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Parallel GPU-based collision detection of irregular vessel wall for massive particles

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Abstract

In this paper, we present a novel GPU-based limit space decomposition collision detection algorithm (LSDCD) for performing collision detection between a massive number of particles and irregular objects, which is used in the design of the Accelerator Driven Sub-Critical (ADS) system. Test results indicate that, the collisions between ten million particles and the vessel can be detected on a general personal computer in only 0.5 s per frame. With this algorithm, the collision detection of maximum sixty million particles are calculated in 3.488030 s. Experiment results show that our algorithm is promising for fast collision detection.

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Acknowledgements

This work was supported by National Natural Science Foundation of China under Grant No. 61402210 and 60973137, Program for New Century Excellent Talents in University under Grant No. NCET-12-0250, Strategic Priority Research Program of the Chinese Academy of Sciences with Grant No. XDA03030100, Gansu Sci. and Tech. Program under Grant No. 1104GKCA049, 1204GKCA061 and 1304GKCA018, Google Research Awards and Google Faculty Award, China.

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Authors and Affiliations

  1. School of Information Science and Engineering, Lanzhou University, Lanzhou, China

    Binbin Yong & Qingguo Zhou

  2. School of Computing and Information Technology, University of Wollongong, Wollongong, Australia

    Jun Shen & Huaming Chen

  3. Computer Science, Swinburne University of Technology, Melbourne, Australia

    Hongyu Sun

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  1. Binbin Yong
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  2. Jun Shen
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  3. Hongyu Sun
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  4. Huaming Chen
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  5. Qingguo Zhou
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Correspondence to Qingguo Zhou.

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Yong, B., Shen, J., Sun, H. et al. Parallel GPU-based collision detection of irregular vessel wall for massive particles. Cluster Comput 20, 2591–2603 (2017). https://doi.org/10.1007/s10586-017-0741-7

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