Performance prediction based on inherent program similarity

  • Authors:

  • Kenneth Hoste;Aashish Phansalkar;Lieven Eeckhout;Andy Georges;Lizy K. John;Koen De Bosschere

  • Affiliations:

  • Ghent University, Belgium;The University of Texas at Austin;Ghent University, Belgium;Ghent University, Belgium;The University of Texas at Austin;Ghent University, Belgium

  • Venue:
  • Proceedings of the 15th international conference on Parallel architectures and compilation techniques
  • Year:
  • 2006

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Abstract

A key challenge in benchmarking is to predict the performance of an application of interest on a number of platforms in order to determine which platform yields the best performance. This paper proposes an approach for doing this. We measure a number of microarchitecture-independent characteristics from the application of interest, and relate these characteristics to the characteristics of the programs from a previously profiled benchmark suite. Based on the similarity of the application of interest with programs in the benchmark suite, we make a performance prediction of the application of interest. We propose and evaluate three approaches (normalization, principal components analysis and genetic algorithm) to transform the raw data set of microarchitecture-independent characteristics into a benchmark space in which the relative distance is a measure for the relative performance differences. We evaluate our approach using all of the SPEC CPU2000 benchmarks and real hardware performance numbers from the SPEC website. Our framework estimates per-benchmark machine ranks with a 0.89 average and a 0.80 worst case rank correlation coefficient.