  To face future reliability challenges, it is necessary to quantify the risk
of error in any part of a computing system. To this goal, the Architectural
Vulnerability Factor (AVF) has long been used for chips. However, this metric
is used for offline characterisation, which is inappropriate for memory. We
survey the literature and formalise one of the metrics used, the Memory
Vulnerability Factor, and extend it to take into account false errors. These
are reported errors which would have no impact on the program if they were
ignored. We measure the False Error Aware MVF (FEA) and related metrics
precisely in a cycle-accurate simulator, and compare them with the effects of
injecting faults in a program's data, in native parallel runs. Our findings
show that MVF and FEA are the only two metrics that are safe to use at runtime,
as they both consistently give an upper bound on the probability of incorrect
program outcome. FEA gives a tighter bound than MVF, and is the metric that
correlates best with the incorrect outcome probability of all considered
metrics.
