Quantum chemistry in dataflow: Density-fitting MP2

Cooper, Bridgette, Girdlestone, Stephen, Burovskiy, Pavel, Gaydadjiev, Georgi, Averbukh, Vitali, Knowles, Peter J. ORCID: https://orcid.org/0000-0003-4657-6331 and Luk, Wayne 2017. Quantum chemistry in dataflow: Density-fitting MP2. Journal of Chemical Theory and Computation 13 (11) , pp. 5265-5272. 10.1021/acs.jctc.7b00649

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Abstract

We demonstrate the use of dataflow technology in the computation of the correlation energy in molecules at the Møller–Plesset perturbation theory (MP2) level. Specifically, we benchmark density fitting (DF)-MP2 for as many as 168 atoms (in valinomycin) and show that speed-ups between 3 and 3.8 times can be achieved when compared to the MOLPRO package run on a single CPU. Acceleration is achieved by offloading the matrix multiplications steps in DF-MP2 to Dataflow Engines (DFEs). We project that the acceleration factor could be as much as 24 with the next generation of DFEs.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Advanced Research Computing @ Cardiff (ARCCA) Chemistry
Subjects:	Q Science > QD Chemistry
Publisher:	American Chemical Society
ISSN:	1549-9618
Funders:	EPSRC and European Union Horizon 2020
Date of First Compliant Deposit:	19 October 2017
Date of Acceptance:	11 October 2017
Last Modified:	26 Nov 2024 05:45
URI:	https://orca.cardiff.ac.uk/id/eprint/105686

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