Advertisement

Acceleration of Blender Cycles Path-Tracing Engine Using Intel Many Integrated Core Architecture

  • Milan JarošEmail author
  • Lubomír Říha
  • Petr Strakoš
  • Tomáš Karásek
  • Alena Vašatová
  • Marta Jarošová
  • Tomáš Kozubek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9339)

Abstract

This paper describes the acceleration of the most computationally intensive kernels of the Blender rendering engine, Blender Cycles, using Intel Many Integrated Core architecture (MIC). The proposed parallelization, which uses OpenMP technology, also improves the performance of the rendering engine when running on multi-core CPUs and multi-socket servers. Although the GPU acceleration is already implemented in Cycles, its functionality is limited. Our proposed implementation for MIC architecture contains all features of the engine with improved performance. The paper presents performance evaluation for three architectures: multi-socket server, server with MIC (Intel Xeon Phi 5100p) accelerator and server with GPU accelerator (NVIDIA Tesla K20m).

Keywords

Intel xeon phi Blender Cycles Quasi-Monte Carlo Path Tracing Rendering 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [CGW]
    Intel: Animation Evolution: A Biopic Through the Eyes of Shrek, Computer Graphic World, December 2010Google Scholar
  2. [KAJ]
    Kajiya, J.: The rendering equation. In: Computer Graphics, vol. 20, pp. 143–150, August 1986Google Scholar
  3. [KAJ]
    Lafortune, E.: MathematicalModels and Monte Carlo Algorithms for Physically Based Rendering, Cornell University, PhD. Dissertation, February 1996Google Scholar
  4. [GRE]
    Gregor, L.: Ověření ocenění opcí metodou quasi-Monte-Carlo, 5. mezinárodní konference Finanční řízení podniku a finančních institucí, VŠB-TU Ostrava (2005)Google Scholar
  5. [NIE]
    Niederreiter, H.: Random number Generation and quasi-Monte Carlo Methods. SIAM, Philadelphia (1992). ISBN 0-89871-295-5Google Scholar
  6. [MOR]
    Morokoff, W.J.: Generating quasi-Random Paths for Stochastic Processes, Working Paper, Mathematics Dept. of UCLA (1997)Google Scholar
  7. [JO3]
    Joe, S., Kuo, F.Y.: Remark on Algorithm 659: Implementing Sobol’s quasi-random sequence generator. ACM Trans. Math. Softw. 29, 49–57 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  8. [JO8]
    Joe, S., Kuo, F.Y.: Constructing Sobol sequences with better two-dimensional projections. SIAM J. Sci. Comput. 30, 2635–2654 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  9. [BRA]
    Bratley, P., Fox, B.L.: Algorithm 659: Implementing Sobol’s quasi-random sequence generator. ACM Trans. Math. Software 14, 88–100 (1988)CrossRefzbMATHGoogle Scholar
  10. [LEM]
    Lemieux, Ch.: Monte Carlo and Quasi-Monte Carlo Sampling. Springer (2009). ISBN 978-1441926760Google Scholar
  11. [AHW]
  12. [SHW]
  13. [BLE]
  14. [BLS]

Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  • Milan Jaroš
    • 1
    • 2
    Email author
  • Lubomír Říha
    • 1
  • Petr Strakoš
    • 1
  • Tomáš Karásek
    • 1
  • Alena Vašatová
    • 1
    • 2
  • Marta Jarošová
    • 1
    • 2
  • Tomáš Kozubek
    • 1
    • 2
  1. 1.IT4InnovationsVŠB-Technical University of OstravaOstravaCzech Republic
  2. 2.Department of Applied MathematicsVŠB-Technical University of OstravaOstravaCzech Republic

Personalised recommendations