CUTLASS Academy

What is CUTLASS?

CUTLASS (CUDA Templates for Linear Algebra Subroutines) is a collection of CUDA C++ templates and abstractions for implementing high-performance matrix-multiplication and related computations at all levels and scales within CUDA. CUTLASS provides:

• Threadblock-level abstractions for matrix multiply-accumulate operations
• Warp-level primitives for matrix multiply-accumulate operations
• Epilogue components for various activation functions and tensor operations
• Utilities for efficiently loading and storing tensors in memory

CUTLASS is designed to deliver high performance for deep learning and HPC applications, with a focus on matrix multiplication operations that are fundamental to neural networks.

What is CUTE?

CUTE (CUDA Template Library for Tensors) is a modern C++ library built on top of CUTLASS that provides a more flexible and composable approach to tensor operations. CUTE introduces:

• A unified tensor abstraction that works across different hardware levels
• Powerful layout mapping capabilities for tensors
• Composable building blocks for tensor algorithms
• A more intuitive programming model for complex tensor operations

CUTE was introduced in CUTLASS 3.0 and represents a significant evolution in NVIDIA's approach to tensor computing.

How do CUTLASS, CUTE, and CUDA relate?

• CUDA is the base programming model and platform for NVIDIA GPUs. It provides the fundamental parallel computing architecture and programming interface.
• CUTLASS is a library built on top of CUDA that provides optimized implementations of matrix operations.
• CUTE is a higher-level abstraction built on top of CUTLASS that simplifies tensor programming.

Key Differences

Resources

Documentation

CUTLASS Docs

• CUTLASS GitHub Repository
• CUTLASS Wiki Documentation (great markdowns with examples and images)
• CUTLASS Documentation

CUTE Docs

• CUTE Documentation
• CUTE Tutorial

GTC

• CUTLASS: CUDA TEMPLATE LIBRARY FOR DENSE LINEAR ALGEBRA AT ALL LEVELS AND SCALES (GTC 2018)

• PROGRAMMING TENSOR CORES: NATIVE VOLTA TENSOR CORES WITH CUTLASS (GTC 2019)

• Developing CUDA Kernels to Push Tensor Cores to the Absolute Limit on NVIDIA A100 (GTC 2020)

• Accelerating Convolution with Tensor Cores in CUTLASS (GTC 2021)

• Accelerating Backward Data Gradient by Increasing Tensor Core Utilization in CUTLASS (GTC 2022)

• CUTLASS: Python API, Enhancements, and NVIDIA Hopper (GTC 2022)

• Developing Optimal CUDA Kernels on Hopper Tensor Cores (GTC 2023)

• CUTLASS: A Performant, Flexible, and Portable Way to Target Hopper Tensor Cores (GTC 2024)

GTC 2025 Coming soon (Cutlass in python coming soon !)

Articles

PyTorch

• Deep Dive on CUTLASS Ping-Pong GEMM Kernel

Nvidia

• Implementing High Performance Matrix Multiplication Using CUTLASS v2.8
• CUTLASS: Fast Linear Algebra in CUDA C++

Colfax

• CUTLASS Tutorial: Fast Matrix-Multiplication with WGMMA on NVIDIA® Hopper™ GPUs
• Tutorial: Matrix Transpose in CUTLASS
• CUTLASS Tutorial: Persistent Kernels and Stream-K
• CUTLASS Tutorial: Mastering the NVIDIA® Tensor Memory Accelerator (TMA)
• Developing CUDA Kernels for GEMM on NVIDIA Hopper Architecture using CUTLASS
• A note on the algebra of CuTe Layouts

Miscellaneous

• Build and Develop CUTLASS CUDA Kernels (How to create a CUDA Docker container for CUTLASS kernel development)
• learn-cutlass

Videos

• Lecture 15: CUTLASS (GPU MODE)
• CUTLASS: A CUDA C++ Template Library for Accelerating Deep Learning Computations (The Linux Foundation)
• Lecture 36: CUTLASS and Flash Attention 3 (GPU MODE)
• GTC 2024 : CUTLASS: A Performant, Flexible, and Portable Way to Target Hopper Tensor Cores

Repos using CUTLASS/CUTE

• DeepGEMM
• FlashMLA
• flash-attention