Parallel Algorithms for Balanced Truncation Model Reduction of Sparse Systems

We describe the parallelization of an efficient algorithm for balanced truncation that allows to reduce models with state-space dimension up to O(105 ). 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.

• Type: Conference
• Year: 2004
• Where: PARA
• Authors: José M. Badía, Peter Benner, Rafael Mayo, Enrique S. Quintana-Ortí
• Keywords: Applied Computing, Intel Xeon Processors, Large-scale Sparse Lyapunov, Model Reduction Algorithm, PARA 2004