Collective computing
- Autores
- Gonzalez, Jesús Alberto; León, Coromoto; Piccoli, María Fabiana; Printista, Alicia Marcela; Roda García, José Luis; Rodriguez, C.; Sande, Francisco de
- Año de publicación
- 2001
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- The parallel computing model used in this paper, the Collective Computing Model (CCM), is a variant of the well-known Bulk Synchronous Parallel (BSP) model. The synchronicity imposed by the BSP model restricts the set of available algorithms and prevents the overlapping of computation and communication. Other models, like the LogP model, allow asynchronous computing and overlapping but depend on the use of specific libraries. The CCM describes a system exploited through a standard software platform providing facilities for group creation, collective operations and remote memory operations. Based in the BSP model, two kinds of supersteps are considered: division supersteps and normal supersteps. To illustrate these concepts, the Fast Fourier Transform Algorithm is used. Computational results prove the accuracy of the model in four different parallel computers: a Parsytec Power PC, a Cray T3E, a Silicon Graphics Origin 2000 and a Digital Alpha Server.
Eje: Disribución y tiempo real
Red de Universidades con Carreras en Informática (RedUNCI) - Materia
-
Ciencias Informáticas
Parallel
Real time
Distributed
Bulk Synchronous Parallel Model
Supersteps
Performance Prediction
Parallel Computer - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/23541
Ver los metadatos del registro completo
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Collective computingGonzalez, Jesús AlbertoLeón, CoromotoPiccoli, María FabianaPrintista, Alicia MarcelaRoda García, José LuisRodriguez, C.Sande, Francisco deCiencias InformáticasParallelReal timeDistributedBulk Synchronous Parallel ModelSuperstepsPerformance PredictionParallel ComputerThe parallel computing model used in this paper, the Collective Computing Model (CCM), is a variant of the well-known Bulk Synchronous Parallel (BSP) model. The synchronicity imposed by the BSP model restricts the set of available algorithms and prevents the overlapping of computation and communication. Other models, like the LogP model, allow asynchronous computing and overlapping but depend on the use of specific libraries. The CCM describes a system exploited through a standard software platform providing facilities for group creation, collective operations and remote memory operations. Based in the BSP model, two kinds of supersteps are considered: division supersteps and normal supersteps. To illustrate these concepts, the Fast Fourier Transform Algorithm is used. Computational results prove the accuracy of the model in four different parallel computers: a Parsytec Power PC, a Cray T3E, a Silicon Graphics Origin 2000 and a Digital Alpha Server.Eje: Disribución y tiempo realRed 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/10915/23541enginfo: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:UNLP2025-09-29T10:55:30Zoai:sedici.unlp.edu.ar:10915/23541Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:55:30.79SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Collective computing |
title |
Collective computing |
spellingShingle |
Collective computing Gonzalez, Jesús Alberto Ciencias Informáticas Parallel Real time Distributed Bulk Synchronous Parallel Model Supersteps Performance Prediction Parallel Computer |
title_short |
Collective computing |
title_full |
Collective computing |
title_fullStr |
Collective computing |
title_full_unstemmed |
Collective computing |
title_sort |
Collective computing |
dc.creator.none.fl_str_mv |
Gonzalez, Jesús Alberto León, Coromoto Piccoli, María Fabiana Printista, Alicia Marcela Roda García, José Luis Rodriguez, C. Sande, Francisco de |
author |
Gonzalez, Jesús Alberto |
author_facet |
Gonzalez, Jesús Alberto León, Coromoto Piccoli, María Fabiana Printista, Alicia Marcela Roda García, José Luis Rodriguez, C. Sande, Francisco de |
author_role |
author |
author2 |
León, Coromoto Piccoli, María Fabiana Printista, Alicia Marcela Roda García, José Luis Rodriguez, C. Sande, Francisco de |
author2_role |
author author author author author author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas Parallel Real time Distributed Bulk Synchronous Parallel Model Supersteps Performance Prediction Parallel Computer |
topic |
Ciencias Informáticas Parallel Real time Distributed Bulk Synchronous Parallel Model Supersteps Performance Prediction Parallel Computer |
dc.description.none.fl_txt_mv |
The parallel computing model used in this paper, the Collective Computing Model (CCM), is a variant of the well-known Bulk Synchronous Parallel (BSP) model. The synchronicity imposed by the BSP model restricts the set of available algorithms and prevents the overlapping of computation and communication. Other models, like the LogP model, allow asynchronous computing and overlapping but depend on the use of specific libraries. The CCM describes a system exploited through a standard software platform providing facilities for group creation, collective operations and remote memory operations. Based in the BSP model, two kinds of supersteps are considered: division supersteps and normal supersteps. To illustrate these concepts, the Fast Fourier Transform Algorithm is used. Computational results prove the accuracy of the model in four different parallel computers: a Parsytec Power PC, a Cray T3E, a Silicon Graphics Origin 2000 and a Digital Alpha Server. Eje: Disribución y tiempo real Red de Universidades con Carreras en Informática (RedUNCI) |
description |
The parallel computing model used in this paper, the Collective Computing Model (CCM), is a variant of the well-known Bulk Synchronous Parallel (BSP) model. The synchronicity imposed by the BSP model restricts the set of available algorithms and prevents the overlapping of computation and communication. Other models, like the LogP model, allow asynchronous computing and overlapping but depend on the use of specific libraries. The CCM describes a system exploited through a standard software platform providing facilities for group creation, collective operations and remote memory operations. Based in the BSP model, two kinds of supersteps are considered: division supersteps and normal supersteps. To illustrate these concepts, the Fast Fourier Transform Algorithm is used. Computational results prove the accuracy of the model in four different parallel computers: a Parsytec Power PC, a Cray T3E, a Silicon Graphics Origin 2000 and a Digital Alpha Server. |
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 |
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http://sedici.unlp.edu.ar/handle/10915/23541 |
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http://sedici.unlp.edu.ar/handle/10915/23541 |
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) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) |
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