The Journal of Supercomputing ( IF 3.3 ) Pub Date : 2021-03-04 , DOI: 10.1007/s11227-021-03680-0 Wang Jun-Feng , Ding Gang-Yi , Wang Yi-Ou , Li Yu-Gang , Zhang Fu-Quan
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.
中文翻译:
基于HPM模型的光线跟踪算法的并行优化
本文提出了一种并行计算分析模型HPM,并基于该模型分析了CPU-GPU的并行体系结构。在此基础上,我们研究了光线跟踪算法在CPU–GPU并行体系结构上的并行优化,并充分发挥了节点之间的并行性,节点内部多核CPU的并行性以及GPU的并行性,从而提高了光线跟踪算法的计算速度。本文采用空分技术对地面数据进行分割,构造出KD树的组织结构,并改进了KD树的构造方法,以减少算法的时间复杂度。地面数据平均分配到每个计算节点,并且计算节点使用CPU-GPU的组合进行并行优化。