Abstract
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.
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D’Ambrosio, D., Di Gregorio, S., Filippone, G., Rongo, R., Spataro, W., Trunfio, G.A. (2014). A Multi-GPU Approach to Fast Wildfire Hazard Mapping. In: Obaidat, M., Filipe, J., Kacprzyk, J., Pina, N. (eds) Simulation and Modeling Methodologies, Technologies and Applications. Advances in Intelligent Systems and Computing, vol 256. Springer, Cham. https://doi.org/10.1007/978-3-319-03581-9_13
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DOI: https://doi.org/10.1007/978-3-319-03581-9_13
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