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Approaches to large‐scale parallel self‐consistent field calculations
Author(s) -
Wong Adrian T.,
Harrison Robert J.
Publication year - 1995
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.540161010
Subject(s) - hessian matrix , massively parallel , computer science , conjugate gradient method , computation , mathematics , parallel computing , quadratic equation , field (mathematics) , convergence (economics) , scale (ratio) , stability (learning theory) , computational science , mathematical optimization , algorithm , physics , geometry , quantum mechanics , machine learning , pure mathematics , economics , economic growth
Abstract The availability of massively parallel computers with high computation rates but limited memory and input/output bandwidth prompts the reevaluation of appropriate solution schemes for the self‐consistent field (SCF) equations. Several algorithms are considered which exhibit between linear and quadratic convergence using various approximations to the orbital Hessian. A prototype is developed to understand the computational expense of each approach. The optimal choice is found to be a conjugate–gradient method preconditioned with a level‐shifted approximation to the orbital Hessian. This is a compromise between efficiency, stability, and low memory usage. Sample benchmarks on two parallel supercomputers are also reported. © 1995 John Wiley & Sons, Inc.