Predicting the Silent Data Corruption Vulnerability of Instructions in Programs With the decreasing size and voltage level of internal device components, soft errors are increasing and constitute a major threat on electronic devices. Silent data corruption (SDC) is the most dangerous result type of soft errors as there is no indication that an error occurs during one program execution. Identifying the SDC vulnerability of instructions is the premise for applying selective SDC detection techniques to programs. We propose proPVInsiden to predict the SDC vulnerability of instructions with a lower cost of fault injection and a better adaptability for programs and program inputs. Reducing the number of fault injections will impair the performance of the prediction. Partial fault injection is applied to control the downward slope of the performance of the prediction to maximize the reduction of fault injection. Experimental results show that the number of fault injections is reduced by 55% and 45% fault injections are sufficient to predict the relative SDC vulnerability of an instruction with respect to other instructions. The averaged Spearmanu0027s rank correlation coefficient is 0.81. proPVInsiden also shows a better applicability for programs and program inputs.

GroundTruth Prediction to Accelerate SoftError Impact Analysis for Iterative Methods Understanding the impact of soft errors on applications can be expensive. Often, it requires an extensive error injection campaign involving numerous runs of the full application in the presence of errors. In this paper, we present a novel approach to arriving at the ground truththe true impact of an error on the final outputfor iterative methods by observing a small number of iterations to learn deviations between normal and errorimpacted execution. We develop a machine learning based predictor for three iterative methods to generate groundtruth results without running them to completion for every error injected. We demonstrate that this approach achieves greater accuracy than alternative prediction strategies, including three existing soft error detection strategies. We demonstrate the effectiveness of the ground truth prediction model in evaluating vulnerability and the effectiveness of soft error detection strategies in the context of iterative methods.