On Optimizing Operator Fusion Plans for Large-Scale Machine Learning in SystemML
Summary: Exact, cost-based fusion optimization for large-scale ML in SystemML; handles complex operator DAGs and hybrid local/distributed execution. Integrated with candidate exploration and code generation for dense, sparse, and compressed data; up to 22x speedups with negligible compilation overhead. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Matthias Boehm (IBM)
- 2. Berthold Reinwald (IBM)
- 3. Dylan Hutchison (University of Washington)
- 4. Prithviraj Sen (IBM)
- 5. Alexandre V. Evfimievski (IBM)
- 6. Niketan Pansare (IBM)
BibTeX Citation
@article{boehm_vldb18,
title = {{On Optimizing Operator Fusion Plans for Large-Scale Machine Learning in SystemML}},
author = {Boehm, Matthias and Reinwald, Berthold and Hutchison, Dylan and Sen, Prithviraj and Evfimievski, Alexandre V. and Pansare, Niketan},
journal = {PVLDB},
series = {{VLDB} '18},
volume = {11},
number = {12},
pages = {1755--1768},
doi = {10.14778/3229863.3229865},
url = {https://doi.org/10.14778/3229863.3229865},
year = {2018}
}
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