Large-scale floods are one of the major events that impact the national economy and people’s livelihood every year during the flood season. Predicting the factors of flood evolution is a worldwide problem. We use the two-dimensional Saint-Venant equations as an example and for high-performance computing in modelling the flood behavior. Discretization of the two-dimensional Saint-Venant equations with initial and boundary conditions with the finite difference method in the explicit leapfrog scheme is carried out. Afterwards, we employed a large-scale heterogeneous parallel solution on the “SunRising-1” supercomputer system using MPI, OpenMP, Pthread, and OpenCL runtime libraries. On this basis, we applied communication/calculation overlapping and the local memory acceleration to optimize the performance. Finally, various performance tests of the parallel scheme are carried out from different perspectives. We have found this method is efficient and recommend this approach be used in solving systems of partial differential equations similar to the Saint-Venant equations.

Currently, research on the lattice Boltzmann method mainly focuses on its numerical simulation and applications, and there is an increasing demand for large-scale simulations in practical scenarios. In response to this situation, this study successfully implemented a large-scale heterogeneous parallel algorithm for the lattice Boltzmann method using OpenMP, MPI, Pthread, and OpenCL parallel technologies on the “Dongfang” supercomputer system. The accuracy and effectiveness of this algorithm were verified through the lid-driven cavity flow simulation. The paper focused on optimizing the algorithm in four aspects: Firstly, non-blocking communication was employed to overlap communication and computation, thereby improving parallel efficiency. Secondly, high-speed shared memory was utilized to enhance memory access performance and reduce latency. Thirdly, a balanced computation between the central processing unit and the accelerator was achieved through proper task partitioning and load-balancing strategies. Lastly, memory access efficiency was improved by adjusting the memory layout. Performance testing demonstrated that the optimized algorithm exhibited improved parallel efficiency and scalability, with computational performance that is 4 times greater than before optimization and 20 times that of a 32-core CPU.