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Volume data imaging is a computationally expensive process. Imaging small volume data sets is slow when executed on typical workstation-class machines and imaging large volume data sets on such machines is nearly impossible. Executing the same tasks on mini-supercomputer class systems can significantly reduce imaging times and job-size restrictions, but at the cost of increased user-interface and accessibility problems. In a well-integrated system, computational tasks should be handled by high-powered compute engines, while user-interface tasks are left to graphics workstations.At the San Diego Supercomputer Center (SDSC), an experimental volume visualization system is being tested that distributes volume imaging tasks to appropriate network resources. Remote high-powered compute engines process rendering tasks, while local workstations run the user-interface.
Elvins et al. (Tue,) studied this question.