High-productivity Framework on GPU-rich Supercomputers for Operational Weather Prediction Code ASUCA
High-productivity Framework on GPU-rich Supercomputers for Operational Weather Prediction Code ASUCA
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富含 GPU 的超级计算机上用于操作天气预报代码 ASUCA 的高生产力框架
DOI：
10.1109/sc.2014.26
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Takayuki Aoki and Naoyuki Onodera
中科院分区：
文献类型：
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作者：
Takashi Shimokawabe;Takayuki Aoki and Naoyuki Onodera
天气预报代码需要大的计算性能，以实现快速和高分辨率的模拟。为了在GPU超级计算机上获得良好的并行效率，需要熟练的编程技术。ASUCA是日本气象厅正在开发的下一代高分辨率中尺度大气模式，具有良好的可扩展性，隐藏了分布式GPU所需的复杂实现和优化，有助于提高可维护性。我们的框架自动翻译用户编写的模板函数，更新网格点，并生成GPU和CPU代码。用户编写的代码通过MPI并行化，节点内GPU点对点直接访问。这些代码可以很容易地利用优化，如重叠技术，隐藏通信开销的计算。我们在东京工业大学的GPU丰富的超级计算机TSUBAME 2.5上进行的模拟已经证明了良好的强和弱可扩展性，对于我们使用4，108个NVIDIA K20 X GPU的最大模型，单精度达到209.6 TFlops。
The weather prediction code demands large computational performance to achieve fast and high-resolution simulations. Skillful programming techniques are required for obtaining good parallel efficiency on GPU supercomputers. Our framework-based weather prediction code ASUCA has achieved good scalability with hiding complicated implementation and optimizations required for distributed GPUs, contributing to increasing the maintainability, ASUCA is a next-generation high resolution meso-scale atmospheric model being developed by the Japan Meteorological Agency. Our framework automatically translates user-written stencil functions that update grid points and generates both GPU and CPU codes. User-written codes are parallelized by MPI with intra-node GPU peer-to-peer direct access. These codes can easily utilize optimizations such as overlapping technique to hide communication overhead by computation. Our simulations on the GPU-rich supercomputer TSUBAME 2.5 at the Tokyo Institute of Technology have demonstrated good strong and weak scalability achieving 209.6 TFlops in single precision for our largest model using 4,108 NVIDIA K20X GPUs.