Optimizing the Betts-Miller-Janjic cumulus parameterization with Intel Many Integrated Core (MIC) architecture

Melin Huang; Bormin Huang; Allen H.-L. Huang

doi:10.1117/12.2196554

20 October 2015 Optimizing the Betts-Miller-Janjic cumulus parameterization with Intel Many Integrated Core (MIC) architecture

Melin Huang, Bormin Huang, Allen H.-L. Huang

Author Affiliations +

Proceedings Volume 9646, High-Performance Computing in Remote Sensing V; 96460O (2015) https://doi.org/10.1117/12.2196554
Event: SPIE Remote Sensing, 2015, Toulouse, France

Abstract

The schemes of cumulus parameterization are responsible for the sub-grid-scale effects of convective and/or shallow clouds, and intended to represent vertical fluxes due to unresolved updrafts and downdrafts and compensating motion outside the clouds. Some schemes additionally provide cloud and precipitation field tendencies in the convective column, and momentum tendencies due to convective transport of momentum. The schemes all provide the convective component of surface rainfall. Betts-Miller-Janjic (BMJ) is one scheme to fulfill such purposes in the weather research and forecast (WRF) model. National Centers for Environmental Prediction (NCEP) has tried to optimize the BMJ scheme for operational application. As there are no interactions among horizontal grid points, this scheme is very suitable for parallel computation. With the advantage of Intel Xeon Phi Many Integrated Core (MIC) architecture, efficient parallelization and vectorization essentials, it allows us to optimize the BMJ scheme. If compared to the original code respectively running on one CPU socket (eight cores) and on one CPU core with Intel Xeon E5-2670, the MIC-based optimization of this scheme running on Xeon Phi coprocessor 7120P improves the performance by 2.4x and 17.0x, respectively.

Citation Download Citation

Melin Huang, Bormin Huang, and Allen H.-L. Huang "Optimizing the Betts-Miller-Janjic cumulus parameterization with Intel Many Integrated Core (MIC) architecture", Proc. SPIE 9646, High-Performance Computing in Remote Sensing V, 96460O (20 October 2015); https://doi.org/10.1117/12.2196554

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