Changjiang Gou, A. Benoit, Mingsong Chen, L. Marchal, Tongquan Wei
{"title":"Reliable and Energy-aware Mapping of Streaming Series-parallel Applications onto Hierarchical Platforms","authors":"Changjiang Gou, A. Benoit, Mingsong Chen, L. Marchal, Tongquan Wei","doi":"10.1109/SBAC-PAD49847.2020.00026","DOIUrl":null,"url":null,"abstract":"Streaming applications come from various application fields such as physics, and many can be represented as a series-parallel dependence graph. We aim at minimizing the energy consumption of such applications when executed on a hierarchical platform, by proposing novel mapping strategies. Dynamic voltage and frequency scaling (DVFS) is used to reduce the energy consumption, and we ensure a reliable execution by either executing a task at maximum speed, or by triplicating it. In this paper, we propose a structure rule to partition the series-parallel applications, and we prove that the optimization problem is NP-complete. We are able to derive a dynamic programming algorithm for the special case of linear chains, which provides an interesting heuristic and a building block for designing heuristics for the general case. The heuristics performance is compared to a baseline solution, where each task is executed at maximum speed. Simulations demonstrate that significant energy savings can be obtained.","PeriodicalId":202581,"journal":{"name":"2020 IEEE 32nd International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)","volume":"171 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 32nd International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SBAC-PAD49847.2020.00026","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
Streaming applications come from various application fields such as physics, and many can be represented as a series-parallel dependence graph. We aim at minimizing the energy consumption of such applications when executed on a hierarchical platform, by proposing novel mapping strategies. Dynamic voltage and frequency scaling (DVFS) is used to reduce the energy consumption, and we ensure a reliable execution by either executing a task at maximum speed, or by triplicating it. In this paper, we propose a structure rule to partition the series-parallel applications, and we prove that the optimization problem is NP-complete. We are able to derive a dynamic programming algorithm for the special case of linear chains, which provides an interesting heuristic and a building block for designing heuristics for the general case. The heuristics performance is compared to a baseline solution, where each task is executed at maximum speed. Simulations demonstrate that significant energy savings can be obtained.