$\nu$-LPA: Fast GPU-based Label Propagation Algorithm (LPA) for Community Detection

TL;DR

This paper introduces $ u$-LPA, a GPU-optimized label propagation algorithm for community detection that significantly outperforms existing methods in speed while maintaining competitive modularity results.

Contribution

The paper presents a novel GPU-based implementation of LPA with asynchronous updates and a new collision resolution method, achieving unprecedented speedups in community detection.

Findings

$ u$-LPA outperforms existing algorithms by up to 364x in speed.

Achieves 3.0 billion edges per second on a 2.2 billion edge graph.

Provides modularity results close to state-of-the-art methods.

Abstract

Community detection is the problem of identifying natural divisions in networks. Efficient parallel algorithms for identifying such divisions are critical in a number of applications. This report presents an optimized implementation of the Label Propagation Algorithm (LPA) for community detection, featuring an asynchronous LPA with a Pick-Less (PL) method every 4 iterations to handle community swaps, ideal for SIMT hardware like GPUs. It also introduces a novel per-vertex hashtable with hybrid quadratic-double probing for collision resolution. On an NVIDIA A100 GPU, our implementation, $\nu$-LPA, outperforms FLPA (sequential), NetworKit LPA (multicore), Gunrock LPA (GPU), and cuGraph Louvain (GPU) by 364x, 62x, 2.6x, and 37x, respectively, while running FLPA and NetworKit LPA on a server with dual 16-core Intel Xeon Gold 6226R processors - processing 3.0B edges/s on a 2.2B edge graph - and achieves 4.7% higher modularity than FLPA, but 6.1% and 9.6% lower than NetworKit LPA and cuGraph Louvain.