2009•Unpublished venueRequires access

3D finite-difference time-domain simulations of well-logging problems on graphic processing unit

Alexandra Mendoza, Rui Qiang, Ji Chen, Dagang Wu

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Abstract

Three dimensional Finite-Difference Time-Domain (FDTD) code is implemented on graphics processing units (GPU) to reduce the overall simulation time. NVIDIA's Compute Unified Device Architecture (CUDA) is used for the implementation of the code on a GTX 260 graphics card. It is observed that the technique can significantly reduce the computational time for parallel FDTD simulations by a factor over 30.

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What this paper is about

Three dimensional Finite-Difference Time-Domain (FDTD) code is implemented on graphics processing units (GPU) to reduce the overall simulation time. NVIDIA's Compute Unified Device Architecture (CUDA) is used for the implementation of the code on a GTX 260 graphics card. It is observed that the technique can significantly reduce the computational time for parallel FDTD simulations by a factor over 30.

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OpenAlex reports 2 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Three dimensional Finite-Difference Time-Domain (FDTD) code is implemented on graphics processing units (GPU) to reduce the overall simulation time. NVIDIA's Compute Unified Device Architecture (CUDA) is used for the implementation of the code on a GTX 260 graphics card. It is observed that the technique can significantly reduce the computational time for parallel FDTD simulations by a factor over 30.

Key concepts: Computer science, Logging, Unit (ring theory), Finite difference method, Domain (mathematical analysis), Computational science, Physics, Mathematics

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