GEVO-ML: A proposal for optimizing ML code with evolutionary computation

Jhe Yu Liou, Xiaodong Wang, Stephanie Forrest, Carole Jean Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Parallel accelerators, such as GPUs, are a key enabler of large-scale Machine Learning (ML) applications. However, programmers often lack detailed knowledge of the underlying architecture and fail to fully leverage their computational power. This paper proposes GEVO-ML, a tool for automatically discovering optimization opportunities and tuning the performance of ML kernels. GEVO-ML extends earlier work on GEVO (Gpu optimization using EVOlutionary computation) by focusing directly on ML frameworks, intermediate languages, and target architectures. It retains the multi-objective evolutionary search developed for GEVO, which searches for edits to GPU code compiled to LLVM-IR and improves performance on desired criteria while retaining required functionality. In earlier work, we studied some ML workloads in GPU settings and found that GEVO could improve kernel speeds by factors ranging from 1.7X to 2.9X, even with access to only a small portion of the overall ML framework. This workshop paper examines the limitations and constraints of GEVO for ML workloads and discusses our GEVO-ML design, which we are currently implementing.

Original languageEnglish (US)
Title of host publicationGECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery, Inc
Pages1849-1856
Number of pages8
ISBN (Electronic)9781450371278
DOIs
StatePublished - Jul 8 2020
Event2020 Genetic and Evolutionary Computation Conference, GECCO 2020 - Cancun, Mexico
Duration: Jul 8 2020Jul 12 2020

Publication series

NameGECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

Conference

Conference2020 Genetic and Evolutionary Computation Conference, GECCO 2020
CountryMexico
CityCancun
Period7/8/207/12/20

Keywords

  • Genetic improvement
  • Machine learning
  • Multi-objective evolutionary computation

ASJC Scopus subject areas

  • Computational Mathematics

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  • Cite this

    Liou, J. Y., Wang, X., Forrest, S., & Wu, C. J. (2020). GEVO-ML: A proposal for optimizing ML code with evolutionary computation. In GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (pp. 1849-1856). (GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion). Association for Computing Machinery, Inc. https://doi.org/10.1145/3377929.3398139