STAT for deadlock detection in ML/AI stack
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- Dominant language
- C
- Stars
- 70
- Forks
- 25
- PR merge metrics
- No merged PRs in 30d
Description
Hello @lee218llnl,
I have been a happy user of STAT for a long time and used STAT for deadlock detection at scale on many systems.
Now working with AI/ML stack and wonder if STAT would be also useful for distributed training workloads as well.
Especially, I wonder about the frameworks like PyTorch where NCCL is default backend. Any experience or suggestion about this?
Also, as I don't see many updates on STAT repo, I was wondering if there are other efforts ongoing or alternative tools being developed at LLNL (or outside).
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
No file, test, or entry point is named. Start by reviewing STAT's current capabilities and repository history against PyTorch distributed training with NCCL, then identify whether support exists and which LLNL or external alternatives address the use case.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- pytorch
- Domain
- distributed-systems, machine-learning, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100