Silent Data Corruptions in Computing Systems: Early Predictions and Large-Scale Measurements

Silent Data Corruptions (SDCs) due to defects in computing chips (CPUs, GPUs, AI accelerators) is a critical threat to the quality of large-scale computing in different application domains: cloud computing, high-performance computing, edge computing. Recent public reports by cloud hyperscalers have emphasized that apart from the usual suspects for SDCs (memory, storage, network), the heart of the computations, the processing elements of all types generate an unexpectedly large rate of SDCs which can cause erroneous calculations and severe information loss. We report, in a consolidated form, recent efforts to correlate early microarchitecture-level simulation-based predictions about the likelihood, rates, severity, and root causes of SDCs and large-scale in-field studies in cloud data centers. Early microarchitecture-level prediction of SDC characteristics (susceptible units, workloads, instructions) can shed light to the cryptic problem of SDCs. The findings of a diligent pre-silicon analysis can assist better understanding of SDCs and can thus drive effective protection decisions either at the hardware or at the software levels at deployment stages.