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• W63923496 abstract "ABSTRACT: Advances in software and hardware technologies in many-core graphics processing units (GPU) create new opportunities to accelerate computational turn-around time of computa-tional fluid dynamics (CFD) simulations for wind engineering applications, where rapid simula-tions can make a tremendous impact on the current practice. We investigate the computational performance of a GPU-accelerated incompressible Navier-Stokes solver for 3D flows (GIN3D) for complex terrain simulations on multi-GPU desktop platforms and on the recently deployed NCSA Lincoln Tesla Cluster. Our results demonstrate the potential of GPU computing to accele-rate complex terrain wind forecasting applications. 1 INTRODUCTION Detailed knowledge of meteorological conditions (e.g. winds, temperature, moisture and turbu-lence fluctuations) in complex terrain and urban domains are becoming increasingly important in weather forecasting, wind energy, homeland security and defense applications. In many applica-tions short-term forecasts for domains that range from 1-2 km to 10-20 km in the horizontal plane are needed. Surface-layer parameterizations in meso-scale models may not directly apply to micro-scale applications in the atmospheric boundary layer because of the scale-dependent as-sumptions inherent in the physical models. Therefore, there has been an interest in application of engineering CFD models to micro-scale atmospheric boundary layer events. However, engineer-ing CFD models appear to be computationally expensive because they are designed to be appli-cable to a variety of problems (aerodynamics, two-phase flows, combustion etc.). For these rea-sons, there is a need to develop a computationally fast micro-scale complex terrain CFD model for short-term wind forecasting. With recent developments in many-core computing hardware, this task appears to be realizable as we discuss next. GPUs have emerged as general-purpose many-core computing platforms that can substantially accelerate compute-intensive applications in various domains (Owens et al. 2007, 2008). Modern GPUs provide memory bandwidth and floating-point performances that are orders of magnitude faster than conventional central processing units (CPU). For instance, NVIDIA Tesla C1060 GPU provides a memory bandwidth of 102 GB/s and almost a teraFLOPS (floating point opera-tions per seconds) of single precision floating-point peak performance through 240 cores distri-buted over 30 multiprocessors. The most recent Fermi architecture from NVIDIA is based on 512 cores with error correcting codes (ECC) on memory. Today, four graphics processing units can be installed on a single motherboard to build a" @default.
• W63923496 created "2016-06-24" @default.
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• W63923496 date "2010-01-01" @default.
• W63923496 modified "2023-09-27" @default.
• W63923496 title "Acceleration of Complex Terrain Wind Predictions using Many-core Computing Hardware" @default.
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