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Faster Approximation of Minimum Enclosing Balls by Distance Filtering and GPU Parallelization
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-6969-6793
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1550-0994
2015 (English)In: Journal of Graphics Tools, ISSN 2165-3488, Vol. 17, no 3, 67-84 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
United States: Taylor & Francis , 2015. Vol. 17, no 3, 67-84 p.
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-29243DOI: 10.1080/2165347X.2015.1037471OAI: oai:DiVA.org:mdh-29243DiVA: diva2:859225
Projects
RALF3 - Software for Embedded High Performance Architectures
Available from: 2015-10-06 Created: 2015-09-29 Last updated: 2015-10-06Bibliographically approved

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