As data analysis places increasing demands on visualization environments, it becomes more difficult to address all requirements on a single computing platform or even in a single location. At the same time, high-speed networks and the advent of multidisciplinary science make using remote resources both feasible and necessary. In distance visualization, networks with varying capabilities can connect geographically distributed data sources, image consumers, and the visualization engines that translate data into images. For example, our distance visualization system allows collaborative online reconstruction and analysis of tomographic data from remote X-ray sources and electron microscopes. The Department of Energy s ESnet and the National Science Foundation s vBNS provide network access to supercomputers and to remote users, while the Transpac connection through Star Tap enables high-speed access to Osaka.
Geographic distribution introduces significant challenges into the visualization process.
Each heterogeneous collection of resources can have different configurations, local resource management systems, and security requirements. Furthermore, adding the network increases the complexity as the design space expands to include a variety of protocols and communication technologies. Finally, we must deal with traditional concerns about distributed systems such as ensuring that an application responds robustly to resource failure. The visualization architectures in common use today were not designed for easy distribution. In the few cases where the architecture provides some distribution capability, it typically integrates into the application at the lowest level, limiting the application s ability to run in widely distributed, highly heterogeneous environments.