Liu-xiandong/How_to_optimize_in_GPU resource
This is a series of GPU optimization topics. Here we will introduce how to optimize the CUDA kernel in detail. I will introduce several basic kernel optimizations, including: elementwise, reduce, sgemv, sgemm, etc. The performance of these kernels is basically at or near the theoretical limit. observed · 2026-08-28
Health v2 · maintenance only
32/100
- Activity 0
- Release rhythm 35
- Longevity 100
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How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 1781
- days_rel: n/a
- days_push: 1131
- n_releases_24m: 0
Adoption not part of the score
1350 stars · 187 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A tutorial series repository teaching CUDA kernel optimization with detailed walkthroughs of elementwise, reduce, sgemv, and sgemm kernels. The kernels achieve near-theoretical-limit performance on V100 GPUs, benchmarked with Nsight against cuBLAS.
Use cases
- learn how to optimize CUDA kernels
- understand vectorized memory access in CUDA
- write a high-performance sgemm kernel
- optimize reduce operations on GPU
- compare my kernel performance against cuBLAS
- learn SASS-level tuning with register remapping
When to choose
- you want to learn GPU kernel optimization from worked examples
- you need reference implementations of near-peak elementwise, reduce, sgemv, or sgemm kernels
- you are studying HPC or preparing for GPU performance engineering work
When to avoid
- you need a production linear algebra library - use cuBLAS or CUTLASS instead
- you want a maintained framework rather than educational code
- you target GPUs other than NVIDIA V100-class hardware without adapting the tuning
Facets
learning-resource · maturity stable
gpu-computing benchmarking developer-tools gpu-computing performance tutorials developer-tools cuda-kernels high-performance-computing sgemm sgemv kernel-optimization nsight v100 gpu cuda linux
1 source
- readme: https://github.com/Liu-xiandong/How_to_optimize_in_GPU · fetched 2026-08-28 · 071694161be4
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| Liu-xiandong/How_to_optimize_in_GPU | main | 32 |
For agents
markdown · JSON · MCP: product_card(name="Liu-xiandong/How_to_optimize_in_GPU")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem