Compression and load balancing for efficient sparse matrix-vector product on multicore processors and graphics processing units
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Otros documentos de la autoría: Aliaga Estellés, José Ignacio; Anzt, Hartwig; Grützmacher, Thomas; Quintana-Orti, Enrique S.; Tomás Domínguez, Andrés Enrique
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7036
comunitat-uji-handle3:10234/8620
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INVESTIGACIONMetadatos
Título
Compression and load balancing for efficient sparse matrix-vector product on multicore processors and graphics processing unitsAutoría
Fecha de publicación
2021Editor
John Wiley and SonsISSN
1532-0634; 1532-0626Cita bibliográfica
Aliaga, JI, Anzt, H, Grützmacher, T, Quintana-Ortí, ES, Tomás, AE. Compression and load balancing for efficient sparse matrix-vector product on multicore processors and graphics processing units. Concurrency Computat Pract Exper. 2021;e6515. https://doi.org/10.1002/cpe.6515Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://onlinelibrary.wiley.com/doi/full/10.1002/cpe.6515Versión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
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 ... [+]
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. [-]
Publicado en
Concurrency and Computation: Practice and Experience, 2021Entidad financiadora
Ministerio de Ciencia, Innovación y Universidades (España) | Helmholtz Association | United States Department of Energy (DOE)
Código del proyecto o subvención
TIN2017-82972 | VH-NG-1241 | 17-SC-20-SC
Derechos de acceso
Copyright © John Wiley & Sons, Inc.
http://rightsstatements.org/vocab/CNE/1.0/
info:eu-repo/semantics/openAccess
http://rightsstatements.org/vocab/CNE/1.0/
info:eu-repo/semantics/openAccess
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- ICC_Articles [423]