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

Research group:

Publication Type:

Conference/Workshop Paper


Proceedings of SIGRAD 2014


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.


author = {Linus K{\"a}llberg and Thomas Larsson},
title = {Accelerated Computation of Minimum Enclosing Balls by GPU Parallelization and Distance Filtering},
month = {June},
year = {2014},
booktitle = {Proceedings of SIGRAD 2014},
url = {}