KernelAbstractions.jl VS oneAPI.jl

For myself, I use Julia to write my own software (that is run on AMD supercomputer) on Fedora system, using 6800XT. For my experience, everything worked nicely. To install you need to install rocm-opencl package with dnf, AMD Julia package (AMDGPU.jl), add yourself to video group and you are good to go. Also, Julia's KernelAbstractions.jl is a good to have, when writing portable code.

>Higher level abstractions

like these?

https://github.com/JuliaGPU/KernelAbstractions.jl

For kernel programming, https://github.com/JuliaGPU/KernelAbstractions.jl (shortened to KA) is what the JuliaGPU team has been developing as a unified programming interface for GPUs of any flavor. It's not significantly different from the (basically identical) interfaces exposed by CUDA.jl and AMDGPU.jl, so it's easy to transition to. I think the event system in KA is also far superior to CUDA's native synchronization system, since it allows one to easily express graphs of dependencies between kernels and data transfers.

https://github.com/JuliaGPU/oneAPI.jl

As for syntax, Julia syntax scales from a scripting language to a fully typed language. You can write valid and performant code without specifying any types, but you can also specialize methods for specific types. The type notation uses `::`. The types also have parameters in the curly brackets. The other aspect that makes this specific example complicated is the use of Lisp-like macros which starts with `@`. These allow for code transformation as I described earlier. The last aspect is that the author is making extensive use of Unicode. This is purely optional as you can write Julia with just ASCII. Some authors like to use `ε` instead of `in`.

https://github.com/JuliaGPU/AMDGPU.jl

https://github.com/JuliaGPU/oneAPI.jl

These are both less mature than CUDA.jl, but are in active development.

OpenCL and various other solutions basically require that one writes kernels in C/C++. This is an unfortunate limitation, and can make it hard for less experienced users (researchers especially) to write correct and performant GPU code, since neither language lends itself to writing many mathematical and scientific models in a clean, maintainable manner (in my opinion).

What oneAPI (the runtime), and also AMD's ROCm (specifically the ROCR runtime), do that is new is that they enable packages like oneAPI.jl [1] and AMDGPU.jl [2] to exist (both Julia packages), without having to go through OpenCL or C++ transpilation (which we've tried out before, and it's quite painful). This is a great thing, because now users of an entirely different language can still utilize their GPUs effectively and with near-optimal performance (optimal w.r.t what the device can reasonably attain).

[1] https://github.com/JuliaGPU/oneAPI.jl