Influence maximization (IM) is an interesting study in the domain of social and complex networks which has gained widespread importance in recent years due to the growth in viral marketing and targeted advertisements through the use of online social networks. There have been several enriching contributions which focus on efficient algorithms for IM; a fundamental question remains as to how can the IM computations be done efficiently with better performance that can aid evaluations in real time. In this work, we present a novel mechanism of IM computation, using two case studies HBUTA and HSSA, that utilizes all the available computing hardware present in a current-generation high-end computing system. We present techniques for efficiently partitioning work, computations of IM in a distributed manner, consolidation, and addressing challenges towards ensuring maximum coverage of the input graph. We see that with our initial implementations, we get a maximum of 29.05% and 35.67% improvement in performance due to our HBUTA and HSSA algorithms, respectively, when used on a graph consisting of 1.6 million vertices and 30 million edges. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.