Under the influence of ground motion loads

saturated soil is susceptible to liquefaction

leading to secondary disasters such as ground settlement

foundation failure

and sand ejection with water seepage. When performing dynamic response analysis of saturated media using the traditional central processing unit (CPU)-based serial numerical calculation method

the large number of coupled model coefficient matrices and the strong coupling between equations result in issues such as low computational efficiency and precision. To address the numerical computing bottleneck associated with ultra-large-scale complex fluid-structure coupling models under the traditional numerical framework

this study proposes a computational framework based on CPU and graphics processing unit (GPU) heterogeneous parallelism. By optimizing the parallel computing process for large-scale data

minimizing data transfer between the CPU and GPU

and employs a data packaging strategy to reduce unnecessary time consumption. By directly constructing the global matrix in compressed sparse row (CSR) compression format through a preprocessing non-assembly method

it avoids the significant memory consumption typically incurred by traditional assembly methods. This enables the program to compute ultra-large-scale finite element models with reduced memory resources

thereby lowering both memory and time costs. Leveraging an intrinsic CUDA function

a novel set of iterative solution schemes for equations was developed

and a parallel iterative solver tailored for multi-field coupling problems was constructed. For complex fluid-structure interaction dynamic response problems

the proposed parallel solver demonstrates an order of magnitude improvement over traditional serial computation methods. The computational capacity threshold surpasses tens of millions of degrees of freedom. Compared to ABAQUS software

the computational efficiency has increased by more than 15 times.