AMDGPU.jl
oneAPI.jl
Our great sponsors
AMDGPU.jl | oneAPI.jl | |
---|---|---|
6 | 4 | |
264 | 173 | |
1.9% | 2.3% | |
9.1 | 8.1 | |
6 days ago | 11 days ago | |
Julia | Julia | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
AMDGPU.jl
-
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.
-
[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.
-
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>
-
IA et Calcul scientifique dans Kubernetes avec le langage Julia, K8sClusterManagers.jl
GitHub - JuliaGPU/AMDGPU.jl: AMD GPU (ROCm) programming in Julia
-
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?
oneAPI.jl
-
GPU vendor-agnostic fluid dynamics solver in Julia
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`.
- Writing GPU shaders in Julia?
-
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?
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
What are some alternatives?
Vulkan.jl - Using Vulkan from Julia
ROCm - AMD ROCm™ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
KernelAbstractions.jl - Heterogeneous programming in Julia
NeuralPDE.jl - Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
Makie.jl - Interactive data visualizations and plotting in Julia
StaticCompiler.jl - Compiles Julia code to a standalone library (experimental)
GPUCompiler.jl - Reusable compiler infrastructure for Julia GPU backends.
julia-distributed-computing - The ultimate guide to distributed computing in Julia