Increasing the Performance of Data Centers by Combining Remote GPU Virtualization with Slurm
Ver/ Abrir
Impacto
Scholar |
Otros documentos de la autoría: Iserte, Sergio; Prades, Javier; Reaño, Carlos; Silla, Federico
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7036
comunitat-uji-handle3:10234/146069
comunitat-uji-handle4:
INVESTIGACIONMetadatos
Título
Increasing the Performance of Data Centers by Combining Remote GPU Virtualization with SlurmFecha de publicación
2016Editor
IEEEISBN
78-1-5090-2453-7/16Cita bibliográfica
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.Tipo de documento
info:eu-repo/semantics/bookPartVersión de la editorial
http://ieeexplore.ieee.org/abstract/document/7515675/Palabras clave / Materias
Resumen
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 ... [+]
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. [-]
Descripción
Ponencia presentada al 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) 2016, Cartagena, Colombia, May 16-19 2016