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feat(issue9): multi-table AlltoAllv aggregation fusion analysis - #22

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xyaohubery:feat/issue9-multi-table-alltoall-fusion
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feat(issue9): multi-table AlltoAllv aggregation fusion analysis#22
xyaohubery wants to merge 1 commit into
Tencent:mainfrom
xyaohubery:feat/issue9-multi-table-alltoall-fusion

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Summary

Analytical model for fusing multiple embedding table AlltoAllv communications into a single aggregated operation, addressing Issue #9.

Key Findings

Scenario Separate Fused Speedup Launch Reduction
16 small tables 130.6 us 42.6 us 3.1x 94%
32 medium tables 277.0 us 93.0 us 3.0x 97%
14 mixed tables 125.4 us 49.4 us 2.5x 93%

Three Fusion Strategies Analyzed

  1. Full fusion: All tables aggregated — best for many small tables
  2. Dim-grouped: Group by embedding_dim for size-aligned fusion
  3. Threshold-based: Fuse small tables (<64KB), keep large ones separate

🤖 Generated with Claude Code

Analytical model for fusing multiple embedding table AlltoAllv
communications in recommendation/advertising training.

Key finding: 16-table fusion achieves 3x speedup and 94% launch
reduction vs separate per-table AlltoAllv calls.

Co-Authored-By: Claude <noreply@anthropic.com>
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