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An efficient GPU version of the preconditioned GMRES method

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An efficient GPU version of the preconditioned GMRES method

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dc.contributor.author Aliaga, Jose I. es_ES
dc.contributor.author Dufrechou, Ernesto es_ES
dc.contributor.author Ezzatti, Pablo es_ES
dc.contributor.author Quintana-Orti, Enrique S. es_ES
dc.date.accessioned 2021-02-03T04:33:46Z
dc.date.available 2021-02-03T04:33:46Z
dc.date.issued 2019-03 es_ES
dc.identifier.issn 0920-8542 es_ES
dc.identifier.uri http://hdl.handle.net/10251/160601
dc.description.abstract [EN] In a large number of scientific applications, the solution of sparse linear systems is the stage that concentrates most of the computational effort. This situation has motivated the study and development of several iterative solvers, among which preconditioned Krylov subspace methods occupy a place of privilege. In a previous effort, we developed a GPU-aware version of the GMRES method included in ILUPACK, a package of solvers distinguished by its inverse-based multilevel ILU preconditioner. In this work, we study the performance of our previous proposal and integrate several enhancements in order to mitigate its principal bottlenecks. The numerical evaluation shows that our novel proposal can reach important run-time reductions. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof The Journal of Supercomputing es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject GPUs es_ES
dc.subject GMRES es_ES
dc.subject Sparse triangular solver es_ES
dc.subject MGSO es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title An efficient GPU version of the preconditioned GMRES method es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s11227-018-2658-1 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors es_ES
dc.description.bibliographicCitation Aliaga, JI.; Dufrechou, E.; Ezzatti, P.; Quintana-Orti, ES. (2019). An efficient GPU version of the preconditioned GMRES method. The Journal of Supercomputing. 75(3):1455-1469. https://doi.org/10.1007/s11227-018-2658-1 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s11227-018-2658-1 es_ES
dc.description.upvformatpinicio 1455 es_ES
dc.description.upvformatpfin 1469 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 75 es_ES
dc.description.issue 3 es_ES
dc.relation.pasarela S\387471 es_ES
dc.description.references Aliaga JI, Badia RM, Barreda M, Bollhöfer M, Dufrechou E, Ezzatti P, Quintana-Ortí ES (2016) Exploiting task and data parallelism in ILUPACK’s preconditioned CG solver on NUMA architectures and many-core accelerators. Parallel Comput 54:97–107 es_ES
dc.description.references Aliaga JI, Bollhöfer M, Dufrechou E, Ezzatti P, Quintana-Ortí ES (2016) A data-parallel ILUPACK for sparse general and symmetric indefinite linear systems. In: Lecture Notes in Computer Science, 14th Int. Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Platforms—HeteroPar’16. Springer es_ES
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dc.description.references Aliaga JI, Dufrechou E, Ezzatti P, Quintana-Ortí ES (2018) Accelerating a preconditioned GMRES method in massively parallel processors. In: CMMSE 2018: Proceedings of the 18th International Conference on Mathematical Methods in Science and Engineering (2018) es_ES
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