Data structures and algorithms 3: multi-dimensional searching and computational geometry
Data structures and algorithms 3: multi-dimensional searching and computational geometry
Computational geometry: an introduction
Computational geometry: an introduction
K-d trees for semidynamic point sets
SCG '90 Proceedings of the sixth annual symposium on Computational geometry
Algorithms in C
Handbook of algorithms and data structures: in Pascal and C (2nd ed.)
Handbook of algorithms and data structures: in Pascal and C (2nd ed.)
Nonlinear time series analysis
Nonlinear time series analysis
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
Parallel Construction of Multidimensional Binary Search Trees
IEEE Transactions on Parallel and Distributed Systems
Multidimensional divide-and-conquer
Communications of the ACM
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The False Nearest Neighbors (FNN) method is particularly relevant in different fields of science and engineering (medicine, economy, oceanography, biological systems, etc.). In some of these applications, it is important to give results within a reasonable time scale, so the execution time of the FNN method has to be reduced. This paper describes three parallel implementations of the FNN method for shared memory architectures. The computationally intensive part of the method lies mainly in the neighbors search and therefore this task is parallelized and executed using 2 up 64 processors. The accuracy and performance of the three parallel approaches are then assessed and compared to the best sequential implementation of the FNN method which appears in the TISEAN project. The results indicate that the three parallel approaches, when the method is run using 64 processors on a SGI Origin 3800, are between 25 and 75 times faster than the sequential one. The efficiency is around 35-115%.