Tools for Developing Fast Python Code

Center for Scientific Computation (CSC)

• Maintains our primary shared resource for research computing, ManeFrame II (M2), in collaboration with OIT
• Provides research computing tools, support, and training to all faculty, staff, and students using research computing resources www.smu.edu/csc has documentation and news
• help@smu.edu or rkalescky@smu.edu for help

CSC Workshop Series

Date	Workshop
January 21	M2 Introduction
January 28	Introduction to LAPACK and BLAS
February 4	Text Mining with Python on M2 (Lead by Dr. Eric Godat)
February 11	Using the New HPC Portal
February 18	Using GitHub
February 25	Writing Portable Accelerator Code with KOKKOS, RAJA, and OCCA
March 3	M2 Introduction
March 10	Introduction to Parallelization Using MPI
March 17	No Workshop Spring Break
March 24	Writing High Performance Python Code
March 31	Creating Portable Environments with Docker and Singularity
April 7	M2 Introduction
April 14	Introduction to Parallelization Using OpenMP and OpenACC
April 21	Profiling Applications on M2
April 28	Improving Code Vectorization

Using This Notebook on ManeFrame II (M2)

1. Go to hpc.smu.edu.
2. Sign in using your SMU ID and SMU password.
3. Select "JupyterLab from the "Interactive Apps" drop-down menu.
4. Select options required for your JupyterLab instance. These options are the same as those requested via a standard Slurm script on M2. For this tutorial: beginning with the "Partition" field the values should be: "htc", 4, 1, 2, 0, 6.
5. Select "Launch" Wait for the job to start on M2. When the job starts a new button "Connect to JupyterLab" button will appear.
6. Select "Connect to JupyterLab" The JupyterLab graphical interface will be presented and running on the M2 resource requested.
7. From "Launcher" tab select "Terminal".
8. Type git clone https://github.com/SouthernMethodistUniversity/fast_python.git and press "Enter"
9. In the file browser on the left double-click "Text_Mining_Python" and then double click "text_mining_python.ipynb"