Graph-based approximate nearest neighbor search (ANNS) methods (e.g., HNSW) have become the de facto state of the art for their high precision and low latency. To scale beyond main memory, recent out-of-memory ANNS systems leverage SSDs to store large vector indexes. However, they still suffer from severe CPU underutilization and read amplification (i.e., storage stalls) caused by limited access locality during graph traversal. We present VeloANN, which mitigates storage stalls through a locality-aware data layout and a coroutine-based asynchronous runtime. VeloANN utilizes hierarchical compression and affinity-based data placement scheme to co-locate related vectors within the same page, effectively reducing fragmentation and over-fetching. We further design a record-level buffer pool, where each record groups the neighbors of a vector; by persistently retaining hot records in memory, it eliminates excessive page swapping under constrained memory budgets. To minimize CPU scheduling overheads during disk I/O interruptions, VeloANN employs a coroutine-based asynchronous runtime for lightweight task scheduling. On top of this, it incorporates asynchronous prefetching and a beam-aware search strategy to prioritize cached data, ultimately improving overall search efficiency. Extensive experiments show that VeloANN outperforms state-of-the-art disk-based ANN systems by up to 5.8x in throughput and 3.25x in latency reduction, while achieving 0.92x the throughput of in-memory systems using only 10% of their memory footprint.

翻译：基于图的近似最近邻搜索（ANNS）方法（如HNSW）因其高精度与低延迟已成为当前事实上的最优方法。为突破主存容量限制，近期出现的外存ANNS系统利用SSD存储大规模向量索引。然而，由于图遍历过程中访问局部性有限，这些系统仍面临严重的CPU利用率不足与读取放大（即存储停滞）问题。本文提出VeloANN系统，通过局部感知数据布局与基于协程的异步运行时机制缓解存储停滞问题。VeloANN采用分层压缩与亲和性数据放置策略，将关联向量聚集存储于同一页面，有效减少碎片化与过度读取。我们进一步设计了记录级缓冲池，每条记录聚合向量的邻居集合；通过持久化保留热点记录于内存中，该系统在有限内存预算下消除了过度的页面交换。为最小化磁盘I/O中断时的CPU调度开销，VeloANN采用基于协程的异步运行时实现轻量级任务调度。在此基础上，系统集成异步预取与束感知搜索策略，优先访问缓存数据，最终提升整体搜索效率。大量实验表明，VeloANN在吞吐量上超越现有基于磁盘的ANN系统达5.8倍，延迟降低3.25倍，同时仅需内存系统10%的内存占用即可达到其0.92倍的吞吐量。