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Multi Objective Optimization of HPC Kernels for Performance, Power, and Energy

  • Prasanna Balaprakash
  • Ananta Tiwari
  • Stefan M. WildEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8551)

Abstract

Code optimization in the high-performance computing realm has traditionally focused on reducing execution time. The problem, in mathematical terms, has been expressed as a single objective optimization problem. The expected concerns of next-generation systems, however, demand a more detailed analysis of the interplay among execution time and other metrics. Metrics such as power, performance, energy, and resiliency may all be targeted together and traded against one another. We present a multi objective formulation of the code optimization problem. Our proposed framework helps one explore potential tradeoffs among multiple objectives and provides a significantly richer analysis than can be achieved by treating additional metrics as hard constraints. We empirically examine a variety of metrics, architectures, and code optimization decisions and provide evidence that such tradeoffs exist in practice.

Keywords

Multi Objective Optimization Pareto Front Clock Frequency Decision Space Code Variant 
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.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Prasanna Balaprakash
    • 1
    • 2
  • Ananta Tiwari
    • 3
  • Stefan M. Wild
    • 1
    Email author
  1. 1.Argonne National Laboratory, Mathematics and Computer Science DivisionArgonneUSA
  2. 2.Argonne National Laboratory, Leadership Computing FacilityArgonneUSA
  3. 3.Performance Modeling and Characterization (PMaC) LabSan Diego Supercomputer CenterLa JollaUSA

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