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Modeling and analyzing the energy consumption of fork‐join‐based task parallel programs
Author(s) -
Rauber Thomas,
Rünger Gudula
Publication year - 2015
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.3219
Subject(s) - computer science , energy consumption , frequency scaling , computation , parallel computing , energy minimization , scheduling (production processes) , exploit , dynamic voltage scaling , scaling , minification , spec# , fork–join queue , energy (signal processing) , distributed computing , algorithm , mathematical optimization , mathematics , engineering , computer network , chemistry , statistics , computational chemistry , computer security , geometry , queueing theory , queue management system , programming language , electrical engineering
SUMMARY Because of environmental and monetary concerns, it is increasingly important to reduce the energy consumption in all areas, including parallel and high performance computing. In this article, we propose an approach to reduce the energy consumption needed for the execution of a set of tasks computed in parallel in a fork‐join fashion. The approach consists of an analytical model for the energy consumption of a parallel computation in fork‐join form on dynamic voltage frequency scaling processors, a theoretical specification of an energy‐optimal frequency‐scaled state, and the energy minimization by computing optimal scaling factors. For larger numbers of tasks, the approach is extended by scheduling algorithms, which exploit the analytical result and aim at a reduction of the energy. Energy measurements of a complex numerical method and the SPEC CPU2006 benchmarks as well as simulations for a large number of randomly generated tasks illustrate and validate the energy modeling, the minimization, and the scheduling results. Copyright © 2014 John Wiley & Sons, Ltd.