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

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Title: A Novel Statistical Learning-Based Methodology for Measuring the Goodness of Energy Profiles of Applications Executing on Multicore Computing Platforms
Authors: Fahad, MuhammadShahid, ArsalanManumachu, RaviLastovetsky, Alexey
Permanent link: http://hdl.handle.net/10197/12476
Date: 1-Aug-2020
Online since: 2021-09-22T13:57:13Z
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.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: MDPI
Journal: Energies
Volume: 13
Issue: 15
Copyright (published version): 2020 the Authors
Keywords: Energy efficient computingAccurate energy modellingGreen computingSimilarity matchingPattern recognitionAnomaly detection
DOI: 10.3390/en13153944
Language: en
Status of Item: Peer reviewed
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by/3.0/ie/
Appears in Collections:Computer Science Research Collection

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