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AutoMOMML: Automatic Multi-objective Modeling with Machine Learning

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Abstract

In recent years, automatic data-driven modeling with machine learning (ML) has received considerable attention as an alternative to analytical modeling for many modeling tasks. While ad hoc adoption of ML approaches has obtained success, the real potential for automation in data-driven modeling has yet to be achieved. We propose AutoMOMML, an end-to-end, ML-based framework to build predictive models for objectives such as performance, and power. The framework adopts statistical approaches to reduce the modeling complexity and automatically identifies and configures the most suitable learning algorithm to model the required objectives based on hardware and application signatures. The experimental results using hardware counters as application signatures show that the median prediction error of performance, processor power, and DRAM power models are 13 %, 2.3 %, and 8 %, respectively.

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Notes

  1. 1.

    We will make the packages and the framework available on our website (http://www.sdsc.edu/~tiwari/AutoMOMML) at the time of publication.

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Acknowledgments

This work was supported by the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research program under contract number DE-AC02-06CH11357.

Author information

Authors and Affiliations

  1. Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, USA

    Prasanna Balaprakash, Stefan M. Wild & Paul D. Hovland

  2. Leadership Computing Facility, Argonne National Laboratory, Argonne, IL, USA

    Prasanna Balaprakash

  3. Performance Modeling and Characterization (PMaC) Laboratory, San Diego Supercomputer Center, La Jolla, CA, USA

    Ananta Tiwari & Laura Carrington

Authors
  1. Prasanna Balaprakash
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  2. Ananta Tiwari
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  3. Stefan M. Wild
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  4. Laura Carrington
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  5. Paul D. Hovland
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Corresponding author

Correspondence to Prasanna Balaprakash .

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Editors and Affiliations

  1. Deutsches Klimarechenzentrum, Hamburg, Germany

    Julian M. Kunkel

  2. Argonne National Laboratory, Lemont, Illinois, USA

    Pavan Balaji

  3. University of Tennessee, Knoxville, Tennessee, USA

    Jack Dongarra

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Balaprakash, P., Tiwari, A., Wild, S.M., Carrington, L., Hovland, P.D. (2016). AutoMOMML: Automatic Multi-objective Modeling with Machine Learning. In: Kunkel, J., Balaji, P., Dongarra, J. (eds) High Performance Computing. ISC High Performance 2016. Lecture Notes in Computer Science(), vol 9697. Springer, Cham. https://doi.org/10.1007/978-3-319-41321-1_12

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  • DOI: https://doi.org/10.1007/978-3-319-41321-1_12

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