Workload placement on heterogeneous CPU-GPU systems

The popularity of heterogeneous CPU-GPU processing has increased considerably in recent years. To efficiently utilize heterogeneous resources, data processing systems depend on an appropriate workload placement strategy to assign the right amount of compute to the right processor. However, finding a...

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Autores: Nogueira Lobo de Carvalho, Marcos|||0000-0001-7015-9517, Simitsis, Alkis, Queralt Calafat, Anna|||0000-0003-2782-2955, Romero Moral, Óscar|||0000-0001-6350-8328
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/426961
Acceso en línea:https://hdl.handle.net/2117/426961
https://dx.doi.org/10.14778/3685800.3685845
Access Level:acceso abierto
Palabra clave:Computer graphics equipment
Data reduction
Digital storage
Graphics processing unit
Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors
Àrees temàtiques de la UPC::Informàtica::Enginyeria del software
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spelling Workload placement on heterogeneous CPU-GPU systemsNogueira Lobo de Carvalho, Marcos|||0000-0001-7015-9517Simitsis, AlkisQueralt Calafat, Anna|||0000-0003-2782-2955Romero Moral, Óscar|||0000-0001-6350-8328Computer graphics equipmentData reductionDigital storageGraphics processing unitÀrees temàtiques de la UPC::Informàtica::Arquitectura de computadorsÀrees temàtiques de la UPC::Informàtica::Enginyeria del softwareThe popularity of heterogeneous CPU-GPU processing has increased considerably in recent years. To efficiently utilize heterogeneous resources, data processing systems depend on an appropriate workload placement strategy to assign the right amount of compute to the right processor. However, finding an optimal placement strategy is not trivial due to various complex and conflicting tradeoffs related to the characteristics of processors, the nature of the workload, and data locality. In addition, placement decisions impact workload runtime and performance cost, and also depend on the availability of potentially different implementations for CPUs and GPUs, which adds extra complexity in such heterogeneous environments. In this tutorial, we review and compare state-of-the-art strategies for workload placement on heterogeneous CPU-GPU architectures, along with runtime prediction techniques and methods to support multi-device code. We also discuss open issues and identify potentially promising future research directions.This work has been partially supported by the H2020-MSCAITN2020 DEDS (GA.955895), and the EU-HORIZON programmes CREXDATA (GA.101092749) and FAIR-CORE4EOSC (GA.101057264) and the Spanish MICIU DOGO4ML (PID2020-117191RB-I00).Peer ReviewedAssociation for Computing Machinery (ACM)20242024-08-0120252025-03-25journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/426961https://dx.doi.org/10.14778/3685800.3685845reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengEuropean Commission http://doi.org/10.13039/100010661 Horizon 2020 Framework Programme 955895 Data Engineering for Data ScienceAgencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-117191RB-I00 DESARROLLO, OPERATIVA Y GOBERNANZA DE DATOS PARA SISTEMAS SOFTWARE BASADOS EN APRENDIZAJE AUTOMATICOopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4269612026-05-27T15:37:01Z
dc.title.none.fl_str_mv Workload placement on heterogeneous CPU-GPU systems
title Workload placement on heterogeneous CPU-GPU systems
spellingShingle Workload placement on heterogeneous CPU-GPU systems
Nogueira Lobo de Carvalho, Marcos|||0000-0001-7015-9517
Computer graphics equipment
Data reduction
Digital storage
Graphics processing unit
Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors
Àrees temàtiques de la UPC::Informàtica::Enginyeria del software
title_short Workload placement on heterogeneous CPU-GPU systems
title_full Workload placement on heterogeneous CPU-GPU systems
title_fullStr Workload placement on heterogeneous CPU-GPU systems
title_full_unstemmed Workload placement on heterogeneous CPU-GPU systems
title_sort Workload placement on heterogeneous CPU-GPU systems
dc.creator.none.fl_str_mv Nogueira Lobo de Carvalho, Marcos|||0000-0001-7015-9517
Simitsis, Alkis
Queralt Calafat, Anna|||0000-0003-2782-2955
Romero Moral, Óscar|||0000-0001-6350-8328
author Nogueira Lobo de Carvalho, Marcos|||0000-0001-7015-9517
author_facet Nogueira Lobo de Carvalho, Marcos|||0000-0001-7015-9517
Simitsis, Alkis
Queralt Calafat, Anna|||0000-0003-2782-2955
Romero Moral, Óscar|||0000-0001-6350-8328
author_role author
author2 Simitsis, Alkis
Queralt Calafat, Anna|||0000-0003-2782-2955
Romero Moral, Óscar|||0000-0001-6350-8328
author2_role author
author
author
dc.subject.none.fl_str_mv Computer graphics equipment
Data reduction
Digital storage
Graphics processing unit
Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors
Àrees temàtiques de la UPC::Informàtica::Enginyeria del software
topic Computer graphics equipment
Data reduction
Digital storage
Graphics processing unit
Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors
Àrees temàtiques de la UPC::Informàtica::Enginyeria del software
description The popularity of heterogeneous CPU-GPU processing has increased considerably in recent years. To efficiently utilize heterogeneous resources, data processing systems depend on an appropriate workload placement strategy to assign the right amount of compute to the right processor. However, finding an optimal placement strategy is not trivial due to various complex and conflicting tradeoffs related to the characteristics of processors, the nature of the workload, and data locality. In addition, placement decisions impact workload runtime and performance cost, and also depend on the availability of potentially different implementations for CPUs and GPUs, which adds extra complexity in such heterogeneous environments. In this tutorial, we review and compare state-of-the-art strategies for workload placement on heterogeneous CPU-GPU architectures, along with runtime prediction techniques and methods to support multi-device code. We also discuss open issues and identify potentially promising future research directions.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-08-01
2025
2025-03-25
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/426961
https://dx.doi.org/10.14778/3685800.3685845
url https://hdl.handle.net/2117/426961
https://dx.doi.org/10.14778/3685800.3685845
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission http://doi.org/10.13039/100010661 Horizon 2020 Framework Programme 955895 Data Engineering for Data Science
Agencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-117191RB-I00 DESARROLLO, OPERATIVA Y GOBERNANZA DE DATOS PARA SISTEMAS SOFTWARE BASADOS EN APRENDIZAJE AUTOMATICO
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Association for Computing Machinery (ACM)
publisher.none.fl_str_mv Association for Computing Machinery (ACM)
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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