VStore
Shengwen Liang, Ying Wang, Ziming Yuan, Cheng Liu, Huawei Li, Xiaowei Li
Abstract
Graph-based vector search that finds best matches to user queries based on their semantic similarities using a graph data structure, becomes instrumental in data science and AI application. However, deploying graph-based vector search in production systems requires high accuracy and cost-efficiency with low latency and memory footprint, which existing work fails to offer. We present VStore, a graph-based vector search solution that collaboratively optimizes accuracy, latency, memory, and data movement on large-scale vector data based on in-storage computing. The evaluation shows that VStore exhibits significant search efficiency improvement and energy reduction while attaining accuracy over CPU, GPU, and ZipNN platforms.