diff options
| author | Guray Ozen <guray.ozen@gmail.com> | 2024-04-24 12:00:12 +0200 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2024-04-24 12:00:12 +0200 |
| commit | 4d3308202e52b213a05023c8b8b470b346151de6 (patch) | |
| tree | 533094638c052fc79ee7898fb788fb231396a49e /llvm/lib/CodeGen/ModuloSchedule.cpp | |
| parent | 506c84a7198630b7476b02d985c6ed09338f757d (diff) | |
| download | llvm-4d3308202e52b213a05023c8b8b470b346151de6.zip llvm-4d3308202e52b213a05023c8b8b470b346151de6.tar.gz llvm-4d3308202e52b213a05023c8b8b470b346151de6.tar.bz2 | |
[mlir][nvgpu] NVGPU Tutorials (#87065)
I have a tutorial at EuroLLVM 2024 ([Zero to Hero: Programming Nvidia
Hopper Tensor Core with MLIR's NVGPU
Dialect](https://llvm.swoogo.com/2024eurollvm/session/2086997/zero-to-hero-programming-nvidia-hopper-tensor-core-with-mlir's-nvgpu-dialect)).
For that, I implemented tutorial codes in Python. The focus is the nvgpu
dialect and how to use its advanced features. I thought it might be
useful to upstream this.
The tutorial codes are as follows:
- **Ch0.py:** Hello World
- **Ch1.py:** 2D Saxpy
- **Ch2.py:** 2D Saxpy using TMA
- **Ch3.py:** GEMM 128x128x64 using Tensor Core and TMA
- **Ch4.py:** Multistage performant GEMM using Tensor Core and TMA
- **Ch5.py:** Warp Specialized GEMM using Tensor Core and TMA
I might implement one more chapter:
- **Ch6.py:** Warp Specialized Persistent ping-pong GEMM
This PR also introduces the nvdsl class, making IR building in the
tutorial easier.
Diffstat (limited to 'llvm/lib/CodeGen/ModuloSchedule.cpp')
0 files changed, 0 insertions, 0 deletions