| Title | Additivity: A Selection Criterion for Performance Events for Reliable Energy Predictive Modeling |
| Publication Type | Journal Article |
| Year of Publication | 2017 |
| Authors | Shahid, A., M. Fahad, R. Reddy, and A. Lastovetsky |
| Journal Title | Supercomputing Frontiers and Innovations |
| Volume | 4 |
| Issue | 4 |
| Pages | 50-65 |
| Journal Date | 12/2017 |
| 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’ 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 |
