Soft Error Detection for Iterative Applications Using Offline Training

Silent data corruption (SDC) from soft errors is one of the challenges for Exascale systems as the number of cores is increasing and the feature size is decreasing. In recent years, a variety of soft error handling methods have been proposed, including replicating computation, feature based runtime detection, algorithm-level protection and others. However, these methods are either relatively expensive or not sufficiently accurate or not even applicable for certain applications. For convergent iterative applications, we observe that their progression of values of the residual leaves a signature of SDC, which is specific to an application but independent of the input dataset size. Based on this observation, we explore a different approach to soft error detection, which involves machine learning technique for off-line training of an application with representative inputs, and on-line detection using the model, applied even to a different dataset. Our experimental evaluation shows that our method is low-cost and effective, and outperforms online detection.