Extreme scaling of the metadynamics of paths algorithm on the pre-exascale JUWELS Booster supercomputer

TL;DR

This paper presents an efficient, highly scalable implementation of the metadynamics of paths (MoP) algorithm in GROMACS, enabling long trajectory sampling for complex biomolecular systems on supercomputers with high parallel efficiency.

Contribution

The paper introduces a scalable implementation of MoP in GROMACS, demonstrating near-linear scaling on thousands of GPUs for large biomolecular systems.

Findings

Achieved over 70% parallel efficiency on 3200 GPUs

Successfully simulated a 150,000-atom membrane protein

Enabled long trajectory sampling with high computational efficiency

Abstract

Molecular dynamics (MD)-based path sampling algorithms are a very important class of methods used to study the energetics and kinetics of rare (bio)molecular events. They sample the highly informative but highly unlikely reactive trajectories connecting different metastable states of complex (bio)molecular systems. The metadynamics of paths (MoP) method proposed by Mandelli, Hirshberg, and Parrinello [Pys. Rev. Lett. 125 2, 026001 (2020)] is based on the Onsager-Machlup path integral formalism. This provides an analytical expression for the probability of sampling stochastic trajectories of given duration. In practice, the method samples reactive paths via metadynamics simulations performed directly in the phase space of all possible trajectories. Its parallel implementation is in principle infinitely scalable, allowing arbitrarily long trajectories to be simulated. Paving the way for future applications to study the thermodynamics and kinetics of protein-ligand (un)binding, a problem of great pharmaceutical interest, we present here the efficient implementation of MoP in the HPC-oriented biomolecular simulation software GROMACS. Our benchmarks on a membrane protein (150,000 atoms) show an unprecedented weak scaling parallel efficiency of over 70% up to 3200 GPUs on the pre-exascale JUWELS Booster machine at the J\"ulich Supercomputing Center.