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.