JuliaDiff / JuliaDiff/ChainRules.jl
Make `OneElement` more GPU friendly
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- Dominant language
- Julia
- Stars
- 475
- Forks
- 98
- PR merge metrics
- No merged PRs in 30d
Description
Ref. https://github.com/FluxML/Flux.jl/pull/2368. I see a couple of possibly complementary ways to go about this. Easiest would be to define an Adapt rule for OneElement so it's materialized or substituted with some GPU-friendly equivalent when run through CUDA.cu. The other would be defining overloads for certain functions such as mul! which can take advantage of the sparsity.
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading the referenced FluxML/Flux.jl pull request 2368 and the existing OneElement implementation. Compare the two suggested directions—an Adapt rule used by CUDA.cu or overloads such as mul!—and establish which GPU behavior and sparse operations should be supported before defining done.
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Assessment
- Tech stack
- julia
- Domain
- performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100