Building representative and balanced datasets of OpenMP parallel regions
Alcaraz, Jordi (Universitat Autònoma de Barcelona)
Sleder, Steven (Iowa State University)
Tehranijamsaz, Ali (Iowa State University)
Sikora, Anna (Universitat Autònoma de Barcelona)
Jannesari, Ali (Iowa State University)
Sorribes Gomis, Joan (Universitat Autònoma de Barcelona)
César Galobardes, Eduardo (Universitat Autònoma de Barcelona)

Publicació:	Piscataway : Institute of Electrical and Electronics Engineers, 2021
Descripció:	8 pàg.
Resum:	Incorporating machine learning into automatic performance analysis and tuning tools is a promising path to tackle the increasing heterogeneity of current HPC applications. However, this introduces the need for generating balanced and representative datasets of parallel applications' executions. This work proposes a methodology for building datasets of OpenMP parallel code regions patterns. It allows for determining whether a given code region covers a unique part of the pattern input space not covered by the patterns already included in the dataset. The proposed methodology uses hardware performance counters to represent the execution of the region, which is referred to as the region signature for a given number of cores. Then, a complete representation of the region is built by joining the signatures for every different thread configuration in the system. Next, correlation analysis is performed between this representation and the representation of all the patterns already in the training set. Finally, if this correlation is below a given threshold, the region is considered to cover a unique part of the pattern input space and is subsequently added to the dataset. For validating this methodology, an example dataset, obtained from well known benchmarks, has been used to train a carefully designed neural network model to demonstrate that it is able to classify different patterns of OpenMP parallel regions.
Ajuts:	Agencia Estatal de Investigación TIN2017-84553-C2-1-R Generalitat de Catalunya 2017/SGR-313
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Llengua:	Anglès
Document:	Capítol de llibre ; recerca ; Versió acceptada per publicar
Matèria:	Hardware performance counters ; Maching learning ; Artificial neural networks ; Parallel applications ; OpenMP
Publicat a:	29th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), 2021, p. 67-74, ISBN 978-1-6654-1455-5

DOI: 10.1109/pdp52278.2021.00019

Postprint 8 p, 412.0 KB

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Llibres i col·leccions > Capítols de llibres