发表机构
Zhejiang University; National University of Singapore（浙江大学; 新加坡国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
CORAL是一种GPU加速的图基跨模态向量索引，通过分层内存管理、覆盖感知自适应剪枝等技术，在十亿级规模下实现了更高吞吐量、更短构建时间及动态更新下的鲁棒性。
AI 中文摘要
跨模态向量检索广泛应用于搜索引擎、向量数据库等多模态系统，其通常在分布外（OOD）场景下运行，此时查询向量的分布与数据库中存储的向量分布存在差异。在这类场景中，传统索引会出现显著的性能下降，即便专门针对分布外设计的方法，也受限于查询模态特征利用效率低、GPU并行性受限、动态更新支持不足等问题。本文提出CORAL，一种用于可扩展跨模态检索的新型GPU加速图基向量索引，具备覆盖GPU、CPU和磁盘的分层内存管理机制。具体而言，CORAL会逐步融入查询模态的特征并及时终止索引构建，关键在于引入覆盖感知自适应剪枝以解决查询向量邻居的覆盖不平衡问题；此外，CORAL还提出全邻居感知投影方法，以高效利用GPU实现高度并行的索引构建，以及针对性的连通性增强方法来优化索引结构。同时，CORAL支持基于模态语义的向量插入和恢复节点连通性的拓扑修复删除操作。实验结果表明，在匹配召回率的情况下，CORAL的吞吐量最高可达现有方法的1.6倍，构建时间最多减少56%；此外，其在动态更新下表现出出色的鲁棒性，在十亿级规模下仍保持有效。
英文摘要
Cross-modal vector retrieval is widely used in multimodal systems, such as search engines and vector databases. It typically operates in out-of-distribution (OOD) settings, where query vectors follow a distribution that differs from that of the vectors stored in the database. In such cases, conventional indexes suffer significant performance degradation, and even methods specially designed for OOD remain limited by inefficient use of query modal characteristics, restricted GPU parallelism, and inadequate support for dynamic updates. We present CORAL, a novel GPU-accelerated graph-based vector index for scalable cross-modal retrieval, featuring hierarchical memory management that spans GPU, CPU, and disk. Specifically, CORAL incrementally incorporates the characteristics of query modality and terminates index construction timely. Crucially, it introduces coverage-aware adaptive pruning to address the imbalanced coverage of the query vector's neighbors. Moreover, CORAL presents a fully neighborhood-aware projection approach to efficiently utilize GPUs for highly parallel index construction, and a targeted connectivity enhancement method to refine the index structure. Besides, CORAL also supports modal-semantics-based vector insertion and topology-repairing deletion that restore node connectivity. Experimental results demonstrate that CORAL outperforms existing methods with up to 1.6 times the throughput at matched recall while reducing construction time by up to 56%. Furthermore, it exhibits remarkable resilience under dynamic updates and remains effective at the billion scale.
CommentsAccepted for publication in Proceedings of the VLDB Endowment (PVLDB). To be presented at VLDB 2027
