Accelerated Computation of Minimum Enclosing Balls by GPU Parallelization and Distance Filtering

Linus Källberg
Mälardalen University, Sweden

Thomas Larsson
Mälardalen University, Sweden

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Ingår i: Proceedings of SIGRAD 2014, Visual Computing, June 12-13, 2014, Göteborg, Sweden

Linköping Electronic Conference Proceedings 106:8, s. 57-65

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Publicerad: 2014-10-30

ISBN: 978-91-7519-212-3

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


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. Parallel GPU solutions using CUDA are developed for both low- and high-dimensional cases. Furthermore, 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. Empirical tests show encouraging results. Compared to a sequential CPU version of the algorithm, the GPU parallelization runs up to 11 times faster. When applying the distance filtering techniques, further speedups are observed.


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