Abstract
Monte-Carlo rendering algorithms are known for producing highly realistic images, but at a significant computational cost, because they rely on tracing up to trillions of light paths through a scene to simulate physically based light transport. For this reason, a large body of research exists on various techniques for accelerating these costly algorithms. As one of the Monte-Carlo rendering algorithms, PSSMLT (Primary Sample Space Metropolis Light Transport) is widely used nowadays for photorealistic rendering. Unfortunately, the computational cost of PSSMLT is still very high since the space of light paths in high-dimension and up to trillions of paths are typically required in such path space. Recent research on PSSMLT has proposed a variety of optimized methods for single node rendering, however, multi-node rendering for PSSMLT is rarely mentioned due in large part to the complicated mathematical model, complicated physical processes and the irregular memory access patterns, and the imbalanced workload of light-carrying paths.
In this paper, we present a highly scalable distributed parallel simulation framework for PSSMLT. Firstly, based on light transport equation, we propose the notion of sub-image with certain property for multi-node rendering and theoretically prove that the whole set of sub-images can be combined to produce the final image; Then we further propose a sub-image based assignment partitioning algorithm for multi-node rendering since the traditional demand-driven assignment partitioning algorithm doesn’t work well. Secondly, we propose a physically based parallel simulation for the PSSMLT algorithm, which is revealed on a parallel computer system in master-worker paradigm. Finally, we discuss the issue of granularity of the assignment partitioning and some optimization strategies for improving overall performance, and then a static/dynamic hybrid scheduling strategy is described. Experiments show that framework has a nearly linear speedup along with the CPU core count up to 9,600 on the Tianhe-2 supercomputer, which suggests that the proposed framework has a high scalability and efficiency.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Pharr, M., Jakob, W., Humphreys, G.: Physically Based Rendering: From Theory to Implementation, 3rd edn. Morgan Kaufmann Publishers Inc., San Francisco (2016)
Pharr, M., Humphreys, G.: Physically Based Rendering: From Theory To Implementation, 2nd edn. Morgan Kaufmann Publishers Inc., San Francisco (2010)
Pharr, M., Humphreys, G.: Physically Based Rendering: From Theory To Implementation, 1st edn. Morgan Kaufmann Publishers Inc., San Francisco (2004)
Keller, A.: Quasi-Monte: Carlo image synthesis in a nutshell. In: Dick, J., Kuo, F., Peters, G., Sloan, I. (eds.) Monte Carlo and Quasi-Monte Carlo Methods 2012, pp. 213–249. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41095-6_8
Davidovič, T., Křrivánek, J., Hašan, M., Slusallek, P.: Progressive light transport simulation on the GPU: survey and improvements. ACM Trans. Graph. 33(3), 29:1–29:19 (2014)
Ritschel, T., Dachsbacher, C., Grosch, T., Kautz, J.: The state of the art in interactive global illumination. Comput. Graph. Forum 31(1), 160–188 (2012)
Dutré, P., et al.: State of the art in Monte Carlo global illumination. In: ACM SIGGRAPH 2004 Course Notes, SIGGRAPH 2004. ACM, New York (2004)
Kajiya, J.T.: The rendering equation. SIGGRAPH Comput. Graph. 20(4), 143–150 (1986)
Veach, E., Guibas, L.: Bidirectional estimators for light transport. In: Sakas, G., Müller, S., Shirley, P. (eds.) Photorealistic Rendering Techniques, pp. 145–167. Springer, Heidelberg (1995). https://doi.org/10.1007/978-3-642-87825-1_11
Veach, E., Guibas, L.J.: Metropolis light transport. In: Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1997, pp. 65–76. ACM Press/Addison-Wesley Publishing Co., New York (1997)
Kelemen, C., Szirmay-Kalos, L., Antal, G., Csonka, F.: A simple and robust mutation strategy for the metropolis light transport algorithm. Comput. Graph. Forum 21(3), 531–540 (2002)
Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E.: Equations of state calculations by fast computing machines. J. Chem. Phys. 21, 1087–1091 (1953)
Fan, S., Chenney, S., Lai, Y.C.: Metropolis photon sampling with optional user guidance. In: Proceedings of the 16th Eurographics Symposium on Rendering, pp. 127–138. Eurographics Association (2005)
Jakob, W.: Mitsuba renderer (2018). http://www.mitsuba-renderer.org
Veach, E.: Robust Monte Carlo methods for light transport simulation. Ph.D. thesis, Stanford University, Stanford (1998). AAI9837162
Keller, A., Premoze, S., Raab, M.: Advanced (quasi) Monte Carlo methods for image synthesis. In ACM SIGGRAPH 2012 Courses, SIGGRAPH 2012, pp. 21:1–21:46. ACM, New York (2012)
Wu, C., Zhang, Y., Yang, C.: Large scale satellite imagery simulations with physically based ray tracing on Tianhe-1A supercomputer. In Proceedings of the 15th IEEE International Conference on High Performance Computing and Communications (HPCC), HPCC 2013, Zhangjiajie, China, pp. 549–556. IEEE (2013)
Freisleben, B., Hartmann, D., Kielmann, T.: Parallel raytracing: a case study on partitioning and scheduling on workstation clusters. In: Proceedings of the Thirtieth Hawaii International Conference on System Sciences, vol. 1, pp. 596–605, January 1997
Reinhard, E.: Scheduling and data management for parallel ray tracing. Technical report, Bristol, UK (1999)
Kelemen, C., Szirmay-Kalos, L.: Simple and robust mutation strategy for metropolis light transport algorithm. Technical report TR-186-2-01-18, Institute of Computer Graphics and Algorithms, Vienna University of Technology, Favoritenstrasse 9–11/186, A-1040 Vienna, Austria, July 2001. Human contact: technical-report@cg.tuwien.ac.at
Plachetka, T.: POV Ray: persistence of vision parallel raytracer. In: Proceedings of Spring Conference on Computer Graphics, Budmerice, Slovakia, pp. 123–129 (1998)
Tanenbaum, A.S., Van Steen, M.: Distributed Systems: Principles and Paradigms, 2nd edn. Prentice-Hall, Upper Saddle River (2006)
Tianhe-2 (milkyway-2) (2013). http://www.top500.org/system/177999
Bitterli, B.: Rendering resources (2016). https://benedikt-bitterli.me/resources/
Acknowledgment
The work is supported by the National Natural Science Foundation of China under Grant No. 61672508, No. 61379048. and the National Key Research and Development Program of China under Grant No. 2017YFB1400902. We thank Benedikt Bitterli [24] for providing the test scenes used in our evaluation. Further, special acknowledgement goes to Dong Wang, Yuanyuan Wan and Huizhong Lu for answering our technical questions on how to using Tianhe-2 supercomputer. We are particularly grateful to Matt Pharr and Wenzel Jakob et al. for the making the PBRT and Mitsuba renderers publicly available [1, 14].
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Wu, C., Zhang, C., Sun, Q. (2018). Distributed Parallel Simulation of Primary Sample Space Metropolis Light Transport. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11334. Springer, Cham. https://doi.org/10.1007/978-3-030-05051-1_32
Download citation
DOI: https://doi.org/10.1007/978-3-030-05051-1_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05050-4
Online ISBN: 978-3-030-05051-1
eBook Packages: Computer ScienceComputer Science (R0)