ModernGpuProgrammingForMlsys：在现代为机器学习

A tutorial 在现代 GPU programming 为机器学习 systemsGitHub 524 ⭐ / 57 Fork，主要使用 HTML 开发。访问项目：https://github.com/mlc-ai/modern-gpu-programming-for-mlsys

项目介绍

A tutorial 在现代 GPU programming 为机器学习 systems

核心功能

- Part I — Understanding the GPU. Execution and memory model, the performance model - Part II — Programming a GPU with TIRx. An introduction to TIRx through one runnable - Part III — GEMM: Tiled to SOTA. A tiled GEMM built up through TMA pipelining, - Part IV — Flash Attention 4. A complete attention kernel built from the Part III techniques: - Reference. TIRx language reference and compiler internals.

使用指南

pip install -r requirements-docs.txt sphinx-build -b html . _build/html ```

Preview

``` python -m http.server -d _build/html 8000 ```

Open <http://localhost:8000>. On a remote machine the server runs there, so forward the port — `ssh -L 8000:localhost:8000 user@your-server` — then open the URL locally. (VS Code Remote SSH auto-forwards it.)

Running the kernels (requires a Blackwell GPU)

The kernels in this book target Blackwell (`sm_100a`), so running them needs a Blackwell GPU (such as a B200), the TIRx compiler, and a CUDA build of PyTorch.

1. Install the TIRx compiler. It ships as the `tvm.tirx` module of the Apache TVM wheel:

``` pip install apache-tvm ```

Verify:

``` python -c "import tvm, tvm.tirx; print(tvm.__version__)"

项目信息

项目地址

GitHub 仓库：<https://github.com/mlc-ai/modern-gpu-programming-for-mlsys>

> 完整 URL：[https://github.com/mlc-ai/modern-gpu-programming-for-mlsys](https://github.com/mlc-ai/modern-gpu-programming-for-mlsys)

如需查看完整源码、安装命令、使用示例、API 文档，请直接访问上方项目地址。所有信息均以 GitHub 仓库为准。