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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/34511
Ver los metadatos del registro completo
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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 Articulo http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
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article |
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publishedVersion |
dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/34511 |
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http://sedici.unlp.edu.ar/handle/10915/34511 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
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info:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Dec13-8.pdf info:eu-repo/semantics/altIdentifier/issn/1666-6038 |
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openAccess |
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http://creativecommons.org/licenses/by-nc/3.0/ Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0) |
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