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GPUs, a New Tool of Acceleration in CFD: Efficiency and Reliability on Smoothed Particle Hydrodynamics Methods

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Title
GPUs, a New Tool of Acceleration in CFD: Efficiency and Reliability on Smoothed Particle Hydrodynamics Methods
Published in
PLOS ONE, June 2011
DOI 10.1371/journal.pone.0020685
Pubmed ID
Authors

Alejandro C. Crespo, Jose M. Dominguez, Anxo Barreiro, Moncho Gómez-Gesteira, Benedict D. Rogers

Abstract

Smoothed Particle Hydrodynamics (SPH) is a numerical method commonly used in Computational Fluid Dynamics (CFD) to simulate complex free-surface flows. Simulations with this mesh-free particle method far exceed the capacity of a single processor. In this paper, as part of a dual-functioning code for either central processing units (CPUs) or Graphics Processor Units (GPUs), a parallelisation using GPUs is presented. The GPU parallelisation technique uses the Compute Unified Device Architecture (CUDA) of nVidia devices. Simulations with more than one million particles on a single GPU card exhibit speedups of up to two orders of magnitude over using a single-core CPU. It is demonstrated that the code achieves different speedups with different CUDA-enabled GPUs. The numerical behaviour of the SPH code is validated with a standard benchmark test case of dam break flow impacting on an obstacle where good agreement with the experimental results is observed. Both the achieved speed-ups and the quantitative agreement with experiments suggest that CUDA-based GPU programming can be used in SPH methods with efficiency and reliability.

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Geographical breakdown

Country Count As %
United Kingdom 2 <1%
Germany 1 <1%
Brazil 1 <1%
Indonesia 1 <1%
China 1 <1%
Japan 1 <1%
Unknown 219 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 55 24%
Student > Master 38 17%
Researcher 35 15%
Student > Bachelor 11 5%
Other 8 4%
Other 36 16%
Unknown 43 19%
Readers by discipline Count As %
Engineering 110 49%
Computer Science 15 7%
Earth and Planetary Sciences 12 5%
Mathematics 9 4%
Medicine and Dentistry 5 2%
Other 16 7%
Unknown 59 26%