High Performance Computing of Complex Electromagnetic Algorithms Based on GPU/CPU Heterogeneous Platform and Its Applications to EM Scattering and Multilayered Medium Structure

Abstract

The fast and accurate numerical analysis for large-scale objects and complex structures is essential to electromagnetic simulation and design. Comparing to the exploration in EM algorithms from mathematical point of view, the computer programming realization is coordinately significant while keeping up with the development of hardware architectures. Unlike the previous parallel algorithms or those implemented by means of parallel programming on multicore CPU with OpenMP or on a cluster of computers with MPI, the new type of large-scale parallel processor based on graphics processing unit (GPU) has shown impressive ability in various scenarios of supercomputing, while its application in computational electromagnetics is especially expected. This paper introduces our recent work on high performance computing based on GPU/CPU heterogeneous platform and its application to EM scattering problems and planar multilayered medium structure, including a novel realization of OpenMP-CUDA-MLFMM, a developed ACA method and a deeply optimized CG-FFT method. With fruitful numerical examples and their obvious enhancement in efficiencies, it is convincing to keep on deeply investigating and understanding the computer hardware and their operating mechanism in the future.

Cinética de renderizado y relación de hardware para la digitalización de imágenes del Neurobanco de la Universidad de Antioquia

“…En ambas hay distintos modelos en el mercado que difieren en https://doi.org/10.24050/reia.v17i34.1405 potencia, calidad y precio. Además, muchos software son programados para aprovechar ciertas estructuras físicas de CPU o GPU particulares, por lo que es común que un software tenga mejor desempeño en un procesador de menor potencia comparado con uno de mayor potencia, simplemente porque está mejor optimizado para dicho procesador (Song, Mu and Zhou, 2017;Teodoro et al, 2017).…”