DMR API: Improving cluster productivity by turning applications into malleable
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Other documents of the author: Iserte, Sergio; Mayo, Rafael; Quintana-Orti, Enrique S.; Beltrán, Vicenç; Peña Monferrer, Antonio J.
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Title
DMR API: Improving cluster productivity by turning applications into malleableAuthor (s)
Date
2018Publisher
ElsevierISSN
0167-8191Bibliographic citation
ISERTE, Sergio, et al. DMR API: Improving cluster productivity by turning applications into malleable. Parallel Computing, 2018, vol. 78, p. 54-66.Type
info:eu-repo/semantics/articlePublisher version
https://www.sciencedirect.com/science/article/pii/S0167819118302229Version
info:eu-repo/semantics/submittedVersionSubject
Abstract
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. [-]
Is part of
Parallel Computing 78 (2018)Investigation project
TIN2014-53495-R and TIN2015-65316-PRights
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