GPU-accelerated Proximity Graph Approximate Nearest Neighbor Search and Construction

Abstract

The approximate nearest neighbor (ANN) search in high-dimensional space offers a wide spectrum of applications across many domains such as database, machine learning, multimedia and computer vision. A variety of ANN search algorithms have been proposed in the literature. In recent years, proximity graph-based approaches have attracted considerable attention from both industry and academic settings due to the superior search performance in terms of speed and accuracy. A recent work utilizes a graphics processing unit (GPU) to accelerate the ANN search on proximity graphs. Though significantly reducing the distance computation time by taking advantage of the massive parallelism of GPUs, the algorithm suffers from the high expenses of data structure operations. In this paper, we propose a novel GPU -accelerated algorithm that designs a novel GPU-friendly search framework on proximity graphs to fully exploit the massively parallel processing power of GPUs at key steps of the search. Also, we propose GPU-accelerated proximity graph construction algorithms which can build high-quality representative proximity graphs with efficient parallel implementations. Extensive experiments on benchmark high-dimensional datasets demonstrate the outstanding performance of our proposed algorithms in both ANN search and proximity graph construction.