基于CUDA的流线仿真并行化

Mulan Luo, Xu-sheng Wang, Xiaohui Ji
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引用次数: 1

摘要

为了加速流线仿真并满足实时性要求,本文提出了一种基于gpu的流线仿真并行化方法。采用CUDA架构在单GPU和多GPU计算机上实现并行算法。在我们的方法中,一个网格被组织成一个二维的块数组,一个块中的所有线程被组织成一个一维数组,这样一个块中的每个线程计算一个流线。为了在多gpu上实现该方法,将物理单元模型划分为子模型,使子模型的数量与gpu的数量相等。以Tóthian流域为例,给出了该算法的应用。实验分析表明,基于不同gpu数量的并行算法具有不同的加速度。对于单个GPU,加速达到170倍;对于5个gpu,对于具有40×106单元的物理模型,它是808倍。结论是gpu可以大大加快流线化仿真。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Parallelization of the streamline simulation based on CUDA
To accelerate the streamline simulation and satisfy the real-time demands, in this paper, we proposed a method based on GPUs to parallelize the streamline simulation. CUDA architecture was used to implement the parallel algorithm on a single GPU and a multi-GPU computer. In our method, a grid is organized into a 2D array of blocks, and all threads in a block are organized into a 1D array, such that each thread in a block computes one streamline. To implement the method on multiple GPUs, the physical cell model is divided into sub-models to make the number of sub-models equal to the number of GPUs. The algorithm is applied to a Tóthian basin as an example. The experimental analysis shows that the parallel algorithm based on different numbers of GPUs has different accelerations. For a single GPU, the speedup reaches 170 times; and for five GPUs, it is 808 times, for a physical model with 40×106 cells. The conclusion is that GPUs can greatly accelerate the streamline simulation.
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