Understand the project
Practical overview
When it fits
Use the repository as a topic-organized reference when studying GPU programming or exploring optimization approaches for kernels and LLM infrastructure.
Who it is for
- GPU programmers studying CUDA kernels and optimization techniques.
- Engineers exploring Triton, CUTLASS/CuTe, PTX, or PyTorch internals.
- Readers researching GPU systems and LLM training or serving optimization.
Documented uses
- Study handwritten kernels for common tensor operations such as reductions, softmax, GEMV and linear attention.
- Consult CUTLASS/CuTe and PTX notes for GPU programming topics.
- Review Triton kernels and PyTorch interoperability examples.
- Browse LLM serving, training, GPU architecture and ML systems optimization material.
Before you choose
Requirements & limits
- The collection is organized into topic directories, including cuda-kernels, cutlass, triton, large-language-model, pytorch and ptx-isa.
- It combines code examples with notes, course material and paper notes; it is not presented as a hosted service.
- The README identifies some older material as deprecated or retained for reference and notes that some Chinese-language notes are being consolidated or replaced with English entry points.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Source-based practical guide
Find CUDA kernel examples and optimization notes
Use the repository map to locate material on a specific CUDA or GPU optimization topic.
Before you start
- Access to the public GitHub repository.
Steps
Open the repository README.
Choose a topic area from the repository map, such as
cuda-kernels/for handwritten kernels orcutlass/for CUTLASS and CuTe notes.Open the corresponding directory to explore its examples and notes.
Expected outcome
You can locate the repository section associated with the CUDA or GPU topic you want to study.
Guide limitations
- The README identifies topic areas but does not provide instructions for building or running the examples.
Evidence and transparency
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 14 October 2026
- Original website title
- BBuf/how-to-optim-algorithm-in-cuda · Official homepage title · retained in its source language