Introduction to CUTLASS
CUTLASS, currently in version 3.6.0 (released in October 2024), is an advanced and flexible collection of CUDA C++ template abstractions aimed at facilitating high-performance matrix-matrix computations (GEMM) across various scales and levels within CUDA. The library borrows strategies from cuBLAS and cuDNN for hierarchical decomposition and movement of data. By translating these processes into modular C++ template classes, CUTLASS offers a flexible framework for building efficient and tunable algorithms tailored for custom kernels and applications.
Key Features and Advantages
CUTLASS supports a wide array of numeric data types and mixed-precision computations, making it highly versatile. It includes specialized functions for half-precision floating point (FP16), BFloat16, Tensor Float 32, and even binary data types. Additionally, NVIDIA's Tensor Cores facilitate these operations, showcasing optimized warp-synchronous matrix multiply functions on NVIDIA's Volta, Turing, Ampere, and Hopper architectures.
Besides traditional GEMM operations, CUTLASS excels in high-performance convolution calculations by framing convolution operations as GEMM equations. This allows CUTLASS to utilize its optimized components to achieve efficient convolution computations.
CuTe: The Core Library
With the introduction of CUTLASS 3.0, a new core library, named CuTe, was included to assist in the manipulation of complex tensors and layouts. CuTe simplifies programming by offering tools that abstract away the complexity of tensor layout, enhancing the productivity of developers working on dense linear algebra operations. Hierarchically multidimensional layouts allow powerful tensor representations, enabling efficient tiling and partitioning, among other advanced operations. The library’s flexibility vastly enhances the simplicity and readability of code, making CUTLASS applications more manageable and adaptable.
Recent Updates in Version 3.6
CUTLASS 3.6.0 introduces several new features:
- Hopper structured sparse GEMM support with specialized optimizations for various data types such as FP16, FP8, INT8, and TF32.
- Enhancements to the convolution API, consolidating its features with the GEMM API.
- Improved mixed input GEMM capabilities and a new lookup table implementation for certain modes.
- New EVT nodes for Top-K selection and softmax operations, enhancing support for complex machine learning tasks.
- Introduction of Programmatic Dependent Launch (PDL) to improve performance by overlapping kernel operations.
- A new debugging tool, synclog, for detailed synchronization logging within kernels.
Performance and Compatibility
CUTLASS is designed to harness the computing power of NVIDIA GPUs effectively, providing performance on par with NVIDIA's cuBLAS and cuDNN libraries. Its ability to deliver robust performance across complex linear algebra operations is benchmark-managed, ensuring that it scales effectively with the latest NVIDIA hardware.
The library requires a C++17 host compiler and is optimized for use with CUDA 12.4, though it maintains compatibility with previous versions back to 11.4. Support extends across multiple operating systems like Ubuntu and Windows, with builds possible for a comprehensive range of NVIDIA's modern GPU architectures.
Conclusion
CUTLASS stands as an exceptional tool for developers aiming to perform efficient, high-performance GEMM and convolution computations on CUDA-capable GPUs. By abstracting complex operations into flexible templates, it simplifies development while simultaneously enabling optimized solutions that leverage NVIDIA's powerful hardware innovations. Whether you are an experienced GPU programmer or new to CUDA, CUTLASS offers the resources and capabilities to build and refine advanced computational algorithms quickly.