Abstract
In this paper we review the effect of two high-performance techniques for the solution of matrix equations arising in control theory applications on CPU-GPU platforms, in particular advanced optimization via look-ahead and iterative refinement. Our experimental evaluation on the last GPU-generation from NVIDIA, “Kepler”, shows the slight advantage of matrix inversion via Gauss-Jordan elimination, when combined with look-ahead, over the traditional LU-based procedure, as well as the clear benefits of using mixed precision and iterative refinement for the solution of Lyapunov equations.
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Benner, P., Ezzatti, P., Quintana-Ortí, E.S., Remón, A. (2013). Unleashing CPU-GPU Acceleration for Control Theory Applications. In: Caragiannis, I., et al. Euro-Par 2012: Parallel Processing Workshops. Euro-Par 2012. Lecture Notes in Computer Science, vol 7640. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36949-0_13
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DOI: https://doi.org/10.1007/978-3-642-36949-0_13
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