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

how to optimize some algorithm in cuda.

Repository: https://github.com/BBuf/how-to-optim-algorithm-in-cuda
Canonical: https://ross.abutalabs.com/products/how-to-optim-algorithm-in-cuda
Language: Cuda
License Family: other
Topics: cuda, llm
Last push: 2026-08-22T06:39:47+00:00

## Health v2 (maintenance only)
Score: 98/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 97, longevity 100
- inputs: {"age_days": 2968, "days_push": 11, "days_rel": 22, "gap_med": 4, "n_releases_24m": 2}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3223, forks 290 (observed 2026-08-28T04:07:49.800931+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: active
- function: gpu-computing, llm-inference, llm-training, benchmarking
- domain: gpu-computing, deep-learning, large-language-models, developer-tools, tutorials
- platform: python, cpp
- tags: cuda-kernels, cutlass, triton, ptx-isa, gemm-optimization, study-notes, ai-infrastructure, gpu, cuda

## Member repositories
- BBuf/how-to-optim-algorithm-in-cuda (main) score 98

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:49.800931+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:44:18.158160+00:00, confidence not recorded.
  - readme: https://github.com/BBuf/how-to-optim-algorithm-in-cuda (fetched 2026-08-28T04:07:49.800931+00:00, sha 1d36abf96a8a)
- Data as of 2026-08-30T08:39:29.467469+00:00.