This repository provides a comprehensive course on CUDA programming, targeting individuals looking to understand and optimize high-performance computing (HPC) tasks, particularly within the deep learning ecosystem. It aims to lower the entry barrier for GPU programming and consolidate scattered resources into an organized learning path, benefiting aspiring AI researchers and developers.

How It Works

The course focuses on GPU kernel optimization for performance improvement, covering CUDA, PyTorch, and Triton. It emphasizes the technical details of writing faster kernels, tailored for NVIDIA GPUs, and includes practical applications like optimizing matrix multiplication. The approach aims to build a strong foundation for understanding advanced projects and GPU performance bottlenecks, especially memory bandwidth.

Quick Start & Requirements

• Prerequisites: Python programming (required), basic differentiation/vector calculus (recommended), linear algebra fundamentals (recommended).
• Hardware: Any NVIDIA GTX, RTX, or datacenter-level GPU. Cloud GPU options are available.
• Environment: Designed for Ubuntu Linux; Windows users can use WSL or Docker.
• Resources: GitHub repo (this repository), Stack Overflow, NVIDIA Developer Forums, NVIDIA/PyTorch documentation.

Highlighted Details

• Covers CUDA, PyTorch extensions, and Triton for GPU programming.
• Includes optimization techniques for matrix multiplication.
• Culminates in a simple MLP MNIST project implemented in CUDA.
• Explores GPU architecture and parallel processing concepts.

Maintenance & Community

• The project is associated with FreeCodeCamp and has a Discord community via discord.gg/gpumode.
• Links to relevant YouTube channels and other CUDA programming resources are provided.

Licensing & Compatibility

• The repository does not explicitly state a license in the provided README.

Limitations & Caveats

• The course is primarily designed for Ubuntu Linux, requiring workarounds for Windows users.
• While recommended, calculus and linear algebra are not strictly enforced prerequisites, which might pose a challenge for some learners.