Abstract
Matrices appearing in Hartree–Fock or density functional theory coming from discretization with help of atom–centered local basis sets become sparse when the separation between atoms exceeds some system–dependent threshold value. Efficient implementation of sparse matrix algebra is therefore essential in large–scale quantum calculations. We describe a unique combination of algorithms and data representation that provides high performance and strict error control in blocked sparse matrix algebra. This has applications to matrix–matrix multiplication, the Trace–Correcting Purification algorithm and the entire self–consistent field calculation.
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Rubensson, E.H., Rudberg, E., Sałek, P. (2007). Sparse Matrix Algebra for Quantum Modeling of Large Systems. In: Kågström, B., Elmroth, E., Dongarra, J., Waśniewski, J. (eds) Applied Parallel Computing. State of the Art in Scientific Computing. PARA 2006. Lecture Notes in Computer Science, vol 4699. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75755-9_11
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DOI: https://doi.org/10.1007/978-3-540-75755-9_11
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