CUDA Course is an educational resource that teaches GPU programming with CUDA, covering kernel optimization and parallel computing fundamentals.
The course addresses the challenge of learning GPU programming by consolidating scattered resources into a structured curriculum. It teaches developers how to write and optimize CUDA kernels for performance improvement, with emphasis on understanding GPU memory bandwidth bottlenecks. The material progresses from foundational concepts through practical kernel implementation, covering CUDA APIs like cuBLAS and cuDNN, matrix multiplication optimization, and integration with PyTorch for custom extensions.
This course suits developers with Python experience who want to understand GPU acceleration for deep learning and high-performance computing. It works best for those targeting NVIDIA GPUs and aiming to build optimized kernels for research or production systems. The curriculum assumes basic familiarity with differentiation, vector calculus, and linear algebra, though these are listed as recommended rather than required. The course is designed for Ubuntu Linux environments, with Windows users directed toward Windows Subsystem for Linux or Docker alternatives. It culminates in a practical project implementing a simple MLP for MNIST classification in CUDA, providing hands-on experience with the concepts covered.
The project maintains a structured, modular approach with eleven sections spanning from ecosystem overview through setup, C/C++ review, GPU fundamentals, kernel writing, API usage, optimization techniques, and alternative frameworks like Triton. The course includes a dedicated extras section with supplementary materials and a cheatsheet for reference. Development activity shows consistent organization with clear progression from theory to implementation, supporting learners at different stages of GPU programming expertise.
