This repository is an extensive curated list of public projects, resources, and learning materials related to CUDA, High-Performance Computing (HPC), and various GPU-accelerated libraries and frameworks. It serves as a comprehensive knowledge base for developers, researchers, and engineers working with NVIDIA GPUs and parallel computing.

How It Works

The project is structured as an "Awesome List," categorizing resources by technology (CUDA, TensorRT, Triton, MLIR, etc.), programming language (C++, Python, Rust, Julia, Mojo, Zig, Go), application domain (scientific computing, machine learning, robotics, ZKP), and learning materials (documentation, blogs, videos, tutorials). It aims to provide a centralized hub for discovering and learning about cutting-edge GPU computing technologies and their implementations.

Quick Start & Requirements

This repository is a curated list, not a software package. It requires no installation. Users can navigate the Markdown file to find links to external projects, documentation, and learning resources.

Highlighted Details

• Extensive coverage of NVIDIA's CUDA ecosystem, including libraries like cuBLAS, cuDNN, and TensorRT.
• Detailed sections on emerging frameworks like Triton and MLIR for custom kernel development and compiler infrastructure.
• Comprehensive resources for learning GPU programming, optimization techniques, and specific HPC applications.
• Includes projects and tutorials in multiple programming languages, showcasing GPU computing across different ecosystems (e.g., Rust, Julia, Mojo).

Maintenance & Community

This is a community-driven project, maintained through GitHub contributions. It links to various community resources like Discord servers and blogs for specific technologies.

Licensing & Compatibility

The repository itself is licensed under the MIT License. However, it links to numerous external projects, each with its own licensing terms. Users must consult the individual project licenses for compatibility and usage restrictions.

Limitations & Caveats

As a curated list, the quality and maintenance status of linked projects can vary. Users should verify the activity and relevance of external repositories. The sheer volume of information may require significant time to explore thoroughly.