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.
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Mohamed, H., Osipyan, H., Marchand-Maillet, S. (2014). Multi-Core (CPU and GPU) for Permutation-Based Indexing. In: Traina, A.J.M., Traina, C., Cordeiro, R.L.F. (eds) Similarity Search and Applications. SISAP 2014. Lecture Notes in Computer Science, vol 8821. Springer, Cham. https://doi.org/10.1007/978-3-319-11988-5_26
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DOI: https://doi.org/10.1007/978-3-319-11988-5_26
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11987-8
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