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ViralBShah merged 1 commit into from
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README: add Reference BLAS and LAPACK to the supported list #174

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13 changes: 7 additions & 6 deletions README.md
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Expand Up @@ -7,12 +7,13 @@ Using [PLT trampolines](https://en.wikipedia.org/wiki/Trampoline_(computing)) to
These BLAS libraries are known to work with libblastrampoline (successfully tested in Julia):

1. [OpenBLAS](https://github.com/OpenMathLib/OpenBLAS) (supported by default in Julia)
2. [Intel oneMKL](https://www.intel.com/content/www/us/en/developer/tools/oneapi/onemkl.html) (use in Julia through [MKL.jl](https://github.com/JuliaLinearAlgebra/MKL.jl))
3. [Apple Accelerate](https://developer.apple.com/documentation/accelerate/blas) (use in Julia through [AppleAccelerate.jl](https://github.com/JuliaMath/AppleAccelerate.jl))
4. [BLIS](https://github.com/flame/blis/) (use in Julia through [BLISBLAS.jl](https://github.com/carstenbauer/BLISBLAS.jl))
5. Fujitsu BLAS (use in Julia through [FujitsuBLAS.jl](https://github.com/giordano/FujitsuBLAS.jl))
6. [ARMPL BLAS](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Libraries)
7. [NVPL BLAS](https://docs.nvidia.com/nvpl/_static/blas/)
2. [Reference BLAS and LAPACK](https://github.com/Reference-LAPACK/lapack) (use in Julia through [ReferenceBLAS_jll](https://github.com/JuliaBinaryWrappers/ReferenceBLAS_jll.jl) and [LAPACK_jll](https://github.com/JuliaBinaryWrappers/LAPACK_jll.jl))
3. [Intel oneMKL](https://www.intel.com/content/www/us/en/developer/tools/oneapi/onemkl.html) (use in Julia through [MKL.jl](https://github.com/JuliaLinearAlgebra/MKL.jl))
4. [Apple Accelerate](https://developer.apple.com/documentation/accelerate/blas) (use in Julia through [AppleAccelerate.jl](https://github.com/JuliaMath/AppleAccelerate.jl))
5. [BLIS](https://github.com/flame/blis/) (use in Julia through [BLISBLAS.jl](https://github.com/carstenbauer/BLISBLAS.jl))
6. Fujitsu BLAS (use in Julia through [FujitsuBLAS.jl](https://github.com/giordano/FujitsuBLAS.jl))
7. [ARMPL BLAS](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Libraries)
8. [NVPL BLAS](https://docs.nvidia.com/nvpl/_static/blas/)

## Basic usage

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