{"title":"模拟连续时间马尔可夫链的并行算法","authors":"D. Nicol, P. Heidelberger","doi":"10.1145/158459.158461","DOIUrl":null,"url":null,"abstract":"We have previously shown that the mathematical technique of uniformization can serve as the basis of synchronization for the parallel simulation of continuous-time Markov chains. This paper reviews the basic method and compares four different methods based on uniformization, evaluating their strengths and weaknesses as a function of problem characteristics. The methods vary in their use of optimism, logical aggregation, communication management, and adaptivity. Touchstone Delta multiprocessor, using up to 256 processors.","PeriodicalId":194781,"journal":{"name":"Workshop on Parallel and Distributed Simulation","volume":"347 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1993-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Parallel algorithms for simulating continuous time Markov chains\",\"authors\":\"D. Nicol, P. Heidelberger\",\"doi\":\"10.1145/158459.158461\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We have previously shown that the mathematical technique of uniformization can serve as the basis of synchronization for the parallel simulation of continuous-time Markov chains. This paper reviews the basic method and compares four different methods based on uniformization, evaluating their strengths and weaknesses as a function of problem characteristics. The methods vary in their use of optimism, logical aggregation, communication management, and adaptivity. Touchstone Delta multiprocessor, using up to 256 processors.\",\"PeriodicalId\":194781,\"journal\":{\"name\":\"Workshop on Parallel and Distributed Simulation\",\"volume\":\"347 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1993-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Workshop on Parallel and Distributed Simulation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/158459.158461\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Workshop on Parallel and Distributed Simulation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/158459.158461","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Parallel algorithms for simulating continuous time Markov chains
We have previously shown that the mathematical technique of uniformization can serve as the basis of synchronization for the parallel simulation of continuous-time Markov chains. This paper reviews the basic method and compares four different methods based on uniformization, evaluating their strengths and weaknesses as a function of problem characteristics. The methods vary in their use of optimism, logical aggregation, communication management, and adaptivity. Touchstone Delta multiprocessor, using up to 256 processors.
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