The invention discloses an influence maximization parallel accelerating method based on a graphic 
processing unit. The purpose of the invention is to provide the influence maximization parallel accelerating method based on the GPU (graphic 
processing unit). 
Algorithm implementation is accelerated and the implementation time is shortened by parallel calculating ability of the GPU. The influence maximization parallel accelerating method is characterized by comprising the following steps: in each Monte Carlo 
simulation, firstly, finding out strong 
connectivity in a network diagram, merging all nodes in the same strong 
connectivity into a node, wherein the weight is the sum of the weights of all nodes in the strong 
connectivity; then calculating an influence value of each node in parallel by a strategy of traversing upwards from the bottom; using different threads by the GPU calculation cores to calculate in a parallel way the influence values of different nodes with the help of the parallel calculation capability of the GPU, and obtaining the K most influential nodes. According to the invention, a pattern is converted into a 
directed acyclic graph; the calculation quantity of an influence value can be obviously reduced, meanwhile, the overall 
operation time is shortened by scheduling parallel calculation of each node in the calculation core of the GPU to the maximal extent.