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23 October 2013 GPU-based ray tracing algorithm for fast coverage zone prediction under urban microcellular environment
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In this study, an improved ray tracing propagation prediction model, which is based on creating a new virtual source tree, is used because of their high efficiency and reliable prediction accuracy. In addition, several acceleration techniques are also adopted to improve the efficiency of coverage prediction over large areas. However, in the process of employing the ray tracing method for coverage zone prediction, runtime is linearly proportional to the total number of prediction points, leading to large and sometimes prohibitive computation time requirements under complex geographical environments. In order to overcome this bottleneck, the compute unified device architecture (CUDA), which provides fine-grained data parallelism and thread parallelism, is implemented to accelerate the calculation. Taking full advantage of tens of thousands of threads in CUDA program, the decomposition of the coverage prediction problem is firstly conducted by partitioning the image tree and the visible prediction points to different sources. Then, we make every thread calculate the electromagnetic field of one propagation path and then collect these results. Comparing this parallel algorithm with the traditional sequential algorithm, it can be found that computational efficiency has been improved dramatically.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Z-Y. Liu, L-X. Guo, and C.-G. Jia "GPU-based ray tracing algorithm for fast coverage zone prediction under urban microcellular environment", Proc. SPIE 8895, High-Performance Computing in Remote Sensing III, 88950L (23 October 2013);


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