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Acceleration of Solving Maxwell’s Equations Using Cluster of GPUs

E. Arianyan, S. A. Motamedi, M. Hekmatpanah, I. Arianyan
Amirkabir University of Technology, Tehran, Iran
Journal of Telecommunications, Volume 12, Issue 2, 2012

@article{arianyan2012acceleration,

   title={Acceleration of Solving Maxwell’s Equations Using Cluster of GPUs},

   author={Arianyan, E. and Motamedi, SA and Hekmatpanah, M. and Arianyan, I.},

   year={2012}

}

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Finite difference time domain (FDTD) is a numerical method for solving differential equations like Maxwell’s equations. Normally, simulation time of these equations is very long and there has been a great effort to reduce it. The most recent and useful way to reduce the simulation time of these equations is through using GPUs. Graphical processing units (GPUs) are powerful hardware which have parallel architectures and are suitable for running parallel applications. In this paper we evaluate three different configurations for implementing FDTD on GPU: one single GPU system, cluster of GPUs on the same system, and cluster of GPUs on different systems. We present the problems of these implementations and how to solve them. We apply some methods to solve data divergence and data conflict problems which lead to 1.6-times increase in speed. Moreover, we devise a new overlap algorithm to run FDTD computations on discrete memories which results in 5-times increase in speed. Furthermore, we divide data in the right direction of memory which causes 1.6-times increase in speed. The speed of running FDTD, on our most powerful cluster system based on GPU, has increased by a factor of 40 compared to CPU cluster.
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