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MPC Toolbox with GPU Accelerated Optimization Algorithms

Nicolai Fog Gade-Nielsen, John Bagterp Jorgensen, Bernd Dammann
DTU Informatics, Technical University of Denmark, 2800 Kgs, Lyngby, Denmark
The 10th European Workshop on Advanced Control and Diagnosis (ACD 2012), 2012
@article{gade2012mpc,

   title={MPC Toolbox with GPU Accelerated Optimization Algorithms},

   author={Gade-Nielsen, N.F. and J{o}rgensen, J.B. and Dammann, B.},

   year={2012}

}

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The introduction of Graphical Processing Units (GPUs) in scientific computing has shown great promise in many different fields. While GPUs are capable of very high floating point performance and memory bandwidth, its massively parallel architecture requires algorithms to be reimplemented to suit the different architecture. Interior point method can be used to solve convex optimization problems. These problems often arise in fields such as in Model Predictive Control (MPC), which may have real-time requirements for the solution time. This paper presents a case study in which we utilize GPUs for a Linear Programming Interior Point Method to solve a test case where a series of power plants must be controlled to minimize the cost of power production. We demonstrate that using GPUs for solving MPC problems can provide a speedup in solution time.
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