Enabling efficient execution of a variational data assimilation application
Dennis, J. M., Baker, A. H., Dobbins, B., Bell, M. M., Sun, J., et al. (2023). Enabling efficient execution of a variational data assimilation application. The International Journal of High Performance Computing Applications, doi:10.1177/10943420221119801
| Title | Enabling efficient execution of a variational data assimilation application |
|---|---|
| Author(s) | John M. Dennis, Allison H. Baker, Brian Dobbins, Michael M. Bell, Jian Sun, Youngsung Kim, Ting-Yu Cha |
| Abstract | Remote sensing observational instruments are critical for better understanding and predicting severe weather. Observational data from such instruments, such as Doppler radar data, for example, are often processed for assimilation into numerical weather prediction models. As such instruments become more sophisticated, the amount of data to be processed grows and requires efficient variational analysis tools. Here we examine the code that implements the popular SAMURAI (Spline Analysis at Mesoscale Utilizing Radar and Aircraft Instrumentation) technique for estimating the atmospheric state for a given set of observations. We employ a number of techniques to significantly improve the code's performance, including porting it to run on standard HPC clusters, analyzing and optimizing its single-node performance, implementing a more efficient nonlinear optimization method, and enabling the use of GPUs via OpenACC. Our efforts thus far have yielded more than 100x improvement over the original code on large test problems of interest to the community. |
| Publication Title | The International Journal of High Performance Computing Applications |
| Publication Date | Mar 1, 2023 |
| Publisher's Version of Record | https://dx.doi.org/10.1177/10943420221119801 |
| OpenSky Citable URL | https://n2t.net/ark:/85065/d7154n2f |
| OpenSky Listing | View on OpenSky |
| CISL Affiliations | TDD, ASAP |