A hierarchic sparse matrix data structure for large-scale Hartree-Fock/Kohn-Sham calculations.
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.
Rubensson
Emanuel H
EH
Department of Physics and Chemistry, University of Southern Denmark, Odense M, Denmark. emanuel@theochem.kth.se
Rudberg
Elias
E
Sałek
Paweł
P