Reduce memory usage in distributed triangle updates
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 25/100
- Issue type
- Feature
- Clarity
- Mostly clear
- Activity status
- Stale
- Tech stack
- cpp
- Domain
- distributed-systems, performance
Research direction
Start by reading tests/cpp/test_overlap.cpp around line 70 to understand the Swizzle1D reference, then review the distributed triangle update context from NVIDIA/Fuser#5890. The issue's proposed direction is to stream-parallelize allgather and reducescatter; done means reducing per-GPU memory to the stated O(b * s/dy * s/dx * c) bound.
Written by the indexing model from the issue text.
Description
This request comes from https://github.com/NVIDIA/Fuser/pull/5890. Currently, nvFuser uses too much memory by allgathering one of the einsum's operands.
A better (not sure if the best) approach is to stream-parallelize the allgather and the reducescatter:
This way, each GPU only has to store O(b * s/dy * s/dx * c).
In nvFuser, this can be represented as
Note that
dy
|
s
is a Swizzle1D similar to https://github.com/NVIDIA/Fuser/blob/231d48c002629b6f8b33a9bae4ed9ff440d1777c/tests/cpp/test_overlap.cpp#L70
- Dominant language
- C++
- Stars
- 404
- Forks
- 84
- PR merge metrics
- No merged PRs in 30d
Contributor guide
No contributing guide indexed for this repository
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.
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