Understanding Silent Data Corruptions in a Large Production CPU Population

Silent Data Corruption (SDC) in processors can lead to various application-level issues, such as incorrect calculations and even data loss. Since traditional techniques are not effective in detecting processor SDCs, it is very hard to address problems caused by SDCs. For the same reason, knowledge about SDCs in the wild is limited.

In this paper, we conduct an extensive study on SDCs in a large production CPU population, encompassing over one million processors. In addition to collecting overall statistics, we perform a detailed study to understand 1) whether certain processor features are particularly vulnerable and their potential impacts on applications; 2) the reproducibility of SDCs and the triggering conditions (e.g., temperature) of those less reproducible SDCs; and 3) the challenges and opportunities to mitigate SDCs.

Inspired by the above observations, we design an efficient SDC mitigation approach called Farron, which relies on prioritized testing to detect highly reproducible SDCs and temperature control to mitigate less reproducible SDCs. Our experimental results indicate that Farron can achieve lower overall overhead with better coverage of SDCs, compared to the baseline used in Alibaba Cloud.