ECE415 — High-Performance Computing

Coursework repository for ECE415 — High-Performance Computing at the University of Thessaly. The labs start with CPU-side profiling and loop-level optimization, then move through OpenMP, CUDA kernels, CUDA streams, memory-hierarchy tuning, and multi-GPU execution.

Quick overview

Area	What is included
CPU optimization	Sobel edge-detection optimization with loop transformations, strength reduction, inlining, and serial/OpenMP comparisons.
OpenMP parallelism	K-Means clustering experiments using OpenMP constructs, SIMD, atomics, critical sections, and timing comparisons.
CUDA kernels	2D convolution and CLAHE implementations covering block sizing, memory access patterns, streams, events, and profiling.
Final application	A galaxy-style N-body simulator with CPU, OpenMP, CUDA, stream, shared-memory, coarsening, multi-GPU, and divergence-reduction versions.

Standout work: N-body simulator

The strongest part of the repository is Lab 5, where the final assignment builds an N-body simulator for independent galaxy-style particle systems. The lab starts from a sequential gravitational simulation and grows into a full CPU/OpenMP/CUDA performance study with multiple GPU implementations and correctness comparisons.

Key pieces of the Lab 5 implementation include:

• CPU and OpenMP baselines: sequential and OpenMP implementations provide reference outputs and performance baselines for the GPU work.
• CUDA optimization ladder: the GPU versions explore naive kernels, streams, Structure-of-Arrays layout, shared-memory tiling, fast math/FMA variants, thread coarsening, launch-bound tuning, divergence-reduction variants, and multi-GPU execution.
• Measurement workflow: run logs and CSV summaries are used to compare implementations with throughput plots instead of only raw terminal timings.
• Extra visualizer work: beyond the base simulator, the repository includes a custom dataset generator and an animation of the simulation output.

Course contents

Lab handouts are collected under docs/handouts/. Recovered reports for Labs 1–5 are collected under docs/reports/

Path	Topic	Implementation summary
Lab1/	Sobel CPU optimization	Serial C optimization passes for Sobel edge detection, including loop interchange, unrolling, fusion, inlining, common-subexpression elimination, strength reduction, and OpenMP comparison.
Lab2/	OpenMP K-Means	Parallel K-Means clustering with OpenMP, SIMD/critical/atomic experiments, timing scripts, and report plots.
Lab3/	CUDA convolution	CUDA 2D convolution kernels covering block sizing, precision behavior, image padding, divergence effects, and runtime comparisons.
Lab4/	CUDA CLAHE	Contrast Limited Adaptive Histogram Equalization with multiple kernels, privatized histograms, scans, coalesced memory access, streams, events, and profiling plots.
Lab5/	N-body simulation	CPU, OpenMP, CUDA, stream, shared-memory, coarsening, multi-GPU, and divergence-reduction versions of a galaxy N-body simulator.

Requirements

• Linux environment
• gcc and make
• Python 3 for data generation and plotting scripts
• OpenMP-capable GCC
• NVIDIA CUDA toolkit (nvcc) for Labs 3–5
• NVIDIA GPU hardware for CUDA execution
• Optional: NVIDIA Nsight Systems / Nsight Compute for profiling

Python plotting scripts use packages such as numpy, pandas, matplotlib, and seaborn.

Build and run

Each lab keeps its own scripts and Makefiles. Typical entry points are:

cd Lab1 && ./run.sh
cd Lab2 && ./run.sh
cd Lab3 && ./run.sh
cd Lab4 && ./run.sh
cd Lab5 && ./run.sh --help

Lab 5 examples:

cd Lab5
./run.sh --iterations=10 --cpu=omp --gpu=on --file=06_coarsening_2.cu --input=galaxy_data.bin
./run.sh --iterations=5 --cpu=seq --gpu=off --input=galaxy_data.bin
python3 generate_custom_dataset.py 1 4096 galaxy_1x4096.bin