Exploring stream parallel patterns in distributed MPI environments
Impacto
Scholar |
Otros documentos de la autoría: López-Gómez, Javier; Fernández Muñoz, Javier; del Río Astorga, David; Dolz, Manuel F.; García, J. Daniel
Metadatos
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https://doi.org/10.1016/j.parco.2019.03.004 |
Metadatos
Título
Exploring stream parallel patterns in distributed MPI environmentsAutoría
Fecha de publicación
2019Editor
ElsevierISSN
0167-8191Cita bibliográfica
LÓPEZ-GÓMEZ, Javier, et al. Exploring stream parallel patterns in distributed MPI environments. Parallel Computing, 2019.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.sciencedirect.com/science/article/pii/S0167819118303442Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
In 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 ... [+]
In 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. [-]
Publicado en
Parallel Computing, Volume 84, May 2019.Proyecto de investigación
801091 ; TIN2013-41350-PDerechos de acceso
http://rightsstatements.org/vocab/CNE/1.0/
info:eu-repo/semantics/restrictedAccess
info:eu-repo/semantics/restrictedAccess
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