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    A Scalable Streamline Generation Algorithm Via Flux-Based Isocontour Extraction

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    Date
    2016
    Author
    Biswas, Ayan
    Strelitz, Richard
    Woodring, Jonathan
    Chen, Chun-Ming
    Shen, Han-Wei
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    Abstract
    Streamlines are commonly used for visualizing flow fields, but particle-tracing based streamline computation usually does not scale well as the data size and complexity increase. Large flow simulations like global ocean or climate models can obtain near perfect load balancing and the resulting data sets are generally analyzed in two dimensional slices. To match the computational properties of these simulations, we propose the use of flux- based stream functions for generating streamlines in parallel. In our method, local stream functions are efficiently generated per block based on flux conservation property, followed by low-cost communication of flux offsets among neighboring blocks. A scalar field is thus generated where streamlines can be extracted through parallel iso- contouring. Experimental results show that our system offers higher streamline computation performance with higher scalability than traditional particle-tracing based method.
    BibTeX
    @inproceedings {10.2312:pgv.20161183,
    booktitle = {Eurographics Symposium on Parallel Graphics and Visualization},
    editor = {Enrico Gobbetti and Wes Bethel},
    title = {{A Scalable Streamline Generation Algorithm Via Flux-Based Isocontour Extraction}},
    author = {Biswas, Ayan and Strelitz, Richard and Woodring, Jonathan and Chen, Chun-Ming and Shen, Han-Wei},
    year = {2016},
    publisher = {The Eurographics Association},
    ISSN = {1727-348X},
    ISBN = {978-3-03868-006-2},
    DOI = {10.2312/pgv.20161183}
    }
    URI
    http://dx.doi.org/10.2312/pgv.20161183
    https://diglib.eg.org:443/handle/10
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    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA