Scalable and Fast Characteristic Mode Analysis using GPUs

Abstract

Characteristic mode analysis (CMA) is used in the design and analysis of a wide range of electromagnetic devices such as antennas and nanostructures. The implementation of CMA involves the evaluation of a large method of moments (MoM) complex impedance matrix at every frequency. In this work, we use different open-source software for the GPU acceleration of the CMA. This open-source software comprises a wide range of computer science numerical and machine learning libraries not typically used for electromagnetic applications. Specifically, this paper shows how these different Python-based libraries can optimize the computational time of the matrix operations that compose the CMA algorithm. Based on our computational experiments and optimizations, we propose an approach using a GPU platform that is able to achieve up to 16×× and 26×× speedup for the CMA processing of a single 15k ×× 15k MoM matrix of a perfect electric conductor scatterer and a single 30k ×× 30k MoM matrix of a dielectric scatterer, respectively. In addition to improving the processing speed of CMA, our approach provided the same accuracy as independent CMA simulations. The speedup, efficiency, and accuracy of our CMA implementation will enable the analysis of electromagnetic systems much larger than what was previously possible at a fraction of the computational time.