Toward high-performance computational chemistry: I. Scalable Fock matrix construction algorithms
1996; Wiley; Volume: 17; Issue: 1 Linguagem: Inglês
10.1002/(sici)1096-987x(19960115)17
ISSN1096-987X
AutoresIan Foster, Jeffrey L. Tilson, Albert F. Wagner, Ron Shepard, Robert J. Harrison, Rick A. Kendall, Rik J. Littlefield,
Tópico(s)Quantum Computing Algorithms and Architecture
ResumoJournal of Computational ChemistryVolume 17, Issue 1 p. 109-123 Toward high-performance computational chemistry: I. Scalable Fock matrix construction algorithms Ian T. Foster, Corresponding Author Ian T. Foster Argonne National Laboratory, Argonne, Illinois 60439Argonne National Laboratory, Argonne, Illinois 60439Search for more papers by this authorJeffrey L. Tilson, Jeffrey L. Tilson Argonne National Laboratory, Argonne, Illinois 60439Search for more papers by this authorAlbert F. Wagner, Albert F. Wagner Argonne National Laboratory, Argonne, Illinois 60439Search for more papers by this authorRon L. Shepard, Ron L. Shepard Argonne National Laboratory, Argonne, Illinois 60439Search for more papers by this authorRobert J. Harrison, Robert J. Harrison Molecular Science Research Center, Pacific Northwest Laboratory, Richland, Washington 99352Search for more papers by this authorRick A. Kendall, Rick A. Kendall Molecular Science Research Center, Pacific Northwest Laboratory, Richland, Washington 99352Search for more papers by this authorRik J. Littlefield, Rik J. Littlefield Molecular Science Research Center, Pacific Northwest Laboratory, Richland, Washington 99352Search for more papers by this author Ian T. Foster, Corresponding Author Ian T. Foster Argonne National Laboratory, Argonne, Illinois 60439Argonne National Laboratory, Argonne, Illinois 60439Search for more papers by this authorJeffrey L. Tilson, Jeffrey L. Tilson Argonne National Laboratory, Argonne, Illinois 60439Search for more papers by this authorAlbert F. Wagner, Albert F. Wagner Argonne National Laboratory, Argonne, Illinois 60439Search for more papers by this authorRon L. Shepard, Ron L. Shepard Argonne National Laboratory, Argonne, Illinois 60439Search for more papers by this authorRobert J. Harrison, Robert J. Harrison Molecular Science Research Center, Pacific Northwest Laboratory, Richland, Washington 99352Search for more papers by this authorRick A. Kendall, Rick A. Kendall Molecular Science Research Center, Pacific Northwest Laboratory, Richland, Washington 99352Search for more papers by this authorRik J. Littlefield, Rik J. Littlefield Molecular Science Research Center, Pacific Northwest Laboratory, Richland, Washington 99352Search for more papers by this author First published: 15 January 1996 https://doi.org/10.1002/(SICI)1096-987X(19960115)17:1 3.0.CO;2-VCitations: 39AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract Several parallel algorithms for Fock matrix construction are described. The algorithms calculate only the unique integrals, distribute the Fock and density matrices over the processors of a massively parallel computer, use blocking techniques to construct the distributed data structures, and use clustering techniques on each processor to maximize data reuse. Algorithms based on both square and row-blocked distributions of the Fock and density matrices are described and evaluated. Variants of the algorithms are discussed that use either triple-sort or canonical ordering of integrals, and dynamic or static task clustering schemes. The algorithms are shown to adapt to screening, with communication volume scaling down with computation costs. Modeling techniques are used to characterize algorithm performance. Given the characteristics of existing massively parallel computers, all the algorithms are shown to be highly efficient for problems of moderate size. The algorithms using the row-blocked data distribution are the most efficient. © 1996 by John Wiley & Sons, Inc. Citing Literature Volume17, Issue115 January 1996Pages 109-123 RelatedInformation
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