  We propose Hierarchical Optimization Time Integration (HOT) for efficient
implicit time-stepping of the Material Point Method (MPM) irrespective of
simulated materials and conditions. HOT is an MPM-specialized hierarchical
optimization algorithm that solves nonlinear time step problems for large-scale
MPM systems near the CFL-limit. HOT provides convergent simulations
"out-of-the-box" across widely varying materials and computational resolutions
without parameter tuning. As an implicit MPM time stepper accelerated by a
custom-designed Galerkin multigrid wrapped in a quasi-Newton solver, HOT is
both highly parallelizable and robustly convergent. As we show in our analysis,
HOT maintains consistent and efficient performance even as we grow stiffness,
increase deformation, and vary materials over a wide range of finite strain,
elastodynamic and plastic examples. Through careful benchmark ablation studies,
we compare the effectiveness of HOT against seemingly plausible alternative
combinations of MPM with standard multigrid and other Newton-Krylov models. We
show how these alternative designs result in severe issues and poor
performance. In contrast, HOT outperforms the existing state-of-the-art,
heavily optimized implicit MPM codes with an up to 10x performance speedup
across a wide range of challenging benchmark test simulations.
