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Toward high‐performance computational chemistry: II. A scalable self‐consistent field program
Author(s) -
Harrison Robert J.,
Guest Martyn F.,
Kendall Rick A.,
Bernholdt David E.,
Wong Adrian T.,
Stave Mark,
Anchell James L.,
Hess Anthony C.,
Littlefield Rik J.,
Fann George L.,
Nieplocha Jaroslaw,
Thomas Greg S.,
Elwood David,
Tilson Jeffrey L.,
Shepard Ron L.,
Wagner Albert F.,
Foster Ian T.,
Lusk Ewing,
Stevens Rick
Publication year - 1996
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/(sici)1096-987x(19960115)17:1<124::aid-jcc10>3.0.co;2-n
Subject(s) - scalability , computer science , distributed memory , replication (statistics) , exploit , field (mathematics) , parallel computing , computation , key (lock) , process (computing) , distributed computing , computational science , theoretical computer science , shared memory , algorithm , mathematics , programming language , database , pure mathematics , statistics , computer security
We discuss issues in developing scalable parallel algorithms and focus on the distribution, as opposed to the replication, of key data structures. Replication of large data structures limits the maximum calculation size by imposing a low ratio of processors to memory. Only applications which distribute both data and computation across processors are truly scalable. The use of shared data structures that may be independently accessed by each process even in a distributed memory environment greatly simplifies development and provides a significant performance enhancement. We describe tools we have developed to support this programming paradigm. These tools are used to develop a highly efficient and scalable algorithm to perform self‐consistent field calculations on molecular systems. A simple and classical strip‐mining algorithm suffices to achieve an efficient and scalable Fock matrix construction in which all matrices are fully distributed. By strip mining over atoms, we also exploit all available sparsity and pave the way to adopting more sophisticated methods for summation of the Coulomb and exchange interactions. © 1996 by John Wiley & Sons, Inc.