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Issue No. 06 - November/December (2008 vol. 28)
ISSN: 0272-1732
pp: 20-36
Zhanpeng Jin , University of Pittsburgh
Allen C. Cheng , University of Pittsburgh
To improve simulation efficiency and relieve burdened benchmarking efforts, this research proposes a biologically inspired, 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.
benchmark, subsetting, genetic algorithm, center of mass, convex hull, fitness

Z. Jin and A. C. Cheng, "Evolutionary Benchmark Subsetting," in IEEE Micro, vol. 28, no. , pp. 20-36, 2008.
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