Parallel algorithms for balanced truncation model reduction of sparse systems

Abstract

We describe the parallelization of an efficient algorithm for balanced truncation that allows to reduce models with state-space dimension up to sricpt O sign (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. © Springer-Verlag Berlin Heidelberg 2006.