Parallel monotone spline interpolation and approximation on GPUs

Beliakov, G; Liu, S

Parallel monotone spline interpolation and approximation on GPUs

Beliakov, G Liu, S

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Publication Type:: Chapter
Citation:: Designing Scientific Applications on GPUs, 2013, pp. 295 - 310
Issue Date:: 2013-01-01

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Field	Value	Language
dc.contributor.author	Beliakov, G	en_US
dc.contributor.author	Liu, S https://orcid.org/0000-0001-6062-6580	en_US
dc.date.issued	2013-01-01	en_US
dc.identifier.citation	Designing Scientific Applications on GPUs, 2013, pp. 295 - 310	en_US
dc.identifier.isbn	9781466571624	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119686
dc.description.abstract	© 2014 by Taylor & Francis Group, LLC. Monotonicity preserving interpolation and approximation have received substantial attention in the last thirty years because of their numerous applications in computer aided-design, statistics, and machine learning [9, 10, 19]. Constrained splines are particularly popular because of their exibility in modeling diffierent geometrical shapes, sound theoretical properties, and availability of numerically stable algorithms [9,10,26]. In this work we examine parallelization and adaptation for GPUs of a few algorithms of monotone spline interpolation and data smoothing, which arose in the context of estimating probability distributions.	en_US
dc.relation.ispartof	Designing Scientific Applications on GPUs	en_US
dc.title	Parallel monotone spline interpolation and approximation on GPUs	en_US
dc.type	Chapter
utslib.for	080109 Pattern Recognition and Data Mining	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.publication-status	Published	en_US

Abstract:

© 2014 by Taylor & Francis Group, LLC. Monotonicity preserving interpolation and approximation have received substantial attention in the last thirty years because of their numerous applications in computer aided-design, statistics, and machine learning [9, 10, 19]. Constrained splines are particularly popular because of their exibility in modeling diffierent geometrical shapes, sound theoretical properties, and availability of numerically stable algorithms [9,10,26]. In this work we examine parallelization and adaptation for GPUs of a few algorithms of monotone spline interpolation and data smoothing, which arose in the context of estimating probability distributions.

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http://hdl.handle.net/10453/119686