Energy predictive modeling is the leading method for determining the energy consumption of an application. Performance monitoring counters (PMCs) and resource utilizations have been the principal source of model variables primarily due to their high positive correlation with energy consumption. Performance events, however, have come to dominate the landscape due to their better prediction accuracy compared to utilization variables. Recently, the theory of energy of computing has been proposed whose practical implications for constructing accurate and reliable linear energy predictive models are unified in a consistency test that includes a selection criterion of additivity for model variables. In this work, we analyze the prediction accuracy of models employing utilization variables only, PMCs only, and combination of both utilization variables and PMCs, through the lens of this theory for modern multicore CPU platforms. We discover that employing utilization variables only in linear energy predictive models does not capture all the energy-consuming activities during an application execution. However, combination of utilization variables with PMCs that are highly additive and highly correlated with energy consumption, gives the most accurate linear energy predictive model. Our experimental results show that application-specific and platform-level models using both utilization variables and PMCs exhibit up to 3.6$\times$ and 2.6$\times$ better average prediction accuracy respectively when compared with models employing utilization variables only and highly additive PMCs only.

能源预测建模是确定应用程序能源消耗的主要方法。性能监控计数器（PMC）和资源利用率已成为模型变量的主要来源，这主要是因为它们与能源消耗高度相关。但是，由于性能事件相比使用率变量具有更好的预测准确性，因此性能事件已成为主导领域。近来，已经提出了计算能量的理论，其对构建精确和可靠的线性能量预测模型的实际意义在一致性测试中得到了统一，该一致性测试包括模型变量的可加性选择标准。在这项工作中，我们分析仅采用利用率变量，仅采用PMC的模型的预测准确性，通过这种理论将现代多核CPU平台与应用变量和PMC结合起来。我们发现仅在线性能量预测模型中采用利用率变量并不能捕获应用程序执行期间的所有能耗活动​​。但是，利用变量与高度相加且与能耗高度相关的PMC的组合提供了最准确的线性能量预测模型。我们的实验结果表明，同时使用利用率变量和PMC的特定于应用程序和平台级别的模型最多可显示3.6 我们发现仅在线性能量预测模型中采用利用率变量并不能捕获应用程序执行期间的所有能耗活动​​。但是，利用变量与高度相加且与能耗高度相关的PMC的组合提供了最准确的线性能量预测模型。我们的实验结果表明，同时使用利用率变量和PMC的特定于应用程序和平台级别的模型最多可显示3.6 我们发现仅在线性能量预测模型中采用利用率变量并不能捕获应用程序执行期间的所有能耗活动​​。但是，利用变量与高度相加且与能耗高度相关的PMC的组合提供了最准确的线性能量预测模型。我们的实验结果表明，同时使用利用率变量和PMC的特定于应用程序和平台级别的模型最多可显示3.6$\times$ 和2.6$\times$ 与仅使用利用率变量和仅使用高度累加的PMC的模型相比，分别具有更好的平均预测精度。