{"title":"CFD中并行预条件共轭梯度解的网格划分度量","authors":"Miao Wang, Xiaoguang Ren, Hao Li, Juan Chen","doi":"10.1109/ICISCE.2016.104","DOIUrl":null,"url":null,"abstract":"This paper focuses on mesh-partitioning metrics in large-scale parallel computational fluid dynamics (CFD) simulations. Mesh partitioning has a significant influence on the efficiency of parallel preconditioned conjugated gradient (PCG) solving procedure, which is the most representative and time-consuming part in parallel CFD. As the efficiency of parallel PCG depends on load balancing, communication overhead and iterative convergence rate comprehensively, we present a detailed review of mesh-partitioning metrics on these three aspects respectively. Three typical large-scale CFD applications are built to numerically testify the validity of all those metrics.","PeriodicalId":6882,"journal":{"name":"2016 3rd International Conference on Information Science and Control Engineering (ICISCE)","volume":"32 1","pages":"448-455"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Mesh-Partitioning Metrics for Parallel Preconditioned Conjugated Gradient Solvers in CFD\",\"authors\":\"Miao Wang, Xiaoguang Ren, Hao Li, Juan Chen\",\"doi\":\"10.1109/ICISCE.2016.104\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper focuses on mesh-partitioning metrics in large-scale parallel computational fluid dynamics (CFD) simulations. Mesh partitioning has a significant influence on the efficiency of parallel preconditioned conjugated gradient (PCG) solving procedure, which is the most representative and time-consuming part in parallel CFD. As the efficiency of parallel PCG depends on load balancing, communication overhead and iterative convergence rate comprehensively, we present a detailed review of mesh-partitioning metrics on these three aspects respectively. Three typical large-scale CFD applications are built to numerically testify the validity of all those metrics.\",\"PeriodicalId\":6882,\"journal\":{\"name\":\"2016 3rd International Conference on Information Science and Control Engineering (ICISCE)\",\"volume\":\"32 1\",\"pages\":\"448-455\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-07-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 3rd International Conference on Information Science and Control Engineering (ICISCE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICISCE.2016.104\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 3rd International Conference on Information Science and Control Engineering (ICISCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICISCE.2016.104","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Mesh-Partitioning Metrics for Parallel Preconditioned Conjugated Gradient Solvers in CFD
This paper focuses on mesh-partitioning metrics in large-scale parallel computational fluid dynamics (CFD) simulations. Mesh partitioning has a significant influence on the efficiency of parallel preconditioned conjugated gradient (PCG) solving procedure, which is the most representative and time-consuming part in parallel CFD. As the efficiency of parallel PCG depends on load balancing, communication overhead and iterative convergence rate comprehensively, we present a detailed review of mesh-partitioning metrics on these three aspects respectively. Three typical large-scale CFD applications are built to numerically testify the validity of all those metrics.