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    Complexity Plots

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    Type
    Article
    Authors
    Thiyagalingam, Jeyarajan
    Walton, Simon
    Duffy, Brian
    Trefethen, Anne
    Chen, Min
    KAUST Grant Number
    KUK-C1-013-04
    Date
    2013-07-01
    Online Publication Date
    2013-07-01
    Print Publication Date
    2013-06
    Permanent link to this record
    http://hdl.handle.net/10754/597818
    
    Metadata
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    Abstract
    In this paper, we present a novel visualization technique for assisting the observation and analysis of algorithmic complexity. In comparison with conventional line graphs, this new technique is not sensitive to the units of measurement, allowing multivariate data series of different physical qualities (e.g., time, space and energy) to be juxtaposed together conveniently and consistently. It supports multivariate visualization as well as uncertainty visualization. It enables users to focus on algorithm categorization by complexity classes, while reducing visual impact caused by constants and algorithmic components that are insignificant to complexity analysis. It provides an effective means for observing the algorithmic complexity of programs with a mixture of algorithms and black-box software through visualization. Through two case studies, we demonstrate the effectiveness of complexity plots in complexity analysis in research, education and application. © 2013 The Author(s) Computer Graphics Forum © 2013 The Eurographics Association and Blackwell Publishing Ltd.
    Citation
    Thiyagalingam J, Walton S, Duffy B, Trefethen A, Chen M (2013) Complexity Plots. Computer Graphics Forum 32: 111–120. Available: http://dx.doi.org/10.1111/cgf.12098.
    Sponsors
    The authors wish to thank the partial support from several funding bodies, including James Martin Foundation, and UK EPSRC. This publication is based on work supported by Award No. KUK-C1-013-04, made by King Abdullah University of Science and Technology (KAUST).
    Publisher
    Wiley
    Journal
    Computer Graphics Forum
    DOI
    10.1111/cgf.12098
    ae974a485f413a2113503eed53cd6c53
    10.1111/cgf.12098
    Scopus Count
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    Publications Acknowledging KAUST Support

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