As far as I am aware, this is the first cross-architecture parallel standard library from a unified codebase - that is, the code is written as KernelAbstractions.jl backend-agnostic kernels, which are then transpiled to a GPU backend; that means we benefit from all the optimisations available on the native platform and official compiler stacks. For example, unlike open standards like OpenCL that require GPU vendors to implement that API for their hardware, we target the existing official compilers. And while performance-portability libraries like Kokkos and RAJA are powerful for large C++ codebases, they require US National Lab-level development and maintenance efforts to effectively forward calls from a single API to other OpenMP, CUDA Thrust, ROCm rocThrust, oneAPI DPC++ libraries developed separately. In comparison, this library was developed effectively in a week by a single person because developing packages in Julia is just a joy.

Again, this is only possible because of the unique Julia compilation model, the JuliaGPU organisation work for reusable GPU backend infrastructure, and especially the KernelAbstractions.jl backend-agnostic kernel language. Thank you.

This is the very first release of this library; while tests are included for all algorithms, I only ran them locally on the oneAPI (laptop Intel UHD Graphics 620), CUDA (laptop with Nvidia Quadro RTX 4000 and data centre Nvidia A100-40), Metal (Mac M2 and M3), and AMD (data centre AMD MI210) backends. Some kinks might still exist for some platform permutations before a CI is set up. The API may undergo some changes in the following weeks as we discuss it with the Julia community - please join the conversation!

AcceleratedKernels.jl will also be a fundamental building block of applications developed at EvoPhase, so it will see continuous heavy use with industry backing. Long-term stability, performance improvements and support are priorities for us.