12 October 2011 Development of the GPU-based Stony-Brook University 5-class microphysics scheme in the weather research and forecasting model
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Abstract
The weather research and Forecasting (WRF) model in an atmospheric simulation system, which is designed for both operational and research use. This common tool aspect promotes closer ties between research and operational communities. It contains a lot a different physics and dynamics options reflecting the experience and input of the broad scientific community. The WRF physics categories and microphysics, cumulus parametrization, planetary boundary layer, land-surface model and radiation. Explicitly resolved water vapor, cloud and precipitation processes are included in microphysics. Several bulk water microphysics schemes are available within the Weather Research and Forecasting (WRF) model, with different numbers of simulated hydrometeor classes and methods for estimating their size fall speeds, distributions and densities. Stony-Brook University (SBU-YLIN) microphysics scheme is a 5-class scheme with riming intensity predicted to account for mixed-phase processes. In this paper, we develop an efficient graphics processing unit (GPU) based SBU-YLIN scheme. WRF computation domain is 3D grid layed over the earth. SBU-YLIN performs the same computation for each spatial position in the whole domain. This repletion of the same computation on different data sets allows using GPU's Single Instruction Multiple Dataset (SIMD) architecture. The GPU-based SBUYLIN scheme will be compared to a CPU-based single-threaded counterpart. The implementation achieves 213x speedup with I/O compared to a Fortran implementation running on a CPU. Without I/O the speedup is 896x.
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Jarno Mielikainen, Jarno Mielikainen, Bormin Huang, Bormin Huang, Allen H.-L. Huang, Allen H.-L. Huang, Mitchell D. Goldberg, Mitchell D. Goldberg, } "Development of the GPU-based Stony-Brook University 5-class microphysics scheme in the weather research and forecasting model", Proc. SPIE 8183, High-Performance Computing in Remote Sensing, 81830V (12 October 2011); doi: 10.1117/12.901829; https://doi.org/10.1117/12.901829
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