Abstract We present a CUDA-based parallel implementation on GPU architecture of a modified version of the Smoothed Particle Hydrodynamics (SPH) method. This modified formulation exploits a strategy based on the Taylor series expansion, which simultaneously improves the approximation of a function and its derivatives with respect to the standard formulation. The improvement in accuracy comes at the cost of an additional computational effort. The computational demand becomes increasingly crucial as problem size increases but can be addressed by employing fast summations in a parallel computational scheme. The experimental analysis showed that our parallel implementation significantly reduces the runtime, when compared to the CPU-based implementation.

中文翻译：

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一种基于 CUDA 的改进 SPH 方法在 GPU 上的实现

摘要 我们在平滑粒子流体动力学 (SPH) 方法的修改版本的 GPU 架构上提出了基于 CUDA 的并行实现。这种修改后的公式利用了基于泰勒级数展开的策略，同时改进了函数及其导数相对于标准公式的近似。准确性的提高是以额外的计算工作为代价的。随着问题规模的增加，计算需求变得越来越重要，但可以通过在并行计算方案中采用快速求和来解决。实验分析表明，与基于 CPU 的实现相比，我们的并行实现显着减少了运行时间。