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MACORD is a new SDC detector for detecting SDCs in HPC applications. 


Future HPC systems with ever-increasing resource capacity (such as compute cores, memory and storage) may significantly increase the risks on reliability. Silent data corruptions (SDCs) or silent errors are one of the major sources
that corrupt HPC execution results. Unlike fail-stop errors, SDCs are rather harmful and dangerous in that they cannot be detected by hardware. We propose an online MAchine-learning based CORruption Detection framework (abbreviated
as MACORD) for detecting SDCs in HPC applications. In particular, we comprehensively investigate the prediction ability of lots a multitude of machine-learning algorithms , and enable the in our study, and enable the detector to automatically select the bestfit ones
algorithms at runtime to adapt to adapt to the data dynamics. Our learning framework exhibits low memory overhead (less than 1%), since it takes only spatial features (i.e., neighboring data values for each data point in the current time step) into
the training data. Experiments based on real-world scientific applications/benchmarks show that our framework can get the detection sensitivity (i.e., recall) up to 99% while the false positive rate is limited down to 0.1% in most cases, which is one order of magnitude improvement compared with the latest state-of-art spatial technique.

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