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Parallel optimization of the ray-tracing algorithm based on the HPM model

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Abstract

This paper proposes a parallel computing analysis model HPM and analyzes the parallel architecture of CPU–GPU based on this model. On this basis, we study the parallel optimization of the ray-tracing algorithm on the CPU–GPU parallel architecture and give full play to the parallelism between nodes, the parallelism of the multi-core CPU inside the node, and the parallelism of the GPU, which improve the calculation speed of the ray-tracing algorithm. This paper uses the space division technology to divide the ground data, constructs the KD-tree organization structure, and improves the construction method of KD-tree to reduce the time complexity of the algorithm. The ground data is evenly distributed to each computing node, and the computing nodes use a combination of CPU–GPU for parallel optimization. This method dramatically improves the drawing speed while ensuring the image quality and provides an effective means for quickly generating photorealistic images.

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Acknowledgements

This research was carried out with the support of the Youth Science Foundation project "Very high resolution satellite stereo image simulation and accuracy evaluation method for DSM extraction in urban areas" (Project ID: 41701427), thank you.

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Authors and Affiliations

  1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, People’s Republic of China

    Wang Jun-Feng, Ding Gang-Yi & Li Yu-Gang

  2. Beijing Institute of Science and Technology Information, Beijing, 100044, People’s Republic of China

    Wang Yi-Ou

  3. Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, 350117, People’s Republic of China

    Zhang Fu-Quan

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  1. Wang Jun-Feng
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Correspondence to Zhang Fu-Quan.

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Jun-Feng, W., Gang-Yi, D., Yi-Ou, W. et al. Parallel optimization of the ray-tracing algorithm based on the HPM model. J Supercomput 77, 10307–10332 (2021). https://doi.org/10.1007/s11227-021-03680-0

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