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dc.contributor.authorExpósito, Roberto R.
dc.contributor.authorTaboada, Guillermo L.
dc.contributor.authorPardo, Xoán C.
dc.contributor.authorTouriño, Juan
dc.contributor.authorDoallo, Ramón
dc.date.accessioned2018-08-10T07:52:46Z
dc.date.available2018-08-10T07:52:46Z
dc.date.issued2013
dc.identifier.citationExpósito, R. R., Taboada, G. L., Pardo, X. C., Touriño, J., & Doallo, R. (2018). Running scientific codes on amazon EC2: a performance analysis of five high-end instances. Journal of Computer Science and Technology, 13(03), 153-159. Retrieved from http://journal.info.unlp.edu.ar/JCST/article/view/597es_ES
dc.identifier.issn1666-6046
dc.identifier.issn1666-6038
dc.identifier.urihttp://hdl.handle.net/2183/20958
dc.descriptionThis is the peer reviewed version of the following article: Expósito, R. R., Taboada, G. L., Pardo, X. C., Touriño, J., & Doallo, R. (2018). Running scientific codes on amazon EC2: a performance analysis of five high-end instances. Journal of Computer Science and Technology, 13(03), 153-159. Retrieved from http://journal.info.unlp.edu.ar/JCST/article/view/597, which has been published in final form at http://journal.info.unlp.edu.ar/JCST/article/view/597. This article may be used for non-commercial purposes in accordance with JCS&T Terms and Conditions.”es_ES
dc.description.abstract[Abstract] 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.es_ES
dc.description.sponsorshipMinisterio de Educación, Cultura y Deporte; AP2010-4348es_ES
dc.description.sponsorshipMinisterio de Ciencia e Innovación; TIN2010-16735,es_ES
dc.language.isoenges_ES
dc.publisherSpringer New York LLCes_ES
dc.relation.urihttp://journal.info.unlp.edu.ar/JCST/article/view/597es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectCloud computinges_ES
dc.subjectHigh performance computinges_ES
dc.subjectHigh throughput computinges_ES
dc.subjectAmazon EC2es_ES
dc.subjectOpenMPes_ES
dc.titleRunning scientific codes on amazon EC2: a performance analysis of five high-end instanceses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleUniversidad Nacional de la Plata * Facultad de Informaticaes_ES
UDC.volume13es_ES
UDC.issue3es_ES
UDC.startPage153es_ES
UDC.endPage159es_ES


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