh6klHKHASmsHJN3/+/FpPkHDShwMnPS4iuuRAdeITDVX39uKd8tv6P5VounDz0pD5ddOT0mSfzkOVaDqoQ986PwD4UdhSuM3Wj4iddlsta6Wcl8dCF3Too9dtcLP+aOqyuy/93jt06KNf/F4HW/kNjEMwjdOYdhgKY1qnOXrG6X66Ul86KYOMpDTJK62J0Fu4ZZks/H6Z9GzTTQmpe3caLLE2cmBGA3294UBeU+RCxSZta0cchqOqrERFsVYV7goZ0Vq5W2gt3bVTCnfukBax1gTX6spyqczfprb6QAxjSkysdEhJCwissehHRobEZbWUuBYtJS4FAmzbGvE1MU1FuhaVS0Bkxd9Ck8haWFwuuVt2iMQmKVHWeK+opG5gQkT7VIvkF5WrTervSh1aJMdLVmqptE2skNTkeBXRig3iau3X8ZKS0UrS2mc1yqJb389eL4+9MVstFGbkuoWI+t3s9fLtrPVy7RkjZOyI2hGYJDS85jnHjjbi9qK00dLY2kzHjh0taTMNAQVUUmfhEyuioNUcEaR5srlgq/y6/g+1ENSSbTU5RoJpk9JKRnUeJvt2GS792vWMuFI9fhQu6/8XaZO5Z/pFY/6IEEIIIW6CyAmkIpo9e3bIz5G/y7iZGDFiRNT76YiddFJtU9vIGUNOkGmLv5AN+TXT/VdsXyP//Onf0iWjk5zQf5zs32Vvx7OhSNMgNjFZbZJRe8pxOJHtH2/8LmmxpZIixdIiplTSYkukZWyptIgpkQP6tpTMFpVSWbw7whWibHG+SJWFiFFEz+4WbmsyENZPTGKKpKamS/ruxbNijQWz2te83pRZINm9smveS0mXmKRUtSBWTWqBsj3CqynNwJ7/jffLakXC2k1fnVi2Uy5JmSoJZVUi9WjP5dWxcnPe8bKjqiaCMTkxLhDlGkg/YF54q07k656I2MSEOFuRpxBPVUR7UP8MMRWfd81MZyQq0YrgRWmJHlBA9SHBuUSsYM5v4UZZt8r4HV366MQn3K57fd5GFWWKnKarTCvnmmmf2lb26zpC9s0eriJF7ESI9O/WN5DfxM122ylrpZyXx0IXdOij121ws/5o6rK7L/3eO3Too1/8Xgdb+Q1MN8P0NwihyJ/Wt2/fWp8jDxgeXiPXH/KNRbufjtiZsojZLQd220fGdh8tv+bMkfcWfiZrdtZE2q7L2yD/N+NFeXv+h3J8/6NkTLd9JT7Om1se+nr9xKWmqynviNqsD5RD+cYAEYgQ0ab9sEJ+mJMjG41p3kM7y/gxoad5Q/SvKi0KpAwwolr3RLju2v3a+H+XVJdbC0KpLiuWCmw7axZACgYj6A0/md6Ii1eRrLAfRNWM1JbSxpRWILZVxu7P2khcaobEpqRJjCl4AeJrUWlFjbAalNO10BBaVSSskYKgTFoUFkmClby0uGeKqVKi9A6pEVBLyirVtjXPflBOYnysElcD6QdMYqs5Hyze+/SXmsWQIoHIVBz3q0/T+yGTDvCa53/dwI8kaGQ7bQVUrOKFHAt//PGHyuuEJMfIbYA8C1iNKxTIh/Df//5XDRaxahtCpJHAedKkSSofQrin0A29X7RgcSU7QhJAQmOrIeNWyrpVxu/o0kcnPhFt3Rg0rtqxdnek6R+Skx86uT0iQTA1H6JpcmlCreTgbrTDjfJWy1op5+Wx0AUd+uh1G9ysP5q66Pf6oEMf/eL3OtjKb2ChCuT7mjZtmlrEEnm/jNxeWKkWednBpZdeWmthBKf76YjTKYv7ddlbRmePUAtSQkhdunvmS27BFvn3b6/JOws+luP6HiGH9ThAEuNrL9wRLfT1+onPaC9dLn2iZhEoE9t3bJc2rWtHV0HgQ/nGAiIpRLQrTxkuZeWVkpQYFzGvLj6LS26htoQ2tRffCkdVealUFefXRLBCeFURrbuksnC3CGu8VqJsvlQV43ywEBpaWSGVBdvVZo0YiU1psTulwB6hNSG1pbRNbSkdUnenF2iP91upHK+xCbVTvJVuXCk5L/7P4veJ7N2vo2RWtd0T/bpbjMV0ejuUVVTJ9l2lanMDRKJCNL/q1OENnkfZb/Ca53/dwI9UaWQ7LUdSSBD7/PPPq/+xQh1EyVWrVsmHH34on3/+uTz55JMyduzYOkZFEtrPPvsssKIe3ps3b57avvnmG3nmmWfqqNcNvZ8bIDFv8Op39QGx12pbrJR1q4zf0aWPTnzCSd1V1VWydOuq3ZGmc2RLUehBGqJLIZiOyh4mndL3CKZYGc+pgGq3j3bKWy1rpZyXx0IXdOij121ws/5o6qLf64MOffSL3+tgKz+CxReWLVsmCxculAkTJqiAAUSPrllTkxMUD+dPPvlk1/ZrSlMWMRYf0WmwDM8aJAs2L5X3F30q8zbVrIS8rWiHTJnztry38FM5pu9hcmSvMZKaUHt1bafQ160BUTRYGC0srJKsEItF6UBsbIwkJ3lzmwwRElt8y/rTUoHqqkolotZeNCtPNq9fLa2SE2oiXAOCbM0mIRZpDVGzqrdGoN1gqS0xCUmmPK4ta1ahssFZ4/uHXCCstLwyZOSrWnSreE9KAiMFgbksoljdACJuaVmFJCc1/n2dzvCa53/dwI9UamQ77QTU//3vf0o8xVPyO+64Qw34MCjCCqM33XSTEiZvuOEG+eqrryQ9fc8Uj2effVaJmRBcn3vuORkwYIB6HxGsf/vb3+THH39Uwus111xT6/saej834JMx0pA+AdF0bu4iJZr+lvOn7CzZVff7JUb6te9ZI5p2HibtWrRxvZ1297VT3mpZK+Waw/mpQx+9boOb9dPvmwY69NEvfq+DrfwIFkF48803ZcqUKfLJJ5+oxaEwm2nYsGFyyimnqBlObu7XFIHvDerYV21Lt66U9xd9JrM2zFOf5ZXmyxtzp8q0RZ/L+D6HyPjeh6hFLKP9PuIM2s4amGYf1yJDbWa2tFoh7XZHmwfPEMP0/9qC666Ir1HeCtXlpVKRt0VtbpKUEKe2Ni2THQmfRkSrOccr/uYXlcqbny+tyX9qgSse+U72G9JJ9huUJX27tVZCOqkNz1vS3ImpxlVWE0pLS2XMmDFqyj6epp999tm1Pi8oKJCDDz5Y8vPz5f7775cTTzwx8P6hhx6qRFZMXTrssMNq7Qcx84ILLpCUlBQ1nQkDzcbYLxoWLVokRUVFaoWz/v371/oMhxB5r/AX07YSE92dnkTsg0UbkNYBPzLIR+a3H5uyynIlmmJq/u8b5kpBWWGdMnExseoGBaLpyM5DpVVyy0ZpKyGEEP8RaVxD9MdPxw+5Ud9f+JlMXze7Vn7VpPgkObLnQXJs38OlVQoXjyHNl6qKslp5WquKd4urhUGvA58XqIWx7JKU1UtSeg6X5C79JLlzH4lNShWvmfzmbPlu9vrAglFWaZ2eJPsOylJi6uBe7VQ+XELqA3rM4sWL1f+YRZ2UVDvtBdFXm7E6rtEqAhXRpRBPu3TpIn/5y1/qfJ6Wlia33Xab7Nixo1YeVESjQsxs166dEjaDOeigg1Sd69atU2UN4bWh93MLpDPYa6+9Aq/hABBNIUDn5OSohQKC81zhM6snsJWybpXxO6H6WFFRoY4DwHFpCPE02CecUFxeInM2LlBT82dvnC8lFXXzCiHf2NDMATI6e7iM6DRI0hJbNFg77e5rp7zVslbKuXEsdEeHPnrdBjfrp983DXToo1/8XgdbEWLQrVW2XL3/BXJK/iaZuuhz+XH1r1JZXSWlFaXy4ZKv5LNl38khPfaXif2OlPYt2tqqm77uHNpOH/vFxidKbMu2Et+yrfW0AiWFUrxmgWx+7xHL31O6cbnaFDGxkti+a42Ymt1Pkrr0lfiW7V2/b5o4tqd8O6tmgblw4Ct7ZbeSFTl5ajEtsCO/VD6bvlptLZLjZWT/TNlvcJaM6NdBUjxK7+AHeN5GBv6LHLHIFQs9AOkOoBUgf2dz0Ea8Ihrbua3NaHX2//LLL+ovRMlwSWKPP/74Ou/NmTNH/d17773DGgSfQdCcOXNmQNBs6P3czAERTKdOnWTt2rUq5xXU9WCQn9XqolZWyrpVxu9E6iN8GMelIQjlE1ZAZOnvOXNVpOmfuQulPETOpMTYBBmZPVQtBDU8c6AkJyQ3eDud7GunvNWyVspF00e/oEMfvW6Dm/XT75sGOvTRL36vg60ICQY52S8bdbacPPAY+WDxl/LNyp/VuAfbF8t/kK9X/CQHddtXjh9wVK387ZGgrzuHtvOv/VRaASw41crZugaK6iop27xabbtmfabeiktvo8RUQ1RN7NhdfVe0C4Nde8YIeeyN2UooNUeixsXGCObi4vOxI7JlV2GZzFyQKzPmb5Q5SzarxapAYUmFfD9nvdoS42NleN8OMnpQlowamCktWzSvmZ88b+sHQXbQZCD6QQ9qTtqIV7hhO7e0Ga0EVExDB4guRfgzIlIRwblx40Zp2bKlHHDAAXLCCSfUmaKOXE8AUZ/hyM7OrlW2MfZzC0TiBpOcnKxWYt2wYYMKUQ7OzICLnVWns1LWrTJ+J1QfjYhgnKA4Lg1BKJ8Ix87iPJmZ86cSTRdsXqKiMOrUl9hCRnYeoqbnd5DW0qVTdoO3M9p97ZS3WtZKuWj66Bd06KPXbXCzfvp900CHPvrF73WwFSHhQJTp+XufJicOGC8fLf1aPl/+g4pGxXjou9XT5fvVM2R0lxFyQv9x0r115PEPfd05tF3zs1/HU26W6tJiKVm/WErWLZayLWtrpQKozN8uhYt+URuISUiWpM69JTm7b42wimn/yfZXgIc42jUzXab9sEJ+mJOj8qZiSv6Y4Z1l4pieSmQFEEMPH9VVbSWlFTJryWaZMW+j/LYwV4moAKLqrwty1YYcqYN6tFViKrb2rd1ZnE5n/Oh3DQ2mgCOt4pYtW1TqSURANhdtxCuisZ3b2oxWAirEP4AVts455xyZMWNGrc8///xzefnll9WiTYZACbZvr1kJvE2b8Kt2tmrVSv3F9P/G2s8twuVUhUMg14ZKHh4koBYXF6ucrFawUtatMn4nVB9xkjZ0ztP68uxuKdymBFMsBLVk68paOcAMWidnyD4q0nS49G/fW+J3P/FFHxuqnW7ua6e81bJWyrmR81h3dOij121ws376fdNAhz76xe91sBUh9YG8p2cOnaSm7n+67Fv5dOm3UlherMZI09fNUtvenQbLpAHjpXfb0FNW6evOoe2an/3i09pIUu8ekjboIPW6qrRISnKWKjG1FKJqzjKpLi8JlMf/Javnqa2GGEns0HX3lP+aKNX4DGvT/iGSXn3aCLnylOGSl18grVqmRdwvOSleDhjSSW0QXOet2KrEVESnYnq/an9VtcxdvlVtz02dJ726tFI5UzHVv0vHPYtdNyX86HeNAaIdMzMz1WZoM81BG/GKaGzntjajlYCKXBHgoYceUgs13XzzzXLccccpFR9i6r333qump1900UXy3nvvBRRkQ+CJlBchuGxj7OcWyOHQq1cvW06CKN5I+9gt61YZv6NLH0P5xIZdufLrbtF05Y61YaMw9u08TPbtMlzdHMTGxNr2t2jb6dW+dspbLWulnJv20hUd+uh1G+j39srp4BNeo0Mf/eL3OtiKEKukJ6XJKYOOlQl9D1dT+T9e8rXklearz2ZtmKe2wR37ygn9x8vADn1qjbHp686h7fxvv7jUdImJS5DqyvJ6y6IcypvBIlKpPYapzcitWrZpTU2E6u4o1cr8baY9qqVs8xq1yezPa9qQ1kaSu+yOUDWm/ceFlzgQNbptyyZpnWFd4ES06oi+HdR2yaQhsnTtDpk+b6PaNm7bs9Du8nU71fbqp4ukc/s0JaRi692lle8WFNbZ7/yGoc3oohv4kY0a2U4rARV5IowIz8cff1zGjRsX+Ozggw9WCYuPOeYYJaK+8847ctZZZ6nPwuVLDYX54tXQ+7kB8mlgij7CmHEBw/9Q49u3b69yoBp5N/CUY9u2mh+c7t27KyF3+fLlSvTNysoKpBZo27atCodGiDlAGgB8B8oi1BmRvitXrlSftW7dWkUHb968WYndOF44Vvgfi1Z169YtkH8VEbgIV0c9AAtbYeEtCOOwH44lyqKdSM+AZMs4MQDCq1Fu165dyn4IgUcbkPsiPT1dlTcSAeOpDvqGugFOLPQN34060eb162sSh3fs2FHZy4gKRrQu8pKUl5crkR52M2wIe8LGRrQx2osIafQZ4jjqWrNmjeo7Fj4DW7duVX9hh02bNik7wt7oDxJuG1HL6L/Z3tgPK77BtkgLYbY3jgHqAjgWaLthbxxXw77oF6YIzFk1TxbsXCZLClbJhvya/YLJTG0v+3QeKt3iMyUrpYNqH+pcuWJlwN5oL/qPaRr4a3wP7I1+GX1GWdjBsDf6Z+R6QdJs2NZsb8MPg30W/+P4mn0W/mDYG9+LNmJf+CzaadgbNoQ9UTfsBV9D+1Eexw+2gs8C2BffYdgb+8IPURb14XiZ7Y1+mn3WaAOevsJnzD4L+2ND3cDss9iMCHucfyhj9lmzvXHumH0WNjDbGzYzfBa2MNsbx8Hss3auEbm5uQGfdeMaYdg70jUCxxbf6+QaAbugDV5dI9Bm1BftNQJgfxxDJ9cItAH1Wr1GGMcm+BoBn4UfmO1t2BDHF+en2d7wRcNnYSscU/M12eyzqNs4N6K9RuBYhPJZK9cIs89auUYY9rZyjTBfZyNdI8z2DneNMOzt5BqB/sK+Xl0jjOPYENcIlCFEJ1ITUuT4/kfJ0b0Pka9X/qzypG4rrrk+zdu0RG192vaQSQPGyfCsQU1GECHEKYj+7HLpE1JZVPPAIRIQT1E+Esh3mpTVQ20Z+xyt3qvI2xIQU0vWL6kRT83T/gsw7X+62lQdCUmS1Kn3nlyqDqf9RxJg+3Vvo7ZzJgyQNbn5SkhFdOrKDTW/2yBnS4H875tlamuXkVwzzX9wlpryHxfHadyE+JWY6uC53o3IiBEj1OAdOVA/+uijkGVuuukmef/992W//faTl156Sb2HvKgLFy6UG264Qc4///yQ+7366qsqghU3AUgF0Bj7RcOiRYvUDRVuiHCDZzf/CG7wrO5jpaxbZfxOY/exqrpKlm9bLT+tmilzNi2QTYU1gkEwe7Xuoqbmj8oeJtktsxqtj9HUZXdft33earnG9omGQIc+et0G+r29cjr4hNfo0Ee/+L2Veszjmv79+0f9naRhaQ7Hr6KyQn5Y86tMXfS55BbUPCAw6NYqW+VIHdS6t7RMb9lobfQzOlxT/UxztV8Vcqhi2v96Y9r/Uqkui/RADtP+u0jS7ghViKolcanqwaXb5G4rlBnzaxahWrhqm1qgKpj01ATZZ0CmikzFYlRJCdEtktXQNFe/cwPaTm/bWR3XaBWBiqgXCKj9+vULWwbiKjCvaIaIBmBEYITCiHAx5y1t6P3cAlEbdh3Izj5WyrpVxu80Rh8rqypl0Zblamr+zJw/ZEfxnqedBjESI33a9ZB9s4fJqM7DpENaOy36GE1ddvd12+etlqPfN4020O/tldPBJ7xGhz76xe91sBUh0RIfFy+H9jhAxnYfLdPXzZb3F30m6/JqIsXX7Fwvk6e/IB1T28mJg46WA7uNCuSOJ9bgdSI6mqv9YpNSJLXHULUFpv1vXrM7QrUmSrVy19agaf9r1ZY/+wv1TkxqhhR1HaDEVAirSfVM+7dKZtsWcvzYnmrbmV+qFpqCmPrH0i1SUVkTNZtfVC7f/L5ObUmJcSotAMRUiKppKQmiO83V79yAtmsattNKQMXUM0x3w3SycBjT5zEtzLzfzz//HJiyGQpjuhsiQhtrP7eAcIvpdV7tY6WsW2X8TkP1sbyyXE0dg2j6e86fkl+2J9+OAfKXIjcXRNN9Og+T1ikZ2vUxmrrs7uu2z1stR79vGm2g39srp4NPeI0OffSL3+tgK0LcIi42Tg7sto/s33VvlQ/1vYWfyortNSlaNhVtladnviLvLPhYJvY7Qg7ea39JjNNfBNEBXieig/YzTfvP7KG2wLT/XVv3CKrrFteZ9l9dlCeFi6erbc+0/16BPKpJ2X0lLspp/63Sk+So0d3UVlRSLrMWbZZf5m2QWYs3SXFppSpTWlYZyKUaFxsjg3u1U2Iqpvu3aRn9auFeQL9zDm3XNGynlYA6bNgw+emnn2Tu3Lkq91ao3EJGHi3k2DIYOrTmCdScOXPC1j179uxAmoDG2o8QO5RUlMofGxco0XT2hvlSXFF3ekp8bLwMyewveyV0kqOHH64WQiCEEEIIIe6CB9XIIT+y0xCZt2mxElIXblmmPttSuE1emPVfeXfBp2oxqiN6HijJCXoKIIQ0deJbtpO0gQeqLTDtf8NSKV23RImqResWS0xFzdoroLq8VErWLFBbDTGS0L7L7in/NQtUxbfq6DjvcWpyghw0vLPaysor5c9lW5RoOnNhruQV1ASOVVZVq0hVbP9+b6707dq6RkwdnCWd2vH+jhBd0CoHKhZJGD9+vBJPH330UZkwYUKtz7GYwRFHHKGm+T/wwAMyadIk9T5e77///iq099lnn1ULTpn54Ycf5MILL1QLWHz77bdqcYfG2M+tnAxIcWD3Ah5OkHZa1q0yfsftPhaWFanohpnr/5A/chdIWYhVLZPik2R41kAZnT1chmUNVIseeGlrN+uOpi67+7rt81bL0e+bRhvo9/bK6eATXqNDH/3i91bqaQ45NJsyPH41IKXS1EWfyZyNhvBSQ1piCzm6z6EyvvfB0iIxtdHapzM6XFP9DO3nnKrKCinfsm73lP/FUrpusYpajURci1aBKf8qSjVzr6in/UM0XbRqm0yfX7MI1eYdxSHLdctMV0LqfoOypEfnjEY97vQ759B2etvO6rhGqyXgsCLsqaeeqv6//fbb5YsvavKUAKyme/XVVyvxEivSHnPMMYHPIFSed9556v+bb745EP0J/vzzT/UeOPvss2uJmQ29n1sYKzt7tY+Vsm6V8Ttu9DGvZJd8teInuf/7J+SCaTfIk7++pHKbmsXTFgkpMqb7vnL9gZfIfyY+LNfuf6Hs33WkEk/dakc43Kw7mrrs7uu2z1stR79vGm2g39srp4NPeI0OffSL3+tgK0IagpTCeLl5zOXy4BE3q8U6kYMeFJQVytvzP5TLPrxV3pg7VY31SG14nYgO2s85a9etVwJoxsjx0vH4a6TrFc9K1yuekw4nXCstRx4tiZk9kBug1j6VhTulcPEM2f7VS7LhpZtk9SNnyYZX/yHbv31dipbPksriAtvtwLT9QT3byYUTB8sLtx4hk68ZK6ce0UcJpmbW5ObLW18ulav/9b1ccP9X8vy0eTJ/xVYlwDY09Dvn0HZNw3ZaTeEHt9xyi2zevFm++eYbueKKKyQzM1Patm0rS5culfLycsnKypLJkydLUlJSrf0uvfRSNfUfKQBOP/10lacUKvXy5cvV54cccohcfvnldb6vofdzg4qKCk/3sVLWrTJ+x2kftxZtV1Gm2BZtXa6eqgSTkZQu+2QPUzlNB3boG3FxAi9t7Wbd0dRld1+3fd5qOfp902gD/d5eOR18wmt06KNf/F4HWxHSEBi+3qNNV7nugItkfd5GtdjUz2t/l6rqKpV+aeqiz+WTpd/IYT0OlOP6HSFtU2sWo23u8DoRHbSfu7aLb9lW0gYcoDZQVVYspTnLAlGqJeuXSnXZngjR6ooyKVm7QG0GgWn/auq/vWn/KNczu5XazhzXXzZsKVALUP0yb6MsWVOzODXYvL1IPvhhpdoy0hJl1IBM2X9IJxnau50kxHu/kB39zjm0XdOwnXYCKoTRp59+Wj7++GN55513VCgtRMns7Gw58sgj5Zxzzgm5sj0WlcJ0+rfeekvee+89lSu1srJSTXc//vjj5cwzz5T4+PhG388NEAHr5T5WyrpVxu/Y6WNu/maZsX6OEk2Xb18dsky71DYyardo2rdtT4mNtRYk7qWt3aw7mrrs7uu2z1stR79vGm2g39srp4NPeI0OffSL3+tgK0IagmBfz87IkitGnyunDJog0xZ9Id+tniEVVRVqVtGny76VL1b8IGO7j5bj+x8lmWntpTnD60R00H7e2i42MUVS9hqiNlBdVSllmPa/brGU7hZVK/K21NoHaQGw5c/5MjDtHwtSQUzdM+3f2iJzndqnyaRDeqttW16x/LogV+VNnbd8T+Qp8qd+OXOt2lKS4mVk/45qmv/e/TuovKteQL9zDm3XNGynVQ5UYi0nQ48ePepE4NZHaWmp5X2slHWrjN+J1EecWuvyNqhFoH5d/4eszcsJWS4rvYOa9oWtR+uujvJ7eGlrN+uOpi67+7rt81bLNXe/byptoN/bK6eDT3iNDn30i99bqYc5NP0Nj581X99etFM+WPKlfLXix1qpmTDWO6DLSDlhwDjpktFJmiM6XFP9DO3X+Lar2LVtT4TquiVStmkVlNaw5WPiEyWpU69AlGpSdh+JS6k9Xb8+CorK5LdFm5SYOnvJZiktq6xTJj4uVob1aS+jB2XJvgMzpVW6e35Cv3MObae37ayOayig+gTzAU1ISFB5YO2AKF6r+1gp61YZvxPcR5xOK7avUaIpIk03FmwOuV+3VtkqyhSiaXbLrKiTIntpazfrjqYuu/u67fNWyzVHv2+KbaDf2yung094jQ599IvfW6mHApy/4fGzd87sKsmXj5d+I58t/06Ky0tqfbZP56EyacB46dmmmzQndLim+hnaTz/bqWn/G5arKFUlquYslerSooj7JLTLDkz5x9/41pmW7wtLyirkj6VblJj628JcyS+qu/hwbIxI/73aKjF1v8FZ0rFNdIva0e+cQ9vpbTur4xrtpvAT4jeqqqpk8dblKsoUoum24j15asz0bruXEk1HZQ9v9tO2CCGEEEKaCy2T0+X0IRNVDtTPl38vHy/5WvLLCtVnv+X8qbahmf3lhP7jZUCH3o3dXEKIA9S0/+6D1Wae9l8z5X+JElYr8moH15RvXa+2/D++Uq/jWmRIkimPaqRp/8mJ8UoYxVZRWSULVm5TYipyp27Lq3lQg9n+eB/bfz6YLz06Zch+Q7LUVP+umelcFZ4QmzAC1SeYFfHOnTtLy5Ytbe2/a9cuy/tYKetWGb9SUVkh8zcvlZ9W/Sp/bF4ou0rrrryIH6QB7XurKFNEF3i5aICXtnaz7mjqsruv2z5vtVxT9nud+uh1G+j39srp4BNeo0Mf/eL3VuphBKO/4fGL7pwpqSiVr1b8JB8u+VJ2FOfV+qxfu54qInVo5oAmLW7ocE31M7SfP21Xkb9995T/mlyqpbkWpv1n9awRU5Ww2rfeaf9VVdWyfP1OJaZiy9lS9z4VZLVroYRURKb26dpaYhGuWg/0O+fQdnrbjhGoTZjy8nJP97FS1q0yfqK0okz+zF2opufP2jBPisr3rMRoEBcbJ0M69lOi6chOQ1TEQUPgpa3drDuauuzu67bPN1e/17WPXreBfm+vnA4+4TU69NEvfq+DrQhpCJz6enJ8kkzoe5gc1WuMfLdqhkxb/LlsLtymPlu8dYXc/8OTKi8+cqTiIXxsjLVFRf0ErxPRQfv503bx6W0krf/+agNVZSVSumFZIEK1NGeJVJmm/VdXlEnJukVqqzPtf/cCVfGta6eDgxAKQRTbX48ZIOs25deIqfM3yvJ1OwPlNm4tlPe+W662Ni2TZN+BWTJ6cJYM7tlOEuJDX3Pod86h7ZqG7Sig+pAdO3ZI27ZtPdvHSlm3yugORNLZG+arqflzNs6X0sqyOmUS4xJkWNZAGZ09XEZkDZbUxJQGb6eXtnaz7mjqsruv2z7fnPy+PnToo9dtoN/bK6eDT3iNDn30i9/rYCtCGoJofT0hLkGO6HWQHNpjf/l57e/y/qLPJGdXrvps5Y618ujPz6lc+Sf0Hyf7d91bPahvKvA6ER20X9OwXWxicu1p/9VVUr5l3Z48qusXS8XOyNP+Y1Nb1sqjmpTZQ2Li90z779IxXW2nHN5HtuwoVlP8sc1fuU1Fq4Ltu0rl0+mr1dYiOV72GZCpxNS9+3aQ5KR4LW3nN2i7pmE7CqiEBIHp+L/nzJWZ6+fI3E2LpaKqok6ZlIRk2bvTEOkalynjhx8mSfGJjdJWQgghhBDibyCMjum+rxzYbR/10P79hZ/Jqp3r1Gfrd22UJ36dIm/P/1Am9j9KxnbfVwmvhJCmR0xMrCR26Ka2lnsfpd6ryN8REFNLEaW6aZVIVWVgn6qiXVK0dKbaVB1xCZLUqZckIULVmPafWjP9uX3rFDn2oB5qyysoVYtPTZ+XK3OWbpbyippUAoUlFfLd7PVqS4yPleF9O6hp/qMGZjaKTQjRCeZA9QnmnAx9+/aV2NhY2wsdWd3HSlm3yujC9uKdasCKbeGWZVIVIhdNelKamkaFhaAGd+gn8XHx2vTRy3a4WXc0ddnd122ft1pOF5/wEh366HUb6Pf2yungE16jQx/94vdW6mEOTX/D4+ftOYnbsz9yF8h7Cz+TJVtX1PqsTUortRjVYT0O9PUDfB2uqX6G9mu+tqsqL62Z9r87SrU0Z6lUldQsSheOhLad9yxMld1PEtrUnvZfXFohsxdvVlP9f1uUK0UlFdI6tkBaxJTWSg3Qs3OGDOnVTgb3aiet0pLV+3Gp6RKfwQWSm7rfNXXbLbI4rqGA6hPMB7RFixbStWtXW/uvXbvW8j5WyrpVpjHZVLBFft0tmi7dtjJkGQxSR2UPUzlNkdQ/eOqULn30sh1u1h1NXXb3ddvnrZbTxSe8RIc+et0G+r29cjr4hNfo0Ee/+L2VeijA+Rsev4Y5J3GbtmjLMnl34acyb9PiWp+1TEqTY/ogj+rYRkkf1RSuqX6G9nNOU7NdzbT/9YEoVQirFTs3RdynZtp/3z2iambPwLR/RKLOn7tU0j67Q+Kq687ErENcgnS99AmKqM3M75qa7biIVBOmrKzM032slHWrTEOCQSjySmERKGyrd64PWa5jWnslmCLStGebbhET9+vSRy/b4Wbd0dRld1+3fd5qOV18wkt06KPXbaDf2yung094jQ599Ivf62ArQhoCr30dEWIDOvRR2/Jtq+W9hZ/K7xvmBlJOvTlvmkxb/IWM632wHN3nUCWq+gVeJ6KD9nNOU7NdzbT/rmprOeLIPdP+c2qm/GOBqtLclSGm/f+mNlVHXIIkZvUM5FHt2zpJcq2Ip6CyXO555mvpOWSIjB6UJb27tKoV3Uqapt81V9tRQPUhKSkpnu5jpaxbZRpCNF21Y62KNIVouiE/9NO4LhmdlGAK4bRrRmfLF30d+uh1O9ysO5q67O7rts9bLaeLT3iJDn30ug30e3vldPAJr9Ghj37xex1sRUhD0JC+3qttd7nhoEtl7c4ceW/RZzJ93Sw1zsWCpxBWP17ytRzR8yCZ0O9wNYNKd3idiA7azznNwXbx6a0lrd9+atsz7X95IEK1NGdJrWn/1ZXlUop0AOsXS56D79u8vUhmf71M3vl6mbRrlSKjB2WqvKkD92orcXGctt5c/K452I5T+H2COaS4Z8+ekpiYaFu1t7qPlbJulfEqRwam5NdMz58jW4q2hyyH6FIIppii3ym9o6Pvaqw+NmQ73Kw7mrrs7uu2z1stp4tPeIkOffS6DfR7e+V08Amv0aGPfvF7K/VwCri/4fFr/OvCxvzNMnXR5/LD6hlSacrdHx8bL4fstZ9acKpDCz1WLdb1mupnaD/n0Ha7p/1vXb87j+oSJaxW7Mh1XN8ju46RdRV1rzfpqYkyamBH2W9Qlgzr20GSEmqnw2tO0O/0tp3VcQ0fB/g0B4SX+1gp61YZt6ioqpS5uYvk+d/fkEs+vFn+8c2j8vHSr2uJpzESI/3b95Zzhp8sT0+4Tx444iY5vv9RjsXThu5jY7XDzbqjqcvuvm77vNVyuviEl+jQR6/bQL+3V04Hn/AaHfroF7/XwVaENASN6etZ6R3k0lFnyRPH3CPjeh0sCXE1+QsrqirkyxU/ypUf/0Oe/PUllb5KR3idiA7azzm03e5p/+1rpvx3OO4K6XrZU9L1qhek44nXS8a+x0pCuy626rvrov3k8pOHyt79Okh83J6ZnPlFZfL1b+vk3ikz5cx/fCoPvDxTvpu1TgqKy6W5Qb9rGrbjFH7iW8oqy2Vu7kIVaYp8UIVlRXXKxMXEyqCO/dT0/JGdh0qr5JaN0lZCCCGEEELcpl2LNnLe3qfKpIHj5aMlX8sXy7+XkopSqaqukh9W/yo/rp6pZlydMGCc7NXanihCCGk+xKe1lvh+o6VFv9GSNnCl5Lx4veV9W6YmyVGju6utqKRcfl+0SabP2yizFm+S4tKa3KslZZXyy9yNaoPIOrhnOzXNf99BWdKmZbKHPSPEPSig+pD27dt7uo+Vsm6VsUtxeYnM2bhA5TOds3G+GiAGgyfwQzMHyOjs4TKi0yBJS2whXuFFH3Vrh5t1R1OX3X3d9nmr5XTxCS/RoY9et4F+b6+cDj7hNTr00S9+r4OtCGkIdPJ1BAmcOfQEOb7fkfLpsm/lk2XfquCCaqmWGetnq2141iCZNGCc9G3Xs7Gbq5Xt/Ajt5xzazltSkxNkzPBstZWVV8ofy7bIjHkb5dcFubKrsGYxoIrKapmzdIvannlvrvTr1kYtQAVBNaudd/fujQn9rmnYjgKqD0GOTy/3sVLWrTJWKCgtVBGmEE0xTb+8qu6KgMnxSTKi02Almg7LHCDJCQ3zFMutPurcDjfrjqYuu/u67fNWy+niE16iQx+9bgP93l45HXzCa3Too1/8XgdbEdIQ6OjraUkt5ORBE2RC38PlyxU/yIdLvpa8kl3qMwQfYBvYoY+c0H+cDO7Yr9FWy9bRdn6C9nMObddwJCbEyagBmWqrrKyShau3KzF1+vyNsmVHsSqDFXkWrd6utikfLZDuWS0DYupenVpaukZVVVUrsRbfFxvbONe0+qDfNQ3bUUD1Idu2bZPWrVt7to+Vsm6VCcfO4jyZmfOnEk0XbF6qpiEFg8jSkZ2HKNEU0/QTd+d+akii6aNf2uFm3dHUZXdft33eajldfMJLdOij122g39srp4NPeI0OffSL3+tgK0IaAp19PSUhWY7rd6TKj/rNql/kg8VfytbdawNgbI2tV5vuKiJ1705DGlxI1dl2foD2cw5t5z6lG5dJUlaPiGXi4mLVtH1sF0wcJCty8gJi6trc/EC51Rt3qe2/Xy6Rjm1SA2Jqv+5tJC5IHF21IU+mfb9CfvgjR8orqiQhPlbGDOssE8f2lL06ZYhO0O+ahu0ooBJt2Fy4TWau/0Nmrp8jS7auVFOOgmmdnCH7ZA9VuZwGtO8tcbHNdyU/QgghhBBCIpEYnyjjeh8sh/c4UH5cM1OmLvpcNhZsVp8t375aHv7p39I1o7OcMOAo2S97b4mN5RrDhBB7bP3sPxKb1ELSBh5oqTwe2PTKbqW2M8f3lw1bClTOVGxL1u4IlNu0vUim/bBCba3SkmTUwEwlpg7t3U7lUn3sjdmCZz+VVTW6AUTU72avl29nrZdrzxghY0dke9Zn0jyJqa5G0DTRnUWLFklRUZGkpqZK7969JT7envZdUVFheR8rZd0qs2FXrsxYP0cJpyt3hF5drX2LtkowxUJQvdvuJbEx+gzs7NjVr+1ws+5o6rK7r9s+b7WcLj7hJTr00es20O/tldPBJ7xGhz76xe+t1GMe1/Tv3z/q7yQNC4+fPtcFJ1MhkQ/1/YWfyZq8nFqfZaV1kIn9j5Ix3UZJfJy3/fKj7XSC9nMObVc/FXlbZN0zV0h1Zbmt/doeca5kjJoQ1XdvyyuWGfNzVXTqvBVbA+KomaSEOCktr1mcKhyxMTEy+dqx2kSi0u/0tp3VcQ0FVJ9gPqBpaWnSpYu9VTTXrVtneR8rZZ2Wgbut2bk+IJqu37Ux5L6dW2buFk2HS/dW2Y2Wn8lNu/q1HW7WHU1ddvd12+etltPFJ7xEhz563Qb6vb1yOviE1+jQR7/4vZV6KMD5Gx4/fa4LTsGYfNaGefLewk9VJKqZdqlt5Lh+R8ihe+2vIli9wM+20wHazzm0nXURtbJoz9R6sGlTrnTsmBl4XV1dKXm/fiSFC38KvJcx+jhpc+hZEuNC0FNBUZnMXLhJZszfKLMWb1Z5Tq2C6f4H750tV582QnSAfqe37ayOayiB+5DS0lJP97FS1k4Z5C9dvm31btF0jpqqH4q9WndRgumo7GGS3TJLmuqx8Fs73Kw7mrrs7uu2z1stp4tPeIkOffS6DfR7e+V08Amv0aGPfvF7HWxlh/Lyctm0aZPs2LFD4uLipG3bttKuXTv1PyFNydfNIDgBawns3WmwzNu0WN5f9JnKiwqQK/XF2W/Juws/lWP7HiZH9Byjcqq6iZ9tpwO0n3NoO2vEZ7RXm5mywqo6uU6Tjr9adrTJkp0/vaNe5834QCoLdkr7CZdJTJRrlKSlJsqhI7uoraSsQuYs2SLT521QU/TrA5GrP8zJkatOHa5FMBb9rmnYjgKqD0lOTvZ0Hytl6ytTWVUp60py5ftZv8vMnD9kR3FenTIxEiN92vVQU/NHZQ+XDi3aSnM4Fn5rh5t1R1OX3X3d9nmr5XTxCS/RoY9et4F+b6+cDj7hNTr00S9+r4Ot6gNi6TvvvCPff/+9zJs3T4moZhITE2XkyJEyZswYOfbYY6VNmzaN1laiL37w9fqAsDAks7/almxdIe8t/EzmbJyvPssr2SWv/fm+vL/oczm69yEyvvchkpbUwpXvbQq2a0xoP+fQds4JZTtcQ9qMPU3i09vI1s+eF6mukoL5P0hlYZ50PPF6iU1Kcee7E+NV/tPhfdpbElCNnKiY6o99Gxv6XdOwHafw+wQ/5EAtryxXT7B/Xf+H/J7zp+SXFdbZD/lLB3boo0TTfToPk9YpeuQk8XsuE+ZAja48c0HaQ4c++iUXZLR10e/1QYc++sXvdc6BunXrVpk8ebJ8+OGHKhfk4MGDpU+fPmpqGFIk4b2dO3dKbm6u/Pnnn7Js2TJJSEiQiRMnyqWXXiqZmXumLjZnOIVfn+uCF6zasU7lSP11/Zxai7omxyfJkb3GyoS+h0mr5JZRfUdTtV1DQfs5h7bzznaFS36VzVMnS3VFmXqdmNlDMk+9VeLTWrnWhqqqajnp5o+UOFofCfGx8u6DE7SIQKXfNY0cqPqsxkMss3r1ak/3sVLWKFNSUSoz1s2Wx6f/Ry6YeoM8+OPT8u2qX2qJpwmx8Wp60GWjzpbnJz4ktx98lRp8+V08dXos/NYON+uOpi67+7rt81bL6eITXqJDH71uA/3eXjkdfMJrdOijX/xeB1uF4tVXX5Vx48ZJTk6OPPDAA/Lbb7/JG2+8IXfeeaecf/75cuqpp8rpp5+uhNK77rpLpk6dKjNmzJBbbrlFli9fLsccc4yqgxDdfT1akFbr2gMulEfH3y5juu8bWMAV4/4PFn8hf/voNnlx1luytXC74+9oqrZrKGg/59B23tmuRd99JeuMOyQ2OU29LstdKRtevkXKt29wrQ2xsTEyZlhnleM0EtBMxwzvrIV4Cuh3TcN2lMBJLebmLpJnF7wmF6edqabyhKKwrEjmbFso7+d+JX/kLlSRp8EkxSdJn/Rucli/g2R41iDX8yYRQgghhNjhm2++kRdffFGGDBlieZ/09HQ55ZRT1Pb777/L//3f/8lZZ53laTsJ0QWsSXD5vufIKQMnyLTFX8i3q6ZLRVWFGvt/tvw7+XLFDzKm+2g5vv9RkpXeobGbSwjRgOQu/aTT2ffKxv/eK5W7tkrFzk2S8/KtKhI1uVMvV75j4tie9U7jxzzrTu3cSTlCiAEFVB+CxQ282AfZHN6cO022lGxXfwd37Bd4YoMcSL/lzFVTeeZvWiyV1XVD5lskpMjenYfI6OzhMqRjfynML5TWrVtLU8bJsfBbO9ysO5q67O5rp7zVslbK6eITXqJDH71uA/3eXjkdfMJrdOijX/xeB1uFYsqUKVHtj5yor7zyimvtIf5HV193mw5p7eTCkWfIiQOPlo8WfyVfrvhRSivL1P0AZp59t3q67Ndlb5nUf5x0bdXZUp3NxXZeQfs5h7bz3naJ7btI57/er0TU8i1rpapol2x87R8qJ2pqz+FRt2OvThly7Rkj5LE3ZqtIUywYZYDXRpLKNz5fIj06t5KR/TtKY0O/axq2o4DqQ5yEoVvZ58/cRbJixxr1P/7+uGamFJQVqpymi7cuVwJrMBlJ6bJP9jAlmg7o0EfiY/esWFsUUyRNHV2mBHjZDjfrjqYuu/vaKW+1rJVyuviEl+jQR6/bQL+3V04Hn/AaHfroF7/XwVaENATNzdfbpLSSs4efJMcPGCefLP1GPlv2nRSVF6t7hF/W/q62kZ2GyKQB46VX2+4R62putnMb2s85tF3D2C6+ZVsVibrpnQelZO1CqS4vldy3H5D2x1wq6UMOibotY0dkS9fMdJn2wwr5YU6OyomKnKeYtl9aVik//blBCasPvPyb3Hvx/tJ/r8ZdDJJ+1zRsRwHVh2ABhFatWrm6DwY+b837QGIkJpAs/slfXwpZtl1qG+mb1l2OHHSw9G3bU2JjY11rp9/QpY9etsPNuqOpy+6+dspbLWulnC4+4SU69NHrNtDv7ZXTwSe8Roc++sXvdbCVE6ZPny4ff/yxbN68WTp27Khyno4ePbqxm0U0xq++Hi0tk9LktMHHyXF9j5DPl38vHy39WvJLC9Rnv2+YqzbMZIOQOqB975A3v83Vdm5B+zmHtms428Ult5DM02+XLdMel8LFM0SqKmXLh09KZcEOydjvhKiFMUSiXn3aCLnylOFSVl4pSYlxqs6aiNTflYiK9+/6zwx56G8HSres6Ba/iwb6XdOwHReRIrWiT80rbZpBXiPkN3rgiJvkqQn3yjFdDpH+7XuHFU8JIYQQQvwCFpO68MILZePGjSrv6bJly+Tcc89V7xNCQpOamCInDBgnT0+4T84ZfrKKUDWYt2mx3PXtv+QfXz8iszfMrzWTDWsu/GvBFPWXENK0iY1PlA4nXCst9x4XeG/7t6/Lti/+I9VVle58R2yMJCfFBwRZLDCFKf7DerdXrwuLy+Ufz02Xzdub/gxZ4i0x1aHmZRPtWLRokRQVFUlqaqr07NlTEhMTbe1fVlYWdh+4wC1fPiQrlYBam9YpGXLrmCukS0anWk+IItVnp4zf0aWPXrbDzbqjqcvuvnbKWy1Lv9enj163gX5vr5wOPuE1OvTRL35vpR7zuKZ//9CLVjYkRx11lFxzzTUybtyeG7yHH35YPvnkE/nuu+8atW06otvxa87XBZ3A4lLfr/5Vpi36XDYVbq312V6tuiixdZ/OQ+W2r/6pAjd6tu4m9x9xo1bTM/0Cfc85tF3j2A6aw85f3pcd370eeK9Fv/2k/cQrlcjqBUUl5XLbv3+RZet2qted27eQhy4/SDLSkqShod/pbTur4xqGD/qQLVu2uLrPnujTuuwozpPtxXl1BjZW2uCknX5Dlz562Q43646mLrv72ilvtSz9Xp8+et0G+r29cjr4hNfo0Ee/+L0OtgrFX//6V5k9e3bIzyoqKuqMddQ0wEp3omNI00RXX28sEuIS5PCeB8rko++Uy/c9R7JbZgU+W7VznTz2y/Ny+Ue311pzAfchxD70PefQdo1jO/ymtj5gkrSf8DeRmBoZqnDxdMl9816pLCkUL0hNTpA7Lhgtndunqdc5WwrlzuenK2G1oaHfNQ3bMQeqDykuLnZtHyP3aWxMjFSFCEbG+/h8aGb/WjcWVtrgpJ1+Q5c+etkON+uOpi67+9opb7Us/V6fPnrdBvq9vXI6+ITX6NBHv/i9DrYKxaBBg+T888+X4cOHy5VXXinDhg0LfHbGGWfIddddJ2+//ba0adNGcnJyZM6cOSoqlRC/+XpjExcbJ2O67ysHdttHfsv5U95b+Kms2rFOfbateEetso9P/49aeCo5IUmS4pMkOT5RkuLwf6Ikx9f8xWv1/u7Xybs/x2vzArbNCfqec2i7xrVd+tBDJa5FK9n03iNqYamStQtk46u3Seapt6mFp9wG0aZ3X7yf3PDEj7Itr0SWr8+T+6bMlDsvHC0J8Q13/aDfNQ3bUUD1IU7Cl8PtY0SfhgOiqvF0eFjWAFttaA4h6rr00ct2uFl3NHXZ3ddOeatl6ff69NHrNtDv7ZXTwSe8Roc++sXvdbBVKK6//noloD733HNyzjnnyN577y1XXXWVDBkyRL3fq1cv+eyzz9RiBd27d5eLLrpIDj744AZv544dO+SFF16Qb775RtavXy/x8fGy1157qUWtzjrrrLD2LS0tlSlTpshHH30ka9askeTkZOnbt6+cfvrpal/iPrr6ui7ExsTKvtnDZVTnYfJn7kJ55Y93Zf2ujbXKFJYXyfdrZkQl1ibHhRZXg1/bFWcTYvfkVNQN+p5zaLvGt11qrxGS9Ze7JPft+6WqaJeUbV4rOS/fIlmn3y6J7bLFbTq0TpW7L9pPbnzyJykoLpe5y7fKo6/PluvPGqnypTYE9LumYTvmQPUJ5pwMffr0kbg4e09LMAUteJ89uU/Xhl08CsRIjPRo3bVWjqJQ9Vn5zqaGLn30sh1u1h1NXXb3tVPealn6vT599LoN9Ht75XTwCa/RoY9+8Xsr9TR2Dk1MB4OQiojTUaNGqYjUwYMHS2Ozbt06OfPMMyU3N1fZsFu3bir3FyJiMW5DG1966SVJS6uZjmhQUlIi5513nsyaNUvth7FiQUGBqg+cdNJJct9997nWzsY+frqgw3XBL1i979AJ3PfUEmBNQm3w63rF2TjT+/FJkhiXoATmpuZ7WBhsypy35dzhp8iQTD2vDbrazg+4bbuybRsk97/3SMXOzep1bHKaZJ56syRn9xMvWLxmu8qJWlpWk55n3H7d5bIThzTIgxL6nd62szquYQSqD1m1apWKkoh2n4qqCtlatL3eQQw+x3QblEduI6ttcNJOv6FLH71sh5t1R1OX3X3tlLdaln6vTx+9bgP93l45HXzCa3Too1/8Xgdb1Uf79u3l1ltvlQsuuEAJqX/5y19k//33lyuuuEIGDhzYaO264YYblHiKBUOfeOIJ9Rf89ttvcvnll8u8efOUEPrAAw/U2u/ee+9V4ins/u9//1u6dOmi3scCWFdffbX873//UykLTj755EbpV1PFD76uC/XNejtn+MnSrVW2lFaUSklFmfpbWlkmJfi7+3VJ5e738brSVK6iTEoq95SrrK5yTfQtrihRmxdAVLUmztYIsOYo2h1bt0n37O5ho2hjYxt+qRPY68250yRnV676O7hjPy0jeHne6mO7xLadpNNf75fc/94nZZtWSVVJgWx8/S7pcPw10qLvKHGbft3ayM1/3Ufu+c+vUllVLZ9NXy0ZaYly5jjvxX76XdOwHQXUZgzE0AeOvEl2lRTUen/d+nXSJbtm4G2QkZweEE8JIYQQQvxIVVWVzJ07V4mUrVu3VmLp7bffLhdeeKE888wzctppp8mBBx6oIlIbOrIS0+6NRa7uueeegHgK9tlnH/n73/8ut912m5qif8cdd6gp+gDT/N9//30lVDz66KMB8RQgBcFNN92kykOQPfHEExtFWCHNGytrLvy4eqaMP+IQVwS3isqKWoKqElp3vy4JJcDWEmoji7PlVRXiFvhebFJa+17MMivCf4T0A/WlMQgnztaX4iA+Lr5ekTxUCjhCQhGf1lo6nXW3bHr3n1K8aq5UV5Sp/9uNu1BajjjS9e/bu19Hufr0EfLo67PU67e+XCoZLZLk2IN6uP5dpOlBAdWHYHEDt/Zpl9pGbWZaSVq932GlDU7a6Td06aOX7XCz7mjqsruvnfJWy9Lv9emj122g39srp4NPeI0OffSL3+tgq1CsWLFC/va3v8nq1asD72VkZMjdd98tRx11lNx1110q7ykiOE855RQZM2aMikjt18+bqYTBbNy4JzdkqO9ErlaAKf3I05qdXZMnbtq0aVJRUaE+D7XfpEmTVMTqpk2bZObMmTJ69GhP+9Gc0NXXdcPpmgtOgcCXhi2xhbhNZVVljfDpSJwNiqKtE1Vb5lo7IfSWl1VIQZn7q5vHxcTWEWcT4xJlXd6GWuWenvmyjO99iKQmpEhKQrKkJiRLSkLKntfxeJ3c4EE6PG/1s11sUqpknnqLbPnwKSlY8KNIdZVs/fRZqSjYIa0POsX1SOaDR2TLrsJSeX7qfPX6uanzpGWLRBk7wv38qwb0u6ZhOwqoPgSLCXi5j5WybpXxO7r00ct2uFl3NHXZ3ddtn7daThef8BId+uh1G+j39srp4BNeo0Mf/eL3OtgqFJiy37ZtWxVpCvFx586darGmG2+8Ufbbbz9p2bKldO7cWUV/Qkh9+umn1ZR3TJtvCDp16lQrF9fIkSNrfb548WL1NyEhQTp06BB4f86cOepvcHnz4gvInYo0ABRQ3UVXX9cx+hRrKtS35gLKDc3sr+W0b/OiVamxNSKg21RVV0lZZfmeyNfdAmwooXZXYb66k68RamvE13DCrBFBi/rdACkSisqL1RaJnSW75M150+qtLz42vpagCpE1+LUSX3e/NgRYvDZEWbxGJK0V3+F56xwvbRcTlyDtJ14pcWmtJe/XD9R7O398Wyrzt0u78RdJTKy7OTCPO6in7Cook7e+Wqpe/+vN2ZKWmqAiVL2Aftc0bKdPS4hlNm/erAb5Xu1jpaxbZfyOLn30sh1u1h1NXXb3ddvnrZbTxSe8RIc+et0G+r29cjr4hNfo0Ee/+L0OtgoFBMjrr79erWhv5EFF7tOXX35ZTZ83LyKFafCI2rzssssarH1du3ZV6QN++uknNeX+ySefDLQVaQf++c9/qv/RZvOKtEZErXnqfjAQjCGgmqNviTRZX9eJaNZcaG5gUSlMncdWH8uXL7eVExBCNmwbnJKg3gjaoPLB6Q7wWUk58s7WLMrjBLQrv7RAbdEA8bRGdK0twNYIrnuiYAt3FkiXrC67xVdDiN0tyiYkK/tHs8BXU8bra15MTKy0PfyvEpfeWrZ/9bJ6L/+Pr6SyME86nHCNxCbUf27Y4S/j+kleYZnKhYqcqA+8/Jvcd8n+0reb+xGP/L1oGrajgEoIIYQQQpo8Q4cOVRGnEB8RaZqfny+vvfaatGvXTnr37h1yn0iipBc8/vjjKlL2888/l2OOOUa6deum8rZC4E1KSlKRsVgUysy2bdvqneLWqlUr9XfHjh0e94CQ2nDNBT2AuAjbYksTd1Mb/LFxodz/wxNhPz+2z+HSPq2tilgtLi/Z8xcLdJVj2/ManyFNghMgEheWF6utXtZH/tiIdt0jxO4RWGuJtAERdneUrBExu3t/RCw3FebmLpJnF7wmF6edKUMyvc0R3mrf41Ru1M0fPClSVSFFy35Ti0tlnnKzxKWmu3peXDJpiJrO/8vcjVJaVil3vTBDHvzbgdI1Uw/BjugFBVQf4mQwb2cfK2XdKuN3dOmjl+1ws+5o6rK7r9s+b7WcLj7hJTr00es20O/tldPBJ7xGhz76xe91sFUoHnnkEXnwwQdVztPy8nJ144SV6ZHz1FiQqbHBAk8DBgyQX3/9VYmdK1euDHzWokULSU9PV4JqXNyem/KSkpoVwiGwhsP4rLjYgrBAfO/ruhFqzYXOLTpG9FniD9+zskDYwi3L5P5hkyynZiivLK8jsBapvyVSXGH6X5WpEV9DvY4mp2wxvruiRCTKSybSCuwRXveIsMGva1IU1M4V25h5YkMd5zfnTpMtJdvV38Ed+3meaiNt4EESl5ohuf97WKrLiqU0Z4lseOVWyTz9NknI2JPGJlriYmPk73/ZW+4smiFzl2+V/KJyueO56fLQFQdJh9apTe6c9SNdNLIdBVQfgkgDc54st/exUtatMn5Hlz562Q43646mLrv7uu3zVsvp4hNeokMfvW4D/d5eOR18wmt06KNf/F4HW4UCU/axSj0EyO3bt6sFpJBPVBcKCgrk3HPPVdP1ERGLKfvIawqx98cff1TiL9o/a9YseeqppwI5wSCmok9WcPuGF+ItphIj1UBOTo5a4ColJUXZeu3ataoMInxx821Eynbv3l1yc3PVvhDQsrKyAqkFkKMWIvKWLVsCaQ3wP4RfRA4jFYEhKrdu3VodP0ztM26wcFwLCwuVbRC9i4XDjAhciOT4XoAI5Ly8PGVz2A/tR1m0E9MEIVYbi3rBl1Fu165dyn49e/ZUbYDNseF70XeQmZmp2oq6AaZYo29Y5At1os3r19eEvnXs2FHZy4gK7tGjh6xbt04d79TUVGU3w4awZ2VlpeofQHs3bNggpaWlql+oC1HKhr0BFhoDsAMWEDPsjf6sWrUqELWM/pvtjf2KioqUbdE3s71xDFAXwLFA2w1747jCFwDOLfiB2d6wH6K+cXzRV5RFe2DvtLQ01R8Af0CdZnujveg/yqFus73RL+QzBigLOxj2Rv9gU4C8wbCt2d44FqF8Fv/j2Jp9Fv5g2Bvfa/ZZtNOwd7DPou9me8NWZp/Fdxj2xr5mn4V9zPZGPw2fxWfYz7A3fMbss7C12d6Gz+IhDDazvVGP2WfN9kY7zD4LG5jt/eXc7ywtEDZnw3xpV5lh+Rqxa2tewGf7ZPVQ9m4lqbauES0zWkpFTJXkbMpROWEz2rWSzTu2yNad20QSYiQ1PVVytmxUQmt1nEhZdbnkFe6S0qoyqYypksKyopp0BVXOhViVm7ayTOWDjYa4mDiVZiAhJl6SYhPU/y1T06WqrFKSYpOkdXor9T5eY2GvrlldpGhXocRWxqhyXTKzZcvGzWp/2N7uNeLn5b8FjjP+fjr7axnYrren14iaa3KqtDjmGin9/CmpKsqT8m05kjPlZkk66m9SmNDS1WvEFSf1l3tfmi1rcgtla16J/OPZX+TKE3pIYlyVK9cInEf4rKGuEfidMNu7sa4Ra9euDfyuwRbmazKOg/l3Ldw4Am2Ef3k5jjAeRtdHTDWuVkR7sJgAHBeOh4NsJ+eN3Tw5Vsq6Vcbv6NJHL9vhZt3R1GV3X7d93mo5XXzCS3Too9dtoN/bK6eDT3iNDn30i99bqcc8runf39upgAa4icMNUzTgxgQ3DF4wefJktcAVbmymTp2qbnjM4KZj4sSJqh/33nuvWuAKjBo1St3YYPr/uHHjQtYN8XXKlClywAEHyIsvvhh1Wxvj+OmIDtcFv0Lb+d9+kBFu+fIhWbljbb0LhPVo3VXuP+JGLRYIs2s7LL4FITUQ5YqUA7ujY2uiYWteG5GxNSkK6qYpwP+NLb0gt2vINARhXqfuzgn78pz/yYb8Teo4I6p4r1YNezzLd26S3DfvkfLtNQJgTFKqZJ58o6R0G+Tq9+zML5Ubn/xRNmwtVK97d2kl9116gKQkxTeJc9avLG8A21kd1zAC1Yc4iZaws4+Vsm6V8Tu69NHLdrhZdzR12d3XbZ+3Wk4Xn/ASHfrodRvo9/bK6eATXqNDH/3i9zrYKhRHHXWUXHDBBXL66afbnjqMQfUrr7yitl9++cWT9n322Wfq71lnnVVHPDUiUU488UTVhg8//DAgoCKCAgKqEfERCiPqLlKeVNJ0fN0P0Hb+t59fFwizazuIjoj4xBYNEE8RjVorLUFAiN2deiAgxHqTJxZiMCJrsTnFiCr+M3eRDMsaIA1BQquO0uns+yT37QekdMMyqS4tko1v3iMdJl4laf33d+17WqUnyd0X7y83PPGjbN9VIsvW7ZT7X5op/zh/tCTEx/r+nPUrCRrZjgKqD0EYspf7WCnrVhm/o0sfvWyHm3VHU5fdfd32eavldPEJL9Ghj163gX5vr5wOPuE1OvTRL36vg61C8dxzz8mdd96pojwPP/xwOeKII2T//fevtaJ9cLQppst//PHH8uWXX6rpZajDK4zpcphOGQ4jAsOYnmhMkcPUNfN7wRjT6zDtjbiHrr7uB2g7/9sv3AJhodBpgbDGsh2iNRHNia11St2HZHYw54ktKjMiYs25Yk2ibECIrfu6rLLc0fcjChW5b4dm9m+wKNS4FhmS9Zc7ZdN7j0rxitkilRWy+b3HpPLInZKxz9GufU/HNqly90X7yY1P/SSFxeXyx9It8q83Z8t1f9lb5Uv18znrV7pqZDsKqD4EeS/shjDb2cdKWbfK+B1d+uhlO9ysO5q67O7rts9bLaeLT3iJDn30ug30e3vldPAJr9Ghj37xex1sFQoszvTWW2/JtGnTVBTnu+++q6IaICoiBxmm5iNCCJGcyBMGURKv+/XrpxaeOuaYY1ReLa9AzjHkDjOE1FAY+b/MaQSGDh0qX3/9tcyePTvkPsgnNn/+fPX/iBEjXG93c0ZXX/cDtF3TsF+oBcJ0RxfbRQPEaGwtJboV6SuqKqUkSGCtiYyt+X/5tjXyzaqftYhCBbGJyWrq/pZPnpWCud+o+OZtX/xHKvK3SZtDznRNzO2W1VL+cf6+cvuz06WsvFJ+/CNHWrZIlItPGOz4O5qC3zUWOtmOAiohhBBCCGkW4Mbn+OOPVxsEx++//179Xbx4sRJO8TkWNEEe0mOPPVbGjBkjAwcObJC2jR49Wj766CN555135LTTTqszZQ1C6AcffKD+32+//QLvjx8/Xh577DHVjyVLlkjfvn1r7QehGIsjQCRGvlRCCCEExMfGSVpSC7UFgweIX6/4WUWbQjDVIQoVxMTFS/sJl0l8emvZ+fO76r286VOlsmCntD/mUvW5GwzYq63cdPZIuXfKTKmqqpaPf14lGS0S5fSj+rlSP/EnFFB9CFY683IfK2XdKuN3dOmjl+1ws+5o6rK7r9s+b7WcLj7hJTr00es20O/tldPBJ7xGhz76xe91sJUVEI2pU0TmpZdeqlIFQAS95ppr5I477gislowVZG+99Va18i1WLT/33HNrTW3D4lKIrL3yyivl6aefVtP6AQTihx9+OFA/Vvcl7uEXX9cR2i46aD/n0HbWQHQpokzD0VhRqACCbZuDz5C4tDay7fMXVCRqwbzvpLJwp3Q88e8SmxhdvlqDfQZkylWnDldT+MEbXyyRlmlJcswBe9mui37nHJ1sx1GUD7G78IHdfayUdauM39Glj162w826o6nL7r5u+7zVcrr4hJfo0Eev20C/t1dOB5/wGh366Be/18FWfgTT0yZPnizXXXedElK//fZb6dGjh0obgBVoKyoq1E3EU089JR07dqy1L8TVZcuWycKFC2XChAnSu3dvFXW6Zk3NzS8iWo1Fp4h70NedQ9tFB+3nHNqufhB9iujSGImJuEgYPm+MKFSDjJHjJD6tlWyeOlmqkRd25R+y8bU7JPPUW1XOVDc4dGQX2VVYJv/5oCYVzrPvz1XT+Q8a1tlWPfQ75+hkO+8SORHP2LRpk6f7WCnrVhm/o0sfvWyHm3VHU5fdfd32eavldPEJL9Ghj163gX5vr5wOPuE1OvTRL36vg638yqGHHqqm6Z911lmSnZ2tBFDkYkWe1gsuuEBN8R85cmSd/TIyMuTNN9+Uq6++Wgmx2Gfz5s0ybNgwuf/++9XiWcR96OvOoe2ig/ZzDm1XPxVVFbK1aHtE8RTg823FO1T5xqJFv9GSecY/JDa5Jg1B6cYVkvPyLVK+I9e17zh+bE85+bDe6n9kM3jsjVkyZ8lmW3XQ75yjk+0YgUoIIYQQQogmdOnSRW677Tbb+yUnJ6tp+tgIIYQQp2CBqgeOvEl2lRTUen/d+nXSJbtLrfcyktNV+cYkpesA6XTWvbLxv/dIZf52qdiRKxtevkVFoiZl1aS0iZazxveXnfml8uXMtVJRWS33vzRT7rv0AOnTtbUr9RN/EFON+GyiPYsWLZKioiJJTU2VvfbaSw2S7YBpXFb3sVLWrTJ+R5c+etkON+uOpi67+7rt81bL6eITXqJDH71uA/3eXjkdfMJrdOijX/zeSj3mcU3//v2j/s7/Z+88oKOqtj6+0wuEQOi9Nx9IEXsBK6IoghUFRH32jr0rAvb2rNgQG3bEh6ifFXyKooIFadJJ6CSE9Drf2ifcYZLMJPeee87MuZP/b61Z0/Y5s/d/diZn9pwCwgveP3M+F7wKtHMH9JMH2kWvduV7dtKWWfdT2c5McT8mIZlan3ETpXYbqKT/iopKevD1X+inpVWzW9NSE+mhq46gjq3TPK+dyYRDO7vjGizh9yB8SqzONnZsVdl4HVNi1OmHyr7d9OW0reqct2tnSk7oxIQYdfuAvHdmZ0JO6MaEGL2S9yZoBUA4QK7LA+3cAf3kgXbRq118kxbUbsIUSu5YVQDzlRXT1nenUd5f85X0HxcXSzeNG0L9ujcX9/MKS+nuFxfSzt1FntfOZHYbpJ2rAmpWVhb9/PPP9Pnnn4vN7hcvXkxbt6rbawIEJz8/X2sbO7aqbLyOKTHq9ENl3276ctpWdc7btTMlJ3RiQoy6fUDeO7MzISd0Y0KMXsl7E7SywyOPPEIrVqyItBvAw3gl100E2rkD+skD7aJbu7iUNGoz9i5K7X1w1QOVFbTjk//Q7oUfi8Ox3JKYEEd3XnAwdWtXdUgVF0/vfvFHcdCU17UzlXyDtHO8B+qqVavozTffpO+//95fLLUS0Tp5rVOnTjR06FA644wzqFevXqp9bvDExcVpbWPHVpWN1zElRp1+qOzbTV9O26rOebt2puSETkyIUbcPyHtndibkhG5MiNEreW+CVnZ444036NVXXxUn3fPJ9Xzh/UcBiLZcNxFo5w7oJw+0i37tYhOSqPWYG2jXF6/QnsVfiMeyv3mDyvNzqPlx51NMjLuF2I1SEujeSw6hW57+H23ZVUCbtuXT5Fd+oimXHkbJSfGe1s5E4gzSzvYeqFw45VM8f/rpJ2rTpg0deOCBojjKA83GjRtTZWWlmFrLRdU//viDfv/9d8rOzqbDDjuMJk2aRP/617/0RxPFYK8pAAAAAEQLJoxreEbDF198QfPmzRMrqioqKqhfv350yimn0IgRI6hly5YR8csLmPD+AQAAAHXBpa7dP3xIOfNn+R9rtN/h1OqUqykm3v3BV1t3FdDNT39POXkl4v7g3q3ozgsPpoR47JTpNZTugfrggw/S2WefTR06dKB3332XvvvuO7Hs6eKLL6YTTzyRjjjiCDrqqKPo1FNPpUsuuYSeffZZ+t///kcvv/wyZWRk0Lnnniv6AGpYvXq11jZ2bFXZeB1TYtTph8q+3fTltK3qnLdrZ0pO6MSEGHX7gLx3ZmdCTujGhBi9kvcmaGUHngBw+umn0yuvvEILFiygu+66SxxS8NBDD9GwYcNo4sSJ9MEHH1BeXl6kXQWG4pVcNxFo5w7oJw+0azja8QrpZkecQS1Ovpxo76zTgmU/0JZ3plBlcYHr/ts0b0T3XXIoNUqumnW6eOV2evKdxVRZ6fO8diax2iDtYu3+Qs+/zk+ZMoUGDBhgO1kPP/xwUWidO3cu5ebmuvUVAAAAAAAA5Vg/+POy/q+++oqGDx8uVl1xUZUnCtxwww30999/R9pNAAAAADikycDjqPWZt1BMfKK4X7xhKW1+4y4qz8t23XfXdul010WHUOLeWacLlmTRS3P+UrLfKjAPWwVULpy2bdtW+kV4mf8DDzwg3R5UJz09XWsbO7aqbLyOKTHq9ENl3276ctpWdc7btTMlJ3RiQoy6fUDeO7MzISd0Y0KMXsl7E7RyCs8ynT17tlhJxcVTnjjQs2dPuv766+mqq66iZcuW0VlnnUXvv/9+pF0FBuHFXDcFaOcO6CcPtGuY2jXqOYTajruPYlPSxP3S7Rto88zbqXRXluu+/9WtOd0y4UCKja06E2ju/9bRe1+vihrtIk26Qdo5PkSqLv755x+KjY2l7t27q+wW1ID3ZdDZxo6tKhuvY0qMOv1Q2bebvpy2VZ3zdu1MyQmdmBCjbh+Q987sTMgJ3ZgQo1fy3gSt7K6w4tmmn332Gf34449UVlZG7dq1owkTJoh9UHv37u23Pf/888Vy/yeeeILOPPPMiPoNzMEruW4i0M4d0E8eaNdwtUtu34vanT+Vts66n8pzd4gLF1HbnHU7JXfY9z9fhoP+1YauPnMgPfXuEnH/zc9WUJNGSTTi0C5RoV0kSTVIO6ndbXk68osvvki33XabuM8HSPEv9rwHKp9getFFF1FBgfs9JUBwtmzZorWNHVtVNl7HlBh1+qGybzd9OW2rOuft2pmSEzoxIUbdPiDvndmZkBO6MSFGr+S9CVrZ4dBDDxVjWT78dMyYMfTmm2/SN998QzfeeGO14imTmJgoJgiYdBIsiDxeyXUTgXbugH7yQLuGrV1i8/bU7vwHKLFVVWGzsiiftrx1LxX886vrvo87qBNdMHLf4enPf/gH/fDH5qjRLlKYpJ1UAZU323/88cdp586d4j7/cs+b759wwgl05ZVX0q+//ioOkgIAAAAAAMBEeNz63HPPiYNP77vvPhoyZEid9lxs5RmrAAAAAPAu8WnNqN34yZTcpb+47ysvpW3vP0R7fnf/P37M0T1ozLAeVf36iB596zf6458drvsFHi6g8h5Rxx9/PL300kviPu8TlZKSIk4t5X2ieBP+zz//XLWvYC+8vExnGzu2qmy8jikx6vRDZd9u+nLaVnXO27UzJSd0YkKMun1A3juzMyEndGNCjF7JexO0ssNjjz1GBx54oNjXNHDl1AcffCAOkyouLq5m37p1a0pKSoqAp8BUvJLrJgLt3AH95IF28kSTdrHJjajt2XdQo/0Or3rAV0k7P32ecr5/3/UBUBNH7kfHHdhJ3C6vqKSpM36mYl9jFW43SNoZlHdSBdRNmzbRUUcdJW7zflELFy6kgw46iJKTk8VjvMTJmp0K9Bx0oLONHVtVNl7HlBh1+qGybzd9OW2rOuft2pmSEzoxIUbdPiDvndmZkBO6MSFGr+S9CVrZISsri0aPHk2TJ0+mdevW+R9fvHgxTZ06Vex1mp3t/oReEL14JddNBNq5A/rJA+3kiTbtYuITqNVp11GTg0b6H8tZ8A7t/PxF8lVWyPcbE0NXnTmADv5XG3G/qKSCpr3+G2XtyFfid0Mjz6C8kyqgNmnSRGy8z/z8889UWFjoL6gyGzdupBYtWqjzElQDBVRzMCVGFFDd2aOQ5AwTYvRKIcltX8h7czAhRq/kvQla2Z2Byr6++uqr1K9fP//j06ZNo7feektMBuAtqwDweq6bCLRzB/STB9rJE43axcTEUvPjJlLGsRP8j+Ut/j/a9uGjVFlWIt1vXFws3TR+CP2rW/OqPgvL6e7pP9Ku3CIlfjck8rxeQB00aJDYaP///u//xMAyPj5e7CPFs1H5sVmzZtHBBx+s3lsgiI2N1drGjq0qG69jSow6/VDZt5u+nLZVnfN27UzJCZ2YEKNuH5D3zuxMyAndmBCjV/LeBK3ssGjRIrrwwgvFYVI1OeCAA2j8+PFij38AvJ7rJgLt3AH95IF28kSrdjxjtOkho6jlqGuJYqsOiyxctYi2vD2ZKorki3dJCXF054UHU5e2TcT97TlFdPeLCymvsFSZ7w2BWIPyLsYnscEDn4J10UUX0dq1a0Wy3XzzzXTBBReI2ajnn38+devWTfya36ZN1ZRl4J7ly5eLmb6pqanUt2/fSLsDAAAAAODpcc3gwYPpmmuuoYkTJwZ9/vXXXxcTBX7//few+2Y6Jrx/AAAAgGoK1/5O2z58hHylVfugJ7ToQG3H3kXxTeRXWGfvKaZbnvmetu4qFPf7dsmgyZceSsmJ8cr8BuEZ10iVctu2bUuffPIJvffee/Tdd9+J4inTp08fMdD88MMPUTzVCBeudbaxY6vKxuuYEqNOP1T27aYvp21V57xdO1NyQicmxKjbB+S9MzsTckI3JsTolbw3QSs77LfffuJg1NLS2jNBeFUVj3V5bAuA13PdRKCdO6CfPNBOnoagXWq3gdRu3P0U1yhd3C/bmUlZr91Gpds3SveZ0SSZLh7RgZqmVR1EuXx9Nj30+q/igCngrbyTngvLy/b3339/cSKpRXp6Op100kmUkpKiyj8QhMrKSq1t7NiqsvE6psSo0w+Vfbvpy2lb1Tlv186UnNCJCTHq9gF578zOhJzQjQkxeiXvTdDKDhdffDGtWrWKzj77bHr77bfphx9+oB9//JHeeecdOu+882jZsmV02WWXRdpNYDBeyXUTgXbugH7yQDt5Gop2SW27Ubvzp1F8s6pJgRV52bT59TuoaOPf0n1mpCXQvf8+hFKTq2ad/rp8Gz317hKqrHS8ILzBUWlQ3knPGf7444/FQHPHjh1BA+Kl/TNnznTrHwhCWlqa1jZ2bFXZeB1TYtTph8q+3fTltK3qnLdrZ0pO6MSEGHX7gLx3ZmdCTujGhBi9kvcmaGWHoUOH0qOPPkoPPvggTZ48WYxdGd7dKiMjQzw+bNiwSLsJDMYruW4i0M4d0E8eaCdPQ9IuoVkban/+NNr67lQq2bKGKksKaevb91PL066lxn1q751uR7vWrZuKPVHveXEhlZVX0ne/ZVJ6oyS66NR/+ccgwOy8kyqgPvHEEzR9+nRKSEig5s2bG7Wpa0MABVRzMCVGFFDd2aOQ5AwTYvRKIcltX8h7czAhRq/kvQla2eXkk08Wq6eWLl1KWVlZYlIAb1XVr18/Mc4FIFpy3TSgnTugnzzQTp6Gph0v42877j7a9uFjVLR2Cfkqymj7h49RxfCLKH3ICCnt+ndvQTeNG0IPzlxEPPl0zoI1lN44kc48tpemKLxPmkF5J1X55P2ijjjiCHF6Ke+B+s033wS9AD1s3rxZaxs7tqpsvI4pMer0Q2Xfbvpy2lZ1ztu1MyUndGJCjLp9QN47szMhJ3RjQoxeyXsTtHICz/ro378/nXjiiaKYOmjQIBRPQVTmuklAO3dAP3mgnTwNUbvYxBRqc9at1Li/tSLFR7u+eJmyv31LrFiR0e7Q/m3pyjMH+u+/Pm85ffHTBqV+RxObDco7qRmo+fn5NHz4cOx1CgAAAAAAPMvq1atp7ty5tHPnTqqoqAhaXJ02bVpEfAMAAABA5ImJi6eWp1xF8WkZtPvHj8RjfF2en0MtT7pMPO+UEw7uTHsKSmnmp8vE/ec++J2aNEqgQ/u3U+4/iHAB9cgjj6SffvqJzjzzTIWuALvw0jKdbezYqrLxOqbEqNMPlX276ctpW9U5b9fOlJzQiQkx6vYBee/MzoSc0I0JMXol703Qyg6ff/45TZo0qc7DCVBABdGQ6yYC7dwB/eSBdvI0ZO14PJBx9HkU17gZ7fq/V8VM1Pw/v6WKgt3UesyNFJuY7Fi704/uQbn5JfTx/DViOf8jb/5G912cSP17tNAYifdoa1DeSS3hv+uuu8SppTfccAN99tlnYin/L7/8UusC9FBYWKi1jR1bVTZex5QYdfqhsm83fTltqzrn7dqZkhM6MSFG3T4g753ZmZATujEhRq/kvQla2eHZZ5+ldu3a0XvvvUd//vknrVixotZl+fLlkXYTGIxXct1EoJ07oJ880E4eaEeUfuBJ1GrMJKK9s06L1iyhLW/dSxUFuY6146LsBSP/RUcf0EHc54Ol7n/1Z1qTuVuT996k0KC8i5XdgyAvL48+/fRT8cv9+eefTxMmTPBfxo8fL66BHnJzc7W2sWOrysbrmBKjTj9U9u2mL6dtVee8XTtTckInJsSo2wfkvTM7E3JCNybE6JW8N0ErO6xfv54mTpxI+++/PyUmJkbaHeBBvJLrJgLt3AH95IF28kC7Khr3PYzajr2LYpJSxf2Szf/Q5tfvoLKcrY61i42NoWvOHkRD+rYW94tKyunel36izTvzNXnvPXINyjupJfyTJ0+mPXv20EUXXURdunSh+HipbgAAAAAAAIgIbdq0oeLi4ki7AQAAAACPkdK5H7Ubfz9tfWcqVeRnU1n2Fto88w5qc84dlNSmm6O+4uNi6ZYJQ+ju6Qtp+fps2p1fIm4/fPWRlNGk7q0BQHiJ8Tk5OmwvAwYMoKuuuoouvvhiPV6BWvASMp66nJqaSn379o20OwAAAAAAnh7XvPbaazRz5kz68MMPKSMjIyI+eBUT3j8AAAAg0pTn7qAts+6nsl1Z4n5MYjK1PuNmSu06wHFf+YWldOuz/6MNW/PE/S5tm9ADVx5BjVMSlPsN5MY1sbK/2MfGSjUFCli3bp3WNnZsVdl4HVNi1OmHyr7d9OW0reqct2tnSk7oxIQYdfuAvHdmZ0JO6MaEGL2S9yZoZYeysjKx/9hxxx1Hl1xyCd1666102223VbvcfvvtkXYTGIxXct1EoJ07oJ880E4eaFeb+PSW1G7CVErq0Fvc95UW09Z3plH+0u8da9c4NZHuu+RQatUsRdxfv2UP3f/KT1RSVkENmXUG5Z3U2vt///vf9PTTT9PQoUOpR48e6r0CdVJRUaG1jR1bVTZex5QYdfqhsm83fTltqzrn7dqZkhM6MSFG3T4g753ZmZATujEhRq/kvQla2eGxxx7z316wYEFQGy6wTps2LYxeAS/hlVw3EWjnDugnD7STB9oFJy41jdqeew9tn/0EFf7zC1FlOW2f8ySV5+dQ00NOdaRd8/QUuv/Sw+jmZ76n3PxSWrYumx5+/Ve6feKBFBfXMCcxVhiUd1IFVD6VlAeUp556KnXs2JFatGhBcXFx1Wz4eV4WBdTTuHFjrW3s2Kqy8TqmxKjTD5V9u+nLaVvVOW/XzpSc0IkJMer2AXnvzM6EnNCNCTF6Je9N0MrueBaAhpDrJgLt3AH95IF28kC70MQmJFHrM26inZ+/RHlLvhSPZX89U+yPmnHsBEfatWvZmO69+FC6/bkfxKFSi5Ztpf+89ztdd84gUWdraDQ2KO+kStjffvutKJjyUn5e/rRlyxbKzMysdtm0aZNSR6+55hrq3bs3PfrooyGr0m+99RaNGTNG7NE6ePBgOuOMM+jtt9+mysrKkP2Gu50KmjZtqrWNHVtVNl7HlBh1+qGybzd9OW2rOuft2pmSEzoxIUbdPiDvndmZkBO6MSFGr+S9CVo5hcduO3fupNLS0ki7AjyEF3PdFKCdO6CfPNBOHmhXNzGxcdRixKXU7Miz/Y/l/vxf2j7nKUpPc1YE7NGhKd1xwUHigCnmm1830Wtzl1FDpKlBeSdVQP3mm29sXVTx8ccf0xdffFHnoHfSpEk0efJkWrZsmZgVy8Xdv/76i+677z6xrxUXeiPdThVcoNbZxo6tKhuvY0qMOv1Q2bebvpy2VZ3zdu1MyQmdmBCjbh+Q987sTMgJ3ZgQo1fy3gSt7LJhwwa6+uqr6YADDqAjjzySfvvtN1q4cCGdeeaZ9Ouvv0baPWA4Xsp104B27oB+8kA7eaBd/fAM0WZHnUUtTrqMK6risYK//0ebZ91PlSWFjvoa0LMl3TjuAIrdO+n0o+9W00ff/kMNjUyD8s5WATUrq+pEMTfIzkjl2a1Tpkyp02b69On0+eefU8uWLemjjz6iuXPn0rx58+jdd98V2wt8//339Mwzz0S8HQAAAAAAMIP169eLQumiRYtE8dSCV1mtXbuWLrzwQvr9998j6iMAAAAAvEeTQcdT6zNuppj4RHE/Zttq2vzG3WJfVCccvn87uvz0Af77M+Yuo68WbVDuL1BYQD399NNFEXPbtm0kUzi96667xPJ2p/h8PnEial5eHqWkVJ1EVpP8/HyaMWOGuM2zP/fbbz//cwMHDqQHH3xQ3Ob9WHNzcyPWTiWtW7fW2saOrSobr2NKjDr9UNm3m76ctlWd83btTMkJnZgQo24fkPfO7EzICd2YEKNX8t4Erezw+OOPU3JysvgR/N577xXjTuaggw4Sj/GP4vhBHERDrpsItHMH9JMH2skD7ZzRqNeB1Pa8eyg2pWr5fum2dbT5tdupdNdmR/2ceGgXGjeij//+0+//QT8v3UINhdYG5Z2tAuqcOXNo+/btdOyxx9LEiRPF3p+hZpTy4HPlypXC5rzzzqMTTjiBdu3aJfpwChchf/rpJxo+fDj1798/qM1XX30lCpU8yD3mmGNqPc8zCniJfVFRkbCNVDuVlJSUaG1jx1aVjdcxJUadfqjs201fTtuqznm7dqbkhE5MiFG3D8h7Z3Ym5IRuTIjRK3lvglZ24DHm2LFjqXnz5rUOZeDB+rnnnktLly6NmH/AfLyS6yYC7dwB/eSBdvJAO+ckd+hD7SZMpZjGGeJ+ee522vz6HVSctcpRP2cd24tOPbKbuF1Z6aOH3/iVlq7ZSQ2BEoPyzlYBlQeR//nPf8TMy0aNGtG0adNEYZQPTjrllFPE4POcc84RhU5+7LTTTqOpU6dSs2bNRCH1ueeeE3uEOmHNmjViZgAPanlWQCiWLFkirnnvqlAnkvFzDC/RilQ7lezevVtrGzu2qmy8jikx6vRDZd9u+nLaVnXO27UzJSd0YkKMun1A3juzMyEndGNCjF7JexO0sgMfGNWkSZOQzyckJBg1aAfm4ZVcNxFo5w7oJw+0kwfayZHYogNVHH0ZJbbqJO5XFu6hLW/dS4Wrf7PdB9eeLjq1Hw0d1EHcLy2vpCmv/kzrNutZ9WwSuw3Ku3gnxgceeKC4bN26lRYsWECLFy8WM1E5oNjYWGrbtq0oHh5yyCF0xBFHUEZGVZXdKXwA00033SQGrVxErasf3r+K4VmfoejQoUM120i0AwAAAAAA5tCnTx9x6CmvmKpJeXk5ffLJJ9S7d++I+AYAAACAKCI1ndqOn0Lb3n+Iijf+Tb6yEtr63oPU8uTLKW1A7ZXNwYiNjaFrzxlEeUWltHjFdiooLqd7XlxID199JLVp3kh7CMBhAdWCZ5OeddZZ4qKDZ599lv7++28xk/W4446r0zY7O1tc11Vkbdq0qbjOycmJWDuVdO/eXWsbO7aqbLyOKTHq9ENl3276ctpWdc7btTMlJ3RiQoy6fUDeO7MzISd0Y0KMXsl7E7Syw6WXXkpXXHEF3XjjjWKbKuvg1K+//ppeeeUVWrZsGT355JORdhMYjFdy3USgnTugnzzQTh5o5047nkXaZuydtOOT/1DB8oVEvkraMfdZKs/LpqaHnx5yhXMgCfGxdNuEA+nO6T/Syg05lJNXQndPX0gPXXUENWuSTNFId4PyztYS/nDyxx9/0IsvviiKtHfeeWe99rzXKJOUlBTShg8ICLSNRDuVbNy4UWsbO7aqbLyOKTHq9ENl3276ctpWdc7btTMlJ3RiQoy6fUDeO7MzISd0Y0KMXsl7E7Syw9FHHy22nPruu+9o0qRJ4jE++PTKK68UP+TfcsstYnsqALye6yYC7dwB/eSBdvJAO/faxcYnUqvRk6jJkJP8z+XMn0W7vniZfJUVtvpKToqne/59CHVsnSbub9lVQPe8tJAKisooGtloUN5JzUDVBRccb775ZqqsrBT7rKalVSVEXcTFxdnuP7CiH+52qiguLhZ7dvE2ATxLgm+npKRQy5Yt/YnFB1zxYV58eBfTpUsX2rNnD61evVoUfnmrBWt7Ad5jlrdf2LFjh7jfqVMnysvLE7aJiYniddauXSue4z1teT8wPlCsoKBAFLl5Ri7fjo+Pp86dO4u9a61ZuPx+cj9M+/btxeFb+fn5QsOuXbsKW/aT9x/jvXW3bKk6Sa5du3bCjn1mDfkXB/aB84Jzgu05doZ94NfhvpkePXqI2HjpHffJPmdmZvr38mW9rJnB3bp1E1tQ8JYRqampQjdLQ9azoqLCP+OY/d28ebPYVoIL5NzXhg0bROzp6enCZufOqk2cWYdt27aJ94r15njWrVvnn7nM8Qfqze0KCwuFtrw1RKDe/B5wXwy/F+y7pTe/r5a+HBdrxttrWHqzfvxe8vvLsQbq3bhxYxEPw/nAfQbqzf5y/GzHcVivw3rzfWsfErZlHSy9OT7rgLlWrVoJbQP1tvKwZs7ybX5/A3OW88HSm1+XX5P7sw77sPRmDVlPzgPWi2Nn/zkmzhfWinOWYX35NSy9uS3rwrbsP79fgXrzawbmrOUDv+ecM4E5y1rzhftmAnOWL4F6s01gzgbqzX87gTnLGgTqzZpZOctaBOrN70Ngzjr5jODcsXJWxWeEpXddnxH83gbmrNPPCPZB12cE+8z+u/2MYLg9v4cynxEcE/dr9zPCem9qfkZwznIeBOptaVjfZwRrxe9pqM8I7tv623D7GcHvRbCctfMZEZizdj4jLL3tfEZYn7Osb12fEYF6h/qMsPSW+YzgeFlfXZ8R1vsYjs8I7ssExowZI/b1/+GHH4RW/J5wjhx22GHCXwDqgv9egBzQzh3QTx5oJw+0U6NdTEwsNT/hQopPy6Dsb98Uj+357XMqz8+hVqddJ4qs9ZGWmkiTLzmUbn7me9qRU0TrNu+h+1/9WTyWmGC/ZuUFygzKuxgfj5ANgQ+LmjVrljj19J577qn23Pjx48WhTBdffLFYamUxevRoscSKC68XXXRR0H7feOMNmjJlivgC8MUXX0SknVuWL18uvlDxFyL+YsZfxpzAX8zstrFjq8rG65gSo04/VPbtpi+nbVXnvF07U3JCJybEqNsH5L0zOxNyQjcmxOiVvLfTT+C4pm/fvq5fE4QXvH/mfC54FWjnDugnD7STB9qp1y7vz+9ox6fPEe2dfZrcaT9qfcYtFJfS2Fa/mdvz6JZn/kd7CkrF/UP6taFbJxxIcXHGLTY3Ou/sjmuMmYHKh1Jx8ZRnLvABUnaxZgfUdTKXNbslcN/ScLdTCc/20NnGjq0qG69jSow6/VDZt5u+nLZVnfN27UzJCZ2YEKNuH5D3zuxMyAndmBCjV/LeBK2Ccdttt9E555xDAwYM8N+vD57NzKuiAPBSrnsBaOcO6CcPtJMH2qnXLm3/YRTXKJ22ffgo+cqKqXjjMtr8xp3U9py7KL5J/Xp3aJUmlvPf8fwPVFxaQT8t3UrPfvAHXX3WQC0roht63hlTlp43b5645qVngwYNEqeeBl549inz0ksvifvHHHNMtQ1lreWawbCWuvGMUItwt1OJtRxPVxs7tqpsvI4pMer0Q2Xfbvpy2lZ1ztu1MyUndGJCjLp9QN47szMhJ3RjQoxeyXsTtArG7Nmzq+2jxfftXADwWq57AWjnDugnD7STB9rp0S61+yBqO+4+ik1tIu6X7dhEWa/dRqU77O392atTM7rjgoMoPq6qYPrloo30+rzlFC1sMijvXM9A5X2seI8v3s+L98XivcR4LyyncLFx8ODBIZ9ftWqV2GeM9znj6bu8VxdjzSJYsmRJyLaLFy8W14H9h7sdAAAAAACIHCtWrKjzPgAAAABAJEhu14Panz+Ntsy6n8p3b6OKvF20+fU7qc1Zt1Fyx/q3yhnYqxXdcN4B9PAbvxJv0vnBN/9QeuNEOm1oj7D431CQnoH622+/iY33hw4dKpZDLV26VMwSHTZsmH82qRMuu+wysYQ/1GW//fYTdqeccoq4/5///Md/giofIMH7IvApqsG2BuCKNR9ewYcEWIS7nUr4IAidbezYqrLxOqbEqNMPlX276ctpW9U5b9fOlJzQiQkx6vYBee/MzoSc0I0JMXol703Qyi58aBSP3fgwLouvv/6avv3224j6BbyBl3LdNKCdO6CfPNBOHminV7uEjLbU7vxplNimm7hfWVxAW96eTAUrfrb1GkcMaE+Xjdnff/+VT/6mb3415wT7aMg7qQLqn3/+SRdccIE4pfj888/3P86HG/EMVD7kaf78+RQOuFB54YUX+veysmZ/Mn/88Yd/f6sJEyb4T0uPRDuV8Cm6OtvYsVVl43VMiVGnHyr7dtOX07aqc96unSk5oRMTYtTtA/LemZ0JOaEbE2L0St6boJUdeM96ngRw6aWX0tq1a/2P//e//6XLL79cjHX5QAEAvJ7rJgLt3AH95IF28kA7/drFN25K7cZNppRuVSuffeWltO2jR2nPb/YOJz/psK507vA+/vtPvfs7/bJsK3mZcoPyTqqA+tRTT1GHDh1ozpw5dMkll5CP5wgTUf/+/emTTz4R+4ROnz6dwgUPco844gjKzs6msWPH0kknnUQnn3wynXXWWbRz504xa/Sqq66KeDtV8OvqbGPHVpWN1zElRp1+qOzbTV9O26rOebt2puSETkyIUbcPyHtndibkhG5MiNEreW+CVnZ44okn6J9//qHJkyf797hnHn74YXH5/fff6emnn46IbwsXLhRjSR5v9uvXT1zzBAX2NxQVFRX01ltviRVivOUUbyV1xhln0Ntvvy1m2gL1eCXXTQTauQP6yQPt5IF24dEuNilFLN1v3O+oqgd8lbTz8xcpe/4sf+2tLs45vheNPLyruF1Z6aMHX/+Vlq3bRV4l26C8kyqg8v6fPDjjpew1T/Zq3LixKCTWNcBTTWJioijY3n333WKQyXuy8jL6Pn360K233ioGvzwzNtLtAAAAAACAGfBqqYkTJ9KZZ54pxnYWfPvUU0+lcePG0f/93/+F3a9HH31U+PXll1+K+3zOQG5urpgZy+PvYKu8uEA6adIkUQxetmwZdezYUZwb8Ndff9F9990nJjyUlZWFPRYAAAAAOCcmLoFanno1pR8yyv/Y7v99QDs/fZ58lRV1t42JoYtP609HDWwv7peWVdDkV36m9Vv2aPc72onx2Slh14B/0b7uuuvEMnVe/nTooYfSjBkzxDXz8ssv0/PPPy/2SQVqWL58uVhGlpqaSr169aK4uDhH7XlWgt02dmxV2XgdU2LU6YfKvt305bSt6py3a2dKTujEhBh1+4C8d2ZnQk7oxoQYvZL3dvoJHNf07Vv/4Qg6GDRoEN1www2iUBoMns3JM1F5i6Zw8cEHH9Add9whfoi/5557RHGXvwhxAZV/pP/mm2+oadOm9NVXX1FaWpq/HY+7n3zySXHI6osvvug/O4Bn0V555ZVihRSfN3D99dcr8dOE988ETPhc8CrQzh3QTx5oJw+0i4x2uYvm0q4vZ/jvp/Y4gFqNuYFiE5LqbFdWXkn3v/ITLVm1Q9zPaJJED111JLVp3oi8REUY8s7uuEZqBiovC5o7d27Q5/hF33//fbGcH+ghKytLaxs7tqpsvI4pMer0Q2Xfbvpy2lZ1ztu1MyUndGJCjLp9QN47szMhJ3RjQoxeyXsTtLIDL9v/4osvQi6H48OkunatWgIXDvggq0ceeUTcvuWWW8SKLmulF++rz89x0XT37t3VZsbm5+eLiQwMzza1iqfMwIED6cEHHxS3Z86cKQqxQB1eyXUTgXbugH7yQDt5oF1ktEs/aCS1Gj2JKK5qlXPh6t9oy1v3UkVh3TNKE+Jj6baJB1GvTk3F/ew9JXT3iwtpd96+gzO9QJZBeSdVQL3mmmvE8iD+xf7jjz8Wgzs+WOr111+nUaNGUWZmpviVG+ihtLRUaxs7tqpsvI4pMer0Q2Xfbvpy2lZ1ztu1MyUndGJCjLp9QN47szMhJ3RjQoxeyXsTtLLD+PHj6ZdffhFjVl4Wv379etqwYQN9//33dPXVV4t9SHm1Vbjg2aVcHOXl9+edd16t53mbrDvvvFPMRO3Zs6f/cZ6NyoXRFi1a0DHHHFOr3ZFHHin6LCoqErZAHV7JdROBdu6AfvJAO3mgXeS0a7zf4dT2nDspJjFF3C/JWkWbZ95BZbu319kuJSme7r7oEOrQqrG4v2VnAd378kIqLPbOtj6lBuVdvOySJ94DlJcWPfTQQ/6N+BleOvT444/TIYccotZT4CclJUVrGzu2qmy8jikx6vRDZd9u+nLaVnXO27UzJSd0YkKMun1A3juzMyEndGNCjF7JexO0sgP/6L99+3Z65plnaMGCBdWe4yX01157rdhzNFz8+OOP4pqLoKGWqp122mlBzyZgDjjggFpnE1jwc7xf/6JFi+j0009X6ndDxiu5biLQzh3QTx5oJw+0i6x2KV36U7sJU2jrO1OoIj+HyrI30+bXbqM2Y++ipNZdQrZLb5xE911yKN3y9Pe0M7eY1mTm0tQZi+iefx9CiQnmb8mQYlDeSZ90dPjhh4vN7f/++28xIOPN69u3by8OVcIBSnrhIrXONnZsVdl4HVNi1OmHyr7d9OW0reqct2tnSk7oxIQYdfuAvHdmZ0JO6MaEGL2S9yZoZZeLL76Yzj77bPrhhx9o8+bNYp+tdu3a0WGHHUYZGRlh9WXlypXimmeX8rYCPCOVZ4zyYaVNmjQRY+/Ro0dXO/CK4ZmzDM8yDUWHDh2q2QJqcLluGtDOHdBPHmgnD7SLvHZcKG13/jTa+s79VLZrM1UU7KbNb9xFbc64WRRYQ9GqWSpNvvQwuuWZ7ymvsIz+XL2THn3rN7plwoEUFxv8x1dTaGlQ3kkt4bfgX7m5YDpixAg6+eSTxT5LKJ7qZ+PGjVrb2LFVZeN1TIlRpx8q+3bTl9O2qnPerp0pOaETE2LU7QPy3pmdCTmhGxNi9Erem6CVE7g4yWPZiy66SJxWP3LkyLAXTxku4DIJCQk0ceJEuuKKK+ijjz4SWwnwXq133323mIHKW2UFkp2dLa7r8pkPnmL48FegDq/luklAO3dAP3mgnTzQzgztEpq2onYTplJSu6rtfHwlhbRl1hTKX/ZDne06tk4Ts06TEqtmnS78aws9/+EfIfeCN4WNBuVdvOweBC+99JL4tX7Hjh1i9mmw4ir2WQIAAAAAACbAS/VPOOEE6tWrl/9+ffB4lk+xDwcFBQXimrfH4oOhbrvtNjr11FPFibA//fQTTZkyhdasWSOKvFxYTU5OFva8tymTlBT6NN6atgAAAADwLnGpTajteffS9tmPi0OlqLJc3Oal/XzoVCh6d86g288/iO5/9Scqr/DRFz9tEEv8x48IffI8cFlAnTp1Kr377rvUpk0bsWw/NtbVRFbgED4kQGcbO7aqbLyOKTHq9ENl3276ctpWdc7btTMlJ3RiQoy6fUDeO7MzISd0Y0KMXsl7E7QKBhdMO3fubGwBtaSkxD+j9KmnnqITTzzR/9ywYcOoa9euYsUXF1Hff/99cQgWE2q/1GCE2iNVluLiYlq9erXwjU/J5UkWvFcZL7ezZoxwPvDsll27don7Xbp0oa1bt4q2XPRt27atf2uB5s2bi+8VPEGD6dSpk7jNhV/euoC3Ili7dq14rlmzZmK2Lu9ja21hwNpxIZpXxPF7zVpZM3C5iMyvy/D3Fz54iwvVrB/7z7bsJ89IbtSokdg6geEtHdhuz549Qr/u3bsLH3gCCb8O+2adEMzfjfg+98306NFDxFZeXi76ZJ+tGcStW7cWelmzgrt16ya2RSsrKxNFc9bN0pD15O0lrNnG7C/PWOac4bi4Lz4AzdKb2blzp7hmHbZt2+bXm+NZt26df9Yyxx+oN7crLCwU2rKmgXrze8B9MfxesO+W3vy+ci4w6enpIg8C9Wb98vLyxPvLsbImbM968wFp1gxszgfuM1Bv9pfjZzvuO1BvjosPX2PYlnWw9Ob4WFOmVatWQttAvfm9CJazfJvf38Cc5Xyw9ObXDcxZ9tPSu2bOcuyBerNWgTnLr2HpzW0Dc5bfr0C9OU4rZ/l9DtSbcyYwZ1nrQL2tnE1LSxOXQL359QNzNlBv9iMwZ1mDQL1ZMytnWYtAvfl9CMxZUz4j2F/uLxyfEaw120fLZwTnGvcbjs+IQL2j4TOC++b4VX9G5A86nWIqYilm3S/i8V1fzqBdmeuo6+grac2atUE/I/p0akcXjOhOL89dTTz39L2vVlFZcR4d1T/DyM+IlJQU8b7o/IzgvuwQ45OYr8v7QvHl0UcfddoUSLJ8+XKRuJx4/AfJb7YTOOHstrFjq8rG65gSo04/VPbtpi+nbVXnvF07U3JCJybEqNsH5L0zOxNyQjcmxOiVvLfTT+C4pm/f8Mx6WLp0qRj4W7MxrS9Y9cFfbsLB4MGDxZdC3gN17ty5QW1uvfVWmj17Nh166KH02muvicd4X9Rly5bRzTffLLYhCMYbb7whZrDylw7eDsAtkXj/TMSEzwWvAu3cAf3kgXbyQDszteOSXs6Cd2j3/z7wP9a431HUcuQVFBOXELLd3P+tpemz//Lfv+HcwTTsgND7qUdz3tkd10hNHeVK8YEHHujGP+ACq/Kuq40dW1U2XseUGHX6obJvN305bas65+3amZITOjEhRt0+IO+d2ZmQE7oxIUav5L0JWoU6MOq///2v/z4XIrlgyQXSui7hgmfZMH369Alpw8VVxprBwVhfKqwZH8GwZtREYm/XaMbUXPcC0M4d0E8eaCcPtDNTO57hmjF0LLU48RKimKoSX/7SBbT13QeosiT01j0jj+hG5xzf23//yXeW0K/Lq2YQm8Qug/JOqoDKS4q+/PJL9d4AAAAAAACgAZ5ZEDgI5yX8q1atIlPgpW4ML18LhbVcn5eh1WxX14xaa3kdz0AFAAAAQPTR5IDh1HrMjf5Zp0Xr/qDNb95N5fmhf2A9d3hvGnFo1digotJHD8z8hVasr1pWDxQt4edf63kDe9734LjjjvPv61ATPikUqJ9SzLMPeG8Mp7OG7baxY6vKxuuYEqNOP1T27aYvp21V57xdO1NyQicmxKjbB+S9MzsTckI3JsTolby3008kloDznqGLFy8WMzx577NFixaJ4iOPY0PB49uZM2eGxb+nn35aFHV5L69vv/026Nj67rvvFucQ8J6o06dPF4/xcv8bbrhB7FvH7YLB43WetcrL+M8880zXvmIJvzmfC14F2rkD+skD7eSBdt7QrnjTctr63gNUWVx1OGV809bUduxdlJDRNqg9F04feeNX+uHPqn1PG6ck0INXHUGd21StjGkI2i3XuYT/t99+E3st/f7772IfVD4llPdkCrzwY0AP1mbLutrYsVVl43VMiVGnHyr7dtOX07aqc96unSk5oRMTYtTtA/LemZ0JOaEbE2L0St6boFUw+HT7Y489Vhz2wTMyuUDJhxfw7VCXwKXyuhk5cqTwiQ95+PTTT2s9z7Nnrb1Rhw8f7n/86KOPFvu68oEP3333Xa12CxYsEHFw0fiEE07QHEXDwtRc9wLQzh3QTx5oJw+084Z2yR37UrsJUygureoH4vLd2yhr5u1UvLnqEK+axMXG0A3nDaYBPasOGMsvKqN7XlxI27MLyQS2GpR3UmXchx9+WFRmb7rpJnFalpPTP4F77J4QJtvGjq0qG69jSow6/VDZt5u+nLZVnfN27UzJCZ2YEKNuH5D3zuxMyAndmBCjV/LeBK2CMX/+fJo0aZJ/GTvPRL399tvplFNOIRPgMfXZZ59N77zzDt11111imb5V8OTTkq+//nqxCowPwjr55JP97bgweuGFF9Jzzz0nJjA8++yz4kAq5o8//vBPapgwYYI4iReow9Rc9wLQzh3QTx5oJw+08452iS07UfuJD9CWd6ZQ2Y6NVFm4h7a8eQ+1Pv1GSu0+qJZ9Qnwc3T7xILrjhR9p9abdtCu3mO5+8Ud66KojKb1xEkWSYoPyTqqAunHjRlE8Pffcc9V7BOolKSlJaxs7tqpsvI4pMer0Q2Xfbvpy2lZ1ztu1MyUndGJCjLp9QN47szMhJ3RjQoxeyXsTtAo1AeDOO+/0F1B5ybtpSxG5oLt9+3b65ptv6Oqrr6Y2bdqILQZ4r9aysjKxvP/JJ5+spfHll19Of/75J/3vf/+jsWPHiq0JeDbr6tWr/bNUr7rqqghFFb2YmuteANq5A/rJA+3kgXbe0i6+SXNqN/5+2vb+g2JZv6+smLa+O42aHnEmNeo5pJY9T4u889TW9J93N9O67ErK2kF078s/0dTLDqPU5Kp9VRt63kntgTpq1CixdOiKK67Q4xWoBfZANRNTYsQeqO7ssRekM0yI0St7QbrtC3lvDibE6JW8N3UP1IMPPpgGDRpEN954I6WkpIjl/HfccYe4rgsutIYTHprzEv73339f6MQzL9gHno06ceJEysjICKk774/60Ucf0dq1a8VZBTyrlc8kGDduHCUkqPvygz1Qzflc8CrQzh3QTx5oJw+086Z2leWltGPOU1Sw4ifbbcp8cTQ1dxTlVDYWS/vv+fchYpZqJPD8HqjXXHON2FCfl0JVVla68RNIsH79eq1t7NiqsvE6psSo0w+Vfbvpy2lb1Tlv186UnNCJCTHq9gF578zOhJzQjQkxeiXvTdAqGGeccYbYI5SX7POhSsy0adNEAbWuS7jhmaO8HyqPtfmgK55Z+vnnn4vtB0IVTxn+cnHeeefRhx9+SEuWLBHt5syZQxdccIHS4ikwP9e9ALRzB/STB9rJA+28qV1sfCK1Gj2JGvU91HabhJgKaplcIW7/8c9OeuytxeKwqYaed1JlXP5FnPdluuyyy8R02qZNm9baB5UHf1999ZUqPwEAAAAAAJCGt5868MADaeXKlVRaWir2CuVCau/evSPtGgAAAACANmJi4yj90NFUsHyh7TaXjO5Pt7+XRaVlFfTDn5upyUd/0uWn7y9qfQ0VqQIqb2DP+0dZe0iB8MJ7YelsY8dWlY3XMSVGnX6o7NtNX07bqs55u3am5IROTIhRtw/Ie2d2JuSEbkyI0St5b4JWoRg2bJi4MLNnzxbL2yMxyxREBybnuulAO3dAP3mgnTzQztvaxZCzwmeXtul02/ntaMqrP4vZp58tXC8OlDrvxD7U0LRzVUB944031HsCbBMbG6u1jR1bVTZex5QYdfqhsm83fTltqzrn7dqZkhM6MSFG3T4g753ZmZATujEhRq/kvQla2YEPagKgIeS6iUA7d0A/eaCdPNCu4Wk3pG9ruu6cQfTY24vF/Xe+XEnpjRNp5BHdGqR25ngCbLNjxw6tbezYqrLxOqbEqNMPlX276ctpW9U5b9fOlJzQiQkx6vYBee/MzoSc0I0JMXol703QKhh8Cv2vv/5a7THey3/FihVUVFRUy/6TTz5p0AckAe/muheAdu6AfvJAO3mgXcPUbtgBHeniUf3891/8+C+avzizQWpnawYqL226/fbb/Uuc7Cx1wh6oAAAAAADAFHhcOnz48GqP5ebm0ujRo+nVV1+lQw+1f7gCAAAAAEBD4dSjutPu/BJ6/+t/yOcjemLWYkpLTaTBfVpRQ8JWAbVdu3aUmppa7T6IHJ06ddLaxo6tKhuvY0qMOv1Q2bebvpy2VZ3zdu1MyQmdmBCjbh+Q987sTMgJ3ZgQo1fy3gStnODjbwIANIBcNwlo5w7oJw+0kwfaNWztxo/oS3sKSumLnzaIPVEfmLmIplx2GPXunNFgtIu1u+dp4K/yfN/OBegBS/jNwZQYsYTfnT2WMjvDhBi9spTZbV/Ie3MwIUav5L0JWgEQDpDr8kA7d0A/eaCdPNCuYWsXExNDl58+gA7t31bcLy6toPte/ok2bctrMNpJ7YF622230R9//BHy+Z9++okuvvhiN36BOgi2T5fKNnZsVdl4HVNi1OmHyr7d9OW0reqct2tnSk7oxIQYdfuAvHdmZ0JO6MaEGL2S9yZoBUA4QK7LA+3cAf3kgXbyQDt5okW7uNgYuvG8A2j/Hi3E/bzCMrp7+o+0I8cb9YiIFFBnz55NmzZtCvn8zz//LC5AD4mJiVrb2LFVZeN1TIlRpx8q+3bTl9O2qnPerp0pOaETE2LU7QPy3pmdCTmhGxNi9Erem6AVAOEAuS4PtHMH9JMH2skD7bytXVxqGsXEJdiyZTu2D0ZiQhzdccFB1K19uri/M7eY7n7xR8rNL1Hqr0naWcT4bGz8xMXSkSNHUmlpqbjPTXj6bl3079+f3nvvPXWeNnCWL19OhYWFYi/a3r17U2yss9o3nzJrt40dW1U2XseUGHX6obJvN305bas65+3amZITOjEhRt0+IO+d2ZmQE7oxIUav5L2dfgLHNeE66b5Pnz70yCOP0CmnnOJ/LCcnR2xTNWPGjFqHSH3yySd0yy23CF9B5N8/EzHhc8GrQDt3QD95oJ080M772pXn7qCKwvqX3HPxND69ZZ02OXnFdMsz/6MtOwvE/V6dmtKUyw6nlCRbRy0ZpZ3dcY0tLzp27Eh33303nXbaaTRq1Cjx2AEHHCDu17yMGTOG/v3vf9OTTz6pLhpQjbVr12ptY8dWlY3XMSVGnX6o7NtNX07bqs55u3am5IROTIhRtw/Ie2d2JuSEbkyI0St5b4JWodi9ezdt3rzZf9m6dat4PDs7u9rjfOHiKgBezXXTgXbugH7yQDt5oJ33teOiaFLbbghQMwIAAI2ySURBVPVe6iueMs3SkmnyJYdSRpMkcX/Vxt30wGuLqKy8kqJRO8Z2afj0008XFyYrK4uuuOKKWr/UAwAAAAAAYCrTpk0Tl5rceOONEfEHAAAAAMCrtGneiO675DC69dn/UUFRGS1ZtYOenLWYbjjvAIqNrXvVuheRmlv7xhtvqPcE2KZZs2Za29ixVWXjdUyJUacfKvt205fTtqpz3q6dKTmhExNi1O0D8t6ZnQk5oRsTYvRK3pugVTBGjx4daRdAlGFqrnsBaOcO6CcPtJMH2skTzdp1aduE7rrwYHGYVGl5JS34PYuaNEqkS0b3r3frT69pJ705werVq2nu3Lm0c+dOqqioqPU8CxXsF37gnoSEBK1t7NiqsvE6psSo0w+Vfbvpy2lb1Tlv186UnNCJCTHq9gF578zOhJzQjQkxeiXvTdAqGA888ECkXQBRhqm57gWgnTugnzzQTh5oJ0+0a/evbs3plvMPpKkzFlFlpY/m/rCOmjROorEn9I4q7aR2Yv3888/p1FNPpRdeeIE++OADmj17dtAL0MP27du1trFjq8rG65gSo04/VPbtpi+nbVXnvF07U3JCJybEqNsH5L0zOxNyQjcmxOiVvDdBKwDCAXJdHmjnDugnD7STB9rJ0xC0O2i/NnTt2QP999/+YgXN+3FdVGknNQP12WefpXbt2tHjjz8uTjRNTExU7xkAAAAAAAAAAAAAAMB4jhnSifYUlNIrn/wt7r/w0Z9iOf8RA9pTNBDj8/l8Thv179+fbrnlFho3bpwer0Atli9fToWFhZSamkrdunWjpKSqk87sUlJSYruNHVtVNl7HlBh1+qGybzd9OW2rOuft2pmSEzoxIUbdPiDvndmZkBO6MSFGr+S9nX4CxzV9+/Z1/ZogvOD9M+dzwatAO3dAP3mgnTzQTp6Gpt1rc/+mD79dLW7Hx8XQPf8+hAb2amWsdnbHNVJL+Nu0aUPFxcVu/AMuyM7O1trGjq0qG69jSow6/VDZt5u+nLZVnfN27UzJCZ2YEKNuH5D3zuxMyAndmBCjV/LeBK0ACAfIdXmgnTugnzzQTh5oJ09D0+78k/ej4w/qJG6XV/jE3qirNuZ4XjupAup5551Hb731llGBNCQKCgq0trFjq8rG65gSo04/VPbtpi+nbVXnvF07U3JCJybEqNsH5L0zOxNyQjcmxOiVvDdBKwDCAXJdHmjnDugnD7STB9rJ09C0i4mJoSvPGEAH/6uNuF9cWkH3vfwTbdqW52ntpPZALSsrE4Icd9xxNGTIEMrIyBD3A+H706ZNU+UnCCA+Pl5rGzu2qmy8jikx6vRDZd9u+nLaVnXO27UzJSd0YkKMun1A3juzMyEndGNCjF7JexO0AiAcINflgXbugH7yQDt5oJ08DVG7uLhYumn8ELrnxYX099pdYm/Ue15aSA9fdSS1aJriSe2k9kDlg6Pq7TgmRuwjANTvycD61yxY1we/zXbb2LFVZeN1TIlRpx8q+3bTl9O2qnPerp0pOaETE2LU7QPy3pmdCTmhGxNi9Ere2+knEntoTpgwwXEbjmPmzJla/PEy2APVnM8FrwLt3AH95IF28kA7eRqydgVFZXTbc/+jdZv3iPsdW6fRQ1cdQWmpicZop3UP1BUrVtR7QfFUH2vWrNHaxo6tKhuvY0qMOv1Q2bebvpy2VZ3zdu1MyQmdmBCjbh+Q987sTMgJ3ZgQo1fy3gStgpGZmen4smnTpki7DQzG1Fz3AtDOHdBPHmgnD7STpyFr1yglge67+FBq0zxV3Odl/Lycv7ik3HPamTMXFgAAAAAAAE188803kXYBAAAAAKDB0axJMk2+5DC6+ZnvaXdeCa3ckEMPvP4L3XXhwRQfJzWv0zsF1Ntuu82W3QMPPCDTPaiHpk2bam1jx1aVjdcxJUadfqjs201fTtuqznm7dqbkhE5MiFG3D8h7Z3Ym5IRuTIjRK3lvglZO2b59O23ZsoW6detGSUlJYr+t2FjvDOhBZPBirpsCtHMH9JMH2skD7eSBdkRtWzSiyZccSrc++z8qLC6nxSu205OzltCkcwdTbGyMJ7STKqDOnj27zuebN28uDpYCekhOTtbaxo6tKhuvY0qMOv1Q2bebvpy2VZ3zdu1MyQmdmBCjbh+Q987sTMgJ3ZgQo1fy3gSt7PLbb7/R1KlT/VtPvfrqq1RRUUG333473XrrrXTSSSdF2kVgMF7KddOAdu6AfvJAO3mgnTzQroqu7dLFrNO7X1xIZeWVNH9JJjVpnEgXj+oXcp9Tk7RTtgfqsmXLaP78+XTLLbeIgeejjz6q3lsg2Lp1q9Y2dmxV2XgdU2LU6YfKvt305bSt6py3a2dKTujEhBh1+4C8d2ZnQk7oxoQYvZL3Jmhlhz///JMuuOACKigooPPPP9//eHp6upiBeuONN4qxLQBez3UTgXbugH7yQDt5oJ080G4f/bq3oJvHDyFr0ul/v19L73/9D3lBO2Vrk3iZU+vWrcVAdMSIEfTggw+q6hoAAAAAAAClPPXUU9ShQweaM2cOXXLJJeKUV6Z///70ySefUPfu3Wn69OmRdhMAAAAAIKo4pF9buvqsgf77b3y2nD5fuJ5MR8vmTvvttx/9/vvvOroGRNS+fXutbezYqrLxOqbEqNMPlX276ctpW9U5b9fOlJzQiQkx6vYBee/MzoSc0I0JMXol703Qyg5LliyhMWPGiKVhNZeNNW7cmM466yz655/QMyIA8Equmwi0cwf0kwfayQPt5IF2tTnuoM408eT9/Pef//AP+uHPzUZrp6WAysudGjVqpKNrQES5ubla29ixVWXjdUyJUacfKvt205fTtqpz3q6dKTmhExNi1O0D8t6ZnQk5oRsTYvRK3puglV0SExNDPldSUkKVlZVh9Qd4Cy/lumlAO3dAP3mgnTzQTh5oF5zTj+lJo4f1ELcrfUSPvvkb/bl6h7HaSR0iddtttwV9vLS0lFauXElr1qyhCRMmuPUNhCA/P19rGzu2qmy8jikx6vRDZd9u+nLaVnXO27UzJSd0YkKMun1A3juzMyEndGNCjF7JexO0ssOAAQNo7ty5QceshYWF9P7774vl/AB4PddNBNq5A/rJA+3kgXbyQLvQ8CzU3PwS+ubXTVReUUlTXl1E0644nHp0aEqVlT7albOHWrVqTbHWpqleK6DOnj075D6oLVq0oIkTJ9J1113n1jcQgri4OK1t7NiqsvE6psSo0w+Vfbvpy2lb1Tlv186UnNCJCTHq9gF578zOhJzQjQkxeiXvTdDKDtdccw2NHz+exo0bR8cee6xYxs8HS/Gy/TfeeIM2b95M9913X6TdBAbjlVw3EWjnDugnD7STB9rJA+1Cw4VR3g81r7CUflm2jYpKyumuF36g/t1b0K8rtlNZeSUlxK+kowa2p1FDu1PXdukUKWJ81o75wGiWL18uZkOkpqZS3759I+0OAAAAAIDnxzU//PAD3XPPPZSZmVnt8ZYtW9Kdd95Jw4cPj5hvJmPK+wcAAACA6KC4tJzueXEhLVuXHfT5uNgY4urlpHMH09DBHSIyrtGyByrDJ5oCPfAWCTrb2LFVZeN1TIlRpx8q+3bTl9O2qnPerp0pOaETE2LU7QPy3pmdCTmhGxNi9Erem6CVXQ4//HD68ssv6YMPPqAnnniCHnvsMXrnnXfo22+/RfEURFWumwa0cwf0kwfayQPt5IF29ZOcGE8TTtp3qFRNKip9VOnz0eNvL6Z1m3PNXsJfXl5OX331Ff3xxx/Ek1a5Kjty5MhaU5GzsrLo7rvvph9//JFGjRqlw+cGj8ykYSdt7NiqsvE6psSo0w+Vfbvpy2lb1Tlv186UnNCJCTHq9gF578zOhJzQjQkxeiXvTdDKCbx0v1+/fuICQDTnuklAO3dAP3mgnTzQTh5oZ48vf94glvTz3qehiIkhmrNgDV13zmAysoC6a9cuuuiii8QBUdYbz4PNl19+md58801KT6/ag2DmzJn05JNPUlFRER1wwAF6PW/ANGnSRGsbO7aqbLyOKTHq9ENl3276ctpWdc7btTMlJ3RiQoy6fUDeO7MzISd0Y0KMXsl7E7QKhswBpzze5fEtAF7KdS8A7dwB/eSBdvJAO3mgXf1w0XTB71l1Fk+tmagLlmTRtWcPEuM04wqojz/+OK1YsYLGjh1Lo0ePppSUFFqwYAE988wzNGXKFJo6dSpdf/319M0334jEuP322+nMM8/U730DpVGjRlrb2LFVZeN1TIlRpx8q+3bTl9O2qnPerp0pOaETE2LU7QPy3pmdCTmhGxNi9Erem6BVMGrucwpAtOa6F4B27oB+8kA7eaCdPNCufkrLKsSBUXZgu5KyCrHsP5zYerWFCxfSCSecIDbZt+jZsyclJyfTo48+SgkJCfT111+LE0wnT55MzZs31+lzg2fLli3Uo0cPbW3s2Kqy8TqmxKjTD5V9u+nLaVvVOW/XzpSc0IkJMer2AXnvzM6EnNCNCTF6Je9N0CoY/EM/ACoxNde9ALRzB/STB9rJA+3kgXb1k5gQRwnxsbaKqGyXlFB9O9FwYOsQqZ07d9Khhx5a6/GjjjpKLNfnA6P4pNJnn30WxVMAAAAAAAAAAAAAAIAteO/Towa2p7jYupfl8/NHDWof9uX7tmeglpaWUuPGjWs9bj127rnn0rhx49R7B4LSrl07rW3s2Kqy8TqmxKjTD5V9u+nLaVvVOW/XzpSc0IkJMer2AXnvzM6EnNCNCTF6Je9N0CoY2AMVqMbUXPcC0M4d0E8eaCcPtJMH2tlj1NDu9O1vdW+5xMcyjTqqO0UCWzNQ64NnooLwkZ+fr7WNHVtVNl7HlBh1+qGybzd9OW2rOuft2pmSEzoxIUbdPiDvndmZkBO6MSFGr+S9CVqF2gPV6WXTpk2RdhsYjKm57gWgnTugnzzQTh5oJw+0s0fXduk06dzBFBsTU2smKt/nx/l5tosESnZcjY8P78atDZ09e/ZQq1attLWxY6vKxuuYEqNOP1T27aYvp21V57xdO1NyQicmxKjbB+S9MzsTckI3JsTolbw3QatgYA9UoBpTc90LQDt3QD95oJ080E4eaGefoYM7UKc2aTRnwRpasCRL7InKe57ysn2eeRqp4ilju/K5e/du2rx5c7XHcnNzxXV2dnat5xhMU9aDzF4PTtrYsVVl43VMiVGnHyr7dtOX07aqc96unSk5oRMTYtTtA/LemZ0JOaEbE2L0St6boJUqeIybkZERaTeAoURTrocbaOcO6CcPtJMH2skD7ZzBRdLrzhlM15w1iFasWk19e/cwQsMYn493EKibPn36hHSWmwd7jh9btmyZGi8BLV++nAoLCyk1NZX69u0baXcAAAAAADw/rpk1axZ9//33wpfKyn2nvlZUVFBBQQGtXr2ali5dGjH/TMWU9w8AAAAAIFzjGlszUEePHu3aIaCOtWvXUrdu3bS1sWOrysbrmBKjTj9U9u2mL6dtVee8XTtTckInJsSo2wfkvTM7E3JCNybE6JW8N0ErO7z00kv02GOPUWJiojgYNScnh9q0aSNWXRUVFVFycjKNHz8+0m4Cg/FKrpsItHMH9JMH2skD7eSBdtGhna0C6gMPPKDfE2CbwBkSOtrYsVVl43VMiVGnHyr7dtOX07aqc96unSk5oRMTYtTtA/LemZ0JOaEbE2L0St6boJUdPvroIzHL4I033hDF0+OPP55ef/11sQXVu+++S/fffz8NGDAg0m4Cg/FKrpsItHMH9JMH2skD7eSBdtGhXWykHQDOSUtL09rGjq0qG69jSow6/VDZt5u+nLZVnfN27UzJCZ2YEKNuH5D3zuxMyAndmBCjV/LeBK3skJWVRaNGjRKzTzt27Ejp6en066+/UlxcHJ177rl00kkn0cyZMyPtJjAYr+S6iUA7d0A/eaCdPNBOHmgXHdqhgOpBmjRporWNHVtVNl7HlBh1+qGybzd9OW2rOuft2pmSEzoxIUbdPiDvndmZkBO6MSFGr+S9CVrZIT4+nho1auS/37lzZ1q5cqX//sEHH0zr168nE7jmmmuod+/e9OijjwZ9nvdsfeutt2jMmDFi1uzgwYPpjDPOoLffftuomRvRhldy3USgnTugnzzQTh5oJw+0iw7tUED1IDxjQmcbO7aqbLyOKTHq9ENl3276ctpWdc7btTMlJ3RiQoy6fUDeO7MzISd0Y0KMXsl7E7SyQ/fu3WnJkiX++127dq12YFRubi6VlpZSpPn444/piy++CPk8F0gnTZpEkydPFge48mxa3sv1r7/+ovvuu48uueQSKisrC6vPDQWv5LqJQDt3QD95oJ080E4eaBcd2qGACgAAAAAAGhw8W5P3Qb3xxhvFyavHHHOMWML/zDPP0Lx588Ty/T59+kTUxy1bttCUKVPqtJk+fTp9/vnn1LJlSxHP3Llzhf+8j2uLFi3o+++/FzEBAAAAAAB5UED1IDyrQGcbO7aqbLyOKTHq9ENl3276ctpWdc7btTMlJ3RiQoy6fUDeO7MzISd0Y0KMXsl7E7Syw9ixY+nSSy+l7777TiznP+GEE2jYsGGi2MgzOouLi0VxNVL4fD669dZbKS8vj1JSUoLa5Ofn04wZM8Rtnm263377+Z8bOHAgPfjgg+I2F4N5Ri1Qi1dy3USgnTugnzzQTh5oJw+0iw7tUED1IEVFRVrb2LFVZeN1TIlRpx8q+3bTl9O2qnPerp0pOaETE2LU7QPy3pmdCTmhGxNi9Erem6CVHXbv3k3XX389/fTTT5SYmEgxMTH0wgsv0JtvvimKqLxsftCgQRHzj4ue7Nvw4cOpf//+QW2++uorURjlmaY8g7YmRx55pFjSz+8J2wK1eCXXTQTauQP6yQPt5IF28kC76NAOBVQPIjODwEkbO7aqbLyOKTHq9ENl3276ctpWdc7btTMlJ3RiQoy6fUDeO7MzISd0Y0KMXsl7E7Syw2mnnUbPPvusmH0ayJAhQ+i4446j5s2bR8y3NWvW0OOPPy58uPfee0PaWXu4HnDAAaIAHAx+jlm0aJEmbxsuXsl1E4F27oB+8kA7eaCdPNAuOrSrPmIEAAAAAACgAZCTkyP2DTUNPvDppptuopKSElFEzcjICGm7fv16cc2zTEPRoUOHarYAAAAAAMA5mIHqQXr06KG1jR1bVTZex5QYdfqhsm83fTltqzrn7dqZkhM6MSFG3T4g753ZmZATujEhRq/kvQla2WHkyJH0/vvv086dO8kkeFbs33//LWbI8kzYusjOzhbXdRVZmzZt6i8YA7V4JddNBNq5A/rJA+3kgXbyQLvo0A4FVA8iM4PASRs7tqpsvI4pMer0Q2Xfbvpy2lZ1ztu1MyUndGJCjLp9QN47szMhJ3RjQoxeyXsTtLJDbGwsrV69moYOHUojRoygcePG0YQJE6pdzj///LD69Mcff9CLL74oDky48847be8LlpSUFNImOTm5mi1Qh1dy3USgnTugnzzQTh5oJw+0iw7tsITfg5SXl2ttY8dWlY3XMSVGnX6o7NtNX07bqs55u3am5IROTIhRtw/Ie2d2JuSEbkyI0St5b4JWdvjhhx+oWbNm4jYvl9+8eXNE/eEC580330yVlZU0bdo0SktLq7dNXFyc7f5D7ZHqhuLiYlGE7tq1K2VlZVFpaSmlpKSIrRE2btwobPiAK5/PR7t27RL3u3TpQlu3bhVtufDbtm1b/5cj3vOVC9s7duwQ9zt16iRuszZ80BdvR7B27VrxHL93CQkJtH37dv82Bjwjt6CgQOxr27lzZ7GXrDULlwvJ/LpM+/btxZ5q+fn5QkP2n23ZzyZNmlCjRo1oy5YtwrZdu3bCbs+ePULD7t27Cx/4feK8Yd84doYL33zf2q+NZ81wbPw3wX2yz5mZmeK51q1bC72smcHdunWjTZs2iS0cUlNThW6WhqxnRUWFf8Yx+8v5yq/PcXFfGzZs8OvNWDOrWYdt27b59eZ41q1b55+5zPEH6s3tCgsLhbasaaDe/B5wXwy/F+y7pTe/r5wLTHp6usiDQL1Zv7y8PPH+cqysEduz3o0bN/b//XE+cJ+BerO/HD/bcd+BenNcfCAcw7asg6U3x8eaMq1atRLaBurN70WwnOXb/P4G5izng6U3v25gzrKflt41c5ZjD9SbtQrMWX4NS29uG5iz/H4F6s1xWjnLegTqzTkTmLOsdaDeVs7y5wpfAvXm1w/M2UC92Y/AnGUNAvVmzaycZS0C9eb3ITBnTfmM4Fi5v3B8RrDWbB8tnxEcE/cbjs+IQL2j4TPC+swL12cE50C0fEaU7X1fdH5GcF92iPFxVgLjWb58uUhcTjxOUk4SJ/Afi902dmxV2XgdU2LU6YfKvt305bSt6py3a2dKTujEhBh1+4C8d2ZnQk7oxoQYvZL3dvoJHNf07dvX9WtGA3xY1KxZs+jcc8+le+65p9pz48ePF4dAXXzxxXTjjTf6Hx89ejQtW7ZMFF4vuuiioP2+8cYbNGXKFPGF44svvlDiK94/cz4XvAq0cwf0kwfayQPt5IF2Zmtnd1yDJfwexJotoauNHVtVNl7HlBh1+qGybzd9OW2rOuft2pmSEzoxIUbdPiDvndmZkBO6MSFGr+S9CVqpwpoZoZsFCxaI4inPlOADpJxqbc32CIY1m6aufVKBHNGU6+EG2rkD+skD7eSBdvJAu+jQDgVUD2JNkdbVxo6tKhuvY0qMOv1Q2bebvpy2VZ3zdu1MyQmdmBCjbh+Q987sTMgJ3ZgQo1fy3gSt7MJFyyuuuIImTpxYbe/T8847TxzidNRRR4XFj3nz5olrXq42aNAg6t27d7ULzz5lXnrpJXH/mGOO8S+PY6zlinW9HzwDFajFS7luGtDOHdBPHmgnD7STB9pFh3bYAxUAAAAAADQ4uBj52GOPiT2xeB8vnqnJ+5bxbE7eK4v3MeOl8+GAi5uDBw8O+fyqVavEvmbsH+9dxnuDMQMGDBDXS5YsCdl28eLF4rqu/gEAAAAAQN2ggOpBeHNenW3s2Kqy8TqmxKjTD5V9u+nLaVvVOW/XzpSc0IkJMer2AXnvzM6EnNCNCTF6Je9N0MoOH330kdjnivcI5eLp8ccfT6+//rooUL777rt0//33+wuUurnsssvEJRTWHqinnHJKtT1Qjz76aFHo5cMevvvuOxo2bFitrQH4wAY+LOOEE07QGkNDxCu5biLQzh3QTx5oJw+0kwfaRYd2WMLvQfhkMp1t7NiqsvE6psSo0w+Vfbvpy2lb1Tlv186UnNCJCTHq9gF578zOhJzQjQkxeiXvTdDKDrzsfdSoUWL2KZ90y6fU/vrrr+L0Wj7I6aSTTqKZM2eSyXBh9MILLxS3b7vtNv9sU+aPP/4QjzG8LQHHB9TilVw3EWjnDugnD7STB9rJA+2iQzsUUD2IdRiArjZ2bFXZeB1TYtTph8q+3fTltK3qnLdrZ0pO6MSEGHX7gLx3ZmdCTujGhBi9kvcmaGWH+Ph4UYC06Ny5M61cudJ//+CDD6b169eT6Vx++eV0xBFHiAOvxo4dKwq/J598Mp111lm0c+dOMUv1qquuirSbUYlXct1EoJ07oJ880E4eaCcPtIsO7VBABQAAAAAADQ4+gClw79CuXbvS0qVL/fdzc3ONmvUQCt7Ddfr06XT33XdTv379aMuWLWLZfp8+fejWW2+lp59+WhSLAQAAAACAPDE+n8/noj0IE8uXL6fCwkJKTU0Vp6/GxjqrfVdWVtpuY8dWlY3XMSVGnX6o7NtNX07bqs55u3am5IROTIhRtw/Ie2d2JuSEbkyI0St5b6efwHEN70MaCWbNmkX33XcfjRw5kiZPnkzff/89XXvttWK2Zrdu3WjatGliVupbb70VEf9MxoT3zwRM+FzwKtDOHdBPHmgnD7STB9qZrZ3dcY2R7+DWrVvpgQceEEuQePN+vvDthx56iHbs2BG0TUlJCb3wwgtiENy/f3868MADady4cfTpp5/W+VrhbqcCnlWgs40dW1U2XseUGHX6obJvN305bas65+3amZITOjEhRt0+IO+d2ZmQE7oxIUav5L0JWtmBl7tfeuml4vAlnqHJhyzxIUzPPPMMTZo0iYqLi6sd2ASAV3PdRKCdO6CfPNBOHmgnD7SLDu2MW8/Dm/fzXk579uwRm/jzpv4M70G1Zs0a+vjjj+mll14SS5QseIDLG+j/9ttvok2vXr0oPz+ffvnlF3H58ccfaerUqbVeK9ztVFFWVqa1jR1bVTZex5QYdfqhsm83fTltqzrn7dqZkhM6MSFG3T4g753ZmZATujEhRq/kvQla2eX666+nq6++2r/EnX8c57Ho7t27adCgQdS8efNIuwgMxku5bhrQzh3QTx5oJw+0kwfaRYd2Rs1A5aIpD2L5+sgjjxQzAr744gv/ZfDgwWKD/CuvvFJMr7WYMmWKKGb26NFD2HGR9auvvhL7QaWkpNAHH3xA77//fq3XC3c7VfC0Yp1t7NiqsvE6psSo0w+Vfbvpy2lb1Tlv186UnNCJCTHq9gF578zOhJzQjQkxeiXvTdCqvoE4L9X666+/qKioqNb+oEOGDKHjjjsOxVPg+Vw3GWjnDugnD7STB9rJA+2iQzujCqgfffSRKJC2atWKnnzySXFtwTNRn332WUpPTxdL/K2l8pmZmTR79myKiYmhxx57zD9jleFlWLx5PsMb6PPeCRbhbqeSFi1aaG1jx1aVjdcxJUadfqjs201fTtuqznm7dqbkhE5MiFG3D8h7Z3Ym5IRuTIjRK3lvglaheO211+iwww6jMWPGiFPqDznkELFFVHl5eaRdAx7E5Fw3HWjnDugnD7STB9rJA+2iQzujCqg///yzuD766KOpcePGtZ7PyMgQy6kYnjXAzJkzRwx6eR9SPm20JjxATk5Opm3bttGiRYv8j4e7nUo2btyotY0dW1U2XseUGHX6obJvN305bas65+3amZITOjEhRt0+IO+d2ZmQE7oxIUav5L0JWgWDVww9+OCD1KRJEzrvvPNo/Pjx1KVLF1FUffjhhyPtHvAgpua6F4B27oB+8kA7eaCdPNAuOrQzqoDKe5/yLIDTTz89pI3P5xPXFRUV4nrJkiX+5VbBSExMFMVOJrCgGe52AAAAAAAgcrz99ts0cOBAmjdvHt155510++23i6Lq8OHD6d1336XS0tJIuwgAAAAAAAzFqALq/vvvT6eddhoNGDAg6PO8vN8qSvLBTdbhUkzgUvqadOjQoZptJNqppGXLllrb2LFVZeN1TIlRpx8q+3bTl9O2qnPerp0pOaETE2LU7QPy3pmdCTmhGxNi9Erem6BVMPgw0lNOOYWSkpL8j/GWTBMnThTF07Vr10bUP+A9TM11LwDt3AH95IF28kA7eaBddGhnVAG1Pvhke97sn5fIjxgxQjy2a9cu//L+UDRt2lRc5+Tk+B8LdzuVWLNvdbWxY6vKxuuYEqNOP1T27aYvp21V57xdO1NyQicmxKjbB+S9MzsTckI3JsTolbw3Qatg8BgyLS0t6A/fvMKJDzEFIBpy3QtAO3dAP3mgnTzQTh5oFx3aeaaA+txzz9HcuXPF7csuu8x/wFRxcbG4DpxNUBPrOR44W4S7nUp4Jq7ONnZsVdl4HVNi1OmHyr7d9OW0reqct2tnSk7oxIQYdfuAvHdmZ0JO6MaEGL2S9yZoFQw+3JNnnNYkLi7OuAE68Aam5roXgHbugH7yQDt5oJ080C46tIsnD/DMM8+IU+2tk+4vvfTSaoNeu6fdBw6aw91OFVzA5WVmPMjPysoSt1NSUsS0ZmtzXT6ljGdSWLNl+YAELuauXr1aFHfbtm3r316gefPmFBsbSzt27BD3O3XqJF6DbXk/V56VYS1pa9asGSUkJND27dupoKCASkpKRDLz7fj4eOrcubNYHmfNwuXDtrgfpn379pSbm0v5+flCw65duwpb9pMPc2jUqBFt2bJF2LZr107Y8UwQ1rB79+7CB9adZ46wPcfOtGnTRsTGfTM9evQQsfFrc5/sc2ZmpniudevWQi9rZnC3bt1o06ZNVFZWRqmpqUI3S0PWkzW2/ljZ382bN4uYeQY097VhwwYR++7du4XNzp07xTXrwIeIsY6sN8ezbt06/8xljj9Qb25XWFgotOWtIQL15veA+2L4vWDfLb35fbX05bhYs61bt/r1Zv3y8vLE+8uxBurNh7RxPAznA/cZqDf7y/GzHV9br8N6c1xWzGzLOlh6c3ysKcM/crC2gXpbeVgzZ/k2v7+BOcv5YOnNr8s+clvOWfbT0ps1ZD25b9aLY2f/2Z7fP9aKc5Zhffk1LL25LevCttwfv1+BenOcgTlr+ZCeni5yJjBnWWu+cN9MYM7yJVBvtgnM2UC9+W8nMGdZg0C9WTMrZ1mLQL35fQjMWSefEZw7Vs6q+Iyw9K7rM4Lf28CcdfIZwbqwD7o+I9hn7s/tZwTD7fk9lPmMYB+4X7ufEdZ7U/MzgnOW8yBQb0vD+j4jWCt+T0N9RnDf1t+G288Ifi+C5aydz4jAnLXzGWHpbeczwvqc5b/xuj4jAvUO9Rlh6S3zGcHxsr66PiOs9zEcnxHWD9IAAAAAAAB4gRifdSqTgfBAf/LkyWJjf+awww6j559/XnxZsjjooIPEl4ynnnqKTjzxxKD98ImrM2bMoMMPP5xeffXViLRzy/Lly8UXKv5CxPu/WrMl7MJfvOy2sWOrysbrmBKjTj9U9u2mL6dtVee8XTtTckInJsSo2wfkvTM7E3JCNybE6JW8t9NP4Limb9++FA769OlDd9xxBx177LHVHudx3ejRo+mxxx6jQYMG1WrHBXAQ+ffPREz4XPAq0M4d0E8eaCcPtJMH2pmtnd1xjbFL+HlGxyWXXOIvnvIJqdOnT69WPLVmMzDW7ItgWLNbAvctDXc7lVizVHS1sWOrysbrmBKjTj9U9u2mL6dtVee8XTtTckInJsSo2wfkvTM7E3JCNybE6JW8N0GrUEybNk0UUAMvY8aMEc/deOONtZ477rjjIu0yMBiTc910oJ07oJ880E4eaCcPtIsO7Yxcws/LxC6++GJatWqVuH/BBRfQzTffLJaI1YSXq/EyMmu5ZjCspW68BC1S7VTCy/V0trFjq8rG65gSo04/VPbtpi+nbVXnvF07U3JCJybEqNsH5L0zOxNyQjcmxKjDh/LcHVRRmFfV/5ZNVBJfFtQuLjWN4tNbekarYPAsUwBUYmquewFo5w7oJw+0kwfayQPtokM74wqovLfY+PHjxR5eXDDlpVbjxo0LaT9gwAD6+uuvafHixUGf5729li5dKm4PHjw4Yu1UUnMWruo2dmxV2XgdU2LU6YfKvt305bSt6py3a2dKTujEhBh1+4C8d2ZnQk7oxoQYVfvAxdNNz19Nvoqqoin/TB3q5+GYuATqePnTtoqoJmgVjAceeCDSLoAow9Rc9wLQzh3QTx5oJw+0kwfaRYd2Ri3h5+Lj5ZdfLoqnfMjAk08+WWfxlBkxYoS45oLmypUraz3/4YcfioMK+NAI3r80Uu1UwodG6Gxjx1aVjdcxJUadfqjs201fTtuqznm7dqbkhE5MiFG3D8h7Z3Ym5IRuTIhRtQ8889QqntYH21kzVb2gFQDhALkuD7RzB/STB9rJA+3kgXbRoZ1RBdSXXnqJ/v77b3H7rrvuEvue1gef9jpq1Chxku0111zjPy2XmT9/Pj388MPiNhdm+aTdSLVTiXWys642dmxV2XgdU2LU6YfKvt305bSt6py3a2dKTujEhBh1+4C8d2ZnQk7oxoQYTfAhmvwEwC3IdXmgnTugnzzQTh5oJw+0iw7t4k2afTpz5kxxmwuPH3/8sbiE4rDDDqOrr75a3OZl/v/88w8tW7aMRo4cST179hSzQC2hzznnHDrzzDNr9RHudgAAAAAAAAAAAAAAAG9hTAGVD4zKzc0Vt8vLy0PuMWrRpk0b/+309HSaNWsWzZgxg+bNmycOeeL9UwcOHEhnnXWW/3TVmoS7nSpatGihtY0dW1U2XseUGHX6obJvN305bas65+3amZITOjEhRt0+IO+d2ZmQE7oxIUa3Pvgqyql0ZyaVbl1LJVvXUdHGZRStWgEQDpDr8kA7d0A/eaCdPNBOHmgXHdoZU0Dt169f0D1FnWwsy8vm+WJyOwAAAAAA3VSWlVDp9o1VxdJt68Q137e75ykAAAAAAADA0D1QgT127typtY0dW1U2XseUGHX6obJvN305bas65+3amZITOjEhRt0+IO+d2ZmQE7oxIcZQPlSWFIrZpLmL5tL2T56mTS9eT+sfGUebX7uVdn7+IuUt+ZJKtqwJW/HUBK0ACAfIdXmgnTugnzzQTh5oJw+0iw7tjJmBCgAAAAAA6qaicI9Yfh84s7Qse4uNljGU0LwtJbbpRkl7Lz4i2vrWvWHwGgAAAAAAAG8T4/P5ePwMDGf58uVUWFhIqamp1KNHD0pISHDUvqyszHYbO7aqbLyOKTHq9ENl3276ctpWdc7btTMlJ3RiQoy6fUDeO7MzISd0E+4YeXhWkZ9DJbz0XuxZWrVvacUeG7/Cx8ZRYouOlNimq79Ymti6M8UmplQzK9mylrJevcm2T+0vfISS2nZTolXguKZv3762fQBmgPev4Xz26QLauQP6yQPt5IF28kA7s7WzO67BEn4Psm3bNq1t7NiqsvE6psSo0w+Vfbvpy2lb1Tlv186UnNCJCTHq9gF578zOhJzQjc4YuVhalrOV8lcspOxv36Its6bQxqcuoo3/uZi2vfcA5Sx4lwpX/RK0eBoTl0BJ7XpS2uATqMWIS6n9BQ9Rl5vepA4XP0atTrmK0g88iZI79qlVPNVJQ8gHABjkujzQzh3QTx5oJw+0kwfaRYd2WMLvQYqLi7W2sWOrysbrmBKjTj9U9u2mL6dtVee8XTtTckInJsSo2wfkvTM7E3JCN6pi9FVWiCX3VTNL11Vdb1tHlcUF9beNT6KUtt0CluF3pYTm7SkmTm44F5eaJgqwdvZGZTu2t0NDyAcAGOS6PNDOHdBPHmgnD7STB9pFh3YooHqQpKQkrW3s2Kqy8TqmxKjTD5V9u+nLaVvVOW/XzpSc0IkJMer2AXnvzM6EnNCNTIxcmCzdkRmwDH8dlW5fT76yknrbxqakVS299y/D70pb8suoXafOpIr49JbU8fKnadHi1fTW5yvYY6oM2NgpNjaGH6LzTuxDBw3uIezt0BDyAQAGuS4PtHMH9JMH2skD7eSBdtGhHfZA9QiBezL06tWL4uLiHLWvqKiw3caOrSobr2NKjDr9UNm3m76ctlWd83btTMkJnZgQo24fkPfO7EzICd3UF2NlWQmVbt8g9hXlGaWiaLpjI1FFeb19xzXOEAXSqpmlVQXTuCYtKCYmxpEPMqzbnEvXPT6fKusYDsbGxNCTk4ZS13bptvq04yf20PQ2eP8azmefLqCdO6CfPNBOHmgnD7QzWzvsgRrFrFu3TmsbO7aqbLyOKTHq9ENl3276ctpWdc7btTMlJ3RiQoy6fUDeO7MzISd0ExgjL7cv2vA37f75v7R9zlO06cXraP0j42jza7fRri9eorzfvxIzToMVT+ObtqZGfQ6hZsPOpTbn3Emdrn2FOl/7ErU5+3bKGHoONep9sJjpWbN4WtMHVcyZv4ao9ktVg12Zs2CN7T4bQj4AwCDX5YF27oB+8kA7eaCdPNAuOrTDEn4AAAAAgBBUFOSK2aS0/Ffa9ufHYhl+ec5WGy1jKKFF+6pl+K15VmlXcR2X0pgiSUlZBa3J3E0rN+TQig3Z9OOfW+ptU1HpowVLsujaswcFLewCAAAAAAAQ7aCA6kEyMjK0trFjq8rG65gSo04/VPbtpi+nbVXnvF07U3JCJybEqNsH5L0zOxNywi28o1FFXrYolgYe8FSRt8u/ZCfkMU+x8ZTYsmO1ZfiJrbpQbGKyUh+d6swxbd5ZQCs3ZIuC6cqNObR+8x5REHVKWXmlKL4mJ8Y3iHwAwA7IdXmgnTugnzzQTh5oJw+0iw7tUED1IDL7PzhpY8dWlY3XMSVGnX6o7NtNX07bqs55u3am5IROTIhRtw/Ie2d2JuSE08Ji+e5tAYc7VR3wVFm4p962MfGJlNi6CyW1toql3UTxNCY+Qbvf9emcV1hKqzbm+IulqzbkUH5RmZLXToiPpaQEdTkDQDSAXJcH2rkD+skD7eSBdvJAu+jQDgVUD7Jjxw5KT0/X1saOrSobr2NKjDr9UNm3m76ctlWd83btTMkJnZgQo24fkPfO7EzIiVD4KiuobFdWwKzSdVSybR35SgrrbRuTlCoKpTyjNCemEXUacAglNG9PMbGRGcgF6lxeUSlmk3KhlGeYcuE0a0fIObICXn3fsXUa9e7UjHp35ksGzf5uNc1fnFnnrNS42Bg6alB728v3Tc4HAFSCXJcH2rkD+skD7eSBdvJAu+jQDgVUAAAAAEQFvvIyKt2xad/M0m3rqHTbevKVl9bbNja1iSiUihmlPLO0dVeKb9aaYmKqztvMWb2aElt2okjNmN25u5h+X5NL85ctFTNMeR/T0vLKOts1bZxEvaxiaadm1LNTU0pNrj5T9rSh3em73zLreX2iUUd1VxILAAAAAAAAXiTGx6NyYDzLly+nwsJCSk1Npe7du1NiYqKj9qWlpbbb2LFVZeN1TIlRpx8q+3bTl9O2qnPerp0pOaETE2LU7QPy3pldJHKisrSYSrevFzNKq5bhrxPFU6qsffJ9TeLSmu8tlFYVTPkSl5ZR5wzLcMZYVFJOq/ce9FS1JD+bsveU1NkmPi6WundI988u5cJp64xUW7NGeQbq428v5nOvqDJgJirPPOVR4qRzB9PQwR1s+29Hq8BxTd++fW33DcwA7585/w+9CrRzB/STB9rJA+3kgXZma2d3XIMZqB5k586d1K5dO21t7NiqsvE6psSo0w+Vfbvpy2lb1Tlv186UnNCJCTHq9gF578xO9/tRUVywr0i6repwp7Jdm3l9fr1t45u12TezVCzH70ZxjZwvA9IVIxcsM7fniULpir0F0w1b9lB95zy1aZ5KvTtlUK/OTalP5wzq2q4JJcTLbS3AxdFObdLonc//ol9W5ogDo3jPU162zzNPu7ZL99xnBADhALkuD7RzB/STB9rJA+3kgXbRoR0KqB6EK+M629ixVWXjdUyJUacfKvt205fTtqpz3q6dKTmhExNi1O0D8t6ZnUq9yvN3+4ukVtGUD3yql5hYSmjebt8SfLEMvwvFJjdS4peqGHPzS/wHPIkZpptyqLC47lmzqcnx1KtjM2rZxEeHDOwuZpmmN04ilXCR9Myj2tAtEw+n0rIKSkqMs73nqYmfEQCEA+S6PNDOHdBPHmgnD7STB9pFh3YooHqQhIQErW3s2Kqy8TqmxKjTD5V9u+nLaVvVOW/XzpSc0IkJMer2AXnvzE4mRt5BqGLPzqpDnQKKpRX52fU3jo2nxFad9s0qbduNElt1ptgEtUVFtzGWlVfQ2qzcvQXT3bRyYzZt3VX3IDA2hqhz2yZiCX6fvUvxO7RKo9jYGNqwYQN17tyGdMbIr5OcFO/5zwgAwgFyXR5o5w7oJw+0kwfayQPtokM77IHqEQL3ZOjduzfFxlYdamGXyspK223s2Kqy8TqmxKjTD5V9u+nLaVvVOW/XzpSc0IkJMer2AXnvzK4+G5+vkspztgYUS6uuK4vy6n39mIQkSmzVRSzDr5pZ2pUSW3akmLjwDqbqj9FH27ILA/YtzaE1WblUXlH3NgMZTZKod+cMMau0V+dm1KNDU0oJUcD0St7b6Qd7aHobvH/m/D/0KtDOHdBPHmgnD7STB9qZrR32QI1i1q5dSz169NDWxo6tKhuvY0qMOv1Q2bebvpy2VZ3zdu1MyQmdmBCjbh+Q987sAm18lRVUtjOz+szSbevJV1pU72vFJqXuK5LuXYafkNGWYmLl9vZUSU0dCovL6J+Nu2nFxmz/7NLc/NI6+0iMj6UeHZvunV2aIa5bNE22vVTeK3lvwmcEAOEAuS4PtHMH9JMH2skD7eSBdtGhHQqoAAAAAJCisryUyrZvJFrzM+3452sxs7R0+wbyldddSGRiU5tQUpvu1WaWxjdtLb3vpk4qKn20eVcxrdm5Xsws5SX5m7bliRPq66J9y0ZidikXSnt3bkZd2jah+DjMPgAAAAAAAMBroIDqQZo1a6a1jR1bVTZex5QYdfqhsm83fTltqzrn7dqZkhM6MSFG3T4g72vbVZYWUem2DWJWqbUMv3TnJqLKCuKSYF2L8eOatBAFUv8BT627UlxahpHFUiZ7T3G1pfirM3OoqKSizjaNUxLEEvw+e5fic9E0LTWxQea9CZ8RAIQD5Lo80M4d0E8eaCcPtJMH2kWHdiigepDExEStbezYqrLxOqbEqNMPlX276ctpW9U5b9fOlJzQiQkx6vahoed9RVE+lW6rWoJfvGkVbdq1kcp2beEF+vX2y0vurSKpNbM0LrUJmUpJWQWtzeSDnrL9s0t35NS93UBcbAx1bVd10JPYv7RzM2rXopH2grBX8t6EzwgAwgFyXR5o5w7oJw+0kwfayQPtokM7FFA9yLZt2ygtLU1bGzu2qmy8jikx6vRDZd9u+nLaVnXO27UzJSd0YkKMun1oSHlfnp9TtU9pwAFP5bnb6+8gJpYSWnQQs0r3xKdR+34HiqIp72NqKnzQ05adBaJIahVL12XliiX6ddGyWQq1y0igA/brKIql3Ts0paSE8O/L6pW8N+EzAoBwgFyXB9q5A/rJA+3kgXbyQLvo0A4FVAAAAKABwMXD8twdokDKhdKYtUtpw6fbqCI/p/7GcfGU2LJztWX4ia06UWxCknh6z+rVlNLJjM3dA8kvLKVVG3fvLZhmiyX5eYVldbZJToyjnh15CX5T/+zSjCbJtHr1amM2sAcAAAAAAACEFxRQPUiHDh20trFjq8rG65gSo04/VPbtpi+nbVXnvF07U3JCJybEqNsHr+e9z1dJZdlb/DNLrevK4ny/LS82D7arZ0xCMiW27iIKpVwwpWbtqXH77hQTF290TpRXVNL6LXv8+5byJWvHvniDwSvuO7RKoz579yzlYmmn1mkUF+SgJxNi9Erem6AVAOEAuS4PtHMH9JMH2skD7eSBdtGhHQqoHiQnJ4fatm2rrY0dW1U2XseUGHX6obJvN305bas65+3amZITOjEhRt0+eCnvfRXlVLozUxRJs9cupYS87VSybR35Sovr7T82uZF/n1Ixs7R1V7GHaUzsvuXpW7ZsobQ6iqd2/VTNzt1F/mX4PLt0dWYulZbVfdBTeuNEf6G0d6dmYqZpo5QEW6+HvA9/PwCYDnJdHmjnDugnD7STB9rJA+2iQzsUUD1IQUGB1jZ2bFXZeB1TYtTph8q+3fTltK3qnLdrZ0pO6MSEGHX74KZ/XiZfUbjvTPqCTZuohGofShSXmkbx6S0d+VFZXkql2zfunVHK+5WuFfd9FfuWpYcqIcY1alqtWLqlJJa67n9gvQcfmZD3xSXltDpzd0DBNIey99RdII6Pi6Xu7dOp195iKRdNW2ekSh/0hLwPfz8AmA5yXR5o5w7oJw+0kwfayQPtokM7FFA9SHx8vNY2dmxV2XgdU2LU6YfKvt305bSt6py3a2dKTujEhBh1+yDbPxdPNz1/dbWCJi8GzwpiGxOXQB0vfzpkEbWypIjisjdS7qIVYkapKJbuyCTyVdbvf3rLvcXSbuJgJ74dn9asus369baKieHO+8pKn1h6bxVLV23IofVb94jH64KLo9bMUr7u1j6dEuLVHfSEvA9/Pw2ZrVu30owZM+j777+nrKyqT5D27dvT0KFD6cILL6SWLWt/bpSUlIg2c+fOpQ0bNlBycjL17t2bxo4dSyeffHIEooh+kOvyQDt3QD95oJ080E4eaBcd2sX4+FQJYDzLly+nwsJCSk1Npb59+0baHQAAAEEo2bKWsl69ybZ9+wsfoaS23cSM1ZJta/0HPPE172FKVN+/6Bix5D5x76xSaxk+z271Crn5JVX7lu6dWfrPxhwqKC6vs01KUrwolIrZpbx/acdm1DSt6kAr4A0wrgnNr7/+Spdffjnt2bOH4uLiqGPHjuLxTZs2UUVFBWVkZNBLL71E/fr187cpLi4WhdXffvtNtOnVqxfl5+eLNswZZ5xBU6dOVeYj3j8AAAAARAt2xzXmlHKBbWROAnbSxo6tKhuvY0qMOv1Q2bebvpy2VZ3zdu1MyQmdmBCjbh/CFeOur16j8t3bqHzPzvqNY2IpsWXH6nuWtupCsUkp1f22WTyNRN6XlVfSus25olBqHfa0ZVfdy3JiY4g6tWlSbXYpH/wUy0+EEeR9+PtpiHDR9OqrrxbXRx55JE2bNo1atWolnuNi6M0330yLFy+mK6+8kj777DMx0GemTJkiiqes+wsvvOAvun733Xd03XXX0QcffEADBw6kM888M6LxRRvIdXmgnTugnzzQTh5oJw+0iw7tUEAFAAAA6kAs1KgsJ195WdWlgm+XimX6Vff3XpeXUenOqtledine+HfQx3l5f2KrzlSc2pxa9hooiqaJrTpRbHwieUm37TlFYgn+io3Z4npNVq4ootZFs7SkqmJp5wxRMO3RsamYcQpAQ+Cjjz6i7OxsUTR98sknqXHjxv7nuCj67LPP0oknniiW+H/66aeiIJqZmUmzZ88WW3I89thj/uIpM2zYMLr11lvpnnvuoaeffppOP/10io3ljUUAAAAAAIAT8I3Eg6Snp2ttY8dWlY3XMSVGnX6o7NtNX2lxFWJ5dH1YB/Ooznm7dqbkhE7CFaOvsqKqUFkeULDcW7xsXLyLijaU7H28fG8RM9A24DHRJrDYGfDY3uInVZRRZcB1XFkJrRevX2Wjk5jE5L37lO5bhp/QvD3FxMXTjh07qEmQvQ5DEcm8Lywuo3827fbPLOUl+bvzSupslxgfS907NN1bMG1GvTo1o5ZNU6QPetKJCX/bun1Q1b8JWnmVn3/+WVwfffTR1YqnFrx8f9CgQWJm6V9//SUKqHPmzKHy8nLaf//9qU+fPrXajBkzhh544AHatm0bLVq0iA455JCwxNIQQK7LA+3cAf3kgXbyQDt5oF10aIcCqgdJSUlR1qbmadFMfFEhlZTn1XlatB0fZPz0GqbEqNMPlX3L9sV5WvDuPVRgo5BlHczj5LXs2jaEvBdFy5oFx4AZlnw7Lm8PFezeUGv2pSg0BhQiqxctg13XmMlZ7bHyeg9LKtStBemn1ek3U6PeB1JMTKySfApX3ldU+mjTtryqQumGbFqxfhdl7iig+nZVb9eiUcBS/Azq0q4Jxcd5YzacCX/bun1Q1b8JWnkV3vt0+PDh1LVr15A21vEFvB8qs2TJEnE9ZMiQoPaJiYnUv39/+uWXX1BAVQxyXR5o5w7oJw+0kwfayQPtokM7FFA9CC/bcroHRLA2wU6Ltsip57RoOz7I+Ok1TIlRpx8q+5btSxT5bc4C5Hxm+60FubZfy65fuvKevwyLv8PygOJj0KJi7SXjgbMyQxcneTbm3tmZ1fqo/RhVVn0hb3DExFJMfIL4rOOZn+UUQ4nJKRQTl7j38XiKiU/ce50QcDvgsbhEqijaQ3t+/cz2yyaktwxZPJXJJyf2TvK+easOYkapNbv0n005VFRSd640TkkQM0qtmaV8adLIO1sQmPh5r9sHVf2boJVX4VmkfAkFL+/nIijDB0Ux69evF9eBS/dr0qFDB1FAtWyBGpDr8kA7d0A/eaCdPNBOHmgXHdqhgNqA4SKTz2FRKnAWKgDRsa9lOVFxPpXv2RWkqFhafUZkVibt2bM2YHm4VXjc1yYmZxdt+ysl+OzLELMyiftuoAQtRO4tZFJcAsXGV10HPs7Xe/IKqGmLlv77ta6DPVbzucD7sXG1NivvKPGPmreZcFJANZHSsgpam5VLK/Ye9LR0zXbKyQu+V6sFn+fUtX26KJL22VswbdeicdgPegIg2pk6dSoVFRVRcnIyjRgxQjy2a9cu//L+UDRt2lRc5+TU/IkcAAAAAADYAQVUD9K+ffuwtHHbn+rXNBFTYmQ/fLzcmYuCvkryVVq39973P+fb+xwvjQ64zY/vtfXf39tHy7gSKs76J8AusE0lUcBrVX9uX3/W7WYlJbTn9w1+vwKf29eeC5uBj1dSWV62Iz12fjadkmJiKWtBRcjl4WLfy72LtHn+30Yb/bJdfWelc7mo7jPFDYALlfUVFUMWIuOpwhdDCcmpNQqfIWZqihmc1mMBz8fHE8XGS+912bioSOtyDpP+tnXZV31u+GjLrgJxwJO1b+m6zblUXlH3WvwWTVPEMnxrhmn75knUNL32fo3RhAk5odsHVf2boFU08txzz9HcuXPF7csuu0wcNMUUFxeL66SkpJBtree4+ArUgVyXB9q5A/rJA+3kgXbyQLvo0A4FVA+yZ88ex4UDmTY1qSguoIqCXFHk2r19OyW0aEE+X0Xtwtze+zk7d1BsRrN9z3GxqlpxrkbRrEbhz0c1i3MBrxVQuAtWqNtXOKwqxgUt/AXa1NlfjSJk5b7bJcXFlJSYELy/gCJk7f4qqvcdoghZy++A/gKLovXtFdnQKNmymowjNq5WITJYUdFfcBQ2gYVIq6C597GQBc46ZnJaBc46lozbgQ8iada6NUUSFZ9pkexflx/12ecXlYlZpXz5c9VWWr+1kPIKS+vsMzEhtqpQGrAcv3l6Sq2coCgvoJqQE17JexO0ijaeeeYZevrpp8XtYcOG0aWXXup/Li4ujirFOKt+VB/QxsVbnrHPe7ZmZWVRaWmpeO9btmxJGzdW/TTZQowXff6Zsl26dBHL8bgtF3bbtm3r31qgefPmFBsbKw7QYzp16iRuc+GX93LlrQjWrq06ULJZs2aUkJBA27dv929hwFscFBQUUHx8PHXu3JnWrFnjn4HLs3b5da0vY7m5uZSfny/0Y//Zlv1s0qQJNWrUiLZs2SJs27VrJ+w4r1m/7t27Cx8szbkvjp1p06aN8JX7ZnjJIcfGh3xxn+xzZmameK5169ZCL2tWcLdu3WjTpk1UVlZGqampQjdLQ9aT97zl+Bj2d/PmzVRSUiLi4r42bNjg15vZubPqJ1/WgT+jLb05nnXr1vlnLXP8gXpzu8LCQqEtaxqoN78H4vN+77YQ7LulN7+vnAvWgR+cB4F6s355eXni/eVY2Qfuj/XmA9M4HobzgfsM1JttOX62474D9ea4du/eLe6zLetg6c3xsaYM/+DA2gbqze9FsJzl2/z+BuYs54OlN79uYM6yn5beNXOWYw/Um7UKzFl+DUtvbhuYs/x+BerNcVo5yzli6cCa8P3AnGWtA/W2cjYtLU1cAvXm1w/M2UC92Y/AnGUNAvVmzaycZS0C9eb3ITBnTfmM4AvnRzg+I1hrto+Wzwh+7zjGcHxGBOodDZ8R7BPrG67PCM6BQL29/BkRHx8vYtD5GWH9GF0fMT5rJ3pgNMuXLxeJy4nHb7LTPSD4j6dmG15qmvXqTYo9BcCwfS1DLA8PfKywpIwaN0mvVojkYqO1fNy63pm9m1q1bVfn8vBNWVuoc7fuNQqc8bWWiHuZYJ8n0eaDbP917S1N9ewtrcKPQPuKikrasJUPesoWM0t5hmnm9vx6++jYujH17pRBvTpXLccvzd9OvXv1tP260YoJMZqa9zL9BI5r+vbt6/o1oxX+cjF58mR69913xf3DDjuMnn/+efEFzeKggw4SX2yeeuopOvHEE4P28+CDD9KMGTPo8MMPp1dffdW1X3j/zPlc8CrQzh3QTx5oJw+0kwfama2d3XENZqB6EK6oh6MNUAzP+uOCHs/+EJdYfmOq3RczA/2399rExFAMv3/++zWei4mlktJSSuIDb/x2NfursgveX43naviwJy+fmjRNr7u/vbFU3d7nZ837/KtRC15yGOo1rT6oen9lOdto1/+9YlvqthPupy0lCdStR91FHwv+Fap1t2712mWvXUtN6rGLLaikhKZVyyqjFRM+T3T7INs/F0O5KCoOPttLVlYmtW/foZZtXGpavftKO/FjV24R/bUuj75f/rcomK7O3E0lpXUf9MSHOgXuW9qzUzNx+FMga9dW/dqryk+vYkKMpua9rn4aOjyL5JprrqEffvhB3B8+fDg9+uijYgZFIDyDgguo1oyPYFgzauraJxU4B7kuD7RzB/STB9rJA+3kgXbRoR0KqB6Ep1iHo01Nkjv0pdiURrWLc7H1FNbqLRayXVz1wl9sqP5qFOcCCoEhi48Bxb2QhT/a23dskCKndd9f0Kuyrx53wGsGKSSqXjIXTqoWeaghXbIdz5Z2Qmx8MnXr2E3534cdOxV/a6ZjQoy6fXDTPxdFAwuj3dp2U+5HcWk5rcnM9c8u5T1Md+bWvfQkPi6Guu096Kl35wyxJL9N89R6P5+Q9+bEaHLe6+inIcNL0y6++GJatWqVuH/BBRfQzTffHPRLBC+R46Vr1pLFYFjL63jZG1AHcl0eaOcO6CcPtJMH2skD7aJDOxRQPQjve8GDZd1tatL8hAspaW8hwE5/Kl7TdEyJUacfKvsOp15OXsuuLfLenBh1+6Cy/39Wr6aOHbtQYkKc41Pp2Y+uXbtR1o58sW+pddDT+i17qLKy7h14WmWkUh8+6Klz1d6l3dqlCx8C+45pgby3iwkxeiXvTdDKy/B+ZuPHjxd7f3HB9I477qBx48aFtB8wYAB9/fXXtHjx4qDP835iS5cuFbcHDx6sze+GCHJdHmjnDugnD7STB9rJA+2iQzsUUD2IzLa1qre6tdNfQ9he15QYdfqhsu9w6uXktezaIu/NiVG3Dyr659Ps58xfQ/OXZFJ5xd+UEB9LRw1sT6OGdqeu7ULPx95TUOovli5ZkUmZO1dSQVHde6qmJMVTr05NqWUa0SEDuomiabO0fXskBgN57wwTYvRC3qvspyHCxc7LL79cFE95z/vHHntMLN2vixEjRtDjjz8uCqgrV66k3r17V3v+ww8/FIcj8EEVvF8qUAdyXR5o5w7oJw+0kwfayQPtokM7FFA9CJ9EF442bvtT/ZomYkqMOv1Q2bdsX7xXJMXFE1WU2zqYh+2bxPmU+4W8NydG3T647X/+4kx6/O3FYoePir0zRcvKK+m7xZn07W+ZNOncwTR0cAfx2Potuf6ZpXy9ZWdBnX3zJNZObZqIWaVVy/GbUYdWaRQXGyNOkeRTLVXGiLw3J0bT8151Pw2Rl156if7++29x+6677qq3eGqdMDtq1CiaM2eO2DP1ueee88/UmD9/Pj388MPiNhdm+SRboA7kujzQzh3QTx5oJw+0kwfaRYd2GEV5kMaNGytpw0UmLjbZPS1aFLEc+CDjp9cwJUadfqjsW7Yv3k+y5cRHKNFXfwHVOpincWGhcr+Q9+bEqNsHN/3zzFMunlbyr6U16vhWMfWxt36jD7/9hzK354sial00TUsS+5VyoZQvPTo0pdTkBNd+I++dYUKMJue9jn4a4uzTmTNnittc6Pz444/FJRSHHXYYXX311eI2L/P/559/aNmyZTRy5Ejq2bOnmHW6YcMG8fw555xDZ555ZpgiaTgg1+WBdu6AfvJAO3mgnTzQLjq0QwHVg2zevJl69Ojhuk2w06KZTZs2UceOHes8LdqODzJ+eg1TYtTph8q+3fS1Lb/UUVsnr2XXFnlvToy6fOAlIlzkXLt+E3Xs1EUUN0vLK8R1WVnAbXGpoNKyqusqu0oqLaug73/PIl/NymnN1xGF1j21Hudl/lwgtWaWJvly6cCBfW0fRIe814cJMer2QVX/JmjlRfjAqNzcXHG7vLw85J6mFm3atPHfTk9Pp1mzZtGMGTNo3rx54lAp3j914MCBdNZZZ9GYMWO0+98QQa7LA+3cAf3kgXbyQDt5oF10aIcCagOn5mnRgoJK/2FRAJgKH6DDxSqZg3mAmXDhkouR5QHFyGrFyr1FTL69MTOXNu7eWFW4LKthI+ys21XXos+9j1X1v7f4WVFlaxVA2X7fNjsrtMfcpnkq9emS4Z9h2qVtuiiiWqxeXWS7eAoA8Db9+vUTe5jKkpycLJbp8wUAAAAAAKgFBVQP0rZtW21tuCjVrHkrcV1XUcpOfzJ+eg1TYtTph8q+3fRltbUO5lnwe5YoeoU6mMfJa9m1jda851mX5VxIDFqMrD3LMr8ghtbt2rCvWFnDxj9rc2+BMrAQGnw2Z9V1eYXTDcIzyes8fePRlJwYryyfkPf6MCFG3T6o6t8ErQAIB8h1eaCdO6CfPNBOHmgnD7SLDu1QQPUgBQUF1KhRI6Vt7BalnPgg46fXMCVGnX6o7NtNX9z215U5tg7mcfpadm115H2FVbgMKCRWn3m5b7akf+akZRusQMm3K2ovN99nEzDbMmDmZUMjMT5WfM4lJMTtux0fR4kJVde+ynJqlJpMifFxlMCPxcWK2c5Vdntvx3H72Cobfiw+juLjY+ih13/152ddcJukhDil+eSVvPciJsSo2wdV/ZugFQDhALkuD7RzB/STB9rJA+3kgXbRoR0KqB5kz549tk9ZttPG7mnRTn2Q8dNrmBKjTj9U9u2mr+Vrt9PjH6yt82AezuNObdJE0b++1+JZl1bxMGtrNlF843qLkVmbt1KzZvn+mZelQZab5+zOpcSkdf52gTM5983w3Fe45NneDQk+Kd4qUlpFR1GgtG7XUaD0Px4fS7m7s6ltm9ZVxc+9jyX6i5hst7cgurcwWtW26nZ8XEy9y+JXr14tvdfOsMEdxOdnXUVU1uGoQe3r9cPp34wTe7u2+Lw3J0bdPqjq3wStAAgHyHV5oJ07oJ880E4eaCcPtIsO7VBA9SAy++GFamPntOjAopQTH6Jp3z4utlkSWbf5XlmFj4pLy6ue27txopCNbfZuiVDVfm+7wL6CPcb3fNyH9RpVt63HrL6sx9ieb27aXkTlCdnkq9z3WM2+/f1WuR7Q3z77KncD/CMfbd26hzbnZdaKP1AHbhd424rfV2n5UGW/Y+cuWppFNuKvalQZ8NiCxZn1HszDMd338k/UoVVjyssvpNj4rGqzLAP3vuQl69Wxu+/cZhs2tQ8HijT857ivaLm36ChmVQYUHa1io3gsoAAZUKCsKl7mULu2rfbN2gxoF6wQys/H77XhwqEK1qxZQ927dyVduPn84pn7/ONTXXC+jzqqu3I/nNjbtW1on/cmx6jbB1X9m6AVAOEAuS4PtHMH9JMH2skD7eSBdtGhXYzPqvoAo1m+fDkVFhZSamoq9e3bV1m/T85aXO9MKaZVsxTq2bFZ9eJcQJGtWjFsb9EsaNEvSAGudsEvsDi3r5hYs2BnFRDrKtiFKlhSjWJhYMFuXwFxX18AuCGeZ1JaMyDjQi8b9xcdaxQjrQKkNfuy+qzKGu1EwbN2EZMLlyb984l2gs3sZ/h94M+UYDP7AWhI6BrXgPC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mRlFQEP9OYidiOp4FiPE4KE9b7atAgpLYiucQZvtzX3k+4TmAOI64lWeBu7u7UFxBzFrFswlt/CSj6ZBj3i2H7sxE3d7eDgUXVBJTccuzAnFcvtBEUneYQJXUESRzSBAyZ/T09DQk+khGHR4ehgrLiKQUCSSCBQazk3CiIo9ZQJeXlyEhRNBAYEEQxp8nYKmiSpJqRxJ5sV0mImjZ3d0N1+d6BDAErbT1PDw8ZFUhkTc+Ph42anK6zf2hNSt9b/E+kvzjXhKojY6Ohn/ndXbys+mUTl+fwJIB+1RpkKAkObmxsREWUN3e3mZHR0chYU0lB8Fx2fzT+LBAG9fT01Phf2cuFb/SxVlUgB4fH4dB/ymuT9KWz5jxAVQ2MKqBClISoK1g9hZJab6rLBrgYYLvz/n5eXjdKVr7eY2Tk5MtXUuSJPUOLdsczjLjPEVSkziSw2gSfrS6n5ychJ8j0VkVkqIkcmORA3EGxQgUPkTEMyw3pWKUWIhRU3QfEW9NTEy0dF3+TpZE8X6IdTj8ZkTR+vp6R9rYqZYlQUrBAglbEqgHBwfh+jyfEMPynojpdnZ2wniCdnEPWXbK+4tLbrkehQVU2nJAz3MLnzXvW1Jv/PNvva0WkiT9z1FRSuUqif7h4eGsXy0sLIQ2Px6qJEmSJEmNcwaqJEl1UBVNuz1jJPoV7XwvLy/ZyspKr1+KJEmSJPUdE6iSJNVBu9rW1tZfi6/6CWMxGPnA2AJJkiRJUnNMoEqS9AvmqJJ8vLi4yPrNx8dHmNPKYgJJkiRJUvOcgSpJkiRJkiRJJaxAlSRJkiRJkqQSJlAlSZIkSZIkqYQJVEmSJEmSJEkqYQJVkiRJkiRJkkqYQJUkSZIkSZKkEiZQJUmSJEmSJKmECVRJkiRJkiRJKmECVZIkSZIkSZJKmECVJEmSJEmSpKzYH9Xn0URT2icvAAAAAElFTkSuQmCC", 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" ] @@ -302,42 +775,39 @@ } ], "source": [ - "weak_scaling([df0, df1, df2], names=[\"CCX\", \"CCD\", \"Socket\"], title=\"Weak Scaling up to 4e9 Points on 512 nodes of ARCHER2 (Uniform)\")" + "weak_scaling([df0, df1, df2], names=[\"CCX\", \"CCD\", \"Socket\"], title=\"Weak Scaling up to 4e9 Points on 512 nodes of ARCHER2 (Uniform)\", plot_err=False, stat='p99')" ] }, { "cell_type": "code", - "execution_count": 13, - "id": "447bedda-cd0e-4ff3-9201-e46d6d66c62c", + "execution_count": 157, + "id": "b5461bbe-e6c7-4fec-a5e4-1d637566a545", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "experiment_id\n", - "0 409.062500\n", - "1 407.945312\n", - "2 756.889648\n", - "3 1081.428162\n", - "Name: runtime, dtype: float64\n", - "experiment_id\n", - "0 611.687500\n", - "1 513.593750\n", - "2 735.462891\n", - "3 1032.433472\n", - "Name: runtime, dtype: float64\n", - "experiment_id\n", - "0 1332.000000\n", - "1 1438.875000\n", - "2 1431.273438\n", - "3 1904.823242\n", - "Name: runtime, dtype: float64\n" - ] - }, + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "weak_scaling([df3, df4, df5], names=[\"CCX\", \"CCD\", \"Socket\"], title=\"Weak Scaling up to 4e9 Points on 512 nodes of ARCHER2 (Sphere)\", plot_err=False, stat='p99')" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "id": "10dc2192-ec20-4c00-bbe1-34697ecc73f0", + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -347,30 +817,18 @@ } ], "source": [ - "weak_scaling([df3, df4, df5], names=[\"CCX\", \"CCD\", \"Socket\"], title=\"Weak Scaling up to 4e9 Points on 512 nodes of ARCHER2 (Sphere)\", plot_err=False)" + "weak_scaling([df_big_uniform], names=[\"Socket\"], title=\"Weak Scaling up to 3.2e10 Points on 512 nodes of ARCHER2 (Uniform)\", plot_err=False, stat='max')" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 9, "id": "acbc24f2-9682-4719-9e7e-43ceeb63d62e", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "experiment_id\n", - "0 9035.000000\n", - "1 9191.687500\n", - "2 9307.953125\n", - "3 9746.737305\n", - "Name: runtime, dtype: float64\n" - ] - }, { "data": { - "image/png": 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", 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"text/plain": [ "
" ] @@ -380,13 +838,21 @@ } ], "source": [ - "weak_scaling([df_big], names=[\"Socket\"], title=\"Weak Scaling up to 3.2e10 Points on 512 nodes of ARCHER2 (Uniform)\", plot_err=True)" + "weak_scaling([df_big_sphere, df_big_sphere_1], names=[\"Socket\"], title=\"Weak Scaling up to 3.2e10 Points on 512 nodes of ARCHER2 (Sphere)\", plot_err=False, stat='p90')" + ] + }, + { + "cell_type": "markdown", + "id": "ff5677a1-cd72-40ab-b6dc-843e928becc0", + "metadata": {}, + "source": [ + "# Breadown Communication Times" ] }, { "cell_type": "code", - "execution_count": 40, - "id": "add61ffe-1504-409b-95ba-8c06b3706c59", + "execution_count": 10, + "id": "7281b367-b065-43cb-b418-92405d3e1621", "metadata": {}, "outputs": [ { @@ -410,56 +876,112 @@ " \n", " \n", " \n", - " local_depth\n", + " experiment_id\n", + " runtime\n", + " p2p\n", + " m2l\n", + " scatter_v_runtime\n", + " gather_v_runtime\n", + " neighbour_all_to_all_v_runtime\n", + " n_points\n", " \n", " \n", " experiment_id\n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " 0\n", - " 5.0\n", + " 0.0\n", + " 10676.000000\n", + " 7257.000000\n", + " 3128.000000\n", + " 0.000000\n", + " 0.000000\n", + " 41.000000\n", + " 32000000.0\n", " \n", " \n", " 1\n", - " 5.0\n", + " 1.0\n", + " 10596.562500\n", + " 3825.500000\n", + " 6263.000000\n", + " 21.187500\n", + " 2.750000\n", + " 103.500000\n", + " 32000000.0\n", " \n", " \n", " 2\n", - " 5.0\n", + " 2.0\n", + " 10846.882812\n", + " 7249.820312\n", + " 3150.437500\n", + " 10.796875\n", + " 58.359375\n", + " 151.671875\n", + " 32000000.0\n", " \n", " \n", " 3\n", - " 5.0\n", + " 3.0\n", + " 14846.920898\n", + " 3823.790039\n", + " 5739.640625\n", + " 108.542969\n", + " 3879.185547\n", + " 914.461914\n", + " 32000000.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " local_depth\n", - "experiment_id \n", - "0 5.0\n", - "1 5.0\n", - "2 5.0\n", - "3 5.0" + " experiment_id runtime p2p m2l \\\n", + "experiment_id \n", + "0 0.0 10676.000000 7257.000000 3128.000000 \n", + "1 1.0 10596.562500 3825.500000 6263.000000 \n", + "2 2.0 10846.882812 7249.820312 3150.437500 \n", + "3 3.0 14846.920898 3823.790039 5739.640625 \n", + "\n", + " scatter_v_runtime gather_v_runtime \\\n", + "experiment_id \n", + "0 0.000000 0.000000 \n", + "1 21.187500 2.750000 \n", + "2 10.796875 58.359375 \n", + "3 108.542969 3879.185547 \n", + "\n", + " neighbour_all_to_all_v_runtime n_points \n", + "experiment_id \n", + "0 41.000000 32000000.0 \n", + "1 103.500000 32000000.0 \n", + "2 151.671875 32000000.0 \n", + "3 914.461914 32000000.0 " ] }, - "execution_count": 40, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_big.groupby('experiment_id')[['local_depth']].mean()" + "df_big_sphere_1.groupby('experiment_id')[runtime_cols].mean()" ] }, { "cell_type": "code", - "execution_count": 41, - "id": "28145053-9c61-48cb-a25f-18960b6124a9", + "execution_count": 11, + "id": "e707dacb-1631-4268-b143-08b765c38186", "metadata": {}, "outputs": [ { @@ -483,11 +1005,14 @@ " \n", " \n", " \n", + " experiment_id\n", " runtime\n", - " m2l\n", " p2p\n", - " m2m\n", - " l2l\n", + " m2l\n", + " scatter_v_runtime\n", + " gather_v_runtime\n", + " neighbour_all_to_all_v_runtime\n", + " n_points\n", " \n", " \n", " experiment_id\n", @@ -496,67 +1021,446 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " 0\n", - " 9035.000000\n", - " 4713.50000\n", - " 4055.500000\n", - " 20.000000\n", - " 44.500000\n", + " 0.0\n", + " 98.994949\n", + " 1.414214\n", + " 31.112698\n", + " 0.000000\n", + " 0.000000\n", + " 25.455844\n", + " 0.0\n", " \n", " \n", " 1\n", - " 9191.687500\n", - " 4718.81250\n", - " 4125.937500\n", - " 18.687500\n", - " 45.125000\n", + " 0.0\n", + " 1703.371753\n", + " 645.033539\n", + " 1021.543473\n", + " 13.417495\n", + " 11.000000\n", + " 54.166410\n", + " 0.0\n", " \n", " \n", " 2\n", - " 9307.953125\n", - " 4776.18750\n", - " 4161.781250\n", - " 18.671875\n", - " 45.179688\n", + " 0.0\n", + " 3294.702213\n", + " 2296.363913\n", + " 998.870258\n", + " 1.096590\n", + " 25.046016\n", + " 55.296639\n", + " 0.0\n", " \n", " \n", " 3\n", - " 9746.737305\n", - " 4655.40625\n", - " 4180.361328\n", - " 17.872070\n", - " 47.135742\n", + " 0.0\n", + " 9179.649659\n", + " 2623.943684\n", + " 4200.543822\n", + " 13.236332\n", + " 10294.242764\n", + " 1368.828403\n", + " 0.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " runtime m2l p2p m2m l2l\n", - "experiment_id \n", - "0 9035.000000 4713.50000 4055.500000 20.000000 44.500000\n", - "1 9191.687500 4718.81250 4125.937500 18.687500 45.125000\n", - "2 9307.953125 4776.18750 4161.781250 18.671875 45.179688\n", - "3 9746.737305 4655.40625 4180.361328 17.872070 47.135742" + " experiment_id runtime p2p m2l \\\n", + "experiment_id \n", + "0 0.0 98.994949 1.414214 31.112698 \n", + "1 0.0 1703.371753 645.033539 1021.543473 \n", + "2 0.0 3294.702213 2296.363913 998.870258 \n", + "3 0.0 9179.649659 2623.943684 4200.543822 \n", + "\n", + " scatter_v_runtime gather_v_runtime \\\n", + "experiment_id \n", + "0 0.000000 0.000000 \n", + "1 13.417495 11.000000 \n", + "2 1.096590 25.046016 \n", + "3 13.236332 10294.242764 \n", + "\n", + " neighbour_all_to_all_v_runtime n_points \n", + "experiment_id \n", + "0 25.455844 0.0 \n", + "1 54.166410 0.0 \n", + "2 55.296639 0.0 \n", + "3 1368.828403 0.0 " ] }, - "execution_count": 41, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_big.groupby('experiment_id')[['runtime', 'm2l', 'p2p', 'm2m', 'l2l', ] ].mean()" + "df_big_sphere_1.groupby('experiment_id')[runtime_cols].std()" ] }, { "cell_type": "code", - "execution_count": 42, - "id": "1d2daf53-bfec-4778-96f1-32960de3adc3", + "execution_count": 12, + "id": "ec3dfd92-0631-401d-871b-434224b957e4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1024" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_big_sphere[df_big_sphere['experiment_id'] == 3].shape[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "87c0da8c-5b2b-4119-b726-0bfe8e1d0ac7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "62" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "col = df_big_sphere[df_big_sphere['experiment_id'] == 3]['gather_v_runtime']\n", + "df_big_sphere[df_big_sphere['experiment_id'] == 3][col > col.mean()].shape[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "53f8cd8c-074f-4768-9028-644a9a95a3cf", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define quantile threshold\n", + "q = 0.95 # change to 0.95, 0.99, etc.\n", + "\n", + "# Loop through each experiment\n", + "for exp_id, group in df_big_sphere.groupby('experiment_id'):\n", + " # print(exp_id, group)\n", + " # Compute this experiment's threshold\n", + " threshold = group['gather_v_runtime'].quantile(q)\n", + "\n", + " # Filter rows within this experiment\n", + " filtered = group[group['gather_v_runtime'] > threshold]\n", + "\n", + " if exp_id == 3:\n", + " # Plot histogram for this experiment\n", + " plt.figure()\n", + " plt.hist(sorted(filtered['rank']), bins=50)\n", + " plt.title(f'Experiment {exp_id} — Ranks above {int(q*100)}th percentile runtime')\n", + " plt.xlabel('Rank')\n", + " plt.ylabel('Count')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "1f75bd28-1504-4ec2-9346-9c2106e52861", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define quantile threshold\n", + "q = 0.95 # change to 0.95, 0.99, etc.\n", + "\n", + "# Loop through each experiment\n", + "for exp_id, group in df_big_sphere_1.groupby('experiment_id'):\n", + " # print(exp_id, group)\n", + " # Compute this experiment's threshold\n", + " threshold = group['gather_v_runtime'].quantile(q)\n", + "\n", + " # Filter rows within this experiment\n", + " filtered = group[group['gather_v_runtime'] > threshold]\n", + "\n", + " if exp_id == 3:\n", + "\n", + " # Plot histogram for this experiment\n", + " plt.figure()\n", + " plt.hist(sorted(filtered['rank']), bins=50)\n", + " plt.title(f'Experiment {exp_id} — Ranks above {int(q*100)}th percentile runtime')\n", + " plt.xlabel('Rank')\n", + " plt.ylabel('Count')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2fb33d4b-b978-4469-93b8-486e766233bb", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "85e4532f-8c15-434e-8c5d-08900c84c40f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "12 0\n", + "18 17\n", + "21 1\n", + "34 24\n", + "36 121\n", + " ... \n", + "1044 620\n", + "1086 613\n", + "1094 17\n", + "1139 87\n", + "1153 622\n", + "Name: rank, Length: 114, dtype: int64" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "filtered['rank']" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "94cb41be-c8db-41bf-b4e2-862e452dae12", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(filtered['rank'], bins=50)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f8fb8789-dfb0-40e7-9749-104c713fa1f6", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "0768750f-b998-441e-8664-1ef6317fab05", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "experiment_id\n", + "0 0.0\n", + "1 0.0\n", + "2 94.0\n", + "3 1001.6\n", + "Name: gather_v_runtime, dtype: float64" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_big_sphere_1.groupby('experiment_id')['gather_v_runtime'].quantile(0.90)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "cc40bb5d-3eb3-4351-8ba0-3be5119a8d1c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(sorted(list(df_big_sphere_1[df_big_sphere_1['gather_v_runtime'] > 35000]['rank'])), bins=50)\n", + "plt.xlabel('Rank')\n", + "plt.ylabel('Count')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "id": "ddfbe25b-bc60-4211-86bc-8f59186066ca", "metadata": {}, "outputs": [ { @@ -580,80 +1484,186 @@ " \n", " \n", " \n", + " experiment_id\n", " runtime\n", - " m2l\n", " p2p\n", - " m2m\n", - " l2l\n", - " \n", - " \n", - " experiment_id\n", - " \n", - " \n", - " \n", - " \n", - " \n", + " m2l\n", + " scatter_v_runtime\n", + " gather_v_runtime\n", + " neighbour_all_to_all_v_runtime\n", + " n_points\n", " \n", " \n", " \n", " \n", " 0\n", - " 9035.000000\n", - " 4713.50000\n", - " 4055.500000\n", - " 20.000000\n", - " 44.500000\n", + " 0\n", + " 10664\n", + " 7244\n", + " 3169\n", + " 0\n", + " 0\n", + " 69\n", + " 32000000\n", " \n", " \n", " 1\n", - " 9191.687500\n", - " 4718.81250\n", - " 4125.937500\n", - " 18.687500\n", - " 45.125000\n", + " 0\n", + " 10803\n", + " 7247\n", + " 3191\n", + " 6\n", + " 0\n", + " 73\n", + " 32000000\n", " \n", " \n", " 2\n", - " 9307.953125\n", - " 4776.18750\n", - " 4161.781250\n", - " 18.671875\n", - " 45.179688\n", + " 1\n", + " 7735\n", + " 2684\n", + " 4385\n", + " 64\n", + " 0\n", + " 171\n", + " 32000000\n", " \n", " \n", " 3\n", - " 9746.737305\n", - " 4655.40625\n", - " 4180.361328\n", - " 17.872070\n", - " 47.135742\n", + " 1\n", + " 10047\n", + " 3673\n", + " 5863\n", + " 39\n", + " 0\n", + " 212\n", + " 32000000\n", + " \n", + " \n", + " 4\n", + " 1\n", + " 10174\n", + " 3675\n", + " 6083\n", + " 10\n", + " 0\n", + " 165\n", + " 32000000\n", + " \n", + " \n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " \n", + " \n", + " 1165\n", + " 3\n", + " 26103\n", + " 8572\n", + " 15186\n", + " 103\n", + " 1482\n", + " 41\n", + " 32000000\n", + " \n", + " \n", + " 1166\n", + " 3\n", + " 26220\n", + " 8566\n", + " 14955\n", + " 128\n", + " 1247\n", + " 97\n", + " 32000000\n", + " \n", + " \n", + " 1167\n", + " 3\n", + " 30017\n", + " 8773\n", + " 15416\n", + " 123\n", + " 1506\n", + " 3634\n", + " 32000000\n", + " \n", + " \n", + " 1168\n", + " 3\n", + " 27543\n", + " 9623\n", + " 15889\n", + " 86\n", + " 1090\n", + " 48\n", + " 32000000\n", + " \n", + " \n", + " 1169\n", + " 3\n", + " 30765\n", + " 10459\n", + " 18371\n", + " 87\n", + " 1069\n", + " 52\n", + " 32000000\n", " \n", " \n", "\n", + "

1170 rows × 8 columns

\n", "" ], "text/plain": [ - " runtime m2l p2p m2m l2l\n", - "experiment_id \n", - "0 9035.000000 4713.50000 4055.500000 20.000000 44.500000\n", - "1 9191.687500 4718.81250 4125.937500 18.687500 45.125000\n", - "2 9307.953125 4776.18750 4161.781250 18.671875 45.179688\n", - "3 9746.737305 4655.40625 4180.361328 17.872070 47.135742" + " experiment_id runtime p2p m2l scatter_v_runtime \\\n", + "0 0 10664 7244 3169 0 \n", + "1 0 10803 7247 3191 6 \n", + "2 1 7735 2684 4385 64 \n", + "3 1 10047 3673 5863 39 \n", + "4 1 10174 3675 6083 10 \n", + "... ... ... ... ... ... \n", + "1165 3 26103 8572 15186 103 \n", + "1166 3 26220 8566 14955 128 \n", + "1167 3 30017 8773 15416 123 \n", + "1168 3 27543 9623 15889 86 \n", + "1169 3 30765 10459 18371 87 \n", + "\n", + " gather_v_runtime neighbour_all_to_all_v_runtime n_points \n", + "0 0 69 32000000 \n", + "1 0 73 32000000 \n", + "2 0 171 32000000 \n", + "3 0 212 32000000 \n", + "4 0 165 32000000 \n", + "... ... ... ... \n", + "1165 1482 41 32000000 \n", + "1166 1247 97 32000000 \n", + "1167 1506 3634 32000000 \n", + "1168 1090 48 32000000 \n", + "1169 1069 52 32000000 \n", + "\n", + "[1170 rows x 8 columns]" ] }, - "execution_count": 42, + "execution_count": 178, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_big.groupby('experiment_id')[['runtime', 'm2l', 'p2p', 'm2m', 'l2l', ] ].mean()" + "df_big_sphere[runtime_cols]['" ] }, { "cell_type": "code", "execution_count": null, - "id": "1aba6245-2460-430c-b7fa-418a4e9c4ec0", + "id": "f97097a2-a227-436a-afb8-cb59ecf9aa81", "metadata": {}, "outputs": [], "source": [] diff --git a/hpc/ijhpca/weak_scaling.py b/hpc/ijhpca/weak_scaling.py index 9cb66d59..efb9eb04 100644 --- a/hpc/ijhpca/weak_scaling.py +++ b/hpc/ijhpca/weak_scaling.py @@ -18,6 +18,18 @@ "sphere": 1 } +MACHINE = { + "lumi": { + "project": "65001872", + "partition": "standard" + }, + + "archer2": { + "project": "e738", + "partition": "standard" + } +} + def parse_process_mapping(arch): ccd_threads_per_rank = arch["cores_per_ccd"] ccx_threads_per_rank = arch["cores_per_ccx"] @@ -114,7 +126,20 @@ def experiment_parameters( return n_nodes.tolist(), n_ranks.tolist(), global_depth.tolist(), max_threads_per_rank, distribution -def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, points_per_rank, local_depth, distribution="uniform", script_name="fmm_m2l_fft_mpi_f32"): +def write_slurm( + machine, + script_path, + n_nodes, + n_tasks, + global_depths, + max_threads, + points_per_rank, + local_depth, + hrs, + mins, + distribution="uniform", + script_name="fmm_m2l_fft_mpi_f32", + ): expansion_order = 3 n_points = points_per_rank n_samples = 500 @@ -126,13 +151,13 @@ def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, point max_points = last_tasks * n_points slurm = f"""#!/bin/bash -#SBATCH --job-name=weak_scaling_fft -#SBATCH --time=00:30:00 +#SBATCH --job-name=weak_scaling_fft_n={max_points}_p={last_nodes}_points_per_rank={n_points}_distribution={distribution} +#SBATCH --time={hrs}:{mins}:00 #SBATCH --nodes={last_nodes} #SBATCH --ntasks-per-node={int(n_tasks[-1] // n_nodes[-1])} #SBATCH --cpus-per-task={cpus_per_task} -#SBATCH --account=e738 -#SBATCH --partition=standard +#SBATCH --account={MACHINE[machine]["project"]} +#SBATCH --partition={MACHINE[machine]["partition"]} #SBATCH --qos=standard """ slurm += f""" @@ -141,8 +166,6 @@ def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, point module load cray-mpich-ucx export UCX_UD_TIMEOUT=20m -export HOME="/home/e738/e738/skailasa" -export WORK="/work/e738/e738/skailasa" script_name="{script_name}" @@ -187,6 +210,10 @@ def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, point parser.add_argument("--method", type=str, default="ccx") parser.add_argument("--output", type=str, default="job.slurm") parser.add_argument("--distribution", type=str, default="uniform") + parser.add_argument("--machine", type=str, default="archer2") + parser.add_argument("--hrs", type=str, default="00") + parser.add_argument("--mins", type=str, default="30") + parser.add_argument("--config", action='append') args = parser.parse_args() @@ -212,7 +239,7 @@ def write_slurm(script_path, n_nodes, n_tasks, global_depths, max_threads, point args.min_nodes, args.max_nodes, ranks_per_node[args.method], args.points_per_rank, args.local_depth, distribution=args.distribution ) - write_slurm(args.output, n_nodes, n_tasks, global_depths, max_threads, args.points_per_rank, args.local_depth, distribution=distribution) + write_slurm(args.machine, args.output, n_nodes, n_tasks, global_depths, max_threads, args.points_per_rank, args.local_depth, args.hrs, args.mins, distribution=distribution) else: raise ValueError("Unknown method") \ No newline at end of file diff --git a/hpc/ijhpca/weak_scaling_big.json b/hpc/ijhpca/weak_scaling_big.json index 5f89b254..02c7267d 100644 --- a/hpc/ijhpca/weak_scaling_big.json +++ b/hpc/ijhpca/weak_scaling_big.json @@ -7,5 +7,8 @@ "output": "weak_scaling_socket_32B.slurm", "method": "socket", "scaling_func": 8, - "distribution": "uniform" + "distribution": "uniform", + "machine": "archer2", + "hrs": "1", + "mins": "00" } \ No newline at end of file diff --git a/hpc/ijhpca/weak_scaling_big_sphere.json b/hpc/ijhpca/weak_scaling_big_sphere.json index 2fa7b581..cb4e4114 100644 --- a/hpc/ijhpca/weak_scaling_big_sphere.json +++ b/hpc/ijhpca/weak_scaling_big_sphere.json @@ -7,5 +7,8 @@ "output": "weak_scaling_socket_32B_sphere.slurm", "method": "socket", "scaling_func": 8, - "distribution": "sphere" + "distribution": "sphere", + "machine": "archer2", + "hrs": "1", + "mins": "30" } \ No newline at end of file diff --git a/hpc/ijhpca/weak_scaling_ccd.json b/hpc/ijhpca/weak_scaling_ccd.json index 6082da31..faaf8b9a 100644 --- a/hpc/ijhpca/weak_scaling_ccd.json +++ b/hpc/ijhpca/weak_scaling_ccd.json @@ -7,5 +7,8 @@ "output": "weak_scaling_ccd_uniform.slurm", "method": "ccd", "scaling_func": 8, - "distribution": "uniform" + "distribution": "uniform", + "machine": "archer2", + "hrs": "0", + "mins": "30" } \ No newline at end of file diff --git a/hpc/ijhpca/weak_scaling_ccx.json b/hpc/ijhpca/weak_scaling_ccx.json index a5f6be3d..5e86038b 100644 --- a/hpc/ijhpca/weak_scaling_ccx.json +++ b/hpc/ijhpca/weak_scaling_ccx.json @@ -7,5 +7,8 @@ "output": "weak_scaling_ccx_uniform.slurm", "method": "ccx", "scaling_func": 8, - "distribution": "uniform" + "distribution": "uniform", + "machine": "lumi", + "hrs": "0", + "mins": "30" } \ No newline at end of file diff --git a/hpc/ijhpca/weak_scaling_socket.json b/hpc/ijhpca/weak_scaling_socket.json index ae73468d..92c9b360 100644 --- a/hpc/ijhpca/weak_scaling_socket.json +++ b/hpc/ijhpca/weak_scaling_socket.json @@ -7,5 +7,8 @@ "output": "weak_scaling_socket_uniform.slurm", "method": "socket", "scaling_func": 8, - "distribution": "uniform" + "distribution": "uniform", + "machine": "archer2", + "hrs": "0", + "mins": "30" } \ No newline at end of file From a6e9c6af6195fbbbf358ebe762bc589021f55010 Mon Sep 17 00:00:00 2001 From: Srinath Kailasa Date: Fri, 24 Oct 2025 16:15:54 +0100 Subject: [PATCH 52/52] Fix gen script --- hpc/ijhpca/weak_scaling.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/hpc/ijhpca/weak_scaling.py b/hpc/ijhpca/weak_scaling.py index efb9eb04..19d274b9 100644 --- a/hpc/ijhpca/weak_scaling.py +++ b/hpc/ijhpca/weak_scaling.py @@ -20,7 +20,7 @@ MACHINE = { "lumi": { - "project": "65001872", + "project": "project_465001872", "partition": "standard" }, @@ -150,6 +150,7 @@ def write_slurm( last_tasks = n_tasks[-1] max_points = last_tasks * n_points + slurm = f"""#!/bin/bash #SBATCH --job-name=weak_scaling_fft_n={max_points}_p={last_nodes}_points_per_rank={n_points}_distribution={distribution} #SBATCH --time={hrs}:{mins}:00 @@ -158,8 +159,12 @@ def write_slurm( #SBATCH --cpus-per-task={cpus_per_task} #SBATCH --account={MACHINE[machine]["project"]} #SBATCH --partition={MACHINE[machine]["partition"]} -#SBATCH --qos=standard """ + if machine == "archer2": + slurm += """ +#SBATCH --qos=standard + """ + slurm += f""" module load PrgEnv-aocc module load craype-network-ucx