clblast

{
  "aliases": [],
  "alternatives": [],
  "build_dependencies": [
    "cmake"
  ],
  "categories": [],
  "caveats": null,
  "conflicts_with": [],
  "dependencies": [],
  "deprecated": 0,
  "deprecation_reason": null,
  "desc": "Tuned OpenCL BLAS library",
  "disable_reason": null,
  "disabled": 0,
  "enrichment_fetched_at": "2026-06-20T23:36:25+00:00",
  "first_seen": "2026-06-20T23:34:18+00:00",
  "full_name": "clblast",
  "github_default_branch": "master",
  "github_last_commit_at": "2026-04-13T18:52:03Z",
  "github_readme_excerpt": "\nCLBlast: The tuned OpenCL BLAS library\n================\n\n| Platform | Build status |\n|-----|-----|\n| Windows | [![Build Status](https://ci.appveyor.com/api/projects/status/github/cnugteren/clblast?branch=master\u0026svg=true)](https://ci.appveyor.com/project/CNugteren/clblast) |\n| Linux/macOS | [![Build Status](https://github.com/cnugteren/clblast/actions/workflows/build_and_test.yml/badge.svg?branch=master)](https://github.com/CNugteren/CLBlast/actions/workflows/build_and_test.yml) |\n\nCLBlast is a lightweight, performant and tunable OpenCL BLAS library written in C++11. It is designed to leverage the full performance potential of a wide variety of OpenCL devices from different vendors, including desktop and laptop GPUs, embedded GPUs, and other accelerators. CLBlast implements BLAS routines: basic linear algebra subprograms operating on vectors and matrices. See [the CLBlast website](https://cnugteren.github.io/clblast) for performance reports on some devices.\n\nThe library is not tuned for all possible OpenCL devices: __if out-of-the-box performance is poor, please run the tuners first__. See [the docs for a list of already tuned devices](doc/tuning.md#already-tuned-for-devices) and [instructions on how to tune yourself](doc/tuning.md) and contribute to future releases of the CLBlast library.\n\n\nWhy CLBlast and not clBLAS or cuBLAS?\n-------------\n\nUse CLBlast instead of clBLAS:\n\n* When you care about achieving maximum performance.\n* When you want to be able to inspect the BLAS kernels or easily customize them to your needs.\n* When you run on exotic OpenCL devices for which you need to tune yourself.\n* When you are still running on OpenCL 1.1 hardware.\n* When you prefer a C++ API over a C API (C API also available in CLBlast).\n* When you value an organized and modern C++ codebase.\n* When you target Intel CPUs and GPUs or embedded devices.\n* When you can benefit from the increased performance of half-precision fp16 data-types.\n\nUse CLBlast instead of cuBLAS:\n\n* When you w",
  "github_repo": "CNugteren/CLBlast",
  "github_stars": 1180,
  "github_topics": [
    "blas",
    "blas-libraries",
    "clblas",
    "gemm",
    "gpu",
    "matrix-multiplication",
    "opencl"
  ],
  "homepage": "https://github.com/CNugteren/CLBlast",
  "homepage_og_description": null,
  "homepage_og_image": null,
  "homepage_title": null,
  "installs_30d": 57,
  "installs_365d": 564,
  "installs_90d": 217,
  "keg_only": 0,
  "keg_only_reason": null,
  "last_seen": "2026-06-20T23:34:18+00:00",
  "license": "Apache-2.0",
  "llm_generated_at": null,
  "llm_model": null,
  "name": "clblast",
  "oldnames": [],
  "one_liner": null,
  "optional_dependencies": [],
  "rank_30d": 4412,
  "rank_365d": 4868,
  "rank_90d": 4064,
  "raw_hash": "1000b4eb5c51461e",
  "recommended_dependencies": [],
  "revision": 0,
  "ruby_source_path": "Formula/c/clblast.rb",
  "tap": "homebrew/core",
  "test_dependencies": [],
  "uses_from_macos": [],
  "version_head": null,
  "version_stable": "1.7.0",
  "versioned_formulae": [],
  "why_use_this": null
}