Comparing scalable programming techniques for weather prediction

Abstract

In this paper we study the of issues of programmability and performance in the parallelization of weather prediction models. We compare parallelization using a high level library (the Nearest Neighbor Tool: NNT) and a high level language/directive approach (High Performance Fortran: HPF). We report on the performance of a complete weather prediction model (the Rapid Update Cycle, which is currently run operationally at the National Meteorological Center at Washington) coded using NNT. We quantify the performance effects of optimizations possible with NNT that must be performed by an HPF compiler.