@article {449,
	title = {Additivity: A Selection Criterion for Performance Events for Reliable Energy Predictive Modeling},
	journal = {Supercomputing Frontiers and Innovations},
	volume = {4},
	year = {2017},
	month = {12/2017},
	pages = {50-65},
	abstract = {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{\textquoteright} 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.
},
	doi = {10.14529/jsfi170404},
	attachments = {https://hcl.ucd.ie/system/files/153-992-1-PB.pdf},
	author = {Arsalan Shahid and Muhammad Fahad and Reddy, R. and Lastovetsky, A.}
}
