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

Konstantin KomarovVladimir MironovSeunghoon LeeBuu Q PhamMark S GordonCheol-Ho Choi
Published in: The Journal of chemical physics (2023)
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.
Keyphrases
  • density functional theory
  • molecular dynamics
  • climate change
  • electronic health record
  • transcription factor
  • single molecule
  • drug discovery
  • electron transfer
  • deep learning