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Energy‐aware load balancing of parallel evolutionary algorithms with heavy fitness functions in heterogeneous CPU‐GPU architectures
Author(s) -
Escobar Juan José,
Ortega Julio,
Díaz Antonio Francisco,
González Jesús,
Damas Miguel
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.4688
Subject(s) - computer science , workload , frequency scaling , energy consumption , scheduling (production processes) , parallel computing , fitness function , symmetric multiprocessor system , distributed computing , load balancing (electrical power) , evolutionary algorithm , efficient energy use , genetic algorithm , mathematical optimization , operating system , artificial intelligence , grid , geometry , mathematics , engineering , machine learning , electrical engineering , biology , ecology
Summary By means of the availability of mechanisms such as Dynamic Voltage and Frequency Scaling (DVFS) and heterogeneous architectures including processors with different power consumption profiles, it is possible to devise scheduling algorithms that are aware of both runtime and energy consumption in parallel programs. In this paper, we propose and evaluate a multi‐objective (more specifically, a bi‐objective) approach to distribute the workload among the processing cores in a given heterogeneous parallel CPU‐GPU architecture. The aim of this distribution may be either to save energy without increasing the running time or to reach a trade‐off among time and energy consumption. The parallel programs considered here are master‐worker evolutionary algorithms where the evaluation of the fitness function for the individuals in the population demands the most part of the computing time. As many useful bioinformatics and data mining applications exhibit this kind of parallel profile, the proposed energy‐aware approach for workload scheduling could be frequently applied.