{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/n-lpa-fast-gpu-based-label-propagation","title":"$ν$-LPA: Fast GPU-based Label Propagation Algorithm (LPA) for Community Detection","arxiv_id":"2411.11468","date":"2024-11-18","proceeding":null,"authors":["Subhajit Sahu"],"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.","url_abs":"https://arxiv.org/abs/2411.11468v2","url_pdf":"https://arxiv.org/pdf/2411.11468v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"n-lpa-fast-gpu-based-label-propagation","repo_url":"https://github.com/puzzlef/rak-communities-cuda","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}