Solving Algebraic Riccati Equations on Hybrid CPU-GPU Platforms
- Autores
- Ezzatti, Pablo; Quintana-Ortí, Enrique S.; Remón, Alfredo
- Año de publicación
- 2011
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- The solution of Algebraic Riccati Equations is required in many linear optimal and robust control methods such as LQR, LQG, Kalman filter, and in model order reduction techniques like the balanced stochastic truncation method. Numerically reliable algorithms for these applications rely on the sign function method, and require O(8n3) floating-point arithmetic operations, with n in the range of 103 −105 for many practical applications. In this paper we investigate the use of graphics processors (GPUs) to accelerate the solution of Algebraic Riccati Equations by off-loading the computationally intensive kernels to this device. Experiments on a hybrid platform compose by state-of-the-art general-purpose multi-core processors and a GPU illustrate the potential of this approach.
Sociedad Argentina de Informática e Investigación Operativa - Materia
-
Ciencias Informáticas
Algebraic Riccati Equations
Hybrid CPU-GPU Platforms - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/126121
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Solving Algebraic Riccati Equations on Hybrid CPU-GPU PlatformsEzzatti, PabloQuintana-Ortí, Enrique S.Remón, AlfredoCiencias InformáticasAlgebraic Riccati EquationsHybrid CPU-GPU PlatformsThe solution of Algebraic Riccati Equations is required in many linear optimal and robust control methods such as LQR, LQG, Kalman filter, and in model order reduction techniques like the balanced stochastic truncation method. Numerically reliable algorithms for these applications rely on the sign function method, and require O(8n3) floating-point arithmetic operations, with n in the range of 103 −105 for many practical applications. In this paper we investigate the use of graphics processors (GPUs) to accelerate the solution of Algebraic Riccati Equations by off-loading the computationally intensive kernels to this device. Experiments on a hybrid platform compose by state-of-the-art general-purpose multi-core processors and a GPU illustrate the potential of this approach.Sociedad Argentina de Informática e Investigación Operativa2011-08info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf41-47http://sedici.unlp.edu.ar/handle/10915/126121enginfo:eu-repo/semantics/altIdentifier/url/https://40jaiio.sadio.org.ar/sites/default/files/T2011/HPC/849.pdfinfo:eu-repo/semantics/altIdentifier/issn/1851-9326info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:30:27Zoai:sedici.unlp.edu.ar:10915/126121Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:30:28.117SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Solving Algebraic Riccati Equations on Hybrid CPU-GPU Platforms |
title |
Solving Algebraic Riccati Equations on Hybrid CPU-GPU Platforms |
spellingShingle |
Solving Algebraic Riccati Equations on Hybrid CPU-GPU Platforms Ezzatti, Pablo Ciencias Informáticas Algebraic Riccati Equations Hybrid CPU-GPU Platforms |
title_short |
Solving Algebraic Riccati Equations on Hybrid CPU-GPU Platforms |
title_full |
Solving Algebraic Riccati Equations on Hybrid CPU-GPU Platforms |
title_fullStr |
Solving Algebraic Riccati Equations on Hybrid CPU-GPU Platforms |
title_full_unstemmed |
Solving Algebraic Riccati Equations on Hybrid CPU-GPU Platforms |
title_sort |
Solving Algebraic Riccati Equations on Hybrid CPU-GPU Platforms |
dc.creator.none.fl_str_mv |
Ezzatti, Pablo Quintana-Ortí, Enrique S. Remón, Alfredo |
author |
Ezzatti, Pablo |
author_facet |
Ezzatti, Pablo Quintana-Ortí, Enrique S. Remón, Alfredo |
author_role |
author |
author2 |
Quintana-Ortí, Enrique S. Remón, Alfredo |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas Algebraic Riccati Equations Hybrid CPU-GPU Platforms |
topic |
Ciencias Informáticas Algebraic Riccati Equations Hybrid CPU-GPU Platforms |
dc.description.none.fl_txt_mv |
The solution of Algebraic Riccati Equations is required in many linear optimal and robust control methods such as LQR, LQG, Kalman filter, and in model order reduction techniques like the balanced stochastic truncation method. Numerically reliable algorithms for these applications rely on the sign function method, and require O(8n3) floating-point arithmetic operations, with n in the range of 103 −105 for many practical applications. In this paper we investigate the use of graphics processors (GPUs) to accelerate the solution of Algebraic Riccati Equations by off-loading the computationally intensive kernels to this device. Experiments on a hybrid platform compose by state-of-the-art general-purpose multi-core processors and a GPU illustrate the potential of this approach. Sociedad Argentina de Informática e Investigación Operativa |
description |
The solution of Algebraic Riccati Equations is required in many linear optimal and robust control methods such as LQR, LQG, Kalman filter, and in model order reduction techniques like the balanced stochastic truncation method. Numerically reliable algorithms for these applications rely on the sign function method, and require O(8n3) floating-point arithmetic operations, with n in the range of 103 −105 for many practical applications. In this paper we investigate the use of graphics processors (GPUs) to accelerate the solution of Algebraic Riccati Equations by off-loading the computationally intensive kernels to this device. Experiments on a hybrid platform compose by state-of-the-art general-purpose multi-core processors and a GPU illustrate the potential of this approach. |
publishDate |
2011 |
dc.date.none.fl_str_mv |
2011-08 |
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info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
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conferenceObject |
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http://sedici.unlp.edu.ar/handle/10915/126121 |
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eng |
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eng |
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info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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