A Comparative Study of Techniques for Energy Predictive Modeling Using Performance Monitoring Counters on Modern Multicore CPUs

Accurate and reliable measurement of energy consumption is essential to energy optimization at an application level. Energy predictive modelling using performance monitoring counters (PMCs) emerged as a promising approach, one of the main drivers being its capacity to provide fine-grained component-level breakdown of energy consumption. In this work, we compare two types of energy predictive models constructed from the same set of experimental data and at two levels, platform and application. The first type contains linear regression (LR) models employing PMCs selected using a theoretical model of energy of computing. The second type contains sophisticated statistical learning models, random forest (RF) and neural network (NN), that are based on PMCs selected using correlation and principal component analysis. Our experimental results performed on two modern Intel multicore processors using a diverse set of applications and a wide range of application configurations, show that the average proportional prediction accuracy of platform-level LR models is 5.09× and 4.37× times better than the platform-level RF and NN models. We also present an experimental methodology to select a reliable subset of four PMCs for constructing accurate application-specific online models. Using the methodology, we demonstrate that LR models perform 1.57× and 1.74× times better than RF and NN models. The consistent accuracy of LR models stress the importance of taking into account domain-specific knowledge for model variable selection, in this case, the physical significance of the PMCs originating from the conservation of energy of computing. The results also endorse the guidelines of the theory of energy of computing, which states that any non-linear energy model (in this case, the RF and NN models) employing PMCs only, will be inconsistent and hence inherently inaccurate.