Abstract
Energy of computing is a serious environmental concern and mitigating it is an important technological challenge. Accurate measurement of energy consumption during an application execution is key to application-level energy minimization techniques. 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. In this work, we present a comprehensive study comparing the accuracy of state-of-the-art on-chip power sensors and energy predictive models against system-level physical measurements using external power meters, which we consider to be the ground truth. We show that the average error of the dynamic energy profiles obtained using on-chip power sensors can be as high as 73% and the maximum reaches 300% for two scientific applications, matrix-matrix multiplication and 2D fast Fourier transform for a wide range of problem sizes. The applications are executed on three modern Intel multicore CPUs, two Nvidia GPUs and an Intel Xeon Phi accelerator. The average error of the energy predictive models employing performance monitoring counters (PMCs) as predictor variables can be as high as 32% and the maximum reaches 100% for a diverse set of seventeen benchmarks executed on two Intel multicore CPUs (one Haswell and the other Skylake). We also demonstrate that using inaccurate energy measurements provided by on-chip sensors for dynamic energy optimization can result in significant energy losses up to 84%. We show that, owing to the nature of the deviations of the energy measurements provided by on-chip sensors from the ground truth, calibration can not improve the accuracy of the on-chip sensors to an extent that can allow them to be used in optimization of applications for dynamic energy. Finally, we present the lessons learned, our recommendations for the use of on-chip sensors and energy predictive models and future directions.
Highlights
IntroductionInternational Energy Agency (IEA) has highlighted energy efficiency (in buildings, transport and industry) as a key measure to mitigate the impact of climate change [1]
International Energy Agency (IEA) has highlighted energy efficiency as a key measure to mitigate the impact of climate change [1]
We show that, owing to the nature of the deviations of the energy measurements provided by on-chip sensors from the ground truth, calibration can not improve the accuracy of the on-chip sensors to an extent that can allow them to be used in optimization of applications for dynamic energy
Summary
International Energy Agency (IEA) has highlighted energy efficiency (in buildings, transport and industry) as a key measure to mitigate the impact of climate change [1]. Information and Communications Technology (ICT) devices and systems are presently consuming about 2000 terawatt hours (TWh) per year which is about 10% of the global electricity demand [2] Energy of computing is a serious environmental concern and mitigating it has become an important technological challenge. Energy efficiency in computing is driven by innovations in hardware represented by the micro-architectural and chip-design advancements and software that can Energies 2019, 12, 2204; doi:10.3390/en12112204 www.mdpi.com/journal/energies
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