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A Comparative Study of Methods for Measurement of Energy of Computing

School of Computer Science, University College Dublin, Belfield, Dublin-4, Ireland
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Energies 2019, 12(11), 2204; https://doi.org/10.3390/en12112204
Received: 5 May 2019 / Revised: 27 May 2019 / Accepted: 28 May 2019 / Published: 10 June 2019
(This article belongs to the Special Issue Model Coupling and Energy Systems)
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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. View Full-Text
Keywords: energy efficiency; energy predictive models; performance monitoring counters; multicore CPU; GPU; Xeon Phi; RAPL; NVML; power aensors; power meters energy efficiency; energy predictive models; performance monitoring counters; multicore CPU; GPU; Xeon Phi; RAPL; NVML; power aensors; power meters
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Fahad, M.; Shahid, A.; Manumachu, R.R.; Lastovetsky, A. A Comparative Study of Methods for Measurement of Energy of Computing. Energies 2019, 12, 2204.

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