Abstract
Visibility computing is a basic problem in computer graphics, and is often the bottleneck in realistic rendering algorithms. Some of the most common include the determination of the objects visible from a viewpoint, virtual reality, real-time simulation and 3D interactive design. As one technique to accelerate the rendering speed, the research on visibility computing has gained great attention in recent years. Traditional visibility computing on single processor machine has been unable to meet more and more large-scale and complex scenes due to lack parallelism. However, it will face many challenges to design parallel algorithms on a cluster due to imbalance workload among compute nodes, the complicated mathematical model and different domain knowledge. In this paper, we propose an efficient and highly scalable framework for visibility computing on Tianhe-2 supercomputer. Firstly, a new technique called hemispheric visibility computing is designed, which can overcome the visibility missing of traditional perspective algorithm. Secondly, a distributed parallel algorithm for visibility computing is implemented, which is based on the master-worker architecture. Finally, we discuss the issue of granularity of visibility computing and some optimization strategies for improving overall performance. Experiments on Tianhe-2 supercomputer show that our distributed parallel visibility computing framework almost reaches linear speedup by using up to 7680 CPU cores.
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Acknowledgment
The authors would like to thank the anonymous referees for their valuable comments and helpful suggestions. 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.
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Xu, Z., Wang, X., Zhang, C., Wu, C. (2018). Parallel Implementation and Optimizations of Visibility Computing of 3D Scene on Tianhe-2 Supercomputer. 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_12
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