2775

Accelerating urban fast response Lagrangian dispersion simulations using inexpensive graphics processor parallelism

B. Singh, E. R. Pardyjak, A. Norgren, P. Willemsen
University of Utah, Department of Mechanical Engineering, Salt Lake City, UT, 84112, USA
Environmental Modelling & Software (21 January 2011)

@article{singh2011accelerating,

   title={Accelerating urban fast response Lagrangian dispersion simulations using inexpensive graphics processor parallelism},

   author={Singh, B. and Pardyjak, ER and Norgren, A. and Willemsen, P.},

   journal={Environmental Modelling & Software},

   issn={1364-8152},

   year={2011},

   publisher={Elsevier}

}

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Owing to the potential consequences associated with accidental or deliberate releases of chemical or biological agents in urban areas, fast response urban dispersion models must rapidly provide solutions that can be easily analyzed by researchers and emergency responders. In this paper, we describe a novel application of an existing Lagrangian dispersion modeling system to achieve real-time simulation and visualization of an urban plume that a user can interact with in a virtual environment (VE) through the utilization of commodity graphics hardware, utilizing the highly parallel computational capabilities available on graphics processing units (GPU). GPUs have quickly developed from video game technology to open up new avenues for enhancing simulation performance and visualization of engineering and science applications. For computer graphics applications, GPUs provide highly parallel and inexpensive data paths for processing geometry and pixels, but for simulation these parallel paths are exploited for solving general problems. In this paper, a newly developed dispersion model (GPU Plume) is tested against an analytical solution, a CPU implementation of the Lagrangian dispersion model and wind tunnel data for dispersion around a single building. GPU Plume is shown to provide results that are similar in accuracy to the CPU model, but with computation times that are up to two orders magnitude smaller. In addition, challenges associated with the implementation of Lagrangian dispersion models onto the GPU architecture are discussed in this paper.
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