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.

随着具有较小晶体管尺寸的多核系统的广泛使用，软错误成为并行程序执行的重要问题。故障注入是一种量化应用程序软错误率的流行方法。但是，执行详细的故障注入实验非常耗时。因此，已经提出了基于预测的技术以更快的方式评估软错误漏洞。在这项工作中，我们提出了一种使用机器学习算法的并行应用程序的软错误漏洞预测方法。我们定义了一组功能，包括线程通信，数据共享，并行编程和性能特征。并基于三种ML算法训练我们的模型。本研究使用并行编程功能，以及并行程序漏洞预测中所有功能的首次组合。我们提出了两个用于软错误漏洞预测的模型：（1）具有严格特征选择分析的回归模型，用于估计正确的执行率；（2）一种新颖的分类模型，用于预测目标程序的漏洞级别。对于基于回归的模型，我们获得的最大预测准确率为73.2％，而对于我们的分类模型，则可达到89％的F得分。