Tomography is a computationally intensive process by which the three-dimensional structure of an object can be reconstructed from a series of two-dimensional projections. In this thesis, we address on-line execution of tomography to provide real-time feedback to users collecting data from an on-line instrument. Context for this work is provided by a powerful electron microscope located at the National Center for Microscopy and Imaging Research (NCMIR). Acquiring data from NCMIR's microscope is a lengthy process and is susceptible to configuration errors. Thus, real-time tomography feedback will allow users to quickly NCMIR's microscope is a lengthy process and is susceptible to configuration errors. Thus, real-time tomography feedback will allow users to quickly identify configuration problems and interact with the microscope in order to more efficiently acquire data from it. We present an implementation of on-line parallel tomography which allows for production runs in Computational Grid environments. Developing applications that leverage this type of platform is difficult because resources are heterogenous and dynamic. In our approach, on-line parallel tomography is designed to be tunable such that it can be configured to adapt to different resource availabilities. It is coupled with an user-directed, application-level scheduler which exploits the tunability of the application to determine a schedule for soft real-time execution. The scheduler utilizes user constraints, an application model, and dynamic resource load predictions to determine feasible run-time configurations. The configurations are displayed as choices to the user where each configuration involves trade-offs between resolution of the reconstruction, frequency of feedback, and cost of execution. Once an appropriate configuration is chosen by the user, the scheduler selects resources, allocates work, and executes the application.
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