We present a new, high-performance coupled electrodynamics-micromagnetics solver for full physical modeling of signals in microelectronic circuitry. The overall strategy couples a finite-difference time-domain (FDTD) approach for Maxwell's equations to a magnetization model described by the Landau-Lifshitz-Gilbert (LLG) equation. The algorithm is implemented in the Exascale Computing Project software framework, AMReX, which provides effective scalability on manycore and GPU-based supercomputing architectures. Furthermore, the code leverages ongoing developments of the Exascale Application Code, WarpX, primarily developed for plasma wakefield accelerator modeling. Our novel temporal coupling scheme provides second-order accuracy in space and time by combining the integration steps for the magnetic field and magnetization into an iterative sub-step that includes a trapezoidal discretization for the magnetization. The performance of the algorithm is demonstrated by the excellent scaling results on NERSC multicore and GPU systems, with a significant (59x) speedup on the GPU using a node-by-node comparison. We demonstrate the utility of our code by performing simulations of an electromagnetic waveguide and a magnetically tunable filter.

中文翻译：

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大规模并行时域耦合电动力学-微磁解算器

我们提出了一种新型的高性能耦合电动力学-微电磁求解器，可对微电子电路中的信号进行完整的物理建模。整体策略将Maxwell方程的时域有限差分（FDTD）方法与Landau-Lifshitz-Gilbert（LLG）方程描述的磁化模型耦合。该算法在Exascale Computing Project软件框架AMReX中实现，该框架在许多核和基于GPU的超级计算体系结构上提供了有效的可伸缩性。此外，该代码利用了Exascale应用程序代码WarpX的不断发展，该代码主要是为等离子流场加速器建模而开发的。我们新颖的时间耦合方案通过将磁场和磁化强度的积分步骤组合到一个迭代子步骤（包括用于磁化的梯形离散化）中，从而在空间和时间上提供了二阶精度。在NERSC多核和GPU系统上，出色的缩放结果证明了该算法的性能，并通过逐节点比较在GPU上显着提高了（59x）速度。通过执行电磁波导和可磁调谐滤波器的仿真，我们演示了代码的实用性。