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CLC number: TP391.9

On-line Access: 2012-11-02

Received: 2012-05-23

Revision Accepted: 2012-08-09

Crosschecked: 2012-10-12

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Journal of Zhejiang University SCIENCE C 2012 Vol.13 No.11 P.816-827


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

Author(s):  Jing Fan, Hai-feng Ji, Xin-xin Guan, Ying Tang

Affiliation(s):  School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China

Corresponding email(s):   fanjing@zjut.edu.cn, tangying@gmail.com

Key Words:  GPU, Seed dispersal, Large-scale, Multi-resolution, Data clustering

Jing Fan, Hai-feng Ji, Xin-xin Guan, Ying Tang. A GPU-based multi-resolution algorithm for simulation of seed dispersal[J]. Journal of Zhejiang University Science C, 2012, 13(11): 816-827.

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T1 - A GPU-based multi-resolution algorithm for simulation of seed dispersal
A1 - Jing Fan
A1 - Hai-feng Ji
A1 - Xin-xin Guan
A1 - Ying Tang
J0 - Journal of Zhejiang University Science C
VL - 13
IS - 11
SP - 816
EP - 827
%@ 1869-1951
Y1 - 2012
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1200147

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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]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].

[2]Barnes, J., Hut, P., 1986. A hierarchical O(nlogn) force calculation algorithm. Nature, 324(6096):446-449.

[3]Bugmann, H., 2001. A review of forest gap models. Climate Change, 51(3/4):259-305.

[4]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.

[5]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.

[6]Gelbard, R., Goldman, O., Spiegler, I., 2007. Investigating diversity of clustering methods: an empirical comparison. Data. Knowl. Eng., 63(1):155-166.

[7]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.

[8]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.

[9]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].

[10]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.

[11]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/researchpubs/sortie_nz_model_dev.pdf [Accessed on May 5, 2012].

[12]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.

[13]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).

[14]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.

[15]Nickolls, J., Buck, I., Garland, M., Skadron, K., 2008. Scalable parallel programming with CUDA. Queue, 6(2):40-53.

[16]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].

[17]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.

[18]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.

[19]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.

[20]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.

[21]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.

[22]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.

[23]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).

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