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Automatic Optimization of Matrix Implementations for Distributed Machine Learning and Linear Algebra

Summary: Proposes automatic optimization of physical data layout for ML/LA, selecting among row/column, tiled, or relational representations to boost performance. Algorithms solve the layout-choice problem; a DBMS prototype yields speedups for ML/LA workloads. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6269
Venue
SIGMOD
Year
2021
Pagerank
5.9956597e-05
Overall Rank
6,046 | 58.52%
DOI
10.1145/3448016.3457317

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{luo_sigmod21,
        title = {{Automatic Optimization of Matrix Implementations for Distributed Machine Learning and Linear Algebra}},
        author = {Luo, Shangyu and Jankov, Dimitrije and Yuan, Binhang and Jermaine, Chris},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3457317},
        url = {https://dl.acm.org/doi/10.1145/3448016.3457317},
        year = {2021}
}

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