This repository provides supplementary materials for a lecture series focused on GPU programming and optimization, targeting engineers and researchers interested in high-performance computing. It offers a curated collection of notebooks, slides, and code examples covering a wide range of topics from CUDA basics to advanced techniques like fused kernels and speculative decoding.

How It Works

The project serves as a central repository for educational content, organizing lecture materials by topic and speaker. It leverages a mix of Jupyter notebooks for hands-on coding demonstrations and presentation slides for conceptual explanations. The content spans various GPU programming frameworks and libraries, including PyTorch, CUDA, Triton, CUTLASS, and SYCL, providing practical insights into performance optimization strategies.

Quick Start & Requirements

• Installation: Clone the repository using git clone.
• Prerequisites: Specific lectures may require Python, PyTorch, CUDA-enabled GPUs, and potentially other specialized libraries as indicated within individual lecture folders. Colab links are provided for some lectures.
• Resources: Setup time and resource requirements vary significantly depending on the lecture and its associated code.

Highlighted Details

• Comprehensive coverage of CUDA programming, from introductory concepts to advanced performance tuning.
• In-depth exploration of modern GPU optimization techniques, including fused kernels, quantization, and attention mechanisms.
• Practical examples and code for integrating GPU acceleration into Python frameworks like PyTorch.
• Inclusion of materials on emerging GPU programming models and libraries like Triton, CUTLASS, and SYCL.

Maintenance & Community

The lectures feature contributions from various speakers, indicating a community-driven effort to share knowledge. Specific community channels or roadmaps are not detailed in the README.

Licensing & Compatibility

The repository's licensing is not explicitly stated in the README. Users should exercise caution regarding the use of provided code and materials, especially in commercial or closed-source projects, until a license is clarified.

Limitations & Caveats

The repository is a collection of lecture materials, not a cohesive software library. Users will need to individually set up the environment and dependencies for each lecture's code. The rapidly evolving nature of GPU technologies means some content may become dated.