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Journal Articles Scalable Computing : Practice and Experience Year : 2000

Partitioning and Scheduling Large Radiosity Computations in Parallel


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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Dates and versions

inria-00099086 , version 1 (26-09-2006)


  • HAL Id : inria-00099086 , version 1


Xavier Cavin, Jean-Claude Paul, Laurent Alonso. Partitioning and Scheduling Large Radiosity Computations in Parallel. Scalable Computing : Practice and Experience, 2000, 3 (3), 12 p. ⟨inria-00099086⟩
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