Brahma.FSharp GPGPU Examples: Image Processing & Matrix Multiplication

GitHub Actions

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Practical GPGPU examples in F# using the Brahma.FSharp library for OpenCL-accelerated computing.

Projects

Project	Source	README
Image Convolution	src/ImageProcessing/	details — filters, CPU/GPU backends, streaming pipeline, benchmarks
Matrix Multiplication	src/MatrixMultiplication/	details — progressive GPU kernels K0–K4, benchmarks

📁 Repository Structure

The project is organized for clarity and ease of navigation:

• src/: Contains all source code.
  • ImageProcessing/: Image type, filter kernels, CPU/GPU convolution, MailboxProcessor streaming pipeline, CLI entry point.
  • MatrixMultiplication/: Matrix multiplication kernels (K0–K4) and CLI entry point.
• tests/: Unit tests for the examples, ensuring correctness.
  • ImageProcessing.Tests/: Xunit tests for all CPU and GPU filter variants.
  • MatrixMultiplication.Tests/: Xunit tests for matrix multiplication kernels.
• benchmarks/: Performance benchmarks.
  • MatrixMultiplication.Benchmarks/: Benchmarks for matrix multiplication kernels K0–K4.
  • ImageProcessing.Benchmarks/: Benchmarks for image convolution on all CPU and GPU backends.
• .github/workflows/: GitHub Actions CI/CD pipelines for automated building and testing.

🚀 Getting Started

Follow these instructions to get the project up and running on your local machine for development and experimentation.

Prerequisites

Before you begin, ensure you have the following installed:

• .NET 9.0 SDK or higher.
• Option A (Recommended for GPU acceleration): An OpenCL-compatible device (e.g., a discrete or integrated GPU) with the respective vendor driver installed. (e.g., NVIDIA drivers for NVIDIA GPUs, ROCm or AMD drivers for AMD GPUs, or Intel OpenCL runtime for Intel GPUs/CPUs).
• Option B (CPU fallback - great for testing/learning): If you don't have a GPU or want to experiment on CPU first, install POCL (Portable Computing Language). POCL is an open-source OpenCL implementation that runs on CPUs, allowing you to run all examples without dedicated graphics hardware.
  • On Ubuntu/Debian: sudo apt install pocl-opencl-icd
  • Check the official POCL installation guide for installation options.

Build

git clone https://github.com/gsvgit/ImageProcessing.git
cd ImageProcessing
dotnet build -c Release

Benchmarks

Both projects include BenchmarkDotNet performance benchmarks. See each sub-project's README for details, run instructions, and analysis figures.

Analysis script

The Python script benchmarks/analyze_benchmarks.py can be used to plot benchmarks results. For retails look at sub-project's README.

Analysis requirements: pandas, matplotlib, numpy

pip install pandas matplotlib numpy