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Improving Application Resilience by Extending Error Correction with Contextual Information.

, , , , , , , and . FTXS@SC, page 19-28. IEEE, (2018)

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Thermal neutrons: a possible threat for supercomputer reliability., , , , , , , , , and 2 other author(s). J. Supercomput., 77 (2): 1612-1634 (2021)Physics-Informed Machine Learning for DRAM Error Modeling., , , , , , , and . DFT, page 1-6. IEEE Computer Society, (2018)Enhancing HPC System Log Analysis by Identifying Message Origin in Source Code., , , , , , and . ISSRE Workshops, page 100-105. IEEE Computer Society, (2018)Analyzing Reliability of Memory Sub-systems with Double-Chipkill Detect/Correct., , , , and . PRDC, page 88-97. IEEE Computer Society, (2013)Memory Errors in Modern Systems: The Good, The Bad, and The Ugly., , , , , , and . ASPLOS, page 297-310. ACM, (2015)Do Solar Proton Events Reduce the Number of Faults in Supercomputers?: A Comparative Analysis of Faults During and without Solar Proton Events., , , and . IRPS, page 1-5. IEEE, (2019)Event Block Identification and Analysis for Effective Anomaly Detection to Build Reliable HPC Systems., , , , and . HPCC/SmartCity/DSS, page 781-788. IEEE, (2018)Feng shui of supercomputer memory: positional effects in DRAM and SRAM faults., , , , and . SC, page 22:1-22:11. ACM, (2013)Exploring Time and Frequency Domains for Accurate and Automated Anomaly Detection in Cloud Computing Systems., , , and . PRDC, page 196-205. IEEE Computer Society, (2013)Harnessing Unreliable Cores in Heterogeneous Architecture: The PyDac Programming Model and Runtime., , , and . DSN, page 744-749. IEEE Computer Society, (2014)