Tensor Relational Algebra for Distributed Machine Learning System Design
Summary: Proposes tensor relational algebra (TRA), a set-based abstraction for tensor-centric ML workloads. TRA operates on binary tensor relations with multi-dimensional keys to enable scalable distributed execution and auto-optimization; experiments show TRA back-end outperforms kernel-based ML workflows on clusters. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Binhang Yuan (Rice University)
- 2. Dimitrije Jankov (Rice University)
- 3. Jia Zou (Arizona State University)
- 4. Yuxin Tang (Rice University)
- 5. Daniel Bourgeois (Rice University)
- 6. Chris Jermaine (Rice University)
BibTeX Citation
@article{yuan_vldb21,
title = {{Tensor Relational Algebra for Distributed Machine Learning System Design}},
author = {Yuan, Binhang and Jankov, Dimitrije and Zou, Jia and Tang, Yuxin and Bourgeois, Daniel and Jermaine, Chris},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {8},
pages = {1338--1350},
doi = {10.14778/3457390.3457399},
url = {https://doi.org/10.14778/3457390.3457399},
year = {2021}
}
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