Energy of Computing on Multicore CPUs: Predictive Models and Energy Conservation Law

TL;DR

This paper develops a formal theory of energy in multicore CPUs using a model-theoretic approach, incorporating energy conservation laws to improve prediction accuracy and highlight potential energy losses in current measurement practices.

Contribution

It introduces a formal framework that extends existing energy models with conservation properties, significantly enhancing prediction accuracy and exposing flaws in current energy measurement methods.

Findings

Improved energy prediction accuracy from 31% to 18%.

Current measurement tools can cause 56-65% energy losses.

Formal energy conservation principles enhance modeling accuracy.

Abstract

Energy is now a first-class design constraint along with performance in all computing settings. Energy predictive modelling based on performance monitoring counts (PMCs) is the leading method used for prediction of energy consumption during an application execution. We use a model-theoretic approach to formulate the assumed properties of existing 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 energy models from 31% to 18%. We also demonstrate that use of state-of-the-art measurement tools for energy optimisation may lead to significant losses of energy (ranging from 56% to 65% for applications used in experiments) since they do not take into account the energy conservation properties.