A sample implementation for parallelizing Divide-and-Conquer algorithms on the GPU. Issue 1 (January 2018)

• Record Type: Journal Article
• Authors: Mei, Gang; Zhang, Jiayin; Xu, Nengxiong; Zhao, Kunyang
• Is Part Of: Heliyon. Volume 4:Issue 1(2018)
• Publication Date: 2018-01
• Subjects: Computer science; Research -- Periodicals; Medical sciences -- Periodicals; Natural history -- Periodicals; Social sciences -- Periodicals; Earth sciences -- Periodicals; Physical sciences -- Periodicals; 507.2
• DOI: 10.1016/j.heliyon.2018.e00512
• Languages: English
• ISSNs: 2405-8440

Abstract

Abstract: 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.