cff-version: 1.2.0
title: CUTLASS
message: >-
  If you use this software, please cite using the
  following metadata.
type: software
authors:
  - given-names: Andrew
    email: akerr@nvidia.com
    family-names: Kerr
    affiliation: NVIDIA
  - given-names: Haicheng
    family-names: Wu
    affiliation: NVIDIA
    email: haichengw@nvidia.com
  - given-names: Manish
    family-names: Gupta
    affiliation: Google
    email: manigupta@google.com
  - given-names: Dustyn
    family-names: Blasig
    email: dblasig@nvidia.com
    affiliation: NVIDIA
  - given-names: Pradeep
    family-names: Ramini
    email: prramani@nvidia.com
    affiliation: NVIDIA
  - given-names: Duane
    family-names: Merrill
    email: dumerrill@nvidia.com
    affiliation: NVIDIA
  - given-names: Aniket
    family-names: Shivam
    email: ashivam@nvidia.com
    affiliation: NVIDIA
  - given-names: Piotr
    family-names: Majcher
    email: pmajcher@nvidia.com
    affiliation: NVIDIA
  - given-names: Paul
    family-names: Springer
    email: pspringer@nvidia.com
    affiliation: NVIDIA
  - given-names: Markus
    family-names: Hohnerbach
    affiliation: NVIDIA
    email: mhohnerbach@nvidia.com
  - given-names: Jin
    family-names: Wang
    email: jinw@nvidia.com
    affiliation: NVIDIA
  - given-names: Matt
    family-names: Nicely
    email: mnicely@nvidia.com
    affiliation: NVIDIA
repository-code: 'https://github.com/NVIDIA/cutlass'
abstract: >-
  CUTLASS is a collection of CUDA C++ template
  abstractions for implementing high-performance
  matrix-multiplication (GEMM) and related
  computations at all levels and scales within CUDA.
  It incorporates strategies for hierarchical
  decomposition and data movement similar to those
  used to implement cuBLAS and cuDNN. CUTLASS
  decomposes these "moving parts" into reusable,
  modular software components abstracted by C++
  template classes. These thread-wide, warp-wide,
  block-wide, and device-wide primitives can be
  specialized and tuned via custom tiling sizes, data
  types, and other algorithmic policy. The resulting
  flexibility simplifies their use as building blocks
  within custom kernels and applications.
keywords:
  - 'cutlass, tensor cores, cuda'
license: BSD-3-Clause
license-url: https://github.com/NVIDIA/cutlass/blob/v2.10.0/LICENSE.txt
version: '2.10.0'
date-released: '2022-09-15'
identifiers:
  - type: url
    value: "https://github.com/NVIDIA/cutlass/tree/v2.10.0"
    description: The GitHub release URL of tag 2.10.0
