English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

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

Kutzner, C., Kniep, C., Cherian, A., Nordstrom, L., Grubmüller, H., de Groot, B. L., et al. (2022). GROMACS in the cloud: A global supercomputer to speed up alchemical drug design. Journal of Chemical Infomation and Modeling, 62(7), 1691-1711. doi:10.1021/acs.jcim.2c00044.

Item is

Basic

show hide
Genre: Journal Article

Files

show Files
hide Files
:
3377752.pdf (Publisher version), 7MB
Name:
3377752.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-

Locators

show

Creators

show
hide
 Creators:
Kutzner, C.1, Author              
Kniep, C., Author
Cherian, A., Author
Nordstrom, L., Author
Grubmüller, H.1, Author              
de Groot, B. L.2, Author              
Gapsys, V.2, Author              
Affiliations:
1Department of Theoretical and Computational Biophysics, Max Planck Institute for Multidisciplinary Sciences, Max Planck Society, ou_3350132              
2Research Group of Computational Biomolecular Dynamics, Max Planck Institute for Multidisciplinary Sciences, Max Planck Society, ou_3350134              

Content

show
hide
Free keywords: -
 Abstract: 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.

Details

show
hide
Language(s): eng - English
 Dates: 2022-01-142022-03-302022-04-11
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1021/acs.jcim.2c00044
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Journal of Chemical Infomation and Modeling
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 62 (7) Sequence Number: - Start / End Page: 1691 - 1711 Identifier: -