julia-distributed-computing VS AMDGPU.jl

GitHub - juliohm/julia-distributed-computing: The ultimate guide to distributed computing in Julia

However, I don't really find documentation on how to do that! I read the README in this repository, which suggests to use SLURM, PBS or LSF as a job scheduler. Also, there's K8sClusterManager.jl, which seems like it could do what I wanted - I'm just surprised, that it is such a small project! I expected distributed computing via Kubernetes to be a big topic in Julia, yet I can't seem to find good documentation on how to actually set this up.

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.

The Julia package AMDGPU.jl provides a Julia interface for AMD GPU (ROCm) programming. As they say, the package is being developed for Julia 1.7, 1.9 and above, but not 1.8. Therefore I downloaded the Julia binary of version 1.7.3 from the older releases Julia page.

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 be portable to AMD, NVIDIA, Intel, and Apple GPUs, and also CPUs. Similarly, the GPUArrays.jl library allows the same behavior for idiomatic array operations, and will automatically dispatch calls to BLAS, FFT, RNG, linear solver, and DNN vendor-provided libraries when appropriate.

I'm personally looking forward to helping researchers get their Julia code up and running on Frontier so that we can push scientific computing to the max!

Library link: <https://github.com/JuliaGPU/AMDGPU.jl>

GitHub - JuliaGPU/AMDGPU.jl: AMD GPU (ROCm) programming in Julia

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.