.png){kind=icon}
.png){kind=icon}
Return
Accelerating wildfire susceptibility mapping through GPGPU
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jpdc.2013.03.014.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In the field of wildfire risk management the so-called burn probability maps (BPMs) are increasingly used with the aim of estimating the probability of each point of a landscape to be burned under certain environmental conditions. Such BPMs are usually computed through the explicit simulation of thousands of fires using fast and accurate models. However, even adopting the most optimized algorithms, the building of simulation-based BPMs for large areas results in a highly intensive computational process that makes mandatory the use of high performance computing. In this paper, General-Purpose Computation with Graphics Processing Units (GPGPU) is applied, in conjunction with a wildfire simulation model based on the Cellular Automata approach, to the process of BPM building. Using three different GPGPU devices, the paper illustrates several implementation strategies to speedup the overall mapping process and discusses some numerical results obtained on a real landscape. (C) 2013 Elsevier Inc. All rights reserved.
Keywords:
GPGPU
Cellular automata
Wildfire simulation
Wildfire susceptibility
Hazard maps
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4
Papers:
3.8K
Citations:
4.8K