Range-filtered approximate nearest neighbor search (RFANNS) is increasingly critical for modern vector databases. However, existing solutions suffer from severe index inflation and construction overhead. Furthermore, they rely exclusively on CPUs for the heavy indexing and query processing, significantly restricting the throughput due to the limited memory bandwidth and parallelism. In this paper, we present Garfield, a GPU-accelerated framework for multi-attribute range filtered ANNS that overcomes these bottlenecks through designing a lightweight index structure and hardware-aware execution pipeline. Garfield introduces the GMG index, which partitions data into cells and builds local graph indexes. It guarantees linear storage and indexing overhead by adding a constant number of cross-cell edges. For queries, Garfield utilizes a cluster-guided ordering strategy that reorders query-relevant cells, enabling a highly efficient cell-by-cell traversal on the GPU that aggressively reuses candidates as entry points across cells. To handle datasets exceeding GPU memory, Garfield features a cell-oriented out-of-core pipeline. It dynamically schedules cells to minimize the number of active queries per batch and overlaps GPU computation with CPU-to-GPU index streaming. Extensive evaluations demonstrate that Garfield reduces index size by 4.4x, while delivering 119.8x higher throughput than state-of-the-art RFANNS methods.

翻译：范围过滤近似最近邻搜索在现代向量数据库中日益关键。然而，现有方案面临严重的索引膨胀与构建开销问题。此外，它们完全依赖CPU进行繁重的索引构建与查询处理，受限于有限的内存带宽与并行性，严重制约了处理吞吐量。本文提出Garfield——一种基于GPU加速的多属性范围过滤近似最近邻搜索框架，通过设计轻量级索引结构与硬件感知执行流水线突破上述瓶颈。Garfield引入GMG索引：将数据划分为单元格并构建局部图索引，通过增加恒定数量的跨单元格边保证线性存储与索引开销。查询处理时，Garfield采用簇引导排序策略对查询相关单元格进行重排，实现在GPU上高效逐单元格遍历，激进复用候选点作为跨单元格入口点。针对超GPU内存数据集，Garfield设计了面向单元格的外存流水线：动态调度单元格以最小化每批活跃查询数，并实现GPU计算与CPU到GPU索引流传输的异步重叠。大量实验表明，Garfield可将索引体积缩减4.4倍，同时相比最先进的RFANNS方法实现119.8倍吞吐量提升。