I/O Optimizations for Graph-Based Disk-Resident Approximate Nearest Neighbor Search: A Design Space Exploration

2026-02-24

AI Summary

In SSD-backed approximate nearest neighbor (ANN) search, I/O overhead accounts for 70%–90% of total query cost, severely limiting performance. This work systematically investigates the joint optimization space across memory layout, disk layout, and search algorithms for graph-based disk ANN systems. We introduce a page-level complexity model to quantify how page locality and path length impact I/O, revealing cross-dimensional synergies. Building on these insights, we design OctopusANN, a high-performance system that integrates memory-resident navigation, dynamic beam width, page reordering, and intra-page search strategies, all deeply tailored to graph index structures and SSD characteristics. Experimental results show that at Recall@10 = 90%, OctopusANN achieves 4.1%–37.9% higher throughput than Starling and 87.5%–149.5% higher throughput than DiskANN, significantly reducing I/O overhead.

Technology Category

• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Social Networks and Social Media: Social mining and social search on the Web

Abstract

Approximate nearest neighbor (ANN) search on SSD-backed indexes is increasingly I/O-bound (I/O accounts for 70--90\% of query latency). We present an I/O-first framework for disk-based ANN that organizes techniques along three dimensions: memory layout, disk layout, and search algorithm. We introduce a page-level complexity model that explains how page locality and path length jointly determine page reads, and we validate the model empirically. Using consistent implementations across four public datasets, we quantify both single-factor effects and cross-dimensional synergies. We find that (i) memory-resident navigation and dynamic width provide the strongest standalone gains; (ii) page shuffle and page search are weak alone but complementary together; and (iii) a principled composition, OctopusANN, substantially reduces I/O and achieves 4.1--37.9\% higher throughput than the state-of-the-art system Starling and 87.5--149.5\% higher throughput than DiskANN at matched Recall@10=90\%. Finally, we distill actionable guidelines for selecting storage-centric or hybrid designs across diverse concurrency levels and accuracy constraints, advocating systematic composition rather than isolated tweaks when pushing the performance frontier of disk-based ANN.

Problem

• Approximate Nearest Neighbor
• I/O Optimization
• Disk-Resident Index
• SSD
• Page Locality

Innovation

• I/O optimization
• disk-based ANN
• page-level complexity model
• OctopusANN
• design space exploration