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A compiler-guided approach for reducing disk power consumption by exploiting disk access locality

机译:一种通过利用磁盘访问局部降低磁盘功耗的编译方法

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Power consumption of large servers and clusters has recently been a popular research topic, since this issue is important from both technical and environmental viewpoints. The prior research proposed disk power management as one of the important ways of reducing overall power of a large system and considered both hardware-based and software-guided disk power reduction schemes. One of the common characteristics of the previously proposed approaches to disk power reduction is that they work with a given disk access pattern. In comparison, the goal of the approach proposed in this paper is to restructure application code using an optimizing compiler so that disk idle periods are lengthened. This in turn allows the underlying disk power management scheme to be more effective since such schemes usually prefer the long idle periods over the short ones. Our approach targets at large scientific applications that operate on disk-resident arrays using nested loops and exhibit regular data access patterns. To test the effectiveness of the proposed approach, we implemented it within an optimizing compiler and performed experiments with six data-intensive applications that manipulate disk-resident data. Our experimental analysis shows that the proposed approach is very successful in practice and reduces the total disk energy consumption on average by 18.17%, as compared to an execution without any disk power management, and by 11.55%, as compared to an execution that employs disks with low-power capabilities without our code restructuring approach.
机译:大型服务器和集群的功耗最近是一个流行的研究主题,因为这个问题对于技术和环境观点来说都很重要。先前的研究提出了磁盘电源管理作为减少大型系统的整体功率的重要方法之一,并考虑基于硬件和软件引导的磁盘减速方案。先前提出的磁盘功率降低方法的一个共同特征是它们与给定的磁盘访问模式一起工作。相比之下,本文提出的方法的目标是使用优化编译器重组应用程序代码,以便延长磁盘空闲周期。这又允许底层磁盘电源管理方案更有效,因为这种方案通常更喜欢短的空闲周期。我们的方法在使用嵌套环路上运行的大型科学应用程序,在磁盘驻留阵列上运行,并展示常规数据访问模式。为了测试所提出的方法的有效性,我们在优化编译器中实现了它,并使用六种数据密集型应用程序进行操作,该应用程序操纵磁盘驻留数据。我们的实验分析表明,与没有任何磁盘电源管理的执行相比,所提出的方法在实践中非常成功,并将平均磁盘能耗平均减少了18.17%,与采用磁盘的执行相比,增加了11.55%没有我们的代码重组方法的低功耗功能。

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