The Louvain algorithm is one of the most popular algorithms for community detection. Observing that existing implementations suffer from inaccurate pruning and inefficient intermediate state management, we introduce GALA, \underline{G}PU-\underline{A}ccelerated \underline{L}ouvain \underline{A}lgorithm, which incorporates two key innovations. The first innovation is a novel modularity gain-based pruning strategy, supported by rigorous theoretical guarantees of optimality and able to reduce up to 76% of vertices as well as their corresponding computations. To take advantage of the memory hierarchy and parallelism of GPUs, the second innovation is workload-aware kernels, featuring a shuffle-based kernel founded on the warp-level primitives for exchange states and a hash-based kernel that prioritizes shared memory in hashtable design. GALA further scales to multiple GPUs by minimizing the synchronization overhead between GPUs through a dense-sparse synchronization strategy. We evaluate the performance of GALA through theoretical analysis and practical experiments on various real-world graphs. The experimental results confirm that GALA significantly improves the performance of the parallel Louvain algorithm on GPUs, surpassing state-of-the-art solutions by $6\times$ on average.