Design and Study of Elastic Recovery in HPC Applications

The efficient utilization of current supercomputing systems with deep storage hierarchies demands scientific applications that are capable of leveraging such heterogeneous hardware. Fault tolerance, and checkpointing in particular, is one of the most time-consuming aspects if not handled correctly. High checkpoint performance can be achieved using optimized multilevel checkpoint and restart libraries. Unfortunately, those libraries do not allow for restarts with a modified number of processes or scientific post-processing of the checkpointed data. This is because they typically use an N-N checkpointing scheme and opaque file-formats. In this article, we present a novel mechanism to asynchronously store checkpoints into a self-descriptive file format and load the data upon recovery with a different number of processes. We provide an API that defines the process-local data as part of a globally shared dataset. Our measurements demonstrate a low overhead between 0.6% and 2.5% for a 2.25 TB checkpoint with 6K processes.