Acceleration of the WRF Monin-Obukhov-Janjic Surface Layer Parameterization Scheme on an MIC-Based Platform for Weather Forecast

作者：Huang Melin^*; Huang Bormin; Huang Hung Lung Allen

来源：IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(10): 4399-4408.

DOI：10.1109/JSTARS.2017.2725743

• Computing performance
• Intel many integrated core (MIC) architecture
• Monin-Obukhov-Janjic (MOJ) scheme
• surface layer
• weather research and forecasting (WRF)
• Xeon Phi coprocessor

A state-of-the-art numerical weather prediction (NWP) model, comprising weather research and forecast (WRF) model and analysis techniques, has been extensively exercised for weather prophecy all over the world. The WRF model, the soul role in NWP, constitutes dynamic solvers and elaborate physical components for conducting fluid behavior, all of which are sketched for both atmospheric research analyses and operational weather foretell. One salient physical ingredient in WRF is the surface layer simulation, which provides surface heat and moisture fluxes through calculation of surface friction velocities and exchange coefficients. TheMonin-Obukhov-Janjic (MOJ) scheme is one popular surface layer option in WRF. This is one of the schemes in WRF we choose to expedite toward an end-to-end accelerated weather model. One advantageous aspect in WRF is the independence among grid points that facilitates programming implementations in parallel computation. We here present a parallel construction on theMOJ module with application of vectorization elements and efficient parallelization essentials furnished by Intel many integrated core (MIC) architecture. To achieve high computing performance, apart from the fundamental usage of Intel MIC architecture, this paper offers some new approaches related to code structure and art of optimization skills. At the end, in comparison with the original code separately executing on one CPU core and on one CPU socket (eight cores) with Intel Xeon E5-2670, the optimized MIC-based MOJ module running on Xeon Phi coprocessor 7120P ameliorates the computing performance by 9.6x and 1.5x, respectively.

• 出版日期2017-10