Direct methods for sparse matrices
Direct methods for sparse matrices
Reduced-order modeling of large passive linear circuits by means of the SYPVL algorithm
Proceedings of the 1996 IEEE/ACM international conference on Computer-aided design
A Cyclic Low-Rank Smith Method for Large Sparse Lyapunov Equations
SIAM Journal on Scientific Computing
Analysis of Multiconductor Transmission Lines
Analysis of Multiconductor Transmission Lines
Interconnect Analysis and Synthesis
Interconnect Analysis and Synthesis
Model Reduction for Control System Design
Model Reduction for Control System Design
Low Rank Solution of Lyapunov Equations
SIAM Journal on Matrix Analysis and Applications
MUMPS: A General Purpose Distributed Memory Sparse Solver
PARA '00 Proceedings of the 5th International Workshop on Applied Parallel Computing, New Paradigms for HPC in Industry and Academia
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)
Parallel Implementation of LQG Balanced Truncation for Large-Scale Systems
Large-Scale Scientific Computing
Efficient model order reduction of large-scale systems on multi-core platforms
ICCSA'11 Proceedings of the 2011 international conference on Computational science and Its applications - Volume Part V
Parallel order reduction via balanced truncation for optimal cooling of steel profiles
Euro-Par'05 Proceedings of the 11th international Euro-Par conference on Parallel Processing
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We describe the parallelization of an efficient algorithm for balanced truncation that allows to reduce models with state-space dimension up to $\mathcal{O}(10^5)$. The major computational task in this approach is the solution of two large-scale sparse Lyapunov equations, performed via a coupled LR-ADI iteration with (super-)linear convergence. Experimental results on a cluster of Intel Xeon processors illustrate the efficacy of our parallel model reduction algorithm.