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
Application-level scheduling on distributed heterogeneous networks
Supercomputing '96 Proceedings of the 1996 ACM/IEEE conference on Supercomputing
MPI: The Complete Reference
Strategies for Dynamic Load Balancing on Highly Parallel Computers
IEEE Transactions on Parallel and Distributed Systems
The Vision of Autonomic Computing
Computer
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
Design and Evaluation of a Resource Selection Framework for Grid Applications
HPDC '02 Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing
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
A Component-Based Programming Model for Autonomic Applications
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
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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; they include fuel characteristics and configurations, chemical reactions, balances between different modes of heat transfer, topography, and fire/atmosphere interactions. These factors make static parallel algorithms inefficient, especially when large number of processors are used because we cannot predict accurately the propagation of the fire and its computational requirements at runtime. In this paper, we present 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 the state of the simulation and analyzing it, and use 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 that does not take into considerations the state of the computations.