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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
- 7042
- Venue
- SIGMOD
- Year
- 2025
- Pagerank
- 4.1905499e-05
- Overall Rank
- 10,390 | 27.79%
- DOI
-
10.1145/3709695
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| Rank |
Citing Paper |
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Venue |
Pagerank |
Outgoing Citations (Sorted by Pagerank)
Showing 14 of 14 cited papers.
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