Skip to main content
Log in

A GPU-based multi-resolution algorithm for simulation of seed dispersal

  • Published:
Journal of Zhejiang University SCIENCE C Aims and scope Submit manuscript

Abstract

In forest dynamics models, the intensive computation and load involved in the simulation of seed dispersal can become unbearably huge for large-scale forest analysis. To solve this problem, we propose a multi-resolution algorithm to compute seed dispersal on GPU. By exploiting the computation parallelism of seed dispersal, the computation of the whole forest plot is divided into multiple small plot cells, which are computed independently by parallel threads on GPU. To further improve the calculation efficiency with limited threads scale for GPU computation, we propose a hierarchical method to cluster the plot cells into a multi-resolution form according to the biological curves of tree seed dispersal. Experimental results show that our algorithm not only greatly reduces computational time but also obtains comparably correct results as compared to the naive GPU algorithm, which makes it especially suitable for large-scale forest modeling.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Astrup, R., Coates, D.K., Hall, E., Trowbridge, A., 2007. Documentation for the SOTIE-ND SBS Research Parameter File Version 1.0. Natural Resources Research and Management Report, Bulkley Valley Centre. Available from http://www.bvcentre.ca/files/SORTIE-ND_SBS_Research_Parameter_File_Version_1.0.pdf [Accessed on May 5, 2012].

  • Barnes, J., Hut, P., 1986. A hierarchical O(nlogn) force calculation algorithm. Nature, 324(6096):446–449. [doi:10. 1038/324446a0]

    Article  Google Scholar 

  • Bugmann, H., 2001. A review of forest gap models. Climate Change, 51(3/4):259–305. [doi:10.1023/A:1012525626 267]

    Article  Google Scholar 

  • Clark, J.S., Lewis, M., Horvath, L., 2001. Invasion by extremes: population spread with variation in dispersal and reproduction. Am. Nat., 157(5):537–554. [doi:10.1086/319934]

    Article  Google Scholar 

  • Du, Z.H., Yin, Z.M., Bader, D.A., 2010. A Tile-Based Parallel Viterbi Algorithm for Biological Sequence Alignment on GPU with CUDA. IEEE Int. Symp. on Parallel & Distributed Processing Workshops and PhD Forum, p.1–8. [doi:10.1109/IPDPSW.2010.5470903]

  • Gelbard, R., Goldman, O., Spiegler, I., 2007. Investigating diversity of clustering methods: an empirical comparison. Data. Knowl. Eng., 63(1):155–166. [doi:10.1016/j.datak.2007.01.002]

    Article  Google Scholar 

  • Govindarajan, S., Dietze, M., Agarwal, P.K., Clark, J., 2004. A Scalable Simulator for Forest Dynamics. Proc. 20th Annual Symp. on Computational Geometry, p.106–115. [doi:10.1145/997817.997836]

  • Govindarajan, S., Dietze, M.C., Agarwal, P.K., Clark, J.S., 2007. A scalable algorithm for dispersing population. J. Intell. Inf. Syst., 29(1):39–61. [doi:10.1007/s10844-006-0030-z]

    Article  Google Scholar 

  • Hamada, T., Titala, I., 2007. The Chamomile Schema: an Optimized Algorithm for N-Body Simulations on Programmable Graphics Processing Units. Available from http://arxiv.org/abs/astro-ph/0703100 [Accessed on June 25, 2012].

  • Hamada, T., Narumi, T., Yokota, R., Yasuola, K., Nitadori, K., Taiji, M., 2009. 42 TFlops Hierarchical N-Body Simulations on GPUs with Applications in Both Astrophysics and Turbulence. Proc. Conf. on High Performance Computing Networking, Storage and Analysis, p.14–20. [doi:10.1145/1654059.1654123]

  • Kunstler, G., Allen, R.B., Coomes, D.A., Canham, C.D., Wright, E.F., 2011. SORTIE/NZ Model Development. Landcare Research New Zealand Ltd. Available from http://www.Landcareresearch.co.nz/publications/resear-chpubs/sortie_nz_model_dev.pdf [Accessed on May 5, 2012].

  • Lepage, P.T., Canham, C.D., Coates, K.D., Bartemucci, P., 2000. Seed abundance versus substrate limitation of seedling recruitment in northern temperate forests of British Columbia. Can. J. Forest Res., 30(3):415–427. [doi:10.1139/x99-223]

    Article  Google Scholar 

  • Lin, J., Tang, M., Tong, R.F., 2010. GPU accelerated biological sequence alignment. J. Comput.-Aided Des. Comput. Graph., 22(3):420–427 (in Chinese).

    Google Scholar 

  • Mielikainen, J., Huang, B., Huang, H.L.A., 2011. GPU-accelerated multi-profile radiative transfer model for the infrared atmospheric sounding Interferometer. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 4(3):691–700. [doi:10.1109/JSTARS.2011.2159195]

    Article  Google Scholar 

  • Nickolls, J., Buck, I., Garland, M., Skadron, K., 2008. Scalable parallel programming with CUDA. Queue, 6(2):40–53. [doi:10.1145/1365490.1365500]

    Article  Google Scholar 

  • NVIDIA Corporation, 2007. CUDA Programming Guide, Version 3.0. NVIDIA Corporation. Available from http://developer.nvidia.com/nvidia-gpu-programming-guide [Accessed on May 5, 2012].

  • Nyland, L., Harris, M., Prins, J., 2007. Fast N-Body Simulation with CUDA. In: Nguyen, H. (Ed.), GPU Gems 3. Addison-Wesley, London, p.677–795.

    Google Scholar 

  • Pacala, S.W., Canham, C.D., Silander, J.A.Jr., 1993. Forest models defined by field measurements: I. The design of a northeastern forest simulator. Can. J. Forest Res., 23(10): 1980–1988. [doi:10.1139/x93-249]

    Article  Google Scholar 

  • Ryoo, S., Rodrigues, C.I., Banghsorkhi, S.S., Stone, S.S., Kirk, D.B., Hwu, W.W., 2008. Optimization Principles and Application Performance Evaluation of a Multithreaded GPU Using CUDA. Proc. 13th ACM SIGPLAN Symp. on Principles and Practice of Parallel Programming, p.73–82. [doi:10.1145/1345206.1345220]

  • Stone, J.E., Phillips, J.C., Freddolino, P.L., Hardy, D.J., Trabuco, L.G., Schulten, K., 2007. Accelerating molecular modeling applications with graphics processors. J. Comput. Chem., 28(16):2618–2640. [doi:10.1002/jcc.20829]

    Article  Google Scholar 

  • Tang, Y., Guan, X.X., Fan, J., 2011. Design and Implementation of Seeds Dispersion on Graphic Processor Unit. Proc. 10th Int. Conf. on Virtual Reality Continuum and Its Applications in Industry, p.403–406. [doi:10.1145/2087756.2087828]

  • Xia, Y.J., Kuang, L., Li, X.M., 2011. Accelerating geospatial analysis on GPUs using CUDA. J. Zhejiang Univ.-Sci. C (Comput. & Electron.), 12(12):990–999. [doi:10.1631/jzus. C1100051]

    Article  Google Scholar 

  • Zhang, S., Chu, Y.L., Zhao, K.Y., Zhang, Y.B., 2009. High Performance GPU Computing of CUDA. China Water Publishing House, Beijing, China, p.155–157 (in Chinese).

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ying Tang.

Additional information

Project supported by the National Natural Science Foundation of China (Nos. 61173097 and 61003265), the Natural Science Foundation of Zhejiang Province, China (No. Z1090459), the Science and Technology Planning Project of Zhejiang Province, China (No. 2010C33046), and Tsinghua-Tencent Joint Laboratory for Internet Innovation Technology

Rights and permissions

Reprints and permissions

About this article

Cite this article

Fan, J., Ji, Hf., Guan, Xx. et al. A GPU-based multi-resolution algorithm for simulation of seed dispersal. J. Zhejiang Univ. - Sci. C 13, 816–827 (2012). https://doi.org/10.1631/jzus.C1200147

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1631/jzus.C1200147

Key words

CLC number

Navigation