Additivity: A Selection Criterion for Performance Events for Reliable Energy Predictive Modeling

TitleAdditivity: A Selection Criterion for Performance Events for Reliable Energy Predictive Modeling
Publication TypeJournal Article
Year of Publication2017
AuthorsShahid, A., M. Fahad, R. R. Manumachu, and A. Lastovetsky
Journal TitleSupercomputing Frontiers and Innovations
Volume4
Issue4
Pages50-65
Journal Date12/2017
AbstractPerformance 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.
DOI10.14529/jsfi170404
AttachmentSize
153-992-1-PB.pdf666.73 KB