Partitioning and Scheduling Large Radiosity Computations in Parallel

Xavier Cavin 1 Jean-Claude Paul 1 Laurent Alonso 1
1 ISA - Models, algorithms and geometry for computer graphics and vision
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : We show, in this paper, how it is feasible to efficiently perform large radiosity computations on a conventional (distributed) shared memory multiprocessor machine. Hierarchical radiosity algorithms, although computationally expensive, are an efficient view-independent way to compute the global illumination which gives the visual ambiance to a scene. Their effective parallelization is made challenging, however, by their non-uniform, dynamically changing characteristics, and their need for long-range communication. To address this need, we have developed appropriate partitioning and scheduling techniques, that deliver an optimal load balancing, while still exhibiting excellent data locality. We provide the detailed implementation of these techniques and present results of experiments showing very good acceleration and scalability performances. The accurate radiosity solutions required to render high quality images of an extremely large model are computed in a reasonable time. The rendering capabilities of modern graphics hardware are then used to visualize this virtual pre-lit environment in real-time.
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Article dans une revue
Journal on Parallel and Distributed Computer Practices - Special Issue on Parallel and Distributed Computer Graphics, 2000, 3 (3), 12 p
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Soumis le : mardi 26 septembre 2006 - 08:50:52
Dernière modification le : jeudi 11 janvier 2018 - 06:19:48

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  • HAL Id : inria-00099086, version 1

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Xavier Cavin, Jean-Claude Paul, Laurent Alonso. Partitioning and Scheduling Large Radiosity Computations in Parallel. Journal on Parallel and Distributed Computer Practices - Special Issue on Parallel and Distributed Computer Graphics, 2000, 3 (3), 12 p. 〈inria-00099086〉

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