{"title":"Load balancing and parallel implementation of iterative algorithms for row-continuous Markov chains","authors":"M. Colajanni, M. Angelaccio","doi":"10.1109/SHPCC.1992.232656","DOIUrl":null,"url":null,"abstract":"Presents the first parallel algorithms for solving row-continuous or generalized birth-death (GBD) Markov chains on distributed memory MIMD multiprocessors. These systems are characterized by very large transition probability matrices, decomposable in heterogeneous tridiagonal blocks. The parallelization of three aggregation/disaggregation iterative methods is carried out by a unique framework that keeps into account the special matrix structure. Great effort has been also devoted to define a general algorithm for approximating the optimum workload. Various computational experiments show that Vantilborgh's (1985) method is the fastest of the three algorithms on any data set dimension.<<ETX>>","PeriodicalId":254515,"journal":{"name":"Proceedings Scalable High Performance Computing Conference SHPCC-92.","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings Scalable High Performance Computing Conference SHPCC-92.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SHPCC.1992.232656","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
Presents the first parallel algorithms for solving row-continuous or generalized birth-death (GBD) Markov chains on distributed memory MIMD multiprocessors. These systems are characterized by very large transition probability matrices, decomposable in heterogeneous tridiagonal blocks. The parallelization of three aggregation/disaggregation iterative methods is carried out by a unique framework that keeps into account the special matrix structure. Great effort has been also devoted to define a general algorithm for approximating the optimum workload. Various computational experiments show that Vantilborgh's (1985) method is the fastest of the three algorithms on any data set dimension.<>
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