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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/34505
Ver los metadatos del registro completo
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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 |
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 |
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http://sedici.unlp.edu.ar/handle/10915/34505 |
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http://sedici.unlp.edu.ar/handle/10915/34505 |
dc.language.none.fl_str_mv |
eng |
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eng |
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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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