B. Rountree, D. Lowenthal, S. Funk, V. Freeh, B. Supinski, M. Schulz
{"title":"Bounding energy consumption in large-scale MPI programs","authors":"B. Rountree, D. Lowenthal, S. Funk, V. Freeh, B. Supinski, M. Schulz","doi":"10.1145/1362622.1362688","DOIUrl":null,"url":null,"abstract":"Power is now a first-order design constraint in large-scale parallel computing. Used carefully, dynamic voltage scaling can execute parts of a program at a slower CPU speed to achieve energy savings with a relatively small (possibly zero) time delay. However, the problem of when to change frequencies in order to optimize energy savings is NP-complete, which has led to many heuristic energy-saving algorithms. To determine how closely these algorithms approach optimal savings, we developed a system that determines a bound on the energy savings for an application. Our system uses a linear programming solver that takes as inputs the application communication trace and the cluster power characteristics and then outputs a schedule that realizes this bound. We apply our system to three scientific programs, two of which exhibit load imbalance---particle simulation and UMT2K. Results from our bounding technique show particle simulation is more amenable to energy savings than UMT2K.","PeriodicalId":274744,"journal":{"name":"Proceedings of the 2007 ACM/IEEE Conference on Supercomputing (SC '07)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"177","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2007 ACM/IEEE Conference on Supercomputing (SC '07)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1362622.1362688","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 177
Abstract
Power is now a first-order design constraint in large-scale parallel computing. Used carefully, dynamic voltage scaling can execute parts of a program at a slower CPU speed to achieve energy savings with a relatively small (possibly zero) time delay. However, the problem of when to change frequencies in order to optimize energy savings is NP-complete, which has led to many heuristic energy-saving algorithms. To determine how closely these algorithms approach optimal savings, we developed a system that determines a bound on the energy savings for an application. Our system uses a linear programming solver that takes as inputs the application communication trace and the cluster power characteristics and then outputs a schedule that realizes this bound. We apply our system to three scientific programs, two of which exhibit load imbalance---particle simulation and UMT2K. Results from our bounding technique show particle simulation is more amenable to energy savings than UMT2K.