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


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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  1. 1.

    NVIDIA: Particle Simulation Using CUDA, 1st ed. NVIDIA (2013)

  2. 2.

  3. 3.

    Bergen, G.: Efficient collision detection of complex deformable models using aabb trees. J. Graph.Tools 2(4), 1–13 (1997)

    Article  MATH  Google Scholar 

  4. 4.

    Chang, J.-W., Wang, W., Kim, M.-S.: Efficient collision detection using a dual obb-sphere bounding volume hierarchy. Comput. Aided Des. 42(1), 50–57 (2010)

    Article  Google Scholar 

  5. 5.

    Tang, M., Curtis, S., Yoon, S.E., Manocha, D.: ICcd: interactive continuous collision detection between deformable models using connectivity-based culling. IEEE Trans. Vis. Comput. Graph. 15(4), 544–557 (2009)

    Article  Google Scholar 

  6. 6.

    Tang, M., Manocha, D., Tong, R.: Mccd: multi-core collision detection between deformable models using front-based decomposition. Graph. Model. 72(2), 7–23 (2010)

    Article  Google Scholar 

  7. 7.

    Lauterbach, C., Mo, Q., Manocha, D.: Hierarchical gpu-based operations for collision and distance queries. In: Eurographics (2010)

  8. 8.

    Sulaiman, H.A. , Othman, M.A., Ismail, M.M., Said, M.A.M., Bade, A., Abdullah, M.H.: Methodology of performing narrow phase collision detection for virtual environment. In: 2014 International Symposium on Technology Management and Emerging Technologies (ISTMET), May 2014, pp. 511–515

  9. 9.

    Liu, F., Harada, T., Lee, Y., Kim, Y.J.: Real-time collision culling of a million bodies on graphics processing units. ACM Trans. Graph. 29(6), 154 (2010)

    Article  Google Scholar 

  10. 10.

    Xiong, Q., Li, B., Xu, J., Wang, X., Wang, L., Ge, W.: Efficient 3d dns of gassolid flows on fermi gpgpu. Comput. Fluids 70, 86–94 (2012)

    Article  MATH  Google Scholar 

  11. 11.

    Garland, M.: Parallel computing with CUDA (2010)

  12. 12.

    Purcell, T.J., Buck, I., Mark, W.R., Hanrahan, P.: Ray tracing on programmable graphics hardware. ACM Trans. Graph. (TOG) 21(3), 703–712 (2002)

    Article  Google Scholar 

  13. 13.

    Baciu, G., Wong, W.S.-K., Sun, H.: Recode: an image-based collision detection algorithm. In: Sixth Pacific Conference on Computer Graphics and Applications: Pacific Graphics’ 98, pp. 125–133. IEEE (1998)

  14. 14.

    Myszkowski, K., Okunev, O.G., Kunii, T.L.: Fast collision detection between complex solids using rasterizing graphics hardware. Vis. Comput. 11(9), 497–511 (1995)

    Article  Google Scholar 

  15. 15.

    Govindaraju, N.K., Redon, S., Lin, M.C., Manocha, D.: Cullide: interactive collision detection between complex models in large environments using graphics hardware. In Proceedings of the ACM SIGGRAPH/EUROGRAPHICS Conference on Graphics Hardware, pp. 25–32. Eurographics Association (2003)

  16. 16.

    Kipfer, P., Segal, M., Westermann, R.: Uberflow: a gpu-based particle engine. In: Proceedings of the ACM SIGGRAPH/EUROGRAPHICS Conference on Graphics Hardware, pp. 115–122. ACM (2004)

  17. 17.

    Zheng, J., An, X., Huang, M.: Gpu-based parallel algorithm for particle contact detection and its application in self-compacting concrete flow simulations. Comput. Struct. 112, 193–204 (2012)

    Article  Google Scholar 

  18. 18.

    Shen, Y., Jia, Q., Chen, G., Wang, Y., Sun, H.: Study of rapid collision detection algorithm for manipulator. In: 2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA), pp. 934–938 (2015)

  19. 19.

    Xue, S., Ji, Z.: Research of collision detection algorithm based on particle swarm optimization. Comput. Des. Appl. (ICCDA), 1 (2010)

  20. 20.

    Qu, H., Zhao, W.: Fast collision detection algorithm based on parallel ant. In: Virtual Reality and Visualization (ICVRV), pp. 261–264 (2013)

  21. 21.

    Xue-li, S., Tao, L.: Fast collision detection based on projection parallel algorithm. In: Future Computer and Communication (ICFCC), vol. 1 (2010)

  22. 22.

    Qu, H., Zhao, W.: Fast collision detection of space-time correlation. Comput. Sci. Electron. Eng. (ICCSEE) 3, 567–571 (2012)

    Google Scholar 

  23. 23.

    Tang, M., Manocha, D., Lin, J., Tong, R.: Collision-streams: Fast GPU-based collision detection for deformable models. In: I3D ’11: Proceedings of the 2011 ACM SIGGRAPH symposium on Interactive 3D Graphics and Games, pp. 63–70 (2011)

  24. 24.

    Zhang, X., Kim, Y.J.: Scalable collision detection using p-partition fronts on many-core processors. IEEE Trans. Vis. Comput. Graph. 20(3), 447–456 (2014)

    Article  Google Scholar 

  25. 25.

    Wang, L., Shi, Y., Li, R.: An image-based collision detection optimization algorithm. In: 2015 IEEE China Summit and International Conference on Signal and Information Processing (China SIP), pp. 220–224 (2015)

  26. 26.

    Xu, R., Kang, L., Tian, H.: A g-octree based fast collision detection for large-scale particle systems. Comput. Sci. Electron. Eng. (ICCSEE) 3, 269–273 (2012)

    Google Scholar 

  27. 27.

    Yisheng, Z., Xiaoli, Z., Guofu, D., Yong, H., Meiwei, J.: A GPGPU-based collision detection algorithm. In: Image and Graphics, pp. 938–942 (2009)

  28. 28.

    Fan, Z., Wan, H., Gao, S.: Streaming real time collision detection using programmable graphics hardware. J. Softw. 15:1505–1513 (2004)

  29. 29.

    Karunasena, H., Senadeera, W., Gu, Y., Brown, R.: A coupled sph-dem model for fluid and solid mechanics of apple parenchyma cells during drying. In: 18th Australian Fluid Mechanics Conference. Australasian Fluid Mechanics Society Launceston, Australia (2012)

  30. 30.

    Rhodes, M., Wang, X.S., Nguyen, M., Stewart, P., Liffman, K.: Study of mixing in gas-fluidized beds using a dem model. Chem. Eng. Sci. 56(8), 2859–2866 (2001)

    Article  Google Scholar 

  31. 31.

    Ericson, C.: Real-Time Collision Detection. Elsevier (2010)

  32. 32.

    Devillers, O., Guigue, P.: Faster triangle-triangle intersection tests. In: INRIA (2002)

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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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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).

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  • GPU-based
  • collision detection
  • irregular objects
  • space decomposition