论文标题
lbmpy：自动代码生成，用于有效的平行格boltzmann方法
lbmpy: Automatic code generation for efficient parallel lattice Boltzmann methods
论文作者
论文摘要
晶格玻尔兹曼方法是宏观计算流体动力学求解器的流行介观替代品。已经开发出许多变体的复杂性，准确性和计算成本各不相同。扩展可用于模拟多相，多组分，湍流或非牛顿流。在这项工作中，我们提出了LBMPY，这是一个支持多种不同方法的代码生成包，并为新方案提供了通用的开发环境。高级域特异性语言允许用户制定，扩展和测试各种晶格Boltzmann方案。方法规范在符号中间表示中表示。在此中间表示方面进行的转换优化和并行化了该方法，不仅用于单次和两 - 释放时间方案，还用于多余时间，累积时间和熵稳定的方法，产生了高效的晶格Boltzmann计算内核。在HPC框架中的集成使沃尔伯拉（Walberla）可以实现大规模平行的分布式模拟，这是通过在SuperMuc-NG超级计算系统上的缩放实验来证明的
Lattice Boltzmann methods are a popular mesoscopic alternative to macroscopic computational fluid dynamics solvers. Many variants have been developed that vary in complexity, accuracy, and computational cost. Extensions are available to simulate multi-phase, multi-component, turbulent, or non-Newtonian flows. In this work we present lbmpy, a code generation package that supports a wide variety of different methods and provides a generic development environment for new schemes as well. A high-level domain-specific language allows the user to formulate, extend and test various lattice Boltzmann schemes. The method specification is represented in a symbolic intermediate representation. Transformations that operate on this intermediate representation optimize and parallelize the method, yielding highly efficient lattice Boltzmann compute kernels not only for single- and two-relaxation-time schemes but also for multi-relaxation-time, cumulant, and entropically stabilized methods. An integration into the HPC framework waLBerla makes massively parallel, distributed simulations possible, which is demonstrated through scaling experiments on the SuperMUC-NG supercomputing system
