Addressing statistical significance of fault injection: empirical studies of the soft error susceptibility

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Soft errors are becoming an important issue in computing systems. Near-threshold voltage (NTV), reduced circuit sizes, high performance computing (HPC), and high altitude computing all present interesting challenges in this area. Much of the existing literature has focused on hardware techniques to mitigate and measure soft errors at the hardware level. Instead, in this paper, we explore the soft error susceptibility of three common sorting algorithms at the software layer. We focus on the comparison operator and use our software fault injection tool to place faults with fine precision during the execution of these algorithms. We explore how the algorithm susceptibilities vary based on input and bit position and relate these faults back to the source code to study how algorithmic decisions impact the reliability of the codes. Finally, we look at the question of the number of fault injections required for statistical significance. Using standard practice equations used in hardware fault injection experiments, we calculate the number of injections that should be required to achieve confidence in our results. Then we show, empirically, that more fault injections are required before we gain confidence in our experiments.