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.