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On visualizing large multidimensional datasets with a multi-threaded radial approach

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Abstract

In this paper, we study how to visualize large amounts of multidimensional data with a radial visualization. For such a visualization, we study a multi-threaded implementation on the CPU and the GPU. We start by reviewing the approaches that have visualized the largest multidimensional datasets and we focus on the approaches that have used CPU or GPU parallelization. We consider the radial visualizations and we describe our approach (called POIViz) that uses points of interest to determine a layout of a large dataset. We detail its parallelization on the CPU and the GPU. We study the efficiency of this approach with different configurations and for large datasets. We show that it can visualize, in less than one second, millions of data with tens of dimensions, and that it can support “real-time” interactions even for large datasets. We conclude on the advantages and limits of the proposed visualization.

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  1. http://lib.stat.cmu.edu/multi/pca.c.

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Authors and Affiliations

  1. Computer Science Laboratory, University François-Rabelais of Tours, 64 Avenue Jean Portalis, 37200, Tours, France

    Tianyang Liu & Gilles Venturini

  2. University of Lille2, IUT, 25-27 Rue du Maréchal Foch, 59100, Roubaix, France

    Fatma Bouali

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  1. Tianyang Liu
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  2. Fatma Bouali
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  3. Gilles Venturini
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Correspondence to Gilles Venturini.

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Liu, T., Bouali, F. & Venturini, G. On visualizing large multidimensional datasets with a multi-threaded radial approach. Distrib Parallel Databases 34, 321–345 (2016). https://doi.org/10.1007/s10619-015-7174-1

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