Introduction
CUTLASS is a CUDA template library from NVIDIA for linear algebra subroutines (such as GEMM), designed to help developers build high-performance, reusable matrix computation kernels. It includes various optimization strategies and examples, making it easy to achieve efficient computation across different GPU architectures.
Key Features
- Templated GEMM and linear algebra building blocks for easy customization and extension.
- Performance optimizations and example implementations targeting multiple GPU architectures.
- Comprehensive documentation and examples for easy integration and tuning.
Use Cases
- Implementing custom high-performance matrix multiplication and linear algebra operators.
- Quickly building hardware-specific kernels using CUTLASS templates and examples.
- Replacing default operators in training and inference pipelines for better performance.
Technical Highlights
- Highly customizable operator building blocks implemented with CUDA and template metaprogramming.
- Optimized paths for different data types and memory layouts.