Abstract. This paper presents a GPU computing algorithm, used to accelerate the Continuous-based Discrete Element Method (CDEM). Using a NVIDIA GTX VGA card, the computing speed achieved an average 650 times speedup ratio vs. Intel Core-Dual 2.66 GHz CPU. To parallelize the CDEM algorithm, the clone node force refreshing process is separated from the elemental calculation, and is replaced by a "Node Group" force assignment process, which ensures the data independence in parallel execution.

In this study, in order to improve the search efficiency of causal Bayesian network structure learning, a new tag-based search algorithm is developed. The algorithm uses tags and the topology structure of tags to mark different types of variables, thus narrowing the search space of causal Bayesian network structure learning. With this algorithm, the task of combining causal Bayesian network theory with existing theories or models in certain application establishments when causal analysis is required becomes simpler. The time complexity of the tag-based search algorithm, compared with other search algorithms, has been reduced. Moreover, the experimental results show that the efficiency and accuracy of the tag-based search algorithm are both high.