GVF: GPU-Based Vector Fitting for Modeling of Multiport Tabulated Data Networks

Abstract

Modeling of multiport data characterizing high-speed modules, such as packages, vias, and complex multiconductor interconnects is becoming increasingly important in signal and power integrity applications. Vector fitting (VF) algorithm has been widely used by designers for macromodeling and system identification from such multiport tabulated data. Since VF and strategies based on it require many iterations to arrive at an optimal number of converged poles, it is highly desired to reduce the computational cost of each VF iteration. This article advances the applicability of VF to exploit the emerging massively parallel graphical processing units (GPUs) by developing necessary parallelization strategies and investigates their performance while using different GPU libraries. For large problem sizes (an increasing number of poles and ports), numerical results demonstrate that the proposed method while using MAGMA libraries provides significant speedup compared with existing multi-CPU-based parallel VF techniques.