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HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework
Summary: HET scales huge embedding training with a cache-enabled distributed framework that exploits skewed popularity. Embedding-level consistency with write-time staleness enables cache coherence, yielding up to 88% comms reduction and 20.68x speedup.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 12794
- Venue
- VLDB
- Year
- 2022
- Pagerank
- 8.3224016e-05
- Overall Rank
- 2,678 | 81.39%
- DOI
-
10.14778/3489496.3489511
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 22 of 22 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 3,475 |
BlindFL: Vertical Federated Machine Learning without Peeking into Your Data |
2022 |
SIGMOD |
7.0590045e-05 |
| 4,054 |
Orca: Scalable Temporal Graph Neural Network Training with Theoretical Guarantees |
2023 |
SIGMOD |
6.4910804e-05 |
| 5,016 |
DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks |
2023 |
SIGMOD |
5.7512359e-05 |
| 5,169 |
HET-GMP: A Graph-based System Approach to Scaling Large Embedding Model Training |
2022 |
SIGMOD |
5.642415e-05 |
| 5,356 |
NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams |
2024 |
VLDB |
5.5514335e-05 |
| 6,361 |
Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism |
2023 |
VLDB |
5.0903244e-05 |
| 6,997 |
PetPS: Supporting Huge Embedding Models with Persistent Memory |
2023 |
VLDB |
4.8629617e-05 |
| 7,535 |
Angel-PTM: A Scalable and Economical Large-scale Pre-training System in Tencent |
2023 |
VLDB |
4.7131061e-05 |
| 8,117 |
SDPipe: A Semi-Decentralized Framework for Heterogeneity-aware Pipeline-parallel Training |
2023 |
VLDB |
4.5788485e-05 |
| 8,430 |
Accelerating Graph Indexing for ANNS on Modern CPUs |
2025 |
SIGMOD |
4.508568e-05 |
| 8,733 |
Scheduling Data Processing Pipelines for Incremental Training on MLP-based Recommendation Models |
2025 |
SIGMOD |
4.4520434e-05 |
| 8,807 |
FlexMoE: Scaling Large-scale Sparse Pre-trained Model Training via Dynamic Device Placement |
2023 |
SIGMOD |
4.4413307e-05 |
| 9,091 |
FEC: Efficient Deep Recommendation Model Training with Flexible Embedding Communication |
2023 |
SIGMOD |
4.393856e-05 |
| 9,408 |
CAFE: Towards Compact, Adaptive, and Fast Embedding for Large-scale Recommendation Models |
2024 |
SIGMOD |
4.3399748e-05 |
| 9,414 |
Experimental Analysis of Large-scale Learnable Vector Storage Compression |
2024 |
VLDB |
4.3399748e-05 |
| 9,596 |
Scalable Graph Convolutional Network Training on Distributed-Memory Systems |
2023 |
VLDB |
4.3150788e-05 |
| 9,677 |
Apt-Serve: Adaptive Request Scheduling on Hybrid Cache for Scalable LLM Inference Serving |
2025 |
SIGMOD |
4.3006524e-05 |
| 9,786 |
MEMO: Fine-grained Tensor Management For Ultra-long Context LLM Training |
2025 |
SIGMOD |
4.2799988e-05 |
| 9,965 |
Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updates |
2022 |
VLDB |
4.2229209e-05 |
| 10,011 |
A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,977 |
GE2: A General and Efficient Knowledge Graph Embedding Learning System |
2024 |
SIGMOD |
4.1905499e-05 |
| 11,267 |
EmbedX: A Versatile, Efficient and Scalable Platform to Embed Both Graphs and High-Dimensional Sparse Data |
2023 |
VLDB |
4.1905499e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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