A Gaussian Process Approach for Effective Soft Error Detection

In this paper, we present a non-parametric dataanalytic soft-error detector. Our detector uses the key properties of Gaussian process regression. First, because Gaussian process regression provides confidence on the prediction, this confidence can be used to automatize construction of the detection range. Second, because the correlation model of a Gaussian process captures the similarity among neighboring point values, only one-time online training is needed. This leads to very low online performance overheads. Finally, Gaussian process regression localizes the detection range computation, thereby avoiding communication costs. We compare our detector with the adaptive impact-driven (AID) and spatial supportvector-machine (SSD) detectors, two effective detectors based on observation of the temporal and spatial evolution of data, respectively. Experiments with five failure distributions and six real-world high-performance computing applications reveal that the Gaussian-process-based detector achieves low false positive rate and high recall while incurring less than 0.1% performance and memory overheads. Considering the detection performance and overheads, our Gaussian process detector provides the best trade-off.