GROMACS in the cloud: A global supercomputer to speed up alchemical drug design

TL;DR

This paper evaluates the cost and performance of cloud-based GROMACS molecular dynamics simulations for drug design, demonstrating that cloud resources can outperform traditional clusters in speed and cost-efficiency for large-scale ligand screening.

Contribution

It provides a comprehensive benchmarking of GROMACS on various cloud instances and introduces strategies for cost-effective, large-scale molecular simulations in the cloud environment.

Findings

Cloud simulations can be as cost-effective as on-premises clusters.

Large-scale ligand screening can be completed in days using cloud resources.

Optimal instance selection and checkpoint-restart protocols reduce costs significantly.

Abstract

We assess costs and efficiency of state-of-the-art high performance cloud computing compared to a traditional on-premises compute cluster. Our use case are atomistic simulations carried out with the GROMACS molecular dynamics (MD) toolkit with a focus on alchemical protein-ligand binding free energy calculations. We set up a compute cluster in the Amazon Web Services (AWS) cloud that incorporates various different instances with Intel, AMD, and ARM CPUs, some with GPU acceleration. Using representative biomolecular simulation systems we benchmark how GROMACS performs on individual instances and across multiple instances. Thereby we assess which instances deliver the highest performance and which are the most cost-efficient ones for our use case. We find that, in terms of total costs including hardware, personnel, room, energy and cooling, producing MD trajectories in the cloud can be as cost-efficient as an on-premises cluster given that optimal cloud instances are chosen. Further, we find that high-throughput ligand-screening for protein-ligand binding affinity estimation can be accelerated dramatically by using global cloud resources. For a ligand screening study consisting of 19,872 independent simulations, we used all hardware that was available in the cloud at the time of the study. The computations scaled-up to reach peak performances using more than 4,000 instances, 140,000 cores, and 3,000 GPUs simultaneously around the globe. Our simulation ensemble finished in about two days in the cloud, while weeks would be required to complete the task on a typical on-premises cluster consisting of several hundred nodes. We demonstrate that the costs of such and similar studies can be drastically reduced with a checkpoint-restart protocol that allows to use cheap Spot pricing and by using instance types with optimal cost-efficiency.