Power Characterisation of Shared-Memory HPC Systems

Autores
Balladini, Javier; Rucci, Enzo; De Giusti, Armando Eduardo; Naiouf, Marcelo; Suppi, Remo; Rexachs del Rosario, Dolores; Luque Fadón, Emilio; De Giusti, Armando Eduardo; Simari, Guillermo Ricardo; Pesado, Patricia Mabel
Año de publicación
2013
Idioma
inglés
Tipo de recurso
parte de libro
Estado
versión publicada
Descripción
Energy consumption has become one of the greatest challenges in the field of High Performance Computing (HPC). Besides its impact on the environment, energy is a limiting factor for the HPC. Keeping the power consumption of a system below a threshold is one of the great problems; and power prediction can help to solve it. The power characterisation can be used to know the power behaviour of the system under study, and to be a support to reach the power prediction. Furthermore, it could be used to design power-aware application programs. In this article we propose a methodology to characterise the power consumption of shared-memory HPC systems. Our proposed methodology involves the finding of influence factors on power consumed by the systems. It is similar to previous works, but we propose an in-deep approach that can help us to get a better power characterisation of the system. We apply our methodology to characterise an Intel server platform and the results show that we can find a more extended set of influence factors on power consumption.
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
power characterisation
shared memory systems
microbenchmarks
green computing
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/81224
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/81224
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Power Characterisation of Shared-Memory HPC SystemsBalladini, JavierRucci, EnzoDe Giusti, Armando EduardoNaiouf, MarceloSuppi, RemoRexachs del Rosario, DoloresLuque Fadón, EmilioDe Giusti, Armando EduardoSimari, Guillermo RicardoPesado, Patricia MabelCiencias Informáticaspower characterisationshared memory systemsmicrobenchmarksgreen computingEnergy consumption has become one of the greatest challenges in the field of High Performance Computing (HPC). Besides its impact on the environment, energy is a limiting factor for the HPC. Keeping the power consumption of a system below a threshold is one of the great problems; and power prediction can help to solve it. The power characterisation can be used to know the power behaviour of the system under study, and to be a support to reach the power prediction. Furthermore, it could be used to design power-aware application programs. In this article we propose a methodology to characterise the power consumption of shared-memory HPC systems. Our proposed methodology involves the finding of influence factors on power consumed by the systems. It is similar to previous works, but we propose an in-deep approach that can help us to get a better power characterisation of the system. We apply our methodology to characterise an Intel server platform and the results show that we can find a more extended set of influence factors on power consumption.Red de Universidades con Carreras en Informática (RedUNCI)Editorial de la Universidad Nacional de La Plata (EDULP)2013-03-31info:eu-repo/semantics/bookPartinfo:eu-repo/semantics/publishedVersionCapitulo de librohttp://purl.org/coar/resource_type/c_3248info:ar-repo/semantics/parteDeLibroapplication/pdf53-65http://sedici.unlp.edu.ar/handle/10915/81224enginfo:eu-repo/semantics/altIdentifier/isbn/978-987-1985-20-3info:eu-repo/semantics/reference/hdl/10915/58940info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/4.0/Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-10T12:17:44Zoai:sedici.unlp.edu.ar:10915/81224Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-10 12:17:45.228SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Power Characterisation of Shared-Memory HPC Systems
title Power Characterisation of Shared-Memory HPC Systems
spellingShingle Power Characterisation of Shared-Memory HPC Systems
Balladini, Javier
Ciencias Informáticas
power characterisation
shared memory systems
microbenchmarks
green computing
title_short Power Characterisation of Shared-Memory HPC Systems
title_full Power Characterisation of Shared-Memory HPC Systems
title_fullStr Power Characterisation of Shared-Memory HPC Systems
title_full_unstemmed Power Characterisation of Shared-Memory HPC Systems
title_sort Power Characterisation of Shared-Memory HPC Systems
dc.creator.none.fl_str_mv Balladini, Javier
Rucci, Enzo
De Giusti, Armando Eduardo
Naiouf, Marcelo
Suppi, Remo
Rexachs del Rosario, Dolores
Luque Fadón, Emilio
De Giusti, Armando Eduardo
Simari, Guillermo Ricardo
Pesado, Patricia Mabel
author Balladini, Javier
author_facet Balladini, Javier
Rucci, Enzo
De Giusti, Armando Eduardo
Naiouf, Marcelo
Suppi, Remo
Rexachs del Rosario, Dolores
Luque Fadón, Emilio
Simari, Guillermo Ricardo
Pesado, Patricia Mabel
author_role author
author2 Rucci, Enzo
De Giusti, Armando Eduardo
Naiouf, Marcelo
Suppi, Remo
Rexachs del Rosario, Dolores
Luque Fadón, Emilio
Simari, Guillermo Ricardo
Pesado, Patricia Mabel
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
power characterisation
shared memory systems
microbenchmarks
green computing
topic Ciencias Informáticas
power characterisation
shared memory systems
microbenchmarks
green computing
dc.description.none.fl_txt_mv Energy consumption has become one of the greatest challenges in the field of High Performance Computing (HPC). Besides its impact on the environment, energy is a limiting factor for the HPC. Keeping the power consumption of a system below a threshold is one of the great problems; and power prediction can help to solve it. The power characterisation can be used to know the power behaviour of the system under study, and to be a support to reach the power prediction. Furthermore, it could be used to design power-aware application programs. In this article we propose a methodology to characterise the power consumption of shared-memory HPC systems. Our proposed methodology involves the finding of influence factors on power consumed by the systems. It is similar to previous works, but we propose an in-deep approach that can help us to get a better power characterisation of the system. We apply our methodology to characterise an Intel server platform and the results show that we can find a more extended set of influence factors on power consumption.
Red de Universidades con Carreras en Informática (RedUNCI)
description Energy consumption has become one of the greatest challenges in the field of High Performance Computing (HPC). Besides its impact on the environment, energy is a limiting factor for the HPC. Keeping the power consumption of a system below a threshold is one of the great problems; and power prediction can help to solve it. The power characterisation can be used to know the power behaviour of the system under study, and to be a support to reach the power prediction. Furthermore, it could be used to design power-aware application programs. In this article we propose a methodology to characterise the power consumption of shared-memory HPC systems. Our proposed methodology involves the finding of influence factors on power consumed by the systems. It is similar to previous works, but we propose an in-deep approach that can help us to get a better power characterisation of the system. We apply our methodology to characterise an Intel server platform and the results show that we can find a more extended set of influence factors on power consumption.
publishDate 2013
dc.date.none.fl_str_mv 2013-03-31
dc.type.none.fl_str_mv info:eu-repo/semantics/bookPart
info:eu-repo/semantics/publishedVersion
Capitulo de libro
http://purl.org/coar/resource_type/c_3248
info:ar-repo/semantics/parteDeLibro
format bookPart
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/81224
url http://sedici.unlp.edu.ar/handle/10915/81224
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/978-987-1985-20-3
info:eu-repo/semantics/reference/hdl/10915/58940
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc/4.0/
Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/4.0/
Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
dc.format.none.fl_str_mv application/pdf
53-65
dc.publisher.none.fl_str_mv Editorial de la Universidad Nacional de La Plata (EDULP)
publisher.none.fl_str_mv Editorial de la Universidad Nacional de La Plata (EDULP)
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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score 12.993085

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