Unleashing CPU-GPU Acceleration for Control Theory Applications
نویسندگان
چکیده
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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