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Efficient Parallel All-Electron Four-Component Dirac-Kohn-Sham Program Using a Distributed Matrix Approach II.

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We developed a new memory-distributed algorithm to enhance the parallel computation of the Dirac-Kohn-Sham (DKS) module for large molecular systems. This advancement enables more efficient calculations on massively parallel systems, improving applicability for complex chemistry problems.

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Area of Science:

  • Computational Chemistry
  • Quantum Chemistry
  • Materials Science

Background:

  • The Dirac-Kohn-Sham (DKS) module in BERTHA is crucial for all-electron relativistic calculations.
  • Previous implementations faced limitations in handling arbitrarily large molecular systems and parallel processing.

Purpose of the Study:

  • To introduce a novel memory-distributed algorithm for the DKS module.
  • To significantly improve the parallel implementation of DKS calculations.
  • To enhance the scalability of DKS methods for large molecular systems.

Main Methods:

  • Developed a new memory-distributed algorithm for the DKS module.
  • Implemented an original procedure for mapping DKS matrices between integral-driven and block-cyclic distributions.
  • Utilized the ScaLAPACK library for linear algebra operations.

Main Results:

  • Achieved significant improvements in the parallel implementation of the DKS module.
  • Demonstrated enhanced efficiency in memory distribution for large-scale computations.
  • Successfully converged DKS self-consistent procedures for gold clusters (Au20, Au34) with over 39k basis functions.

Conclusions:

  • The new algorithm represents a significant advancement in the applicability of DKS procedures for large molecular systems.
  • The implementation is well-suited for last-generation massively parallel systems.
  • The method enables detailed analysis, such as reporting density of electronic states for complex clusters.