K. Straube, Jason Lowe-Power, C. Nitta, M. Farrens, V. Akella
{"title":"Improving Provisioned Power Efficiency in HPC Systems with GPU-CAPP","authors":"K. Straube, Jason Lowe-Power, C. Nitta, M. Farrens, V. Akella","doi":"10.1109/HiPC.2018.00021","DOIUrl":null,"url":null,"abstract":"In this paper we propose a microarchitectural technique called GPU Constant Average Power Processing (GPU-CAPP) that improves the power utilization of power provisioning-limited systems by using provisioned power as much as possible to accelerate computation on parallel work-loads. GPU-CAPP uses a flexible, decentralized control to ensure fast response times and the scalability required for increasingly parallel GPU designs. We use GPGPU-Sim and GPUWattch to simulate GPU-CAPP and evaluate its capabilities on a subset of the Rodinia benchmark suite. Overall, GPU-CAPP enables speedup by an average of 26% and 12% over equivalent fixed frequency systems at two power targets.","PeriodicalId":113335,"journal":{"name":"2018 IEEE 25th International Conference on High Performance Computing (HiPC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 25th International Conference on High Performance Computing (HiPC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HiPC.2018.00021","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
In this paper we propose a microarchitectural technique called GPU Constant Average Power Processing (GPU-CAPP) that improves the power utilization of power provisioning-limited systems by using provisioned power as much as possible to accelerate computation on parallel work-loads. GPU-CAPP uses a flexible, decentralized control to ensure fast response times and the scalability required for increasingly parallel GPU designs. We use GPGPU-Sim and GPUWattch to simulate GPU-CAPP and evaluate its capabilities on a subset of the Rodinia benchmark suite. Overall, GPU-CAPP enables speedup by an average of 26% and 12% over equivalent fixed frequency systems at two power targets.