GPU-Accelerated Parallel FDTD on Distributed Heterogeneous Platform
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...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | International Journal of Antennas and Propagation |
| Online Access: | http://dx.doi.org/10.1155/2014/321081 |
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| _version_ | 1832564438793715712 |
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| author | Ronglin Jiang Shugang Jiang Yu Zhang Ying Xu Lei Xu Dandan Zhang |
| author_facet | Ronglin Jiang Shugang Jiang Yu Zhang Ying Xu Lei Xu Dandan Zhang |
| author_sort | Ronglin Jiang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ed596d3fd7cb4694b4123feef97e967d |
| institution | Kabale University |
| issn | 1687-5869 1687-5877 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Antennas and Propagation |
| spelling | doaj-art-ed596d3fd7cb4694b4123feef97e967d2025-02-03T01:11:08ZengWileyInternational Journal of Antennas and Propagation1687-58691687-58772014-01-01201410.1155/2014/321081321081GPU-Accelerated Parallel FDTD on Distributed Heterogeneous PlatformRonglin Jiang0Shugang Jiang1Yu Zhang2Ying Xu3Lei Xu4Dandan Zhang5Research and Development Department, Shanghai Supercomputer Center, Shanghai 201203, ChinaSchool of Electronic Engineering, Xidian University, Xi’an 710071, ChinaSchool of Electronic Engineering, Xidian University, Xi’an 710071, ChinaResearch and Development Department, Shanghai Supercomputer Center, Shanghai 201203, ChinaResearch and Development Department, Shanghai Supercomputer Center, Shanghai 201203, ChinaResearch and Development Department, Shanghai Supercomputer Center, Shanghai 201203, ChinaThis 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.http://dx.doi.org/10.1155/2014/321081 |
| spellingShingle | Ronglin Jiang Shugang Jiang Yu Zhang Ying Xu Lei Xu Dandan Zhang GPU-Accelerated Parallel FDTD on Distributed Heterogeneous Platform International Journal of Antennas and Propagation |
| title | GPU-Accelerated Parallel FDTD on Distributed Heterogeneous Platform |
| title_full | GPU-Accelerated Parallel FDTD on Distributed Heterogeneous Platform |
| title_fullStr | GPU-Accelerated Parallel FDTD on Distributed Heterogeneous Platform |
| title_full_unstemmed | GPU-Accelerated Parallel FDTD on Distributed Heterogeneous Platform |
| title_short | GPU-Accelerated Parallel FDTD on Distributed Heterogeneous Platform |
| title_sort | gpu accelerated parallel fdtd on distributed heterogeneous platform |
| url | http://dx.doi.org/10.1155/2014/321081 |
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