CUTLASS Notes

The CUTLASS notes series will begin with a minimal GEMM implementation, gradually expand to incorporate CuTe and various CUTLASS components, as well as features of new architectures, e.g. Hopper and Blackwell, ultimately achieving a high-performance fused GEMM operator.

Usage

git clone https://github.com/ArthurinRUC/cutlass-notes.git
# clone cutlass
cd cutlass-notes
git submodule update --init --recursive

Run sample code

All example code in this GitHub repository can be compiled and run by simply executing the Python script. For example:

cd 01-minimal-gemm
python minimal_gemm.py

Note list

Notes	Summary	Links
00-Intro	Brief introduction to CUTLASS	intro
01-minimal-gemm	Introduces CuTe fundamentals Implements 16x8x8 GEMM kernel using single MMA instruction from scratch Python kernel invocation, precision validation & performance benchmarking Profiling with Nsight Compute (ncu)	minimal-gemm
02-mixed-precision-gemm	Implements mixed-precision GEMM supporting varying input/output/accumulation precisions Explores technical details for numerical precision conversion within kernels Demonstrates custom FP8 GEMM kernel implementation via PTX instructions (for CUTLASS-unsupported MMA ops)	mixed-precision-gemm
03-tiled-mma	Introduces the key conceptual model of GEMM operator: Three-Level Tiling Details the implementation of Tiled MMA operations in CUTLASS CuTe Explains the usage and semantics of various parameters in the Tiled MMA API Extends the GEMM kernel from single instruction to single tile operation	tiled-mma
04-tiled-copy	Coming soon	Stay tuned

License

This project is licensed under the MIT License - see the LICENSE file for details.