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.
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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]
Bugmann, H., 2001. A review of forest gap models. Climate Change, 51(3/4):259–305. [doi:10.1023/A:1012525626 267]
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]
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]
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]
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]
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).
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]
Nickolls, J., Buck, I., Garland, M., Skadron, K., 2008. Scalable parallel programming with CUDA. Queue, 6(2):40–53. [doi:10.1145/1365490.1365500]
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.
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]
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]
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]
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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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
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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
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DOI: https://doi.org/10.1631/jzus.C1200147