A sample implementation for parallelizing Divide-and-Conquer algorithms on the GPU

The strategy of Divide-and-Conquer (D&C) is one of the frequently used programming patterns to design efficient algorithms in computer science, which has been parallelized on shared memory systems and distributed memory systems. Tzeng and Owens specifically developed a generic paradigm for paral...

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Bibliographic Details
Published in:Heliyon Vol. 4; no. 1; p. e00512
Main Authors: Mei, Gang, Zhang, Jiayin, Xu, Nengxiong, Zhao, Kunyang
Format: Journal Article
Language:English
Published: England Elsevier Ltd 01.01.2018
Elsevier
Subjects:
algorithms
Computer science
memory
Computer science
ISSN:2405-8440, 2405-8440
Online Access:Get full text
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Summary:The strategy of Divide-and-Conquer (D&C) is one of the frequently used programming patterns to design efficient algorithms in computer science, which has been parallelized on shared memory systems and distributed memory systems. Tzeng and Owens specifically developed a generic paradigm for parallelizing D&C algorithms on modern Graphics Processing Units (GPUs). In this paper, by following the generic paradigm proposed by Tzeng and Owens, we provide a new and publicly available GPU implementation of the famous D&C algorithm, QuickHull, to give a sample and guide for parallelizing D&C algorithms on the GPU. The experimental results demonstrate the practicality of our sample GPU implementation. Our research objective in this paper is to present a sample GPU implementation of a classical D&C algorithm to help interested readers to develop their own efficient GPU implementations with fewer efforts.
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ISSN:2405-8440
2405-8440
DOI:10.1016/j.heliyon.2018.e00512