Scalable multi-node multi-GPU Louvain community detection algorithm for heterogeneous architectures

Community detection is an important problem that is widely applied for finding cluster patterns in brain, social, biological, and many other kinds of networks. In this work, we have developed a multi-node multi-GPU Louvain community detection algorithm, simultaneously harnessing the CPU and GPU cores of the devices. The algorithm partitions a given graph across multiple nodes and devices in the nodes and performs independent computations of Louvain algorithm on the parts on the devices. The independently formed communities in the devices are refined by identification of doubtful vertices and migrating them to the other processors. The communities are merged using a hierarchical merging algorithm that ensures that at any point the merged component can be accommodated within a processor. Our experiments show that our algorithm is highly scalable with increasing number of devices and provides large-scale performance for BigData graphs.