Survey on Parallel Collision Detection Algorithms

Authors

Fuchang Liu, Digital Media and HCI Research Center, Hangzhou Normal University, Hangzhou 310012, China;
Shuangjian Wang, Digital Media and HCI Research Center, Hangzhou Normal University, Hangzhou 310012, China;
Zhigeng Pan, Digital Media and HCI Research Center, Hangzhou Normal University, Hangzhou 310012, China;
Jinrong Wang, Digital Media and HCI Research Center, Hangzhou Normal University, Hangzhou 310012, China;

Abstract

Abstract: The demand for real-time collision detection is increasing in different applications. Exploiting the parallel computing capability of multi-core CPUs and GPUs to accelerate the speed of collision detection algorithms has attracted abroad attention. This paper reviews the development history of collision detection algorithms and classified the existing algorithms from multiple perspectives. Moreover, we analyze the strengths and weaknesses of more than ten representative parallel collision detection algorithms based on multi-core CPUs and GPUs from the aspects of the scalability, memory consumption and workload balancing. Finally, the problem of present parallel collision detection research and potential direction of following research and some representative benchmark data sets are presented.

Recommended Citation

Liu, Fuchang; Wang, Shuangjian; Pan, Zhigeng; and Wang, Jinrong (2020) "Survey on Parallel Collision Detection Algorithms," Journal of System Simulation: Vol. 29: Iss. 11, Article 1.
DOI: 10.16182/j.issn1004731x.joss.201711001
Available at: https://dc-china-simulation.researchcommons.org/journal/vol29/iss11/1

First Page

2601

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201711001

Last Page

2608

CLC

TP391

Recommended Citation

Liu Fuchang, Wang Shuangjian, Pan Zhigeng, Wang Jinrong. Survey on Parallel Collision Detection Algorithms[J]. Journal of System Simulation, 2017, 29(11): 2601-2608.

DOI

10.16182/j.issn1004731x.joss.201711001