A Multi-GPU Approach to Fast Wildfire Hazard Mapping
Burn probability maps (BPMs) are among the most effective tools to support strategic wildfire and fuels management. In such maps, an estimate of the probability to be burned by a wildfire is assigned to each point of a raster landscape. A typical approach to build BPMs is based on the explicit propagation of thousands of fires using accurate simulation models. However, given the high number of required simulations, for a large area such a processing usually requires high performance computing. In this paper, we propose a multi-GPU approach for accelerating the process of BPM building. The paper illustrates some alternative implementation strategies and discusses the achieved speedups on a real landscape.
KeywordsGPGPU Cellular Automata Wildfire Simulation Wildfire Susceptibility Hazard Maps
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- 2.Ager, A., Finney, M.: Application of wildfire simulation models for risk analysis. In: Geophysical Research Abstracts. EGU2009-5489, EGU General Assembly, vol. 11 (2009)Google Scholar
- 6.Peterson, S.H., Morais, M.E., Carlson, J.M., Dennison, P.E., Roberts, D.A., Moritz, M.A., Weise, D.R.: Using HFIRE for spatial modeling of fire in shrublands. Technical Report PSW-RP-259, U.S. Department of Agriculture, Forest Service, Pacific Southwest Research Station, Albany, CA (2009)Google Scholar
- 9.Rothermel, R.C.: A mathematical model for predicting fire spread in wildland fuels. Technical Report INT-115, U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, Ogden, UT (1972)Google Scholar
- 10.Alexander, M.: Estimating the length-to-breadth ratio of elliptical forest fire patterns. In: Proc. 8th Conf. Fire and Forest Meteorology, pp. 287–304 (1985)Google Scholar
- 12.Rongo, R., Lupiano, V., Avolio, M.V., D’Ambrosio, D., Spataro, W., Trunfio, G.A.: Cellular automata simulation of lava flows - applications to civil defense and land use planning with a cellular automata based methodology. In: Proceedings of SIMULTECH 2011 (2011)Google Scholar
- 13.Filippone, G., Spataro, W., Spingola, G., D’Ambrosio, D., Rongo, R., Perna, G., Di Gregorio, S.: GPGPU programming and cellular automata: Implementation of the SCIARA lava flow simulation code. In: 23rd European Modeling and Simulation Symposium (EMSS), Rome, Italy, September 12-14 (2011)Google Scholar
- 15.CUDA C Programming Guide: v. 3.2 (2010)Google Scholar
- 16.Anderson, H.: Predicting wind-driven wildland fire size and shape. Technical Report INT-305, U.S Department of Agriculture, Forest Service (1983)Google Scholar
- 19.Crisci, G.M., Avolio, M.V., Behncke, B., D’Ambrosio, D., Di Gregorio, S., Lupiano, V., Neri, M., Rongo, R., Spataro, W.: Predicting the impact of lava flows at Mount Etna, Italy. Journal of Geophysical Research: Solid Earth 115(B4) (2010)Google Scholar
- 20.Blecic, I., Cecchini, A., Trunfio, G.A.: A general-purpose geosimulation infrastructure for spatial decision support. Transactions on Computational Science 6, 200–218 (2009)Google Scholar