Evolutionary Benchmark Subsetting

  • Authors:
  • Zhanpeng Jin;Allen C. Cheng

  • Affiliations:
  • University of Pittsburgh;University of Pittsburgh

  • Venue:
  • IEEE Micro
  • Year:
  • 2008

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Abstract

To improve simulation efficiency and relieve burdened benchmarking efforts, this research proposes a survival-of-the-fittest evolutionary methodology. The goal is to subset any given benchmark suite based on its inherent workload characteristics, desired workload space coverage, and total execution time. Given a user-specified workload space coverage threshold, the proposed technique can systematically yield the "fittest" time-efficient benchmark subset.