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NVIDIA/cutlass

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9,904 stars·1,909 forks·C++·10 viewsdocs.nvidia.com/cutlass/index.html↗

Cutlass

Cutlass is a collection of C++ templates and Python interfaces for implementing high-performance linear algebra operations on NVIDIA GPUs. It provides a kernel composition framework for designing custom GPU kernels and a mixed-precision tensor library capable of executing operations across diverse data formats, ranging from 64-bit floating point to 4-bit integers.

The project features a toolkit for operator fusion that integrates activation functions and bias calculations directly into matrix multiplication kernels to reduce memory passes. It also includes a Python-based domain-specific language for defining high-performance GPU operations, which eliminates the need for C++ glue code.

The framework covers broader capabilities in GPU memory layout optimization, hierarchical tiling strategies, and the development of specialized CUDA kernels through modular software hierarchies.

Features

  • GPU Kernel Implementations - Provides a framework for implementing custom-written hardware-level kernels for accelerated parallel computing on NVIDIA GPUs.
  • CUDA-Accelerated Libraries - A CUDA-accelerated library of C++ templates and Python interfaces for high-performance matrix operations.
  • Kernel Composition Frameworks - Provides a modular software hierarchy for composing specialized GPU kernels by tuning tiling sizes and data types.
  • Compute Engines - Implements a mixed-precision tensor library supporting data formats from 64-bit floating point down to 4-bit integers.

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  • Mixed-Precision Compute Engines - Provides a unified interface for hardware-accelerated tensor cores supporting numerical formats from 64-bit float to 4-bit integers.
  • Mixed-Precision Computing - Executes operations across various data types from 64-bit floating point to 4-bit formats to balance precision and speed.
  • Matrix Operation Fusions - Integrates activation functions and bias calculations directly into matrix multiplication kernels to reduce memory passes.
  • Gathered Matrix Multiplication - Implements the fusion of gathering and matrix multiplication into a single optimized operation to reduce memory overhead.
  • Tensor Libraries - Provides a framework for creating and manipulating multidimensional arrays across diverse numerical precisions.
  • Tensor Mappings - Maps multi-dimensional tensor data into linear memory to optimize direct hardware access and movement.
  • Python GPU Kernels - Provides a domain-specific language to define high-performance GPU kernels directly in Python, eliminating the need for C++ glue code.
  • Multidimensional Arrays - Defines multidimensional array structures to simplify indexing and data movement across GPU threads.
  • GPU Linear Algebra Libraries - Implements high-performance matrix multiplication and tensor computations on NVIDIA hardware using modular templates.
  • GPU Matrix Operation Implementations - Builds high-performance matrix multiplication and related GPU computations using modular templates and language interfaces.
  • Memory Layout Optimizations - Optimizes the organization of multidimensional tensors to improve cache locality and reduce memory overhead.
  • Template-Based Kernel Composition - Uses C++ templates to generate specialized GPU kernels by combining modular software components and hardware tuning parameters.
  • Tiled Memory Access Patterns - Organizes data movement into structured blocks to maximize cache locality across the GPU memory hierarchy.
  • Kernel Configuration DSLs - Provides a high-level Python DSL to define kernel configurations and layouts without requiring C++ glue code.
  • AI & Machine Learning - CUDA templates for high-performance linear algebra
  • Tensor Core Optimization - Template-based library for implementing high-performance tensor computations on NVIDIA GPUs.

    • Star history

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    Frequently asked questions

    What does nvidia/cutlass do?

    Cutlass is a collection of C++ templates and Python interfaces for implementing high-performance linear algebra operations on NVIDIA GPUs. It provides a kernel composition framework for designing custom GPU kernels and a mixed-precision tensor library capable of executing operations across diverse data formats, ranging from 64-bit floating point to 4-bit integers.

    What are the main features of nvidia/cutlass?

    The main features of nvidia/cutlass are: GPU Kernel Implementations, CUDA-Accelerated Libraries, Kernel Composition Frameworks, Compute Engines, Mixed-Precision Compute Engines, Mixed-Precision Computing, Matrix Operation Fusions, Gathered Matrix Multiplication.

    What are some open-source alternatives to nvidia/cutlass?

    Open-source alternatives to nvidia/cutlass include: xlite-dev/leetcuda — LeetCUDA is a collection of high-performance GPU kernel libraries focusing on memory optimization, activation… nvidia/isaac-gr00t. rapidsai/cuml — cuml is a GPU-accelerated machine learning library and framework that uses CUDA to accelerate tabular data… dusty-nv/jetson-inference — jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU… facebookresearch/flashlight — Flashlight is a C++ machine learning library and deep learning framework designed for building and training neural… infrasys-ai/aisystem — AISystem is a comprehensive AI full-stack infrastructure project covering the entire pipeline from AI chip…

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