异构处理环境中的功率感知任务匹配与迁移

Christopher Hyatt, Greg Lakomski, Claudia Alvarado, Richard Hay, Apan Qasem, D. Tamir
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引用次数: 1

摘要

在以往的任务匹配研究中,功率不可知的最小最小算法被发现是一种有效的匹配算法,并被许多研究作为基准。我们的研究通过增加能量延迟积(EDP)来增强这些算法,从而提高了考虑功率和执行时间的最小最小值。此外,研究还结合了经典的Min-Min和改进的带有EDP的Min-Min作为在系统核心之间迁移被抢占任务的方法。我们的结果表明,在可接受的性能下降的情况下,有可能在调度相关的功耗方面获得显著的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Power Aware Task Matching and Migration in Heterogeneous Processing Environments
In previous task matching research the power agnostic Min-Min algorithms has been found to be an effective matching algorithm and has been used as a benchmark in numerous studies. Our study has enhanced these algorithms with the addition of the energy delay product (EDP) resulting in an improved Min-Min that takes both power and execution time into account. In addition, the study incorporates the classical Min-Min and an improved Min-Min with EDP as ways to migrate preempted task between system cores. Our results show that it is possible to obtain significant improvements in scheduling related power consumption with acceptable performance degradation.
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