The Anatomy of Silent Data Corruption: GPU Error Pattern Study and Modeling Guidance

Chung-Hsuan Tung; Yanxiang Huang; Nirmal Saxena; Philip Shirvani; Saurabh Hukerikar; Twinkle Jain; Abhishek Tyagi; Sanjay Gongalore

arXiv · 2605.04213

The Anatomy of Silent Data Corruption: GPU Error Pattern Study and Modeling Guidance

Chung-Hsuan Tung·Yanxiang Huang·Nirmal Saxena·Philip Shirvani·Saurabh Hukerikar·Twinkle Jain·Abhishek Tyagi·Sanjay Gongalore

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Abstract

Silent data corruption (SDC) threatens the reliability of large-scale GPU clusters used for training large language models, yet its rarity and lack of explicit error signals make accurate high-level modeling challenging. To address this gap, we conducted a large-scale gate-level stuck-at fault injection on a production-class data-center GPU, consuming over three million simulator hours across 63 CUDA micro-benchmarks. We extracted GPU SDC characteristics in terms of corruption types, bit-flip behavior, and warp-aligned spatial correlation. Our results show that NaN/+INF/-INF account for only 1.01% of SDC outcomes, that single-bit flips constitute less than 40% of bit-flip events, and that corruption addresses exhibit periodicity. These statistics motivate distribution-aware high-level fault modeling and realistic software-based fault injection for resilience evaluation of production-class GPU architectures.

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BibTeX RIS

Chung-Hsuan Tung, Yanxiang Huang, Nirmal Saxena, Philip Shirvani, Saurabh Hukerikar, Twinkle Jain, Abhishek Tyagi, Sanjay Gongalore. 2026-05-05. The Anatomy of Silent Data Corruption: GPU Error Pattern Study and Modeling Guidance. https://arxiv.org/abs/2605.04213

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