{"title":"分数阶系统的并行模拟","authors":"A. Baban, C. Bonchis, A. Fikl, F. Rosu","doi":"10.1109/SYNASC.2016.033","DOIUrl":null,"url":null,"abstract":"In this paper, we explore how numerical calculations can be accelerated by implementing several numerical methods of fractional-order systems using parallel computing techniques. We investigate the feasibility of parallel computing algorithms and their efficiency in reducing the computational costs over a large time interval. Particularly, we present the case of Adams-Bashforth-Mouhlton predictor-corrector method and measure the speedup of two parallel approaches by using GPU and HPC cluster implementations.","PeriodicalId":268635,"journal":{"name":"2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Parallel Simulations for Fractional-Order Systems\",\"authors\":\"A. Baban, C. Bonchis, A. Fikl, F. Rosu\",\"doi\":\"10.1109/SYNASC.2016.033\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we explore how numerical calculations can be accelerated by implementing several numerical methods of fractional-order systems using parallel computing techniques. We investigate the feasibility of parallel computing algorithms and their efficiency in reducing the computational costs over a large time interval. Particularly, we present the case of Adams-Bashforth-Mouhlton predictor-corrector method and measure the speedup of two parallel approaches by using GPU and HPC cluster implementations.\",\"PeriodicalId\":268635,\"journal\":{\"name\":\"2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SYNASC.2016.033\",\"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 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SYNASC.2016.033","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, we explore how numerical calculations can be accelerated by implementing several numerical methods of fractional-order systems using parallel computing techniques. We investigate the feasibility of parallel computing algorithms and their efficiency in reducing the computational costs over a large time interval. Particularly, we present the case of Adams-Bashforth-Mouhlton predictor-corrector method and measure the speedup of two parallel approaches by using GPU and HPC cluster implementations.
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