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Harnessing Voltage Margins for Energy Efficiency in Multicore CPUs

机译:利用多核CPU中的能效的电压边距

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In this paper, we present the first automated system-level analysis of multicore CPUs based on ARMv8 64-bit architecture (8-core, 28nm X-Gene 2 micro-server by AppliedMicro) when pushed to operate in scaled voltage conditions. We report detailed system-level effects including SDCs, corrected/uncorrected errors and application/system crashes. Our study reveals large voltage margins (that can be harnessed for energy savings) and also large Vmin variation among the 8 cores of the CPU chip, among 3 different chips (a nominal rated and two sigma chips), and among different benchmarks. Apart from the Vmin analysis we propose a new composite metric (severity) that aggregates the behavior of cores when undervolted and can support system operation and design protection decisions. Our undervolting characterization findings are the first reported analysis for an enterprise class 64-bit ARMv8 platform and we highlight key differences with previous studies on x86 platforms. We utilize the results of the system characterization along with performance counters information to measure the accuracy of prediction models for the behavior of benchmarks running in particular cores. Finally, we discuss how the detailed characterization and the prediction results can be effectively used to support design and system software decisions to harness voltage margins for energy efficiency while preserving operation correctness. Our findings show that, on average, 19.4% energy saving can be achieved without compromising the performance, while with 25% performance reduction, the energy saving raises to 38.8%.
机译:在本文中,当按下缩放电压条件时,我们介绍了基于ARMv8 64位架构(8核,28nm X-Gene 2微型服务器)的多核CPU自动化系统级分析。我们报告了详细的系统级效果,包括SDC,纠正/未校正错误和应用程序/系统崩溃。我们的研究揭示了大量电压边缘(可以利用节能),也可以在CPU芯片的8个核心中进行大的VMIN变化,其中3种不同的芯片(标称额定值和两个Sigma芯片),以及不同的基准。除了VMIN分析外,我们提出了一种新的综合度量(严重性),可在低压时聚集核心的行为,并可以支持系统操作和设计保护决策。我们的低于企业64位ARMV8平台的第一个报告的分析,我们突出了对X86平台的先前研究的关键差异。我们利用系统表征的结果以及性能计数器信息来测量特定核心运行基准的行为的预测模型的准确性。最后,我们讨论了详细表征和预测结果如何有效地用于支持设计和系统软件决策,以在保持操作效率的同时为能量效率进行线束边缘。我们的研究结果表明,平均而言,在不损害性能的情况下,可以实现19.4%的节能,而在减少25%的性能下,节能升高至38.8%。

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