Notably, the advantage of incorporating the Smagorinsky model in RLBM lies in its completely local computation, preserving the parallel nature of RLBM simulations. This integration signifies a significant advancement in simulating turbulence, enabling researchers to delve deeper into intricate flow dynamics with enhanced precision and computational efficiency. Since the LES problems handled by RLBM are often large in scale, computing platforms are often required to have high computing power and large storage space. Currently, servers [29] and supercomputers [30] with multicore systems are the first choice for solving LES problems using RLBM in computational fluid dynamics. These computational platforms offer high computing power and ample storage space, which are essential for tackling the large-scale LES problems. As fluid mechanics and artificial intelligence continue to integrate deeply [31; 32; 33; 34; 35; 36; 37; 38; 39; 40], a new computing model is anticipated in the future. However, the current situation is that reliance on the use of servers and supercomputers based on multi-core architectures for solving LES with RLBM still performs much better than AI solutions. It is imperative to employ parallel computing strategies to efficiently address these computational challenges in the realm of handling LES problems by RLBM. The high-performance computing (HPC) platform provides for the large amount of computational resources required during LES simulation. RLBM needs to design parallel algorithms and formulate computational domain division strategies according to its own characteristics when solving LES simulation. Due to the local dynamics of RLBM with LES (RLBM-LES), DDM can be used to achieve parallelism. Related studies have been presented. [PERSON] et al. [41] used the domain decomposition method to simulate the 3D incompressible flow past a sphere on multi-GPUs. MPI is the optimum solution for implementing computation across compute nodes in HPC. [PERSON] et al. [42] used MPI to accelerate the RLBM simulation implemented on top of the Palabos [43] library, but they only gave performance results and did not show the details on the parallel algorithm. [PERSON] and [PERSON] [44] used RLBM to simulated 3D transient flows in complex geometries on 128 CPU cores but did not provide a performance analysis. [PERSON] and [PERSON] [45] used the RLBM method to simulate incompressible laminar convection in 2D and 3D channelsand the domain decomposition method to parallelize uniform lattice grids, while also providing a performance analysis on a HPC cluster with 32 nodes. On the one hand, there is no research on the scalability of RLBM-LES on large-scale cluster; on the other hand, parallel algorithms of RLBM only evaluate its parallel performance and still lack practical applications which have no Cartesian grid generation algorithms. Therefore, this paper proposes a parallel algorithm for RLBM to solve LES simulations. The algorithm is based on Cartesian grid generation, domain decomposition method and data exchange strategy on clusters, which empowers RLBM to solve LES with HPC clusters. The scalability of the algorithm on large-scale clusters is also investigated. The rest of the paper is arranged as follows. Section 2 introduces RLBM-LES and the processing method of the boundary. Section 3 illustrates RLBM-LES parallel implementation. Next, the experimental results are shown and discussed in Section 4. Finally, Section 5 summarizes this paper.