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

J. Duato, Pena, A.J. a, F. Silla, R. Mayo, E.S. Quintana-Orti
Univ. Politec. de Valencia (UPV), Valencia, Spain
International Conference on High Performance Computing and Simulation (HPCS), 2010

@inproceedings{duato2010rcuda,

   title={rCUDA: Reducing the number of GPU-based accelerators in high performance clusters},

   author={Duato, J. and Pena, A.J. and Silla, F. and Mayo, R. and Quintana-Ort{‘i}, ES},

   booktitle={High Performance Computing and Simulation (HPCS), 2010 International Conference on},

   pages={224–231},

   year={2010},

   organization={IEEE}

}

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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.
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