An Evaluation of Different I/O Techniques for Checkpoint/Restart

Today's High Performance Computing (HPC) clusters consist of hundreds of thousands of CPUs, memory units, complex networks, and other components. Such an extreme level of hardware parallelism reduces the mean time to failure (MTTF) of the overall cluster. The future of HPC urgently demands to develop environments that facilitate programs to run successfully even in the presence of failures. Checkpoint/Restart (C/R) is one of the most common techniques to provide fault tolerance. C/R is relatively easy to implement, but typically it introduces significant overhead in the runtime of the application. In this paper, a check pointing technique is presented that significantly reduces the checkpoint overhead and is highly scalable. This is achieved by overlapping the I/O for writing the checkpoint with the computation of the application. For this asynchronous check pointing technique, a theoretical model is developed to estimate the checkpoint overhead. An implementation of this technique is then benchmarked and compared with other check pointing strategies. We show our approach to have marginal overhead as opposite to standard synchronous check pointing for typical application scenarios. A comparison with the node-level check pointing technique by using Scalable Checkpoint/Restart (SCR) library is also presented.