On Resiliency in Krylov Solvers

International audienceIn this talk we will discuss possible numerical remedies to survive data loss in some numerical linear algebra solvers namely Krylov subspace linear solvers and some widely used eigensolvers. Assuming that a separate mechanism ensures fault detection, we propose numerical algorithms to extract relevant information from available data after a fault. After data extraction, well chosen part of missing data is regenerated through interpolation strategies to constitute meaningful inputs to numerically restart. We will also present some preliminary investigations to address soft error detection again at the application level in the conjugate gradient framework