Versatile prediction and fast estimation of Architectural Vulnerability Factor from processor performance metrics

The shrinking processor feature size, lower threshold voltage and increasing clock frequency make modern processors highly vulnerable to transient faults. Archi-tectural Vulnerability Factor (AVF) reflects the possibil-ity that a transient fault eventually causes a visible error in the program output, and it indicates a system’s sus-ceptibility to transient faults. Therefore, the awareness of the AVF especially at early design stage is greatly helpful to achieve a trade-off between system perform-ance and reliability. However, tracking the AVF during program execution is extremely costly, which makes accurate AVF prediction extraordinarily attractive to computer architects. In this paper, we propose to use Boosted Regression Trees, a nonparametric tree-based predictive modeling scheme, to identify the correlation across workloads, execution phases and processor configurations between a key processor structure’s AVF and various perform-ance metrics. The proposed method not only makes an accurate prediction but quantitatively illustrates indi-vidual performance variable’s importance to the AVF. Moreover, to reduce the prediction complexity, we also utilize a technique named Patient Rule Induction Method to extract some simple selecting rules on impor-tant metrics. Applying these rules during run time can fast identify execution intervals with a relatively high AVF