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.
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