Louvain community detection with parallel heuristics on GPUs

Abstract

Community detection has become an important operation in numerous graph based applications. It is used to reveal groups that exist within real world networks without imposing prior size or cardinality constraints on the set of communities. Despite its potential, the support for parallel computers is rather limited. This is largely because the algorithm is irregular and the underlying heuristics imply a sequential nature. In this paper I present parallelization heuristics for fast community detection using the Louvain method as it is applied on GPUs. The Louvain method is a multi-phase, iterative heuristic for modularity optimization. It was originally developed by Blondel et al. (2008), the method has become increasingly popular owing to its ability to detect high modularity community partitions in a fast and memory-efficient manner. The parallel heuristics used, were first introduced by Hao Lu et al. (2015). As the Louvain method is inherently sequential, it limits the possibility of scalable usage. Thanks to the proposed parallel heuristics, I observe how this method can behave on GPUs. For evaluation I implemented the heuristics using CUDA on a GeForce GTX 980M GPU and for testing I used organization landscapes from the CERN developed Collaboration Spotting project that involves patents and publications to visualize the connections in technologies among its collaborators. Compared to the parallel Louvain implementation running on 8 threads on the same machine that has the used GPU, the CUDA implementation is able to produce community outputs comparable to the CPU generated results, while providing absolute speedups of up to 12 using the GeForce GTX 980M mobile GPU.