GPU-Accelerated Algorithms for Graph Vector Search: Taxonomy, Empirical Study, and Research Directions

2026-02-10

AI Summary

This work addresses the lack of systematic optimization and end-to-end performance evaluation of graph-based vector search algorithms on modern GPU architectures. It presents the first comprehensive taxonomy framework for GPU-accelerated graph-structured approximate nearest neighbor search (ANNS) algorithms, offering in-depth analysis of the mapping between algorithmic tasks and GPU hardware resources. Through implementations of six representative algorithms across eight large-scale datasets and fine-grained performance profiling, the study identifies distance computation and CPU–GPU data transfer as critical performance bottlenecks and elucidates the trade-offs between scalability and memory usage. The paper further provides practical design guidelines for deploying GPU-based ANNS systems and releases a comprehensive open-source benchmark to support future research in the community.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Search and Optimization: Distributed Search
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

Approximate Nearest Neighbor Search (ANNS) underpins many large-scale data mining and machine learning applications, with efficient retrieval increasingly hinging on GPU acceleration as dataset sizes grow. Although graph-based approaches represent the state of the art in approximate nearest neighbor search, there is a lack of systematic understanding regarding their optimization for modern GPU architectures and their end-to-end effectiveness in practical scenarios. In this work, we present a comprehensive survey and experimental study of GPU-accelerated graph-based vector search algorithms. We establish a detailed taxonomy of GPU optimization strategies and clarify the mapping between algorithmic tasks and hardware execution units within GPUs. Through a thorough evaluation of six leading algorithms on eight large-scale benchmark datasets, we assess both graph index construction and query search performance. Our analysis reveals that distance computation remains the primary computational bottleneck, while data transfer between the host CPU and GPU emerges as the dominant factor influencing real-world latency at large scale. We also highlight key trade-offs in scalability and memory usage across different system designs. Our findings offer clear guidelines for designing scalable and robust GPU-powered approximate nearest neighbor search systems, and provide a comprehensive benchmark for the knowledge discovery and data mining community.

Problem

• Approximate Nearest Neighbor Search
• Graph-based Algorithms
• GPU Acceleration
• Vector Search
• Scalability

Innovation

• GPU acceleration
• graph-based ANNS
• taxonomy of optimization
• distance computation bottleneck
• CPU-GPU data transfer