Enabling efficient execution of a variational data assimilation application

John M. Dennis, Allison H. Baker, Brian Dobbins, Michael M. Bell, Jian Sun, Youngsung Kim, Ting Yu Cha

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish
Pages (from-to)101-114
Number of pages14
JournalInternational Journal of High Performance Computing Applications
Volume37
Issue number2
DOIs
StatePublished - Mar 2023

Funding

We would like to acknowledge high-performance computing support from Cheyenne (doi:10.5065/D6RX99HX ) provided by NCAR’s Computational and Information Systems Laboratory, sponsored by the National Science Foundation. MMB and TYC acknowledge support from NSF award OAC-1661663. We would also like to thank Scott Ellis and Wen-chau Lee of NCAR for supporting the samurai optimization effort and Mike Dixon of NCAR for his software and build support. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Office of the Director (OAC-1661663). We would like to acknowledge high-performance computing support from Cheyenne ( doi:10.5065/D6RX99HX ) provided by NCAR’s Computational and Information Systems Laboratory, sponsored by the National Science Foundation. MMB and TYC acknowledge support from NSF award OAC-1661663. We would also like to thank Scott Ellis and Wen-chau Lee of NCAR for supporting the samurai optimization effort and Mike Dixon of NCAR for his software and build support. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Office of the Director (OAC-1661663).

Keywords

  • GPU
  • data assimilation
  • optimization

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