Parallel bucket sorting on graphics processing units based on convex optimization

Beliakov, G; Li, G; Liu, S

Parallel bucket sorting on graphics processing units based on convex optimization

Beliakov, G Li, G Liu, S

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Publication Type:: Journal Article
Citation:: Optimization, 2015, 64 (4), pp. 1033 - 1055
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Beliakov, G	en_US
dc.contributor.author	Li, G	en_US
dc.contributor.author	Liu, S https://orcid.org/0000-0001-6062-6580	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	Optimization, 2015, 64 (4), pp. 1033 - 1055	en_US
dc.identifier.issn	0233-1934	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119361
dc.description.abstract	© 2013, © 2013 Taylor & Francis. We found an interesting relation between convex optimization and sorting problem. We present a parallel algorithm to compute multiple order statistics of the data by minimizing a number of related convex functions. The computed order statistics serve as splitters that group the data into buckets suitable for parallel bitonic sorting. This led us to a parallel bucket sort algorithm, which we implemented for many-core architecture of graphics processing units (GPUs). The proposed sorting method is competitive to the state-of-the-art GPU sorting algorithms and is superior to most of them for long sorting keys.	en_US
dc.relation.ispartof	Optimization	en_US
dc.relation.isbasedon	10.1080/02331934.2013.836645	en_US
dc.subject.classification	Operations Research	en_US
dc.title	Parallel bucket sorting on graphics processing units based on convex optimization	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	64	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0103 Numerical and Computational Mathematics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	64	en_US

Abstract:

© 2013, © 2013 Taylor & Francis. We found an interesting relation between convex optimization and sorting problem. We present a parallel algorithm to compute multiple order statistics of the data by minimizing a number of related convex functions. The computed order statistics serve as splitters that group the data into buckets suitable for parallel bitonic sorting. This led us to a parallel bucket sort algorithm, which we implemented for many-core architecture of graphics processing units (GPUs). The proposed sorting method is competitive to the state-of-the-art GPU sorting algorithms and is superior to most of them for long sorting keys.

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http://hdl.handle.net/10453/119361