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GVE-Louvain: Fast Louvain Algorithm for Community Detection in Shared Memory Setting
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Community detection is the problem of identifying natural divisions in networks. Efficient parallel algorithms for identifying such divisions is critical in a number of applications, where the size of datasets have reached significant scales. This technical report presents one of the most efficient multicore implementations of the Louvain algorithm, a high quality community detection method. On a server equipped with dual 16-core Intel Xeon Gold 6226R processors, our Louvain, which we term as GVE-Louvain, outperforms Vite, Grappolo, NetworKit Louvain, and cuGraph Louvain (running on NVIDIA A100 GPU) by 50x, 22x, 20x, and 5.8x faster respectively - achieving a processing rate of 560M edges/s on a 3.8B edge graph. In addition, GVE-Louvain improves performance at an average rate of 1.6x for every doubling of threads.
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CPU vs. GPU for Community Detection: Performance Insights from GVE-Louvain and $\nu$-Louvain
A tuned multicore CPU implementation of Louvain is claimed to beat leading CPU and GPU implementations on billion-edge graphs, with a GPU version only matching it.
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