https://github.com/alexkranias/triton_vs_cuda
Building Triton and CUDA kernels side-by-side to create a cuBLAS-performant GEMM kernel.
Science Score: 13.0%
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Low similarity (7.6%) to scientific vocabulary
Keywords
Repository
Building Triton and CUDA kernels side-by-side to create a cuBLAS-performant GEMM kernel.
Basic Info
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Metadata Files
README.md
tritonvscuda
Building Triton and CUDA kernels side-by-side to create a cuBLAS-performant GEMM kernel.
Lately I've been learning Triton, its strengths, and its weaknesses. Inspired by SiBohem's blog, I thought I would show how we can attempt to build a Triton kernel as performant as a near-cuBLAS performant CUDA kernel. In this endeavor I hope to highlight a few things about Triton: - what are the limitations of a Triton's block level programming paradigm? - as a kernel engineer, how much control do we retain in Triton to squeeze more performance out? - where does the Triton compiler take over and attempt to fill in? How successful is it at this task? Where is work still needed at the compiler level? - when should you actually use Triton v.s. CUDA?
Getting Started
I've divided this project into two branches:
- main: template kernel files
- solutions: solution kernel files
I've included dockerfiles in each /triton and /cuda directory to make enviornment setup quick and easy. Open those directories and you'll find README.mds explaining how to get going.
In Progress
I'll have a blog on the subject posted at some point on my personal website: alexkranias.com
I'm actively working on that piece.
In the meantime, you can clone this repo to work on this on your own and follow SiBohem's blog.
Owner
- Name: Alex Kranias
- Login: alexkranias
- Kind: user
- Website: alexkranias.com
- Repositories: 1
- Profile: https://github.com/alexkranias
Feel free to contact me at alexander.kranias@gatech.edu or check out my personal site at alexkranias.com
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