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A High-Performance Progressive Radiosity Method Based on Scene Partitioning

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Book cover High Performance Computing for Computational Science — VECPAR 2002 (VECPAR 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2565))

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Abstract

The radiosity method is one oft he most popular global illumination models in which highly realistic synthetic images can be achieved. In this paper we present a parallel algorithm for computing the progressive radiosity method on distributed memory systems. Our implementation allows to work with large scenes using a partition of the input scene among the processors; besides good results in terms of speedup have been obtained.

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Author information

Authors and Affiliations

  1. Dept. Electronic and Systems, Univ. da Coruña, 15071 A, Coruña, Spain

    Antonio P. Guerra, Margarita Amor, Emilio J. Padrón & Ramón Doallo

Authors
  1. Antonio P. Guerra
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  2. Margarita Amor
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  3. Emilio J. Padrón
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  4. Ramón Doallo
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Editor information

Editors and Affiliations

  1. Faculdade de Engenharia da, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    José M. L. M. Palma  & A. Augusto Sousa  & 

  2. Department of Computer Science, University of Tennessee, 37996-1301, Knoxville, TN, USA

    Jack Dongarra

  3. Departamento de Sistemas Informáticos y Computación, Universidad Politécnica de Valencia, Camino de Vera, s/n, Apartado 22012, 46020, Valencia, Spain

    Vicente Hernández

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© 2003 Springer-Verlag Berlin Heidelberg

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Guerra, A.P., Amor, M., Padrón, E.J., Doallo, R. (2003). A High-Performance Progressive Radiosity Method Based on Scene Partitioning. In: Palma, J.M.L.M., Sousa, A.A., Dongarra, J., Hernández, V. (eds) High Performance Computing for Computational Science — VECPAR 2002. VECPAR 2002. Lecture Notes in Computer Science, vol 2565. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36569-9_36

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  • DOI: https://doi.org/10.1007/3-540-36569-9_36

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00852-1

  • Online ISBN: 978-3-540-36569-3

  • eBook Packages: Springer Book Archive

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