AI 中文总结
FROG是面向GPU的RFANNS索引，采用全局顶点中心设计，经六组实验验证，其混合选择性查询吞吐量及索引构建速度均大幅优于CPU与现有GPU基准。
AI 中文摘要
范围过滤近似最近邻搜索（RFANNS）是现代向量数据库中的基础操作。给定查询向量$q$和数值范围谓词，RFANNS会返回属性满足该范围谓词的对象中，查询$q$的$k$个近似最近邻（$k$-ANN）。然而，现有RFANNS方法并不适配高吞吐量GPU执行：CPU索引的并行可扩展性有限，通用GPU过滤高度依赖选择性，基于局部优化子图构建的GPU索引会导致较长的搜索轨迹和冗余距离计算。为解决这些局限，本文提出FROG，一种面向GPU的RFANNS索引，它以全局感知的顶点中心设计替代了多个局部最优子结构构建，在GPU友好结构中组织每个顶点的多样扩展邻居候选，并在查询时快速识别用于计算的扩展邻居。此外，本文还开发了面向GPU的索引构建与查询处理算法及实现。在六个数据集上的实验表明，FROG的混合选择性查询吞吐量相比44核CPU基准提升了14.7至37.7倍，相比最强GPU基准提升了4.5至7.6倍，索引构建速度相比GPU基准也加快了2.4至14.8倍。
英文摘要
Range-filtering approximate nearest neighbor search (RFANNS) is a fundamental operation in modern vector databases. Given a query vector $q$ and a numerical range predicate, RFANNS returns the $k$-approximate nearest neighbors ($k$-ANN) of the query $q$ among the objects whose attributes satisfy the range predicate. However, existing RFANNS methods are not well suited to high-throughput GPU execution. CPU indexes offer limited parallel scalability, generic GPU filtering is highly selectivity-dependent, and GPU indexes built from locally optimized subgraphs can incur long search trajectories and redundant distance computations. To address these limitations, we present FROG, a GPU-oriented RFANNS index that replaces multiple locally optimal substructure building with a globally aware, vertex-centric design. It organizes diverse expansion neighbor candidates for each vertex in a GPU-friendly structure and rapidly identifies the expansion neighbors used for computation at query time. Moreover, GPU-oriented algorithms and implementations are developed for both index construction and query processing. Experiments on six datasets show that FROG improves mixed-selectivity query throughput by 14.7--37.7$\times$ over 44-core CPU baselines and 4.5--7.6$\times$ over the strongest GPU baseline. It also accelerates index construction by 2.4--14.8$\times$ over the GPU baseline.
