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Heterogeneity in ȁC;HomogeneousȁD; Warehouse-Scale Computers: A Performance Opportunity

机译:ȁC;ȁD;仓库规模的计算机:性能机遇

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摘要

The class of modern datacenters recently coined as “warehouse scale computers” (WSCs) has traditionally been embraced as homogeneous computing platforms. However, due to frequent machine replacements and upgrades, modern WSCs are in fact composed of diverse commodity microarchitectures and machine configurations. Yet, current WSCs are designed with an assumption of homogeneity, leaving a potentially significant performance opportunity unexplored. In this paper, we investigate the key factors impacting the available heterogeneity in modern WSCs, and the benefit of exploiting this heterogeneity to maximize overall performance. We also introduce a new metric, opportunity factor, which can be used to quantify an application’s sensitivity to the heterogeneity in a given WSC. For applications that are sensitive to heterogeneity, we observe a performance improvement of up to 70% when employing our approach. In a WSC composed of state-of-the-art machines, we can improve the overall performance of the entire datacenter by 16% over the status quo.
机译:最近被称为“仓库级计算机”(WSC)的现代数据中心属于传统的同类计算平台。但是,由于频繁的机器更换和升级,现代WSC实际上是由各种商品微体系结构和机器配置组成的。但是,当前的WSC都是在假设同质性的前提下进行的,因此未开发潜在的重大性能机会。在本文中,我们研究了影响现代WSC中可用异构性的关键因素,以及利用这种异构性最大化整体性能的好处。我们还引入了一个新的机会因素度量标准,可以用来量化应用程序对给定WSC中异构性的敏感性。对于对异构性敏感的应用程序,当采用我​​们的方法时,我们观察到性能可提高70%。在由最先进的机器组成的WSC中,我们可以将整个数据中心的整体性能比现状提高16%。

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