A distributed approach for accelerating sparse matrix arithmetic operations for high-dimensional feature selection

Autores
Tommasel, Antonela; Godoy, Daniela Lis; Zunino Suarez, Alejandro Octavio; Mateos Diaz, Cristian Maximiliano
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Matrix computations are both fundamental and ubiquitous in computational science, and as a result, they are frequently used in numerous disciplines of scientific computing and engineering. Due to the high computational complexity of matrix operations, which makes them critical to the performance of a large number of applications, their efficient execution in distributed environments becomes a crucial issue. This work proposes a novel approach for distributing sparse matrix arithmetic operations on computer clusters aiming at speeding-up the processing of high-dimensional matrices. The approach focuses on how to split such operations into independent parallel tasks by considering the intrinsic characteristics that distinguish each type of operation and the particular matrices involved. The approach was applied to the most commonly used arithmetic operations between matrices. The performance of the presented approach was evaluated considering a high-dimensional text feature selection approach and two real-world datasets. Experimental evaluation showed that the proposed approach helped to significantly reduce the computing times of big-scale matrix operations, when compared to serial and multi-thread implementations as well as several linear algebra software libraries.
Fil: Tommasel, Antonela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Godoy, Daniela Lis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Materia
DISTRIBUTED COMPUTING
FEATURE SELECTION
MATRIX ARITHMETIC OPERATION
SPARSE MATRIX
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/58575
Acceder
id CONICETDig_ae19f269f543c20468df894f19088264
oai_identifier_str oai:ri.conicet.gov.ar:11336/58575
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A distributed approach for accelerating sparse matrix arithmetic operations for high-dimensional feature selectionTommasel, AntonelaGodoy, Daniela LisZunino Suarez, Alejandro OctavioMateos Diaz, Cristian MaximilianoDISTRIBUTED COMPUTINGFEATURE SELECTIONMATRIX ARITHMETIC OPERATIONSPARSE MATRIXhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Matrix computations are both fundamental and ubiquitous in computational science, and as a result, they are frequently used in numerous disciplines of scientific computing and engineering. Due to the high computational complexity of matrix operations, which makes them critical to the performance of a large number of applications, their efficient execution in distributed environments becomes a crucial issue. This work proposes a novel approach for distributing sparse matrix arithmetic operations on computer clusters aiming at speeding-up the processing of high-dimensional matrices. The approach focuses on how to split such operations into independent parallel tasks by considering the intrinsic characteristics that distinguish each type of operation and the particular matrices involved. The approach was applied to the most commonly used arithmetic operations between matrices. The performance of the presented approach was evaluated considering a high-dimensional text feature selection approach and two real-world datasets. Experimental evaluation showed that the proposed approach helped to significantly reduce the computing times of big-scale matrix operations, when compared to serial and multi-thread implementations as well as several linear algebra software libraries.Fil: Tommasel, Antonela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Godoy, Daniela Lis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaSpringer London Ltd2017-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/58575Tommasel, Antonela; Godoy, Daniela Lis; Zunino Suarez, Alejandro Octavio; Mateos Diaz, Cristian Maximiliano; A distributed approach for accelerating sparse matrix arithmetic operations for high-dimensional feature selection; Springer London Ltd; Knowledge And Information Systems; 51; 2; 5-2017; 459-4970219-1377CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1007/s10115-016-0981-5info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007%2Fs10115-016-0981-5info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:13:18Zoai:ri.conicet.gov.ar:11336/58575instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:13:19.117CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A distributed approach for accelerating sparse matrix arithmetic operations for high-dimensional feature selection
title A distributed approach for accelerating sparse matrix arithmetic operations for high-dimensional feature selection
spellingShingle A distributed approach for accelerating sparse matrix arithmetic operations for high-dimensional feature selection
Tommasel, Antonela
DISTRIBUTED COMPUTING
FEATURE SELECTION
MATRIX ARITHMETIC OPERATION
SPARSE MATRIX
title_short A distributed approach for accelerating sparse matrix arithmetic operations for high-dimensional feature selection
title_full A distributed approach for accelerating sparse matrix arithmetic operations for high-dimensional feature selection
title_fullStr A distributed approach for accelerating sparse matrix arithmetic operations for high-dimensional feature selection
title_full_unstemmed A distributed approach for accelerating sparse matrix arithmetic operations for high-dimensional feature selection
title_sort A distributed approach for accelerating sparse matrix arithmetic operations for high-dimensional feature selection
dc.creator.none.fl_str_mv Tommasel, Antonela
Godoy, Daniela Lis
Zunino Suarez, Alejandro Octavio
Mateos Diaz, Cristian Maximiliano
author Tommasel, Antonela
author_facet Tommasel, Antonela
Godoy, Daniela Lis
Zunino Suarez, Alejandro Octavio
Mateos Diaz, Cristian Maximiliano
author_role author
author2 Godoy, Daniela Lis
Zunino Suarez, Alejandro Octavio
Mateos Diaz, Cristian Maximiliano
author2_role author
author
author
dc.subject.none.fl_str_mv DISTRIBUTED COMPUTING
FEATURE SELECTION
MATRIX ARITHMETIC OPERATION
SPARSE MATRIX
topic DISTRIBUTED COMPUTING
FEATURE SELECTION
MATRIX ARITHMETIC OPERATION
SPARSE MATRIX
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Matrix computations are both fundamental and ubiquitous in computational science, and as a result, they are frequently used in numerous disciplines of scientific computing and engineering. Due to the high computational complexity of matrix operations, which makes them critical to the performance of a large number of applications, their efficient execution in distributed environments becomes a crucial issue. This work proposes a novel approach for distributing sparse matrix arithmetic operations on computer clusters aiming at speeding-up the processing of high-dimensional matrices. The approach focuses on how to split such operations into independent parallel tasks by considering the intrinsic characteristics that distinguish each type of operation and the particular matrices involved. The approach was applied to the most commonly used arithmetic operations between matrices. The performance of the presented approach was evaluated considering a high-dimensional text feature selection approach and two real-world datasets. Experimental evaluation showed that the proposed approach helped to significantly reduce the computing times of big-scale matrix operations, when compared to serial and multi-thread implementations as well as several linear algebra software libraries.
Fil: Tommasel, Antonela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Godoy, Daniela Lis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
description Matrix computations are both fundamental and ubiquitous in computational science, and as a result, they are frequently used in numerous disciplines of scientific computing and engineering. Due to the high computational complexity of matrix operations, which makes them critical to the performance of a large number of applications, their efficient execution in distributed environments becomes a crucial issue. This work proposes a novel approach for distributing sparse matrix arithmetic operations on computer clusters aiming at speeding-up the processing of high-dimensional matrices. The approach focuses on how to split such operations into independent parallel tasks by considering the intrinsic characteristics that distinguish each type of operation and the particular matrices involved. The approach was applied to the most commonly used arithmetic operations between matrices. The performance of the presented approach was evaluated considering a high-dimensional text feature selection approach and two real-world datasets. Experimental evaluation showed that the proposed approach helped to significantly reduce the computing times of big-scale matrix operations, when compared to serial and multi-thread implementations as well as several linear algebra software libraries.
publishDate 2017
dc.date.none.fl_str_mv 2017-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/58575
Tommasel, Antonela; Godoy, Daniela Lis; Zunino Suarez, Alejandro Octavio; Mateos Diaz, Cristian Maximiliano; A distributed approach for accelerating sparse matrix arithmetic operations for high-dimensional feature selection; Springer London Ltd; Knowledge And Information Systems; 51; 2; 5-2017; 459-497
0219-1377
CONICET Digital
CONICET
url http://hdl.handle.net/11336/58575
identifier_str_mv Tommasel, Antonela; Godoy, Daniela Lis; Zunino Suarez, Alejandro Octavio; Mateos Diaz, Cristian Maximiliano; A distributed approach for accelerating sparse matrix arithmetic operations for high-dimensional feature selection; Springer London Ltd; Knowledge And Information Systems; 51; 2; 5-2017; 459-497
0219-1377
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1007/s10115-016-0981-5
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007%2Fs10115-016-0981-5
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer London Ltd
publisher.none.fl_str_mv Springer London Ltd
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1844614049174650880
score 13.069144

Publicaciones similares