基于 CPU-GPU 协同的迭代物理光学并行算法研究

With the integration of radar technology and unmanned driving, electromagnetic simulation has been widely used in the field of unmanned driving. When solving the radar cross section (RCS) of electrically large scattering bodies using the iterative physical optics (IPO) method, the number of unknowns is large, resulting in significant memory consumption and computation time. To address this issue, this paper introduces parameter space techniques to optimize the iterative physical optics algorithm, aimed at enhancing the computational efficiency of calculating radar cross sections of electrically large objects. Additionally, compute unified device architecture (CUDA) parallel computing technology is employed to achieve parallel computation of radar cross sections for electrically large targets on a collaborative platform of central processing unit (CPU) and graphics processing unit (GPU). Compared with commercial software FEKO, a speedup of 224.35 is achieved on an NVIDIA GeForce RTX 3050 graphics card. The experimental results demonstrate the feasibility and efficiency of the IPO algorithm parallel computation based on CPU-GPU collaboration, enabling the solution of scattering problems of electrically large targets that were previously only feasible on high-performance computers or computer clusters.