- -

A parallel structured banded DC algorithm for symmetric eigenvalue problems

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

A parallel structured banded DC algorithm for symmetric eigenvalue problems

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: LiLiaoLu - A parallel ...
Tamaño: 731.1Kb
Formato: PDF
Descripción: Versión del Autor.
Abrir
[Cerrado]
Nombre: ccfhpc.pdf
Tamaño: 2.386Mb
Formato: PDF
Descripción: Versión editorial
Solicitar una copia al autor
dc.contributor.author Li, Shengguo es_ES
dc.contributor.author Liao, Xia es_ES
dc.contributor.author Lu, Yutong es_ES
dc.contributor.author Roman, Jose E. es_ES
dc.contributor.author Yue, Xiaoqiang es_ES
dc.date.accessioned 2024-07-17T18:07:58Z
dc.date.available 2024-07-17T18:07:58Z
dc.date.issued 2023 es_ES
dc.identifier.issn 2524-4922 es_ES
dc.identifier.uri http://hdl.handle.net/10251/206280
dc.description.abstract [EN] In this paper, a novel parallel structured divide-and-conquer (DC) algorithm is proposed for symmetric banded eigenvalue problems, denoted by PBSDC, which modifes the classical parallel banded DC (PBDC) algorithm by reducing its computational cost. The main tool that PBSDC uses is a parallel structured matrix multiplication algorithm (PSMMA), which can be much faster than the general dense matrix multiplication ScaLAPACK routine PDGEMM. Numerous experiments have been performed on Tianhe-2 supercomputer to compare PBSDC with PBDC and ELPA. For matrices with few defations, PBSDC can be much faster than PBDC since computations are saved. For matrices with many defations and/or small bandwidths, PBSDC can be faster than the tridiagonalization-based DC implemented in LAPACK and ELPA. However, PBSDC would become slower than ELPA for matrices with relatively large bandwidths. es_ES
dc.description.sponsorship The authors would like to thank the referees for their valuable comments. This work is supported in part by NSFC (No. 2021YFB0300101, 62073333, 61902411, 62032023, 12002382, 11275269, 42104078), 173 Program of China (2020-JCJQ-ZD-029), Open Research Fund from State Key Laboratory of High Performance Computing of China (HPCL) (No. 202101-01), Guangdong Natural Science Foundation (2018B030312002), and the Program for Guangdong Introducing Innovative and Entrepreneurial Teams under Grant (No. 2016ZT06D211). Jose E. Roman is supported by the Spanish Agencia Estatal de Investigacion (AEI) under project SLEPc-DA (PID2019-107379RB-I00). On behalf of all authors, the corresponding author states that there is no conflict of interest. es_ES
dc.language Inglés es_ES
dc.publisher Springer es_ES
dc.relation.ispartof CCF Transactions on High Performance Computing es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject ScaLAPACK es_ES
dc.subject Divide-and-conquer es_ES
dc.subject PSMMA es_ES
dc.subject PBSDC es_ES
dc.subject Distributed-memory parallel algorithm es_ES
dc.subject.classification CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL es_ES
dc.title A parallel structured banded DC algorithm for symmetric eigenvalue problems es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s42514-022-00117-9 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-107379RB-I00/ES/ALGORITMOS PARALELOS Y SOFTWARE PARA METODOS ALGEBRAICOS EN ANALISIS DE DATOS/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NSFC//42104078/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NSFC//2021YFB0300101/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NSFC//62073333/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NSFC//61902411/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NSFC//62032023/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NSFC//12002382/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NSFC//11275269/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/Natural Science Foundation of Guangdong Province//2018B030312002/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/Natural Science Foundation of Guangdong Province//2016ZT06D211/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NKRDPC//2020-JCJQ-ZD-029/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/Key Laboratory of High Performance Computing and Stochastic Information Processing//202101-01/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica es_ES
dc.description.bibliographicCitation Li, S.; Liao, X.; Lu, Y.; Roman, JE.; Yue, X. (2023). A parallel structured banded DC algorithm for symmetric eigenvalue problems. CCF Transactions on High Performance Computing. 5:116-128. https://doi.org/10.1007/s42514-022-00117-9 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s42514-022-00117-9 es_ES
dc.description.upvformatpinicio 116 es_ES
dc.description.upvformatpfin 128 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 5 es_ES
dc.relation.pasarela S\496562 es_ES
dc.contributor.funder AGENCIA ESTATAL DE INVESTIGACION es_ES
dc.contributor.funder National Natural Science Foundation of China es_ES
dc.contributor.funder Natural Science Foundation of Guangdong Province es_ES
dc.contributor.funder National Key Research and Development Program of China es_ES
dc.contributor.funder Key Laboratory of High Performance Computing and Stochastic Information Processing es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem