Compute unified device architecture (CUDA)-based parallelization of WRF Kessler cloud microphysics scheme

Authors:
Jarno Mielikainen;Bormin Huang;Jun Wang;H. -L. Allen Huang;Mitchell D. Goldberg
Affiliations:
Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin, Madison, WI, USA;Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin, Madison, WI, USA;Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin, Madison, WI, USA;Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin, Madison, WI, USA;NESDIS Center for Satellite Applications and Research, National Oceanic and Atmospheric Administration, World Weather Building, Suite 701, 200 Auth Road, Camp Springs, MD 20746, USA
Venue:
Computers & Geosciences
Year:
2013

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Abstract

In recent years, graphics processing units (GPUs) have emerged as a low-cost, low-power and a very high performance alternative to conventional central processing units (CPUs). The latest GPUs offer a speedup of two-to-three orders of magnitude over CPU for various science and engineering applications. 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 domain scales ranging from meters to hundreds 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. When a 4 GPU system is used, we achieve an overall speedup of 132x as compared to the single thread CPU version.