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Parallel Monte Carlo radiosity using scene partitioning
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DOI：10.1177/1094342012457191.png)
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Global illumination provides realistic image synthesis but its high computational requirements limit its use in practice. In this paper we present a parallel method of the Monte Carlo radiosity method. Our proposal is based on the utilization of a convex partition to divide the whole scene into a set of disjoint sub-scenes which are allocated among processors of a distributed memory system. We have used two partitioning strategies: uniform and non-uniform. The convex partition employed permits the exploitation of data locality, and the optimization of the ray shooting procedure by minimizing the number of objects to be tested in the intersection calculation. We present three different techniques to increase the performance of parallel implementation and to solve the challenges that the distribution of the data among processors implies: minimization of communications, load balancing, and a distributed test for determining the end of each iteration. The obtained results are good in terms of quality and execution times, increasing the flexibility of previous solutions.
Keyword:
distributed memory system
parallelization
radiosity
Monte Carlo methods
convex partition
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