A domain-decomposed fast poisson solver on a rectangle
SIAM Journal on Scientific and Statistical Computing - Papers from the Second Conference on Parallel Processing for Scientific Computin
Preconditioning and boundary conditions
SIAM Journal on Numerical Analysis
Hierarchical analysis of power distribution networks
Proceedings of the 37th Annual Design Automation Conference
Efficient large-scale power grid analysis based on preconditioned krylov-subspace iterative methods
Proceedings of the 38th annual Design Automation Conference
Multigrid-like technique for power grid analysis
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
Parallel domain decomposition for simulation of large-scale power grids
Proceedings of the 2007 IEEE/ACM international conference on Computer-aided design
Multigrid on GPU: tackling power grid analysis on parallel SIMT platforms
Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design
GPU friendly fast Poisson solver for structured power grid network analysis
Proceedings of the 46th Annual Design Automation Conference
A parallel direct solver for the simulation of large-scale power/ground networks
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Proceedings of the 47th Design Automation Conference
Efficient simulation of power grids
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems - Special section on the ACM IEEE international conference on formal methods and models for codesign (MEMOCODE) 2009
Power grid analysis using random walks
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Proceedings of the International Conference on Computer-Aided Design
Deterministic random walk preconditioning for power grid analysis
Proceedings of the International Conference on Computer-Aided Design
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Robust and efficient algorithms for power grid analysis are crucial for both VLSI design and optimization. Due to the increasing size of power grids IR drop analysis has become more computationally challenging both in runtime and memory consumption. This work presents a fast Poisson solver preconditioned method for unstructured power grid with unideal boundary conditions. In fact, by taking the advantage of analytical formulation of power grids this analytical preconditioner can be considered as sparse approximate inverse technique. By combining this analytical preconditioner with robust conjugate gradient method, we demonstrate that this approach is totally robust for extremely large scale power grid simulations. Experimental results have shown that iterations of our proposed method will hardly increase with grid size increasing once the pads density and the range of metal resistances value distribution have been decided. We demonstrated that this approach solves an unstructured power grid with 2.56M nodes in only 1/3 iterations of classical ICCG solver, and achieves almost 20X speedups over the classical ICCG solver on runtime.