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

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...

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Autores principales: Muresano, Ronal, Wong, Alvaro, Rexachs del Rosario, Dolores, Luque Fadón, Emilio
Formato: Articulo
Lenguaje:Inglés
Publicado: 2013
Materias:
Ciencias Informáticas
performance
PAS2P
prediction
SPMD
cloud
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/34505
http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Dec13-3.pdf
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-34505
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
performance
PAS2P
prediction
SPMD
cloud
spellingShingle Ciencias Informáticas
performance
PAS2P
prediction
SPMD
cloud
Muresano, Ronal
Wong, Alvaro
Rexachs del Rosario, Dolores
Luque Fadón, Emilio
Migration of tools and methodologies for performance prediction and efficient HPC on cloud environments: results and conclusion
topic_facet Ciencias Informáticas
performance
PAS2P
prediction
SPMD
cloud
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.
format Articulo
Articulo
author Muresano, Ronal
Wong, Alvaro
Rexachs del Rosario, Dolores
Luque Fadón, Emilio
author_facet Muresano, Ronal
Wong, Alvaro
Rexachs del Rosario, Dolores
Luque Fadón, Emilio
author_sort Muresano, Ronal
title Migration of tools and methodologies for performance prediction and efficient HPC on cloud environments: results and conclusion
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
publishDate 2013
url http://sedici.unlp.edu.ar/handle/10915/34505
http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Dec13-3.pdf
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AT wongalvaro migrationoftoolsandmethodologiesforperformancepredictionandefficienthpconcloudenvironmentsresultsandconclusion
AT rexachsdelrosariodolores migrationoftoolsandmethodologiesforperformancepredictionandefficienthpconcloudenvironmentsresultsandconclusion
AT luquefadonemilio migrationoftoolsandmethodologiesforperformancepredictionandefficienthpconcloudenvironmentsresultsandconclusion
bdutipo_str Repositorios
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