Resilient Iterative Linear Solvers Running Through Errors

ELLIOTT III, JAMES JOHN. Resilient Iterative Linear Solvers Running Through Errors. (Under the direction of Frank Mueller.) Future extreme-scale computer systems may expose incorrect behavior to applications, in order to save energy or increase performance. However, resilience research struggles to come up with useful abstract programming models for reasoning about faults in applications. This work mainly focuses on silent soft errors, that is, errors that do not cause the system to halt and provide no indication that they occurred. The approach presented is not specific to silent soft errors; it is a general model for tolerating abnormal behavior in numerical algorithms. We present findings targeted at silent faults that impact the data used by an algorithm. Silent faults in data, which we refer to as silent data corruption (SDC), may lead to the algorithm operating on incorrect data and presenting invalid outputs. The overarching goal of this work is to ensure that we obtain valid solutions given soft faults. Existing work in algorithm fault tolerance randomly flips bits in running applications, but this only shows average-case behavior for a low-level, artificial hardware model. Algorith