{"title":"大规模电磁散射计算的并行快速多极子法","authors":"Yaojiang Zhang, Fang Wu, E. Li","doi":"10.1142/S1465876304002368","DOIUrl":null,"url":null,"abstract":"Parallel fast multipole method is developed for radar cross section (RCS) computation of large targets on multiple processor systems. The sparse matrices in fast multipole method (FMM) are calculated and stored in distributed processors and message passing interface (MPI) is used to perform the matrix-vector product in parallel iterative solvor. The accuracy of the code is verified by comparison with the results of benchmark targets. Either the computational complexity or speed-up ratio of parallel FMM and MoM are compared.","PeriodicalId":331001,"journal":{"name":"Int. J. Comput. Eng. Sci.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Parallel fast multipole method for large-scale computation of electromagnetic scattering\",\"authors\":\"Yaojiang Zhang, Fang Wu, E. Li\",\"doi\":\"10.1142/S1465876304002368\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Parallel fast multipole method is developed for radar cross section (RCS) computation of large targets on multiple processor systems. The sparse matrices in fast multipole method (FMM) are calculated and stored in distributed processors and message passing interface (MPI) is used to perform the matrix-vector product in parallel iterative solvor. The accuracy of the code is verified by comparison with the results of benchmark targets. Either the computational complexity or speed-up ratio of parallel FMM and MoM are compared.\",\"PeriodicalId\":331001,\"journal\":{\"name\":\"Int. J. Comput. Eng. Sci.\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Comput. Eng. Sci.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1142/S1465876304002368\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Comput. Eng. Sci.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/S1465876304002368","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Parallel fast multipole method for large-scale computation of electromagnetic scattering
Parallel fast multipole method is developed for radar cross section (RCS) computation of large targets on multiple processor systems. The sparse matrices in fast multipole method (FMM) are calculated and stored in distributed processors and message passing interface (MPI) is used to perform the matrix-vector product in parallel iterative solvor. The accuracy of the code is verified by comparison with the results of benchmark targets. Either the computational complexity or speed-up ratio of parallel FMM and MoM are compared.
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