6 papers
A GPU-Accelerated Framework for Multi-Attribute Range Filtered Approximate Nearest Neighbor Search
Zhonggen Li, Haoran Yu, Zixuan Xu +2
Range-filtered approximate nearest neighbor search (RFANNS) is increasingly critical for modern vector databases. However, existing solutions suffer from severe index inflation and…
Accelerating High-Dimensional Nearest Neighbor Search with Dynamic Query Preference
Yifan Zhu, Ruijie Zhao, Zhonggen Li +4
Approximate Nearest Neighbor Search (ANNS) has emerged as an essential operation in modern database and AI systems. While graph-based methods like NSG demonstrate state-of-the-art…
Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration
Zhonggen Li, Xiangyu Ke, Yifan Zhu +2
Graph embeddings map graph nodes to continuous vectors and are foundational to community detection, recommendation, and many scientific applications. At billion-scale, however, exi…
All-in-one Graph-based Indexing for Hybrid Search on GPUs
Zhonggen Li, Yougen Li, Yifan Zhu +3
Hybrid search has emerged as a promising paradigm that combines lexical and semantic retrieval, enhancing accuracy for applications such as recommendations, information retrieval,…
Scalable Graph Indexing using GPUs for Approximate Nearest Neighbor Search
Zhonggen Li, Xiangyu Ke, Yifan Zhu +3
Approximate nearest neighbor search (ANNS) in high-dimensional vector spaces has a wide range of real-world applications. Numerous methods have been proposed to handle ANNS efficie…
HC-SpMM: Accelerating Sparse Matrix-Matrix Multiplication for Graphs with Hybrid GPU Cores
Zhonggen Li, Xiangyu Ke, Yifan Zhu +2
Sparse Matrix-Matrix Multiplication (SpMM) is a fundamental operation in graph computing and analytics. However, the irregularity of real-world graphs poses significant challenges…