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Update on Performance Analysis of Different Computational Architectures: Molecular Dynamics in Application to Protein-Protein Interactions
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2020 (English)In: Supercomputing Frontiers and Innovations, ISSN 2409-6008, Vol. 7, no 4, p. 62-67Article in journal (Refereed) Published
Abstract [en]

Molecular dynamics has proved itself as a powerful computer simulation method to study dynamics, conformational changes, and interactions of biological macromolecules and their complexes. In order to achieve the best performance and efficiency, it is crucial to benchmark various hardware platforms for the simulations of realistic biomolecular systems with different size and timescale. Here, we compare performance and scalability of a number of commercially available computing architectures using all-atom and coarse-grained molecular dynamics simulations of water and the Ndc80-microtubule protein complex in the GROMACS-2019.4 package. We report typical single-node performance of various combinations of modern CPUs and GPUs, as well as multiple-node performance of the “Lomonosov-2” supercomputer. These data can be used as the practical guidelines for choosing optimal hardware for molecular dynamics simulations. 

Place, publisher, year, edition, pages
South Ural State University, Publishing Center , 2020. Vol. 7, no 4, p. 62-67
Keywords [en]
coarse grain, microtubule, molecular dynamics, Ndc80, tubulin, Benchmarking, Bioinformatics, Computational chemistry, Computer hardware, Program processors, Proteins, Supercomputers, Biological macromolecule, Coarse-grained molecular dynamics simulations, Computational architecture, Computing architecture, Molecular dynamics simulations, Performance and scalabilities, Protein-protein interactions, Single-node performance
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Computer Engineering
Identifiers
URN: urn:nbn:se:kth:diva-302886DOI: 10.14529/jsfi200405Scopus ID: 2-s2.0-85101580336OAI: oai:DiVA.org:kth-302886DiVA, id: diva2:1599804
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QC 20211001

Available from: 2021-10-01 Created: 2021-10-01 Last updated: 2022-06-25Bibliographically approved

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Zhmurov, Artem

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Science for Life Laboratory, SciLifeLabCentre for High Performance Computing, PDCSeRC - Swedish e-Science Research Centre
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