A Parallel Graph Algorithm for Detecting Mesh Singularities in Distributed Memory Ice Sheet Simulations

We present a new, distributed-memory parallel algorithm for detection of degenerate mesh features that can cause singularities in ice sheet mesh simulations. Identifying and removing mesh features such as disconnected components (icebergs) or hinge vertices (peninsulas of ice detached from the land) can significantly improve the convergence of iterative solvers. Because the ice sheet evolves during the course of a simulation, it is important that the detection algorithm can run in situ with the simulation --- running in parallel and taking a negligible amount of computation time --- so that degenerate features (e.g., calving icebergs) can be detected as they develop. We present a distributed memory, BFS-based label-propagation approach to degenerate feature detection that is efficient enough to be called at each step of an ice sheet simulation, while correctly identifying all degenerate features of an ice sheet mesh. Our method finds all degenerate features in a mesh with 13 million vertices in 0.0561 seconds on 1536 cores in the MPAS Albany Land Ice (MALI) model. Compared to the previously used serial pre-processing approach, we observe a 46,000x speedup for our algorithm, and provide additional capability to do dynamic detection of degenerate features in the simulation.