GPU-based Parallel Algorithm for Generating Massive Scale-free Networks Using the Preferential Attachment Model

Citation

M. Alam and K. Perumalla. GPU-based Parallel Algorithm for Generating Massive Scale-free Networks Using the Preferential Attachment Model, IEEE International Conference on Big Data, 2017

Abstract

A novel GPU-based algorithm, named cuPPA, has been presented, with a detailed performance study, and its combination of its scale and speed has been tested by achieving the ability to generate networks with up to two billion edges in under three seconds of wall clock time. The algorithm is customizable with respect to the structure of the network by varying a single parameter, namely, a probability measure that captures the preference style of new edges in the preferential attachment model. Also, a high amount of concurrency in the generator's workload per thread or processor is observed when that probability is at very small fractions greater than zero.

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