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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/58575
Ver los metadatos del registro completo
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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 Técnicas2025-09-03T10:00:49Zoai: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:34982025-09-03 10:00:49.876CONICET 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 |
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1842269661628989440 |
score |
13.13397 |