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. We propose an explicit GPU-based solver within the material point method (MPM) framework using graphics processing units (GPUs) to resolveelastoplastic problems under two- and three-dimensional configurations (i.e. granular collapses and slumping mechanics). Modern GPU architectures,including Ampere, Turing and Volta, provide a computational framework that is well suited to the locality of the material point method in view ofhigh-performance computing. For intense and non-local computational aspects (i.e. the back-and-forth mapping between the nodes of the backgroundmesh and the material points), we use straightforward atomic operations (the scattering paradigm). We select the generalized interpolation materialpoint method (GIMPM) to resolve the cell-crossing error, which typically arises in the original MPM, because of the C0 continuity of the linearbasis function. We validate our GPU-based in-house solver by comparing numerical results for granular collapses with the available experimental datasets. Good agreement is found between the numerical results and experimental results for the free surface and failure surface. We further evaluatethe performance of our GPU-based implementation for the three-dimensional elastoplastic slumping mechanics problem. We report (i) a maximum 200-foldperformance gain between a CPU- and a single-GPU-based implementation, provided that (ii) the hardware limit (i.e. the peak memorybandwidth) of the device is reached. Furthermore, our multi-GPU implementation can resolve models with nearly a billion material points. We finallyshowcase an application to slumping mechanics and demonstrate the importance of a three-dimensional configuration coupled with heterogeneousproperties to resolve complex material behaviour.

An explicit GPU-based material point method solver for elastoplastic problems (ep2-3De v1.0)