Building representative and balanced datasets of OpenMP parallel regions

Joan Sorribes Gomis, Eduardo Cesar Galobardes, Anna Barbara Sikora , Jordi Alcaraz*, Steven Sleder, Ali Tehrani, Ali Janesari

*Corresponding author for this work

Research output: Chapter in BookChapterResearchpeer-review

Abstract

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.

Translated title of the contributionElaboración de conjuntos de datos representativos y balanceados de regiones OpenMP
Original languageEnglish
Title of host publicationProceedings - 29th Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages67-74
Number of pages8
ISBN (Electronic)9781665414555
ISBN (Print)9781665414555
DOIs
Publication statusPublished - 1 Mar 2021

Publication series

NameProceedings - 29th Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2021

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