CUDA Approach for Meshless Local Petrov-Galerkin Method

In this paper, a strategy to parallelize the meshless local Petrov-Galerkin (MLPG) method is developed. It is executed in a high parallel architecture, the well known graphics processing unit. The MLPG algorithm has many variations depending on which combination of trial and test functions is used....

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Bibliographic Details
Published in:IEEE transactions on magnetics 2015-03, Vol.51 (3), p.1-4
Main Authors: Correa, Bruno C., Mesquita, Renato C., Amorim, Lucas P.
Format: Article
Language:English
Subjects:
Algorithms
Approximation
Central Processing Unit
Finite element analysis
Finite element method
Functions (mathematics)
Graphics processing units
Instruction sets
Interpolation
Magnetism
Mathematical analysis
Mathematical model
Meshless methods
Polynomials
Strategy
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Summary:In this paper, a strategy to parallelize the meshless local Petrov-Galerkin (MLPG) method is developed. It is executed in a high parallel architecture, the well known graphics processing unit. The MLPG algorithm has many variations depending on which combination of trial and test functions is used. Two types of interpolation schemes are explored in this paper to approximate the trial functions and a Heaviside step function is used as test function. The first scheme approximates the trial function through a moving least squares interpolation, and the second interpolates using the radial point interpolation method with polynomial reproduction (RPIMp). To compare these two approaches, a simple electromagnetic problem is solved, and the number of nodes in the domain is increased while the time to assemble the system of equations is obtained. Results shows that with the parallel version of the algorithm it is possible to achieve an execution time 20 times smaller than the CPU execution time, for the MLPG using RPIMp versions of the method.
ISSN:0018-9464
1941-0069
DOI:10.1109/TMAG.2014.2359213