Computational Grids are becoming an increasingly important and powerful platform for the execution of large-scale, resource-intensive applications. However, it remains a challenge for applications to tap the potential of Grid resources in order to achieve performance. In this paper, we illustrate how applications can leverage Grids to achieve performance through coallocation. We describe our experiences developing a scheduling strategy for a real-life parallel tomography application targeted to Grids which contain both workstations and parallel supercomputers. Our strategy uses dynamic information exported by a supercomputer's batch scheduler to simultaneously schedule on workstations and immediately available supercomputer nodes. This strategy is of great practical interest because it combines resources available to the typical research lab: time-shared workstations and CPU time in remote space-shared supercomputers. We show that this strategy improves the performance of the parallel tomography application compared to traditional scheduling strategies, which target the application to either type of resource alone.
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