Veritas – Demystifying Silent Data Corruptions: μArch-Level Modeling and Fleet Data of Modern x86 CPUs
Odysseas Chatzopoulos, Nikos Karystinos, George N. Papadimitriou, Dimitris Gizopoulos, Harish Dattatraya Dixit, Sriram Sankar
Abstract
Hyperscalers have reported unexpectedly high numbers of defective CPU chips, with a defect rate of 1 in a 1000, leading to Silent Data Corruptions (SDCs) in their computing fleets. However, there is no public data on the rate of SDC incidents (corrupted program executions) in large fleets, nor nor any detailed information on which CPU units, microarchitectures, or workloads are more likely to generate SDCs due to silicon defects. While CPU array structures have been studied for fault effects, arithmetic units like integer and floating-point units have not been thoroughly analyzed as potential root causes of SDCs. This paper addresses this critical gap by accurately modeling hardware faults in the arithmetic units of modern x86 CPUs and measuring the probability and rates of SDCs. Using a full-system gem5-based fault injector, the paper examines SDC trends across five recent $x 86$ microarchitectures, various arithmetic units, and instruction classes. By integrating real-world defect rates from large-scale datacenter experiments with early-stage modeling and simulation, the paper provides critical insights into SDC incident rates across different systems. This information is essential for guiding hardware-based or software-based fault protection methods and is the paper’s primary contribution to minimizing the impact of silent data corruptions in computing.