Coupling streaming AI and HPC ensembles to achieve 100-1000x faster biomolecular simulations

TL;DR

DeepDriveMD leverages machine learning to significantly accelerate molecular dynamics simulations on large HPC systems, enabling more extensive and faster biomolecular research.

Contribution

This paper introduces DeepDriveMD, a novel framework that couples ML with HPC to achieve 100-1000x speedups in biomolecular simulations, a substantial advancement over existing methods.

Findings

Achieves 100-1000x acceleration in protein folding simulations

Supports large-scale simulations on up to 1020 HPC nodes

Enables exploration of longer time scales and conformational landscapes

Abstract

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.