CudaCHPre2D: A straightforward preprocessing approach for accelerating 2D convex hull computations on the GPU-Reference-Cited by-同舟云学术

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CudaCHPre2D: A straightforward preprocessing approach for accelerating 2D convex hull computations on the GPU

Published:2019-04-08 Issue:10 Volume:32 Page:
ISSN:1532-0626
Container-title:Concurrency and Computation: Practice and Experience
language:en
Short-container-title:Concurrency Computat Pract Exper

Author:

Qin Jiayu¹,Mei Gang¹^ORCID,Cuomo Salvatore²^ORCID,Guo Sixu¹,Li Yixuan¹

Affiliation:

1. School of Engineering and TechnologyChina University of Geosciences (Beijing) Beijing China

2. Department of Mathematics and Applications “R. Caccioppoli”University of Naples Federico II Napoli Italy

Funder

National Natural Science Foundation of China

Ministry of Education of the People's Republic of China

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.5229

Reference30 articles.

1. An efficient algorith for determining the convex hull of a finite planar set

2. On the identification of the convex hull of a finite set of points in the plane

3. A fast convex hull algorithm

4. On-line construction of the convex hull of a simple polyline

5. Gamma Knife treatment planning: MR brain tumor segmentation and volume measurement based on unsupervised Fuzzy C-Means clustering

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An evaluation of GPU filters for accelerating the 2D convex hull;Journal of Parallel and Distributed Computing;2024-02

2. Stream Computation of 3D Approximate Convex Hulls with an FPGA;International Symposium on Highly-Efficient Accelerators and Reconfigurable Technologies;2022-06-09

3. Designing Parallel Adaptive Laplacian Smoothing for Improving Tetrahedral Mesh Quality on the GPU;Applied Sciences;2021-06-15

4. Latest advances in parallel, distributed, and network‐based processing;Concurrency and Computation: Practice and Experience;2020-02-11

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