Parallel implementation of electronic structure eigensolver using a partitioned folded spectrum method
E.L. Briggs, C.T. Kelley, J. Bernholc

TL;DR
This paper introduces a parallel eigensolver method tailored for electronic structure calculations, significantly improving speed and scalability on large systems while maintaining accuracy, applicable to metals, insulators, and semiconductors.
Contribution
The paper presents a novel partitioned folded spectrum method that enhances parallel efficiency for iterative eigenvalue problems in electronic structure calculations.
Findings
Achieves up to tenfold speedup over standard eigensolvers.
Maintains accuracy comparable to traditional methods.
Effective for large, diverse systems including metals and insulators.
Abstract
A parallel implementation of an eigensolver designed for electronic structure calculations is presented. The method is applicable to computational tasks that solve a sequence of eigenvalue problems where the solution for a particular iteration is similar but not identical to the solution from the previous iteration. Such problems occur frequently when performing electronic structure calculations in which the eigenvectors are solutions to the Kohn-Sham equations. The eigenvectors are represented in some type of basis but the problem sizes are normally too large for direct diagonalization in that basis. Instead a subspace diagonalization procedure is employed in which matrix elements of the Hamiltonian operator are generated and the eigenvalues and eigenvectors of the resulting reduced matrix are obtained using a standard eigensolver from a package such as LAPACK or SCALAPACK. While this…
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Taxonomy
TopicsMatrix Theory and Algorithms · Quantum Computing Algorithms and Architecture
