Autotuning of Exascale Applications With Anomalies Detection

The execution of complex distributed applications in exascale systems faces many challenges, as it involves empirical evaluation of countless code variations and application runtime parameters over a heterogeneous set of resources. To mitigate these challenges, the research field of autotuning has g...

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Autores principales:	Dragi Kimovski, Roland Mathá, Gabriel Iuhasz, Fabrizio Marozzo, Dana Petcu, Radu Prodan
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	exascale computing autotuning events and anomalies detection multi-objective optimization IoT applications Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/22e7395ee71a4fc2bee1fcd250b9dabb
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id	oai:doaj.org-article:22e7395ee71a4fc2bee1fcd250b9dabb
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spelling	oai:doaj.org-article:22e7395ee71a4fc2bee1fcd250b9dabb2021-12-01T07:14:08ZAutotuning of Exascale Applications With Anomalies Detection2624-909X10.3389/fdata.2021.657218https://doaj.org/article/22e7395ee71a4fc2bee1fcd250b9dabb2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fdata.2021.657218/fullhttps://doaj.org/toc/2624-909XThe execution of complex distributed applications in exascale systems faces many challenges, as it involves empirical evaluation of countless code variations and application runtime parameters over a heterogeneous set of resources. To mitigate these challenges, the research field of autotuning has gained momentum. The autotuning automates identifying the most desirable application implementation in terms of code variations and runtime parameters. However, the complexity and size of the exascale systems make the autotuning process very difficult, especially considering the number of parameter variations that have to be identified. Therefore, we introduce a novel approach for autotuning exascale applications based on a genetic multi-objective optimization algorithm integrated within the ASPIDE exascale computing framework. The approach considers multi-dimensional search space with support for pluggable objective functions, including execution time and energy requirements. Furthermore, the autotuner employs a machine learning-based event detection approach to detect events and anomalies during application execution, such as hardware failures or communication bottlenecks.Dragi KimovskiRoland MatháGabriel IuhaszGabriel IuhaszFabrizio MarozzoDana PetcuDana PetcuRadu ProdanFrontiers Media S.A.articleexascale computingautotuningevents and anomalies detectionmulti-objective optimizationIoT applicationsInformation technologyT58.5-58.64ENFrontiers in Big Data, Vol 4 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	exascale computing autotuning events and anomalies detection multi-objective optimization IoT applications Information technology T58.5-58.64
spellingShingle	exascale computing autotuning events and anomalies detection multi-objective optimization IoT applications Information technology T58.5-58.64 Dragi Kimovski Roland Mathá Gabriel Iuhasz Gabriel Iuhasz Fabrizio Marozzo Dana Petcu Dana Petcu Radu Prodan Autotuning of Exascale Applications With Anomalies Detection
description	The execution of complex distributed applications in exascale systems faces many challenges, as it involves empirical evaluation of countless code variations and application runtime parameters over a heterogeneous set of resources. To mitigate these challenges, the research field of autotuning has gained momentum. The autotuning automates identifying the most desirable application implementation in terms of code variations and runtime parameters. However, the complexity and size of the exascale systems make the autotuning process very difficult, especially considering the number of parameter variations that have to be identified. Therefore, we introduce a novel approach for autotuning exascale applications based on a genetic multi-objective optimization algorithm integrated within the ASPIDE exascale computing framework. The approach considers multi-dimensional search space with support for pluggable objective functions, including execution time and energy requirements. Furthermore, the autotuner employs a machine learning-based event detection approach to detect events and anomalies during application execution, such as hardware failures or communication bottlenecks.
format	article
author	Dragi Kimovski Roland Mathá Gabriel Iuhasz Gabriel Iuhasz Fabrizio Marozzo Dana Petcu Dana Petcu Radu Prodan
author_facet	Dragi Kimovski Roland Mathá Gabriel Iuhasz Gabriel Iuhasz Fabrizio Marozzo Dana Petcu Dana Petcu Radu Prodan
author_sort	Dragi Kimovski
title	Autotuning of Exascale Applications With Anomalies Detection
title_short	Autotuning of Exascale Applications With Anomalies Detection
title_full	Autotuning of Exascale Applications With Anomalies Detection
title_fullStr	Autotuning of Exascale Applications With Anomalies Detection
title_full_unstemmed	Autotuning of Exascale Applications With Anomalies Detection
title_sort	autotuning of exascale applications with anomalies detection
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/22e7395ee71a4fc2bee1fcd250b9dabb
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Autotuning of Exascale Applications With Anomalies Detection

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