Error resilience of three GMRES implementations under fault injection

The resilience behavior of three GMRES prototyped implementations (with Incomplete LU, Flexible and randomized-SVD-based preconditioners) has been analyzed with a soft errors injection approach. A low-level fault injector is inserted into the GMRES solvers, which randomly select locations in the program to inject the fault across multiple executions. This fault injection approach combines the configurability of high-level and the accuracy of low-level techniques at the same time, so the effect of faults may be closely emulated. In order to gather enough statistical data, a set of eighteen sparse matrix-based linear systems Ax = b has been solved with these GMRES implementations in the injection experiments and monitored. The results of this prototype-based fault injection suggest an improved error resilience behavior of the randomized-SVD-based preconditioned GMRES version in many of the analyzed matrices, which points out to its interest in supercomputing applications where silent errors are more prominent.