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A Run-Time System for Power-Constrained HPC Applications

  • Aniruddha MaratheEmail author
  • Peter E. Bailey
  • David K. Lowenthal
  • Barry Rountree
  • Martin Schulz
  • Bronis R. de Supinski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9137)

Abstract

As the HPC community attempts to reach exascale performance, power will be one of the most critical constrained resources. Achieving practical exascale computing will therefore rely on optimizing performance subject to a power constraint. However, this additional complication should not add to the burden of application developers; optimizing the run-time environment given restricted power will primarily be the job of high-performance system software.

This paper introduces Conductor, a run-time system that intelligently distributes available power to nodes and cores to improve performance. The key techniques used are configuration space exploration and adaptive power balancing. Configuration exploration dynamically selects the optimal thread concurrency level and DVFS state subject to a hardware-enforced power bound. Adaptive power balancing efficiently determines where critical paths are likely to occur so that more power is distributed to those paths. Greater power, in turn, allows increased thread concurrency levels, the DVFS states, or both. We describe these techniques in detail and show that, compared to the state-of-the-art technique of using statically predetermined, per-node power caps, Conductor leads to a best-case performance improvement of up to 30 %, and average improvement of 19.1 %.

Keywords

Execution Time Power Allocation Critical Path Power Constraint Power Limit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

Part of this work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344 (LLNL-CONF-667408).

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Aniruddha Marathe
    • 1
    Email author
  • Peter E. Bailey
    • 1
  • David K. Lowenthal
    • 1
  • Barry Rountree
    • 2
  • Martin Schulz
    • 2
  • Bronis R. de Supinski
    • 2
  1. 1.Department of Computer ScienceThe University of ArizonaTucsonUSA
  2. 2.Lawrence Livermore National LaboratoryLivermoreUSA

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