Investigate using MIOpen's immediate mode for conv algorithm search
Nobody has claimed this yet.
- Dominant language
- Julia
- Stars
- 344
- Forks
- 79
- Avg merge
- 2d 23h
- Merged PRs (30d)
- 27
Description
As described in https://rocm.docs.amd.com/projects/MIOpen/en/latest/find_and_immediate.html#immediate-mode-api. The main benefits would be enabling persistent on-disk caching for users while removing the somewhat tricky algorithm cache handling code on the Julia side. The main drawback would be not being able to control how the caching is done, but on balance I think the benefits outweigh this bit of flexibility. Notably, cuDNN does not support this kind of transparent caching (ref. https://github.com/pytorch/pytorch/issues/88410) and currently requires manual caching on the Julia side (ref. https://github.com/JuliaGPU/CUDA.jl/issues/1947).
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 MIOpen's immediate mode API documentation and locating the Julia-side algorithm cache handling code mentioned in the issue. Compare the current search and caching behavior with immediate mode, and consider the work complete when persistent on-disk caching is enabled without the existing cache handling complexity.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- backend, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100