DisCVar: discovering critical variables using algorithmic differentiation for transient faults

Aggressive technology scaling trends have made the hardware of high performance computing (HPC) systems more susceptible to faults. Some of these faults can lead to silent data corruption (SDC), and represent a serious problem because they alter the HPC simulation results. In this paper, we present a full-coverage, systematic methodology called DisCVar to identify critical variables in HPC applications for protection against SDC. DisCVar uses automatic differentiation (AD) to determine the sensitivity of the simulation output to errors in program variables. We empirically validate our approach in identifying vulnerable variables by comparing the results against a full-coverage code-level fault injection campaign. We find that our DisCVar correctly identifies the variables that are critical to ensure application SDC resilience with a high degree of accuracy compared to the results of the fault injection campaign. Additionally, DisCVar requires only two executions of the target program to generate results, whereas in our experiments we needed to perform millions of executions to get the same information from a fault injection campaign.