You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at http://www.ieee.org/web/publications/rights/copyrightpolicy.html)
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at http://www.acm.org/pubs/copyright_policy/)
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact webmaster@ide.mdh.se

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

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.mdh.se/publications/3971-}
}