Using Community Detection Algorithms to Identify Clusters of Ranks in an MPI Application Based on the Communication Matrix

dc.creator	Calvo-Sánchez, Manfred
dc.creator	Meneses, Esteban
dc.date	2019-08-16
dc.date.accessioned	2021-06-11T01:32:48Z
dc.date.available	2021-06-11T01:32:48Z
dc.identifier	https://revistas.tec.ac.cr/index.php/memorias/article/view/4518
dc.identifier	10.18845/mct.v0i0.4518
dc.identifier.uri	http://hdl.handle.net/2238/13111
dc.description	In the MPI parallel programming model, communication remains the bottleneck that prevents applications to achieve greater performance and scalability. Due to this problem it is important to know the behavior of this communication in each application. For this reason, we propose the use of community detection algorithms to identify from the communication matrix the clusters of ranks that maximize intracluster communication and minimize intercluster communication. The aim of this project is providing another tool to identify how you can improve the performance of a MPI application.	es-ES
dc.format	application/pdf
dc.language	spa
dc.publisher	Instituto Tecnológico de Costa Rica	es-ES
dc.relation	https://revistas.tec.ac.cr/index.php/memorias/article/view/4518/4090
dc.source	Memorias de congresos TEC; 2017: III Jornadas Costarricenses de Investigación en Computación e Informática	es-ES
dc.source	9978-9930
dc.source	10.18845/mct.2017.0
dc.title	Using Community Detection Algorithms to Identify Clusters of Ranks in an MPI Application Based on the Communication Matrix	es-ES
dc.type	artículo original

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