Towards a generalised GPU/CPU shallow-flow modelling tool
Towards a generalised GPU/CPU shallow-flow modelling tool
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DOI：
10.1016/j.compfluid.2013.09.018
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发表时间：
2013-12
影响因子：
2.8
通讯作者：
L. Smith;Q. Liang
中科院分区：
文献类型：
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作者：
L. Smith;Q. Liang
本文提出了一种利用现代图形处理器(GPU)的新软件，与传统的中央处理器(CPU)方法相比，它可以显著加快二维浅流模拟的速度。二阶精度Godunov型MUSCL-HANCOCK格式与HLLC Riemann求解器相结合，建立了适用于不同类型洪水模拟的稳健框架。一个真实的大坝坍塌事件被模拟使用一个180万个单元域，使用三个主流供应商提供的CPU和GPU硬件。结果表明，与事后调查显示出良好的一致性。对程序结构和数据缓存的不同配置进行了评估，结果表明新软件适用于不同类型的现代处理设备。性能扩展类似于供应商提供的报价峰值性能数据的差异。我们还比较了32位和位浮点计算的结果，发现32位精度引入了显著的局部误差。
This paper presents new software that takes advantage of modern graphics processing units (GPUs) to significantly expedite two-dimensional shallow-flow simulations when compared to a traditional central processing unit (CPU) approach. A second-order accurate Godunov-type MUSCL-Hancock scheme is used with an HLLC Riemann solver to create a robust framework suitable for different types of flood simulation. A real-world dam collapse event is simulated using a 1.8 million cell domain with CPU and GPU hardware available from three mainstream vendors. The results are shown to exhibit good agreement with a post-event survey. Different configurations are evaluated for the program structure and data caching, with results demonstrating the new software’s suitability for use with different types of modern processing device. Performance scaling is similar to differences in quoted peak performance figures supplied by the vendors. We also compare results obtained with 32-bit and 64-bit floating-point computation, and find there are significant localised errors introduced by 32-bit precision.