Similarity (range and kNN) queries processing on an Intel Xeon Phi coprocessor

Abstract

Nowadays, the evolution of information technologies requires fast similarity search tools for analyzing new data types as audio, video, or images. The usual search by keys or records is not possible and to search on these databases is a compute-intensive problem. Regarding this, in the latest years, compute-intensive coprocessors (mainly NVIDIA GPUs) have been studied as a tool for accelerating sequential processing algorithms. In this work, we implement kNN and range queries on the recently launched Intel Xeon Phi coprocessor. We developed exhaustive and also indexing algorithms using the LC index. This index has been widely studied in sequential computing to accelerate similarity search on multimedia databases. We implement and compare different exhaustive and indexing versions showing some key factors in Xeon Phi to deal with this type of search. For indexing algorithms, we used a strategy based on cluster distribution among cores LC MIC Dist-C obtaining up to 168$$\times $$× over the sequential exhaustive algorithm. Our algorithms using exhaustive strategies in Xeon Phi for range queries achieve up to 22$$\times $$× speed-up over the sequential counterpart compared to the 12$$\times $$× of a 20-core machine, and a similar advantage is achieved for kNN queries. Comparing with GPUs, we obtain higher performance on our indexing algorithms on Intel Xeon Phi. However, GPU works faster with memory-aligned access exhaustive algorithms. Our exhaustive approaches on Xeon Phi can be used on a wide class of databases, for example, non-metric spaces. Finally, we extend our algorithms to be used with large databases that do not fit in the coprocessor memory, showing a good scalability with the number of elements.