MPC Toolbox with GPU Accelerated Optimization Algorithms

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2012

Standard

MPC Toolbox with GPU Accelerated Optimization Algorithms. / Gade-Nielsen, Nicolai Fog; Jørgensen, John Bagterp; Dammann, Bernd.

The 10th European Workshop on Advanced Control and Diagnosis (ACD 2012). Technical University of Denmark, 2012.

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2012

Harvard

Gade-Nielsen, NF, Jørgensen, JB & Dammann, B 2012, 'MPC Toolbox with GPU Accelerated Optimization Algorithms'. in The 10th European Workshop on Advanced Control and Diagnosis (ACD 2012). Technical University of Denmark.

APA

Gade-Nielsen, N. F., Jørgensen, J. B., & Dammann, B. (2012). MPC Toolbox with GPU Accelerated Optimization Algorithms. In The 10th European Workshop on Advanced Control and Diagnosis (ACD 2012). Technical University of Denmark.

CBE

Gade-Nielsen NF, Jørgensen JB, Dammann B. 2012. MPC Toolbox with GPU Accelerated Optimization Algorithms. In The 10th European Workshop on Advanced Control and Diagnosis (ACD 2012). Technical University of Denmark.

MLA

Gade-Nielsen, Nicolai Fog, John Bagterp Jørgensen, and Bernd Dammann "MPC Toolbox with GPU Accelerated Optimization Algorithms". The 10th European Workshop on Advanced Control and Diagnosis (ACD 2012). Technical University of Denmark. 2012.

Vancouver

Gade-Nielsen NF, Jørgensen JB, Dammann B. MPC Toolbox with GPU Accelerated Optimization Algorithms. In The 10th European Workshop on Advanced Control and Diagnosis (ACD 2012). Technical University of Denmark. 2012.

Author

Gade-Nielsen, Nicolai Fog; Jørgensen, John Bagterp; Dammann, Bernd / MPC Toolbox with GPU Accelerated Optimization Algorithms.

The 10th European Workshop on Advanced Control and Diagnosis (ACD 2012). Technical University of Denmark, 2012.

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2012

Bibtex

@inbook{5695cc20e29d419ba089003b59ea1453,
title = "MPC Toolbox with GPU Accelerated Optimization Algorithms",
keywords = "Linear programming, Interior Point Methods, Model predictive control, Graphical Processing Unit",
publisher = "Technical University of Denmark",
author = "Gade-Nielsen, {Nicolai Fog} and Jørgensen, {John Bagterp} and Bernd Dammann",
year = "2012",
booktitle = "The 10th European Workshop on Advanced Control and Diagnosis (ACD 2012)",

}

RIS

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 -