Unified sparse framework for large-scale material point method simulations
This work addresses the inefficiency of dense grids in MPM for large-scale simulations where material occupies a small fraction of the domain, benefiting geophysical and visual computing applications.
The paper introduces a unified sparse background-grid framework for material point method (MPM) simulations, achieving one to two orders of magnitude reduction in computational time and memory usage for strongly sparse cases while maintaining accuracy.
The material point method (MPM) is a hybrid particle-grid method widely used for simulating large deformation with history-dependent behavior. Standard MPM often relies on a dense background grid, which can be highly inefficient when material occupies a small fraction of the computational domain. Such sparsity is common in many large-scale problems, from geophysical mass flows over large terrain domains to visual-computing applications. Here, we introduce a unified sparse background-grid framework for large-scale MPM simulation. The framework treats sparse grid construction as a general active-node indexing problem. We develop two architecture-specific implementations to realize the same sparse framework: a scan-based strategy for CPUs and a hash-based strategy for GPUs. Through benchmark problems and a large-scale landslide simulation, we show that the framework provides the same results as standard dense MPM while reducing computational time and memory usage by one to two orders of magnitude in strongly sparse cases.