{"title":"基于阶段前驱和局部性的执行阶段预测","authors":"Saman Khoshbakht, N. Dimopoulos","doi":"10.1145/3149412.3149415","DOIUrl":null,"url":null,"abstract":"This paper focuses on different methods developed to detect the upcoming execution phase of a workload with regards to power demands. By controlling the state of the processor in power demanding phases, the operating system can maintain a relatively steady power pattern in the workload, leading to higher power headroom in the system. We compared two main approaches in phase prediction. Firstly, we show that by detecting the precursors leading to an upcoming phase, the system can speculate the next phase with high accuracy. Additionally, we compared this method with another approach which relies on the assumption of phase locality, expecting the current dominant phase to continue in the near future. Our results show that by detecting the precursors we can detect 81% of the upcoming phases with lower processor frequency switching overhead compared to most of the proposed locality-based methods.","PeriodicalId":102033,"journal":{"name":"Proceedings of the 5th International Workshop on Energy Efficient Supercomputing","volume":"57 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Execution Phase Prediction Based on Phase Precursors and Locality\",\"authors\":\"Saman Khoshbakht, N. Dimopoulos\",\"doi\":\"10.1145/3149412.3149415\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper focuses on different methods developed to detect the upcoming execution phase of a workload with regards to power demands. By controlling the state of the processor in power demanding phases, the operating system can maintain a relatively steady power pattern in the workload, leading to higher power headroom in the system. We compared two main approaches in phase prediction. Firstly, we show that by detecting the precursors leading to an upcoming phase, the system can speculate the next phase with high accuracy. Additionally, we compared this method with another approach which relies on the assumption of phase locality, expecting the current dominant phase to continue in the near future. Our results show that by detecting the precursors we can detect 81% of the upcoming phases with lower processor frequency switching overhead compared to most of the proposed locality-based methods.\",\"PeriodicalId\":102033,\"journal\":{\"name\":\"Proceedings of the 5th International Workshop on Energy Efficient Supercomputing\",\"volume\":\"57 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-11-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 5th International Workshop on Energy Efficient Supercomputing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3149412.3149415\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 5th International Workshop on Energy Efficient Supercomputing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3149412.3149415","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Execution Phase Prediction Based on Phase Precursors and Locality
This paper focuses on different methods developed to detect the upcoming execution phase of a workload with regards to power demands. By controlling the state of the processor in power demanding phases, the operating system can maintain a relatively steady power pattern in the workload, leading to higher power headroom in the system. We compared two main approaches in phase prediction. Firstly, we show that by detecting the precursors leading to an upcoming phase, the system can speculate the next phase with high accuracy. Additionally, we compared this method with another approach which relies on the assumption of phase locality, expecting the current dominant phase to continue in the near future. Our results show that by detecting the precursors we can detect 81% of the upcoming phases with lower processor frequency switching overhead compared to most of the proposed locality-based methods.