amdahls-law

Field-artillery simulation supplement to the Coordination Tax working paper/manuscript.

Repository of the lab2 assignment for the Parallel Programming course.

Parallelizing Federated Learning client simulation with ProcessPoolExecutor: 1.27x stable speedup, empirical Amdahl analysis (~24% parallel fraction), NumPy IPC to avoid PyTorch pickling deadlocks

Interactive dashboard for learning parallel computing speedup using Amdahl’s and Gustafson’s Laws with real-time simulations

Multi-threaded matrix multiplication written in java to test Amdahl's Law.

High-performance $\pi$ digit calculation service designed for the Software Architecture (ARSW) course at Escuela Colombiana de Ingeniería Julio Garavito. Features a transition from sequential to parallel execution using Java 21 platform threads, layered architectural patterns, and performance benchmarking across diverse CPU architectures.

3D visualization toolkit for parallel computing performance analysis: speedup, efficiency, and iso-efficiency metrics with Amdahl's and Gustafson's Law implementations.

A parameterized multi-cycle Harvard architecture processor designed from the ground up in Verilog. Developed using a complete RTL design workflow including simulation, linting, synthesis, Sky130 technology mapping, area estimation, static timing analysis, cocotb self-checking testbenches and post synthesis GLS 🖥️.

Data-parallel pipeline for detecting DGA-based malware using linguistic feature extraction on multi-core CPUs. Achieves 5.75× speedup on 8 cores with 93.18% classification accuracy. Built with Python multiprocessing and Random Forest. [AMLCCZG516 — BITS Pilani]