Altough this technique is capable of producing high quality visualizations, its slowness prevents the interactive use. The major method to overcome this speed limitation is the parallelization. The most important issues in image-space parallelization are load balancing and minimization of the data redistribution overhead introduced at successive visualization instances.
Load balancing in volume rendering requires the estimation of screen work load correctly.
For this purpose, we tested three different load assignment schemes. Since the data used in this work is made up of unstructured tetrahedral grids, for efficiency purposes, clusters of data is used instead of individual cells. Two different cluster-processor distribution schemes are employed to see the effects of initial data distribution. The major contribution of the thesis comes at the hypergraph partitioning model proposed as a solution to the remapping problem. The model is tested on a Parsytec CC system, and satisfactory results are obtained.