A Numerical Soft Fault Model for Iterative Linear Solvers

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 sig-nificantly fewer resources, while punishing our solvers with undetectable errors that require notable overhead for recov-ery. 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