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Large-Scale Visualization of Sparse Matrices

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F14%3A00217664" target="_blank" >RIV/68407700:21240/14:00217664 - isvavai.cz</a>

  • Result on the web

    <a href="http://www.scpe.org/index.php/scpe/article/view/963" target="_blank" >http://www.scpe.org/index.php/scpe/article/view/963</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.12694/scpe.v15i1.963" target="_blank" >10.12694/scpe.v15i1.963</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Large-Scale Visualization of Sparse Matrices

  • Original language description

    An efficient algorithm for parallel acquisition of visualization data for large sparse matrices is presented and evaluated both analytically and empirically. The algorithm was designed to be application-independent, i.e., it works with any matrix-processors mapping and with any sparse storage format/scheme. The empirical scalability study of the algorithm was carried on using multiple modern HPC systems. In our largest experiment, we utilized 262,144 processors for 73 seconds to gather and store to a file the visualization data for a matrix with 1.17x10^13 nonzero elements. Using the proposed algorithm, one can thus visualize large sparse matrices with a minimal runtime overhead imposed on executed HPC codes.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

    <a href="/en/project/GAP202%2F12%2F2011" target="_blank" >GAP202/12/2011: Parallel Input/Output Algorithms for Very Large Sparse Matrices</a><br>

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2014

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Scalable Computing: Practice and Experience

  • ISSN

    1895-1767

  • e-ISSN

  • Volume of the periodical

    15

  • Issue of the periodical within the volume

    1

  • Country of publishing house

    RO - ROMANIA

  • Number of pages

    11

  • Pages from-to

    21-31

  • UT code for WoS article

  • EID of the result in the Scopus database