Atom: An Efficient Query Serving System for Embedding-based Knowledge Graph Reasoning with Operator-level Batching
Summary: Atom uses operator-level batching for embedding-based KGR, decomposing queries into operators and batching by type. Hybrid scheduling with a two-level pipeline and GPU kernels yields 20x throughput and 5x latency reduction over prior systems. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Qihui Zhou (Chinese University of Hong Kong)
- 2. Peiqi Yin (Chinese University of Hong Kong)
- 3. Xiao Yan (Centre for Perceptual and Interactive Intelligence)
- 4. Changji Li (Chinese University of Hong Kong)
- 5. Guanxian Jiang (Chinese University of Hong Kong)
- 6. James Cheng (Chinese University of Hong Kong)
BibTeX Citation
@inproceedings{zhou_sigmod24,
title = {{Atom: An Efficient Query Serving System for Embedding-based Knowledge Graph Reasoning with Operator-level Batching}},
author = {Zhou, Qihui and Yin, Peiqi and Yan, Xiao and Li, Changji and Jiang, Guanxian and Cheng, James},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3677129},
url = {https://dl.acm.org/doi/10.1145/3677129},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,163 | GPS: Revisiting the Data Layout for Disk-based High-Dimensional Vector Search | 2026 | SIGMOD | 5.0682654e-05 |
| 10,609 | SG-Serve: Efficient Model Serving for Subgraph-based Graph Representation Learning | 2026 | SIGMOD | 4.9793485e-05 |
| 11,178 | CARINA: An Efficient CXL-Oriented Embedding Serving System for Recommendation Models | 2025 | SIGMOD | 4.9793485e-05 |
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