2 citations · 2 across the 2 of their papers we have counts for
3 papers
The Anatomy of Silent Data Corruption: GPU Error Pattern Study and Modeling Guidance
Chung-Hsuan Tung, Yanxiang Huang, Nirmal Saxena +5
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 a…
LLM-PRISM: Characterizing Silent Data Corruption from Permanent GPU Faults in LLM Training
Abhishek Tyagi, Saurabh Hukerikar, Nirmal Saxena +4
Large-scale LLM training is increasingly susceptible to hardware defects stemming from manufacturing escapes and silicon aging. These defects manifest as Silent Data Corruption (SD…
Estimating Silent Data Corruption Rates Using a Two-Level Model
Siva Kumar Sastry Hari, Paolo Rech, Timothy Tsai +7
High-performance and safety-critical system architects must accurately evaluate the application-level silent data corruption (SDC) rates of processors to soft errors. Such an evalu…