High-performance Material Point Method for Landslide Simulation in Julia

The study of landslides spans from pre-failure mechanisms to post-failure propagation. The risk posed by landslides often relies more on the latter, and quantitative analysis for it can also describe the hazard caused by landslides more intuitively. Traditional numerical methods, such as the finite element method (FEM), suffer from severe mesh distortions when dealing with the highly nonlinear problems of landslides, especially in the post-failure propagation, resulting in inefficient or even failed computations. Meshfree methods such as the material point method (MPM) can efficiently describe the large deformation process of a structure using material points by reducing the dependence on the mesh. However, its computational efficiency is much lower compared to FEM. Currently, MPM programs are written in languages like C/C++/Fortran, which are performant but difficult to implement and read, and in languages like MATLAB/Python, which are flexible and easy to read but at the cost of much lower performance. This is known as the “two-language problem”. A new programming language, Julia, recently rose to prominence in scientific computing. It is designed for high-performance computing, has many of the features of advanced programming languages, and solves the "two-language problem". Benefiting from the native support for GPU computing in Julia, we can easily introduce GPU computing in the program to efficiently simulate the dynamic process in the post-failure of landslide. Consequently, for such a computationally intensive task, programming a high-performance MPM in Julia would be an attractive alternative. We use the Generalized Interpolation Material Point (GIMP) method, a variant of MPM, to perform the simulations and demonstrate the capabilities of the Julia language for high-performance scientific computing.