Numerical weather prediction (NWP) models use mathematical models of the atmosphere to predict the weather. Ongoing efforts in the weather and climate community continuously try to improve the fidelity of weather models by employing higher order numerical methods suitable for solving model equations at high resolutions. In realistic weather forecasting scenario, simulating and tracking multiple regions of interest (nests) at fine resolutions is important in understanding the interplay between multiple weather phenomena and for comprehensive predictions. These multiple regions of interest in a simulation can be significantly different in resolution and other modeling parameters. Currently, the weather simulations involving these nested regions process them one after the other in a sequential fashion. There exists a lot of prior work in performance evaluation and optimization of weather models, however most of this work is either limited to simulations involving a single domain or multiple nests with same resolution and model parameters such as model physics options. In this paper, we evaluate and enhance the performance of popular WRF model on IBM Blue Gene/Q system. We consider nested simulations with multiple child domains and study how parameters such as physics options and simulation time steps for child domains affect the computational requirements. We also analyze how such configurations can benefit from parallel execution of the children domains rather than processing them sequentially. We demonstrate that it is important to allocate processors to nested child domains in proportion to the work load associated with them when executing them in parallel. This ensures that the time spent in the different nested simulations is nearly equal, and the nested domains reach the synchronization step with the parent simulation together. Our experimental evaluation using a simple heuristic for allocation of nodes shows that the performance of WRF simulations can be improved by up to 14% by parallel execution of sibling domains with different configuration of domain sizes, temporal resolutions and physics options.

Accurate and timely prediction of weather phenomena, such as hurricanes and flash floods, require high-fidelity compute intensive simulations of multiple finer regions of interest within a coarse simulation domain. Current weather applications execute these nested simulations sequentially using all the available processors, which is sub-optimal due to their sub-linear scalability. In this work, we present a strategy for parallel execution of multiple nested domain simulations based on partitioning the 2-D processor grid into disjoint rectangular regions associated with each domain. We propose a novel combination of performance prediction, processor allocation methods and topology-aware mapping of the regions on torus interconnects. Experiments on IBM Blue Gene systems using WRF show that the proposed strategies result in performance improvement of up to 33% with topology-oblivious mapping and up to additional 7% with topology-aware mapping over the default sequential strategy.