Abstract
A message passing interface (MPI) parallel scheme on distributed memory platforms is developed for the low-rank decomposition to accommodate the memory requirement during angular sweeping of a rough surface in terms of tapered wave incidence. Numerical examples, including that conducted on a $160\times 160$ square wavelength rough surface, are carried out to demonstrate the performance of the proposed MPI angular sweeping with respect to accuracy, efficiency, scalability, and the peak memory requirement.
Highlights
T HE electromagnetic simulation of scattering from a random rough surface plays a fundamental role in many areas, such as remote sensing, target recognition, and radar surveillance [1]–[15]
We focus on the message passing interface (MPI) parallelization of the randomized low-rank matrix decomposition, which can be integrated smoothly with the MPI parallel multilevel fast multipole algorithm (MLFMA) [33]–[37]
It should be pointed out that we employ the strategy of finding skeleton Rao–Wilton–Glisson functions [30] to generate a size-reduced excitation matrix that feeds into the randomized low-rank decomposition
Summary
T HE electromagnetic simulation of scattering from a random rough surface plays a fundamental role in many areas, such as remote sensing, target recognition, and radar surveillance [1]–[15]. Encouraged by the angular sweeping algorithm first proposed in [25], this work presents our attempt to improve the efficiency of the MC simulation on large-scale rough surfaces where solve each single deterministic computation. The skeletonization has been proved efficient in angular sweeping in terms of plane waves and Gaussian beams, its capability in accelerating the simulation in terms of tapered waves is not well documented As it is known, tapered waves are always employed to truncate the infinite rough surfaces into finite ones.
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