A Novel Statistical Learning-Based Methodology for Measuring the Goodness of Energy Profiles of Applications Executing on Multicore Computing Platforms

Muhammad Fahad, Arsalan Shahid, Ravi Reddy Manumachu and Alexey Lastovetsky
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Muhammad Fahad: School of Computer Science, University College Dublin, Belfield, Dublin-4, Ireland
Arsalan Shahid: School of Computer Science, University College Dublin, Belfield, Dublin-4, Ireland
Ravi Reddy Manumachu: School of Computer Science, University College Dublin, Belfield, Dublin-4, Ireland
Alexey Lastovetsky: School of Computer Science, University College Dublin, Belfield, Dublin-4, Ireland

Energies, 2020, vol. 13, issue 15, 1-22

Abstract: Accurate energy profiles are essential to the optimization of parallel applications for energy through workload distribution. Since there are many model-based methods available for efficient construction of energy profiles, we need an approach to measure the goodness of the profiles compared with the ground-truth profile, which is usually built by a time-consuming but reliable method. Correlation coefficient and relative error are two such popular statistical approaches, but they assume that profiles be linear or at least very smooth functions of workload size. This assumption does not hold true in the multicore era. Due to the complex shapes of energy profiles of applications on modern multicore platforms, the statistical methods can often rank inaccurate energy profiles higher than more accurate ones and employing such profiles in the energy optimization loop of an application leads to significant energy losses (up to 54% in our case). In this work, we present the first method specifically designed for goodness measurement of energy profiles. First, it analyses the underlying energy consumption trend of each energy profile and removes the profiles that exhibit a trend different from that of the ground truth. Then, it ranks the remaining energy profiles using the Euclidean distances as a metric. We demonstrate that the proposed method is more accurate than the statistical approaches and can save a significant amount of energy.

Keywords: energy efficient computing; accurate energy modelling; green computing; similarity matching; pattern recognition; anomaly detection (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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