Running scientific codes on amazon EC2: a performance analysis of five high-end instances

Autores
Expósito, Roberto R.; Taboada, Guillermo L.; Pardo, Xoán C.; Touriño, Juan; Doallo, Ramón
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Amazon Web Services (AWS) is a well-known public Infrastructure-as-a-Service (IaaS) provider whose Elastic Computing Cloud (EC2) o ering includes some instances, known as cluster instances, aimed at High-Performance Computing (HPC) applications. In previous work, authors have shown that the scalability of HPC communication-intensive applications does not bene t from using higher computational power cluster instances as much as it could be expected. Cost analysis recommends using lower computational power cluster instances unless high memory requirements preclude their use. Moreover, it has been observed that scalability is very poor when more than one instance is used due to network virtualization overhead. Based on those results, this paper gives more insight into the performance of running scienti c applications on the Amazon EC2 platform evaluating ve (of which two have been recently released) of the higher computational power instances in terms of single instance performance, intra-VM (Virtual Machine) scalability and cost-e ciency. The evaluation has been carried out using both an HPC benchmark suite and a real High-Troughput Computing (HTC) application.
Facultad de Informática
Materia
Ciencias Informáticas
cloud computing
high performance computing
high throughput computing
Amazon EC2
OpenMP
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/34511

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spelling Running scientific codes on amazon EC2: a performance analysis of five high-end instancesExpósito, Roberto R.Taboada, Guillermo L.Pardo, Xoán C.Touriño, JuanDoallo, RamónCiencias Informáticascloud computinghigh performance computinghigh throughput computingAmazon EC2OpenMPAmazon Web Services (AWS) is a well-known public Infrastructure-as-a-Service (IaaS) provider whose Elastic Computing Cloud (EC2) o ering includes some instances, known as cluster instances, aimed at High-Performance Computing (HPC) applications. In previous work, authors have shown that the scalability of HPC communication-intensive applications does not bene t from using higher computational power cluster instances as much as it could be expected. Cost analysis recommends using lower computational power cluster instances unless high memory requirements preclude their use. Moreover, it has been observed that scalability is very poor when more than one instance is used due to network virtualization overhead. Based on those results, this paper gives more insight into the performance of running scienti c applications on the Amazon EC2 platform evaluating ve (of which two have been recently released) of the higher computational power instances in terms of single instance performance, intra-VM (Virtual Machine) scalability and cost-e ciency. The evaluation has been carried out using both an HPC benchmark suite and a real High-Troughput Computing (HTC) application.Facultad de Informática2013-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf153-159http://sedici.unlp.edu.ar/handle/10915/34511enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Dec13-8.pdfinfo:eu-repo/semantics/altIdentifier/issn/1666-6038info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/3.0/Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:59:01Zoai:sedici.unlp.edu.ar:10915/34511Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:59:01.768SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Running scientific codes on amazon EC2: a performance analysis of five high-end instances
title Running scientific codes on amazon EC2: a performance analysis of five high-end instances
spellingShingle Running scientific codes on amazon EC2: a performance analysis of five high-end instances
Expósito, Roberto R.
Ciencias Informáticas
cloud computing
high performance computing
high throughput computing
Amazon EC2
OpenMP
title_short Running scientific codes on amazon EC2: a performance analysis of five high-end instances
title_full Running scientific codes on amazon EC2: a performance analysis of five high-end instances
title_fullStr Running scientific codes on amazon EC2: a performance analysis of five high-end instances
title_full_unstemmed Running scientific codes on amazon EC2: a performance analysis of five high-end instances
title_sort Running scientific codes on amazon EC2: a performance analysis of five high-end instances
dc.creator.none.fl_str_mv Expósito, Roberto R.
Taboada, Guillermo L.
Pardo, Xoán C.
Touriño, Juan
Doallo, Ramón
author Expósito, Roberto R.
author_facet Expósito, Roberto R.
Taboada, Guillermo L.
Pardo, Xoán C.
Touriño, Juan
Doallo, Ramón
author_role author
author2 Taboada, Guillermo L.
Pardo, Xoán C.
Touriño, Juan
Doallo, Ramón
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
cloud computing
high performance computing
high throughput computing
Amazon EC2
OpenMP
topic Ciencias Informáticas
cloud computing
high performance computing
high throughput computing
Amazon EC2
OpenMP
dc.description.none.fl_txt_mv Amazon Web Services (AWS) is a well-known public Infrastructure-as-a-Service (IaaS) provider whose Elastic Computing Cloud (EC2) o ering includes some instances, known as cluster instances, aimed at High-Performance Computing (HPC) applications. In previous work, authors have shown that the scalability of HPC communication-intensive applications does not bene t from using higher computational power cluster instances as much as it could be expected. Cost analysis recommends using lower computational power cluster instances unless high memory requirements preclude their use. Moreover, it has been observed that scalability is very poor when more than one instance is used due to network virtualization overhead. Based on those results, this paper gives more insight into the performance of running scienti c applications on the Amazon EC2 platform evaluating ve (of which two have been recently released) of the higher computational power instances in terms of single instance performance, intra-VM (Virtual Machine) scalability and cost-e ciency. The evaluation has been carried out using both an HPC benchmark suite and a real High-Troughput Computing (HTC) application.
Facultad de Informática
description Amazon Web Services (AWS) is a well-known public Infrastructure-as-a-Service (IaaS) provider whose Elastic Computing Cloud (EC2) o ering includes some instances, known as cluster instances, aimed at High-Performance Computing (HPC) applications. In previous work, authors have shown that the scalability of HPC communication-intensive applications does not bene t from using higher computational power cluster instances as much as it could be expected. Cost analysis recommends using lower computational power cluster instances unless high memory requirements preclude their use. Moreover, it has been observed that scalability is very poor when more than one instance is used due to network virtualization overhead. Based on those results, this paper gives more insight into the performance of running scienti c applications on the Amazon EC2 platform evaluating ve (of which two have been recently released) of the higher computational power instances in terms of single instance performance, intra-VM (Virtual Machine) scalability and cost-e ciency. The evaluation has been carried out using both an HPC benchmark suite and a real High-Troughput Computing (HTC) application.
publishDate 2013
dc.date.none.fl_str_mv 2013-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
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status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/34511
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dc.language.none.fl_str_mv eng
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info:eu-repo/semantics/altIdentifier/issn/1666-6038
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/3.0/
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