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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
ABSTRACT
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.
INDEX TERMS
benchmark, subsetting, genetic algorithm, center of mass, convex hull, fitness
CITATION

Z. Jin and A. C. Cheng, "Evolutionary Benchmark Subsetting," in IEEE Micro, vol. 28, no. , pp. 20-36, 2008.
doi:10.1109/MM.2008.87
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