Self-optimization of Large Scale Wildfire Simulations

The development of efficient parallel algorithms for large scale wildfire simulations is a challenging research problem because the factors that determine wildfire behavior are complex. These factors make static parallel algorithms inefficient, especially when large number of processors is used because we cannot predict accurately the propagation of the fire and its computational requirements at runtime. In this paper, we propose an Autonomic Runtime Manager (ARM) to dynamically exploit the physics properties of the fire simulation and use them as the basis of our self-optimization algorithm. At each step of the wildfire simulation, the ARM decomposes the computational domain into several natural regions (e.g., burning, unburned, burned) where each region has the same temporal and special characteristics. The number of burning, unburned and burned cells determines the current state of the fire simulation and can then be used to accurately predict the computational power required for ea...

• Type: Conference
• Year: 2005
• Where: ICCS
• Authors: Jingmei Yang, Huoping Chen, Salim Hariri, Manish Parashar
• Keywords: ICCS 2005, Parallel Algorithms, Scale Wildfire Simulations, Wildfire Simulation