GPU-Based Cellular Automata Model for Multi-Orient Dendrite Growth and the Application on Binary Alloy

To simulate dendrite growth with different orientations more efficiently, a high-performance cellular automata (CA) model based on heterogenous central processing unit (CPU)+ graphics (GPU) architecture has been proposed in this paper. Firstly, the decentered square algorithm (DCSA) is used to morphology of orientations. Secondly, parallel algorithms are take full advantage many cores by maximizing computational parallelism. Thirdly, order further improve calculation efficiency, task scheduling scheme using multi-stream designed solve waiting problem among independent tasks, improving Then, present was validated comparing its steady tip velocity Lipton–Glicksman–Kurz (LGK) analytical model, which shows great agreement. Finally, it applied binary alloy, proves that can not only clear and secondary arms, but also show good agreement situ experiment. In addition, compared traditional CPU speedup up 158×, provides acceleration.