An approach for an efficient execution of SPMD applications on Multi-core environments

Executing traditional Message Passing Interface (MPI) applications on multi-core cluster balancing speed and computational efficiency is a difficult task that parallel programmers have to deal with. For this reason, communications on multi-core clusters ought to be handled carefully in order to impr...

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Detalles Bibliográficos
Autores: Muresano, Ronal, Meyer, Hugo|||0000-0002-6803-7550, Rexachs, Dolores, Luque, Emilio
Tipo de recurso: artículo
Fecha de publicación:2016
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/90124
Acceso en línea:https://hdl.handle.net/2117/90124
https://dx.doi.org/10.1016/j.future.2016.06.016
Access Level:acceso abierto
Palabra clave:Large scale systems--Data processing
Scheduling--Computer programs
Performance improvements
Multi-core
Mapping
Scheduling
Scalability analysis
SPMD
Supercomputadors
Enginyeria d'ordinadors
Àrees temàtiques de la UPC::Enginyeria electrònica
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network_acronym_str ES
network_name_str España
repository_id_str
spelling An approach for an efficient execution of SPMD applications on Multi-core environmentsMuresano, RonalMeyer, Hugo|||0000-0002-6803-7550Rexachs, DoloresLuque, EmilioLarge scale systems--Data processingScheduling--Computer programsPerformance improvementsMulti-coreMappingSchedulingScalability analysisSPMDSupercomputadorsEnginyeria d'ordinadorsÀrees temàtiques de la UPC::Enginyeria electrònicaExecuting traditional Message Passing Interface (MPI) applications on multi-core cluster balancing speed and computational efficiency is a difficult task that parallel programmers have to deal with. For this reason, communications on multi-core clusters ought to be handled carefully in order to improve performance metrics such as efficiency, speedup, execution time and scalability. In this paper we focus our attention on SPMD (Single Program Multiple Data) applications with high communication volume and synchronicity and also following characteristics such as: static, local and regular. This work proposes a method for SPMD applications, which is focused on managing the communication heterogeneity (different cache level, RAM memory, network, etc.) on homogeneous multi-core computing platform in order to improve the application efficiency. In this sense, the main objective of this work is to find analytically the ideal number of cores necessary that allows us to obtain the maximum speedup, while the computational efficiency is maintained over a defined threshold (strong scalability). This method also allows us to determine how the problem size must be increased in order to maintain an execution time constant while the number of cores are expanded (weak scalability) considering the tradeoff between speed and efficiency. This methodology has been tested with different benchmarks and applications and we achieved an average improvement around 30.35% of efficiency in applications tested using different problems sizes and multi-core clusters. In addition, results show that maximum speedup with a defined efficiency is located close to the values calculated with our analytical model with an error rate lower than 5% for the applications tested.This research has been supported by the MINECO (MICINN) Spain under contracts TIN2011-24384 and TIN2014- 53172-PPeer ReviewedElsevier20172017-01-0120162016-09-21journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/90124https://dx.doi.org/10.1016/j.future.2016.06.016reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengMinisterio de Economía y Competitividad http://doi.org/10.13039/501100003329 TIN2014-53172-P COMPUTACION EFICIENTE Y SEGURA PARA LA SIMULACION Y OPTIMIZACION DE APLICACIONES SOCIALES.open accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivs 4.0 International Licensehttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/901242026-05-27T15:37:01Z
dc.title.none.fl_str_mv An approach for an efficient execution of SPMD applications on Multi-core environments
title An approach for an efficient execution of SPMD applications on Multi-core environments
spellingShingle An approach for an efficient execution of SPMD applications on Multi-core environments
Muresano, Ronal
Large scale systems--Data processing
Scheduling--Computer programs
Performance improvements
Multi-core
Mapping
Scheduling
Scalability analysis
SPMD
Supercomputadors
Enginyeria d'ordinadors
Àrees temàtiques de la UPC::Enginyeria electrònica
title_short An approach for an efficient execution of SPMD applications on Multi-core environments
title_full An approach for an efficient execution of SPMD applications on Multi-core environments
title_fullStr An approach for an efficient execution of SPMD applications on Multi-core environments
title_full_unstemmed An approach for an efficient execution of SPMD applications on Multi-core environments
title_sort An approach for an efficient execution of SPMD applications on Multi-core environments
dc.creator.none.fl_str_mv Muresano, Ronal
Meyer, Hugo|||0000-0002-6803-7550
Rexachs, Dolores
Luque, Emilio
author Muresano, Ronal
author_facet Muresano, Ronal
Meyer, Hugo|||0000-0002-6803-7550
Rexachs, Dolores
Luque, Emilio
author_role author
author2 Meyer, Hugo|||0000-0002-6803-7550
Rexachs, Dolores
Luque, Emilio
author2_role author
author
author
dc.subject.none.fl_str_mv Large scale systems--Data processing
Scheduling--Computer programs
Performance improvements
Multi-core
Mapping
Scheduling
Scalability analysis
SPMD
Supercomputadors
Enginyeria d'ordinadors
Àrees temàtiques de la UPC::Enginyeria electrònica
topic Large scale systems--Data processing
Scheduling--Computer programs
Performance improvements
Multi-core
Mapping
Scheduling
Scalability analysis
SPMD
Supercomputadors
Enginyeria d'ordinadors
Àrees temàtiques de la UPC::Enginyeria electrònica
description Executing traditional Message Passing Interface (MPI) applications on multi-core cluster balancing speed and computational efficiency is a difficult task that parallel programmers have to deal with. For this reason, communications on multi-core clusters ought to be handled carefully in order to improve performance metrics such as efficiency, speedup, execution time and scalability. In this paper we focus our attention on SPMD (Single Program Multiple Data) applications with high communication volume and synchronicity and also following characteristics such as: static, local and regular. This work proposes a method for SPMD applications, which is focused on managing the communication heterogeneity (different cache level, RAM memory, network, etc.) on homogeneous multi-core computing platform in order to improve the application efficiency. In this sense, the main objective of this work is to find analytically the ideal number of cores necessary that allows us to obtain the maximum speedup, while the computational efficiency is maintained over a defined threshold (strong scalability). This method also allows us to determine how the problem size must be increased in order to maintain an execution time constant while the number of cores are expanded (weak scalability) considering the tradeoff between speed and efficiency. This methodology has been tested with different benchmarks and applications and we achieved an average improvement around 30.35% of efficiency in applications tested using different problems sizes and multi-core clusters. In addition, results show that maximum speedup with a defined efficiency is located close to the values calculated with our analytical model with an error rate lower than 5% for the applications tested.
publishDate 2016
dc.date.none.fl_str_mv 2016
2016-09-21
2017
2017-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/90124
https://dx.doi.org/10.1016/j.future.2016.06.016
url https://hdl.handle.net/2117/90124
https://dx.doi.org/10.1016/j.future.2016.06.016
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Ministerio de Economía y Competitividad http://doi.org/10.13039/501100003329 TIN2014-53172-P COMPUTACION EFICIENTE Y SEGURA PARA LA SIMULACION Y OPTIMIZACION DE APLICACIONES SOCIALES.
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 4.0 International License
https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 4.0 International License
https://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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