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Selecting optimal SpMV realizations for GPUs via machine learning

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Selecting optimal SpMV realizations for GPUs via machine learning

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Dufrechou, E.; Ezzatti, P.; Quintana-Orti, ES. (2021). Selecting optimal SpMV realizations for GPUs via machine learning. International Journal of High Performance Computing Applications. 35(3):254-267. https://doi.org/10.1177/1094342021990738

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/184043

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Title: Selecting optimal SpMV realizations for GPUs via machine learning
Author: Dufrechou, Ernesto Ezzatti, Pablo Quintana-Orti, Enrique S.
UPV Unit: Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors
Issued date:
Abstract:
[EN] More than 10 years of research related to the development of efficient GPU routines for the sparse matrix-vector product (SpMV) have led to several realizations, each with its own strengths and weaknesses. In this ...[+]
Subjects: Sparse numerical linear algebra , Sparse matrix-vector product (SpMV) , Automatic method selection , Machine learning , Parallel architectures , Graphics processing units (GPUs)
Copyrigths: Reserva de todos los derechos
Source:
International Journal of High Performance Computing Applications. (issn: 1094-3420 )
DOI: 10.1177/1094342021990738
Publisher:
SAGE Publications
Publisher version: https://doi.org/10.1177/1094342021990738
Project ID:
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/
info:eu-repo/grantAgreement/MCIU//TIN2017-82972-R/
Thanks:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: ES Quintana-Ort was supported by project TIN2017-82972-R of the MINECO and FEDER
Type: Artículo

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