Big data in multi-block data analysis: An approach to parallelizing Partial Least Squares Mode B algorithm

Partial Least Squares (PLS) Mode B is a multi-block method and a tightly coupled algorithm for estimating structural equation models (SEMs). Describing key aspects of parallel computing, we approach the parallelization of the PLS Mode B algorithm to operate on large distributed data. We show the sca...

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Detalles Bibliográficos
Autores: Martinez Ruiz, Alba, Montañola-Sales, Cristina
Tipo de recurso: artículo
Fecha de publicación:2019
País:España
Institución:Universitat Ramon Llull (URL)
Repositorio:DAU Arxiu Digital de la Universitat Ramon Llull
OAI Identifier:oai:dau.url.edu:20.500.14342/3857
Acceso en línea:http://hdl.handle.net/20.500.14342/3857
https://doi.org/10.1016/j.heliyon.2019.e01451
Access Level:acceso abierto
Palabra clave:Computer science
Computational mathematics
Big data
Dades massives
004
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spelling Big data in multi-block data analysis: An approach to parallelizing Partial Least Squares Mode B algorithmMartinez Ruiz, AlbaMontañola-Sales, CristinaComputer scienceComputational mathematicsBig dataDades massives004Partial Least Squares (PLS) Mode B is a multi-block method and a tightly coupled algorithm for estimating structural equation models (SEMs). Describing key aspects of parallel computing, we approach the parallelization of the PLS Mode B algorithm to operate on large distributed data. We show the scalability and performance of the algorithm at a very fine-grained level thanks to the versatility of pbdR, a R-project library for parallel computing. We vary several factors under different data distribution schemes in a supercomputing environment. Shorter elapsed times are obtained for the square-blocking factor 16 × 16 using a grid of processors as square as possible and non-square blocking factors 1000 × 4 and 10000 × 4 using an one-column grid of processors. Depending on the configuration, distributing data in a larger number of cores allows reaching speedups of up to 121 over the CPU implementation. Moreover, we show that SEMs can be estimated with big data sets using current state-of-the-art algorithms for multi-block data analysis.info:eu-repo/semantics/publishedVersionElsevierUniversitat Ramon Llull. IQS202420242019info:eu-repo/semantics/article29 p.application/pdfhttp://hdl.handle.net/20.500.14342/3857https://doi.org/10.1016/j.heliyon.2019.e01451reponame:DAU Arxiu Digital de la Universitat Ramon Llullinstname:Universitat Ramon Llull (URL)InglésHeliyon© L'autor/aAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:dau.url.edu:20.500.14342/38572026-06-21T06:40:37Z
dc.title.none.fl_str_mv Big data in multi-block data analysis: An approach to parallelizing Partial Least Squares Mode B algorithm
title Big data in multi-block data analysis: An approach to parallelizing Partial Least Squares Mode B algorithm
spellingShingle Big data in multi-block data analysis: An approach to parallelizing Partial Least Squares Mode B algorithm
Martinez Ruiz, Alba
Computer science
Computational mathematics
Big data
Dades massives
004
title_short Big data in multi-block data analysis: An approach to parallelizing Partial Least Squares Mode B algorithm
title_full Big data in multi-block data analysis: An approach to parallelizing Partial Least Squares Mode B algorithm
title_fullStr Big data in multi-block data analysis: An approach to parallelizing Partial Least Squares Mode B algorithm
title_full_unstemmed Big data in multi-block data analysis: An approach to parallelizing Partial Least Squares Mode B algorithm
title_sort Big data in multi-block data analysis: An approach to parallelizing Partial Least Squares Mode B algorithm
dc.creator.none.fl_str_mv Martinez Ruiz, Alba
Montañola-Sales, Cristina
author Martinez Ruiz, Alba
author_facet Martinez Ruiz, Alba
Montañola-Sales, Cristina
author_role author
author2 Montañola-Sales, Cristina
author2_role author
dc.contributor.none.fl_str_mv Universitat Ramon Llull. IQS
dc.subject.none.fl_str_mv Computer science
Computational mathematics
Big data
Dades massives
004
topic Computer science
Computational mathematics
Big data
Dades massives
004
description Partial Least Squares (PLS) Mode B is a multi-block method and a tightly coupled algorithm for estimating structural equation models (SEMs). Describing key aspects of parallel computing, we approach the parallelization of the PLS Mode B algorithm to operate on large distributed data. We show the scalability and performance of the algorithm at a very fine-grained level thanks to the versatility of pbdR, a R-project library for parallel computing. We vary several factors under different data distribution schemes in a supercomputing environment. Shorter elapsed times are obtained for the square-blocking factor 16 × 16 using a grid of processors as square as possible and non-square blocking factors 1000 × 4 and 10000 × 4 using an one-column grid of processors. Depending on the configuration, distributing data in a larger number of cores allows reaching speedups of up to 121 over the CPU implementation. Moreover, we show that SEMs can be estimated with big data sets using current state-of-the-art algorithms for multi-block data analysis.
publishDate 2019
dc.date.none.fl_str_mv 2019
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.14342/3857
https://doi.org/10.1016/j.heliyon.2019.e01451
url http://hdl.handle.net/20.500.14342/3857
https://doi.org/10.1016/j.heliyon.2019.e01451
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Heliyon
dc.rights.none.fl_str_mv © L'autor/a
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © L'autor/a
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 29 p.
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:DAU Arxiu Digital de la Universitat Ramon Llull
instname:Universitat Ramon Llull (URL)
instname_str Universitat Ramon Llull (URL)
reponame_str DAU Arxiu Digital de la Universitat Ramon Llull
collection DAU Arxiu Digital de la Universitat Ramon Llull
repository.name.fl_str_mv
repository.mail.fl_str_mv
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