Exploiting GPU/SIMD Architectures for Solving Linear-Quadratic MPC Problems
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2022
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866914543952723968 |
|---|---|
| author | Cole, David Shin, Sungho Pacaud, François Zavala, Victor M. Anitescu, Mihai |
| author_facet | Cole, David Shin, Sungho Pacaud, François Zavala, Victor M. Anitescu, Mihai |
| contents | We report numerical results on solving constrained linear-quadratic model predictive control (MPC) problems by exploiting graphics processing units (GPUs). The presented method reduces the MPC problem by eliminating the state variables and applies a condensed-space interior-point method to remove the inequality constraints in the KKT system. The final condensed matrix is positive definite and can be efficiently factorized in parallel on GPU/SIMD architectures. In addition, the size of the condensed matrix depends only on the number of controls in the problem, rendering the method particularly effective when the problem has many states but few inputs and moderate horizon length. Our numerical results for PDE-constrained problems show that the approach is an order of magnitude faster than a standard CPU implementation. We also provide an open-source Julia framework that facilitates modeling (DynamicNLPModels.jl) and solution (MadNLP.jl) of MPC problems on GPUs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_13049 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Exploiting GPU/SIMD Architectures for Solving Linear-Quadratic MPC Problems Cole, David Shin, Sungho Pacaud, François Zavala, Victor M. Anitescu, Mihai Optimization and Control We report numerical results on solving constrained linear-quadratic model predictive control (MPC) problems by exploiting graphics processing units (GPUs). The presented method reduces the MPC problem by eliminating the state variables and applies a condensed-space interior-point method to remove the inequality constraints in the KKT system. The final condensed matrix is positive definite and can be efficiently factorized in parallel on GPU/SIMD architectures. In addition, the size of the condensed matrix depends only on the number of controls in the problem, rendering the method particularly effective when the problem has many states but few inputs and moderate horizon length. Our numerical results for PDE-constrained problems show that the approach is an order of magnitude faster than a standard CPU implementation. We also provide an open-source Julia framework that facilitates modeling (DynamicNLPModels.jl) and solution (MadNLP.jl) of MPC problems on GPUs. |
| title | Exploiting GPU/SIMD Architectures for Solving Linear-Quadratic MPC Problems |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2209.13049 |