Towards adaptive exascale workflows for simulating long timescales

Molecular dynamics simulations integrate trillions of short simulation steps and are reaching hardware-bound performance limits for clock time per step. Despite this, most biologically relevant processes occur over seconds to hours, orders of magnitude slower than feasible simulation timescales. Workflow-based methods harnessing complex statistical mechanics analyses are increasingly used with the hope of achieving kinetic estimates on experimentally relevant timescales from swarms of short parallel simulations. We present a highly scalable workflow software, AdaptiveMD, that implements a massively parallel adaptive-sampling algorithm to build a Markov-state model of long-timescale biomolecular kinetics. The software was ported to the Summit pre-exascale machine at the Oak Ridge Leadership Computing Facility (OLCF), after initial development on OLCF Titan, and demonstrated excellent scalability and fault tolerance. Summits software and hardware features allow for an unprecedented ability to simulate large biomolecular systems within this framework. These results suggest that this method at exascale may allow us to tackle grand challenges in biomolecular simulations.