PaddleJitLab/CUDATutorial resource

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1087 stars · 113 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A self-learning tutorial repository for CUDA high-performance programming, structured as progressive lesson series from beginner environment setup through advanced kernel optimization. It includes hands-on implementations and optimization guides for matrix multiplication, reduction, convolution, Triton kernels, and LLM inference techniques like Flash Attention and vLLM.

Use cases

• learn CUDA programming from scratch
• optimize GPU kernels for matrix multiplication
• understand how Flash Attention works
• study vLLM source code and scheduling
• write faster reduction kernels avoiding bank conflicts
• get started with Triton kernel programming
• profile CUDA code with nvprof
• implement convolution operators with im2col and implicit GEMM

When to choose

• you want a structured, hands-on path to learn CUDA and GPU kernel optimization
• you need worked examples of GEMM, reduce, and convolution optimization techniques
• you are preparing for deep learning inference systems work involving CUDA or Triton

When to avoid

• you need production-ready CUDA libraries rather than educational material
• you want a general GPU computing course without a deep learning focus
• you cannot read Chinese, as the primary documentation is written in Chinese

Facets

learning-resource · maturity active

gpu-computing developer-tools benchmarking gpu-computing deep-learning tutorials large-language-models performance cross-platform cuda-programming tutorial high-performance-computing kernel-optimization gemm flash-attention triton vllm matrix-multiplication reduce-operations convolution-optimization notes self-learning chinese-language gpu cuda linux

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