Improving accuracy of parallel SLICOT model reduction routines for stable systems

D. Guerrero-Lopez, J. Román
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引用次数: 1

Abstract

This paper shows part of the work carried out to develop parallel versions of the SLICOT routines for model reduction of stable systems. In particular, the routines that have been parallelised are those based on the solution of Lyapunov equations. The goal is to be able to work with larger unreduced models and also to obtain better performance in the reduction process. New routines have been developed using standard libraries to improve portability and efficiency. A preliminary version was released previously by the authors, which achieved high performance. However, accuracy improvements have been necessary in order to make the new routines similar to the sequential ones in this aspect. Routines presented in this paper preserve good performance obtained by the previous parallel implementation while maintaining high accuracy of sequential SLICOT routines.
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提高稳定系统并行SLICOT模型约简程序的精度
本文展示了为开发用于稳定系统模型简化的SLICOT例程的并行版本所进行的部分工作。特别地,已经并行化的例程是那些基于李雅普诺夫方程的解的例程。目标是能够处理更大的未约简模型,并在约简过程中获得更好的性能。使用标准库开发了新的例程,以提高可移植性和效率。作者之前发布了一个初步版本,实现了高性能。然而,为了使新例程在这方面与顺序例程相似,精度改进是必要的。本文所提出的例程在保持顺序SLICOT例程高精度的同时,保留了以往并行实现所获得的良好性能。
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