This paper describes a numerical method for the parallel solution of the differential measure inclusion problem posed by mechanical multibody systems containing bilateral and unilateral frictional constraints. The method proposed has been implemented as a set of parallel algorithms leveraging NVIDIA’s Compute Unified Device Architecture (CUDA) library support for multi-core stream computing. This allows the proposed solution to run on a wide variety of GeForce and TESLA NVIDIA graphics cards for high performance computing. Although the methodology relies on the solution of cone complementarity problems known to be fine-grained in terms of data dependency, a suitable approach has been developed to exploit parallelism with low overhead in terms of memory access and thread synchronization. Additionally, a parallel collision detection algorithm has been incorporated to further exploit available parallelism. Initial numerical tests described in this paper demonstrate a speedup of one order of magnitude for the solution time of both the collision detection and the cone complementarity problems when performed in parallel. Since stream multiprocessors are becoming ubiquitous as embedded components of next-generation graphic boards, the solution proposed represents a cost-efficient way to simulate the time evolution of complex mechanical problems with millions of parts and constraints, a task that used to require powerful supercomputers. The proposed methodology facilitates the analysis of extremely complex systems such as granular material flows and off-road vehicle dynamics.

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