Fluid simulations are often performed using the incompressible Navier-Stokes equations (INSE), leading to sparse linear systems which are difficult to solve efficiently in parallel. Recently, kinetic methods based on the adaptive-central-moment multiple-relaxation-time (ACM-MRT) model have demonstrated impressive capabilities to simulate both laminar and turbulent flows, with quality matching or surpassing that of state-of-the-art INSE solvers. Furthermore, due to its local formulation, this method presents the opportunity for highly scalable implementations on parallel systems such as GPUs. However, an efficient ACM-MRT-based kinetic solver needs to overcome a number of computational challenges, especially when dealing with complex solids inside the fluid domain. In this paper, we present multiple novel GPU optimization techniques to efficiently implement high-quality ACM-MRT-based kinetic fluid simulations in domains containing complex solids. Our techniques include a new communication-efficient data layout, a load-balanced immersed-boundary method, a multi-kernel launch method using a simplified formulation of ACM-MRT calculations to enable greater parallelism, and the integration of these techniques into a parametric cost model to enable automated parameter search to achieve optimal execution performance. We also extended our method to multi-GPU systems to enable large-scale simulations. To demonstrate the state-of-the-art performance and high visual quality of our solver, we present extensive experimental results and comparisons to other solvers.

中文翻译：

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GPU优化可进行高质量的动力学流体模拟

流体模拟通常使用不可压缩的Navier-Stokes方程（INSE）进行，导致稀疏的线性系统难以并行高效求解。最近，基于自适应中心矩多重弛豫时间（ACM-MRT）模型的动力学方法已经展示出令人印象深刻的功能，能够模拟层流和湍流，其质量与最新的INSE相匹配或超越解算器。此外，由于其局部公式化，该方法为并行系统（例如GPU）上的高度可扩展实现提供了机会。但是，基于ACM-MRT的高效动力学求解器需要克服许多计算难题，尤其是在处理流体域内的复杂固体时。在本文中，我们提出了多种新颖的GPU优化技术，以在包含复杂固体的域中有效实施基于ACM-MRT的高质量运动流体仿真。我们的技术包括新的高效通信数据布局，负载平衡的沉浸式边界方法，使用ACM-MRT计算的简化公式以实现更大并行度的多内核启动方法，以及将这些技术集成到参数成本中模型以启用自动参数搜索以获得最佳执行性能。我们还将方法扩展到多GPU系统，以实现大规模仿真。为了证明我们的求解器具有最先进的性能和较高的视觉质量，我们提供了广泛的实验结果并与其他求解器进行了比较。