Distributed volume visualization: A step towards integrated data analysis and image synthesis

Integrated on-the-fly data analysis and image synthesis is one of the most dominant challenges on modern volume visualization tools. Since volume visualization algorithms are them self computationally complex and memory intensive there is hardly a chance to efficiently integrate data analysis tools on standard single processor architectures. The development and spreading of multiprocessor systems with large scale memory allow for the direct analysis and modification of the data during the rendering process. But to efficiently integrate both tasks similar parallelization strategies and a unique data layout must be chosen. Furthermore, since more and more systems with hardware assisted processing and visualization options can be accessed across high performance networks, distributed visualization tools should benefit from these options whenever possible. In the following paper a prototyped parallel visualization environment will be exemplified in which data analysis and image synthesis are simultaneously performed on demand. The objective is to outline basic parallelization and distribution aspects which enable flexible integration of different tasks rather than to present a concrete implementation. In this context the development and design of a unique algorithmic framework for integrated and/or distributed data analysis and image synthesis dominates the work hereafter.