Paper Info
Title
Similarity Search of Sparse Histograms on GPU Architecture.
Abstract
Searching for similar objects within large-scale database is a hard problem due to the exponential increase of multimedia data. The time required to find the nearest objects to the specific query in a high-dimensional space has become a serious constraint of the searching algorithms. One of the possible solution for this problem is utilization of massively parallel platforms such as GPU architectures. This solution becomes very sensitive for the applications working with sparse dataset. The performance of the algorithm can be totally changed depending on the different sparsity settings of the input data. In this paper, we study four different approaches on the GPU architecture for finding the similar histograms to the given queries. The performance and efficiency of observed methods were studied on sparse dataset of half a million histograms. We summarize our empirical results and point out the optimal GPU strategy for sparse histograms with different sparsity settings.
Year
DOI
Venue
2016
10.1007/978-3-319-46759-7_25
Lecture Notes in Computer Science
Keywords
Field
DocType
GPU,Similarity search,High-dimensional space,Sparse dataset
Histogram,Architecture,Exponential function,Search algorithm,Pattern recognition,Massively parallel,Computer science,Artificial intelligence,High dimensional space,Nearest neighbor search,Machine learning
Conference
Volume
ISSN
Citations 
9939
0302-9743
0
PageRank 
References 
Authors
0.34
21
3
Name
Order
Citations
PageRank
Hasmik Osipyan1112.65
Jakub Lokoc218229.05
Stéphane Marchand-Maillet31039104.97