A Numerical Soft Fault Model for Iterative Linear Solvers
James John Elliott, Mark Frederick Hoemmen, Frank Mueller · 2015
We present a fault model designed to bring out the "worst" in iterative solvers based on mathematical properties. Our model introduces substantially higher overhead, but smaller variance, than a fault model based on random bit flips. We also relate the statistics from our experiments back to the solvers' configuration, and briefly address the computational effort that each model requires. Our approach requires significantly fewer resources, while punishing our solvers with undetectable errors that require notable overhead for recovery. This work also illustrates the robustness of our resilient algorithms: Not only do we make forward progress in the presence of pathological faults, we always obtain the correct answer.