Migration of tools and methodologies for performance prediction and efficient HPC on cloud environments: results and conclusion

Autores
Muresano, Ronal; Wong, Alvaro; Rexachs del Rosario, Dolores; Luque Fadón, Emilio
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Progress in the parallel programming field has allowed scientific applications to be developed with more complexity and accuracy. However, such precision requires greater computational power in order to be executed. How- ever, updating the local systems could be considered an expensive decision. For this reason, cloud computing is emerging as a commercial infrastructure that allows us to eliminate maintaining the computing hardware. For this reason, cloud is promising to be a computing alternative to clusters, grids and supercomputing for executing these applications. In this sense, this work is focused on describing the manner of migrating our prediction tool PAS2P (parallel application signature for performance prediction), and how we have to analyze our method for executing SPMD ap- plications efficiently on these cloud environments. In both cases, cloud could be considered a huge challenge due to the environment virtualization and the communication heterogeneities, which can seriously affect the application performance. However, our experimental evaluations make it clear that our prediction tool can predict with an error rate lower than 6,46%, considering that the signature for prediction represents a small portion of the execution time. On the other hand, analyzing the application parameters over the cloud computing allows us to find through an analytical model, which is the ideal number of virtual cores needed to obtain the maximum speedup under a defined efficiency. In this case the error rate was lower that 9% for the application tested.
Facultad de Informática
Materia
Ciencias Informáticas
performance
PAS2P
prediction
SPMD
cloud
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:10915/34505

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spelling Migration of tools and methodologies for performance prediction and efficient HPC on cloud environments: results and conclusionMuresano, RonalWong, AlvaroRexachs del Rosario, DoloresLuque Fadón, EmilioCiencias InformáticasperformancePAS2PpredictionSPMDcloudProgress in the parallel programming field has allowed scientific applications to be developed with more complexity and accuracy. However, such precision requires greater computational power in order to be executed. How- ever, updating the local systems could be considered an expensive decision. For this reason, cloud computing is emerging as a commercial infrastructure that allows us to eliminate maintaining the computing hardware. For this reason, cloud is promising to be a computing alternative to clusters, grids and supercomputing for executing these applications. In this sense, this work is focused on describing the manner of migrating our prediction tool PAS2P (parallel application signature for performance prediction), and how we have to analyze our method for executing SPMD ap- plications efficiently on these cloud environments. In both cases, cloud could be considered a huge challenge due to the environment virtualization and the communication heterogeneities, which can seriously affect the application performance. However, our experimental evaluations make it clear that our prediction tool can predict with an error rate lower than 6,46%, considering that the signature for prediction represents a small portion of the execution time. On the other hand, analyzing the application parameters over the cloud computing allows us to find through an analytical model, which is the ideal number of virtual cores needed to obtain the maximum speedup under a defined efficiency. In this case the error rate was lower that 9% for the application tested.Facultad de Informática2013-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf123-129http://sedici.unlp.edu.ar/handle/10915/34505enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Dec13-3.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:10915/34505Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:59:01.753SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Migration of tools and methodologies for performance prediction and efficient HPC on cloud environments: results and conclusion
title Migration of tools and methodologies for performance prediction and efficient HPC on cloud environments: results and conclusion
spellingShingle Migration of tools and methodologies for performance prediction and efficient HPC on cloud environments: results and conclusion
Muresano, Ronal
Ciencias Informáticas
performance
PAS2P
prediction
SPMD
cloud
title_short Migration of tools and methodologies for performance prediction and efficient HPC on cloud environments: results and conclusion
title_full Migration of tools and methodologies for performance prediction and efficient HPC on cloud environments: results and conclusion
title_fullStr Migration of tools and methodologies for performance prediction and efficient HPC on cloud environments: results and conclusion
title_full_unstemmed Migration of tools and methodologies for performance prediction and efficient HPC on cloud environments: results and conclusion
title_sort Migration of tools and methodologies for performance prediction and efficient HPC on cloud environments: results and conclusion
dc.creator.none.fl_str_mv Muresano, Ronal
Wong, Alvaro
Rexachs del Rosario, Dolores
Luque Fadón, Emilio
author Muresano, Ronal
author_facet Muresano, Ronal
Wong, Alvaro
Rexachs del Rosario, Dolores
Luque Fadón, Emilio
author_role author
author2 Wong, Alvaro
Rexachs del Rosario, Dolores
Luque Fadón, Emilio
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
performance
PAS2P
prediction
SPMD
cloud
topic Ciencias Informáticas
performance
PAS2P
prediction
SPMD
cloud
dc.description.none.fl_txt_mv Progress in the parallel programming field has allowed scientific applications to be developed with more complexity and accuracy. However, such precision requires greater computational power in order to be executed. How- ever, updating the local systems could be considered an expensive decision. For this reason, cloud computing is emerging as a commercial infrastructure that allows us to eliminate maintaining the computing hardware. For this reason, cloud is promising to be a computing alternative to clusters, grids and supercomputing for executing these applications. In this sense, this work is focused on describing the manner of migrating our prediction tool PAS2P (parallel application signature for performance prediction), and how we have to analyze our method for executing SPMD ap- plications efficiently on these cloud environments. In both cases, cloud could be considered a huge challenge due to the environment virtualization and the communication heterogeneities, which can seriously affect the application performance. However, our experimental evaluations make it clear that our prediction tool can predict with an error rate lower than 6,46%, considering that the signature for prediction represents a small portion of the execution time. On the other hand, analyzing the application parameters over the cloud computing allows us to find through an analytical model, which is the ideal number of virtual cores needed to obtain the maximum speedup under a defined efficiency. In this case the error rate was lower that 9% for the application tested.
Facultad de Informática
description Progress in the parallel programming field has allowed scientific applications to be developed with more complexity and accuracy. However, such precision requires greater computational power in order to be executed. How- ever, updating the local systems could be considered an expensive decision. For this reason, cloud computing is emerging as a commercial infrastructure that allows us to eliminate maintaining the computing hardware. For this reason, cloud is promising to be a computing alternative to clusters, grids and supercomputing for executing these applications. In this sense, this work is focused on describing the manner of migrating our prediction tool PAS2P (parallel application signature for performance prediction), and how we have to analyze our method for executing SPMD ap- plications efficiently on these cloud environments. In both cases, cloud could be considered a huge challenge due to the environment virtualization and the communication heterogeneities, which can seriously affect the application performance. However, our experimental evaluations make it clear that our prediction tool can predict with an error rate lower than 6,46%, considering that the signature for prediction represents a small portion of the execution time. On the other hand, analyzing the application parameters over the cloud computing allows us to find through an analytical model, which is the ideal number of virtual cores needed to obtain the maximum speedup under a defined efficiency. In this case the error rate was lower that 9% for the application tested.
publishDate 2013
dc.date.none.fl_str_mv 2013-12
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dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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