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Combining Edge Vector and Event Counter for Time-Dependent Power Behavior Characterization

  • Chunling Hu
  • Daniel A. Jiménez
  • Ulrich Kremer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5470)

Abstract

Fine-grained program power behavior is useful in both evaluating power optimizations and observing power optimization opportunities. Detailed power simulation is time consuming and often inaccurate. Physical power measurement is faster and objective. However, fine-grained measurement generates enormous amounts of data in which locating important features is difficult, while coarse-grained measurement sacrifices important detail.

We present a program power behavior characterization infrastructure that identifies program phases, selects a representative interval of execution for each phase, and instruments the program to enable precise power measurement of these intervals to get their time-dependent power behavior.

We show that the representative intervals accurately model the fine-grained time-dependent behavior of the program. They also accurately estimate the total energy of a program. Our compiler infrastructure allows for easy mapping between a measurement result and its corresponding source code. We improve the accuracy of our technique over previous work by using edge vectors, i.e., counts of traversals of control-flow edges, instead of basic block vectors, as well as incorporating event counters into our phase classification.

We validate our infrastructure through the physical power measurement of 10 SPEC CPU 2000 integer benchmarks on an Intel Pentium 4 system. We show that using edge vectors reduces the error of estimating total program energy by 35% over using basic block vectors, and using edge vectors plus event counters reduces the error of estimating the fine-grained time-dependent power profile by 22% over using basic block vectors.

Keywords

Fast Fourier Transform Root Mean Square Basic Block Program Execution Event Counter 
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-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Chunling Hu
    • 1
  • Daniel A. Jiménez
    • 1
  • Ulrich Kremer
    • 1
  1. 1.Department of Computer ScienceRutgers UniversityUSA

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