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Accelerating Model Reduction of Large Linear Systems with Graphics Processors

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7134)

Abstract

Model order reduction of a dynamical linear time-invariant system appears in many applications from science and engineering. Numerically reliable SVD-based methods for this task require in general \(\mathcal{O}(n^3)\) floating-point arithmetic operations, with n being in the range 103 − 105 for many practical applications. In this paper we investigate the use of graphics processors (GPUs) to accelerate model reduction of large-scale linear systems by off-loading the computationally intensive tasks to this device. Experiments on a hybrid platform consisting of state-of-the-art general-purpose multi-core processors and a GPU illustrate the potential of this approach.

Keywords

  • model reduction
  • dynamical linear systems
  • Lyapunov equations
  • SVD-based methods
  • GPUs

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Benner, P., Ezzatti, P., Kressner, D., Quintana-Ortí, E.S., Remón, A. (2012). Accelerating Model Reduction of Large Linear Systems with Graphics Processors. In: Jónasson, K. (eds) Applied Parallel and Scientific Computing. PARA 2010. Lecture Notes in Computer Science, vol 7134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28145-7_9

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  • DOI: https://doi.org/10.1007/978-3-642-28145-7_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28144-0

  • Online ISBN: 978-3-642-28145-7

  • eBook Packages: Computer ScienceComputer Science (R0)