Approximate Nearest Neighbor Search (ANNS) underpins modern applications such as information retrieval and recommendation. With the rapid growth of vector data, efficient indexing for real-time vector search has become rudimentary. Existing CPU-based solutions support updates but suffer from low throughput, while GPU-accelerated systems deliver high performance but face challenges with dynamic updates and limited GPU memory, resulting in a critical performance gap for continuous, large-scale vector search requiring both accuracy and speed. In this paper, we present SVFusion, a GPU-CPU-disk collaborative framework for real-time vector search that bridges sophisticated GPU computation with online updates. SVFusion leverages a hierarchical vector index architecture that employs CPU-GPU co-processing, along with a workload-aware vector caching mechanism to maximize the efficiency of limited GPU memory. It further enhances performance through real-time coordination with CUDA multi-stream optimization and adaptive resource management, along with concurrency control that ensures data consistency under interleaved queries and updates. Empirical results demonstrate that SVFusion achieves significant improvements in query latency and throughput, exhibiting a 20.9x higher throughput on average and 1.3x to 50.7x lower latency compared to baseline methods, while maintaining high recall for large-scale datasets under various streaming workloads.

翻译：近似最近邻搜索（ANNS）是现代信息检索与推荐等应用的基础支撑技术。随着向量数据的快速增长，面向实时向量搜索的高效索引构建已成为关键需求。现有基于CPU的解决方案虽支持数据更新，但查询吞吐量较低；而基于GPU加速的系统虽能提供高性能查询，却面临动态更新困难与GPU内存受限的挑战，导致在需要兼顾准确性与速度的持续大规模向量搜索场景中存在显著的性能鸿沟。本文提出SVFusion，一种面向实时向量搜索的GPU-CPU-磁盘协同框架，该框架将复杂的GPU计算与在线更新机制相融合。SVFusion采用分层向量索引架构，通过CPU-GPU协同处理以及基于工作负载感知的向量缓存机制，最大化有限GPU内存的利用效率。此外，系统通过CUDA多流优化与自适应资源管理的实时协调，以及保障交错查询与更新下数据一致性的并发控制机制，进一步提升了整体性能。实验结果表明，相较于基线方法，SVFusion在查询延迟与吞吐量方面均取得显著提升：平均吞吐量提高20.9倍，延迟降低1.3至50.7倍，并在多种流式工作负载下对大规模数据集保持了高召回率。