GPU implementation of the Modified Equivalent Current Approximation (MECA) method
Keywords:
CUDA, GPGPU, MECA, parallel programming, Physical OpticsAbstract
This paper investigates two different methods of implementing the Modified Equivalent Current Approximation (MECA) method using CUDA parallel programing and computing platform [1]. The MECA method allows the analysis of dielectric and lossy geometries and reduces to the well-studied Physical Optics (PO) formulation in case of PEC caterers [2]. We discuss the implementation details and performance of using both an add-on toolbox for MATLAB™ to offload computations to the GPU, as well as porting MECA code to CUDA directly. We show through simulations that both methods are effective at significantly reducing the MECA algorithm computation time.
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References
M. Ujaldon, “Using GPUs for Accelerating Electromagnetic Simulations,” ACES Journal, vol. 25, no. 4, pp. 294-302, 2010.
H. Gómez-Sousa, J. A. Martínez-Lorenzo, O. Rubiños-López, J. G. Meana, M. Graña-Varela, N. Gonzalez-Valdes, M. Arias-Acuña, “Strategies for Improving the Use of the Memory Hierarchy in an Implementation of the Modified Equivalent Current Approximation (MECA) Method,” ACES Journal, vol. 25, no. 10, pp. 841-852, 2010.
J. G. Meana, J. A. Martinez-Lorenzo, F. Las-Heras, and C. Rappaport, “Wave Scattering by Dielectric and Lossy Materials using the Modified Equivalent Current Approximation,” IEEE Transactions on Antennas and Propagation, vol. 58, no. 11, pp. 3757-3761, 2010.
J. L. Fernandes, C. Rappaport and D. M. Sheen, “Improved Reconstruction and Sensing Techniques for Personnel Screening in Three-Dimensional Cylindrical Millimeter-Wave Portal Scanning,” Proc. SPIE 8022, 802205, 2011.
AccelerEyes, Jacket, Version 1.8.1, http://www.accelereyes.com, Sep. 2011.
C. A. Balanis, Advanced Engineering Electromagnetics, 1st ed. New York, USA: John Wiley and Sons, 1989.
NVIDIA, “NVIDIA’s Next Generation CUDA™ Compute Architecture: Fermi™,” Version 1.1, http://www.nvidia.com/content/PDF/fermi_white_ papers/NVIDIA_Fermi_Compute_Architecture_W hitepaper.pdf, 2009.
M. Malik, T. Li, U. Sharif, R. Shahid, T. ElGhazawi, G. Newby, “Productivity of GPUs under Different Programming Paradigms,” Concurrency and Computation: Practice and Experience, vol. 24, no. 2, pp. 179-191, 2012.
J. Hoberock, N. Bell, “Thrust: A Parallel Template Library,” V1.3.0, http://www.meganewtons.com, 2010.
NVIDIA, “Thrust Quick Start Guide,” Version 01, http://developer.download.nvidia.com/compute/De vZone/docs/html/CUDALibraries/doc/Thrust_Quic k_Start_Guide.pdf, Jan. 2011.


