# AOCL.jl v5.3.1

**URL:** <https://discourse.julialang.org/t/aocl-jl-v5-3-1/138718>\
**Category:** Package Announcements\
**Tags:** blas, hpc, lapack\
**Created:** [August 10, 2026, 9:53am UTC](https://discourse.julialang.org/t/aocl-jl-v5-3-1/138718 "2026-08-10T09:53:56Z")\
**Posts on this page:** 9\
**Page:** 1

<div class="post-metadata">

**Author:** ![agodbole](https://avatars.discourse-cdn.com/v4/letter/a/97f17d/32.png) [@agodbole](https://discourse.julialang.org/u/agodbole)\
**Post date:** [August 10, 2026, 9:53am UTC](https://discourse.julialang.org/t/aocl-jl-v5-3-1/138718/1 "2026-08-10T09:53:56Z")

</div>

Hello all,

[AOCL](https://www.amd.com/en/developer/aocl.html) is a set of numerical libraries optimized for AMD processors built on the AMD “Zen” architecture, spanning multiple generations. It supports AMD EPYC™, AMD Ryzen™, and AMD Ryzen™ Threadripper™ processor families. These highly tuned, industry-standard math libraries accelerate the development of scientific and high-performance computing applications.

We are happy to announce that [AOCL.jl](https://github.com/JuliaLinearAlgebra/AOCL.jl) [5.3.1](https://github.com/JuliaLinearAlgebra/AOCL.jl/releases/tag/v5.3.1) is now tagged and released.

The AOCL package currently ships the following libraries bundled into a single shared library:

- [AOCL BLAS](https://github.com/amd/blis.git)
- [AOCL LAPACK](https://github.com/amd/libflame.git)
- [AOCL Sparse](https://github.com/amd/aocl-sparse.git)
- [AOCL LibM](https://github.com/amd/aocl-libm-ose.git)
- [AOCL Compression](https://github.com/amd/aocl-compression.git)
- [AOCL OpenRNG](https://github.com/amd/openrng.git)

Currently, AOCL BLAS and AOCL LAPACK are made available to use via [libblastrampoline](https://github.com/JuliaLinearAlgebra/libblastrampoline). This should allow users to make use of the [AOCL](https://www.amd.com/en/developer/aocl.html) BLAS and LAPACK libraries via a simple `using AOCL`. This package should offer better performance on AMD CPUs, so please try it out.

For questions, feature requests, or issues, please contact [toolchainsupport@amd.com](mailto:toolchainsupport@amd.com)

---

<div class="post-metadata">

**Author:** ![RoyiAvital](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/royiavital/32/571_2.png) [@RoyiAvital](https://discourse.julialang.org/u/RoyiAvital)\
**Post date:** [August 10, 2026, 3:38pm UTC](https://discourse.julialang.org/t/aocl-jl-v5-3-1/138718/2 "2026-08-10T15:38:46Z")

</div>

Great to see this.

How does it handle non AMD CPU’s?  
Or does it only choose the processing path by the features of the CPU?

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<div class="post-metadata">

**Author:** ![agodbole](https://avatars.discourse-cdn.com/v4/letter/a/97f17d/32.png) [@agodbole](https://discourse.julialang.org/u/agodbole)\
**Post date:** [August 10, 2026, 4:34pm UTC](https://discourse.julialang.org/t/aocl-jl-v5-3-1/138718/3 "2026-08-10T16:34:26Z")

</div>

AOCL libraries run on any x86 CPU but are optimized to achieve best performance on AMD Zen CPUs (the libraries have dynamic dispatch based on the ISA (instruction set architecture)).

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<div class="post-metadata">

**Author:** ![johnh](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/johnh/32/3615_2.png) [@johnh](https://discourse.julialang.org/u/johnh)\
**Post date:** [August 11, 2026, 1:41pm UTC](https://discourse.julialang.org/t/aocl-jl-v5-3-1/138718/4 "2026-08-11T13:41:46Z")

</div>

I am very impressed by this.

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<div class="post-metadata">

**Author:** ![linwaytin](https://avatars.discourse-cdn.com/v4/letter/l/898d66/32.png) [@linwaytin](https://discourse.julialang.org/u/linwaytin)\
**Post date:** [August 11, 2026, 2:42pm UTC](https://discourse.julialang.org/t/aocl-jl-v5-3-1/138718/5 "2026-08-11T14:42:32Z")

</div>

This can be very useful. Are there some benchmarks? In particular, I am curious how it is compared to OpenBLAS.

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<div class="post-metadata">

**Author:** ![agodbole](https://avatars.discourse-cdn.com/v4/letter/a/97f17d/32.png) [@agodbole](https://discourse.julialang.org/u/agodbole)\
**Post date:** [August 11, 2026, 6:48pm UTC](https://discourse.julialang.org/t/aocl-jl-v5-3-1/138718/6 "2026-08-11T18:48:30Z")

</div>

Just to clarify, we have made the artifacts of AOCL (AOCL\_jll.jl) available only on Linux as of today. Windows support is planned for the future.

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<div class="post-metadata">

**Author:** ![BjarkeHautop](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/bjarkehautop/32/219291_2.png) [@BjarkeHautop](https://discourse.julialang.org/u/BjarkeHautop)\
**Post date:** [August 11, 2026, 9:33pm UTC](https://discourse.julialang.org/t/aocl-jl-v5-3-1/138718/7 "2026-08-11T21:33:36Z")

</div>

Here are some benchmarks I quickly ran. Somewhat mixed results I get:

```julia
using LinearAlgebra
using Random
using Printf
using BenchmarkTools

BLAS.set_num_threads(16)

const SIZES = (256, 512, 1024, 2048)

function bench(f, name)
    t = @belapsed $f()
    @printf("%-25s %10.3f ms\n", name, t * 1000)
end

function benchmark_blas_lapack(N, A, B, C, x, y, SPD, SQR)
    println("===== N = $N =====")

    # Level-3 BLAS
    bench("GEMM A * B") do
        mul!(C, A, B)
    end

    # Level-2 BLAS
    bench("GEMV A * x") do
        mul!(y, A, x)
    end

    # LAPACK
    bench("Cholesky") do
        cholesky(SPD)
    end

    bench("LU") do
        lu(SQR)
    end

    bench("QR") do
        qr(SQR)
    end

    bench("Eigen (symmetric)") do
        eigen(Symmetric(SPD))
    end

    println()
end

Random.seed!(1405)
#> TaskLocalRNG()

INPUTS = [
    let
        A = randn(Float64, N, N)
        B = randn(Float64, N, N)
        C = similar(A)

        x = randn(Float64, N)
        y = similar(x)

        # Symmetric positive-definite matrix for Cholesky.
        SPD = A * A' + N * I

        # Separate copy for LU/QR.
        SQR = copy(A)

        (; N, A, B, C, x, y, SPD, SQR)
    end
    for N in SIZES
]

println("BLAS threads: ", BLAS.get_num_threads())
#> BLAS threads: 16
println(BLAS.get_config())
#> LBTConfig([ILP64] libopenblas64_.so)

for inputs in INPUTS
    benchmark_blas_lapack(inputs...)
end
#> ===== N = 256 =====
#> GEMM A * B 0.117 ms
#> GEMV A * x 0.004 ms
#> Cholesky 0.166 ms
#> LU 0.309 ms
#> QR 0.887 ms
#> Eigen (symmetric) 5.436 ms
#> 
#> ===== N = 512 =====
#> GEMM A * B 0.708 ms
#> GEMV A * x 0.018 ms
#> Cholesky 1.148 ms
#> LU 1.424 ms
#> QR 4.267 ms
#> Eigen (symmetric) 23.321 ms
#> 
#> ===== N = 1024 =====
#> GEMM A * B 5.049 ms
#> GEMV A * x 0.027 ms
#> Cholesky 5.400 ms
#> LU 5.430 ms
#> QR 19.333 ms
#> Eigen (symmetric) 103.687 ms
#> 
#> ===== N = 2048 =====
#> GEMM A * B 40.955 ms
#> GEMV A * x 0.368 ms
#> Cholesky 39.240 ms
#> LU 38.891 ms
#> QR 134.078 ms
#> Eigen (symmetric) 604.034 ms

using AOCL

println("BLAS threads: ", BLAS.get_num_threads())
#> BLAS threads: 16
println(BLAS.get_config())
#> LBTConfig([ILP64] libaocl64.so, [LP64] libaocl.so)

for inputs in INPUTS
    benchmark_blas_lapack(inputs...)
end
#> ===== N = 256 =====
#> GEMM A * B 0.079 ms
#> GEMV A * x 0.004 ms
#> Cholesky 0.139 ms
#> LU 0.256 ms
#> QR 5.201 ms
#> Eigen (symmetric) 6.584 ms
#> 
#> ===== N = 512 =====
#> GEMM A * B 0.669 ms
#> GEMV A * x 0.009 ms
#> Cholesky 1.047 ms
#> LU 1.080 ms
#> QR 14.699 ms
#> Eigen (symmetric) 28.969 ms
#> 
#> ===== N = 1024 =====
#> GEMM A * B 5.218 ms
#> GEMV A * x 0.022 ms
#> Cholesky 4.182 ms
#> LU 4.435 ms
#> QR 41.410 ms
#> Eigen (symmetric) 110.973 ms
#> 
#> ===== N = 2048 =====
#> GEMM A * B 45.948 ms
#> GEMV A * x 0.414 ms
#> Cholesky 20.869 ms
#> LU 29.655 ms
#> QR 169.518 ms
#> Eigen (symmetric) 736.530 ms

```

Created on 2026-08-11 with [MinimalWorkingExamples v1.1.0](https://github.com/BjarkeHautop/MinimalWorkingExamples.jl) using Julia 1.12.6

> **Environment**
>
> ```plaintext
> Julia Version 1.12.6
> Commit 15346901f00 (2026-04-09 19:20 UTC)
> Build Info:
> Official https://julialang.org release
> Platform Info:
> OS: Linux (x86_64-linux-gnu)
> CPU: 16 × AMD Ryzen 7 8845HS w/ Radeon 780M Graphics
> WORD_SIZE: 64
> LLVM: libLLVM-18.1.7 (ORCJIT, znver3)
> GC: Built with stock GC
> Threads: 1 default, 1 interactive, 1 GC (on 16 virtual cores)
> Environment:
> JULIA_LOAD_PATH = @:@stdlib
> 
> ```

---

<div class="post-metadata">

**Author:** ![dwannetts](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/dwannetts/32/219012_2.png) [@dwannetts](https://discourse.julialang.org/u/dwannetts)\
**Post date:** [August 12, 2026, 12:19am UTC](https://discourse.julialang.org/t/aocl-jl-v5-3-1/138718/8 "2026-08-12T00:19:40Z")

</div>

On

```julia-auto
"Julia Version 1.12.6"
 "Commit 15346901f00 (2026-04-09 19:20 UTC)"
 "Build Info:"
 " Official https://julialang.org release"
 "Platform Info:"
 " OS: Linux (x86_64-linux-gnu)"
 " CPU: 24 × AMD Ryzen 9 7900X 12-Core Processor"
 " WORD_SIZE: 64"
 " LLVM: libLLVM-18.1.7 (ORCJIT, znver4)"
 " GC: Built with stock GC"
 "Threads: 24 default, 1 interactive, 24 GC (on 24 virtual cores)"
 "Environment:"
 " JULIA_NUM_THREADS = 24"

```

using your code, I get

```julia-auto
BLAS threads: 24
LBTConfig([ILP64] libopenblas64_.so)
===== N = 256 =====
GEMM A * B 0.143 ms
GEMV A * x 0.003 ms
Cholesky 0.182 ms
LU 0.270 ms
QR 0.836 ms
Eigen (symmetric) 5.488 ms

===== N = 512 =====
GEMM A * B 0.434 ms
GEMV A * x 0.016 ms
Cholesky 1.000 ms
LU 4.142 ms
QR 4.940 ms
Eigen (symmetric) 33.989 ms

===== N = 1024 =====
GEMM A * B 2.781 ms
GEMV A * x 0.028 ms
Cholesky 4.091 ms
LU 19.990 ms
QR 39.375 ms
Eigen (symmetric) 206.905 ms

===== N = 2048 =====
GEMM A * B 26.754 ms
GEMV A * x 0.095 ms
Cholesky 41.085 ms
LU 119.647 ms
QR 273.866 ms
Eigen (symmetric) 1328.123 ms

BLAS threads: 24
LBTConfig([ILP64] libaocl64.so, [LP64] libaocl.so)
===== N = 256 =====
GEMM A * B 0.043 ms
GEMV A * x 0.003 ms
Cholesky 0.130 ms
LU 0.243 ms
QR 5.412 ms
Eigen (symmetric) 7.036 ms

===== N = 512 =====
GEMM A * B 0.309 ms
GEMV A * x 0.013 ms
Cholesky 0.760 ms
LU 1.045 ms
QR 15.458 ms
Eigen (symmetric) 192.932 ms

===== N = 1024 =====
GEMM A * B 2.400 ms
GEMV A * x 0.030 ms
Cholesky 3.028 ms
LU 4.325 ms
QR 481.706 ms
Eigen (symmetric) 547.092 ms

===== N = 2048 =====
GEMM A * B 24.015 ms
GEMV A * x 0.091 ms
Cholesky 27.928 ms
LU 34.289 ms
QR 2195.568 ms
Eigen (symmetric) 6794.954 ms

```

As you say, a mixed bag.

This mirrors testing I did with AOCC & BLISS under F95 a few years ago.

YMMV.

D.

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<div class="post-metadata">

**Author:** ![skypuppy](https://avatars.discourse-cdn.com/v4/letter/s/54ee81/32.png) [@skypuppy](https://discourse.julialang.org/u/skypuppy)\
**Post date:** [August 14, 2026, 5:48am UTC](https://discourse.julialang.org/t/aocl-jl-v5-3-1/138718/9 "2026-08-14T05:48:05Z")

</div>

The stepped increases in timings, based upon the factors of two, are quite surprising to me. What accounts for that, please? (whether int or float? I assume ints would be significantly speedier. As ints, one would guess that their times, per value,would all be identical). What am I missing here?