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On the Benefits of the Remote GPU Virtualization Mechanism: the rCUDA Case

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On the Benefits of the Remote GPU Virtualization Mechanism: the rCUDA Case

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dc.contributor.author Silla Jiménez, Federico es_ES
dc.contributor.author Iserte Agut, Sergio es_ES
dc.contributor.author Reaño González, Carlos es_ES
dc.contributor.author Prades, Javier es_ES
dc.date.accessioned 2020-10-22T03:32:32Z
dc.date.available 2020-10-22T03:32:32Z
dc.date.issued 2017-07-10 es_ES
dc.identifier.issn 1532-0626 es_ES
dc.identifier.uri http://hdl.handle.net/10251/152813
dc.description.abstract [EN] Graphics processing units (GPUs) are being adopted in many computing facilities given their extraordinary computing power, which makes it possible to accelerate many general purpose applications from different domains. However, GPUs also present several side effects, such as increased acquisition costs as well as larger space requirements. They also require more powerful energy supplies. Furthermore, GPUs still consume some amount of energy while idle, and their utilization is usually low for most workloads. In a similar way to virtual machines, the use of virtual GPUs may address the aforementioned concerns. In this regard, the remote GPU virtualization mechanism allows an application being executed in a node of the cluster to transparently use the GPUs installed at other nodes. Moreover, this technique allows to share the GPUs present in the computing facility among the applications being executed in the cluster. In this way, several applications being executed in different (or the same) cluster nodes can share 1 or more GPUs located in other nodes of the cluster. Sharing GPUs should increase overall GPU utilization, thus reducing the negative impact of the side effects mentioned before. Reducing the total amount of GPUs installed in the cluster may also be possible. In this paper, we explore some of the benefits that remote GPU virtualization brings to clusters. For instance, this mechanism allows an application to use all the GPUs present in the computing facility. Another benefit of this technique is that cluster throughput, measured as jobs completed per time unit, is noticeably increased when this technique is used. In this regard, cluster throughput can be doubled for some workloads. Furthermore, in addition to increase overall GPU utilization, total energy consumption can be reduced up to 40%. This may be key in the context of exascale computing facilities, which present an important energy constraint. Other benefits are related to the cloud computing domain, where a GPU can be easily shared among several virtual machines. Finally, GPU migration (and therefore server consolidation) is one more benefit of this novel technique. es_ES
dc.description.sponsorship Generalitat Valenciana, Grant/Award Number: PROMETEOII/2013/009; MINECO and FEDER, Grant/Award Number: TIN2014-53495-R es_ES
dc.language Inglés es_ES
dc.publisher John Wiley & Sons es_ES
dc.relation MINECO/TIN2014-53495-R es_ES
dc.relation GENERALITAT VALENCIANA/PROMETEOII/2013/009 es_ES
dc.relation.ispartof Concurrency and Computation Practice and Experience es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject CUDA es_ES
dc.subject GPU migration es_ES
dc.subject GPU virtualization es_ES
dc.subject InfiniBand es_ES
dc.subject Slurm es_ES
dc.subject Xen es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title On the Benefits of the Remote GPU Virtualization Mechanism: the rCUDA Case es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1002/cpe.4072 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors es_ES
dc.description.bibliographicCitation Silla Jiménez, F.; Iserte Agut, S.; Reaño González, C.; Prades, J. (2017). On the Benefits of the Remote GPU Virtualization Mechanism: the rCUDA Case. Concurrency and Computation Practice and Experience. 29(13):1-17. https://doi.org/10.1002/cpe.4072 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1002/cpe.4072 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 17 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 29 es_ES
dc.description.issue 13 es_ES
dc.relation.pasarela S\327017 es_ES
dc.contributor.funder Generalitat Valenciana es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES


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