Scalability of a multi-physics system for forest fire spread prediction in multi-core platforms

Angel Farguell,Josep R Miró,Ana Cortés,Tomàs Margalef,J Mercader

doi:10.1007/s11227-018-2330-9

Angel Farguell, Josep R Miró + Show 3 more

Open Access

https://doi.org/10.1007/s11227-018-2330-9

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Abstract

Advances in high-performance computing have led to an improvement in modeling multi-physics systems because of the capacity to solve complex numerical systems in a reasonable time. WRF–SFIRE is a multi-physics system that couples the atmospheric model WRF and the forest fire spread model called SFIRE with the objective of considering the atmosphere–fire interactions. In systems like WRF–SFIRE, the trade-off between result accuracy and time required to deliver that result is crucial. So, in this work, we analyze the influence of WRF–SFIRE settings (grid resolutions) into the forecasts accuracy and into the execution times on multi-core platforms using OpenMP and MPI parallel programming paradigms.

Highlights

There are several factors that affect the evolution of a wildland fire
A multi-physics forest fire model that considers the feedback between an atmospheric model and a forest fire spread model could capture the micro-weather generated by a large forest fire and provide more accurate wildland fire predictions
The state of the atmosphere model is modified by the outputs of the fire spread model, which leads to a modification of the micro-meteorology, of local winds near the wildland fire

Summary

Introduction

There are several factors that affect the evolution of a wildland fire. It is well known that wind is one of the key parameters to understand the forest fire propagation. A multi-physics forest fire model that considers the feedback between an atmospheric model and a forest fire spread model could capture the micro-weather generated by a large forest fire and provide more accurate wildland fire predictions. This improvement in accuracy has a cost in terms of execution time. The state of the atmosphere model is modified by the outputs of the fire spread model, which leads to a modification of the micro-meteorology, of local winds near the wildland fire This local wind variation has a feedback on the fire behavior itself that can have an increasing effect during the fire event.

WRF–SFIRE

Experimental study

Quality results

WRF–SFIRE scalability

Findings

Conclusions