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Approaches for building error resilient applications

University of British Columbia

Transient hardware faults have become one of the major concerns affecting the reliability of modern high-performance computing (HPC) systems. They can cause failure outcomes for applications, such as crashes and silent data corruptions (SDCs) (i.e. the application produces an incorrect output). To mitigate the impact of these failures, HPC applications need to adopt fault tolerance techniques. The most common practices of fault tolerance techniques include (i) characterization techniques, such as fault injection and architectural vulnerability factor (AVF)/program vulnerability factor (PVF) analysis; (ii) run-time error detection techniques; and (iii) error recovery techniques. However, these approaches have the following shortcomings: (i) fault injections are generally time-consuming and lack predictive power, while the AVF/PVF analysis offers low accuracy; (ii) prior techniques often do not fully exploit the program’s error resilience characteristics; and (iii) the application constantly pays a performance/storage overhead. This dissertation proposes comprehensive approaches to improve the above techniques in terms of effectiveness and efficiency. In particular, this dissertation makes the following contributions: First, it proposes ePVF, a methodology that distinguishes crash-causing bits from the architecturally correct execution (ACE) bits and obtains a closer estimate of the SDC rate than PVF analysis (by 45% to 67%). To reduce the overall analysis time, it samples representative patterns from ACE bits and obtains a good approximation (less than 1% error) for the overall prediction. This dissertation applies the ePVF methodology to error detection, which leads to a 30% lower SDC rate than well-accepted hot-path instruction duplication. Second, this dissertation combines the roll-forward recovery and the roll-back recovery schemes and demonstrates the improvement in the overall efficiency of the C/R with two systems: LetGo (for faults affecting computational components) and BonVoision (for faults affecting DRAM memory). Overall, LetGo is able to elide 62% of the crashes caused by computational faults and convert them to continued execution (out of these 80% result in correct output while a majority of the rest fall back on the traditional roll-back recovery technique). BonVoision is able to continue to completion 30% of the DRAM memory detectable but uncorrectable errors (DUEs).

Text

2020-03-10

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/73711

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/