Breaking bonds on graphical processing units: Scalable implementations of the density-fitted CR-CC(2,3) and ΛCCSD(T) approaches

Efficient scalable implementations are presented for two single-reference coupled-cluster (CC) methods with non-iterative corrections for triple excitations, which accurately describe single bond dissociations in molecules in contrast to the standard CCSD(T) approach. These methods are (a) the completely renormalized coupled-cluster theory, CR-CC(2,3), and (b) the ΛCCSD(T) approach, the triples energy corrections in both of which are formulated using the left eigenvector of the CC similarity transformed Hamiltonian. The density fitting approximation is employed for factorizing the two-electron repulsion integrals (ERIs), which eliminates memory bottlenecks associated with the storage of the four-index ERIs by allowing an integral-direct algorithm. Massively parallel algorithms have been developed for the above methods within the GAMESS quantum chemistry program using a hybrid MPI and OpenMP based parallelization model. All compute-intensive steps in the CCSD amplitude and the left eigenvector equations, as well as the triples corrections in both methods, have been adapted to execute on graphical processing units (GPUs) using the OpenMP target directives. Performance of these codes is demonstrated on petascale supercomputers equipped with NVIDIA A100 GPUs. While the CR-CC(2,3) and ΛCCSD(T) computations take roughly twice as much time as the standard CCSD(T) method, the GPU-offloaded codes are shown to impart 3.5–5× accelerations relative to the CPU-only parallel codes, thereby significantly reducing the time-to-solution for the CR-CC(2,3) and ΛCCSD(T) methods.