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.

A Multi-GPU Approach to Fast Wildfire Hazard Mapping

D'AMBROSIO, Donato;RONGO, Rocco;SPATARO, William;
2014-01-01

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.
2014
978-3-319-03580-2
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/169591
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