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GPU-Based Ray Tracing Algorithm for High-Speed Propagation Prediction in Typical Indoor Environments

机译:基于GPU的典型室内环境高速传播预测的基于GPU的光线跟踪算法

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A fast 3-D ray tracing propagation prediction model based on virtual source tree is presented in this paper, whose theoretical foundations are geometrical optics(GO) and the uniform theory of diffraction(UTD). In terms of typical single room indoor scene, taking the geometrical and electromagnetic information into account, some acceleration techniques are adopted to raise the efficiency of the ray tracing algorithm. The simulation results indicate that the runtime of the ray tracing algorithm will sharply increase when the number of the objects in the single room is large enough. Therefore, GPU acceleration technology is used to solve that problem. As is known to all, GPU is good at calculation operation rather than logical judgment, so that tens of thousands of threads in CUDA programs are able to calculate at the same time, in order to achieve massively parallel acceleration. Finally, a typical single room with several objects is simulated by using the serial ray tracing algorithm and the parallel one respectively. It can be found easily from the results that compared with the serial algorithm, the GPU-based one can achieve greater efficiency.
机译:本文提出了一种基于虚拟源树的快速3-D射线跟踪传播预测模型,其理论基础是几何光学(GO)和统一的衍射理论(UTD)。就典型的单人间室内场景而言,考虑到几何和电磁信息,采用了一些加速技术来提高光线跟踪算法的效率。仿真结果表明,当单个房间中的物体的数量足够大时,光线跟踪算法的运行时间将急剧增加。因此,GPU加速技术用于解决该问题。正如所有已知的,GPU在计算操作中擅长计算操作而不是逻辑判断,因此CUDA程序中成千上万的线程能够同时计算,以实现大规模平行的加速。最后,通过使用串行射线跟踪算法和平行的典型单个具有多个对象的单个房间。可以从结果中易于发现,与串行算法相比,基于GPU的GPU可以实现更高的效率。

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