HyperMR: Efficient Hypergraph-enhanced Matrix Storage on Compute-in-Memory Architecture
Summary: HyperMR uses hypergraph-based CIM storage; two CIM-tailored NP-hard objectives; solved by two-phase partitioning. An access-aware hypergraph generator handles diverse matrices; results show gains up to 34.9% and 29.65% on synthetic queries. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Yifan Wu (Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security; Zhejiang University)
- 2. Ke Chen (Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security; Zhejiang University)
- 3. Gang Chen (Zhejiang University)
- 4. Dawei Jiang (Zhejiang University)
- 5. Huan Li (Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security; Zhejiang University)
- 6. Lidan Shou (Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security; Zhejiang University)
BibTeX Citation
@inproceedings{wu_sigmod25,
title = {{HyperMR: Efficient Hypergraph-enhanced Matrix Storage on Compute-in-Memory Architecture}},
author = {Wu, Yifan and Chen, Ke and Chen, Gang and Jiang, Dawei and Li, Huan and Shou, Lidan},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3709695},
url = {https://dl.acm.org/doi/10.1145/3709695},
year = {2025}
}
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