Yong Liu, Bin Wang
Many analyses in geotechnical engineering can be conceptualized as the interaction of a continua with rigid bodies or intricate boundaries. The coupling MPM-DEM method is recognized as an effective numerical framework for resolving such problems. Nevertheless, most existing study suffer from intrinsic performance constraints imposed by their program-structure design, and rarely can be extended to simulations involving million-scale particles on the desktop-class graphics processing unit (GPU). In this study, a fully GPU-resident MPM-DEM framework based on CUDA is proposed. A linked-cell neighbour search accelerated with two parallel prefix-sum scans, together with a tunable Verlet buffer, reduces the cost of broad-phase collision detection, whereas race-free particle-to-grid transfers are enforced with CUDA atomic operations. Verification against (i) a half cylinder–plane contact, (ii) projectile penetration, and (iii) granular-flow impact on stacked blocks shows excellent agreement with analytical solutions and laboratory measurements. The Baige landslide in China was simulated with 4,398,192 material points and 641,280 triangular facets. This large-scale case verifies the method’s applicability to real-world geohazards and achieves a computational speed approximately 30 times faster than the runtime reported in a conventional GPU-MPM analysis. These results confirm that the proposed framework delivers strong scalability and substantial computational speed-ups for coupled MPM–DEM analyses of complex, large-scale geotechnical processes.