A Scalable Double In-memory Checkpoint and Restart Scheme towards Exascale

As the size of supercomputers increases, the probability of system failure grows substantially, posing an increasingly significant challenge for scalability. It is important to provide resilience for long running applications. Checkpoint-based fault tolerance methods are effective approaches at dealing with faults. With these methods, the state of the entire parallel application is checkpointed to reliable storage. When a failure occurs, the application is restarted from a recent checkpoint. In previous work, we have demonstrated an efficient double in-memory checkpoint and restart fault tolerance scheme, which leverages Charm++'s parallel objects for checkpointing. In this paper, we further optimize the scheme by eliminating several bottlenecks caused by serialized communication. We extend the in-memory checkpointing scheme to work on MPI communication layer, and demonstrate the performance on very large scale supercomputers. For example, when running a million atom molecular dynamics simulation on up to 64K cores of a BlueGene/P machine, the checkpoint time was in milliseconds. The restart times were measured to be less than 0.15 seconds on 64K cores.

Gengbin Zheng, Xiang Ni and Laxmikant V. Kale, A Scalable Double In-memory Checkpoint and Restart Scheme towards Exascale, Proceedings of the 2nd Workshop on Fault-Tolerance for HPC at Extreme Scale (FTXS 2012), Boston, USA

@inproceedings{ftxs12,
  author = "Gengbin Zheng and Xiang Ni and L. V. Kale",
  title = "{A Scalable Double In-memory Checkpoint and Restart Scheme towards Exascale}",
  booktitle = "Proceedings of the 2nd Workshop on Fault-Tolerance for HPC at Extreme Scale (FTXS)",
  year = "2012",
  month = "June",
  address = {Boston, USA},
}