Efficiency analysis of a physical problem: different parallel computational approaches for a dynamical integrator evolution
- Autores
- Gaudiani, Adriana; Carusela, Florencia; Soba, Alejandro
- Año de publicación
- 2013
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- A great challenge for scientists is to execute their computational applications efficiently. Nowadays, parallel programming has become a fundamental key to achieve this goal. High-performance computing provides a solution to exploit parallel architectures in order to get optimal performance. Both parallel programming model and the system architecture will maximize the benefits if both together are suitable to the inherent parallelism of the problem. We compared three parallelized versions of our algorithm when applied to the study of the heat transport phenomenon in a low dimensional system. We qualitatively analyze the obtained performance data based on the own characteristics of multicore architecture, shared memory and NVIDIA graphical multiprocesors related to the traditional programing models provided by MPI and OpenMP, and Cuda programming environment. We conclude that GPUs parallel computing architecture is the most suitable programing model to achieve a better performance of our algorithm. We obtained an improvement of 15X, quite good for a program whose efficiency is strongly degraded by an integration process that essentially must be carried out in a serial way due to the dependence of the data.
WPDP- XIII Workshop procesamiento distribuido y paralelo
Red de Universidades con Carreras en Informática (RedUNCI) - Materia
-
Ciencias Informáticas
Parallel Architectures
Parallel programming
System architectures
Shared memory
computational applications efficiently
performance computing - 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:10915/31742
Ver los metadatos del registro completo
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Efficiency analysis of a physical problem: different parallel computational approaches for a dynamical integrator evolutionGaudiani, AdrianaCarusela, FlorenciaSoba, AlejandroCiencias InformáticasParallel ArchitecturesParallel programmingSystem architecturesShared memorycomputational applications efficientlyperformance computingA great challenge for scientists is to execute their computational applications efficiently. Nowadays, parallel programming has become a fundamental key to achieve this goal. High-performance computing provides a solution to exploit parallel architectures in order to get optimal performance. Both parallel programming model and the system architecture will maximize the benefits if both together are suitable to the inherent parallelism of the problem. We compared three parallelized versions of our algorithm when applied to the study of the heat transport phenomenon in a low dimensional system. We qualitatively analyze the obtained performance data based on the own characteristics of multicore architecture, shared memory and NVIDIA graphical multiprocesors related to the traditional programing models provided by MPI and OpenMP, and Cuda programming environment. We conclude that GPUs parallel computing architecture is the most suitable programing model to achieve a better performance of our algorithm. We obtained an improvement of 15X, quite good for a program whose efficiency is strongly degraded by an integration process that essentially must be carried out in a serial way due to the dependence of the data.WPDP- XIII Workshop procesamiento distribuido y paraleloRed de Universidades con Carreras en Informática (RedUNCI)2013-10info: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/10915/31742enginfo: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:UNLP2025-09-29T10:58:05Zoai:sedici.unlp.edu.ar:10915/31742Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:58:05.907SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Efficiency analysis of a physical problem: different parallel computational approaches for a dynamical integrator evolution |
title |
Efficiency analysis of a physical problem: different parallel computational approaches for a dynamical integrator evolution |
spellingShingle |
Efficiency analysis of a physical problem: different parallel computational approaches for a dynamical integrator evolution Gaudiani, Adriana Ciencias Informáticas Parallel Architectures Parallel programming System architectures Shared memory computational applications efficiently performance computing |
title_short |
Efficiency analysis of a physical problem: different parallel computational approaches for a dynamical integrator evolution |
title_full |
Efficiency analysis of a physical problem: different parallel computational approaches for a dynamical integrator evolution |
title_fullStr |
Efficiency analysis of a physical problem: different parallel computational approaches for a dynamical integrator evolution |
title_full_unstemmed |
Efficiency analysis of a physical problem: different parallel computational approaches for a dynamical integrator evolution |
title_sort |
Efficiency analysis of a physical problem: different parallel computational approaches for a dynamical integrator evolution |
dc.creator.none.fl_str_mv |
Gaudiani, Adriana Carusela, Florencia Soba, Alejandro |
author |
Gaudiani, Adriana |
author_facet |
Gaudiani, Adriana Carusela, Florencia Soba, Alejandro |
author_role |
author |
author2 |
Carusela, Florencia Soba, Alejandro |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas Parallel Architectures Parallel programming System architectures Shared memory computational applications efficiently performance computing |
topic |
Ciencias Informáticas Parallel Architectures Parallel programming System architectures Shared memory computational applications efficiently performance computing |
dc.description.none.fl_txt_mv |
A great challenge for scientists is to execute their computational applications efficiently. Nowadays, parallel programming has become a fundamental key to achieve this goal. High-performance computing provides a solution to exploit parallel architectures in order to get optimal performance. Both parallel programming model and the system architecture will maximize the benefits if both together are suitable to the inherent parallelism of the problem. We compared three parallelized versions of our algorithm when applied to the study of the heat transport phenomenon in a low dimensional system. We qualitatively analyze the obtained performance data based on the own characteristics of multicore architecture, shared memory and NVIDIA graphical multiprocesors related to the traditional programing models provided by MPI and OpenMP, and Cuda programming environment. We conclude that GPUs parallel computing architecture is the most suitable programing model to achieve a better performance of our algorithm. We obtained an improvement of 15X, quite good for a program whose efficiency is strongly degraded by an integration process that essentially must be carried out in a serial way due to the dependence of the data. WPDP- XIII Workshop procesamiento distribuido y paralelo Red de Universidades con Carreras en Informática (RedUNCI) |
description |
A great challenge for scientists is to execute their computational applications efficiently. Nowadays, parallel programming has become a fundamental key to achieve this goal. High-performance computing provides a solution to exploit parallel architectures in order to get optimal performance. Both parallel programming model and the system architecture will maximize the benefits if both together are suitable to the inherent parallelism of the problem. We compared three parallelized versions of our algorithm when applied to the study of the heat transport phenomenon in a low dimensional system. We qualitatively analyze the obtained performance data based on the own characteristics of multicore architecture, shared memory and NVIDIA graphical multiprocesors related to the traditional programing models provided by MPI and OpenMP, and Cuda programming environment. We conclude that GPUs parallel computing architecture is the most suitable programing model to achieve a better performance of our algorithm. We obtained an improvement of 15X, quite good for a program whose efficiency is strongly degraded by an integration process that essentially must be carried out in a serial way due to the dependence of the data. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013-10 |
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 |
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dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/31742 |
url |
http://sedici.unlp.edu.ar/handle/10915/31742 |
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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