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Faster Approximation of Minimum Enclosing Balls by Distance Filtering and GPU Parallelization

Authors:

Linus Källberg, Thomas Larsson

Research groups:

Programming Languages
Ubiquitous Computing

Publication Type:

Journal article

Venue:

Journal of Graphics Tools

Publisher:

Taylor & Francis

DOI:

10.1080/2165347X.2015.1037471

Abstract

Minimum enclosing balls are used extensively to speed up multidimensional data processing in, e.g., machine learning, spatial databases, and computer graphics. We present a case study of several acceleration techniques that are applicable in enclosing ball algorithms based on repeated farthest-point queries. Two different distance filtering heuristics are proposed aiming at reducing the cost of the farthest-point queries as much as possible by exploiting lower and upper distance bounds. Furthermore, auto-tunable GPU solutions using CUDA are developed for both low- and high-dimensional cases. Empirical tests apply these techniques to two recent algorithms and demonstrate substantial speedups of the ball computations. Our results also indicate that a combination of the approaches has the potential to give further performance improvements.

Bibtex

@article{Kallberg3971,
author = {Linus K{\"a}llberg and Thomas Larsson},
title = {Faster Approximation of Minimum Enclosing Balls by Distance Filtering and GPU Parallelization},
volume = {17},
number = {3},
pages = {67--84},
month = {July},
year = {2015},
journal = {Journal of Graphics Tools},
publisher = {Taylor {\&} Francis},
url = {http://www.es.mdu.se/publications/3971-}
}