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 (East China Normal University)
- 2. Zhizhen Xu (East China Normal University)
- 3. Chen Xu (East China Normal University)
- 4. Juan Soto (Technical University of Berlin)
- 5. Volker Markl (Technical University of Berlin)
- 6. Weining Qian (East China Normal University)
- 7. Aoying Zhou (East China Normal University)
BibTeX Citation
@article{chen_vldb21,
title = {{HyMAC: A Hybrid Matrix Computation System}},
author = {Chen, Zihao and Xu, Zhizhen and Xu, Chen and Soto, Juan and Markl, Volker and Qian, Weining and Zhou, Aoying},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {12},
pages = {2699--2702},
doi = {10.14778/3476311.3476323},
url = {https://doi.org/10.14778/3476311.3476323},
year = {2021}
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Outgoing Citations (Sorted by Pagerank)
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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 |
|---|---|---|---|---|
| 415 | SystemML: Declarative Machine Learning on Spark | 2016 | VLDB | 0.0001888524 |
| 2,681 | Exploiting Matrix Dependency for Efficient Distributed Matrix Computation | 2015 | SIGMOD | 8.2632778e-05 |
| 11,669 | Hybrid Evaluation for Distributed Iterative Matrix Computation | 2021 | SIGMOD | 5.093636e-05 |
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