The Gradient Model Load Balancing Method
IEEE Transactions on Software Engineering - Special issue on distributed systems
Dynamic load balancing for distributed memory multiprocessors
Journal of Parallel and Distributed Computing
Parallel dynamic graph partitioning for adaptive unstructured meshes
Journal of Parallel and Distributed Computing - Special issue on dynamic load balancing
PLUM: parallel load balancing for adaptive unstructured meshes
Journal of Parallel and Distributed Computing
Future Generation Computer Systems - Special issue on metacomputing
Strategies for Dynamic Load Balancing on Highly Parallel Computers
IEEE Transactions on Parallel and Distributed Systems
Dome: Parallel Programming in a Distributed Computing Environment
IPPS '96 Proceedings of the 10th International Parallel Processing Symposium
Adaptive Computing on the Grid Using AppLeS
IEEE Transactions on Parallel and Distributed Systems
Grid Harvest Service: A System for Long-Term, Application-Level Task Scheduling
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
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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 each region. By regularly monitoring and analyzing the state of the simulation, and using that to drive the runtime optimization, we can achieve significant performance gains because we can efficiently balance the computational load on each processor. Our experimental results show that the performance of the fire simulation has been improved by 45% when compared with a static portioning algorithm.