A distributed memory implementation of the False Nearest Neighbors method based on kd-tree applied to electrocardiography

被引:4
|
作者
Aguila, J. J. [1 ]
Arias, E. [2 ]
Artigao, M. M. [3 ]
Miralles, J. J. [3 ]
机构
[1] Univ Magallanes, Dpto Ingn Computac, Avda Bulnes 01855,Casilla 113-D, Punta Arenas, Chile
[2] Comp Syst Dept, Punta Arenas, Chile
[3] Univ Castilla La Mancha, Appl Phys Dept, E-13071 Ciudad Real, Spain
关键词
Parallel Computing; Message Passing Interface; Physics; Nonlinear Time Series Analysis; False Nearest Neighbors method; kd-tree;
D O I
10.1016/j.procs.2010.04.291
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
In different fields of science and engineering (medicine, economy, oceanographic, biologic 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, thus the execution time of the FNN method has to be reduced. This paper describes a parallel implementation of the FNN method for distributed memory architectures based on kd-tree. A "Single-Program, Multiple Data" (SPMD) paradigm is employed using a tree decomposition approach where each processor runs the same program but computes a different sub-tree called local tree. As far as the authors know, there is not any parallel implementation of the FNN method based on kd-tree, consisting this implementation the main contribution of the paper. The accuracy and performance of the parallel approach are then assessed and compared to the best sequential kd-tree based implementation of the FNN method, executing from 2 up to 64 processors and running a Lorenz time series and an electrocardiogram signal as case studies. Results are discussed in terms on execution time, speed-up, and efficiency. In terms of speed, our approach was 3 similar to 20 times faster than sequential algorithm. (C) 2010 Published by Elsevier Ltd.
引用
收藏
页码:2573 / 2581
页数:9
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