Bounding energy consumption in large-scale MPI programs

  • Authors:

  • Barry Rountree;David K. Lowenthal;Shelby Funk;Vincent W. Freeh;Bronis R. de Supinski;Martin Schulz

  • Affiliations:

  • University of Georgia, Athens, GA;University of Georgia, Athens, GA;University of Georgia, Athens, GA;North Carolina State University, Raleigh, NC;Lawrence Livermore National Laboratory, Livermore, CA;Lawrence Livermore National Laboratory, Livermore, CA

  • Venue:
  • Proceedings of the 2007 ACM/IEEE conference on Supercomputing
  • Year:
  • 2007

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Abstract

Power is now a first-order design constraint in large-scale parallel computing. Used carefully, dynamic voltage scaling can execute parts of a program at a slower CPU speed to achieve energy savings with a relatively small (possibly zero) time delay. However, the problem of when to change frequencies in order to optimize energy savings is NP-complete, which has led to many heuristic energy-saving algorithms. To determine how closely these algorithms approach optimal savings, we developed a system that determines a bound on the energy savings for an application. Our system uses a linear programming solver that takes as inputs the application communication trace and the cluster power characteristics and then outputs a schedule that realizes this bound. We apply our system to three scientific programs, two of which exhibit load imbalance---particle simulation and UMT2K. Results from our bounding technique show particle simulation is more amenable to energy savings than UMT2K.