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2012•IEEE Antennas and Propagation MagazineRequires access

Development of a CUDA Implementation of the 3D FDTD Method

Matthew Livesey, James F. Stack, Fumie Costen, Takeshi Nanri, Naotoshi Nakashima, Seiji Fujino

Open publisher page 52 citations

Abstract

The use of general-purpose computing on a GPU is an effective way to accelerate the FDTD method. This paper introduces flexibility to the theoretically best available approach. It examines the performance on both Tesla- and Fermi-architecture GPUs, and identifies the best way to determine the GPU parameters for the proposed method.

About this research paper

What this paper is about

The use of general-purpose computing on a GPU is an effective way to accelerate the FDTD method. This paper introduces flexibility to the theoretically best available approach. It examines the performance on both Tesla- and Fermi-architecture GPUs, and identifies the best way to determine the GPU parameters for the proposed method.

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OpenAlex reports 52 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

The use of general-purpose computing on a GPU is an effective way to accelerate the FDTD method. This paper introduces flexibility to the theoretically best available approach. It examines the performance on both Tesla- and Fermi-architecture GPUs, and identifies the best way to determine the GPU parameters for the proposed method.

Key concepts: CUDA, Finite-difference time-domain method, Computer science, Computational science, Flexibility (engineering), General-purpose computing on graphics processing units, Parallel computing, Architecture

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