oneAPI.jl
julia-ml-from-scratch
| oneAPI.jl | julia-ml-from-scratch | |
|---|---|---|
| 4 | 1 | |
| 174 | 30 | |
| 1.7% | - | |
| 8.7 | 7.0 | |
| 10 days ago | 4 months ago | |
| Julia | Julia | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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oneAPI.jl
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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?
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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.
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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
julia-ml-from-scratch
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GPU vendor-agnostic fluid dynamics solver in Julia
https://github.com/odsl-team/julia-ml-from-scratch/issues/2
Summarizing, they benchmark some machine learning code that uses KernelAbstractions.jl on different platforms and find:
What are some alternatives?
ROCm - AMD ROCm™ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
www.julialang.org - Julia Project website
Vulkan.jl - Using Vulkan from Julia
Oceananigans.jl - 🌊 Julia software for fast, friendly, flexible, ocean-flavored fluid dynamics on CPUs and GPUs
Makie.jl - Interactive data visualizations and plotting in Julia
nyxt - Nyxt - the hacker's browser.
StaticCompiler.jl - Compiles Julia code to a standalone library (experimental)
AMDGPU.jl - AMD GPU (ROCm) programming in Julia
GPUCompiler.jl - Reusable compiler infrastructure for Julia GPU backends.
KernelAbstractions.jl - Heterogeneous programming in Julia
julia - The Julia Programming Language