Accelerating Graph Indexing for ANNS on Modern CPUs

2025-02-25

AI Summary

To address the prohibitively long indexing time and poor CPU-architecture compatibility of graph-based indexes in high-dimensional approximate nearest neighbor search (ANNS), this paper proposes Flash—a hardware-aware compact encoding strategy. Flash innovatively integrates vector quantization with key CPU architectural features, including SIMD parallelism and cache locality, enabling efficient distance computation while maintaining bounded quantization error. By jointly optimizing compact encoding, memory access patterns, and cache-friendly graph construction, Flash achieves 10.4×–22.9× speedup in index construction across eight real-world datasets ranging from 10 million to one billion vectors. Crucially, this acceleration comes without sacrificing retrieval accuracy or query latency—indeed, both are preserved or improved. Flash thus bridges the gap between algorithmic efficiency and modern hardware utilization in large-scale ANNS.

Technology Category

• Search and Optimization: Distributed Search
• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

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
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

In high-dimensional vector spaces, Approximate Nearest Neighbor Search (ANNS) is a key component in database and artificial intelligence infrastructures. Graph-based methods, particularly HNSW, have emerged as leading solutions among various ANNS approaches, offering an impressive trade-off between search efficiency and accuracy. Many modern vector databases utilize graph indexes as their core algorithms, benefiting from various optimizations to enhance search performance. However, the high indexing time associated with graph algorithms poses a significant challenge, especially given the increasing volume of data, query processing complexity, and dynamic index maintenance demand. This has rendered indexing time a critical performance metric for users. In this paper, we comprehensively analyze the underlying causes of the low graph indexing efficiency on modern CPUs, identifying that distance computation dominates indexing time, primarily due to high memory access latency and suboptimal arithmetic operation efficiency. We demonstrate that distance comparisons during index construction can be effectively performed using compact vector codes at an appropriate compression error. Drawing from insights gained through integrating existing compact coding methods in the graph indexing process, we propose a novel compact coding strategy, named Flash, designed explicitly for graph indexing and optimized for modern CPU architectures. By minimizing random memory accesses and maximizing the utilization of SIMD (Single Instruction, Multiple Data) instructions, Flash significantly enhances cache hit rates and arithmetic operations. Extensive experiments conducted on eight real-world datasets, ranging from ten million to one billion vectors, exhibit that Flash achieves a speedup of 10.4$ imes$ to 22.9$ imes$ in index construction efficiency, while maintaining or improving search performance.

Problem

• High indexing time in graph-based ANNS methods
• Distance computation dominates indexing time
• Optimizing graph indexing for modern CPU architectures

Innovation

• Flash compact coding strategy
• Optimized for modern CPUs
• Enhances cache and SIMD utilization