SPOOF: Sum-Product Optimization and Operator Fusion for Large-Scale Machine Learning
Summary: Introduces Spoof, an automatic framework that unifies algebraic simplification (sum-product) rewrites and operator fusion/codegen for ML DAGs to exploit linear-algebra properties and sparsity. Produces fused kernels with performance close to hand-tuned code and modest compile overhead. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Tarek Elgamal (IBM; University of Illinois Urbana-Champaign)
- 2. Shangyu Luo (IBM; Rice University)
- 3. Matthias Boehm (IBM)
- 4. Alexandre V. Evfimievski (IBM)
- 5. Shirish Tatikonda (IBM; Target Corporation)
- 6. Berthold Reinwald (IBM)
- 7. Prithviraj Sen (IBM)
BibTeX Citation
@inproceedings{elgamal_cidr17,
address = {Amsterdam, Netherlands},
series = {{CIDR} '17},
title = {{SPOOF: Sum-Product Optimization and Operator Fusion for Large-Scale Machine Learning}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Elgamal, Tarek and Luo, Shangyu and Boehm, Matthias and Evfimievski, Alexandre V. and Tatikonda, Shirish and Reinwald, Berthold and Sen, Prithviraj},
year = {2017}
}
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