Abstract
The energy efficiency in ICT is becoming a grand technological challenge and is now a first-class design constraint in all computing settings. Energy predictive modelling based on performance monitoring counters (PMCs) is the leading method for application-level energy optimization. However, a sound theoretical framework to understand the fundamental significance of the PMCs to the energy consumption and the causes of the inaccuracy of the models is lacking. In this work, we propose a small but insightful theory of energy predictive models of computing, which formalizes both the assumptions behind the existing PMC-based energy predictive models and properties, heretofore unconsidered, that are basic implications of the universal energy conservation law. The theory’s basic practical implications include selection criteria for model variables, model intercept, and model coefficients. The experiments on two modern Intel multicore servers show that applying the proposed selection criteria improves the prediction accuracy of state-of-the-art linear regression models from 31.2% to 18%. Finally, we demonstrate that employing energy models constructed using the proposed theory for energy optimization can save a significant amount of energy (up to 80% for applications used in experiments) compared to state-of-the-art energy measurement tools.
Highlights
According to the study [1], the energy consumption of Information and Communications Technology (ICT) is 7% of the global electricity usage in 2020 and is forecast to be around the average of the best-case and expected scenarios (7% and 21%) by 2030.Multicore processors are at the heart of modern computing platforms
We prove that a consistent energy predictive model is linear if and only if each performance monitoring counters (PMCs) variable is additive in the sense that the PMC for a serial execution of two applications is the sum of PMCs for the individual execution of each application
Accurate measurement of energy consumption during an application execution is key to energy minimization at the application level
Summary
According to the study [1], the energy consumption of Information and Communications Technology (ICT) is 7% of the global electricity usage in 2020 and is forecast to be around the average of the best-case and expected scenarios (7% and 21%) by 2030.Multicore processors are at the heart of modern computing platforms. Khokhriakhov et al [3] demonstrate that EP does not hold for modern multicore processors using a novel application-level bi-objective optimization method for energy and performance on a single multicore processor. They experiment with four popular and highly optimized multithreaded data-parallel applications on four modern multicore processors. They show that optimizing for performance alone may result in a significant increase in dynamic energy consumption and optimizing for dynamic energy alone – in considerable performance degradation. Their optimization method determined a good number of Pareto-optimal solutions
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