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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
6207
Venue
SIGMOD
Year
2021
Pagerank
5.1642282e-05
Overall Rank
6,191 | 56.94%
DOI
10.1145/3448016.3457317

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