面向GPU的地形遮蔽探测并行算法OA北大核心CSTPCD

Terrain masking algorithms have found wide application in military,civil aviation,and meteorological analy-sis.However,as simulation scale expands and the demand for real-time simulation results increases,traditional computing models are no longer able to meet current requirements.To address this issue,a terrain occlusion detection algorithm is implemented on the compute unified device architecture(CUDA)parallel computing platform,which has successfully solved the issue of slow simulation computation speed.Firstly,the elevation data of discrete sampling points within the radar detection area is matrixed on the CPU end to improve the reading speed of elevation values in parallel computing.Then,the thread allocation method for radar simulation calculation parameters is optimized,and a loop comparison method is utilized to accelerate the calculation of terrain masking angle in parallel.Finally,device side thread synchronization and data alternating transmission technology are adopted to ensure the consistency of calculation results and maximize the uti-lization of GPU computing resources.Moreover,a multi-mode parallel computing method is employed,which includes both multi-threaded parallel computing and single-threaded serial computing,to support degraded computing when the GPU computing resources are insufficient.This ensures high availability of computing.The experimental results show that,compared to the terrain masking serial computation and multithreaded parallel computation of i7-12700H CPU with a simulation granularity of 3 600 detection beams,it achieves 48 times and 17 times acceleration at 3060 Laptop GPU,respectively.Therefore,this approach provides an effective engineering solution for real-time simulation.