Implementation Techniques Building Scalable Cluster-Based 3D Graphics Engines

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The most apparent architectural trends in high-performance computing in the recent years is the emergence of cluster or network of workstations (NOW) architectures, where a set of off-the-shelf high-performance desktop machines connected by a high-speed system-area interconnect can efficiently execute parallel programs and sometimes out-perform custom-built multiprocessors or multicomputers that axe orders of magnitude more expensive. The key driving force behind cluster computing is the unstoppable advances in microprocessor performance, which in turn is enabled by the economies of scale in the PC industry. We are witnessing a similar technological revolution in 3D graphics hardware arena. The performance of low-cost 3D graphics cards is improving at an astonishing rate of 80% per year within the last five years, and if continuing at this rate, is expected to out-pace high-end graphics engines such as those from Sili-con Graphics in 2003. Despite these incredible performance progresses, most of these graphics cards remain relatively inexpensive. The central research questions that this project aims to answer are whether it is feasible to build parallel 3D graphics engines that are based on the clustering architecture and 3D graphics cards and whose overall performance can scale with the number of CPUs/graphics processors, and if so, what the enabling algorithmic and architectural techniques axe. The project will address these questions by designing, implementing, and evaluating efficient software for a cluster-based parallel 3D graphics system called Sunder, which is built on top of Linux and Mesa.