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An analytical methodology to derive power models based on hardware and software metrics

机译：一种基于硬件和软件指标得出功率模型的分析方法

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The use of models to predict the power consumption of a system is an appealing alternative to wattmeters since they avoid hardware costs and are easy to deploy. In this paper, we present an analytical methodology to build models with a reduced number of features in order to estimate power consumption at node level. We aim at building simple power models by performing a per-component analysis (CPU, memory, network, I/O) through the execution of four standard benchmarks. While they are executed, information from all the available hardware counters and resource utilization metrics provided by the system is collected. Based on correlations among the recorded metrics and their correlation with the instantaneous power, our methodology allows (ⅰ) to identify the significant metrics; and (ⅱ) to assign weights to the selected metrics in order to derive reduced models. The reduction also aims at extracting models that are based on a set of hardware counters and utilization metrics that can be obtained simultaneously and, thus, can be gathered and computed on-line. The utility of our procedure is validated using real-life applications on an Intel Sandy Bridge architecture.
机译：使用模型来预测系统的功耗是功率计的一种吸引人的选择，因为它们避免了硬件成本并且易于部署。在本文中，我们提出了一种分析方法来构建具有较少数量特征的模型，以便估算节点级别的功耗。我们旨在通过执行四个标准基准对每个组件进行分析（CPU，内存，网络，I / O），以构建简单的电源模型。在执行它们时，将收集来自系统提供的所有可用硬件计数器和资源利用率指标的信息。基于记录的指标之间的相关性及其与瞬时功率的相关性，我们的方法允许（ⅰ）识别重要指标； （ⅱ）将权重分配给所选指标，以得出简化的模型。减少还旨在提取基于一组硬件计数器和利用率度量的模型，这些模型可以同时获得，因此可以在线收集和计算。我们的程序的实用程序通过使用英特尔Sandy Bridge架构上的实际应用程序进行了验证。

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