Fault Tolerance in the Parareal Method
Allan S. Nielsen, Jan S. Hesthaven · 2016
Parallel-in-time integration is an often advocated approach for extracting parallelism in the solution of PDEs. Due to the comparatively low parallel efficiency of parallel-in-time integration techniques, they are primarily of interest as an extension to classical approaches at parallelism such as spacial domain decomposition. Potential applications are expected to scale across several hundreds, or possibly thousands of nodes, making algorithmic resilience towards hardware induced errors highly relevant. In this work we develop a scheduling scheme for the parareal algorithm that is resilient to node-loss. The fault-tolerant scheme is based on a popular ``Fully-Distributed'' work-scheduler for parareal, modified with a set of MPI interface extensions for implementing recovery strategies available in the ULFM framework. In addition, we demonstrate how the parareal algorithm may be made resilient towards Silent-Data-Corruption (SDC) errors by viewing it as a point-iterative method, locally monitoring the residual between consecutive iterations so to discard potentially corrupt iterations.