xlite-dev/LeetCUDA resource

Health v2 · maintenance only

Adoption not part of the score

11836 stars · 1244 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A collection of modern CUDA learning notes with PyTorch integration for beginners, featuring 200+ CUDA kernels, 100+ LLM/CUDA blogs, and high-performance implementations like HGEMM (98-100% of cuBLAS TFLOPS) and Flash Attention-2 using Tensor Cores with pure MMA PTX. It serves as both an educational resource and a reference for GPU kernel optimization techniques.

Use cases

• learn CUDA programming from scratch with PyTorch integration
• study high-performance HGEMM kernel implementations
• understand Flash Attention-2 with Tensor Core MMA PTX
• find reference CUDA kernel implementations for common operations
• prepare for CUDA/GPU kernel engineering interviews
• benchmark custom CUDA kernels against cuBLAS
• learn about TF32, FP16, BF16, and FP8 tensor core usage

When to choose

• you are a beginner wanting structured CUDA learning materials with runnable PyTorch examples
• you need reference implementations of optimized kernels like HGEMM or Flash Attention
• you are preparing for GPU kernel engineering interviews
• you want to study Tensor Core programming with MMA PTX instructions

When to avoid

• you need a production-ready kernel library for deployment rather than educational notes
• you need a general-purpose CUDA math library like cuBLAS or CUTLASS
• you work on non-NVIDIA GPU platforms
• you need stable, versioned APIs with long-term support guarantees

Facets

learning-resource · maturity active

gpu-computing developer-tools benchmarking machine-learning gpu-computing developer-tools tutorials machine-learning deep-learning python cuda-kernels tensor-cores hgemm flash-attention ptx pytorch cuda-cpp awesome-lists linux cuda gpu

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markdown · JSON · MCP: product_card(name="xlite-dev/LeetCUDA")