Abstract: Due to the plenty of third-party libraries and development productivity,Python is becoming increasingly popular as a programming language in areas such as data science and artificial intelligence.Python has been providing fundamental support for scientific and engineering computing.For example,libraries such as NumPy and SciPy provide efficient data structures for multi-dimensional arrays and rich numerical functions.Traditionally,Python was used as a script language,gluing preprocessors,solvers and postprocessors and enhancing automation in numerical simulations.Recently,some foreign researchers implement their solvers using Python and parallelize their Python codes on high performance computers,with impressive results achieved.Because of its intrinsic features,implementation and optimization of high performance large-scale numerical simulations with Python are quite different with traditional language such as C/C++ and Fortran.This paper presented a large-scale parallel open source 3D Lattice Boltzmann multi-phase flow simulation code PyLBMFlow with Python,and investigated large-scale parallel computing and performance optimization for Python numerical applications.It designed LBM flow data structures and computational kernels with NumPy multi-dimensional arrays and universal functions.Through a range of optimization including reconstruction of boundary processing,it dramatically improves the efficiency of Python computing by about 100X,compared with the baseline version,on a CPU core.Furthermore,this paper designed a 3D decomposition method and implement hybrid MPI+OpenMP parallelization using mpi4py and Cython.Tests for 3D multi-phase(liquid and gases) problem(about 10 Billion lattices) simulating drop impact with gravity effect using D3Q19 Lattice Boltzmann discretization and Shan-Chen BGK single relaxation time collision model were presented,achieving a weak parallel efficiency of above 90% in going from 64 to 1024 compute nodes.

Key words: High performance Python, Lattice Boltzmann method, Multi-phase flow simulation, Parallel computing, Performance optimization