IMI-GPU: Inverted multi-index for billion-scale approximate nearest neighbor search with GPUs

Abstract

Similarity search is utilized in specialized database systems designed to handle multimedia data, often represented by high-dimensional features. In this paper, we focus on speeding up the search process with GPUs. This problem has been previously approached by accelerating the Inverted File with Asymmetric Distance Computation algorithm on GPUs (IVFADC-GPU). However, the most recent algorithm for CPU, Inverted Multi-Index (IMI), was not considered for parallelization, being found too challenging for efficient GPU deployment. Thus, we propose a novel and efficient version of IMI for GPUs called IMI-GPU. We propose a new design of the multi-sequence algorithm of IMI, enabling efficient GPU execution. We compared IMI-GPU with IVFADC-GPU using a billion-scale dataset in which IMI-GPU achieved speedups of about 3.2× and 1.9× at Recall@1 and at Recall@16 respectively. The algorithms have been compared in a variety of scenarios and our novel IMI-GPU has shown to significantly outperform IVFADC on GPUs for the majority of tested cases.