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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)

Paper ID
6185
Venue
SIGMOD
Year
2021
Pagerank
5.093636e-05
Overall Rank
11,669 | 19.95%
DOI
10.1145/3448016.3452843

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Authors

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}
}

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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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