Towards Reliable and Accurate Energy Predictive Modelling using Performance Events on Modern Computing Platforms

Information and Communication Technologies (ICT) systems and devices are forecast to consume up to 50% of global electricity in 2030. Considering the unsustainable future predicted, energy efficiency in ICT is becoming a grand technological challenge and is now a first-class design constraint in all computing settings. Energy efficiency in ICT can be achieved at the hardware level (or system-level) and software level (or application-level). While the mainstream approach is to minimize the energy of the operating environment and is extensively researched, application-level energy optimization is comparatively understudied and forms the focus of this work. The fundamental building block for energy minimization at the application level is an accurate measurement of energy consumption during application execution. There are three popular approaches to providing it: (a) System-level physical measurements using external power meters, (b) Measurements using on-chip power sensors, and (c) Energy predictive models. While the first approach is considered to be the ground truth, it is cost-prohibitive for the energy optimization of applications. The second approach using energy measurements by state-of-the-art on-chip sensors is not recommended for energy optimization of applications due to several issues related to the lack of accurate information in the vendor manuals and poorly reported experimental accuracy. The third approach of energy predictive modelling based on performance monitoring counters (PMCs) as model variables is now the leading method for prediction of energy consumption during application execution. In this thesis, we focus on application-level energy measurement, modelling, and optimization using PMCs. A vast majority of research works propose models where the employed model variables (or PMCs) are selected solely on the basis of their high positive correlation with energy consumption and report prediction accuracies ranging from poor to excellent. There are a few pieces of research that critically examine the inaccuracy of PMC-based models. We present a study to identify the causes of inaccuracies. We formulate a sound theoretical framework to understand the fundamental significance of the model variables with respect to the energy consumption and the causes of inaccuracy or the reported wide variance of the accuracy of the models. We use a model-theoretic approach to formulate the assumed properties of existing energy predictive models in a mathematical form. We extend the formalism by adding properties, heretofore unconsidered, that account for a limited form of energy conservation law. The extended formalism defines our theory of energy of computing. By applying the basic practical implications of the theory, we improve the prediction accuracy of state-of-the-art linear regression models from 31.2% to 18.01%. We demonstrate that use of the state-of-the-art measurement tools for energy optimization may lead to significant losses of energy (up to 84% for applications used in experiments) since they do not take into account the properties of the theory of energy predictive models for computing. Finally, we present the first comprehensive experimental study to compare the energy predictive modelling techniques employing PMCs. We demonstrate that a platform-level and application-level linear regression-based model employing the additive PMCs, irrespective of the applications used for training and testing, performs better in terms of average prediction accuracies.