https://github.com/juliagpu/daggergpu.jl

GPU integrations for Dagger.jl
https://github.com/juliagpu/daggergpu.jl

dagger gpu heterogeneous-computing

Last synced: about 2 months ago
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GPU integrations for Dagger.jl

• Host: GitHub
• URL: https://github.com/juliagpu/daggergpu.jl
• Owner: JuliaGPU
• License: other
• Created: 2020-04-04T18:13:21.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2024-08-26T19:29:03.000Z (4 months ago)
• Last Synced: 2024-08-26T22:45:55.776Z (4 months ago)
• Topics: dagger, gpu, heterogeneous-computing
• Language: Julia
• Homepage:
• Size: 60.5 KB
• Stars: 49
• Watchers: 7
• Forks: 11
• Open Issues: 9
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
# DaggerGPU

**GPU integrations for Dagger.jl**

DaggerGPU.jl makes use of the `Dagger.Processor` infrastructure to dispatch Dagger kernels to NVIDIA, AMD, and Apple GPUs, via CUDA.jl, AMDGPU.jl, and Metal.jl respectively. Usage is simple: `add` or `dev` DaggerGPU.jl and CUDA.jl/AMDGPU.jl/Metal.jl appropriately, load it with `using DaggerGPU`, and add `DaggerGPU.CuArrayDeviceProc`/`DaggerGPU.ROCArrayProc`/`DaggerGPU.MtlArrayDeviceProc` to your scheduler or thunk options (see Dagger.jl documentation for details on how to do this).

DaggerGPU.jl is still experimental, but we welcome GPU-owning users to try it out and report back on any issues or sharp edges that they encounter. When filing an issue about DaggerGPU.jl, please provide:

- The complete error message and backtrace

- Julia version

- GPU vendor and model

- CUDA/AMDGPU version(s)