Quantum Chemistry in Dataflow: Density-Fitting MP2

Bridgette Cooper, Stephen Girdlestone, Pavel Burovskiy, Georgi Gaydadjiev, Vitali Averbukh, Peter J. Knowles, Wayne Luk

Research output: Contribution to journalArticleScientificpeer-review

5 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)5265-5272
Number of pages8
JournalJournal of chemical theory and computation
Volume13
Issue number11
DOIs
Publication statusPublished - 14 Nov 2017
Externally publishedYes

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  • Cite this

    Cooper, B., Girdlestone, S., Burovskiy, P., Gaydadjiev, G., Averbukh, V., Knowles, P. J., & Luk, W. (2017). Quantum Chemistry in Dataflow: Density-Fitting MP2. Journal of chemical theory and computation, 13(11), 5265-5272. https://doi.org/10.1021/acs.jctc.7b00649