Scalable Solvers for Cone Complementarity Problems in Frictional Multibody Dynamics


We present an efficient, hybrid MPI/OpenMP framework for the cone complementarity formulation of large-scale rigid body dynamics problems with frictional contact. Data is partitioned among MPI processes using a Morton encoding in order to promote data locality and minimize communication. We parallelize the state-of-the-art first and second-order solvers for the resulting cone complementarity optimization problems. Our approach is highly scalable, enabling the solution of dense, large-scale multibody problems; a sedimentation simulation involving 256 million particles (apprx. 324 million contacts on average) was resolved using 512 cores in less than half-hour per time-step.

In 2019 IEEE High Performance Extreme Computing Conference
Saibal De
Graduate Student

Graduate Student Research Assistant (GSRA), University of Michigan.