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At present, information technology presents exponential growth characteristics, it have entered the era of large data. Data is a strategic resource as important as self-heating resources and human resources, which implied huge economic value. How to effectively organize and deal with large data will play a huge role in the socio-economic development. The graph search and depth learning algorithms play a more and more important role in the processing of large data because of their strong ability of network analysis and feature recognition and classification. In this paper, we propose a parallel optimization method based on locality principle, synchronization cost reduction and load balancing to solve the problem of width-first search. Finally, this paper combines all the methods together, and proposes a width-first search method using heuristic search. The experimental results showed that the width-first search algorithm with parallel optimization has good acceleration effect.

Parallel algorithm research of graph search and depth learning based on data mining