Coupled cluster theory on modern heterogeneous supercomputers

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Dokumenter

  • Fulltext

    Forlagets udgivne version, 1,65 MB, PDF-dokument

This study examines the computational challenges in elucidating intricate chemical systems, particularly through ab-initio methodologies. This work highlights the Divide-Expand-Consolidate (DEC) approach for coupled cluster (CC) theory—a linear-scaling, massively parallel framework—as a viable solution. Detailed scrutiny of the DEC framework reveals its extensive applicability for large chemical systems, yet it also acknowledges inherent limitations. To mitigate these constraints, the cluster perturbation theory is presented as an effective remedy. Attention is then directed towards the CPS (D-3) model, explicitly derived from a CC singles parent and a doubles auxiliary excitation space, for computing excitation energies. The reviewed new algorithms for the CPS (D-3) method efficiently capitalize on multiple nodes and graphical processing units, expediting heavy tensor contractions. As a result, CPS (D-3) emerges as a scalable, rapid, and precise solution for computing molecular properties in large molecular systems, marking it an efficient contender to conventional CC models.

OriginalsprogEngelsk
Artikelnummer1154526
TidsskriftFrontiers in Chemistry
Vol/bind11
Antal sider24
ISSN2296-2646
DOI
StatusUdgivet - 2023

Bibliografisk note

Funding Information:
This research used resources of the Oak Ridge Leadership Computing Facility (OLCF), which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725. AH-B and KM acknowledge the Danish Council for Independent Research, DFF-0136-00081B, and the European Union’s Horizon 2020 Framework Programme under grant agreement number 951801 for financial support. AYZ acknowledges the additional computational resources that were provided by the Pinnacles cluster at UC Merced, which is supported by the National Science Foundation under OAC-2019144.

Publisher Copyright:
Copyright © 2023 Corzo, Hillers-Bendtsen, Barnes, Zamani, Pawłowski, Olsen, Jørgensen, Mikkelsen and Bykov.

ID: 359598132