GAIPS: Accelerating Maximum Inner Product Search with GPU

Abstract

In this paper, we propose the GAIPS framework for efficient maximum inner product search (MIPS) on GPU. We observe that a query can usually find a good lower bound of its maximum inner product in some large norm items that take up only a small portion of the dataset and utilize this fact to facilitate pruning. In addition, we design norm-based, residue-based and hash-based pruning techniques to avoid computation for items that are unlikely to be the MIPS results. Experiment results show that compared with FAISS, the state-of-the-art GPU-based similarity search framework, GAIPS has significantly shorter query processing time at the same recall.