  While checkpointing is typically combined with a restart of the whole
application, localized recovery permits all but the affected processes to
continue. In task-based cluster programming, for instance, the application can
then be finished on the intact nodes, and the lost tasks be reassigned.
  This extended abstract suggests to adapt a checkpointing and localized
recovery technique that has originally been developed for independent tasks to
nested fork-join programs. We consider a Cilk-like work stealing scheme with
work-first policy in a distributed memory setting, and describe the required
algorithmic changes. The original technique has checkpointing overheads below
1% and neglectable costs for recovery, we expect the new algorithm to achieve a
similar performance.

Distributed, Parallel, and Cluster Computing