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International Journal of Antennas and Propagation
Volume 2014 (2014), Article ID 321081, 8 pages
http://dx.doi.org/10.1155/2014/321081
Research Article

GPU-Accelerated Parallel FDTD on Distributed Heterogeneous Platform

1Research and Development Department, Shanghai Supercomputer Center, Shanghai 201203, China
2School of Electronic Engineering, Xidian University, Xi’an 710071, China

Received 31 October 2013; Revised 27 December 2013; Accepted 10 January 2014; Published 20 February 2014

Academic Editor: Lei Zhao

Copyright © 2014 Ronglin Jiang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper introduces a (finite difference time domain) FDTD code written in Fortran and CUDA for realistic electromagnetic calculations with parallelization methods of Message Passing Interface (MPI) and Open Multiprocessing (OpenMP). Since both Central Processing Unit (CPU) and Graphics Processing Unit (GPU) resources are utilized, a faster execution speed can be reached compared to a traditional pure GPU code. In our experiments, 64 NVIDIA TESLA K20m GPUs and 64 INTEL XEON E5-2670 CPUs are used to carry out the pure CPU, pure GPU, and CPU + GPU tests. Relative to the pure CPU calculations for the same problems, the speedup ratio achieved by CPU + GPU calculations is around 14. Compared to the pure GPU calculations for the same problems, the CPU + GPU calculations have 7.6%–13.2% performance improvement. Because of the small memory size of GPUs, the FDTD problem size is usually very small. However, this code can enlarge the maximum problem size by 25% without reducing the performance of traditional pure GPU code. Finally, using this code, a microstrip antenna array with elements is calculated and the radiation patterns are compared with the ones of MoM. Results show that there is a well agreement between them.