Distributed Point Rendering

  • Ramgopal Rajagopalan
  • Sushil Bhakar
  • Dhrubajyoti Goswami
  • Sudhir P. Mudur
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3769)


Traditionally graphics clusters have been employed in real-time visualization of large geometric models (many millions of 3D points). Data parallel approaches have been the obvious choices when it comes to breaking up the computations over multiple processors. In recent years, programmable graphics hardware has gained widespread acceptance. Today, every processing node in a graphics cluster has two powerful and fully programmable processors – a CPU (Central Processing Unit) and a GPU (Graphics processing unit). It enables distribution of graphics computations targeting an applications’s needs in more flexible ways. In this paper we discuss and analyze our implementation of functionality distributed point-based rendering pipeline with impressive performance improvements. To the best of our knowledge, it is the first attempt to devise a functionality distribution scheme for a large data and compute-intensive application. We discuss the merits and limitations of such a distribution scheme by comparing it against traditional data parallel and single node schemes.


Graphic Processing Unit Graphic Cluster Graphic Application Cache Block Functionality Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Humphreys, G., Houston, M., Ng, R., Frank, R., Ahern, S., Kirchner, P.D., Klosowski, J.T.: Chromium: a stream processing framework for interactive rendering on clusters. In: SIGGRAPH, pp. 693–702 (2002)Google Scholar
  2. 2.
    Muller, C.: The Sort-First Rendering Architecture for High-Performance Graphics. In: Symposium on Interactive 3D Graphics (1995)Google Scholar
  3. 3.
    Buck, I., Foley, T., Horn, D., Sugerman, J., Fatahalian, K., Houston, M., Hanrahan, P.: Brook for GPUs: Stream Computing on Graphics Hardware. In: SIGGRAPH (2004)Google Scholar
  4. 4.
    Sutherland, I.E., Sproull, R.F., Schumacker, R.A.: A Characterization of Ten Hidden Surface Algorithms. ACM Computing Surveys 6, 1–55 (1974)zbMATHCrossRefGoogle Scholar
  5. 5.
    Molnar, S., Cox, M., Ellsworth, D., Fuchs, H.: A Sorting Classification of Parallel Rendering. IEEE Computer Graphics and Algorithms, 23–32 (1994)Google Scholar
  6. 6.
    Govindaraju, N.K., Sud, A., Yoon, S.E., Manocha, D.: Interactive visibility culling in complex environments using occlusion-switches. In: Symposium on Interactive 3D Graphics, pp. 103–112 (2003)Google Scholar
  7. 7.
    Govindaraju, N.K., Lloyd, B., Yoon, S., Sud, A., Manocha, D.: Interactive Shadow Generation in Complex Environments. In: ACM SIGGRAPH (2003)Google Scholar
  8. 8.
    Isard, M., Shand, M., Heirich, A.: Distributed rendering of interactive soft shadows. In: Parallel Graphics and Visualization, EGPGV, pp. 71–76 (2002)Google Scholar
  9. 9.
    Heirich, A., Moll, L.: Scalable Distributed Visualization Using Off-the-Shelf Components. In: IEEE Parallel Visualization and Graphics Symposium (1999)Google Scholar
  10. 10.
    Moll, L., Heirich, A., Shand, M.: Sepia: Scalable 3D Compositing Using PCI Pamette. In: IEEE Symposium on Field Programmable Custom Computing Machines (1999)Google Scholar
  11. 11.
    Zara, F., Faure, F., Vincent, J.M.: Physical cloth simulation on a PC cluster. Parallel Graphics and Visualisation (2002)Google Scholar
  12. 12.
    Fan, Z., Qiu, F., Kaufman, A., Yoakum-Stover, S.: GPU Cluster for High Performance Computing. In: Proceedings of ACM/IEEE Supercomputing Conference, Pittsburgh PA, USA (2004)Google Scholar
  13. 13.
    Kipfer, P., Slusallek, P.: Transparent Distributed Processing for Rendering. In: Parallel Visualization and Graphics Symposium (PVG), San Francisco (1999)Google Scholar
  14. 14.
    Rajagopalan, R., Goswami, D., Mudur, S.: Functionality Distribution for Parallel Rendering. In: IEEE IPDPS (2005)Google Scholar
  15. 15.
    Rajagopalan, R.: Functionality Distribution in Graphics. Master’s thesis, Concordia University, Canada (2005)Google Scholar
  16. 16.
    Levoy, M., Whitted, T.: The use of points as display primitives. Technical report, CS Departement, University of North Carolina at Chapel Hill (1985)Google Scholar
  17. 17.
    Westover, L.: Interactive Volume Rendering. In: Chapel Hill Workshop Volume Visualization, pp. 9–16 (1989)Google Scholar
  18. 18.
    Grossman, J.P.: Point Sample Rendering. Master’s thesis, Dept. of Electrical Engineering and Computer Science, MIT (1998)Google Scholar
  19. 19.
    Rusinkiewicz, S., Levoy, M.: QSplat: A Multiresolution Point Rendering System for Large Meshes. In: SIGGRAPH (2000)Google Scholar
  20. 20.
    Pfister, H., Zwicker, M., Baar, J.V., Gross, M.: Surfels: Surface elements as rendering primitives. In: SIGGRAPH, pp. 335–342 (2000)Google Scholar
  21. 21.
    Zwicker, M., Pfister, H., Baar, J.V., Gross, M.: Surface splatting. In: SIGGRAPH, pp. 371–378 (2001)Google Scholar
  22. 22.
    Ren, L., Pfister, H., Zwicker, M.: Object space EWA surface splatting: A hardware accelerated approach to high quality point rendering. In: Eurographics 2002, pp. 461–470 (2002)Google Scholar
  23. 23.
    Carsten, D., Christian, V., Marc, S.: Sequential Point Trees. In: SIGGRAPH (2003)Google Scholar
  24. 24.
    Hubo, E., Bekaer, P.: A Data Distribution Strategy for Parallel Point-Based Rendering. In: WSCG (2005)Google Scholar
  25. 25.
    Chilimbi, T.M., Hill, M.D., Larus, J.R.: Making Pointer-Based Data Structures Cache Conscious. IEEE Computer (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Ramgopal Rajagopalan
    • 1
  • Sushil Bhakar
    • 1
  • Dhrubajyoti Goswami
    • 1
  • Sudhir P. Mudur
    • 1
  1. 1.Dept. of Computer Science and Software EngineeringConcordia UniversityMontrealCanada

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