{"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/memory-efficient-gpu-based-label-propagation","title":"Memory Efficient GPU-based Label Propagation Algorithm (LPA) for Community Detection on Large Graphs","arxiv_id":"2411.19901","date":"2024-11-29","proceeding":null,"authors":["Subhajit Sahu"],"abstract":"Community detection involves grouping nodes in a graph with dense connections within groups, than between them. We previously proposed efficient multicore (GVE-LPA) and GPU-based ($\\nu$-LPA) implementations of Label Propagation Algorithm (LPA) for community detection. However, these methods incur high memory overhead due to their per-thread/per-vertex hashtables. This makes it challenging to process large graphs on shared memory systems. In this report, we introduce memory-efficient GPU-based LPA implementations, using weighted Boyer-Moore (BM) and Misra-Gries (MG) sketches. Our new implementation, $\\nu$MG8-LPA, using an 8-slot MG sketch, reduces memory usage by 98x and 44x compared to GVE-LPA and $\\nu$-LPA, respectively. It is also 2.4x faster than GVE-LPA and only 1.1x slower than $\\nu$-LPA, with minimal quality loss (4.7%/2.9% drop compared to GVE-LPA/$\\nu$-LPA).","url_abs":"https://arxiv.org/abs/2411.19901v1","url_pdf":"https://arxiv.org/pdf/2411.19901v1.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":"memory-efficient-gpu-based-label-propagation","repo_url":"https://github.com/puzzlef/rak-lowmem-communities-cuda","is_official":1,"mentioned_in_paper":0,"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}