Improving Application Resilience by Extending Error Correction with Contextual Information

Extreme-scale systems are growing in scope and complexity as we approach exascale. Uncorrectable faults in such systems are also increasing, so resilience efforts addressing these are of great importance. In this paper, we extend a method that augments hardware error detection and correction (EDAC) contextually, and show an application-based approach that takes detectable uncorrectable (DUE) data errors and corrects them. We applied this application-based method successfully to data errors found using common EDAC, and discuss operating system changes that will make this possible on existing systems. We show that even when there are many acceptable correction choices (which may be seen in floating point), a large percentage of DUEs are corrected, and even the miscorrected data are very close to correct. We developed two different contextual criteria for this application: local averaging and global conservation of mass. Both did well in terms of closeness, but conservation of mass outperformed averaging in terms of actual correctness. The contributions of this paper are: 1) the idea of application- specific EDAC-based contextual correction, 2) its demonstration with great success on a real application, 3) the development of two different contextual criteria, and 4) a discussion of attainable changes to the OS kernel that make this possible on a real system.