17565

GPU-Accelerated Parallel Finite-Difference Time-Domain Method for Electromagnetic Waves Propagation in Unmagnetized Plasma Media

Xi-min Wang, Lang-lang Xiong, Song Liu, Zhi-yun Peng, Shuang-ying Zhong
Electrical Engineering, Nanchang University, Nanchang Jiangxi 330031, China
arXiv:1709.00821 [physics.comp-ph], (6 Sep 2017)

@article{wang2017gpuaccelerated,

   title={GPU-Accelerated Parallel Finite-Difference Time-Domain Method for Electromagnetic Waves Propagation in Unmagnetized Plasma Media},

   author={Wang, Xi-min and Xiong, Lang-lang and Liu, Song and Peng, Zhi-yun and Zhong, Shuang-ying},

   year={2017},

   month={sep},

   archivePrefix={"arXiv"},

   primaryClass={physics.comp-ph}

}

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The finite-difference time-domain (FDTD) method has been commonly utilized in the numerical solution of electromagnetic (EM) waves propagation through the plasma media. However, the FDTD method may bring about a significant increment in additional run-times consuming for computationally large and complicated EM problems. Graphics Processing Unit (GPU) computing based on Compute Unified Device Architecture (CUDA) has grown in response to increased concern for reduction of run-times. We represent the CUDA-based FDTD method with the Runge-Kutta exponential time differencing scheme (RKETD) for the unmagnetized plasma implemented on GPU. In the paper, we derive the RKETD-FDTD formulation for the unmagnetized plasma comprehensively, and describe the detailed flowchart of CUDA-implemented RKETD-FDTD method on GPU. The accuracy and acceleration performance of the posed CUDA-based RKETD-FDTD method implemented on GPU are substantiated by the numerical experiment that simulates the EM waves traveling through the unmagnetized plasma slab, compared with merely CPU-based RKETD-FDTD method. The accuracy is validated by calculating the reflection and transmission coefficients for one-dimensional unmagnetized plasma slab. Comparison between the elapsed times of two methods proves that the GPU-based RKETD-FDTD method can acquire better application acceleration performance with sufficient accuracy.
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