  Machine learning (ML)-based steering can improve the performance of
ensemble-based simulations by allowing for online selection of more
scientifically meaningful computations. We present DeepDriveMD, a framework for
ML-driven steering of scientific simulations that we have used to achieve
orders-of-magnitude improvements in molecular dynamics (MD) performance via
effective coupling of ML and HPC on large parallel computers. We discuss the
design of DeepDriveMD and characterize its performance. We demonstrate that
DeepDriveMD can achieve between 100-1000x acceleration for protein folding
simulations relative to other methods, as measured by the amount of simulated
time performed, while covering the same conformational landscape as quantified
by the states sampled during a simulation. Experiments are performed on
leadership-class platforms on up to 1020 nodes. The results establish
DeepDriveMD as a high-performance framework for ML-driven HPC simulation
scenarios, that supports diverse MD simulation and ML back-ends, and which
enables new scientific insights by improving the length and time scales
accessible with current computing capacity.
