Title: Parallel adaptations of stochastic modeling techniques for computer graphics
Abstract: Realistic models of natural objects for use in computer graphics require an enormous amount of information. Traditional methods employ large data bases which are unwieldy to enter and manipulate. Stochastic procedural models greatly diminish these problems by performing data amplification on a small data base and, using a parameter constrained stochastic process, mimic the richness found in nature.
Time constraints on real time graphics applications are severe and even applications which are not real time (production of movie sequences) can benefit from a reduction of execution time. Speedups through uniprocessor architectures are almost exhausted leaving parallel architectures as the only hope for major hardware improvements. Many different forms of parallel architectures exist, each with its own balance of resources. Often the resource balance of sequential machines differs drastically from parallel machines. Since resource balance is a major determiner in the optimization of software, there are many dangers in blindly moving sequential software to parallel computers (especially optimized software). Of major concern is the coupling between modeling and rendering modules (special cases of numerical simulation and analysis modules). Special purpose architectures tend to suffer from an offloading problem since their processing resources outweigh their memory and communications resources. General purpose massively parallel processors are proposed as a solution since they can increase the processing load which effectively balances the system.
The effectiveness of massively parallel architectures for stochastic modeling is demonstrated using examples from stochastic subdivision, iterated function systems and chaotic dynamical systems. A general method for mapping recursive generating procedures onto the array is developed. Massively parallel algorithms scale well to different size machines and compete well with special purpose architectures. Software development not constrained by sequential code often leads to powerful generalizations as adaptations to parallel machines are made. As computer graphics models become increasingly complex and other scientific modeling more dependent on graphics, the generality and computing power of massively parallel processors will become an increasingly attractive computing tool.
Publication Year: 1987
Publication Date: 1987-01-01
Language: en
Type: article
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