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. © 2013 Springer-Verlag.
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CITATION STYLE
Benner, P., Ezzatti, P., Quintana-Ortí, E. S., & Remón, A. (2013). Unleashing CPU-GPU acceleration for control theory applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7640 LNCS, pp. 102–111). https://doi.org/10.1007/978-3-642-36949-0_13
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