On the Use of Remote GPUs and Low-Power Processors for the Acceleration of Scientific Applications
Universitat Polit’ecnica de Val’encia, Val’encia, Spain
The Fourth International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies (ENERGY), 2014
@inproceedings{castello2014use,
title={On the Use of Remote GPUs and Low-Power Processors for the Acceleration of Scientific Applications},
author={Castell{‘o}, Adri{‘a}n and Duato, Jos{‘e} and Mayo, Rafael and Pe{~n}a, Antonio J and Quintana-Ort{‘i}, Enrique S and Roca, Vicente and Silla, Federico},
booktitle={ENERGY 2014, The Fourth International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies},
pages={57–62},
year={2014}
}
Many current high-performance clusters include one or more GPUs per node in order to dramatically reduce application execution time, but the utilization of these accelerators is usually far below 100%. In this context, remote GPU virtualization can help to reduce acquisition costs as well as the overall energy consumption. In this paper, we investigate the potential overhead and bottlenecks of several "heterogeneous" scenarios consisting of client GPU-less nodes running CUDA applications and remote GPU-equipped server nodes providing access to NVIDIA hardware accelerators. The experimental evaluation is performed using three general-purpose multicore processors (Intel Xeon, Intel Atom and ARM Cortex A9), two graphics accelerators (NVIDIA GeForce GTX480 and NVIDIA Quadro M1000), and two relevant scientific applications (CUDASW++ and LAMMPS) arising in bioinformatics and molecular dynamics simulations.
April 27, 2014 by hgpu
