Near-optimal adaptive control of a large grid application
ICS '02 Proceedings of the 16th international conference on Supercomputing
Applying scheduling and tuning to on-line parallel tomography
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
Optimisation of component-based applications within a grid environment
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
An Integrated Grid Environment for Component Applications
GRID '01 Proceedings of the Second International Workshop on Grid Computing
Computational Communities: A Marketplace for Federated Resources
HPCN Europe 2001 Proceedings of the 9th International Conference on High-Performance Computing and Networking
Model-Based Control of Adaptive Applications: An Overview
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
A decoupled scheduling approach for the GrADS program development environment
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
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
A decoupled scheduling approach for Grid application development environments
Journal of Parallel and Distributed Computing - Special issue on computational grids
Applying scheduling and tuning to on-line parallel tomography
Scientific Programming - Best papers from SC 2001
Mapping cooperating GRID applications by affinity for resource characteristics
AIS'04 Proceedings of the 13th international conference on AI, Simulation, and Planning in High Autonomy Systems
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Computational Grids have become an important and popular computing platform for both scientific and commercial distributed computing communities. However, users of such systems typically find achievement of application execution performance remains challenging. Although Grid infrastructures such as Legion and Globus provide basic resource selection functionality, work allocation functionality, and scheduling mechanisms, applications must interpret system performance information in terms of their own requirements in order to develop performance-efficient schedules.We describe a new high-performance scheduler that incorporates dynamic system information, application requirements, and a detailed performance model in order to create performance efficient schedules. While the scheduler is designed to provide improved performance for a magneto hydrodynamics simulation in the Legion Computational Grid infrastructure, the design is generalizable to other systems and other data-parallel, iterative codes. We describe the adaptive performance model, resource selection strategies, and scheduling policies employed by the scheduler. We demonstrate the improvement in application performance achieved by the scheduler in dedicated and shared Legion environments.