Parallel Computing of Discontinuous Deformation Analysis Based on Graphics Processing Unit

Abstract

The performance of discontinuous deformation analysis (DDA) needs to be improved for large-scale analysis. In this study, the contact detection and open-close iteration, as the bottlenecks of DDA computing, are reimplemented on graphics processing units (GPUs). For contact detection, the proposed parallel method accelerates DDA computing by maintaining a high loading balance and data reuse ratio on the GPU. The open-close iteration is divided into two parts, simultaneous equations solver and interpenetration checker. For the simultaneous equations solver, both the parallel Jacobi method and the block Jacobi preconditioned conjugate gradient (BJPCG) methods are implemented on the GPU to substitute the original successive overrelaxation (SOR) method. The parallel interpenetration checker is improved by optimizing conditional branches on the GPU. Two applications of the new parallel methods are introduced. The results showed that the broad and narrow phases of contact detection showed 18 and 5 times speedup, respectively. The parallel BJPCG simultaneous equations solver showed 16 times speedup, and the parallel interpenetration checker showed 2 times speedup. The total performance of DDA is improved about 2 and 10 times, respectively, using the proposed methods.