# \[ANN\] AcceleratedKernels.jl - Cross-architecture parallel algorithms for Julia's GPU backends

**URL:** <https://discourse.julialang.org/t/ann-acceleratedkernels-jl-cross-architecture-parallel-algorithms-for-julias-gpu-backends/119698>\
**Category:** Package Announcements\
**Tags:** package, announcement, gpu, performance, parallel\
**Created:** [September 22, 2024, 3:52pm UTC](https://discourse.julialang.org/t/ann-acceleratedkernels-jl-cross-architecture-parallel-algorithms-for-julias-gpu-backends/119698 "2024-09-22T15:52:56Z")\
**Posts on this page:** 1\
**Showing post:** 16

<div class="post-metadata">

**Author:** ![anicusan](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/anicusan/32/30094_2.png) [@anicusan](https://discourse.julialang.org/u/anicusan)\
**Post date:** [September 26, 2024, 11:44pm UTC](https://discourse.julialang.org/t/ann-acceleratedkernels-jl-cross-architecture-parallel-algorithms-for-julias-gpu-backends/119698/16 "2024-09-26T23:44:04Z")

</div>

Thank you for trying it out! ThreadsX.jl is a lovely library, I used it in the past. I just compared sorting times against it:

```julia
# File: sort_benchmark.jl
import ThreadsX
import AcceleratedKernels as AK
using BenchmarkTools

using Random
Random.seed!(0)

# Metal backend; swap with CUDA/CuArray, AMDGPU/ROCArray, oneAPI/oneArray
using Metal
const DeviceArray = MtlArray

# Benchmark settings
const DTYPE = Int64
const N = 10_000_000

function aksort!(v, temp)
    # Separate function to add GPU synchronization
    AK.sort!(v, temp=temp, block_size=256)
    synchronize()
end

println("Julia Base CPU Sort:")
display(@benchmark sort!(v) setup=(v = rand(DTYPE, N)))

println("$DeviceArray AcceleratedKernels GPU Sort:")
temp = DeviceArray(Vector{DTYPE}(undef, N))
display(@benchmark aksort!(v, $temp) setup=(v = DeviceArray(rand(DTYPE, N))))

println("ThreadsX CPU Sort:")
display(@benchmark ThreadsX.sort!(v) setup=(v = rand(DTYPE, N)))

```

On my Mac M3 Max with 10 “performance cores”, when running with `julia --project=. --threads=10 sort_benchmark.jl` I get:

```julia
  Activating project at `~/Prog/Julia/Packages/SortTestThreadsX`

Julia Base CPU Sort:
BenchmarkTools.Trial: 54 samples with 1 evaluation.
 Range (min … max): 82.757 ms … 104.819 ms ┊ GC (min … max): 0.00% … 13.88%
 Time (median): 84.927 ms ┊ GC (median): 1.90%
 Time (mean ± σ): 86.504 ms ± 3.949 ms ┊ GC (mean ± σ): 2.24% ± 2.12%

        ▄█▄                                                     
  ▅▃▁▁▁▅███▅▃▁▆▃▃▅▃▁▁▁▃▁▃▃▁▃▃▁▁▁▁▁▃▁▁▃▁▁▁▁▁▃▁▁▁▁▁▁▁▃▁▁▁▁▁▁▁▁▁▃ ▁
  82.8 ms Histogram: frequency by time 98.3 ms <

 Memory estimate: 76.30 MiB, allocs estimate: 3.

MtlArray AcceleratedKernels GPU Sort:
BenchmarkTools.Trial: 81 samples with 1 evaluation.
 Range (min … max): 42.047 ms … 43.518 ms ┊ GC (min … max): 0.00% … 0.00%
 Time (median): 42.269 ms ┊ GC (median): 0.00%
 Time (mean ± σ): 42.359 ms ± 285.598 μs ┊ GC (mean ± σ): 0.00% ± 0.00%

     ▁▁█▁▃ ▁ ▃ ▁                                    
  ▄▇▄█████▇▇▇▆▇▄▄█▆▆█▆▆▄▁▁▄█▄▁▄▁▄▁▁▁▁▁▁▁▁▁▄▁▁▄▁▄▁▁▆▁▁▄▁▄▁▁▁▁▁▄ ▁
  42 ms Histogram: frequency by time 43.2 ms <

 Memory estimate: 103.91 KiB, allocs estimate: 4012.

ThreadsX CPU Sort:
BenchmarkTools.Trial: 103 samples with 1 evaluation.
 Range (min … max): 32.894 ms … 63.143 ms ┊ GC (min … max): 0.00% … 47.40%
 Time (median): 40.175 ms ┊ GC (median): 16.79%
 Time (mean ± σ): 40.378 ms ± 3.610 ms ┊ GC (mean ± σ): 16.35% ± 5.09%

                █▃                                             
  ▄▁▁▂▁▁▁▁▁▁▁▁▂████▄▄▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂ ▂
  32.9 ms Histogram: frequency by time 61.8 ms <

 Memory estimate: 187.33 MiB, allocs estimate: 183908.

```

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