Argonne National Laboratory

Adaptive Impact-Driven Detection of Silent Data Corruption for HPC Applications

TitleAdaptive Impact-Driven Detection of Silent Data Corruption for HPC Applications
Publication TypeJournal Article
Year of Publication2015
AuthorsDi, S, Cappello, F
JournalIEEE Transactions on Parallel and Distributed Systems
Date Published12/2015
Other NumbersANL/MCS-P5376-0715
AbstractFor exascale HPC applications, silent data corruption (SDC) is one of the most dangerous problems because there is no indication that there are errors during the execution. We propose an adaptive impact-driven method that can detect SDCs dynamically. The key contributions are threefold. (1) We carefully characterize 18 real-world HPC applications and discuss the runtime data features, as well as the impact of the SDCs on their execution results. (2) We propose an impact-driven detection model that does not blindly improve the prediction accuracy, but instead detects only influential SDCs to guarantee user-acceptable execution results. (3) Our solution can adapt to dynamic prediction errors based on local runtime data and can automatically tune detection ranges for guaranteeing low false alarms. Experiments show that our detector can detect 80-99.99% of SDCs with a false alarm rate less that 1% of iterations for most cases. The memory cost and detection overhead are reduced to 15% and 6.3%, respectively, for a large majority of applications.