Local Møller-Plesset Perturbation Theory:  A Massively Parallel Algorithm

A massively parallel algorithm is presented for computation of energies with local second-order Møller-Plesset (LMP2) perturbation theory. Both the storage requirement and the computational time scale linearly with the molecular size. The parallel algorithm is designed to be scalable, employing a di...

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Vydáno v:Journal of chemical theory and computation Ročník 3; číslo 1; s. 71
Hlavní autoři: Nielsen, Ida M B, Janssen, Curtis L
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 01.01.2007
ISSN:1549-9618
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Abstract A massively parallel algorithm is presented for computation of energies with local second-order Møller-Plesset (LMP2) perturbation theory. Both the storage requirement and the computational time scale linearly with the molecular size. The parallel algorithm is designed to be scalable, employing a distributed data scheme for the two-electron integrals, avoiding communication bottlenecks, and distributing tasks in all computationally significant steps. A sparse data representation and a set of generalized contraction routines have been developed to allow efficient massively parallel implementation using distributed sparse multidimensional arrays. High parallel efficiency of the algorithm is demonstrated for applications employing up to 100 processors.
AbstractList A massively parallel algorithm is presented for computation of energies with local second-order Møller-Plesset (LMP2) perturbation theory. Both the storage requirement and the computational time scale linearly with the molecular size. The parallel algorithm is designed to be scalable, employing a distributed data scheme for the two-electron integrals, avoiding communication bottlenecks, and distributing tasks in all computationally significant steps. A sparse data representation and a set of generalized contraction routines have been developed to allow efficient massively parallel implementation using distributed sparse multidimensional arrays. High parallel efficiency of the algorithm is demonstrated for applications employing up to 100 processors.A massively parallel algorithm is presented for computation of energies with local second-order Møller-Plesset (LMP2) perturbation theory. Both the storage requirement and the computational time scale linearly with the molecular size. The parallel algorithm is designed to be scalable, employing a distributed data scheme for the two-electron integrals, avoiding communication bottlenecks, and distributing tasks in all computationally significant steps. A sparse data representation and a set of generalized contraction routines have been developed to allow efficient massively parallel implementation using distributed sparse multidimensional arrays. High parallel efficiency of the algorithm is demonstrated for applications employing up to 100 processors.
A massively parallel algorithm is presented for computation of energies with local second-order Møller-Plesset (LMP2) perturbation theory. Both the storage requirement and the computational time scale linearly with the molecular size. The parallel algorithm is designed to be scalable, employing a distributed data scheme for the two-electron integrals, avoiding communication bottlenecks, and distributing tasks in all computationally significant steps. A sparse data representation and a set of generalized contraction routines have been developed to allow efficient massively parallel implementation using distributed sparse multidimensional arrays. High parallel efficiency of the algorithm is demonstrated for applications employing up to 100 processors.
Author Nielsen, Ida M B
Janssen, Curtis L
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  organization: Sandia National Laboratories, P.O. Box 969, Livermore, California 94551
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  givenname: Curtis L
  surname: Janssen
  fullname: Janssen, Curtis L
  organization: Sandia National Laboratories, P.O. Box 969, Livermore, California 94551
BackLink https://www.ncbi.nlm.nih.gov/pubmed/26627153$$D View this record in MEDLINE/PubMed
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Title Local Møller-Plesset Perturbation Theory:  A Massively Parallel Algorithm
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