MPC Toolbox with GPU Accelerated Optimization Algorithms
Publication: Research - peer-review › Article in proceedings – Annual report year: 2012
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MPC Toolbox with GPU Accelerated Optimization Algorithms. / Gade-Nielsen, Nicolai Fog; Jørgensen, John Bagterp; Dammann, Bernd.
In: The 10th European Workshop on Advanced Control and Diagnosis (ACD 2012). Technical University of Denmark, 2012.Publication: Research - peer-review › Article in proceedings – Annual report year: 2012
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TY - GEN
T1 - MPC Toolbox with GPU Accelerated Optimization Algorithms
A1 - Gade-Nielsen,Nicolai Fog
A1 - Jørgensen,John Bagterp
A1 - Dammann,Bernd
AU - Gade-Nielsen,Nicolai Fog
AU - Jørgensen,John Bagterp
AU - Dammann,Bernd
PB - Technical University of Denmark
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Linear programming
KW - Interior Point Methods
KW - Model predictive control
KW - Graphical Processing Unit
BT - The 10th European Workshop on Advanced Control and Diagnosis (ACD 2012)
T2 - The 10th European Workshop on Advanced Control and Diagnosis (ACD 2012)
ER -