Evolutionary benchmark subsetting

Zhanpeng Jin; Allen C. Cheng

doi:10.1109/MM.2008.87

Evolutionary benchmark subsetting

Zhanpeng Jin
, Allen C. Cheng

Department of Computer Science and Engineering

University of Pittsburgh

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

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.

Original language	English
Pages (from-to)	20-36
Number of pages	17
Journal	IEEE Micro
Volume	28
Issue number	6
DOIs	https://doi.org/10.1109/MM.2008.87
State	Published - 2008

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10.1109/MM.2008.87

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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.",

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AB - 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.

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Evolutionary benchmark subsetting

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