2019Unpublished venueRequires access

Localizing Sparse Direct Solvers for Circuit Simulations

Robert J. Adams, O.T. Wilkerson, John C. Young, Indranil Chowdhury, W. Theil

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Abstract

We report results obtained using a preconditioned fast iterative solver to model electrostatic circuit problems. The method of moments is used to discretize integral equation-based formulations of the underlying circuit problem. A compressed representation of the system matrix is obtained using the fast multipole method, and the resulting linear system is solved using a preconditioned iterative method. Preconditioners are constructed from a class of localization-based sparse direct solvers, and numerical performance is reported for realistic application ns. The impact of incorporating a multilevel matrix binormalization method is also examined, and resulting tradeoffs are discussed.

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What this paper is about

We report results obtained using a preconditioned fast iterative solver to model electrostatic circuit problems. The method of moments is used to discretize integral equation-based formulations of the underlying circuit problem. A compressed representation of the system matrix is obtained using the fast multipole method, and the resulting linear system is solved using a preconditioned iterative method. Preconditioners are constructed from a class of localization-based sparse direct solvers, and numerical performance is reported for realistic application ns. The impact of incorporating a multilevel matrix binormalization method is also examined, and resulting tradeoffs are discussed.

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Available abstract

We report results obtained using a preconditioned fast iterative solver to model electrostatic circuit problems. The method of moments is used to discretize integral equation-based formulations of the underlying circuit problem. A compressed representation of the system matrix is obtained using the fast multipole method, and the resulting linear system is solved using a preconditioned iterative method. Preconditioners are constructed from a class of localization-based sparse direct solvers, and numerical performance is reported for realistic application ns. The impact of incorporating a multilevel matrix binormalization method is also examined, and resulting tradeoffs are discussed.

Key concepts: Solver, Sparse matrix, Iterative method, Discretization, Computer science, Sparse approximation, Matrix (chemical analysis), Representation (politics)

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