rCUDA: Reducing the number of GPU-based accelerators in high performance clusters

The increasing computing requirements for GPUs (Graphics Processing Units) have favoured the design and marketing of commodity devices that nowadays can also be used to accelerate general purpose computing. Therefore, future high performance clusters intended for HPC (High Performance Computing) wil...

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Bibliographic Details
Main Authors: Duato, José, Peña, Antonio J, Silla, F, Mayo, R, Quintana-Orti, E S
Format: Conference Proceeding
Language:eng ; jpn
Subjects:
Acceleration
clusters
CUDA
Driver circuits
Energy saving
Graphics processing unit
high performance computing
Kernel
Libraries
Runtime
Servers
virtualization
Online Access:Request full text
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Summary:The increasing computing requirements for GPUs (Graphics Processing Units) have favoured the design and marketing of commodity devices that nowadays can also be used to accelerate general purpose computing. Therefore, future high performance clusters intended for HPC (High Performance Computing) will likely include such devices. However, high-end GPU-based accelerators used in HPC feature a considerable energy consumption, so that attaching a GPU to every node of a cluster has a strong impact on its overall power consumption. In this paper we detail a framework that enables remote GPU acceleration in HPC clusters, thus allowing a reduction in the number of accelerators installed in the cluster. This leads to energy, acquisition, maintenance, and space savings.
DOI:10.1109/HPCS.2010.5547126