Accelerating the multilevel fast multipole algorithm with the sparse-approximate-inverse (SAI) preconditioning

buir.contributor.authorGürel, Levent
dc.citation.epage1984en_US
dc.citation.issueNumber3en_US
dc.citation.spage1968en_US
dc.citation.volumeNumber31en_US
dc.contributor.authorMalas, T.en_US
dc.contributor.authorGürel, Leventen_US
dc.date.accessioned2019-01-24T13:43:22Z
dc.date.available2019-01-24T13:43:22Z
dc.date.issued2009en_US
dc.departmentComputational Electromagnetics Research Center (BiLCEM)en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractWith the help of the multilevel fast multipole algorithm, integral-equation methods can be used to solve real-life electromagnetics problems both accurately and efficiently. Increasing problem dimensions, on the other hand, necessitate effective parallel preconditioners with low setup costs. In this paper, we consider sparse approximate inverses generated from the sparse near-field part of the dense coefficient matrix. In particular, we analyze pattern selection strategies that can make efficient use of the block structure of the near-field matrix, and we propose a load-balancing method to obtain high scalability during the setup. We also present some implementation details, which reduce the computational cost of the setup phase. In conclusion, for the open-surface problems that are modeled by the electric-field integral equation, we have been able to solve ill-conditioned linear systems involving millions of unknowns with moderate computational requirements. For closed surface problems that can be modeled by the combined-field integral equation, we reduce the solution times significantly compared to the commonly used block-diagonal preconditioner.en_US
dc.description.provenanceSubmitted by Elsa Bitri (elsabitri@bilkent.edu.tr) on 2019-01-24T13:43:22Z No. of bitstreams: 1 Accelerating_the_multilevel_fast_multipole_algorithm_with_the_sparse_approximate_inverse_SAI_preconditioning.pdf: 3781959 bytes, checksum: 6cee64028edd38cc29a7b1686fced6f8 (MD5)en
dc.description.provenanceMade available in DSpace on 2019-01-24T13:43:22Z (GMT). No. of bitstreams: 1 Accelerating_the_multilevel_fast_multipole_algorithm_with_the_sparse_approximate_inverse_SAI_preconditioning.pdf: 3781959 bytes, checksum: 6cee64028edd38cc29a7b1686fced6f8 (MD5) Previous issue date: 2009en
dc.description.sponsorshipThis work was supported by the Scientific and Technical Research Council of Turkey (TUBITAK) under research grants 105E172 and 107E136, the Turkish Academy of Sciences in the framework of the Young Scientist Award Program (LG/TUBAGEBIP/2002-1-12), and contracts from ASELSAN and SSM.en_US
dc.identifier.doi10.1137/070711098en_US
dc.identifier.eissn1095-7197
dc.identifier.issn1064-8275
dc.identifier.urihttp://hdl.handle.net/11693/48309
dc.language.isoEnglishen_US
dc.publisherSociety for Industrial and Applied Mathematicsen_US
dc.relation.isversionofhttps://doi.org/10.1137/070711098en_US
dc.source.titleSIAM Journal on Scientific Computingen_US
dc.subjectPreconditioningen_US
dc.subjectSparse-approximate-inverse preconditionersen_US
dc.subjectIntegral-equation methodsen_US
dc.subjectComputational electromagneticsen_US
dc.subjectParallel computationen_US
dc.subject31A10en_US
dc.subject65F10en_US
dc.subject78A45en_US
dc.subject78M05en_US
dc.subject65Y05en_US
dc.titleAccelerating the multilevel fast multipole algorithm with the sparse-approximate-inverse (SAI) preconditioningen_US
dc.typeArticleen_US

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