GVF:基于GPU的矢量拟合

N. Elumalai, Srinidhi Ganeshan, R. Achar
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引用次数: 3

摘要

向量拟合(VF)算法已广泛应用于多端口表列数据的系统识别。特别是,关注高速模块(如大量耦合互连、封装结构和各种电磁模块)建模的设计界非常感兴趣。本文提出了VF在开发新兴的大规模并行图形处理单元(gpu)方面的适用性。提出了适用于GPU平台的必要并行化策略。对于较大的问题规模(增加端口和极点的数量),数值结果表明,与基于单CPU的VF和现有的基于多CPU的并行VF技术相比,所提出的方法根据所使用的内核数量提供了显着的加速。
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GVF: GPU based Vector Fitting
Vector Fitting (VF) algorithm has been widely used for system identification from multiport tabulated data. Particularly, it is of high interest to the design community focused on modeling of high-speed modules such as large number of coupled interconnects, packaging structures and variety of electromagnetic modules. This paper advances the applicability of VF to exploit the emerging massively parallel graphical processing Units (GPUs). Necessary parallelization strategies suitable for GPU platforms are developed. For large problem sizes (increasing number of ports and poles), numerical results demonstrate that the proposed method provides significant speedup compared to both the single CPU based VF as well as existing multi-CPU based parallel VF techniques depending on the number of cores used.
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