IJulia.jl
AMDGPU.jl
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| IJulia.jl | AMDGPU.jl | |
|---|---|---|
| 3 | 6 | |
| 2,748 | 264 | |
| 0.8% | 1.9% | |
| 4.8 | 9.1 | |
| about 2 months ago | 7 days ago | |
| Julia | Julia | |
| MIT License | 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.
IJulia.jl
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IA et Calcul scientifique dans Kubernetes avec le langage Julia, K8sClusterManagers.jl
GitHub - JuliaLang/IJulia.jl: Julia kernel for Jupyter
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Numbering in REPL like IPython?
You could also look into Pluto notebooks or IJulia.
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Text Editor or IDE: Machine Learning With Julia
Maybe something like https://github.com/JuliaLang/IJulia.jl/issues/1005 ?
AMDGPU.jl
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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?
What are some alternatives?
Pluto.jl - 🎈 Simple reactive notebooks for Julia
Vulkan.jl - Using Vulkan from Julia
nvim-treesitter - Nvim Treesitter configurations and abstraction layer
oneAPI.jl - Julia support for the oneAPI programming toolkit.
REPLHistory.jl - Gain access to the Julia REPL history.
KernelAbstractions.jl - Heterogeneous programming in Julia
K8sClusterManagers.jl - A Julia cluster manager for Kubernetes
NeuralPDE.jl - Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
klipper-lb - Embedded service load balancer in Klipper
ROCm - AMD ROCm™ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
OhMyREPL.jl - Syntax highlighting and other enhancements for the Julia REPL
GPUCompiler.jl - Reusable compiler infrastructure for Julia GPU backends.