{"title":"Coupling-aware graph partitioning algorithms: Preliminary study","authors":"Maria Predari, Aurélien Esnard","doi":"10.1109/HiPC.2014.7116879","DOIUrl":null,"url":null,"abstract":"In the field of scientific computing, load balancing is a major issue that determines the performance of parallel applications. Nowadays, simulations of real-life problems are becoming more and more complex, involving numerous coupled codes, representing different models. In this context, reaching high performance can be a great challenge. In this paper, we present graph partitioning techniques, called co-partitioning, that address the problem of load balancing for two coupled codes: the key idea is to perform a “coupling-aware” partitioning, instead of partitioning these codes independently, as it is usually done. Finally, we present a preliminary experimental study which compares our methods against the usual approach.","PeriodicalId":337777,"journal":{"name":"2014 21st International Conference on High Performance Computing (HiPC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 21st International Conference on High Performance Computing (HiPC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HiPC.2014.7116879","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
In the field of scientific computing, load balancing is a major issue that determines the performance of parallel applications. Nowadays, simulations of real-life problems are becoming more and more complex, involving numerous coupled codes, representing different models. In this context, reaching high performance can be a great challenge. In this paper, we present graph partitioning techniques, called co-partitioning, that address the problem of load balancing for two coupled codes: the key idea is to perform a “coupling-aware” partitioning, instead of partitioning these codes independently, as it is usually done. Finally, we present a preliminary experimental study which compares our methods against the usual approach.