Qingnhua Zhu, Bo Wu, Xipeng Shen, Li Shen, Zhiying Wang
{"title":"Co-Run Scheduling with Power Cap on Integrated CPU-GPU Systems","authors":"Qingnhua Zhu, Bo Wu, Xipeng Shen, Li Shen, Zhiying Wang","doi":"10.1109/IPDPS.2017.124","DOIUrl":null,"url":null,"abstract":"This paper presents the first systematic study on co-scheduling independent jobs on integrated CPU-GPU systems with power caps considered. It reveals the performance degradations caused by the co-run contentions at the levels of both memory and power. It then examines the problem of using job co-scheduling to alleviate the degradations in this less understood scenario. It offers several algorithms and a lightweight co-run performance and power predictive model for computing the performance bounds of the optimal co-schedules and finding appropriate schedules. Results show that the method can efficiently find co-schedules that significantly improve the system throughput (9-46% on average over the default schedules).","PeriodicalId":209524,"journal":{"name":"2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"27","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IPDPS.2017.124","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 27
Abstract
This paper presents the first systematic study on co-scheduling independent jobs on integrated CPU-GPU systems with power caps considered. It reveals the performance degradations caused by the co-run contentions at the levels of both memory and power. It then examines the problem of using job co-scheduling to alleviate the degradations in this less understood scenario. It offers several algorithms and a lightweight co-run performance and power predictive model for computing the performance bounds of the optimal co-schedules and finding appropriate schedules. Results show that the method can efficiently find co-schedules that significantly improve the system throughput (9-46% on average over the default schedules).