Combining workstations and supercomputers to support grid applications: the parallel tomography experience

Abstract

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 into the potential of grid resources in order to achieve performance. In this paper, we illustrate how work queue applications can leverage grids to achieve performance through coallocation. We describe our experiences in developing a scheduling strategy for a production tomography application targeted at grids that contain both workstations and parallel supercomputers. Our strategy uses dynamic information exported by a supercomputer's batch scheduler to simultaneously schedule tasks on workstations and immediately-available supercomputer nodes. This strategy is of great practical interest because it combines resources that are available in a typical research laboratory: time-shared workstations and CPU time in remote space-shared supercomputers. We show that this strategy improves the performance of the tomography application compared to traditional scheduling strategies, which target the application to either type of resource alone.