A scalable version of the Navy Operational Global Atmospheric Prediction System spectral forecast model

Article type: Research Article

Authors: Thomas E. Rosmond,

Affiliations: Naval Research Laboratory, Monterey, California, 7 Grace Hopper Ave, Stop 2, Monterey, CA 93943-5502, USA. Tel.: +1 408 656 4736; Fax: +1 408 656 4769; E-mail: rosmond@nrlmry.navy.mil

Abstract: The Navy Operational Global Atmospheric Prediction System (NOGAPS) includes a state-of-the-art spectral forecast model similar to models run at several major operational numerical weather prediction (NWP) centers around the world. The model, developed by the Naval Research Laboratory (NRL) in Monterey, California, has run operational at the Fleet Numerical Meteorological and Oceanographic Center (FNMOC) since 1982, and most recently is being run on a Cray C90 in a multi-tasked configuration. Typically the multi-tasked code runs on 10 to 15 processors with overall parallel efficiency of about 90%. resolution is T159L30, but other operational and research applications run at significantly lower resolutions. A scalable NOGAPS forecast model has been developed by NRL in anticipation of a FNMOC C90 replacement in about 2001, as well as for current NOGAPS research requirements to run on DOD High-Performance Computing (HPC) scalable systems. The model is designed to run with message passing (MPI). Model design criteria include bit reproducibility for different processor numbers and reasonably efficient performance on fully shared memory, distributed memory, and distributed shared memory systems for a wide range of model resolutions. Results for a wide range of processor numbers, model resolutions, and different vendor architectures are presented. Single node performance has been disappointing on RISC based systems, at least compared to vector processor performance. This is a common complaint, and will require careful re-examination of traditional numerical weather prediction (NWP) model software design and data organization to fully exploit future scalable architectures.

Journal: Scientific Programming, vol. 8, no. 1, pp. 31-38, 2000