Use of Multiple GPUs on Shared Memory Multiprocessors for Ultrasound Propagation Simulations
Research School of Computer Science, College of Engineering and Computer Science, The Australian National University, Canberra, ACT 0200, Australia
The Tenth Australasian Symposium on Parallel and Distributed Computing (AusPDC 2012), 2012
@inproceedings{jaros2012use,
author={Jiri Jaros and E. Bradley Treeby and P. Alistair Rendell},
title={Use of Multiple GPUs on Shared Memory Multiprocessors for Ultrasound Propagation Simulations},
pages={43–52},
booktitle={Australasian Symposium on Parallel and Distributed Computing (AusPDC 2012)},
year={2012},
location={Melbourne, AU},
language={english},
url={http://www.fit.vutbr.cz/research/view_pub.php?id=9881}
}
This paper outlines our effort to migrate a compute intensive application of ultrasound propagation being developed in Matlab to a cluster computer where each node has seven GPUs. Our goal is to perform realistic simulations in hours and minutes instead of weeks and days. In order to reach this goal we investigate architecture characteristics of the target system focusing on the PCI-Express subsystem and new features proposed in CUDA version 4.0, especially simultaneous host to device, device to host and peer-to-peer transfers that the application is going to highly benefit from. We also present the results from a CPU based implementation and discuss future directions to exploit multiple GPUs.
February 23, 2012 by hgpu
