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Acceleration of computation speed for elastic wave simulation using a Graphic Processing Unit

N. Nakata, T. Tsuji, T. Matsuoka
Department of Urban Management, Faculty of Engineering, Kyoto University, C1-118, Kyotodaigaku-Katsura, Nishikyo-ku, Kyoto 615-8540, Japan
Exploration Geophysics, Vol. 42, No. 1, 2011,page(s): 98-104

@article{nakata2011acceleration,

   title={Acceleration of computation speed for elastic wave simulation using a Graphic Processing Unit},

   author={Nakata, N. and Tsuji, T. and Matsuoka, T.},

   journal={Exploration Geophysics},

   volume={42},

   number={1},

   pages={98–104},

   year={2011},

   publisher={CSIRO}

}

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Numerical simulation in exploration geophysics provides important insights into subsurface wave propagation phenomena. Although elastic wave simulations take longer to compute than acoustic simulations, an elastic simulator can construct more realistic wavefields including shear components. Therefore, it is suitable for exploration of the responses of elastic bodies. To overcome the long duration of the calculations, we use a Graphic Processing Unit (GPU) to accelerate the elastic wave simulation. Because a GPU has many processors and a wide memory bandwidth, we can use it in a parallelised computing architecture. The GPU board used in this study is an NVIDIA Tesla C1060, which has 240 processors and a 102 GB/s memory bandwidth. Despite the availability of a parallel computing architecture (CUDA), developed by NVIDIA, we must optimise the usage of the different types of memory on the GPU device, and the sequence of calculations, to obtain a significant speedup of the computation. In this study, we simulate two- (2D) and three-dimensional (3D) elastic wave propagation using the Finite-Difference Time-Domain (FDTD) method on GPUs. In the wave propagation simulation, we adopt the staggered-grid method, which is one of the conventional FD schemes, since this method can achieve sufficient accuracy for use in numerical modelling in geophysics. Our simulator optimises the usage of memory on the GPU device to reduce data access times, and uses faster memory as much as possible. This is a key factor in GPU computing. By using one GPU device and optimising its memory usage, we improved the computation time by more than 14 times in the 2D simulation, and over six times in the 3D simulation, compared with one CPU. Furthermore, by using three GPUs, we succeeded in accelerating the 3D simulation 10 times.
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