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dc.contributor.authordel Río Astorga, David
dc.contributor.authorDolz, Manuel F.
dc.contributor.authorFernández Muñoz, Javier
dc.contributor.authorGarcía, J. Daniel
dc.date.accessioned2019-07-04T09:51:49Z
dc.date.available2019-07-04T09:51:49Z
dc.date.issued2018-10
dc.identifier.citationDEL RÍO ASTORGA, David; DOLZ ZARAGOZÁ, Manuel Francisco; FERNÁNDEZ MUÑOZ, Javier; GARCÍA, J. Daniel (2018). Paving the way towards high-level parallel pattern interfaces for data stream processing. Future Generation Computer Systems, v. 87, p. 228-241ca_CA
dc.identifier.urihttp://hdl.handle.net/10234/183117
dc.description.abstractThe emergence of the Internet of Things (IoT) data stream applications has posed a number of new challenges to existing infrastructures, processing engines, and programming models. In this sense, high-level interfaces, encapsulating algorithmic aspects in pattern-based constructions, have considerably reduced the development and parallelization efforts of this type of applications. An example of parallel pattern interface is GrPPI, a C++ generic high-level library that acts as a layer between developers and existing parallel programming frameworks, such as C++ threads, OpenMP and Intel TBB. In this paper, we complement the basic patterns supported by GrPPI with the new stream operators Split-Join and Window, and the advanced parallel patterns Stream-Pool, Windowed-Farm and Stream-Iterator for the aforementioned back ends. Thanks to these new stream operators, complex compositions among streaming patterns can be expressed. On the other hand, the collection of advanced patterns allows users to tackle some domain-specific applications, ranging from the evolutionary to the real-time computing areas, where compositions of basic patterns are not capable of fully mimicking the algorithmic behavior of their original sequential codes. The experimental evaluation of the new advanced patterns and the stream operators on a set of domain-specific use-cases, using different back ends and pattern-specific parameters, reports considerable performance gains with respect to the sequential versions. Additionally, we demonstrate the benefits of the GrPPI pattern interface from the usability, flexibility and readability points of view.ca_CA
dc.format.extent14 p.ca_CA
dc.language.isoengca_CA
dc.publisherElsevierca_CA
dc.relation.isPartOfFuture Generation Computer Systems (2018), v. 87ca_CA
dc.subjectData stream processingca_CA
dc.subjectParallel programming frameworkca_CA
dc.subjectStream operatorca_CA
dc.subjectDomain-specific parallel patternca_CA
dc.subjectHigh-level APIca_CA
dc.titlePaving the way towards high-level parallel pattern interfaces for data stream processingca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttps://doi.org/10.1016/j.future.2018.05.011
dc.relation.projectID1) This work was partially supported by the EU project ICT 644235 “RePhrase: REfactoring Parallel Heterogeneous Resource-Aware Applications” and 2) The project TIN2013-41350-P “Scalable Data Management Techniques for High-End Computing Systems” from the Ministerio de Economía y Competitividad, Spain .ca_CA
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccessca_CA
dc.relation.publisherVersionhttps://www.sciencedirect.com/science/article/pii/S0167739X17324524ca_CA
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_CA


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