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Genetic scheduling policy on codelet model
Author(s) -
Pei Songwen,
Wang Jinkai,
Jiang Linhua,
Xiong Naixue,
Gaudiot JeanLuc
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4995
Subject(s) - dataflow , computer science , scheduling (production processes) , fair share scheduling , distributed computing , parallel computing , dynamic priority scheduling , two level scheduling , rate monotonic scheduling , round robin scheduling , genetic algorithm , mathematical optimization , operating system , mathematics , machine learning , schedule
Summary The Codelet Model is a fine‐grained event‐driven hybrid parallel model inspired by dataflow, whose computing performance depends on the scheduling policy. An approximate optimal codelet scheduling policy based on the features of the task graphs is important to accelerate the performance of dataflow computer system. Therefore, we have proposed an adaptive genetic scheduling policy (GSP) for codelet by improving a “pure” genetic algorithm (IPGA) for given tasks with complex dependencies. It is verified that the genetic scheduling policy is effective according to bunches of experimental results.