For well-resolving extreme weather events, running numerical weather prediction model with high resolution in time and space is essential. We explore how efficiently such modeling could be, using NURION. We have examined one of community numerical weather prediction models, WRF, and KISTI’s 5th supercomputer NURION of national HPC. Scalability of the model has been tested at first, and we have compared the computational efficiency of hybrid openMP + MPI runs with pure MPI runs. In addition to those parallel computing experiments, we have tested a new storage layer called burst buffer to see whether it can accelerate frequent I/O. We found that there are significant differences between the computational environments for running WRF model. First of all, we have tested a sensitivity of computational efficiency to the number of cores per node. The sensitivity experiments certainly tell us that using all cores per node does not guarantee the best results, rather leaving several cores per node could give more stable and efficient computation. For the current experimental configuration of WRF, moreover, pure MPI runs gives much better computational performance than any hybrid openMP + MPI runs. Lastly, we have tested burst buffer storage layer that is expected to accelerate frequent I/O. However, our experiments show that its impact is not consistently positive. We clearly confirm the positive impact with relatively smaller problem size experiments while the impact was not seen with bigger problem experiments. Significant sensitivity to the different computational configurations shown this paper strongly suggests that HPC users should find out the best computing environment before massive use of their applications
The impact of a thermodynamic sea-ice module in the COSMO numerical weather prediction model on simulations for the Laptev Sea, Siberian Arctic
