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Recent Advances in Computational Optimization pp 161–180Cite as

Efficient and Scalable Pareto Front Generation for Energy and Makespan in Heterogeneous Computing Systems

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 580))

Abstract

The rising costs and demand of electricity for high-performancecomputing systems pose difficult challenges to system administrators that are trying to simultaneously reduce operating costs and offer state-of-the-art performance. However, system performance and energy consumption are often conflicting objectives. Algorithms are necessary to help system administrators gain insight into this energy/performance trade-off. Through the use of intelligent resource allocation techniques, system administrators can examine this trade-off space to quantify how much a given performance level will cost in electricity, or see what kind of performance can be expected when given an energy budget. A novel algorithm is presented that efficiently computes tight lower bounds and high quality solutions for energy and makespan. These solutions are used to bound the Pareto front to easily trade-off energy and performance. These new algorithms are shown to be highly scalable in terms of solution quality and computation time compared to existing algorithms.

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Acknowledgments

This work was supported by the Sjostrom Family Scholarship, Numerica Corporation, the National Science Foundation (NSF) under grants CNS-0905399 and CCF-1302693, the NSF Graduate Research Fellowship, and by the Colorado State University George T. Abell Endowment. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. A preliminary version of portions of this work have been previously presented in [25].

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, 80523, USA

    Kyle M. Tarplee, Ryan Friese, Anthony A. Maciejewski & Howard Jay Siegel

  2. Department of Computer Science, Colorado State University, Fort Collins, CO, 80523, USA

    Howard Jay Siegel

Authors
  1. Kyle M. Tarplee
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  2. Ryan Friese
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  3. Anthony A. Maciejewski
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  4. Howard Jay Siegel
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Corresponding author

Correspondence to Kyle M. Tarplee .

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Editors and Affiliations

  1. Department of Parallel Algorithms, Institute of Information and CommunicationTechnology, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Stefka Fidanova

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Tarplee, K.M., Friese, R., Maciejewski, A.A., Siegel, H.J. (2015). Efficient and Scalable Pareto Front Generation for Energy and Makespan in Heterogeneous Computing Systems. In: Fidanova, S. (eds) Recent Advances in Computational Optimization. Studies in Computational Intelligence, vol 580. Springer, Cham. https://doi.org/10.1007/978-3-319-12631-9_10

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  • DOI: https://doi.org/10.1007/978-3-319-12631-9_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12630-2

  • Online ISBN: 978-3-319-12631-9

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