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A Generic Parallel Pattern Interface for Stream and Data Processing

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Arquitectura de Computadores, Comunicaciones y Sistemases
dc.contributor.authorRío Astorga, David del
dc.contributor.authorDolz Zaragoza, Manuel Francisco
dc.contributor.authorFernández Muñoz, Javier
dc.contributor.authorGarcía Sánchez, José Daniel
dc.contributor.funderEuropean Commissionen
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2023-10-18T07:48:30Z
dc.date.available2023-10-18T07:48:30Z
dc.date.issued2017-05-01
dc.description.abstractCurrent parallel programming frameworks aid developers to a great extent in implementing applications that exploit parallel hardware resources. Nevertheless, developers require additional expertise to properly use and tune them to operate efficiently on specific parallel platforms. On the other hand, porting applications between different parallel programming models and platforms is not straightforward and demands considerable efforts and specific knowledge. Apart from that, the lack of high-level parallel pattern abstractions, in those frameworks, further increases the complexity in developing parallel applications. To pave the way in this direction, this paper proposes GRPPI, a generic and reusable parallel pattern interface for both stream processing and data-intensive C++ applications. GRPPI accommodates a layer between developers and existing parallel programming frameworks targeting multi-core processors, such as C++ threads, OpenMP and Intel TBB, and accelerators, as CUDA Thrust. Furthermore, thanks to its high-level C++ application programming interface and pattern composability features, GRPPI allows users to easily expose parallelism via standalone patterns or patterns compositions matching in sequential applications. We evaluate this interface using an image processing use case and demonstrate its benefits from the usability, flexibility, and performance points of view. Furthermore, we analyze the impact of using stream and data pattern compositions on CPUs, GPUs and heterogeneous configurations.en
dc.description.sponsorshipThis work has been partially supported by the EU project ICT 644235 “REPHRASE: REfactoring Parallel Heterogeneous Resource-aware Applications” and the Spanish “Ministerio de Economía y Competitividad” under the grant TIN2016-79673-P “Towards Unification of HPC and Big Data Paradigms.”en
dc.format.extent12es
dc.identifier.bibliographicCitationDel Rio Astorga, D., Dolz, M. F., Fernández, J. & García, J. D. (2017). A generic parallel pattern interface for stream and data processing. Concurrency and Computation: Practice and Experience, 29(24).en
dc.identifier.doihttps://doi.org/10.1002/cpe.4175
dc.identifier.issn1532-0626
dc.identifier.publicationfirstpage1es
dc.identifier.publicationissue24, e4175es
dc.identifier.publicationlastpage12es
dc.identifier.publicationtitleConcurrency and Computation: Practice and Experienceen
dc.identifier.publicationvolume29es
dc.identifier.urihttps://hdl.handle.net/10016/38618
dc.identifier.uxxiAR/0000020225
dc.language.isoengen
dc.publisherWileyen
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/644235es
dc.relation.projectIDGobierno de España. TIN2016-79673-Pes
dc.rights© 2017 The Authors. Concurrency and Computation: Practice and Experience Published by John Wiley & Sons, Ltden
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaInformáticaes
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherHigh-level apien
dc.subject.otherParallel patternen
dc.subject.otherParallel programming frameworken
dc.subject.otherStream processingen
dc.titleA Generic Parallel Pattern Interface for Stream and Data Processingen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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