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MiDataSets: Creating the Conditions for a More Realistic Evaluation of Iterative Optimization

  • Grigori Fursin
  • John Cavazos
  • Michael O’Boyle
  • Olivier Temam
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4367)

Abstract

Iterative optimization has become a popular technique to obtain improvements over the default settings in a compiler for performance-critical applications, such as embedded applications. An implicit assumption, however, is that the best configuration found for any arbitrary data set will work well with other data sets that a program uses.

In this article, we evaluate that assumption based on 20 data sets per benchmark of the MiBench suite. We find that, though a majority of programs exhibit stable performance across data sets, the variability can significantly increase with many optimizations. However, for the best optimization configurations, we find that this variability is in fact small. Furthermore, we show that it is possible to find a compromise optimization configuration across data sets which is often within 5% of the best possible configuration for most data sets, and that the iterative process can converge in less than 20 iterations (for a population of 200 optimization configurations). All these conclusions have significant and positive implications for the practical utilization of iterative optimization.

Keywords

Program Optimization Continuous Optimization Good Optimization Iterative Optimization Benchmark Suite 
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 Berlin Heidelberg 2007

Authors and Affiliations

  • Grigori Fursin
    • 1
  • John Cavazos
    • 2
  • Michael O’Boyle
    • 2
  • Olivier Temam
    • 1
  1. 1.ALCHEMY Group, INRIA Futurs and LRI, Paris-Sud UniversityFrance
  2. 2.Institute for Computing Systems Architecture, University of EdinburghUK

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