Model Selection to Characterize Performance Using Genetic Algorithms

ISPA(2012)

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摘要
The TIA modeling framework provides analytical models of the performance of parallel applications. The resulting models are obtained using model selection techniques and are accurate enough for various purposes. Its main drawback is that the completion time depends on the number of candidate models and, in some situations, it becomes critical. In this work, a genetic algorithm is proposed for reducing the time for searching of the best candidate model. The use of this genetic algorithm to obtain the performance model of the linear implementation of the broadcast collective communication in a cluster of multicores is shown.
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关键词
genetic algorithms,model selection technique,analytical model,characterize performance,genetic algorithm,best candidate model,broadcast collective communication,completion time,model selection,accurate enough,tia modeling framework,performance model,candidate model,next generation networking,aic,parallel algorithms,sociology,computational modeling,statistics
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