Ronald Gualan-Saavedra, L. Solano-Quinde, Brett M. Bode
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引用次数: 3
Abstract
The Weather Research and Forecasting (WRF) is a next-generation mesoscale numerical weather prediction system. It is designed with a dual purpose, forecasting and research. The WRF software infrastructure consists of a number of components such as dynamic solvers and physical simulation modules. Dynamic solvers are intensive computational components of the WRF model. In this paper, the Horizontal Diffusion method, which is part of the ARW (Advanced Research WRF) dynamic solver, is accelerated using GPUs. The performance of the GPU-based method was compared to that one of a CPU-based single-threaded counterpart on a computational domain of 433x308 horizontal grid points with 35 vertical levels. Thus, the achieved speedup is 19x on a NVIDIA Tesla M2090, without considering data I/O.
天气研究与预报(WRF)是下一代中尺度数值天气预报系统。它的设计具有双重目的，预测和研究。WRF软件基础设施由许多组件组成，如动态求解器和物理模拟模块。动态求解器是WRF模型的密集计算组成部分。本文利用gpu对ARW (Advanced Research WRF)动态求解器中的水平扩散法进行了加速。在433x308个水平网格点和35个垂直级别的计算域上，将基于gpu的方法的性能与基于cpu的单线程方法的性能进行了比较。因此，在不考虑数据I/O的情况下，在NVIDIA Tesla M2090上实现的加速是19倍。
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