{"title":"行连续马尔可夫链迭代算法的负载平衡与并行实现","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":"{\"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}","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}
Load balancing and parallel implementation of iterative algorithms for row-continuous Markov chains
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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