Parallel Rendering With K-Way Replication

With the recent advances in commodity graphics hardware performance, PC clusters have become an attractive alternative to traditional high-end graphics workstations. The main challenge is to develop parallel rendering algorithms that work well within the memory constraints and communication limitations of a networked cluster. Previous systems have required the entire 3D scene to be replicated in memory on every PC. While this approach can take advantage of view-dependent load balancing algorithms and thus largely avoid the problems of inter-process communication, it limits the scalability of the system to the memory capacity of a single PC. We present a k-way replication approach in which each 3D primitive of a large scene is replicated on k out of n PCs (k much less than n). The key idea is to support 3D models larger than the memory capacity of any single PC, while retaining the reduced communication overheads of dynamic view-dependent partitioning. In this paper, we investigate algorithms for distributing copies of primitives among PCs and for dynamic load balancing under the constraints of partial replication. Our main result is that the parallel rendering efficiencies achieved with small replication factors are similar to the ones measured with full replication. By storing one-fourth of Michelangelo's David model (800MB) on each of 24 PCs (each with 256MB of memory), our system is able to render 40 million polygons/second (65% efficiency).