In the near future large-scale parallel computers will feature hundreds of thousands of processing nodes. In such systems, fault tolerance is critical as failures will occur very often. Checkpointing and rollback recovery has been extensively studied as an attempt to provide fault tolerance. However, current implementations do not provide the total transparency and full flexibility that are necessary to support the new paradigm of autonomic computing — systems able to self-heal and self-repair. In this paper we provide an in-depth evaluation of incremental checkpointing for scientific computing. The experimental results, obtained on a state-of-the art cluster running several scientific applications, show that efficient, scalable, automatic and user-transparent incremental checkpointing is within reach with current technology.