Employing GPU architectures for permutation-based indexing View Full Text


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Article Info

DATE

2017-05

AUTHORS

Martin Kruliš, Hasmik Osipyan, Stéphane Marchand-Maillet

ABSTRACT

Permutation-based indexing is one of the most popular techniques for the approximate nearest-neighbor search problem in high-dimensional spaces. Due to the exponential increase of multimedia data, the time required to index this data has become a serious constraint. One of the possible steps towards faster index construction is utilization of massively parallel platforms such as the GPGPU architectures. In this paper, we have analyzed the computational costs of individual steps of the permutation-based index construction in a high-dimensional feature space and summarized our hybrid CPU-GPU solution. Our experience gained from this research may be utilized in other individual problems that require computing Lp distances in high-dimensional spaces, parallel top-k selection, or partial sorting of multiple smaller sets. We also provide guidelines how to balance workload in hybrid CPU-GPU systems. More... »

PAGES

11859-11887

References to SciGraph publications

  • 2015. Optimizing Sorting and Top-k Selection Steps in Permutation Based Indexing on GPUs in NEW TRENDS IN DATABASES AND INFORMATION SYSTEMS
  • 2013. Quantized Ranking for Permutation-Based Indexing in SIMILARITY SEARCH AND APPLICATIONS
  • 2010. Fast In-Place Sorting with CUDA Based on Bitonic Sort in PARALLEL PROCESSING AND APPLIED MATHEMATICS
  • 2014. Multi-Core (CPU and GPU) for Permutation-Based Indexing in SIMILARITY SEARCH AND APPLICATIONS
  • 2014-08. MI-File: using inverted files for scalable approximate similarity search in MULTIMEDIA TOOLS AND APPLICATIONS
  • 2012. Parallel Approaches to Permutation-Based Indexing Using Inverted Files in SIMILARITY SEARCH AND APPLICATIONS
  • 2009. A Brief Index for Proximity Searching in PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11042-016-3677-7

    DOI

    http://dx.doi.org/10.1007/s11042-016-3677-7

    DIMENSIONS

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