Summary
Julia is well-suited for GPU programming, as it allows for both kernel and surrounding code to be written in Julia itself, while running on most GPU hardware.
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JuliaGPU is a Github organization that unifies many packages for GPU programming in Julia.
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Julia is designed with performance in mind, and combines a sophisticated LLVM-based compiler with a high-level syntax and flexible compiler.
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JuliaGPU provides 46 repositories for GPU computing, including CUDA.jl, GPUArrays.jl, KernelAbstractions.jl, AMDGPU.jl, oneAPI.jl, and Metal.jl.
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These repositories provide reusable array functionality for Julia's various GPU backends, and the JuliaGPU landing page provides information about the top languages, most used topics, and people who use JuliaGPU.
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Tutorials are available to help Julia users take their first step into GPU computing.
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Summaries from the best pages on the web
Summary
The good news is Julia, a high level scripting language, allows you to write both kernel and surrounding code in Julia itself, while running on most GPU hardware!
Most highly parallel algorithms need to churn through quite a bit of data to overcome all the threading and latency overheads.
An Introduction to GPU Programming in Julia - Nextjournal
nextjournal.com
Summary
JuliaGPU is a Github organization created to unify the many packages for programming GPUs in Julia. With its high-level syntax and flexible compiler, Julia is well positioned to productively program hardware accelerators like GPUs without sacrificing performance.
JuliaGPU
juliagpu.org
Summary
JuliaGPU is a GitHub repository for GPU computing in Julia, which includes 46 repositories available for download. These repositories include CUDA.jl, GPUArrays.jl, KernelAbstractions.jl, AMDGPU.jl, oneAPI.jl, and Metal.jl, all of which provide reusable array functionality for Julia's various GPU backends. The JuliaGPU landing page provides information about the top languages, most used topics, and people who use JuliaGPU.
JuliaGPU · GitHub
github.com
Summary
Julia has first-class support for GPU programming: you can use high-level abstractions or obtain fine-grained control, all without ever leaving your favorite programming language. The purpose of this tutorial is to help Julia users take their first step into GPU computing.
Introduction · CUDA.jl
juliagpu.gitlab.io
The goal was to match performance of the C++ version, but some of its kernels were implemented in CUDA C… So obviously Julia needed a GPU back-end!
Julia GPU
notamonadtutorial.com
