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AMDGPU.jl Alternatives
Similar projects and alternatives to AMDGPU.jl
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distrobox
Use any linux distribution inside your terminal. Enable both backward and forward compatibility with software and freedom to use whatever distribution you’re more comfortable with. Mirror available at: https://gitlab.com/89luca89/distrobox
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Pyston
(No longer maintained) A faster and highly-compatible implementation of the Python programming language.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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NeuralPDE.jl
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
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k8s-device-plugin
Kubernetes (k8s) device plugin to enable registration of AMD GPU to a container cluster (by ROCm)
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
AMDGPU.jl discussion
AMDGPU.jl reviews and mentions
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Why is AMD leaving ML to nVidia?
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.
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[GUIDE] How to install ROCm for GPU Julia programming via Distrobox
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.
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First True Exascale Supercomputer
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>
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IA et Calcul scientifique dans Kubernetes avec le langage Julia, K8sClusterManagers.jl
GitHub - JuliaGPU/AMDGPU.jl: AMD GPU (ROCm) programming in Julia
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Cuda.jl v3.3: union types, debug info, graph APIs
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.
- Unified programming model for all devices – will it catch on?
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A note from our sponsor - CodeRabbit
coderabbit.ai | 6 Nov 2024
Stats
JuliaGPU/AMDGPU.jl is an open source project licensed under GNU General Public License v3.0 or later which is an OSI approved license.
The primary programming language of AMDGPU.jl is Julia.