PowerRush: A linear simulator for power grid

Jianlei Yang, Zuowei Li, Yici Cai, Qiang Zhou
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引用次数: 25

Abstract

As the increasing size of power grids, IR drop analysis has become more computationally challenging both in runtime and memory consumption. In this paper, we propose a linear complexity simulator named PowerRush, which consists of an efficient SPICE Parser, a robust circuit Builder and a linear solver. The proposed solver is a pure algebraic method which can provide an optimal convergence without geometric information. It is implemented by Algebraic Multigrid Preconditioned Conjugate Gradient method, in which an aggregation based algebraic multigrid with K-Cycle acceleration is adopted as a preconditioner to improve the robustness of conjugate gradient iterative method. In multigrid scheme, double pairwise aggregation technique is applied to the matrix graph in coarsening procedure to ensure low setup cost and memory requirement. Further, a K-Cycle multigrid scheme is adopted to provide Krylov subspace acceleration at each level to guarantee optimal or near optimal convergence. Experimental results on real power grids have shown that PowerRush has a linear complexity in runtime cost and memory consumption. The DC analysis of a 60 Million nodes power grid can be solved by PowerRush for 0.01mV accuracy in 170 seconds with 21.89GB memory used.
PowerRush:一个用于电网的线性模拟器
随着电网规模的不断扩大,IR下降分析在运行时和内存消耗方面变得越来越具有计算挑战性。在本文中,我们提出了一个名为PowerRush的线性复杂性模拟器,它由一个高效的SPICE解析器、一个鲁棒的电路生成器和一个线性求解器组成。所提出的求解方法是一种不需要几何信息的纯代数方法,可以提供最优收敛性。该算法采用代数多网格预条件共轭梯度法实现,采用基于K-Cycle加速的聚集代数多网格作为预条件,提高了共轭梯度迭代法的鲁棒性。在多网格方案中,在粗化过程中对矩阵图采用双对聚合技术,以保证较低的设置成本和存储需求。进一步,采用K-Cycle多重网格方案,在每一级提供Krylov子空间加速,保证最优或接近最优收敛。在实际电网上的实验结果表明,PowerRush在运行成本和内存消耗方面具有线性复杂性。PowerRush在使用21.89GB内存的情况下,可以在170秒内解决6000万个节点电网的直流分析,精度为0.01mV。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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