3POr — Parallel projection based parameterized order reduction for multi-dimensional linear models

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.