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Sustaining Performance While Reducing Energy Consumption: A Control Theory Approach

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Euro-Par 2021: Parallel Processing (Euro-Par 2021)

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Abstract

Production high-performance computing systems continue to grow in complexity and size. As applications struggle to make use of increasingly heterogeneous compute nodes, maintaining high efficiency (performance per watt) for the whole platform becomes a challenge. Alongside the growing complexity of scientific workloads, this extreme heterogeneity is also an opportunity: as applications dynamically undergo variations in workload, due to phases or data/compute movement between devices, one can dynamically adjust power across compute elements to save energy without impacting performance. With an aim toward an autonomous and dynamic power management strategy for current and future HPC architectures, this paper explores the use of control theory for the design of a dynamic power regulation method. Structured as a feedback loop, our approach—which is novel in computing resource management—consists of periodically monitoring application progress and choosing at runtime a suitable power cap for processors. Thanks to a preliminary offline identification process, we derive a model of the dynamics of the system and a proportional-integral (PI) controller. We evaluate our approach on top of an existing resource management framework, the Argo Node Resource Manager, deployed on several clusters of Grid’5000, using a standard memory-bound HPC benchmark.

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Notes

  1. 1.

    https://www.grid5000.fr/w/Hardware with reference API version 9925e0598.

  2. 2.

    Available at https://xgitlab.cels.anl.gov/argo/hnrm.

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Acknowledgments and Data Availability Statement

Experiments presented in this paper were carried out using the Grid’5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https://www.grid5000.fr). Argonne National Laboratory’s work was supported by the U.S. Department of Energy, Office of Science, Advanced Scientific Computer Research, under Contract DE-AC02-06CH11357. This research was supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration. This research is partially supported by the NCSA-Inria-ANL-BSC-JSC-Riken-UTK Joint-Laboratory for Extreme Scale Computing (JLESC, https://jlesc.github.io/).

The datasets and code generated and analyzed during the current study are available in the Figshare repository: https://doi.org/10.6084/m9.figshare.14754468 [5].

Author information

Authors and Affiliations

  1. Université Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, 38000, Grenoble, France

    Sophie Cerf, Raphaël Bleuse & Éric Rutten

  2. Argonne National Laboratory, Lemont, USA

    Valentin Reis & Swann Perarnau

Authors
  1. Sophie Cerf
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  2. Raphaël Bleuse
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  3. Valentin Reis
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  4. Swann Perarnau
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  5. Éric Rutten
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Corresponding author

Correspondence to Sophie Cerf .

Editor information

Editors and Affiliations

  1. Universidade de Lisboa, Lisbon, Portugal

    Leonel Sousa

  2. Universidade de Lisboa, Lisbon, Portugal

    Nuno Roma

  3. Universidade de Lisboa, Lisbon, Portugal

    Pedro Tomás

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Cerf, S., Bleuse, R., Reis, V., Perarnau, S., Rutten, É. (2021). Sustaining Performance While Reducing Energy Consumption: A Control Theory Approach. In: Sousa, L., Roma, N., Tomás, P. (eds) Euro-Par 2021: Parallel Processing. Euro-Par 2021. Lecture Notes in Computer Science(), vol 12820. Springer, Cham. https://doi.org/10.1007/978-3-030-85665-6_21

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  • DOI: https://doi.org/10.1007/978-3-030-85665-6_21

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