HyMAC: A Hybrid Matrix Computation System
Summary: HyMAC enables per-iteration hybrid plans that blend full and incremental evaluation to exploit non-uniform convergence in distributed matrix computation. It shows when hybrid plans beat both full and incremental evaluation on large matrix workloads. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Zihao Chen
- 2. Zhizhen Xu
- 3. Chen Xu
- 4. Juan Soto
- 5. Volker Markl
- 6. Weining Qian
- 7. Aoying Zhou
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 557 | SystemML: Declarative Machine Learning on Spark | 2016 | VLDB | 0.00020197988 |
| 2,848 | Exploiting Matrix Dependency for Efficient Distributed Matrix Computation | 2015 | SIGMOD | 8.0208832e-05 |
| 11,472 | Hybrid Evaluation for Distributed Iterative Matrix Computation | 2021 | SIGMOD | 4.1945683e-05 |
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