Parallel implementation of the four-dimensional lattice spring model on heterogeneous CPU-GPU system

Gao-Feng Zhao & Fuxin Rui & Hua Chen & Qin Li

As a newly developed computational method, the four-dimensional lattice spring model (4D-LSM) is computationally intensive due to the introduction of extra-dimensional interactions. In this work, the 4D-LSM is parallelized to fully utilize the available computational resources of modern computers, namely, the multi-core CPU and the GPU. To utilize computing power of the multi-core CPU, OpenMP with a fork-join scheme is used to assign computational tasks to different CPU threads, whereas CUDA, with a granular computing scheme, is adopted to assign computations to thousands of GPU threads. A domain decomposition with a data communication scheme is proposed to utilize both the multi-core CPU and the GPU. The influence of digital precision and hardware on the parallel computing performance of the 4D-LSM are investigated through a number of numerical examples including elastic deformation, elastic bulking and dynamic fracturing. Finally, the multi-core CPU 4DLSM is used to solve a crack propagation problem and is compared with existing experimental and numerical results.