A hierarchic sparse matrix data structure for large-scale Hartree-Fock/Kohn-Sham calculations

Authors

  • Emanuel H. Rubensson,

    Corresponding author
    1. Department of Physics and Chemistry, University of Southern Denmark, DK-5230 Odense M, Denmark
    2. Department of Theoretical Chemistry, Royal Institute of Technology, SE-10691 Stockholm, Sweden
    • Department of Physics and Chemistry, University of Southern Denmark, DK-5230 Odense M, Denmark
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  • Elias Rudberg,

    1. Department of Theoretical Chemistry, Royal Institute of Technology, SE-10691 Stockholm, Sweden
    2. Department of Chemistry, University of Warwick, Coventry CV4 7AL, UK
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  • Paweł Sałek

    1. Department of Theoretical Chemistry, Royal Institute of Technology, SE-10691 Stockholm, Sweden
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Abstract

A hierarchic sparse matrix data structure for Hartree-Fock/Kohn-Sham calculations is presented. The data structure makes the implementation of matrix manipulations needed for large systems faster, easier, and more maintainable without loss of performance. Algorithms for symmetric matrix square and inverse Cholesky decomposition within the hierarchic framework are also described. The presented data structure is general; in addition to its use in Hartree-Fock/Kohn-Sham calculations, it may also be used in other research areas where matrices with similar properties are encountered. The applicability of the data structure to ab initio calculations is shown with help of benchmarks on water droplets and graphene nanoribbons. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2007

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