Implementation and tests of low-discrepancy sequences
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Computing rank-revealing QR factorizations of dense matrices
ACM Transactions on Mathematical Software (TOMS)
An Asynchronous Parallel Supernodal Algorithm for Sparse Gaussian Elimination
SIAM Journal on Matrix Analysis and Applications
SuperLU_DIST: A scalable distributed-memory sparse direct solver for unsymmetric linear systems
ACM Transactions on Mathematical Software (TOMS)
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
State-space truncation methods for parallel model reduction of large-scale systems
Parallel Computing - Special issue: Parallel and distributed scientific and engineering computing
Approximation of Large-Scale Dynamical Systems (Advances in Design and Control) (Advances in Design and Control)
Variational interconnect analysis via PMTBR
Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
Direct Methods for Sparse Linear Systems (Fundamentals of Algorithms 2)
Direct Methods for Sparse Linear Systems (Fundamentals of Algorithms 2)
Random sampling of moment graph: a stochastic Krylov-reduction algorithm
Proceedings of the conference on Design, automation and test in Europe
An efficient resistance sensitivity extraction algorithm for conductors of arbitrary shapes
Proceedings of the 46th Annual Design Automation Conference
ARMS - automatic residue-minimization based sampling for multi-point modeling techniques
Proceedings of the 46th Annual Design Automation Conference
PRIMA: passive reduced-order interconnect macromodeling algorithm
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Poor man's TBR: a simple model reduction scheme
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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This paper introduces a distributed and shared memory parallel projection based model order reduction framework for parameterized linear systems. The proposed methodology is based on a sampling scheme followed by a projection to build the reduced model. It exploits the parallel nature of the sampling methods to improve the efficiency of the basis generation. The sample selection scheme uses the residue as a proxy for the model error in order to improve automation and maximize the effectiveness of the sampling step. This yields an automatic and reliable methodology, able to handle large systems depending on the frequency and multiple parameters. The framework can be used in shared and distributed memory architectures separately or in conjunction. It is able to deal with different system representations and models of different characteristics, is highly scalable and the parallelization is very effective, as will be demonstrated on a variety of industrial benchmarks, with super linear speed-ups in certain cases. The methodology provides the potential to tackle large and complex models, depending on multiple parameters in an automatic fashion.