High-Performance Multi-GPU Analytic RI-MP2 Energy Gradients

This article presents a novel algorithm for the calculation
of
analytic energy gradients from second-order Møller–Plesset
perturbation theory within the Resolution-of-the-Identity approximation
(RI-MP2), which is designed to achieve high performance on clusters
with multiple graphical processing units (GPUs). The algorithm uses
GPUs for all major steps of the calculation, including integral generation,
formation of all required intermediate tensors, solution of the Z-vector
equation and gradient accumulation. The implementation in the EXtreme
Scale Electronic Structure System (EXESS) software package includes
a tailored, highly efficient, multistream scheduling system to hide
CPU-GPU data transfer latencies and allows nodes with 8 A100 GPUs
to operate at over 80% of theoretical peak floating-point performance.
Comparative performance analysis shows a significant reduction in
computational time relative to traditional multicore CPU-based methods,
with our approach achieving up to a 95-fold speedup over the single-node
performance of established software such as Q-Chem and ORCA. Additionally,
we demonstrate that pairing our implementation with the molecular
fragmentation framework in EXESS can drastically lower the computational
scaling of RI-MP2 gradient calculations from quintic to subquadratic,
enabling further substantial savings in runtime while retaining high
numerical accuracy in the resulting gradients.