Abstract
To compute high quality images with a lot of polygons or patches may take hours. For this reason parallel processing will be a good option in order to decrease the computational cost. On the other hand, Monte Carlo methods offer good alternatives for parallelization, given their intrinsic decomposition properties in independent subtasks. We have implemented the stochastic method for radiosity using a cluster of PCs. Results are presented for 1 to 8 processors, exhibiting a good efficiency and scalability.
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Martínez, R., Coma, J. (2010). Parallel Implementation of the Stochastic Radiosity Method. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2009. Lecture Notes in Computer Science, vol 5910. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12535-5_46
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DOI: https://doi.org/10.1007/978-3-642-12535-5_46
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