An OpenMP-CUDA Implementation of Multilevel Fast Multipole Algorithm for Electromagnetic Simulation on Multi-GPU Computing Systems

A multi-GPU implementation of the multilevel fast multipole algorithm (MLFMA) based on the hybrid OpenMPCUDA parallel programming model (OpenMP-CUDA-MLFMA) is presented for computing electromagnetic scattering of a three-dimensional conducting object. The proposed hierarchical parallelization strate...

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Bibliographic Details
Published in:IEEE transactions on antennas and propagation 2013-07, Vol.61 (7), p.3607-3616
Main Authors: JIAN GUAN, SU YAN, JIN, Jian-Ming
Format: Article
Language:English
Subjects:
Algorithms
Antenna radiation patterns
Applied classical electromagnetism
Applied sciences
Computational modeling
Computer architecture
CUDA
Diffraction, scattering, reflection
electromagnetic scattering
Electromagnetic wave propagation, radiowave propagation
Electromagnetism
electron and ion optics
Exact sciences and technology
Fundamental areas of phenomenology (including applications)
Graphics processing units
hybrid parallel programming model
Instruction sets
Method of moments
multi-GPU
multilevel fast multipole algorithm
OpenMP
Physics
radar cross section
Radiocommunications
Radiolocalization and radionavigation
Radiowave propagation
Studies
Systems, networks and services of telecommunications
Telecommunications
Telecommunications and information theory
Transmission and modulation (techniques and equipments)
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Summary:A multi-GPU implementation of the multilevel fast multipole algorithm (MLFMA) based on the hybrid OpenMPCUDA parallel programming model (OpenMP-CUDA-MLFMA) is presented for computing electromagnetic scattering of a three-dimensional conducting object. The proposed hierarchical parallelization strategy ensures a high computational throughput for the GPU calculation. The resulting OpenMP-based multi-GPU implementation is capable of solving real-life problems with over one million unknowns with a remarkable speed-up. The radar cross sections of a few benchmark objects are calculated to demonstrate the accuracy of the solution. The results are compared with those from the CPU-based MLFMA and measurements. The capability and efficiency of the presented method are analyzed through the examples of a sphere, an aerocraft, and a missile-like object. Compared with the 8-threaded CPU-based MLFMA, the OpenMP-CUDA-MLFMA method can achieve from 5 to 20 total speed-up ratios.
ISSN:0018-926X
1558-2221
DOI:10.1109/TAP.2013.2258882