Comparing Domain Decomposition Methods for the Parallelization of Distributed Land Surface Models

Current research challenges in hydrology require high resolution models, which simulate the processes comprising the water-cycle on a global scale. These requirements stand in great contrast to the current capabilities of distributed land surface models. Hardly any literature noting efficient scalability past approximately 64 processors could be found. Porting these models to supercomputers is no simple task, because the greater part of the computational load stems from the evaluation of highly parametrized equations. Furthermore, the load is heterogeneous in both spatial and temporal dimension, and considerable load-imbalances occur triggered by input data. We investigate different domain decomposition methods for distributed land surface models and focus on their properties concerning load balancing and communication minimizing partitionings. Artificial strong scaling experiments from a single core to 8, 192 cores show that graph-based methods can distribute the computational load of the application almost as efficiently as coordinate-based methods, while the partitionings found by the graph-based method significantly reduce communication overhead.