The document discusses the optimization of the Purdue-Lin microphysics scheme in the Weather Research and Forecasting (WRF) model to enhance weather predictions, especially in light of increasing severe weather due to climate change. It details the performance improvements achieved through code optimizations, resulting in a 4.7× speedup on the Intel Xeon Phi and a 1.3× speedup on dual socket Intel Xeon E5-2670 CPUs. The Purdue-Lin scheme is noted for its effective parallel computation due to minimal interactions among grid points.

1. Optimizing Purdue-Lin Microphysics Scheme for Intel Xeon Phi Coprocessor
Abstract:
Due to severe weather events, there is a growing need for more accurate weather
predictions. Climate change has increased both frequency and severity of such
events. Optimizing weather model source code would result in reduced run times
or more accurate weather predictions. One such weather model is the weather
research and forecasting (WRF) model, which is designed for both numerical
weather prediction (NWP) and atmospheric research. The WRF software
infrastructure consists of several components such as dynamic solvers and physics
schemes. Purdue-Lin scheme is a relatively sophisticated microphysics scheme in
the WRF model. The scheme includes six classes of hydro meteors: 1) water
vapor; 2) cloud water; 3) raid; 4) cloud ice; 5) snow; and 6) graupel. The scheme is
very suitable for massively parallel computation as there are no interactions
among horizontal grid points. Thus, we present our optimization results for the
Purdue-Lin microphysics scheme. Those optimizations included improved
vectorization of the code to utilize multiple vector units inside each processor
code better. Performed optimizations improved the performance of the original
unmodified Purdue-Lin microphysics code running natively on Xeon Phi 7120P by
a factor of 4.7×. Similarly, the same optimizations improved the performance of
the Purdue-Lin microphysics scheme on a dual socket configuration of eight core
Intel Xeon E5-2670 CPUs by a factor of 1.3× compared to the original code.