Title :
Evolutionary Benchmark Subsetting
Author :
Jin, Zhanpeng ; Cheng, Allen C.
Author_Institution :
Univ. of Pittsburgh, Pittsburgh, PA
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.
Keywords :
benchmark testing; computer architecture; evolutionary computation; benchmark suite; evolutionary benchmark subsetting; fittest time-efficient benchmark subset; survival-of-the-fittest evolutionary; user-specified workload space coverage threshold; Biological information theory; Biological system modeling; Computational modeling; Energy consumption; Microarchitecture; Process design; Time factors;
Journal_Title :
Micro, IEEE
