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Accelerated Computation of Minimum Enclosing Balls by GPU Parallelization and Distance Filtering
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
2014 (English)In: Proceedings of SIGRAD 2014 SIGRAD 2014, 2014Conference paper, Published paper (Refereed)
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. 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.

Place, publisher, year, edition, pages
2014.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-26452OAI: oai:DiVA.org:mdh-26452DiVA: diva2:759901
Conference
SIGRAD 2014 SIGRAD 2014, 12 Jun 2014, Gothenburg, Sweden
Projects
RALF3 - Software for Embedded High Performance Architectures
Available from: 2014-10-31 Created: 2014-10-31 Last updated: 2014-10-31Bibliographically approved

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CiteExportLink to record
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