High-performance strategies for the recent MRSF-TDDFT in GAMESS

Konstantin Komarov; Vladimir Mironov; Seunghoon Lee; Buu Q Pham; Mark S Gordon; Cheol Ho Choi

doi:10.1063/5.0148005

High-performance strategies for the recent MRSF-TDDFT in GAMESS

J Chem Phys. 2023 May 21;158(19):194105. doi: 10.1063/5.0148005.

Authors

Konstantin Komarov¹, Vladimir Mironov², Seunghoon Lee³, Buu Q Pham⁴, Mark S Gordon⁴, Cheol Ho Choi²

Affiliations

¹ Center for Quantum Dynamics, Pohang University of Science and Technology, Pohang 37673, South Korea.
² Department of Chemistry, Kyungpook National University, Daegu 41566, South Korea.
³ Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA.
⁴ Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA.

PMID: 37184015
DOI: 10.1063/5.0148005

Abstract

Multiple ERI (Electron Repulsion Integral) tensor contractions (METC) with several matrices are ubiquitous in quantum chemistry. In response theories, the contraction operation, rather than ERI computations, can be the major bottleneck, as its computational demands are proportional to the multiplicatively combined contributions of the number of excited states and the kernel pre-factors. This paper presents several high-performance strategies for METC. Optimal approaches involve either the data layout reformations of interim density and Fock matrices, the introduction of intermediate ERI quartet buffer, and loop-reordering optimization for a higher cache hit rate. The combined strategies remarkably improve the performance of the MRSF (mixed reference spin flip)-TDDFT (time-dependent density functional theory) by nearly 300%. The results of this study are not limited to the MRSF-TDDFT method and can be applied to other METC scenarios.