PARALLELIZING OPERATIONAL WEATHER FORECAST MODELS FOR PORTABLE AND FAST EXECUTION

This paper describes a high-level library (The Nearest Neighbor Tool, NNT) that has been used to parallelize operational weather prediction models. NNT is part of the Scalable Modeling System (SMS), developed a t the Forecast Systems Laboratory (FSL). Programs written in NNT rely on SMS's run-time system and port between a wide range of computing pl atforms, performing well in multiprocessor systems. We show, using exa mples from operational weather models, how large Fortran 77 codes can be parallelized using NNT. We compare the ease of programmability of N NT and High Performance Fortran (HPF). We also discuss optimizations l ike data movement overlap (in interprocessor communication and I/O ope rations), and the minimization of data exchanges through the use of re dundant computations. We show that although HPF provides a simpler pro gramming interface, NNT allows for program optimizations that increase performance considerably and still keeps a simple user interface. The se optimizations have proven essential to run weather prediction model s in real time, and HPF compilers should incorporate them in order to meet operational demands. Throughout the paper, we present performance results of weather models running on a network of workstations, the I ntel Paragon, and the SGI Challenge. Finally, we study the cost of pro gramming global address space architectures with NNT's local address s pace paradigm. (C) 1996 Academic Press, Inc.