Add GPU support#108
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## master #108 +/- ##
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+ Coverage 95.27% 96.64% +1.37%
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Lines 1459 2055 +596
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CPU benchmarks are posted here: https://github.com/JuliaDSP/Wavelets.jl/actions/runs/24744254291?pr=108 It is a bit noisy, as expected on CI servers, but otherwise no significant regression is detected. I run the GPU benchmarks locally, with the following results: |
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Hi @gummif, |
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This PR introduces GPU acceleration to the library. The implementation is designed to be a drop-in solution, with no breaking changes to the existing API. Thanks to KernelAbstractions (https://juliagpu.github.io/KernelAbstractions.jl/stable/), the implementation is fully backend-agnostic.
Besides, the PR brings two additional changes:
benchmark/benchmark.jl), and another that compares the speed of CPU and GPU (benchmark/gpu_benchmark.jl).github/workflows/benchmark.yml).Project.tomlfile in thetestfolder using theworkspacefeature introduced in Julia 1.12, as this is now recommended by approach (https://pkgdocs.julialang.org/v1/creating-packages/#Recommended-approach:-Using-workspaces-with-test/Project.toml)The testing included verification (the GPU implementation's output matches the CPU functions' output) and benchmarking (the CPU implementation didn't get slower, and the GPU implementation provides significant speedup for large arrays).
Disclaimer: The code is mostly generated by GitHub Copilot.