Predictive–corrective incompressible smoothed particle hydrodynamics (PCISPH) is a promising variant of the particle-based fluid modeling technique smoothed particle hydrodynamics (SPH). In PCISPH, a dedication prediction–correction scheme is employed which allows for using a larger time step and thereby outperforms other SPH variants by up to one order of magnitude. However, certain characteristics of the PCISPH lead to severe synchronization problems that, thus far, prevented PCISPH from being applied to industrial scenarios where high performance computing techniques need to leveraged in order to simulate in appropriate resolution. In this work, we are for the first time, presenting a highly accelerated PCISPH implementation which employs a distributed multi-GPU architecture. To that end, dedicated optimization techniques are presented that allow to overcome the drawbacks caused by the algorithmic characteristics of PCISPH. Experimental evaluations on a standard dam break test case and an industrial water splash scenario confirm that PCISPH can be efficiently employed to model real-world scenarios involving a large number of particles.

中文翻译：

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使用分布式多 GPU 加速模拟工业工程应用的 PCISPH 实现

预测-校正不可压缩平滑粒子流体动力学 (PCISPH) 是基于粒子的流体建模技术平滑粒子流体动力学 (SPH) 的一个有前途的变体。在 PCISPH 中，采用了专用预测校正方案，该方案允许使用更大的时间步长，从而比其他 SPH 变体的性能高出一个数量级。然而，PCISPH 的某些特性导致了严重的同步问题，迄今为止，这些问题阻止了 PCISPH 应用于需要利用高性能计算技术以适当分辨率进行模拟的工业场景。在这项工作中，我们首次展示了采用分布式多 GPU 架构的高度加速的 PCISPH 实现。为此，提出了专用的优化技术，可以克服由 PCISPH 的算法特性引起的缺点。对标准溃坝测试案例和工业水溅场景的实验评估证实，可以有效地使用 PCISPH 来模拟涉及大量颗粒的真实场景。