Accelerating Electrostatically-Embedded Fragmentation Methods using Graphics Processing Units

Résumé fourni par la source

Predicting the physicochemical properties of large molecular systems requires quantum chemistry methods that are both accurate and computationally scalable. Electrostatically embedded fragmentation approaches, such as the Fragment Molecular Orbital (FMO) method, have been highly successful in extending Hartree-Fock (HF) and post-HF theories to systems with thousands of atoms, but their iterative electrostatic potential (ESP) cycles and communication patterns pose challenges on modern heterogeneous architectures. In this work, we develop a distributed-memory, multi-GPU algorithms for electrostatically embedded fragmentation, targeting both FMO and the recently proposed Coulomb-Perturbed Fragmentation (CPF) method. CPF removes the expensive self-consistent ESP iterations at the monomer level by converging monomers once in vacuo and then using these fixed densities to construct the electrostatic embedding for all fragments. Our implementations in the Extreme-Scale Electronic Structure System (EXESS) feature GPU-accelerated HF and RI-MP2 kernels, specialised ERI kernels for ESP terms, a multi-layer dynamic load balancing scheme, and MPI Remote Memory Access (RMA) to efficiently distribute monomer densities across nodes. Accuracy is assessed for water hexamers, neutral molecular crystals, and ionic liquids at the HF and RI-MP2 levels, where CPF3 and FMO3 reproduce full-system energies with mean absolute deviations of only a few kJ, mol-1 and correctly recover subtle energetic orderings. Performance benchmarks on Gadi and Perlmutter demonstrate speedups of up to ~6× over the parallel CPU FMO implementation in GAMESS on a single node, and strong scaling efficiencies approaching 90% on up to 128 GPU nodes. Overall, CPF emerges as a highly accurate and markedly more scalable alternative to FMO for large-scale electrostatically embedded quantum chemistry on GPU-accelerated supercomputers.

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