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HET-GMP: A Graph-based System Approach to Scaling Large Embedding Model Training

Summary: HET-GMP uses a graph-based design to scale embedding models via a bigraph of data-sample to embedding-vector access. Graph locality, skewness-aware replication/partitioning, bounded-asynchronous sync reduces comms; 87.5% reduction, 27.5x CTR speedup. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6354
Venue
SIGMOD
Year
2022
Pagerank
5.7337977e-05
Overall Rank
5,052 | 64.86%
DOI
10.1145/3514221.3517902

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