GRIP: Multi-Store Capacity-Optimized High-Performance Nearest Neighbor Search for Vector Search Engine

This paper presents GRIP, an approximate nearest neighbor (ANN) search algorithm for building vector search engine which makes heavy use of the algorithm. GRIP is designed to retrieve documents at large-scale based on their semantic meanings in a scalable way. It is both fast and capacity-optimized. GRIP combines new algorithmic and system techniques to collaboratively optimize the use of memory, storage, and computation. The contributions include: (1) The first hybrid memory-storage ANN algorithm that allows ANN to benefit from both DRAM and SSDs simultaneously; (2) The design of a highly optimized indexing scheme that provides both memory-efficiency and high performance; (3) A cost analysis and a cost function for evaluating the capacity improvements of ANN algorithms. GRIP achieves an order of magnitude improvements on overall system efficiency, significantly reducing the cost of vector search, while attaining equal or higher accuracy, compared with the state-of-the-art.