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Compute Unified Device Architecture (CUDA)-based parallelization of WRF Kessler cloud microphysics scheme
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Paper Abstract

The Weather Research and Forecasting (WRF) model is the latest-generation numerical weather prediction model. It has been designed to serve both operational forecasting and atmospheric research needs. It proves useful for a broad spectrum of applications for scales ranging from meters to thousands of kilometers. WRF computes an approximate solution to the differential equations which govern the air motion of the whole atmosphere. Kessler microphysics module in WRF is a simple warm cloud scheme that includes water vapor, cloud water and rain. Microphysics processes which are modeled are rain production, fall and evaporation. The accretion and auto-conversion of cloud water processes are also included along with the production of cloud water from condensation. In this paper, we develop an efficient WRF Kessler microphysics scheme which runs on Graphics Processing Units (GPUs) using the NVIDIA Compute Unified Device Architecture (CUDA). The GPU-based implementation of Kessler microphysics scheme achieves a significant speedup of 70x over its CPU based single-threaded counterpart. The speedup on a GPU without host-device data transfer time is 816x. Since Kessler microphysics scheme is just an intermediate modules of the entire WRF model, the GPU I/O should not occur, i.e. its input data should be already available in the GPU global memory from previous modules and their output data should reside at the GPU global memory for later usage by other modules. Thus, the limited scaling of Kessler scheme with I/O will not be an issue once all modules have been rewritten using CUDA. High speed WRF running completely on GPUs promises more accurate forecasts in considerably less time.

Paper Details

Date Published: 2 November 2011
PDF: 9 pages
Proc. SPIE 8183, High-Performance Computing in Remote Sensing, 81830U (2 November 2011); doi: 10.1117/12.901828
Show Author Affiliations
Jun Wang, Univ. of Wisconsin-Madison (United States)
Bormin Huang, Univ. of Wisconsin-Madison (United States)
Hung-Lung Allen Huang, Univ. of Wisconsin-Madison (United States)
Mitchell D. Goldberg, National Oceanic and Atmospheric Administration (United States)

Published in SPIE Proceedings Vol. 8183:
High-Performance Computing in Remote Sensing
Bormin Huang; Antonio J. Plaza, Editor(s)

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