Towards scaling community detection on distributed-memory heterogeneous systems

In most real-world networks, nodes/vertices tend to be organized into tightly-knit modules known as communities or clusters such that nodes within a community are more likely to be connected or related to one another than they are to the rest of the network. Community detection in a network (graph) is aimed at finding a partitioning of the vertices into communities. The goodness of the partitioning is commonly measured using modularity. Maximizing modularity is an NP-complete problem. In 2008, Blondel et al. introduced a multi-phase, multi-iteration heuristic for modularity maximization called the Louvain method. Owing to its speed and ability to yield high quality communities, the Louvain method continues to be one of the most widely used tools for serial community detection.

Distributed multi-GPU systems pose significant challenges and opportunities for efficient execution of parallel applications. Graph algorithms, in particular, have been known to be harder to parallelize on such platforms, due to irregular memory accesses, low computation to communication ratios, and load balancing problems that are especially hard to address on multi-GPU systems.

In this paper, we present our ongoing work on distributed-memory implementation of Louvain method on heterogeneous systems. We build on our prior work parallelizing the Louvain method for community detection on traditional CPU-only distributed systems without GPUs. Corroborated by an extensive set of experiments on multi-GPU systems, we demonstrate competitive performance to existing distributed-memory CPU-based implementation, up to 3.2$\times$ speedup using 16 nodes of OLCF Summit relative to two nodes, and up to 19$\times$ speedup relative to the NVIDIA RAPIDS® cuGraph® implementation on a single NVIDIA V100 GPU from DGX-2 platform, while achieving high quality solutions comparable to the original Louvain method. To the best of our knowledge, this work represents the first effort for community detection on distributed multi-GPU systems. Our approach and related findings can be extended to numerous other iterative graph algorithms on multi-GPU systems.