Hybrid Evaluation for Distributed Iterative Matrix Computation
Summary: Proposes matrix reorganization and a hybrid evaluation that interleaves full and incremental updates for distributed iterative matrix computations. A cost model plus selective comparison reduces overhead, and HyMAC (SystemML-based) delivers ~23% average speedups and outperforms SystemML, ScaLAPACK, SciDB on large datasets. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Zihao Chen (East China Normal University)
- 2. Chen Xu (East China Normal University)
- 3. Juan Soto (Technical University of Berlin)
- 4. Volker Markl (Technical University of Berlin)
- 5. Weining Qian (East China Normal University)
- 6. Aoying Zhou (East China Normal University)
BibTeX Citation
@inproceedings{chen_sigmod21,
title = {{Hybrid Evaluation for Distributed Iterative Matrix Computation}},
author = {Chen, Zihao and Xu, Chen and Soto, Juan and Markl, Volker and Qian, Weining and Zhou, Aoying},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3452843},
url = {https://dl.acm.org/doi/10.1145/3448016.3452843},
year = {2021}
}
Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,709 | HyMAC: A Hybrid Matrix Computation System | 2021 | VLDB | 5.093636e-05 |
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