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A GPU IMPLEMENTATION OF THE INVERSE FAST MULTIPOLE METHOD FOR MULTI-BISTATIC IMAGING APPLICATIONS

By L. E. Tirado, G. Ghazi, Y. Alvarez-Lopez, F. Las-Heras, and J. A. Martinez-Lorenzo

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Abstract:
This paper describes a parallel implementation of the Inverse Fast Multipole Method (IFMM) for multi-bistatic imaging configurations. NVIDIAs Compute Unified Device Architecture (CUDA) is used to parallelize and accelerate the imaging algorithm in a Graphics Processing Unit (GPU). The algorithm is validated with synthetic data generated by the Modified Equivalent Current Approximation (MECA) method and experimental data collected by a Frequency-Modulated Continuous Wave (FMCW) radar system operating in the 70-77 GHz frequency band. The presented results show that the IFMM implementation using the CUDA platform is effective at significantly reducing the algorithm computational time, providing a 300X speedup when compared to the single core OpenMP version of the algorithm.

Citation:
L. E. Tirado, G. Ghazi, Y. Alvarez-Lopez, F. Las-Heras, and J. A. Martinez-Lorenzo, "A GPU Implementation of the Inverse Fast Multipole Method for Multi-Bistatic Imaging Applications," Progress In Electromagnetics Research M, Vol. 58, 159-169, 2017.
doi:10.2528/PIERM17021004

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