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As polygonal models rapidly grow to sizes orders of magnitudes bigger than the memory of commodity workstations, a viable approach to simplifying such models is parallel mesh simplification algorithms. A naive approach that divides the model into a number of equally sized chunks and distributes them to a number of potentially heterogeneous workstations is bound to fail. In severe cases the computation becomes virtually impossible due to significant slow downs because of memory thrashing. We present a general parallel framework for simplification of very large meshes. This framework ensures a near optimal utilization of the computational resources in a cluster of workstations by providing an intelligent partitioning of the model. This partitioning ensures a high quality output, low runtime due to intelligent load balancing, and high parallel efficiency by providing total memory utilization of each machine, thus guaranteeing not to trash the virtual memory system. To test the usability of our framework we have implemented a parallel version of R-Simp [Brodsky and Watson 2000].
Date of Conference: 21-21 Oct. 2003