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GPU implementation of Krylov solvers for block-tridiagonal eigenvalue problems

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GPU implementation of Krylov solvers for block-tridiagonal eigenvalue problems

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dc.contributor.author Lamas Daviña, Alejandro es_ES
dc.contributor.author Román Moltó, José Enrique es_ES
dc.date.accessioned 2016-06-27T08:13:46Z
dc.date.available 2016-06-27T08:13:46Z
dc.date.issued 2016-04-02
dc.identifier.isbn 978-3-319-32148-6
dc.identifier.issn 0302-9743
dc.identifier.issn 10.1007/978-3-319-32149-3_18
dc.identifier.uri http://hdl.handle.net/10251/66516
dc.description The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-32149-3_18 es_ES
dc.description.abstract In an eigenvalue problem defined by one or two matrices with block-tridiagonal structure, if only a few eigenpairs are required it is interesting to consider iterative methods based on Krylov subspaces, even if matrix blocks are dense. In this context, using the GPU for the associated dense linear algebra may provide high performance. We analyze this in an implementation done in the context of SLEPc, the Scalable Library for Eigenvalue Problem Computations. In the case of a generalized eigenproblem or when interior eigenvalues are computed with shift-and-invert, the main computational kernel is the solution of linear systems with a block-tridiagonal matrix. We explore possible implementations of this operation on the GPU, including a block cyclic reduction algorithm. es_ES
dc.description.sponsorship This work was partially supported by the Spanish Ministry of Economy and Competitiveness under grant TIN2013-41049-P. Alejandro Lamas was supported by the Spanish Ministry of Education, Culture and Sport through grant FPU13-06655. es_ES
dc.format.extent 10 es_ES
dc.language Inglés es_ES
dc.publisher Springer es_ES
dc.relation.ispartof Parallel Processing and Applied Mathematics es_ES
dc.relation.ispartofseries Lecture Notes in Computer Science;9573
dc.rights Reserva de todos los derechos es_ES
dc.subject GPU computing es_ES
dc.subject Eigenvalue computation es_ES
dc.subject Krylov methods es_ES
dc.subject Block-tridiagonal linear solvers es_ES
dc.subject.classification CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL es_ES
dc.title GPU implementation of Krylov solvers for block-tridiagonal eigenvalue problems es_ES
dc.type Capítulo de libro es_ES
dc.type Comunicación en congreso es_ES
dc.identifier.doi 10.1007%2F978-3-319-32149-3_18
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//TIN2013-41049-P/ES/EXTENSION DE LA LIBRERIA SLEPC PARA POLINOMIOS MATRICIALES, FUNCIONES MATRICIALES Y ECUACIONES MATRICIALES EN PLATAFORMAS DE COMPUTACION EMERGENTES/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MECD//FPU13%2F06655/ES/FPU13%2F06655/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Lamas Daviña, A.; Román Moltó, JE. (2016). GPU implementation of Krylov solvers for block-tridiagonal eigenvalue problems. En Parallel Processing and Applied Mathematics. Springer. 182-191. https://doi.org/10.1007%2F978-3-319-32149-3_18 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename 11th International Conference on Parallel Processing and Applied Mathematics (PPAM 2015) es_ES
dc.relation.conferencedate September 6-9, 2015 es_ES
dc.relation.conferenceplace Krakow, Poland es_ES
dc.relation.publisherversion http://link.springer.com/chapter/10.1007%2F978-3-319-32149-3_18 es_ES
dc.description.upvformatpinicio 182 es_ES
dc.description.upvformatpfin 191 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.senia 311439 es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.contributor.funder Ministerio de Educación, Cultura y Deporte es_ES
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