  Extreme-scale scientific applications can be more vulnerable to soft errors
(transient faults) as high-performance computing systems increase in scale. The
common practice to evaluate the resilience to faults of an application is
random fault injection, a method that can be highly time consuming. While
resilience prediction modeling has been recently proposed to predict
application resilience in a faster way than fault injection, it can only
predict a single class of fault manifestation (SDC) and there is no evidence
demonstrating that it can work on previously unseen programs, which greatly
limits its re-usability. We present PARIS, a resilience prediction method that
addresses the problems of existing prediction methods using machine learning.
Using carefully-selected features and a machine learning model, our method is
able to make resilience predictions of three classes of fault manifestations
(success, SDC, and interruption) as opposed to one class like in current
resilience prediction modeling. The generality of our approach allows us to
make prediction on new applications, i.e., previously unseen applications,
providing large applicability to our model. Our evaluation on 125 programs
shows that PARIS provides high prediction accuracy, 82% and 77% on average for
predicting the rate of success and interruption, respectively, while the
state-of-the-art resilience prediction model cannot predict them. When
predicting the rate of SDC, PARIS provides much better accuracy than the
state-of-the-art (38% vs. -273%). PARIS is much faster (up to 450x speedup)
than the traditional method (random fault injection).
