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On the Use of Remote GPUs and Low-Power Processors for the Acceleration of Scientific Applications

Authors:
Adrián Castelló
José Duato
Rafael Mayo
Antonio J. Peña
Enrique S. Quintana-Ortí
Vicente Roca
Federico Silla

Keywords: High Performance Computing; Graphic Process- ing Units (GPUs); CUDA; Virtualization; Scientific Computing; Energy-Aware Computing;

Abstract:
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.

Pages: 57 to 62

Copyright: Copyright (c) IARIA, 2014

Publication date: April 20, 2014

Published in: conference

ISSN: 2308-412X

ISBN: 978-1-61208-332-2

Location: Chamonix, France

Dates: from April 20, 2014 to April 24, 2014