Prof. Kishore Kothapalli and his students Subhajit Sahu and Mahen N   presented a paper on ν-LPA: Fast GPU-based Label Propagation Algorithm (LPA) for Community Detection at the 26th IEEE International Workshop on Parallel and Distributed Scientific and Engineering Computing (PDSEC-2025) held in conjunction with 39th IEEE International Parallel & Distributed Processing Symposium (IPDPS 2025) in Milan, Italy from 3 – 7 June. Here is the summary of the paper as explained by the authors:

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 paper 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, ν-LPA, outperforms FLPA (sequential), NetworKit LPA (multicore), Gunrock LPA (GPU), and cuGraph Louvain (GPU) by 364×, 62×, 2.6×, and 37×, respectively, while running FLPA and NetworKit LPA on a server with dual 16-core and achieves higher modularity than FLPA, but lower than NetworKit LPA and cuGraph Louvain.

June 2025