Efficient GPU data structures and methods to solve sparse linear systems in dynamics applications

Daniel Weber, Jan Bender, Markus Schnoes, Andre Stork and Dieter Fellner

We present GPU data structures and algorithms to efficiently solve sparse linear systems which are typically required in simulations of multibody systems and deformable bodies. Thereby, we introduce an efficient sparse matrix data structure that can handle arbitrary sparsity patterns and outperforms current state-of-the-art implementations for sparse matrix vector multiplication. Moreover, an efficient method to construct global matrices on the GPU is presented where hundreds of thousands of individual element contributions are assembled in a few milliseconds. A finite element based method for the simulation of deformable solids as well as an impulse-based method for rigid bodies are introduced in order to demonstrate the advantages of the novel data structures and algorithms. These applications share the characteristic that a major computational effort consists of building and solving systems of linear equations in every time step. Our solving method results in a speed-up factor of up to 13 in comparison to other GPU methods.

Efficient GPU data structures and methods to solve sparse linear systems in dynamics applications