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SIESTA-SIPs: Massively parallel spectrum-slicing eigensolver for an ab initio molecular dynamics package

Journal Article · · Journal of Computational Chemistry
DOI:https://doi.org/10.1002/jcc.25350· OSTI ID:1468621
 [1];  [2];  [3];  [3];  [4];  [5];  [6];  [7]
  1. Chemical Sciences and Engineering Division, Argonne National Laboratory, Argonne Illinois 60439; Computational Science Division, Argonne National Laboratory, Argonne Illinois 60439
  2. Departments of Materials and Physics and the Thomas Young Centre for Theory and Simulation of Materials, Imperial College London, London SW7 2AZ United Kingdom
  3. D. Sistemes Informàtics i Computació, Universitat Politècnica de València, Camí de Vera s/n, València 46022 Spain
  4. Mathematics and Computer Science Division, Argonne National Laboratory, Argonne Illinois 60439
  5. Chemical Sciences and Engineering Division, Argonne National Laboratory, Argonne Illinois 60439; Argonne Leadership Computing Facility, Argonne National Laboratory, Argonne Illinois 60439
  6. Materials Science Division, Argonne National Laboratory, Argonne Illinois 60439
  7. Computational Science Division, Argonne National Laboratory, Argonne Illinois 60439
+ Show Author Affiliations

Integration of Shift-and-Invert Parallel spectral transformation (SIPs) eigensolver (as implemented in the SLEPc library) into an ab initio molecular dynamics package, SIESTA, is described. The effectiveness of the code is demonstrated on applications to polyethylene chains, boron nitride sheets, and bulk water clusters. For problems with the same number of orbitals, the performance of the SLEPc eigensolver depends on the sparsity of the matrices involved, favoring reduced dimensional systems such as polyethylene or boron nitride sheets in comparison to bulk systems like water clusters. For all problems investigated, performance of SIESTA-SIPs exceeds the performance of SIESTA with default solver (ScaLAPACK) at the larger number of cores and the larger number of orbitals. A method that improves the load-balance with each iteration in the self-consistency cycle by exploiting the emerging knowledge of the eigenvalue spectrum is demonstrated

Research Organization:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Organization:
Argonne National Laboratory - Laboratory Directed Research and Development (LDRD); USDOE Office of Science - Office of Basic Energy Sciences - Materials Sciences and Engineering Division
DOE Contract Number:
AC02-06CH11357
OSTI ID:
1468621
Journal Information:
Journal of Computational Chemistry, Vol. 39, Issue 22; ISSN 0192-8651
Publisher:
Wiley
Country of Publication:
United States
Language:
English

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DFT
SCF
ab initio
eigensolver
sparse