This paper describes the design of a hierarchical parallel algorithm for accelerating community detection which involves partitioning a network into communities of densely connected nodes. The algorithm is based on the Louvain method developed at the Université Catholique de Louvain, which uses modularity to measure community quality and has been successfully applied on many different types of networks. The proposed hierarchical parallel algorithm targets three levels of parallelism in the Louvain method and it has been implemented on single-GPU and multi-GPU architectures. Benchmarking results on several large web-based networks and popular social networks show that on top of offering speedups of up to 5x, the single-GPU version is able to find better quality communities. On average, the multi-GPU version provides an additional 2x speedup over the single-GPU version but with a 3% degradation in community quality.