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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)

Paper ID
7103
Venue
SIGMOD
Year
2025
Pagerank
5.093636e-05
Overall Rank
10,666 | 26.83%
DOI
10.1145/3709695

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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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