We assess costs and
efficiency of state-of-the-art high-performance
cloud computing and compare the results to traditional on-premises
compute clusters. Our use case is atomistic simulations carried out
with the GROMACS molecular dynamics (MD) toolkit with a particular
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 about as cost-efficient as an on-premises cluster
given that optimal cloud instances are chosen. Further, we find that
high-throughput ligand-screening can be accelerated dramatically by
using global cloud resources. For a ligand screening study consisting
of 19 872 independent simulations or ∼200 μs of
combined simulation trajectory, we made use of diverse hardware available
in the cloud at the time of the study. The computations scaled-up
to reach peak performance using more than 4 000 instances,
140 000 cores, and 3 000 GPUs simultaneously. Our simulation
ensemble finished in about 2 days in the cloud, while weeks would
be required to complete the task on a typical on-premises cluster
consisting of several hundred nodes.