Towards Electronic Structure-Based Ab-Initio Molecular Dynamics Simulations with Hundreds of Millions of Atoms
We push the boundaries of electronic structure-based \textit{ab-initio}
molecular dynamics (AIMD) beyond 100 million atoms. This scale is otherwise
barely reachable with classical force-field methods or novel neural network and
machine learning potentials. We achieve this breakthrough by combining
innovations in linear-scaling AIMD, efficient and approximate sparse linear
algebra, low and mixed-precision floating-point computation on GPUs, and a
compensation scheme for the errors introduced by numerical approximations. The
core of our work is the non-orthogonalized local submatrix method (NOLSM),
which scales very favorably to massively parallel computing systems and
translates large sparse matrix operations into highly parallel, dense matrix
operations that are ideally suited to hardware accelerators. We demonstrate
that the NOLSM method, which is at the center point of each AIMD step, is able
to achieve a sustained performance of 324 PFLOP/s in mixed FP16/FP32 precision
corresponding to an efficiency of 67.7% when running on 1536 NVIDIA A100 GPUs.