CUDA-Accelerated Python Library

A CUDA-focused project for benchmarking matrix multiplication and image convolution workflows across CPU and GPU implementations, with notebook-driven analysis and reproducible outputs.

Project Goals

• Benchmark CPU vs CUDA performance for core numerical workloads.
• Provide executable C/CUDA implementations for image convolution.
• Preserve reproducible benchmarking artifacts and visual outputs.
• Document methodology and results in a final project report.

Repository Layout

CUDA-Accelerated-Python-Library/
├── native_image_convolution_sources/
│   ├── image_convolution_cpu.c
│   ├── image_convolution_cuda.cu
│   └── image_convolution_cuda_python_bridge.cu
├── benchmarks/
│   └── image_convolution/
│       ├── case_01/
│       │   ├── src/
│       │   ├── notebooks/
│       │   ├── data/input/
│       │   ├── results/final/
│       │   ├── results/intermediate/
│       │   └── artifacts/
│       ├── case_02/
│       └── case_03/
├── notebooks/
│   └── matrix_multiplication/
│       └── matrix_multiplication_benchmark.ipynb
├── docs/
│   └── report/
│       ├── CUDA_Project_Report_Final.pdf
│       └── CUDA_Project_Report_Final.docx
├── README.md
└── LICENSE

Workload Modules

Matrix Multiplication

• Notebook: notebooks/matrix_multiplication/matrix_multiplication_benchmark.ipynb
• Covers CPU, naive CUDA, optimized CUDA, and cuBLAS comparisons.
• Includes timing, plotting, and performance analysis.

Image Convolution

Each benchmark case (case_01, case_02, case_03) is organized as:

• native_image_convolution_sources/ (repository root) - canonical source files used by notebooks:
  • image_convolution_cpu.c
  • image_convolution_cuda.cu
  • image_convolution_cuda_python_bridge.cu
• src/ - local case source snapshots
• notebooks/ - image_convolution_benchmark.ipynb
• data/input/ - input.pgm, image.jpg
• results/final/ - final output images (out_*)
• results/intermediate/ - temporary benchmark outputs (tmp_*)
• artifacts/bin and artifacts/lib - compiled executables and shared libraries

Build and Run

From any convolution case directory, for example benchmarks/image_convolution/case_01:

CPU Build

gcc ../../../native_image_convolution_sources/image_convolution_cpu.c -O2 -o artifacts/bin/image_convolution_cpu

CUDA Build

nvcc -O2 -arch=sm_75 ../../../native_image_convolution_sources/image_convolution_cuda.cu -o artifacts/bin/image_convolution_cuda

Python-Interop Shared Library

nvcc -Xcompiler -fPIC -shared ../../../native_image_convolution_sources/image_convolution_cuda_python_bridge.cu -o artifacts/lib/libimage_convolution.so

Sample Execution

./artifacts/bin/image_convolution_cpu data/input/input.pgm results/final/out_edge_n5_512.pgm edge_n5 512
./artifacts/bin/image_convolution_cuda data/input/input.pgm results/final/out_edge_n5_cuda_512.pgm edge_n5 512

Environment

• NVIDIA GPU with CUDA support
• CUDA toolkit (nvcc)
• GCC
• Python 3 + Jupyter for notebook workflows

The benchmark configuration in this project is aligned with Tesla T4-compatible compilation (sm_75), as used in the report workflow.

Report

Project report files are available in docs/report/:

• CUDA_Project_Report_Final.pdf
• CUDA_Project_Report_Final.docx

License

This project is licensed under the MIT License. See LICENSE for details.