Abstract
The kd tree is one of the most commonly used spatial data structures for a variety of graphics applications because of its reliably high-acceleration performance. Several years ago, Zhou et al. devised an effective kd-tree construction algorithm that runs entirely on a GPU. In this chapter, we present improved GPU programming techniques for implementing the algorithm more efficiently on current GPUs. One of the major ideas is to reduce the number of necessary kernel functions by replacing the essential, segmented-scan, and reduction computations by simpler per-block atomic operations, thereby alleviating the overheads from multiple synchronous kernel calls. Combined with the efficient implementation of intrablock scan and reduction, using recently introduced intrinsic functions, these changes achieve remarkable performance enhancement to the kd-tree construction process. Through an example of real-time ray tracing for dynamic scenes of nontrivial complexity, we demonstrate that the proposed GPU techniques can be exploited effectively for various real-time applications.
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Acknowledgments
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MOE) (No. 2012R1A1A2008958).
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Appendix: A Single-Kernel Implementation for the Triangle-Sorting Process (Sect. 13.2.1.2)
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Chang, B., Seo, W., Ihm, I. (2015). On the Efficient Implementation of a Real-Time Kd-Tree Construction Algorithm. In: Cai, Y., See, S. (eds) GPU Computing and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-287-134-3_13
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DOI: https://doi.org/10.1007/978-981-287-134-3_13
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