A Scalable Cluster-based Parallel Simplifi cation Framework for Height Fields

In this paper, we present a method to interactively render 3D large datasets on a PC Cluster. Classical methods use simplification to fill up the gap between such models and graphics card capabilities. Unfortunatelly, simplification algorithms are time and memory consuming and they allow real-time interaction only for a restricted size of models. This work focuses on parallelizing Rottger's simplification algorithm for height fields but the main ideas can be generalized to other scientific areas. The method benefits from the scalable computating power of clusters. As our results show it, this permits us to achieve a data scaling while maintaining an acceptable frame rate with real-time interaction. Moreover, the scheme can take avantage of tiled-display environments.