llnl / llnl/UnifyFS

Optimizing ibverbs network performance

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Dominant language
C
Stars
122
Forks
34
PR merge metrics
No merged PRs in 30d

Description

Eventually, we'll want to be able to switch from TCP to ibverbs to pick up better performance.

To communicate with ibverbs, one has to register memory with the network card, and that registration step has high overhead. To work around this, it's common to pre-register memory regions with the card and then use those regions for network buffers. It looks like margo provides some help with that by using their bulk pools:

https://xgitlab.cels.anl.gov/sds/margo/-/blob/master/include/margo-bulk-pool.h

Most MPI implementations on verbs use pre-pinned memory buffers to transfer small and medium data. They will copy data from the original user buffer into these buffers and send from there (memcpy cost is lower than registration). For large transfers (e.g., MB range), they will dynamically pin the user buffer and send from it, since at that point (memcpy cost is higher than registration).

This refactoring may push us to packetize messages that we are not packetizing right now.

Contributor guide

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First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start by tracing the current TCP data path and reviewing the linked Margo bulk-pool reference. Compare the existing message handling with the proposed pre-registered buffers, dynamic registration, and packetization approach. Done would require a defined implementation plan and verified ibverbs performance behavior, but the issue names no files or tests.

Written by the indexing model from the issue text.

Assessment

Tech stack
c
Domain
networking, performance
Issue type
Refactor
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
20/100

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