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Characterization, modeling and scheduling of power consumption of scientific computing applications in multicores

Cluster Computing volume 22, pages 839–859 (2019)Cite this article

Abstract

This article presents an empirical evaluation of power consumption for scientific computing applications in multicore systems. Three types of applications are studied, in single and combined executions on Intel and AMD servers, for evaluating the overall power consumption of each application. The main results indicate that power consumption behavior has a strong dependency with the type of application. Additional performance analysis shows that the best load of the server regarding energy efficiency depends on the type of the applications, with efficiency decreasing in heavily loaded situations. These results allow formulating a model to characterize applications according to power consumption, efficiency, and resource sharing, which provide useful information for resource management and scheduling policies. Several scheduling strategies are evaluated using the proposed energy model over realistic scientific computing workloads. Results confirm that strategies that maximize host utilization provide the best energy efficiency and performance results.

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Authors and Affiliations

  1. Universidad de la República, Montevideo, Uruguay

    Jonathan Muraña & Sergio Nesmachnow

  2. CICESE Research Center, Ensenada, Mexico

    Fermín Armenta & Andrei Tchernykh

Authors
  1. Jonathan Muraña
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  2. Sergio Nesmachnow
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  3. Fermín Armenta
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  4. Andrei Tchernykh
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Correspondence to Jonathan Muraña.

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Muraña, J., Nesmachnow, S., Armenta, F. et al. Characterization, modeling and scheduling of power consumption of scientific computing applications in multicores. Cluster Comput 22, 839–859 (2019). https://doi.org/10.1007/s10586-018-2882-8

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  • DOI: https://doi.org/10.1007/s10586-018-2882-8

Keywords

  • Green computing
  • Energy efficiency
  • Multicores
  • Energy model
  • Cloud simulator