The recent research trend of Eikonal solver focuses on employing state-of-the-art parallel computing technology, such as GPUs. Even though there exists previous work on GPU-based parallel Eikonal solvers, only little research literature exists on the multi-GPU Eikonal solver due to its complication in data and work management. In this paper, we propose a novel on-the-y, adaptive domain decomposition method for e cient implementation of the Block-based Fast Iterative Method on a multi-GPU system. The proposed method is based on dynamic domain decomposition so that the region to be processed by each GPU is determined on-the-y when the solver is running. In addition, we propose an e cient domain assignment algorithm that minimizes communication overhead while maximizing load balancing between GPUs. The proposed method scales well, up to 6.17× for eight GPUs, and can handle large computing problems that do not t to limited GPU memory. We assess the parallel e ciency and runtime performance of the proposed method on various distance computation examples using up to eight GPUs.
Hong, S., & Jeong, W. K. (2016). A multi-GPU fast iterative method for eikonal equations using on-the-fly adaptive domain decomposition. In Procedia Computer Science (Vol. 80, pp. 190–200). Elsevier B.V. https://doi.org/10.1016/j.procs.2016.05.309