Enhancing data parallel aplications with task parallelism

Autores
Fernández, Jacqueline; Guerrero, Roberto A.; Piccoli, María Fabiana; Printista, Alicia Marcela; Villalobos, M.
Año de publicación
2001
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Most parallel applications contain data parallelism and almost all discussion of its solutions has limited to the simplest and least expressive form: flat data parallelism. Several generalization of the flat data parallel model have been proposed because a large number of those applications need a combination of task and data parallelism to represent their natural computation structure and to achieve good performance in their results. Their aim is to allow the capability of combining the easiness of programming of the data parallel model with the efficiency of the task parallel model. In this work, we examine how to enhance two basic data parallel computation applications with task parallelism. Applications presented: N-body Simulation and Echo Elimination Process have been chosen from an unlimited scope of applications where the benefit of the integration of task and data parallelism can be shown
Eje: Programación concurrente
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Parallel
Concurrent Programming
Data parallelism
task parallelism
nested data parallelism
image-processing
pipeline parallelism
color images
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/23313
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/23313
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Enhancing data parallel aplications with task parallelismFernández, JacquelineGuerrero, Roberto A.Piccoli, María FabianaPrintista, Alicia MarcelaVillalobos, M.Ciencias InformáticasParallelConcurrent ProgrammingData parallelismtask parallelismnested data parallelismimage-processingpipeline parallelismcolor imagesMost parallel applications contain data parallelism and almost all discussion of its solutions has limited to the simplest and least expressive form: flat data parallelism. Several generalization of the flat data parallel model have been proposed because a large number of those applications need a combination of task and data parallelism to represent their natural computation structure and to achieve good performance in their results. Their aim is to allow the capability of combining the easiness of programming of the data parallel model with the efficiency of the task parallel model. In this work, we examine how to enhance two basic data parallel computation applications with task parallelism. Applications presented: N-body Simulation and Echo Elimination Process have been chosen from an unlimited scope of applications where the benefit of the integration of task and data parallelism can be shownEje: Programación concurrenteRed de Universidades con Carreras en Informática (RedUNCI)2001-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/23313enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:55:25Zoai:sedici.unlp.edu.ar:10915/23313Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:55:25.852SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Enhancing data parallel aplications with task parallelism
title Enhancing data parallel aplications with task parallelism
spellingShingle Enhancing data parallel aplications with task parallelism
Fernández, Jacqueline
Ciencias Informáticas
Parallel
Concurrent Programming
Data parallelism
task parallelism
nested data parallelism
image-processing
pipeline parallelism
color images
title_short Enhancing data parallel aplications with task parallelism
title_full Enhancing data parallel aplications with task parallelism
title_fullStr Enhancing data parallel aplications with task parallelism
title_full_unstemmed Enhancing data parallel aplications with task parallelism
title_sort Enhancing data parallel aplications with task parallelism
dc.creator.none.fl_str_mv Fernández, Jacqueline
Guerrero, Roberto A.
Piccoli, María Fabiana
Printista, Alicia Marcela
Villalobos, M.
author Fernández, Jacqueline
author_facet Fernández, Jacqueline
Guerrero, Roberto A.
Piccoli, María Fabiana
Printista, Alicia Marcela
Villalobos, M.
author_role author
author2 Guerrero, Roberto A.
Piccoli, María Fabiana
Printista, Alicia Marcela
Villalobos, M.
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Parallel
Concurrent Programming
Data parallelism
task parallelism
nested data parallelism
image-processing
pipeline parallelism
color images
topic Ciencias Informáticas
Parallel
Concurrent Programming
Data parallelism
task parallelism
nested data parallelism
image-processing
pipeline parallelism
color images
dc.description.none.fl_txt_mv Most parallel applications contain data parallelism and almost all discussion of its solutions has limited to the simplest and least expressive form: flat data parallelism. Several generalization of the flat data parallel model have been proposed because a large number of those applications need a combination of task and data parallelism to represent their natural computation structure and to achieve good performance in their results. Their aim is to allow the capability of combining the easiness of programming of the data parallel model with the efficiency of the task parallel model. In this work, we examine how to enhance two basic data parallel computation applications with task parallelism. Applications presented: N-body Simulation and Echo Elimination Process have been chosen from an unlimited scope of applications where the benefit of the integration of task and data parallelism can be shown
Eje: Programación concurrente
Red de Universidades con Carreras en Informática (RedUNCI)
description Most parallel applications contain data parallelism and almost all discussion of its solutions has limited to the simplest and least expressive form: flat data parallelism. Several generalization of the flat data parallel model have been proposed because a large number of those applications need a combination of task and data parallelism to represent their natural computation structure and to achieve good performance in their results. Their aim is to allow the capability of combining the easiness of programming of the data parallel model with the efficiency of the task parallel model. In this work, we examine how to enhance two basic data parallel computation applications with task parallelism. Applications presented: N-body Simulation and Echo Elimination Process have been chosen from an unlimited scope of applications where the benefit of the integration of task and data parallelism can be shown
publishDate 2001
dc.date.none.fl_str_mv 2001-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/23313
url http://sedici.unlp.edu.ar/handle/10915/23313
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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