Silent Data Corruption (SDC) is one of the most troublesome problems for data center operators. A CPU manufacturing defect causes certain computations to output incorrect results — with no error raised. The program finishes normally, the output looks fine, but the values are wrong. Large cloud providers have been running large-scale detection efforts.
ITHICA's core insight (arXiv:2605.15638) is simple but clever: the most dangerous manufacturing defects cause inconsistent errors — the same instruction, in the same thread, with the same input, produces different results depending on the execution context. Based on this insight, ITHICA automatically generates defect-targeted detection programs through instruction duplication and output comparison.
Results from evaluation on over 3,000 CPU servers: the method detected 39% more defective servers than existing native detection approaches. Some findings also challenge conclusions about defect behavior from earlier hyperscale fleet studies.
Unclear points: repeated instruction execution introduces performance overhead — how large is this overhead in real deployments? If you insert duplicate-and-compare checks into every production program, how much CPU throughput do you lose? The paper does not discuss the cost of production deployment.
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References
1. Vavelidou, I., et al. (2026). *ITHICA: Intra-Thread Instruction Checking Approach for Defect-Induced Silent Data Corruptions*. arXiv:2605.15638 [cs.AR]. 2. Hochschild, P., et al. (2021). *Cores that Don't Count*. HotOS 2021. 3. Dixit, H., et al. (2021). *Silent Data Corruptions at Scale*. arXiv:2102.11245.