Shervin Hajiamini, B. Shirazi, Aaron S. Crandall, Hassan Ghasemzadeh
{"title":"A Dynamic Programming Technique for Energy-Efficient Multicore Systems","authors":"Shervin Hajiamini, B. Shirazi, Aaron S. Crandall, Hassan Ghasemzadeh","doi":"10.1109/IGCC.2018.8752159","DOIUrl":null,"url":null,"abstract":"With a focus on static (compile-time) methods for V/F level assignments, we propose an efficient Dynamic programming (DP) technique using the Viterbi algorithm, which uses the Energy-Delay Product (EDP) as objective function to predict the best V/F levels. By using the profiled information of applications, this technique minimizes energy consumption and execution time. We evaluate and compare the performance of the proposed algorithm against three heuristic methods—a greedy version of our algorithm, a feedback controller method, and a simple heuristic that uses historical performance to make predictions for adjusting the V/F levels. Experimental results show that our algorithm outperforms the heuristics under the study by an average of 12 to 24% using the EDP performance criteria.","PeriodicalId":388554,"journal":{"name":"2018 Ninth International Green and Sustainable Computing Conference (IGSC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Ninth International Green and Sustainable Computing Conference (IGSC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IGCC.2018.8752159","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
With a focus on static (compile-time) methods for V/F level assignments, we propose an efficient Dynamic programming (DP) technique using the Viterbi algorithm, which uses the Energy-Delay Product (EDP) as objective function to predict the best V/F levels. By using the profiled information of applications, this technique minimizes energy consumption and execution time. We evaluate and compare the performance of the proposed algorithm against three heuristic methods—a greedy version of our algorithm, a feedback controller method, and a simple heuristic that uses historical performance to make predictions for adjusting the V/F levels. Experimental results show that our algorithm outperforms the heuristics under the study by an average of 12 to 24% using the EDP performance criteria.