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GPU-Job Migration: The rCUDA Case

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GPU-Job Migration: The rCUDA Case

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dc.contributor.author Prades, Javier es_ES
dc.contributor.author Silla Jiménez, Federico es_ES
dc.date.accessioned 2020-12-15T04:32:24Z
dc.date.available 2020-12-15T04:32:24Z
dc.date.issued 2019-12 es_ES
dc.identifier.issn 1045-9219 es_ES
dc.identifier.uri http://hdl.handle.net/10251/157203
dc.description © 2019 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. es_ES
dc.description.abstract [EN] Virtualization techniques have been shown to report benefits to data centers and other computing facilities. In this regard, not only virtual machines allow to reduce the size of the computing infrastructure while increasing overall resource utilization, but also virtualizing individual components of computers may provide significant benefits. This is the case, for instance, for the remote GPU virtualization technique, implemented in several frameworks during the recent years. The large degree of flexibility provided by the remote GPU virtualization technique can be further increased by applying the migration mechanism to it, so that the GPU part of applications can be live-migrated to another GPU elsewhere in the cluster during execution time in a transparent way. In this paper we present the implementation of the migration mechanism within the rCUDA remote GPU virtualization middleware. Furthermore, we present a thorough performance analysis of the implementation of the migration mechanism within rCUDA. To that end, we leverage both synthetic and real production applications as well as three different generations of NVIDIA GPUs. Additionally, two different versions of the InfiniBand interconnect are used in this study. Several use cases are provided in order to show the extraordinary benefits that the GPU-job migration mechanism can report to data centers. es_ES
dc.description.sponsorship This work was funded by the Generalitat Valenciana under Grant PROMETEO/2017/77. Authors are grateful for the generous support provided by Mellanox Technologies Inc. es_ES
dc.language Inglés es_ES
dc.publisher Institute of Electrical and Electronics Engineers es_ES
dc.relation.ispartof IEEE Transactions on Parallel and Distributed Systems es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Graphics processing units es_ES
dc.subject Virtualization es_ES
dc.subject Servers es_ES
dc.subject Middleware es_ES
dc.subject Proposals es_ES
dc.subject Virtual machining es_ES
dc.subject Resource management es_ES
dc.subject CUDA es_ES
dc.subject GPU es_ES
dc.subject Migration es_ES
dc.subject RCUDA es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title GPU-Job Migration: The rCUDA Case es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1109/TPDS.2019.2924433 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//PROMETEO%2F2017%2F077/ 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 Prades, J.; Silla Jiménez, F. (2019). GPU-Job Migration: The rCUDA Case. IEEE Transactions on Parallel and Distributed Systems. 30(12):2718-2729. https://doi.org/10.1109/TPDS.2019.2924433 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1109/TPDS.2019.2924433 es_ES
dc.description.upvformatpinicio 2718 es_ES
dc.description.upvformatpfin 2729 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 30 es_ES
dc.description.issue 12 es_ES
dc.relation.pasarela S\399115 es_ES
dc.contributor.funder Generalitat Valenciana es_ES


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