{"title":"基于精确工作负载特征的高效集群计算","authors":"S. Huang, W. Feng","doi":"10.1109/CCGRID.2009.88","DOIUrl":null,"url":null,"abstract":"This paper presents an eco-friendly daemon that reduces power and energy consumption while better maintaining high performance via an accurate workload characterization that infers “processor stall cycles due to off-chip activities.” The eco-friendly daemon is an interval-based, run-time algorithm that uses the workload characterization to dynamically adjust a processor’s frequency and voltage to reduce power and energy consumption with little impact on application performance. Using the NAS Parallel Benchmarks as our workload, we then evaluate our eco-friendly daemon on a cluster computer. The results indicate that our workload characterization allows the power-aware daemon to more tightly control performance (5% loss instead of 11%) while delivering substantial energy savings (11% instead of 8%).","PeriodicalId":118263,"journal":{"name":"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid","volume":"60 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"110","resultStr":"{\"title\":\"Energy-Efficient Cluster Computing via Accurate Workload Characterization\",\"authors\":\"S. Huang, W. Feng\",\"doi\":\"10.1109/CCGRID.2009.88\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an eco-friendly daemon that reduces power and energy consumption while better maintaining high performance via an accurate workload characterization that infers “processor stall cycles due to off-chip activities.” The eco-friendly daemon is an interval-based, run-time algorithm that uses the workload characterization to dynamically adjust a processor’s frequency and voltage to reduce power and energy consumption with little impact on application performance. Using the NAS Parallel Benchmarks as our workload, we then evaluate our eco-friendly daemon on a cluster computer. The results indicate that our workload characterization allows the power-aware daemon to more tightly control performance (5% loss instead of 11%) while delivering substantial energy savings (11% instead of 8%).\",\"PeriodicalId\":118263,\"journal\":{\"name\":\"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid\",\"volume\":\"60 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-05-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"110\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCGRID.2009.88\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCGRID.2009.88","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Energy-Efficient Cluster Computing via Accurate Workload Characterization
This paper presents an eco-friendly daemon that reduces power and energy consumption while better maintaining high performance via an accurate workload characterization that infers “processor stall cycles due to off-chip activities.” The eco-friendly daemon is an interval-based, run-time algorithm that uses the workload characterization to dynamically adjust a processor’s frequency and voltage to reduce power and energy consumption with little impact on application performance. Using the NAS Parallel Benchmarks as our workload, we then evaluate our eco-friendly daemon on a cluster computer. The results indicate that our workload characterization allows the power-aware daemon to more tightly control performance (5% loss instead of 11%) while delivering substantial energy savings (11% instead of 8%).