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CU2rCU: towards the Complete rCUDA Remote GPU Virtualization and Sharing Solution

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CU2rCU: towards the Complete rCUDA Remote GPU Virtualization and Sharing Solution

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dc.contributor.author Reaño González, Carlos es_ES
dc.contributor.author Peña Monferrer, Antonio José es_ES
dc.contributor.author Silla Jiménez, Federico es_ES
dc.contributor.author Duato Marín, José Francisco es_ES
dc.contributor.author Mayo Gual, Rafael es_ES
dc.contributor.author Quintana Ortí, Enrique Salvador es_ES
dc.date.accessioned 2016-10-14T10:34:22Z
dc.date.available 2016-10-14T10:34:22Z
dc.date.issued 2012
dc.identifier.isbn 978-1-4673-2371-0
dc.identifier.uri http://hdl.handle.net/10251/71823
dc.description © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising 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 GPUs are being increasingly embraced by the high performance computing and computational communities as an effective way of considerably reducing execution time by accelerating significant parts of their application codes. However, despite their extraordinary computing capabilities, the adoption of GPUs in current HPC clusters may present certain negative side-effects. In particular, to ease job scheduling in these platforms, a GPU is usually attached to every node of the cluster. In addition to increasing acquisition costs this favors that GPUs may frequently remain idle, as applications usually do not fully utilize them. On the other hand, idle GPUs consume non-negligible amounts of energy, which translates into very poor energy efficiency during idle cycles. rCUDA was recently developed as a software solution to address these concerns. Specifically, it is a middleware that allows transparently sharing a reduced number of GPUs among the nodes in a cluster. rCUDA thus increases the GPU-utilization rate, taking care of job scheduling. While the initial prototype versions of rCUDA demonstrated its functionality, they also revealed several concerns related with usability and performance. With respect to usability, in this paper we present a new component of the rCUDA suite that allows an automatic transformation of any CUDA source code, so that it can be effectively accommodated within this technology. In response to performance, we briefly show some interesting results, which will be deeply analyzed in future publications. The net outcome is a new version of rCUDA that allows, for any CUDA-compatible program, to use remote GPUs in a cluster with minimum overhead. es_ES
dc.description.sponsorship The researchers at UPV were supported by the Spanish MICINN, Plan E funds, under Grant TIN2009-14475-C04-01 and also by PROMETEO from Generalitat Valenciana (GVA) under Grant PROMETEO/2008/060. Researchers at UJI were supported by the Spanish Ministry of Science and FEDER (contract no. TIN2011-23283), and by the Fundacion Caixa- ´ Castello/Bancaixa (no. P1-1B2009-35)
dc.format.extent 10 es_ES
dc.language Inglés es_ES
dc.publisher IEEE es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Virtualisation es_ES
dc.subject Graphics processing units es_ES
dc.subject Middleware es_ES
dc.subject Parallel processing es_ES
dc.subject Power aware computing es_ES
dc.subject Scheduling es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title CU2rCU: towards the Complete rCUDA Remote GPU Virtualization and Sharing Solution es_ES
dc.type Comunicación en congreso es_ES
dc.identifier.doi 10.1109/HiPC.2012.6507485
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//TIN2009-14475-C04-01/ES/Arquitecturas De Servidores, Aplicaciones Y Servicios/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//PROMETEO08%2F2008%2F060/ES/Extensión de la tecnología de red hypertransport para la mejora de la escalabilidad de los servidores de internet/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//TIN2011-23283/ES/POWER-AWARE HIGH PERFORMANCE COMPUTING/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UJI//P1·1B2009-35/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació 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.contributor.affiliation Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica es_ES
dc.description.bibliographicCitation Reaño González, C.; Peña Monferrer, AJ.; Silla Jiménez, F.; Duato Marín, JF.; Mayo Gual, R.; Quintana Ortí, ES. (2012). CU2rCU: towards the Complete rCUDA Remote GPU Virtualization and Sharing Solution. IEEE. https://doi.org/10.1109/HiPC.2012.6507485 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename 19th International Conference on High Performance Computing (HiPC 2012) es_ES
dc.relation.conferencedate December 18-22, 2012 es_ES
dc.relation.conferenceplace Pune, India es_ES
dc.relation.publisherversion http://dx.doi.org/10.1109/HiPC.2012.6507485 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.senia 232025 es_ES
dc.contributor.funder Ministerio de Ciencia e Innovación
dc.contributor.funder Generalitat Valenciana
dc.contributor.funder European Regional Development Fund
dc.contributor.funder Fundació Caixa Castelló - Bancaixa
dc.contributor.funder Universitat Jaume I es_ES


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