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dc.contributorUniversitat Ramon Llull. IQS
dc.contributor.authorMartinez Ruiz, Alba
dc.contributor.authorMontañola i Sales, Cristina
dc.date.accessioned2024-02-05T20:28:29Z
dc.date.available2024-02-05T20:28:29Z
dc.date.issued2019-04-29
dc.identifier.issn2405-8440ca
dc.identifier.urihttp://hdl.handle.net/20.500.14342/3857
dc.description.abstractPartial 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.ca
dc.format.extent29 p.ca
dc.language.isoengca
dc.publisherElsevierca
dc.relation.ispartofHeliyonca
dc.rights© L'autor/aca
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subject.otherComputer scienceca
dc.subject.otherComputational mathematicsca
dc.subject.otherBig dataca
dc.subject.otherDades massivesca
dc.titleBig data in multi-block data analysis: An approach to parallelizing Partial Least Squares Mode B algorithmca
dc.typeinfo:eu-repo/semantics/articleca
dc.rights.accessLevelinfo:eu-repo/semantics/openAccess
dc.embargo.termscapca
dc.subject.udc004ca
dc.identifier.doihttps://doi.org/10.1016/j.heliyon.2019.e01451ca
dc.description.versioninfo:eu-repo/semantics/publishedVersionca


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