Finding influential individuals is an important part in Social Networks. The main aim of influence maximization is to find the top influential individuals in a social network. Many basic greedy algorithms have provided good approximation to optimal result but they suffer from low efficiency. The excessively long execution time in application to large-scale social networks is also suffered. A framework is presented to accelerate the influence maximization using parallel processing with capability of graphics processing unit (GPU). Therefore, with the same objective accelerates the influence maximization by taking help of the parallel processing . It has been a NP hard problem. GPU implementation is used for improving existing greedy algorithms and designing a bottom-up traversal algorithm.

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