Efficient Simulation of Power Grids

Modern deep sub-micron ultra-large scale integration designs with hundreds of millions of devices require huge grids for power distribution. Such grids, operating with decreasing power supply voltages, are a design limiting factor and accurate analysis of their behavior is of paramount importance as any voltage drops can seriously impact performance or functionality. As power grid models have millions of unknowns, highly optimized special-purpose simulation tools are required to handle the time and memory complexity of solving for their dynamic behavior. In this paper, we propose a hierarchical matrix representation of the power grid model that is both space and time efficient. With this representation, reduced storage matrix factors are efficiently computed and applied in the analysis at every time-step of the simulation. Results show an almost linear complexity growth, namely O(n loga(n)), for some small constant a, in both space and time, when using this matrix representation. Comparisons of our academic implementation with production-quality code prove this method to be very efficient when dealing with the simulation of large power grid models.

[1]  Charlie Chung-Ping Chen,et al.  Efficient large-scale power grid analysis based on preconditioned Krylov-subspace iterative methods , 2001, Proceedings of the 38th Design Automation Conference (IEEE Cat. No.01CH37232).

[2]  Sanjay Pant,et al.  Power Grid Physics and Implications for CAD , 2007, IEEE Design & Test of Computers.

[3]  Farid N. Najm,et al.  A static pattern-independent technique for power grid voltage integrity verification , 2003, Proceedings 2003. Design Automation Conference (IEEE Cat. No.03CH37451).

[4]  Mario Bebendorf,et al.  F ¨ Ur Mathematik in Den Naturwissenschaften Leipzig Hierarchical Lu Decomposition Based Preconditioners for Bem Hierarchical Lu Decomposition Based Preconditioners for Bem , 2022 .

[5]  正人 木村 Max-Planck-Institute for Mathematics in the Sciences(海外,ラボラトリーズ) , 2001 .

[6]  W. Hackbusch,et al.  On the fast matrix multiplication in the boundary element method by panel clustering , 1989 .

[7]  Chung-Kuan Cheng,et al.  Power network analysis using an adaptive algebraic multigrid approach , 2003, DAC '03.

[8]  S. Oliveira,et al.  An algebraic approach for $${\mathcal{H}}$$-matrix preconditioners , 2007, Computing.

[9]  Wolfgang Hackbusch,et al.  A Sparse Matrix Arithmetic Based on H-Matrices. Part I: Introduction to H-Matrices , 1999, Computing.

[10]  Shashi Shekhar,et al.  Multilevel hypergraph partitioning: applications in VLSI domain , 1999, IEEE Trans. Very Large Scale Integr. Syst..

[11]  Sani R. Nassif,et al.  Multigrid-like technique for power grid analysis , 2001, IEEE/ACM International Conference on Computer Aided Design. ICCAD 2001. IEEE/ACM Digest of Technical Papers (Cat. No.01CH37281).

[12]  L. Greengard The Rapid Evaluation of Potential Fields in Particle Systems , 1988 .

[13]  Ieee Circuits,et al.  IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems information for authors , 2018, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[14]  Sani R. Nassif,et al.  Power grid analysis benchmarks , 2008, 2008 Asia and South Pacific Design Automation Conference.

[15]  Mario Bebendorf,et al.  Mathematik in den Naturwissenschaften Leipzig Existence of H-Matrix Approximants to the Inverse FE-Matrix of Elliptic Operators with L ∞-Coefficients , 2003 .

[16]  Timothy A. Davis,et al.  Multiple-Rank Modifications of a Sparse Cholesky Factorization , 2000, SIAM J. Matrix Anal. Appl..

[17]  Fang Yang,et al.  An Algebraic Approach for H-matrix Preconditioners ∗ Suely , 2006 .

[18]  Wolfgang Hackbusch,et al.  Construction and Arithmetics of H-Matrices , 2003, Computing.

[19]  L. Grasedyck,et al.  Domain-decomposition Based ℌ-LU Preconditioners , 2007 .

[20]  Leslie Greengard,et al.  A fast algorithm for particle simulations , 1987 .

[21]  Boris N. Khoromskij,et al.  A Sparse H-Matrix Arithmetic. Part II: Application to Multi-Dimensional Problems , 2000, Computing.

[22]  Timothy A. Davis,et al.  Row Modifications of a Sparse Cholesky Factorization , 2005, SIAM J. Matrix Anal. Appl..

[23]  Sani R. Nassif,et al.  Fast power grid simulation , 2000, Proceedings 37th Design Automation Conference.

[24]  Timothy A. Davis,et al.  Modifying a Sparse Cholesky Factorization , 1999, SIAM J. Matrix Anal. Appl..