Publications
Evaluating the Viability of Using Compression to Mitigate Silent Corruption of Read-Mostly Application Data
Aggregating millions of hardware components to construct an exascale computing platform will pose significant resilience challenges. In addition to slowdowns associated with detected errors, silent errors are likely to further degrade application performance. Moreover, silent data corruption (SDC) has the potential to undermine the integrity of the results produced by important scientific applications.In this paper, we propose an application-independent mechanism to efficiently detect and correct SDC in read-mostly memory, where SDC may be most likely to occur. We use memory protection mechanisms to maintain compressed backups of application memory. We detect SDC by identifying changes in memory contents that occur without explicit write operations. We demonstrate that, for several applications, our approach can potentially protect a significant fraction of application memory pages from SDC with modest overheads. Moreover, our proposed technique can be straightforwardly combined with many other approaches to provide a significant bulwark against SDC.