DMR API: Improving cluster productivity by turning applications into malleable
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Otros documentos de la autoría: Iserte, Sergio; Mayo, Rafael; Quintana-Orti, Enrique S.; Beltrán, Vicenç; Peña Monferrer, Antonio J.
Metadatos
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Título
DMR API: Improving cluster productivity by turning applications into malleableAutoría
Fecha de publicación
2018Editor
ElsevierISSN
0167-8191Cita bibliográfica
ISERTE, Sergio, et al. DMR API: Improving cluster productivity by turning applications into malleable. Parallel Computing, 2018, vol. 78, p. 54-66.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.sciencedirect.com/science/article/pii/S0167819118302229Versión
info:eu-repo/semantics/submittedVersionPalabras clave / Materias
Resumen
Adaptive workloads can change on–the–fly the configuration of their jobs, in terms of
number of processes. To carry out these job reconfigurations, we have designed a methodology which enables a job to communicate ... [+]
Adaptive workloads can change on–the–fly the configuration of their jobs, in terms of
number of processes. To carry out these job reconfigurations, we have designed a methodology which enables a job to communicate with the resource manager and, through the
runtime, to change its number of MPI ranks. The collaboration between both the workload manager—aware of the queue of jobs and the resources allocation—and the parallel
runtime—able to transparently handle the processes and the program data—is crucial for
our throughput-aware malleability methodology. Hence, when a job triggers a reconfiguration, the resource manager will check the cluster status and return the appropriate action:
i) expand, if there are spare resources; ii) shrink, if queued jobs can be initiated; or iii)
none, if no change can improve the global productivity. In this paper, we describe the internals of our framework and demonstrate how it reduces the global workload completion
time along with providing a more efficient usage of the underlying resources. For this purpose, we present a thorough study of the adaptive workloads processing by showing the
detailed behavior of our framework in representative experiments. [-]
Publicado en
Parallel Computing 78 (2018)Proyecto de investigación
TIN2014-53495-R and TIN2015-65316-PDerechos de acceso
http://rightsstatements.org/vocab/CNE/1.0/
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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