Welcome to Kernelheim – a powerful collection of custom Triton and CUDA kernel functions designed to optimize and accelerate machine learning workloads on NVIDIA GPUs. Inspired by the mythical stronghold of the gods, Kernelheim is a forge where high-performance kernels are crafted to unlock the full potential of your hardware.
Kernelheim is an ongoing project aimed at delivering highly efficient, scalable, and easy-to-integrate kernel functions for machine learning and deep learning tasks, with a specific focus on NVIDIA GPU architectures. By leveraging Triton and CUDA, the project aims to build a suite of optimized kernels to maximize performance in GPU-accelerated machine learning workflows.
This project is currently under active development. The initial focus is on building foundational kernel functions that target NVIDIA GPUs, with optimizations for tasks such as matrix operations, custom neural network layers, and various computation-heavy processes.
More features, enhancements, and documentation will be added as development progresses.
- High-Performance Kernels: Custom kernels optimized for NVIDIA GPUs, specifically targeting machine learning operations.
- Triton & CUDA Integration: Full support for both Triton and CUDA to take full advantage of NVIDIA's GPU architecture.
- Efficient Memory Management: Optimized memory access patterns to reduce latency and improve throughput.
- Scalable and Modular: Designed to be flexible, with future-proofing for additional machine learning tasks and model optimization.
In Kernelheim, we aim to forge kernels that harness the raw power of NVIDIA GPUs, just as the mythical gods forged their tools with divine precision. By combining Triton and CUDA, we’re building tools to help developers achieve superior machine learning performance, scalability, and efficiency on NVIDIA hardware.
Installation instructions will be available once the first version of the project is ready for public use.
Detailed usage instructions, examples, and integration guides will be provided after the initial release.
Contributions are welcome! As this project is still under development, your ideas, suggestions, and optimizations are vital. If you have thoughts on kernel functions or performance improvements, feel free to contribute:
- Fork the repository
- Create your feature branch (
git checkout -b feature/your-feature), better yet use my.gitmessage - Commit your changes (
git commit -m 'Add new feature') - Push to the branch (
git push origin feature/your-feature) - Open a pull request
Make sure your code follows the project’s style guidelines and is well-documented.
- Build foundational kernel functions for core machine learning tasks on NVIDIA GPUs
- Optimize performance for Triton and CUDA on NVIDIA hardware
- Implement kernel-specific testing and benchmarking
- Expand the kernel suite to cover deep learning, image processing, and numerical operations
Special thanks to the following projects and their communities, whose work has been instrumental in the development of Kernelheim:
We deeply appreciate the contributions of these projects to the open-source community.
This project is licensed under the MIT License – see the LICENSE file for details.
Stay tuned as we continue building the project, and feel free to get involved in the early stages of Kernelheim – the future of efficient machine learning on NVIDIA GPUs.