Adaptive Parallel Rendering on Multiprocessors and Workstation Clusters

This paper presents the design and performance of a new parallel graphics renderer for 3-D images. This renderer is based on an adaptive supersampling approach that works for time/space-efficient execution on two classes of parallel computers. Our rendering scheme takes sub-pixel supersamples only along polygon edges. This leads to a significant reduction in rendering time and in buffer memory requirement. Furthermore, we offer a balanced rasterization of all transformed polygons. Experimental results prove these advantages on both a shared-memory SGI multiprocessor server and a Unix cluster of Sun workstations. We reveal performance effects of the new rendering scheme on subpixel resolution, polygon number, scene complexity, and memory requirements. The balanced parallel renderer demonstrates scalable performance with respect to increase in graphics complexity and in machine size. Our parallel renderer outperforms Crow’s scheme in benchmark experiments performed. The improvements are made in three fronts: (i) reduction in rendering time, (ii) higher efficiency with balanced workload, and (iii) adaptive to available buffer memory size. The balanced renderer can be more cost-effectively imbedded within many 3-D graphics algorithms, such as those for edge smoothing and 3-D visualization. Our parallel renderer is MPI-coded, offering high portability and cross-platform performance. These advantages can greatly improve the QoS in 3-D imaging and in real-time interactive graphics.