Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/11477
Title: Predicting the soft error vulnerability of parallel applications using machine learning
Authors: Öz, Işıl
Arslan, Sanem
Keywords: Soft error analysis
Fault injection
Parallel programming
Machine learning
Issue Date: 2021
Publisher: Springer
Abstract: With the widespread use of the multicore systems having smaller transistor sizes, soft errors become an important issue for parallel program execution. Fault injection is a prevalent method to quantify the soft error rates of the applications. However, it is very time consuming to perform detailed fault injection experiments. Therefore, prediction-based techniques have been proposed to evaluate the soft error vulnerability in a faster way. In this work, we present a soft error vulnerability prediction approach for parallel applications using machine learning algorithms. We define a set of features including thread communication, data sharing, parallel programming, and performance characteristics; and train our models based on three ML algorithms. This study uses the parallel programming features, as well as the combination of all features for the first time in vulnerability prediction of parallel programs. We propose two models for the soft error vulnerability prediction: (1) A regression model with rigorous feature selection analysis that estimates correct execution rates, (2) A novel classification model that predicts the vulnerability level of the target programs. We get maximum prediction accuracy rate of 73.2% for the regression-based model, and achieve 89% F-score for our classification model.
URI: https://doi.org/10.1007/s10766-021-00707-0
https://hdl.handle.net/11147/11477
ISSN: 0885-7458
1573-7640
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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