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Performance Monitoring and Analysis of Task-Based OpenMP
OpenMP, a typical shared memory programming paradigm, has been extensively applied in high performance computing community due to the popularity of multicore architectures in recent years. The most significant feature of the OpenMP 3.0 specification is the introduction of the task constructs to expr...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3813764/ https://www.ncbi.nlm.nih.gov/pubmed/24204946 http://dx.doi.org/10.1371/journal.pone.0077742 |
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author | Ding, Yi Hu, Kai Wu, Kai Zhao, Zhenlong |
author_facet | Ding, Yi Hu, Kai Wu, Kai Zhao, Zhenlong |
author_sort | Ding, Yi |
collection | PubMed |
description | OpenMP, a typical shared memory programming paradigm, has been extensively applied in high performance computing community due to the popularity of multicore architectures in recent years. The most significant feature of the OpenMP 3.0 specification is the introduction of the task constructs to express parallelism at a much finer level of detail. This feature, however, has posed new challenges for performance monitoring and analysis. In particular, task creation is separated from its execution, causing the traditional monitoring methods to be ineffective. This paper presents a mechanism to monitor task-based OpenMP programs with interposition and proposes two demonstration graphs for performance analysis as well. The results of two experiments are discussed to evaluate the overhead of monitoring mechanism and to verify the effects of demonstration graphs using the BOTS benchmarks. |
format | Online Article Text |
id | pubmed-3813764 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-38137642013-11-07 Performance Monitoring and Analysis of Task-Based OpenMP Ding, Yi Hu, Kai Wu, Kai Zhao, Zhenlong PLoS One Research Article OpenMP, a typical shared memory programming paradigm, has been extensively applied in high performance computing community due to the popularity of multicore architectures in recent years. The most significant feature of the OpenMP 3.0 specification is the introduction of the task constructs to express parallelism at a much finer level of detail. This feature, however, has posed new challenges for performance monitoring and analysis. In particular, task creation is separated from its execution, causing the traditional monitoring methods to be ineffective. This paper presents a mechanism to monitor task-based OpenMP programs with interposition and proposes two demonstration graphs for performance analysis as well. The results of two experiments are discussed to evaluate the overhead of monitoring mechanism and to verify the effects of demonstration graphs using the BOTS benchmarks. Public Library of Science 2013-10-30 /pmc/articles/PMC3813764/ /pubmed/24204946 http://dx.doi.org/10.1371/journal.pone.0077742 Text en © 2013 Ding et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Ding, Yi Hu, Kai Wu, Kai Zhao, Zhenlong Performance Monitoring and Analysis of Task-Based OpenMP |
title | Performance Monitoring and Analysis of Task-Based OpenMP |
title_full | Performance Monitoring and Analysis of Task-Based OpenMP |
title_fullStr | Performance Monitoring and Analysis of Task-Based OpenMP |
title_full_unstemmed | Performance Monitoring and Analysis of Task-Based OpenMP |
title_short | Performance Monitoring and Analysis of Task-Based OpenMP |
title_sort | performance monitoring and analysis of task-based openmp |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3813764/ https://www.ncbi.nlm.nih.gov/pubmed/24204946 http://dx.doi.org/10.1371/journal.pone.0077742 |
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