Journal Title
Title of Journal: Cluster Comput
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Abbravation: Cluster Computing
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Authors: Carlos M Toledo Ricardo J Barrientos Andrés I Ávila
Publish Date: 2016/01/06
Volume: 19, Issue: 1, Pages: 57-71
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 computeintensive problem Regarding this in the latest years computeintensive 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 DistC obtaining up to 168times over the sequential exhaustive algorithm Our algorithms using exhaustive strategies in Xeon Phi for range queries achieve up to 22times speedup over the sequential counterpart compared to the 12times of a 20core 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 memoryaligned access exhaustive algorithms Our exhaustive approaches on Xeon Phi can be used on a wide class of databases for example nonmetric 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
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