Towards automatic parallelization of stream processing applications

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dc.contributor.author Dolz Zaragoza, Manuel Francisco
dc.contributor.author Río Astorga, David del
dc.contributor.author González Fernández, Francisco Javier
dc.contributor.author Carretero Pérez, Jesús
dc.contributor.author García Sánchez, José Daniel
dc.date.accessioned 2021-01-18T11:14:16Z
dc.date.available 2021-01-18T11:14:16Z
dc.date.issued 2018-07-11
dc.identifier.bibliographicCitation M. F. Dolz, D. Del Rio Astorga, J. Fernández, J. D. García and J. Carretero, "Towards Automatic Parallelization of Stream Processing Applications" in IEEE Access, vol. 6, pp. 39944-39961, 2018, doi: 10.1109/ACCESS.2018.2855064
dc.identifier.issn 2169-3536
dc.identifier.uri http://hdl.handle.net/10016/31718
dc.description.abstract Parallelizing and optimizing codes for recent multi-/many-core processors have been recognized to be a complex task. For this reason, strategies to automatically transform sequential codes into parallel and discover optimization opportunities are crucial to relieve the burden to developers. In this paper, we present a compile-time framework to (semi) automatically find parallel patterns (Pipeline and Farm) and transform sequential streaming applications into parallel using GrPPI, a generic parallel pattern interface. This framework uses a novel pipeline stage-balancing technique which provides the code generator module with the necessary information to produce balanced pipelines. The evaluation, using a synthetic video benchmark and a real-world computer vision application, demonstrates that the presented framework is capable of producing parallel and optimized versions of the application. A comparison study under several thread-core oversubscribed conditions reveals that the framework can bring comparable performance results with respect to the Intel TBB programming framework.
dc.description.sponsorship This work was supported in part by the Spanish Ministerio de Economía y Competitividad through the Project Toward Uni cation of HPC and Big Data Paradigms under Grant TIN2016-79637-P and in part by the EU Project RePhrase: REfactoring Parallel Heterogeneous Resource-Aware Applications under Grant ICT 644235.
dc.language.iso eng
dc.publisher IEEE Xplore
dc.rights Copyright © 2018, IEEE
dc.rights Atribución 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by/3.0/es/
dc.subject.other Refactoring framework
dc.subject.other Automatic parallelization
dc.subject.other Load-balanced pipeline
dc.subject.other Parallel patterns
dc.title Towards automatic parallelization of stream processing applications
dc.type article
dc.subject.eciencia Informática
dc.identifier.doi https://doi.org/10.1109/ACCESS.2018.2855064
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TIN2016-79637-P
dc.relation.projectID info:eu-repo/grantAgreement/644235/Re-Phrase
dc.type.version publishedVersion
dc.identifier.publicationfirstpage 39944
dc.identifier.publicationlastpage 39961
dc.identifier.publicationtitle IEEE Access
dc.identifier.publicationvolume 6
dc.identifier.uxxi AR/0000021931
dc.contributor.funder European Commission
dc.contributor.funder Ministerio de Economía y Competitividad (España)
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