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Increasing the Performance of Data Centers by Combining Remote GPU Virtualization with Slurm
dc.contributor.author | Iserte, Sergio | |
dc.contributor.author | Prades, Javier | |
dc.contributor.author | Reaño, Carlos | |
dc.contributor.author | Silla, Federico | |
dc.date.accessioned | 2017-03-28T15:24:29Z | |
dc.date.available | 2017-03-28T15:24:29Z | |
dc.date.issued | 2016 | |
dc.identifier.citation | ISERTE, Sergio, et al. Increasing the Performance of Data Centers by Combining Remote GPU Virtualization with Slurm. En Cluster, Cloud and Grid Computing (CCGrid), 2016 16th IEEE/ACM International Symposium on. IEEE, 2016. p. 98-101. | ca_CA |
dc.identifier.isbn | 78-1-5090-2453-7/16 | |
dc.identifier.uri | http://hdl.handle.net/10234/166974 | |
dc.description | Ponencia presentada al 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) 2016, Cartagena, Colombia, May 16-19 2016 | |
dc.description.abstract | The use of Graphics Processing Units (GPUs) presents several side effects, such as increased acquisition costs as well as larger space requirements. Furthermore, GPUs require a non-negligible amount of energy even while idle. Additionally, GPU utilization is usually low for most applications. Using the virtual GPUs provided by the remote GPU virtualization mechanism may address the concerns associated with the use of these devices. However, in the same way as workload managers map GPU resources to applications, virtual GPUs should also be scheduled before job execution. Nevertheless, current workload managers are not able to deal with virtual GPUs. In this paper we analyze the performance attained by a cluster using the rCUDA remote GPU virtualization middleware and a modified version of the Slurm workload manager, which is now able to map remote virtual GPUs to jobs. Results show that cluster throughput is doubled at the same time that total energy consumption is reduced up to 40%. GPU utilization is also increased. | ca_CA |
dc.description.sponsorShip | This work was funded by Generalitat Valenciana under Grant PROMETEOII/2013/009 of the PROMETEO program phase II. The author from Universidad Jaume I was supported by project TIN2014-53495-R from MINECO and FEDER. The authors are grateful for the generous support provided by Mellanox Technologies and the equipment donated by NVIDIA Corporation. | ca_CA |
dc.format.extent | 4 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | IEEE | ca_CA |
dc.relation.isFormatOf | 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), 2016 | |
dc.rights | © 2016 IEEE | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | * |
dc.subject | GPGPU | ca_CA |
dc.subject | CUDA | ca_CA |
dc.subject | HPC | ca_CA |
dc.subject | Virtualization | ca_CA |
dc.subject | InfiniBand | ca_CA |
dc.subject | Data centers | ca_CA |
dc.subject | Slurm | ca_CA |
dc.subject | rCUDA | ca_CA |
dc.title | Increasing the Performance of Data Centers by Combining Remote GPU Virtualization with Slurm | ca_CA |
dc.type | info:eu-repo/semantics/bookPart | ca_CA |
dc.identifier.doi | http://dx.doi.org/10.1109/CCGrid.2016.26 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.relation.publisherVersion | http://ieeexplore.ieee.org/abstract/document/7515675/ | ca_CA |