Neural Network Based Silent Error Detector
Neural Network Based Silent Error Detector
复制标题
基于神经网络的无声错误检测器
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
M. Snir
中科院分区：
文献类型：
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作者：
Chen Wang;Nikoli Dryden;F. Cappello;M. Snir
随着我们向百亿亿级平台发展，静默数据损坏（SDC）可能会更频繁地发生。这样的错误会导致不正确的结果。已经尝试使用通用算法来检测此类错误。这类检测器在检测错误方面具有很高的精度和召回率，但前提是它们必须在注入错误后立即运行。在本文中，我们提出了一种神经网络检测器，它可以在注入sdc多次迭代后检测到sdc。我们用6个FLASH应用程序和2个Mantevo迷你应用程序评估了我们的检测器。实验表明，该检测器能检测出89%以上的sdc，假阳性率小于2%。
As we move toward exascale platforms, silent data corruptions (SDC) are likely to occur more frequently. Such errors can lead to incorrect results. Attempts have been made to use generic algorithms to detect such errors. Such detectors have demonstrated high precision and recall for detecting errors, but only if they run immediately after an error has been injected. In this paper, we propose a neural network detector that can detect SDCs even multiple iterations after they were injected. We have evaluated our detector with 6 FLASH applications and 2 Mantevo mini-apps. Experiments show that our detector can detect more than 89% of SDCs with a false positive rate of less than 2%.