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International Conference on High Performance Computing

ISC High Performance 2021: High Performance Computing pp 314–330Cite as

Machine Learning for Generic Energy Models of High Performance Computing Resources

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12761))

Abstract

This article presents an study of the generalization capabilities of forecasting techniques of empirical energy consumption models of high performance computing resources. This is a relevant subject, considering the large energy utilization of modern supercomputing facilities. Different energy models are built, considering several forecasting techniques and using information from the execution of a benchmark over different hardware. A cross-evaluation is performed and the training information of each model is gradually extended with information about other hardware. Each model is analyzed to evaluate how new information impacts on the prediction capabilities. The main results indicate that neural network approaches achieve the highest quality results when the training data of the models is expanded with minimal information from new scenarios.

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Author information

Authors and Affiliations

  1. Universidad de la República, Montevideo, Uruguay

    Jonathan Muraña & Sergio Nesmachnow

  2. Leibniz Supercomputing Center, Munich, Germany

    Carmen Navarrete

Authors
  1. Jonathan Muraña
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  2. Carmen Navarrete
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  3. Sergio Nesmachnow
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Corresponding author

Correspondence to Jonathan Muraña .

Editor information

Editors and Affiliations

  1. University of Tennessee at Knoxville, Knowville, TN, USA

    Heike Jagode

  2. Karlsruhe Institute of Technology, Karlsruhe, Baden-Württemberg, Germany

    Hartwig Anzt

  3. King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    Hatem Ltaief

  4. University of Tennessee System, Knoxville, TN, USA

    Piotr Luszczek

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Muraña, J., Navarrete, C., Nesmachnow, S. (2021). Machine Learning for Generic Energy Models of High Performance Computing Resources. In: Jagode, H., Anzt, H., Ltaief, H., Luszczek, P. (eds) High Performance Computing. ISC High Performance 2021. Lecture Notes in Computer Science(), vol 12761. Springer, Cham. https://doi.org/10.1007/978-3-030-90539-2_21

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  • DOI: https://doi.org/10.1007/978-3-030-90539-2_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90538-5

  • Online ISBN: 978-3-030-90539-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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