The many-body simulation of quantum systems is an active field of research that involves several different methods targeting various computing platforms. Many methods commonly employed, particularly coupled cluster methods, have been adapted to leverage the latest advances in modern high-performance computing. Selected configuration interaction (sCI) methods have seen extensive usage and development in recent years. However, the development of sCI methods targeting massively parallel resources has been explored only in a few research works. Here, we present a parallel, distributed memory implementation of the adaptive sampling configuration interaction approach (ASCI) for sCI. In particular, we will address the key concerns pertaining to the parallelization of the determinant search and selection, Hamiltonian formation, and the variational eigenvalue calculation for the ASCI method. Load balancing in the search step is achieved through the application of memory-efficient determinant constraints originally developed for the ASCI-PT2 method. The presented benchmarks demonstrate near optimal speedup for ASCI calculations of Cr2 (24e, 30o) with 106, 107, and 3 × 108 variational determinants on up to 16 384 CPUs. To the best of the authors’ knowledge, this is the largest variational ASCI calculation to date.
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7 June 2023
Research Article|
June 01 2023
A parallel, distributed memory implementation of the adaptive sampling configuration interaction method Available to Purchase
Special Collection:
High Performance Computing in Chemical Physics
David B. Williams-Young
;
David B. Williams-Young
a)
(Conceptualization, Formal analysis, Investigation, Methodology, Software, Validation, Writing – original draft, Writing – review & editing)
1
Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory
, Berkeley, California 94720, USA
a)Author to whom correspondence should be addressed: [email protected]
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Norm M. Tubman
;
Norm M. Tubman
(Conceptualization, Formal analysis, Investigation, Methodology, Software, Validation, Writing – original draft, Writing – review & editing)
2
NASA Ames Research Center
, Moffett Field, California 94035, USA
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Carlos Mejuto-Zaera
;
Carlos Mejuto-Zaera
(Conceptualization, Formal analysis, Investigation, Methodology, Software, Validation, Writing – original draft, Writing – review & editing)
3
Scuola Internazionale Superiore di Studi Avanzati (SISSA)
, Trieste, TS, Italy
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Wibe A. de Jong
Wibe A. de Jong
(Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Software, Validation, Writing – original draft, Writing – review & editing)
1
Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory
, Berkeley, California 94720, USA
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David B. Williams-Young
1,a)
Norm M. Tubman
2
Carlos Mejuto-Zaera
3
Wibe A. de Jong
1
1
Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory
, Berkeley, California 94720, USA
2
NASA Ames Research Center
, Moffett Field, California 94035, USA
3
Scuola Internazionale Superiore di Studi Avanzati (SISSA)
, Trieste, TS, Italy
a)Author to whom correspondence should be addressed: [email protected]
Note: This paper is part of the JCP Special Topic on High Performance Computing in Chemical Physics.
J. Chem. Phys. 158, 214109 (2023)
Article history
Received:
March 01 2023
Accepted:
May 10 2023
Citation
David B. Williams-Young, Norm M. Tubman, Carlos Mejuto-Zaera, Wibe A. de Jong; A parallel, distributed memory implementation of the adaptive sampling configuration interaction method. J. Chem. Phys. 7 June 2023; 158 (21): 214109. https://doi.org/10.1063/5.0148650
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