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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...

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Detalles Bibliográficos
Autores principales: Ding, Yi, Hu, Kai, Wu, Kai, Zhao, Zhenlong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
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.
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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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