Spatial support vector regression to detect silent errors in the exascale era
Document typeConference report
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
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As the exascale era approaches, the increasing capacity of high-performance computing (HPC) systems with targeted power and energy budget goals introduces significant challenges in reliability. Silent data corruptions (SDCs) or silent errors are one of the major sources that corrupt the executionresults of HPC applications without being detected. In this work, we explore a low-memory-overhead SDC detector, by leveraging epsilon-insensitive support vector machine regression, to detect SDCs that occur in HPC applications that can be characterized by an impact error bound. The key contributions are three fold. (1) Our design takes spatialfeatures (i.e., neighbouring data values for each data point in a snapshot) into training data, such that little memory overhead (less than 1%) is introduced. (2) We provide an in-depth study on the detection ability and performance with different parameters, and we optimize the detection range carefully. (3) Experiments with eight real-world HPC applications show thatour detector can achieve the detection sensitivity (i.e., recall) up to 99% yet suffer a less than 1% of false positive rate for most cases. Our detector incurs low performance overhead, 5% on average, for all benchmarks studied in the paper. Compared with other state-of-the-art techniques, our detector exhibits the best tradeoff considering the detection ability and overheads.
CitationSubasi, O., Di, S., Bautista, L., Balaprakash, P., Unsal, O., Labarta, J., Cristal, A., Cappello, F. Spatial support vector regression to detect silent errors in the exascale era. A: IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. "2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2016: 16-19 May 2016, Cartagena, Colombia: proceedings". Cartagena: Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 413-424.