AMDGPU.jl

This is exciting news! What's also exciting is that it's not just C++ that can run on this supercomputer; there is also good (currently unofficial) support for programming those GPUs from Julia, via the AMDGPU.jl library (note: I am the author/maintainer of this library). Some of our users have been able to run AMDGPU.jl's testsuite on the Crusher test system (which is an attached testing system with the same hardware configuration as Frontier), as well as their own domain-specific programs that use AMDGPU.jl. What's nice about programming GPUs in Julia is that you can write code once and execute it on multiple kinds of GPUs, with excellent performance. The KernelAbstractions.jl library makes this possible for compute kernels by acting as a frontend to AMDGPU.jl, CUDA.jl, and soon Metal.jl and oneAPI.jl, allowing a single piece of code to

Julia has excellent CUDA support[1], I have no idea why nvidia doesn't promote it more. It's fast, flexible and very featureful. There was a recent thread about it here: https://news.ycombinator.com/item?id=27496679 The AMD [2] and Intel [3] support is younger, but developing quickly. There's also [4] for a unified API that works across different GPU vendors to avoid lockin [1] https://cuda.juliagpu.org/dev/ [2] https://github.com/JuliaGPU/AMDGPU.jl [3] https://github.com/JuliaGPU/oneAPI.jl [4] https://github.com/JuliaGPU/KernelAbstractions.jl

I wish more attention would be towards open source alternatives for CUDA, such as AMD's ROCm[1][2] and Julia framework using it - AMDGPU.jl[3]. It's sad to see so many people praise NVIDIA which is the enemy of open source, openly hostile to anything except their oversized proprietary binary blobs. [1] https://rocmdocs.amd.com/en/latest/index.html [2] https://github.com/RadeonOpenCompute/ROCm [3] https://github.com/JuliaGPU/AMDGPU.jl

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. > Edit: the site has one project per GPU type, shame there isn't one interface that works with every GPU type instead. That would be https://juliagpu.github.io/KernelAbstractions.jl

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 t

Most of the julia GPU stuff is being made vendor agnostic via moving infrastructure from CUDA.jl to CPUCompiler.jl. The package AMDGPU.jl is coming along very well and will be plug-able with all this stuff. https://github.com/JuliaGPU/GPUCompiler.jl https://github.com/JuliaGPU/AMDGPU.jl

Yes, I have done non-trivial implementations of a number of SoTA models in Julia. The framework I've used is Flux[1] which I love for it's simplicity, it is very much like the DarkNet[2] framework in that regard which is refreshing after using TensorFlow. PyTorch is much better about not having unnecessary complexity and a sensible API but Flux is certainly better. The ability for Julia to compile directly to PTX assembly[3][4] means that you can even write the GPU kernels in Julia and eliminate the C/C++ CUDA code. Unfortunately, there is still a lot of work to be done to make it as reliably fast and easy as TensorFlow/PyTorch so I don't think it is usable for production yet. I hope it will be production ready soon but it will likely take some time to highly tune the compute stacks. They are already working on AMD GPU support with AMDGPU

And Julia can do custom ROCM codegen, either by itself or through array and kernel abstractions :) https://github.com/JuliaGPU/AMDGPU.jl https://juliagpu.gitlab.io/KernelAbstractions.jl/ https://github.com/JuliaGPU/GPUArrays.jl/pulse