Chun-Xun Lin, Tsung-Wei Huang, T. Yu, Martin D. F. Wong
{"title":"A Distributed Power Grid Analysis Framework from Sequential Stream Graph","authors":"Chun-Xun Lin, Tsung-Wei Huang, T. Yu, Martin D. F. Wong","doi":"10.1145/3194554.3194560","DOIUrl":null,"url":null,"abstract":"The ever-increasing design complexities have overwhelmed what is offered by existing EDA tools. As a result, the recent EDA industry is driving the need for distributed computing to leverage large-scale compute-intensive problems, in particular, power grid analysis. In this paper, we introduce a distributed power grid analysis framework based on the stream graph model. We show that the stream graph model has better programmability over the MPI and enables flexible domain decomposition without limited by hardware resource. In addition, we design an efficient scheduling policy for this particular workload to maximize the cluster utilization to improve the performance. The experimental results demonstrated the promising performance of our framework that scales from single multi-core machines to a distributed computer cluster.","PeriodicalId":215940,"journal":{"name":"Proceedings of the 2018 on Great Lakes Symposium on VLSI","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2018 on Great Lakes Symposium on VLSI","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3194554.3194560","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
The ever-increasing design complexities have overwhelmed what is offered by existing EDA tools. As a result, the recent EDA industry is driving the need for distributed computing to leverage large-scale compute-intensive problems, in particular, power grid analysis. In this paper, we introduce a distributed power grid analysis framework based on the stream graph model. We show that the stream graph model has better programmability over the MPI and enables flexible domain decomposition without limited by hardware resource. In addition, we design an efficient scheduling policy for this particular workload to maximize the cluster utilization to improve the performance. The experimental results demonstrated the promising performance of our framework that scales from single multi-core machines to a distributed computer cluster.