Hierarchical approach for deriving a reproducible unblocked LU factorization
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Otros documentos de la autoría: Iakymchuk, Roman; Graillat, Stef; Defour, David; Quintana-Orti, Enrique S.
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7036
comunitat-uji-handle3:10234/8620
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Hierarchical approach for deriving a reproducible unblocked LU factorizationFecha de publicación
2019-03-17Editor
SageCita bibliográfica
IAKYMCHUK, Roman, et al. Hierarchical approach for deriving a reproducible unblocked LU factorization. The International Journal of High Performance Computing Applications, 2019, 1094342019832968.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://journals.sagepub.com/doi/full/10.1177/1094342019832968Versión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
We propose a reproducible variant of the unblocked LU factorization for graphics processor units (GPUs). For this purpose, we build upon Level-1/2 BLAS kernels that deliver correctly-rounded and reproducible results ... [+]
We propose a reproducible variant of the unblocked LU factorization for graphics processor units (GPUs). For this purpose, we build upon Level-1/2 BLAS kernels that deliver correctly-rounded and reproducible results for the dot (inner) product, vector scaling, and the matrix-vector product. In addition, we draw a strategy to enhance the accuracy of the triangular solve via iterative refinement. Following a bottom-up approach, we finally construct a reproducible unblocked implementation of the LU factorization for GPUs, which accommodates partial pivoting for stability and can be eventually integrated in a high performance and stable algorithm for the (blocked) LU factorization. [-]
Proyecto de investigación
HPC resources of The Institute for Scientific Computing and Simulation financed by Region Iˆle-de-France and the project Equip@Meso (reference ANR-10-EQPX-29-01) overseen by the French National Agency for Research (ANR) as part of the “Investissements d’Avenir” program ; ANR FastRelax (ANR-14-CE25-0018-01) projectDerechos de acceso
Copyright © 2019 by SAGE Publications
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