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Exploring stream parallel patterns in distributed MPI environments

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Arquitectura de Computadores, Comunicaciones y Sistemases
dc.contributor.authorLópez Gómez, Javier
dc.contributor.authorFernández Muñoz, Javier
dc.contributor.authorRío Astorga, David del
dc.contributor.authorDolz Zaragoza, Manuel Francisco
dc.contributor.authorGarcía Sánchez, José Daniel
dc.contributor.funderEuropean Commissionen
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2020-02-17T12:56:46Z
dc.date.available2021-05-01T23:00:06Z
dc.date.issued2019-05
dc.descriptionASPIDE: Exascale programIng models for extreme data processingen
dc.description.abstractIn recent years, the large volumes of stream data and the near real-time requirements of data streaming applications have exacerbated the need for new scalable algorithms and programming interfaces for distributed and shared-memory platforms. To contribute in this direction, this paper presents a new distributed MPI back end for GrPPI, a C++ high-level generic interface of data-intensive and stream processing parallel patterns. This back end, as a new execution policy, supports distributed and hybrid (distributed+shared-memory) parallel executions of the Pipeline and Farm patterns, where the hybrid mode combines the MPI policy with a GrPPI shared-memory one. These patterns internally leverage distributed queues, which can be configured to use two-sided or one-sided MPI primitives to communicate items among nodes. A detailed analysis of the GrPPI MPI execution policy reports considerable benefits from the programmability, flexibility and readability points of view. The experimental evaluation of two different streaming applications with different distributed and shared-memory scenarios reports considerable performance gains with respect to the sequential versions at the expense of negligible GrPPI overheads.en
dc.description.sponsorshipThis work was partially supported by the EU project No. 801091 "ASPIDE: Exascale programming models for extreme data process ing"; and the project TIN2013-41350-P "Scalable Data Management Techniques for High-End Computing Systems" from the Ministerio de Economía y Competitividad , Spain.en
dc.format.extent13es
dc.identifier.bibliographicCitationParallel computing, 84, May 2019, Pp. 24-36en
dc.identifier.doihttps://doi.org/10.1016/j.parco.2019.03.004
dc.identifier.issn0167-8191
dc.identifier.issn1872-7336 (online)
dc.identifier.publicationfirstpage24es
dc.identifier.publicationlastpage36es
dc.identifier.publicationtitlePARALLEL COMPUTINGen
dc.identifier.publicationvolume84es
dc.identifier.urihttps://hdl.handle.net/10016/29711
dc.identifier.uxxiAR/0000023519
dc.language.isoenges
dc.publisherElsevier B.V.es
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/801091/ASPIDEen
dc.relation.projectIDGobierno de España. TIN2013-41350-Pes
dc.rights© 2019 Elsevier B.V. All rights reserved.en
dc.rightsThis work is licensed under a Creative Commons Attribution- NonCommercial-NoDerivatives 4.0 International License.en
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaInformáticaes
dc.subject.otherParallel patternsen
dc.subject.otherStream processingen
dc.subject.otherDistributed patternsen
dc.subject.otherC++ programmingen
dc.subject.otherGeneric programmingen
dc.titleExploring stream parallel patterns in distributed MPI environmentsen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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