Optimizing Purdue-Lin Microphysics Scheme for Intel Xeon Phi Coprocessor

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
$4.7\times$
. 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
$1.3 \times$
compared to the original code.