High performance computing applied to the false nearest neighbors method: Box-assisted and kd-tree approaches

被引:0
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作者
Águila J.J. [1 ,3 ]
Marín I. [2 ]
Arias E. [1 ]
Del Mar Artigao M. [2 ]
Miralles J.J. [2 ]
机构
[1] Albacete Research Institute of Informatics, University of Castilla-La Mancha, Albacete 02071, Avda. España s/n
[2] Applied Physics Department, University of Castilla-La Mancha, Albacete 02071, Avda. España s/n
[3] Depto. Ingeniería en Computación, Universidad de Magallanes, Punta Arenas 01855, Avda. Bulnes
来源
关键词
Box-assisted algorithm; False nearest neighbors method; Kd-tree data structure; Message passing interface; Nonlinear time series analysis;
D O I
10.1007/978-94-007-1192-1_27
中图分类号
学科分类号
摘要
In different fields of science and engineering (medicine, economics, oceanography, biological systems, etc.) the false nearest neighbors (FNN) method has a special relevance. In some of these applications, it is important to provide the results in a reasonable time scale, thus the execution time of the FNN method has to be reduced. To achieve this goal, a multidisciplinary group formed by computer scientists and physicists are collaborative working on developing High Performance Computing implementations of one of the most popular algorithms that implement the FNN method: based on box-assisted algorithm and based on kd-tree data structure. In this paper, a comparative study of the distributed memory architecture implementations carried out in the framework of this collaboration is presented. As a result, two parallel implementations for box-assisted algorithm and one parallel implementation for the kd-tree structure are compared in terms of execution time, speed-up and efficiency. In terms of execution time, the approaches presented here are from 2 to 16 times faster than the sequential implementation, and the kd-tree approach is from 3 to 7 times faster than the box-assisted approaches. © 2011 Springer Science+Business Media B.V.
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页码:323 / 336
页数:13
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