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Compression and load balancing for efficient sparse matrix-vector product on multicore processors and graphics processing units

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Compression and load balancing for efficient sparse matrix-vector product on multicore processors and graphics processing units

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dc.contributor.author Aliaga, José I. es_ES
dc.contributor.author Anzt, Hartwig es_ES
dc.contributor.author Grützmacher, Thomas es_ES
dc.contributor.author Quintana-Ortí, Enrique S. es_ES
dc.contributor.author Tomás Domínguez, Andrés Enrique es_ES
dc.date.accessioned 2023-09-12T18:04:41Z
dc.date.available 2023-09-12T18:04:41Z
dc.date.issued 2022-06-25 es_ES
dc.identifier.issn 1532-0626 es_ES
dc.identifier.uri http://hdl.handle.net/10251/196280
dc.description.abstract [EN] We contribute to the optimization of the sparse matrix-vector product by introducing a variant of the coordinate sparse matrix format that balances the workload distribution and compresses both the indexing arrays and the numerical information. Our approach is multi-platform, in the sense that the realizations for (general-purpose) multicore processors as well as graphics accelerators (GPUs) are built upon common principles, but differ in the implementation details, which are adapted to avoid thread divergence in the GPU case or maximize compression element-wise (i.e., for each matrix entry) for multicore architectures. Our evaluation on the two last generations of NVIDIA GPUs as well as Intel and AMD processors demonstrate the benefits of the new kernels when compared with the optimized implementations of the sparse matrix-vector product in NVIDIA's cuSPARSE and Intel's MKL, respectively. es_ES
dc.description.sponsorship J. I. Aliaga, E. S. Quintana-Ortí, and A. E. Tomás were supported by TIN2017-82972-R of the Spanish MINECO. H. Anzt and T. Grützmacher were supported by the Impuls und Vernetzungsfond of the Helmholtz Association under grant VH-NG-1241 and by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration. The authors would like to thank the Steinbuch Centre for Computing (SCC) of the Karlsruhe Institute of Technology for providing access to an NVIDIA A100 GPU. es_ES
dc.language Inglés es_ES
dc.publisher John Wiley & Sons es_ES
dc.relation.ispartof Concurrency and Computation: Practice and Experience es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Compression es_ES
dc.subject Coordinate sparse matrix format es_ES
dc.subject Graphics processing units (GPUs) es_ES
dc.subject Multicore processors (CPUs) es_ES
dc.subject Sparse matrix-vector product es_ES
dc.subject Workload balancing. es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title Compression and load balancing for efficient sparse matrix-vector product on multicore processors and graphics processing units es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1002/cpe.6515 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-82972-R/ES/TECNICAS ALGORITMICAS PARA COMPUTACION DE ALTO RENDIMIENTO CONSCIENTE DEL CONSUMO ENERGETICO Y RESISTENTE A ERRORES/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/DOE//17-SC-20-SC//Exascale Computing Project/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/Helmholtz Association of German Research Centers//VH-NG-1241/ 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.contributor.affiliation Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica es_ES
dc.description.bibliographicCitation Aliaga, JI.; Anzt, H.; Grützmacher, T.; Quintana-Ortí, ES.; Tomás Domínguez, AE. (2022). Compression and load balancing for efficient sparse matrix-vector product on multicore processors and graphics processing units. Concurrency and Computation: Practice and Experience. 34(14):1-13. https://doi.org/10.1002/cpe.6515 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1002/cpe.6515 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 13 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 34 es_ES
dc.description.issue 14 es_ES
dc.relation.pasarela S\465928 es_ES
dc.contributor.funder Nvidia es_ES
dc.contributor.funder U.S. Department of Energy es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder Helmholtz Association of German Research Centers es_ES


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