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- W1963664501 abstract "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." @default.
- W1963664501 created "2016-06-24" @default.
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- W1963664501 date "2012-07-01" @default.
- W1963664501 modified "2023-09-23" @default.
- W1963664501 title "Model Selection to Characterize Performance Using Genetic Algorithms" @default.
- W1963664501 cites W169711757 @default.
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- W1963664501 doi "https://doi.org/10.1109/ispa.2012.134" @default.
- W1963664501 hasPublicationYear "2012" @default.
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