{"title":"Dynamic load balancing of massively parallel unstructured meshes","authors":"Gerrett Diamond, Cameron W. Smith, M. Shephard","doi":"10.1145/3148226.3148236","DOIUrl":null,"url":null,"abstract":"Simulating systems with evolving relational structures on massively parallel computers require the computational work to be evenly distributed across the processing resources throughout the simulation. Adaptive, unstructured, mesh-based finite element and finite volume tools best exemplify this need. We present EnGPar and its diffusive partition improvement method that accounts for multiple application specified criteria. EnGPar's performance is compared against its predecessor, ParMA. Specifically, partition improvement results are provided on up to 512Ki processes of the Argonne Leadership Computing Facility's Mira BlueGene/Q system.","PeriodicalId":440657,"journal":{"name":"Proceedings of the 8th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 8th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3148226.3148236","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Simulating systems with evolving relational structures on massively parallel computers require the computational work to be evenly distributed across the processing resources throughout the simulation. Adaptive, unstructured, mesh-based finite element and finite volume tools best exemplify this need. We present EnGPar and its diffusive partition improvement method that accounts for multiple application specified criteria. EnGPar's performance is compared against its predecessor, ParMA. Specifically, partition improvement results are provided on up to 512Ki processes of the Argonne Leadership Computing Facility's Mira BlueGene/Q system.
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