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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
6416
Venue
SIGMOD
Year
2022
Pagerank
6.4481656e-05
Overall Rank
4,912 | 66.31%
DOI
10.1145/3514221.3517902

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{miao_sigmod22,
        title = {{HET-GMP: A Graph-based System Approach to Scaling Large Embedding Model Training}},
        author = {Miao, Xupeng and Shi, Yining and Zhang, Hailin and Zhang, Xin and Nie, Xiaonan and Yang, Zhi and Cui, Bin},
        series = {{SIGMOD} '22},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3514221.3517902},
        url = {https://dl.acm.org/doi/10.1145/3514221.3517902},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 12 of 12 citing papers.

Rank Citing Paper Year Venue Pagerank
5,003 Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism 2023 VLDB 6.4065691e-05
5,205 ETC: Efficient Training of Temporal Graph Neural Networks over Large-scale Dynamic Graphs 2024 VLDB 6.318626e-05
5,597 Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses 2024 VLDB 6.1547003e-05
6,900 Apt-Serve: Adaptive Request Scheduling on Hybrid Cache for Scalable LLM Inference Serving 2025 SIGMOD 5.7430032e-05
7,190 PetPS: Supporting Huge Embedding Models with Persistent Memory 2023 VLDB 5.6772818e-05
8,883 FlexMoE: Scaling Large-scale Sparse Pre-trained Model Training via Dynamic Device Placement 2023 SIGMOD 5.351513e-05
8,907 Scheduling Data Processing Pipelines for Incremental Training on MLP-based Recommendation Models 2025 SIGMOD 5.3483178e-05
9,475 BladeDISC: Optimizing Dynamic Shape Machine Learning Workloads via Compiler Approach 2023 SIGMOD 5.2634238e-05
9,520 Experimental Analysis of Large-scale Learnable Vector Storage Compression 2024 VLDB 5.2561283e-05
9,556 CAFE: Towards Compact, Adaptive, and Fast Embedding for Large-scale Recommendation Models 2024 SIGMOD 5.2528121e-05
10,114 Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updates 2022 VLDB 5.1319012e-05
10,400 Scalable Graph Indexing using GPUs for Approximate Nearest Neighbor Search 2026 SIGMOD 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 11 of 11 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
13 Mining Association Rules between Sets of Items in Large Databases 1993 SIGMOD 0.0006567919
20 Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud 2012 VLDB 0.00056944564
521 PyTorch Distributed: Experiences on Accelerating Data Parallel Training 2020 VLDB 0.0001713368
920 APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding 2021 SIGMOD 0.00013209734
2,485 HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework 2022 VLDB 8.5145736e-05
4,956 Heterogeneity-Aware Distributed Machine Learning Training via Partial Reduce 2021 SIGMOD 6.4290135e-05
6,519 Dynamic Parameter Allocation in Parameter Servers 2020 VLDB 5.8536062e-05
6,550 Reliable Data Distillation on Graph Convolutional Network 2020 SIGMOD 5.8430465e-05
6,633 Agile and Accurate CTR Prediction Model Training for Massive-Scale Online Advertising Systems 2021 SIGMOD 5.8185371e-05
8,334 Just Move It! Dynamic Parameter Allocation in Action 2021 VLDB 5.4518674e-05
11,999 LDA*: A Robust and Large-scale Topic Modeling System 2017 VLDB 5.093636e-05
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