GPU-Accelerated Parallel FDTD on Distributed Heterogeneous Platform

• Published in: International journal of antennas and propagation Vol. 2014; no. 2014; pp. 1 - 8
• Main Authors: Xu, Lei, Xu, Ying, Zhang, Yu, Jiang, Shugang, Jiang, Ronglin, Zhang, Dandan
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2014; Hindawi Limited
• Subjects: Arrays; Central processing units; Efficiency; Finite difference time domain method; FORTRAN; Graphics processing units; Magnetic fields; Mathematical analysis; Message passing; Multiprocessing (computers); Permeability; R&D; Research & development; Supercomputers
• ISSN: 1687-5869; 1687-5877
• DOI: 10.1155/2014/321081

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 16×18 elements is calculated and the radiation patterns are compared with the ones of MoM. Results show that there is a well agreement between them.