Mostrar el registro sencillo del ítem
dc.contributor.author | Reaño González, Carlos | es_ES |
dc.contributor.author | Silla Jiménez, Federico | es_ES |
dc.contributor.author | Castello Gimeno, Adrián | es_ES |
dc.contributor.author | Peña Monferrer, Antonio José | es_ES |
dc.contributor.author | Mayo Gual, Rafael | es_ES |
dc.contributor.author | Quintana Ortí, Enrique Salvador | es_ES |
dc.contributor.author | Duato Marín, José Francisco | es_ES |
dc.date.accessioned | 2016-06-02T08:01:58Z | |
dc.date.available | 2016-06-02T08:01:58Z | |
dc.date.issued | 2015-09-25 | |
dc.identifier.issn | 1532-0626 | |
dc.identifier.uri | http://hdl.handle.net/10251/65084 | |
dc.description.abstract | Graphics processing units (GPUs) are being increasingly embraced by the high-performance computing community as an effective way to reduce execution time by accelerating parts of their applications. remote CUDA (rCUDA) was recently introduced as a software solution to address the high acquisition costs and energy consumption of GPUs that constrain further adoption of this technology. Specifically, rCUDA is a middleware that allows a reduced number of GPUs to be transparently shared among the nodes in a cluster. Although the initial prototype versions of rCUDA demonstrated its functionality, they also revealed concerns with respect to usability, performance, and support for new CUDA features. In response, in this paper, we present a new rCUDA version that (1) improves usability by including a new component that allows an automatic transformation of any CUDA source code so that it conforms to the needs of the rCUDA framework, (2) consistently features low overhead when using remote GPUs thanks to an improved new communication architecture, and (3) supports multithreaded applications and CUDA libraries. As a result, for any CUDA-compatible program, rCUDA now allows the use of remote GPUs within a cluster with low overhead, so that a single application running in one node can use all GPUs available across the cluster, thereby extending the single-node capability of CUDA. Copyright © 2014 John Wiley & Sons, Ltd. | es_ES |
dc.description.sponsorship | This work was funded by the Generalitat Valenciana under Grant PROMETEOII/2013/009 of the PROMETEO program phase II. The author from Argonne National Laboratory was supported by the US Department of Energy, Office of Science, under Contract No. DE-AC02-06CH11357. The authors are also grateful for the generous support provided by Mellanox Technologies. | en_EN |
dc.language | Inglés | es_ES |
dc.publisher | Wiley | es_ES |
dc.relation.ispartof | Concurrency and Computation: Practice and Experience | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | GPGPU | es_ES |
dc.subject | CUDA | es_ES |
dc.subject | Virtualización | es_ES |
dc.subject | HPC | es_ES |
dc.subject | Clusters | es_ES |
dc.subject.classification | ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES | es_ES |
dc.title | Improving the User Experience of the rCUDA Remote GPU Virtualization Framework | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1002/cpe.3409 | |
dc.relation.projectID | info:eu-repo/grantAgreement/GVA//PROMETEOII%2F2013%2F009/ES/DESARROLLO DE LIBRERIAS PARA GESTIONAR EL ACCESO A DISPOSITIVOS REMOTOS COMPARTIDOS EN SERVIDORES DE ALTAS PRESTACIONES/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/780529/EU/Easy Reading: A Framework for Personalised Cognitive Accessibility when using Original Digital Content/ | |
dc.relation.projectID | info:eu-repo/grantAgreement/DOE//DE-AC02-06CH11357/ | 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.description.bibliographicCitation | Reaño González, C.; Silla Jiménez, F.; Castello Gimeno, A.; Peña Monferrer, AJ.; Mayo Gual, R.; Quintana Ortí, ES.; Duato Marín, JF. (2015). Improving the User Experience of the rCUDA Remote GPU Virtualization Framework. Concurrency and Computation: Practice and Experience. 27(14):3746-3770. https://doi.org/10.1002/cpe.3409 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | http://dx.doi.org/10.1002/cpe.3409 | es_ES |
dc.description.upvformatpinicio | 3746 | es_ES |
dc.description.upvformatpfin | 3770 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 27 | es_ES |
dc.description.issue | 14 | es_ES |
dc.relation.senia | 294829 | es_ES |
dc.contributor.funder | Generalitat Valenciana | es_ES |
dc.contributor.funder | U.S. Department of Energy | es_ES |
dc.description.references | NVIDIA NVIDIA industry cases http://www.nvidia.es/object/tesla-case-studies | es_ES |
dc.description.references | Figueiredo, R., Dinda, P. A., & Fortes, J. (2005). Guest Editors’ Introduction: Resource Virtualization Renaissance. Computer, 38(5), 28-31. doi:10.1109/mc.2005.159 | es_ES |
dc.description.references | Duato J Igual FD Mayo R Peña AJ Quintana-Ortí ES Silla F An efficient implementation of GPU virtualization in high performance clusters Euro-Par 2009 Workshops, ser. LNCS, 6043 Delft, Netherlands, 385 394 | es_ES |
dc.description.references | Duato J Peña AJ Silla F Mayo R Quintana-Ortí ES Performance of CUDA virtualized remote GPUs in high performance clusters International Conference on Parallel Processing, Taipei, Taiwan 2011 365 374 | es_ES |
dc.description.references | Duato J Peña AJ Silla F Fernández JC Mayo R Quintana-Ortí ES Enabling CUDA acceleration within virtual machines using rCUDA International Conference on High Performance Computing, Bangalore, India 2011 1 10 | es_ES |
dc.description.references | Shi, L., Chen, H., Sun, J., & Li, K. (2012). vCUDA: GPU-Accelerated High-Performance Computing in Virtual Machines. IEEE Transactions on Computers, 61(6), 804-816. doi:10.1109/tc.2011.112 | es_ES |
dc.description.references | Gupta V Gavrilovska A Schwan K Kharche H Tolia N Talwar V Ranganathan P GViM: GPU-accelerated virtual machines 3rd Workshop on System-Level Virtualization for High Performance Computing, Nuremberg, Germany 2009 17 24 | es_ES |
dc.description.references | Giunta G Montella R Agrillo G Coviello G A GPGPU transparent virtualization component for high performance computing clouds Euro-Par 2010 - Parallel Processing, 6271 Ischia, Italy, 379 391 | es_ES |
dc.description.references | Zillians VGPU http://www.zillians.com/vgpu | es_ES |
dc.description.references | Liang TY Chang YW GridCuda: a grid-enabled CUDA programming toolkit Proceedings of the 25th IEEE International Conference on Advanced Information Networking and Applications Workshops (WAINA), Biopolis, Singapore 2011 141 146 | es_ES |
dc.description.references | Barak A Ben-Nun T Levy E Shiloh A Apackage for OpenCL based heterogeneous computing on clusters with many GPU devices Workshop on Parallel Programming and Applications on Accelerator Clusters, Heraklion, Crete, Greece 2010 1 7 | es_ES |
dc.description.references | Xiao S Balaji P Zhu Q Thakur R Coghlan S Lin H Wen G Hong J Feng W-C VOCL: an optimized environment for transparent virtualization of graphics processing units Proceedings of InPar, San Jose, California, USA 2012 1 12 | es_ES |
dc.description.references | Kim J Seo S Lee J Nah J Jo G Lee J SnuCL: an OpenCL framework for heterogeneous CPU/GPU clusters Proceedings of the 26th International Conference on Supercomputing, Venice, Italy 2012 341 352 | es_ES |
dc.description.references | NVIDIA The NVIDIA CUDA Compiler Driver NVCC Version 5, NVIDIA 2012 | es_ES |
dc.description.references | Quinlan D Panas T Liao C ROSE http://rosecompiler.org/ | es_ES |
dc.description.references | Free Software Foundation, Inc. GCC, the GNU Compiler Collection http://gcc.gnu.org/ | es_ES |
dc.description.references | LLVM Clang: a C language family frontend for LLVM http://clang.llvm.org/ | es_ES |
dc.description.references | Martinez G Feng W Gardner M CU2CL: a CUDA-to-OpenCL Translator for Multi- and Many-core Architectures http://eprints.cs.vt.edu/archive/00001161/01/CU2CL.pdf | es_ES |
dc.description.references | LLVM The LLVM compiler infrastructure http://llvm.org/ | es_ES |
dc.description.references | Reaño C Peña AJ Silla F Duato J Mayo R Quintana-Orti ES CU2rCU: towards the complete rCUDA remote GPU virtualization and sharing solution Proceedings of the 19th International Conference on High Performance Computing (HiPC), Pune, India 2012 1 10 | es_ES |
dc.description.references | NVIDIA The NVIDIA GPU Computing SDK Version 4, NVIDIA 2011 | es_ES |
dc.description.references | Sandia National Labs LAMMPS molecular dynamics simulator http://lammps.sandia.gov/ | es_ES |
dc.description.references | Citrix Systems, Inc. Xen http://xen.org/ | es_ES |
dc.description.references | Peña AJ Virtualization of accelerators in high performance clusters Ph.D. Thesis, 2013 | es_ES |
dc.description.references | NVIDIA CUDA profiler user's guide version 5, NVIDIA 2012 | es_ES |
dc.description.references | Igual, F. D., Chan, E., Quintana-Ortí, E. S., Quintana-Ortí, G., van de Geijn, R. A., & Van Zee, F. G. (2012). The FLAME approach: From dense linear algebra algorithms to high-performance multi-accelerator implementations. Journal of Parallel and Distributed Computing, 72(9), 1134-1143. doi:10.1016/j.jpdc.2011.10.014 | es_ES |
dc.description.references | Slurm workload manager http://slurm.schedmd.com | es_ES |