# CNugteren/CLBlast

Tuned OpenCL BLAS

Repository: https://github.com/CNugteren/CLBlast
Canonical: https://ross.abutalabs.com/products/clblast
Language: C++
License: Apache-2.0
License Family: permissive
Topics: blas, opencl, blas-libraries, clblas, matrix-multiplication, gemm, gpu
Last push: 2026-04-13T18:52:03+00:00

## Health v2 (maintenance only)
Score: 70/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 77, release rhythm 45, longevity 100
- inputs: {"age_days": 4113, "days_push": 142, "days_rel": 156, "gap_med": null, "n_releases_24m": 1}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1186, forks 212 (observed 2026-08-28T04:03:55.010719+00:00)

## What it is
CLBlast is a lightweight, tunable OpenCL BLAS library written in C++11 that implements basic linear algebra subprograms for vectors and matrices. It targets a wide variety of OpenCL devices from different vendors, including desktop, laptop, and embedded GPUs, and includes tuners for optimizing performance on specific hardware.

## Use cases
- run BLAS matrix operations on non-NVIDIA GPUs via OpenCL
- accelerate matrix multiplication (GEMM) on embedded or Intel GPUs
- tune BLAS kernels for a specific OpenCL device or matrix shape
- use half-precision fp16 linear algebra routines
- replace clBLAS with a modern, customizable C++ BLAS library

## When to choose
- you need BLAS on OpenCL devices from any vendor, not just CUDA GPUs
- you want to inspect or customize the BLAS kernels yourself
- you run on exotic or older OpenCL 1.1 hardware and can run the tuners
- you prefer a C++ API (with a C API also available) over clBLAS's C API

## When to avoid
- you run exclusively on NVIDIA CUDA-enabled GPUs where cuBLAS's assembly-tuned kernels are faster
- you need out-of-the-box peak performance without running the tuning step on untuned devices

## Facets
- artifact type: library
- maturity: stable
- function: math, gpu-computing, benchmarking
- domain: machine-learning, gpu-computing, performance
- platform: cpp, cross-platform, windows
- tags: blas, opencl, gemm, matrix-multiplication, linear-algebra, tunable-kernels, fp16, algorithms, linux, macos, gpu

## Member repositories
- CNugteren/CLBlast (main) score 70

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:55.010719+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:23:46.730999+00:00, confidence not recorded.
  - readme: https://github.com/CNugteren/CLBlast (fetched 2026-08-28T04:03:55.010719+00:00, sha 83cd0fa06531)
- Data as of 2026-08-30T08:39:29.467469+00:00.