An investigation into the feasibility and benefits of GPU/multicore acceleration of the weather research and forecasting model
An investigation into the feasibility and benefits of GPU/multicore acceleration of the weather research and forecasting model
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DOI：
10.1109/hpcsim.2013.6641457
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发表时间：
2013-07
期刊：
影响因子：
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通讯作者：
W. Vanderbauwhede;T. Takemi
中科院分区：
文献类型：
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作者：
W. Vanderbauwhede;T. Takemi
越来越准确的气候和天气模拟在较短的时间范围内提供了使用GPU或FPGA的硬件加速度。评估这类数字天气预测代码的加速度是否可行且值得。创建了一个简单的性能模型，用于根据WRF分析结果比较多核心CPU和GPU，我们专注于标量广告模块的加速度。与CPU上的原始代码相比，在GPU上，由于GPU和CPU之间的数据传输成本很高（如我们的分析所示）集成的代码。代码，不限于NWP模型。
There is a growing need for ever more accurate climate and weather simulations to be delivered in shorter timescales. Hardware Acceleration using GPUs or FPGAs could potentially result in much reduced run times or higher accuracy simulations. We studied the Weather Research and Forecasting Model in order to assess if GPU acceleration of this type of Numerical Weather Prediction code is both feasible and worthwhile. We studied the performance of the original code and created a simple performance model for comparing multicore CPUs and GPUs. Based on the WRF profiling results, we focused on the acceleration of the scalar advection module. We show that our data-parallel kernel version of the scalar advection module runs up to 7× faster on the GPU compared to the original code on the CPU. However, as the data transfer cost between GPU and CPU is very high (as shown by our analysis), there is only a small speed-up (2×) for the fully integrated code. We also developed an extensible system for integrating OpenCL code into large Fortran code bases such as WRF. In conclusion, we have shown that GPU acceleration of WRF is both feasible and worthwhile. Our findings are generally applicable to multi-physics fluid dynamics code and not limited to NWP models.