Paper Info
Title
Multi-Core (CPU and GPU) for Permutation-Based Indexing.
Abstract
Permutation-based indexing is a technique to approximate k-nearest neighbor computation in high-dimensional spaces. The technique aims to predict the proximity between elements encoding their location with respect to their surrounding. The strategy is fast and effective to answer user queries. The main constraint of this technique is the indexing time. Opening the GPUs to general purpose computation allows to perform parallel computation on a powerful platform. In this paper, we propose efficient indexing algorithms for the permutation-based indexing using multi-core architecture GPU and CPU. We study the performance and efficiency of our algorithms on large-scale datasets of millions of documents. Experimental results show a decrease of the indexing time.
Year
DOI
Venue
2014
10.1007/978-3-319-11988-5_26
Lecture Notes in Computer Science
Keywords
Field
DocType
K-NN,Similarity Search,GPU,Permutation-Based Indexing,Big-Data
General purpose,Computer science,Search engine indexing,Theoretical computer science,Artificial intelligence,Multi-core processor,Nearest neighbor search,Computation,Permutation,Parallel computing,Big data,Machine learning,Encoding (memory)
Conference
Volume
ISSN
Citations 
8821
0302-9743
5
PageRank 
References 
Authors
0.49
9
3
Name
Order
Citations
PageRank
Hisham Mohamed1342.04
Hasmik Osipyan2112.65
Stéphane Marchand-Maillet31039104.97