Exploiting accelerators for efficient high dimensional similarity search

Abstract

Similarity search finds the most similar matches in an object collection for a given query; making it an important problem across a wide range of disciplines such as web search, image recognition and protein sequencing. Practical implementations of High Dimensional Similarity Search (HDSS) search across billions of possible solutions for multiple queries in real time, making its performance and efficiency a significant challenge. Existing clusters and datacenters use commercial multicore hardware to perform search, which may not provide the optimal performance and performance per Watt.This work explores the performance, power and cost benefits of using throughput accelerators like GPUs to perform similarity search for query cohorts even under tight deadlines. We propose optimized implementations of similarity search for both the host and the accelerator. Augmenting existing Xeon servers with accelerators results in a 3× improvement in throughput per machine, resulting in a more than 2.5× reduction in cost of ownership, even for discounted Xeon servers. Replacing a Xeon based cluster with an accelerator based cluster for similarity search reduces the total cost of ownership by more than 6× to 16× while consuming significantly less power than an ARM based cluster.