Similarity search in the blink of an eye with compressed indices

• URL: http://arxiv.org/abs/2304.04759v2
• Date: Mon, 24 Jul 2023 18:10:09 GMT
• Title: Similarity search in the blink of an eye with compressed indices
• Authors: Cecilia Aguerrebere, Ishwar Bhati, Mark Hildebrand, Mariano Tepper, Ted Willke
• Abstract summary: Graph-based indices are currently the best performing techniques for billion-scale similarity search. We present new techniques and systems for creating faster and smaller graph-based indices.
• Score: 3.39271933237479
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Nowadays, data is represented by vectors. Retrieving those vectors, among millions and billions, that are similar to a given query is a ubiquitous problem, known as similarity search, of relevance for a wide range of applications. Graph-based indices are currently the best performing techniques for billion-scale similarity search. However, their random-access memory pattern presents challenges to realize their full potential. In this work, we present new techniques and systems for creating faster and smaller graph-based indices. To this end, we introduce a novel vector compression method, Locally-adaptive Vector Quantization (LVQ), that uses per-vector scaling and scalar quantization to improve search performance with fast similarity computations and a reduced effective bandwidth, while decreasing memory footprint and barely impacting accuracy. LVQ, when combined with a new high-performance computing system for graph-based similarity search, establishes the new state of the art in terms of performance and memory footprint. For billions of vectors, LVQ outcompetes the second-best alternatives: (1) in the low-memory regime, by up to 20.7x in throughput with up to a 3x memory footprint reduction, and (2) in the high-throughput regime by 5.8x with 1.4x less memory.

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