BBuf/how-to-optim-algorithm-in-cuda resource

Health v2 · maintenance only

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3223 stars · 290 forks observed · 2026-08-28

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

A curated collection of notes and hands-on code for optimizing algorithms on CUDA GPUs, covering handwritten kernels, CUTLASS/CuTe, Triton, PTX ISA, and PyTorch internals. It also includes LLM inference and training systems optimization material, serving as a public engineering notebook for GPU systems work.

Use cases

• learn how to write optimized CUDA kernels for softmax and reduction
• understand GEMM optimization with CUTLASS and CuTe
• study Triton kernel examples for PyTorch
• optimize LLM inference performance on GPUs
• learn PTX ISA and GPU architecture internals
• find notes from the CUDA-MODE lecture series

When to choose

• you are learning GPU kernel optimization from first principles
• you want curated notes on CUTLASS, Triton, and PTX in one place
• you work on LLM inference or training systems and need performance background

When to avoid

• you need a production-ready kernel library to drop into your project
• you want a maintained software tool rather than study notes
• you need guaranteed correctness or support, since there is no license or formal releases

Facets

learning-resource · maturity active

gpu-computing llm-inference llm-training benchmarking gpu-computing deep-learning large-language-models developer-tools tutorials python cpp cuda-kernels cutlass triton ptx-isa gemm-optimization study-notes ai-infrastructure gpu cuda

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