State-space truncation methods for parallel model reduction of large-scale systems

We discuss a parallel library of efficient algorithms for model reduction of largescale systems with state-space dimension up to O(104). We survey the numerical algorithms underlying the implementation of the chosen model reduction methods. The approach considered here is based on state-space truncation of the system matrices and includes absolute and relative error methods for both stable and unstable systems. In contrast to serial implementations of these methods, we employ Newton-type iterative algorithms for the solution of the major computational tasks. Experimental results report the numerical accuracy and the parallel performance of our approach on a cluster of Intel Pentium II processors.