Application of FPGA technology to accelerate the finite-difference time-domain (FDTD) method

Schneider, Ryan N.; Turner, L.E.; Okoniewski, M.

doi:10.1145/503062.503063

Cited by 7 publications

(6 citation statements)

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“…Among different approaches, such as using clusters [22] or field programmable gate arrays (FPGA) [23], using graphics processing unit (GPU) is more efficient, and cheaper in reducing runtimes. In spite of large efforts on GPU parallel programming for ordinary FDTD [24][25][26][27][28][29][30][31], GPU parallel programming for SF-FDTD has not been reported yet, to the best of our knowledge.…”

Section: Introductionmentioning

confidence: 99%

Gpu Implementation of Split-Field Finite-Difference Time-Domain Method for Drude-Lorentz Dispersive Media

Shahmansouri¹,

Rashidian²

2012

PIER

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Abstract-Split-field finite-difference time-domain (SF-FDTD) method can overcome the limitation of ordinary FDTD in analyzing periodic structures under oblique incidence. On the other hand, huge run times of 3D SF-FDTD, is practically a major burden in its usage for analysis and design of nanostructures, particularly when having dispersive media. Here, details of parallel implementation of 3D SF-FDTD method for dispersive media, combined with totalfield/scattered-field (TF/SF) method for injecting oblique plane wave, are discussed. Graphics processing unit (GPU) has been used for this purpose, and very large speed up factors have been achieved. Also a previously reported formulation of SF-FDTD based on the Drude model for dispersive media, is extended to cover Drude-Lorentz model, which is usually needed for materials such as gold. The resulting reduction in the number of variables in this formulation, not only helps in reducing the computational time, but also makes it possible to be implemented in GPU, where its memory limitation is a major concern. As an example for demonstrating the importance of this method in optimization of nanophotonics structures, improvement in the performance of a refractive index sensor, made of an array of nanodisks, using suitable angle of incidence is reported. To the best of our knowledge this is the first report of GPU implementation of SF-FDTD method, capable of analyzing periodic dispersive media under oblique incidence.

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Section: Introductionmentioning

confidence: 99%

Gpu Implementation of Split-Field Finite-Difference Time-Domain Method for Drude-Lorentz Dispersive Media

Shahmansouri¹,

Rashidian²

2012

PIER

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“…It can not only solve the large memory requirements, but also greatly reduce the computing time [2].Since 1991, E, K. Miller proposed parallel FDTD computing which based on the idea of domain decomposition [3], parallel FDTD computing have witnessed a rapid development at home and abroad. To sum up, there are three main parallel computing methods at present--personal computer (PC) cluster [4], field programmable gate array [5] (FPGA) and GPu. It has done a great deal of researches on PC cluster, the technology is very mature, but it is expensive and bulky.…”

Section: Introductionmentioning

confidence: 99%

Study on acceleration technique for two-dimensional FDTD algorithm based on GPU

Jin

et al. 2010

Proceedings of the 9th International Symposium on Antennas, Propagation and EM Theory

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The Parallel finite difference time domain (FDTD) algorithm is an important method to enhance the speed in multiple data FDTD operation. The improvement of graphics processing unit (GPU) performance, especially the emergence of Computer Unit Device Architecture (CUDA), offers parallel FDTD method an efficient and simple solution. First of all, this paper explains parallel FDTD method and CUDA in detail, and then elaborates the parallel speedup principle of two-dimensional FDTD algorithm by use of GPU on the CUDA platform. At last, this paper analyzes computing time of the FDTD algorithm on two different CPUs and GPUs. The result shows the consistency of the results between serial and parallel algorithms, and observably speedup is obtained compared with traditional PC computation by use of GPU.

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“…An alternative solution is the hardware implementation of the Finite Difference (FD) representation of the thermal model. In fact, different hardware implementations of FD solutions in the electromagnetics domain can be found in the literature, Durbano et al (2004); Placidi et al (2002);Schneider et al (2002). In previous works, Pardo et al (2009;, we have presented an FPGA implementation of a FD Heat Equation solver which speeds the computation up by a factor of 10 compared to the purely software solution.…”

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confidence: 99%