<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Arsalan Shahid</style></author><author><style face="normal" font="default" size="100%">Muhammad Fahad</style></author><author><style face="normal" font="default" size="100%">Reddy, R.</style></author><author><style face="normal" font="default" size="100%">Lastovetsky, A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Additivity: A Selection Criterion for Performance Events for Reliable Energy Predictive Modeling</style></title><secondary-title><style face="normal" font="default" size="100%">Supercomputing Frontiers and Innovations</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/2017</style></date></pub-dates></dates><urls><related-urls><url><style face="normal" font="default" size="100%">https://hcl.ucd.ie/system/files/153-992-1-PB.pdf</style></url></related-urls></urls><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">50-65</style></pages><abstract><style face="normal" font="default" size="100%">Performance events or performance monitoring counters (PMCs) are now the dominant predictor variables for modeling energy consumption. Modern hardware processors provide a large set of PMCs. Determination of the best subset of PMCs for energy predictive modeling is a non-trivial task given the fact that all the PMCs can not be determined using a single application run. Several techniques have been devised to address this challenge. While some techniques are based on a statistical methodology, some use expert advice to pick a subset (that may not necessarily be obtained in one application run) that, in experts’ opinion, are significant contributors to energy consumption. However, the existing techniques have not considered a fundamental property of predictor variables that should have been applied in the first place to remove PMCs unfit for modeling energy. We address this oversight in this paper.
</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue></record></records></xml>