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JuliaGPU/GPUArrays.jl

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Reusable array functionality for Julia's various GPU backends.

6 mentions on Hacker News by 5 people: 1 story, 5 comments · first 2017-04-12 · latest 2020-09-21

Mentions per year 6 mentions · busiest year 2017 (4)
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  1. …:) https://github.com/JuliaGPU/AMDGPU.jl https://juliagpu.gitlab.io/KernelAbstractions.jl/ https://github.com/JuliaGPU/GPUArrays.jl/pulse
    comment by amkkma · 2020-09-21 · in Swift for TensorFlow – A system for deep learning and differentiable computing
  2. That's how you do it: https://github.com/JuliaGPU/GPUArrays.jl/blob/master/src/lin... Or directly with cuda shfl intrinsics: https://github.com/JuliaGPU/CUDAnative.jl/blob/b249dfd145501...
    comment by simondanisch · 2018-10-19 · in An Introduction to GPU Programming in Julia
  3. …Allowing arbitrary array types means your algorithm can already work on the GPU via GPUArrays.jl ( https://github.com/JuliaGPU/GPUArrays.jl ). This means that all differential equation, optimization, numerical linear algebra, etc. routines…
    comment by ChrisRackauckas · 2017-10-16 · in Switching from Common Lisp to Julia
  4. As replied in the thread above, the GPUArrays package can use OpenCL: https://github.com/JuliaGPU/GPUArrays.jl More details in this package: https://github.com/SimonDanisch/Transpiler.jl
    comment by ViralBShah · 2017-08-25 · in Generic GPU Kernels in Julia
  5. …a bunch of work to make Julia as hardware-agnostic as possible, but you gotta start somewhere. [1]: https://github.com/JuliaGPU/GPUArrays.jl
    comment by one-more-minute · 2017-08-24 · in Generic GPU Kernels in Julia
  6. ▲ 2
    Julia's new GPU Array/Tensor library
    story by simondanisch · 2017-04-12 · 0 comments