‘Scaling molecular dynamics beyond 100, 000 processor cores for large-scale biophysical simulations’
Open accessAbstract
The growing interest in the complexity of biological interactions is continuously driving the need to increase system size in biophysical simulations, requiring not only powerful and advanced hardware but adaptable software that can accommodate a large number of atoms interacting through complex forcefields. To address this, we developed and implemented strategies in the GENESIS molecular dynamics package designed for large numbers of processors. Long‐range electrostatic interactions were parallelized by minimizing the number of processes involved in communication. A novel algorithm was implemented for nonbonded interactions to increase single instruction multiple data (SIMD) performance, reducing memory usage for ultra large systems. Memory usage for neighbor searches in real‐space nonbonded interactions was reduced by approximately 80%, leading to significant speedup. Using experimental data describing physical 3D chromatin interactions, we constructed the first atomistic model of an entire gene locus (GATA4). Taken together, these developments enabled the first billion‐atom simulation of an intact biomolecular complex, achieving scaling to 65,000 processes (130,000 processor cores) with 1 ns/day performance. Published 2019. This article is a U.S. Government work and is in the public domain in the USA.
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Billion-particle simulations have already been achieved.
“The forthcoming generation of supercomputers and simulation software is becoming increasingly efficient, and billion-particle simulations have already been achieved (Jung et al., 2019; Castagna et al., 2020).”
From Molecular dynamics simulation of an entire cell (2023) · cites this paper as Jung et al., 2019
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“The atomistic computer simulations of chromatin fiber with a comparable size to ours were undertaken recently, while the system of 83 kbp consisted of one billion atoms [47].”
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Kulke, M., & Vermaas, J. V. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00327
Yagi, K., Gunst, K., Shiozaki, T., & Sugita, Y. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c00163 · Cited by 9 · see citing works
Self-citationFast Parametrization of Martini3 Models for Fragments and Small Molecules
Szczuka, M., Pereira, G. P., Walter, L. J., Guéroult, M., Poulain, P., Bereau, T., Souza, P. C. T., & Chavent, M. G. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c01178 · Cited by 9 · see citing works
Extension of the iSoLF implicit-solvent coarse-grained model for multicomponent lipid bilayers
Torre, D. U. L., Takada, S., & Sugita, Y. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0160417 · Cited by 8 · see citing works
Self-citationBiomolecular simulations at the exascale: From drug design to organelles and beyond
Gapsys, V., Kopeć, W., Matthes, D., & de Groot, B. L. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102887 · Cited by 8 · see citing works
Kohnke, B., Briand, E., Kutzner, C., & Grubmüller, H. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c01319 · Cited by 8 · see citing works
Matin, S., Allen, A. E. A., Shinkle, E., Pachalieva, A. A., Craven, G. T., Nebgen, B. T., Smith, J. S., Messerly, R. A., Li, Y. W., Tretiak, S., et al. (2025) · Digital Discovery · doi:10.1039/d5dd00085h · Cited by 8 · see citing works
Andoh, Y., Ichikawa, S., Sakashita, T., Yoshii, N., & Okazaki, S. (2021) · Journal of Computational Chemistry · doi:10.1002/jcc.26524 · Cited by 8 · see citing works
GROMEX: A Scalable and Versatile Fast Multipole Method for Biomolecular Simulation
Kohnke, B., Ullmann, T. R., Beckmann, A., Kabadshow, I., Haensel, D., Morgenstern, L., Dobrev, P., Groenhof, G., Kutzner, C., Hess, B., et al. (2020) · Lecture notes in computational science and engineering · doi:10.1007/978-3-030-47956-5_17 · Cited by 8 · see citing works
“Recently, the first MD simulation of an entire gene was reported, comprising about a billion of atoms [21].”
BackgroundLarge-Scale Molecular Dynamics Simulations of Cellular Compartments
Wilson, E., Vant, J., Layton, J., Boyd, R. J., Lee, H., Turilli, M., Hernández, B., Wilkinson, S. R., Jha, S., Gupta, C., et al. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1394-8_18 · Cited by 8 · see citing works
“Dividing and Conquering” and “Caching” in Molecular Modeling
Cao, X., & Tian, P. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22095053 · Cited by 10 · see citing works
Pulido, J., Macaya, L., & Vöhringer‐Martinez, E. (2024) · Journal of Chemical & Engineering Data · doi:10.1021/acs.jced.4c00144 · Cited by 7 · see citing works
Allosteric regulation of β-reaction stage I in tryptophan synthase upon the α-ligand binding
Ito, S., Yagi, K., & Sugita, Y. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0134117 · Cited by 7 · see citing works
Self-citationSarkar, D., Kulke, M., & Vermaas, J. V. (2023) · Biomolecules · doi:10.3390/biom13010107 · Cited by 7 · see citing works
Supercomputing in the biological sciences: Toward Zettascale and Yottascale simulations
Sanbonmatsu, K. Y. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102889 · Cited by 7 · see citing works
Self-citationVTX: real-time high-performance molecular structure and dynamics visualization software
Maria, M., Guionnière, S., Dacquay, N., Plateau–Holleville, C., Guillaume, V., Larroque, V., Lardé, J., Naïmi, Y., Piquemal, J., Levieux, G., et al. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf295 · Cited by 6 · see citing works
“Due to the modern High-Performance Computing hardware and storage, the size of the simulated systems (Jung et al. 2019, Stevens et al. 2023, Coshic et al. 2024) with molecular dynamics has dramatically increased and the storage, analysis and interactive visualization of the resulting data can…”
Meneses-Juárez, E., Beltrán, C. M., & González-Melchor, M. (2019) · Physical review. E · doi:10.1103/physreve.100.012505 · Cited by 6 · see citing works
Jung, J., Kobayashi, C., & Sugita, Y. (2023) · Journal of Computational Chemistry · doi:10.1002/jcc.27124 · Cited by 6 · see citing works
Self-citationSimulating Time-Resolved Dynamics of Biomolecular Systems
Aranda-García, D., Torrens‐Fontanals, M., Medel-Lacruz, B., Lopez‐Balastegui, M., Peralta-García, A., Dieguez-Eceolaza, M., Morales-Pastor, A., Sotillo-Núñez, D., Abbondandolo, D., Stępniewski, T. M., et al. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-820472-6.00214-0 · Cited by 6 · see citing works
Computational Modeling of Molecular Mechanics for the Experimentally Inclined
Kleinschmidt, A. T., Chen, A. X., Pascal, T. A., & Lipomi, D. J. (2022) · Chemistry of Materials · doi:10.1021/acs.chemmater.2c00292 · Cited by 6 · see citing works
Chandra, A., Loeffler, T. D., Chan, H., Wang, X., Stephenson, G. B., Servis, M. J., & Sankaranarayanan, S. K. R. S. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0151050 · Cited by 5 · see citing works
A proposition: feasibility of classical plate theory on bending monolayer graphene
Yan, J., Jiang, S., He, L., & Gong, M. L. (2023) · Physica Scripta · doi:10.1088/1402-4896/acdc63 · Cited by 5 · see citing works
Saurabh, S., Li, Z., Hollowell, P., Waigh, T. A., Li, P., Webster, J. R. P., Seddon, J. M., Kalonia, C. K., Lu, J. R., & Bresme, F. (2023) · Molecular Physics · doi:10.1080/00268976.2023.2236248 · Cited by 5 · see citing works
Multiscale modelling of biopolymers
Scacchi, A., Vuorte, M., & Sammalkorpi, M. (2024) · Advances in Physics X · doi:10.1080/23746149.2024.2358196 · Cited by 5 · see citing works
Connected Component Analysis of Dynamical Perturbation Contact Networks
Gheeraert, A., Lesieur, C., Batista, V. S., Vuillon, L., & Rivalta, I. (2023) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.3c04592 · Cited by 5 · see citing works
Esposito, R., Mensitieri, G., Zhou, Y., Lu, Z.-Y., Zhao, Y., Kawakatsu, T., & Milano, G. (2025) · Journal of Computational Chemistry · doi:10.1002/jcc.70126 · Cited by 3 · see citing works
SPANA : Spatial decomposition analysis for cellular‐scale molecular dynamics simulations
Yu, I., Mori, T., Matsuoka, D., Surblys, D., & Sugita, Y. (2023) · Journal of Computational Chemistry · doi:10.1002/jcc.27260 · Cited by 4 · see citing works
Self-citationChowdhury, A., Sapkota, D., & Girodat, D. J. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102804 · Cited by 4 · see citing works
In the future simulations will replace clinical trials
Ebert, M. A., Gebski, V., & Baldock, C. (2021) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-021-01079-y · Cited by 4 · see citing works
“130,000 processor cores have recently been used to simulate a single gene locus at 1 physical nanosecond per day of simulation [8].”
MethodUgrani, S. (2023) · European Biophysics Journal · doi:10.1007/s00249-023-01695-4 · Cited by 4 · see citing works
“For instance, the cost of calculating non-bonded interactions in an all-atom MD simulation scales by O( n 2 ), where n is the total number of particles in the system (Jung et al. 2019).”
Mörl, M.-C., Zülske, T., Schöpflin, R., & Wedemann, G. (2019) · AIMS Biophysics · doi:10.3934/biophy.2019.3.83 · Cited by 4 · see citing works
Efficient GPU Computation of Large Protein Solvent-Excluded Surface
Plateau–Holleville, C., Maria, M., Mérillou, S., & Montès, M. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3380100 · Cited by 3 · see citing works
“Additionally, recent developments in HPC allowed the generation of massive simulations at the atomic scale [4] that would benefit from appropriate illustration methods.”
Velocity Jumps for Molecular Dynamics
Gouraud, N., Lagardère, L., Adjoua, O., Plé, T., Monmarché, P., & Piquemal, J. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c00023 · Cited by 3 · see citing works
“Because of the discrepancy between the microscopic and macroscopic time and space scales, in order to obtain relevant results, one needs to simulate complex molecular systems with a high precision and for a very long time [6, 7, 8, 9, 10, 11].”
Gonzales, J. E., Hwang, W., & Brooks, B. R. (2025) · Journal of Chemical Physics · doi:10.1063/5.0264935 · Cited by 3 · see citing works
Maria, M., Guillaume, V., Guionnière, S., Dacquay, N., Plateau-Holleville, C., Larroque, V., Larde, J., Naimi, Y., Piquemal, J.-P., Levieux, G., et al. (2025) · Frontiers Bioinform. · doi:10.3389/fbinf.2025.1588661 · Cited by 2 · see citing works
“Additionally, modern high-performance computing hardware has enabled the simulation of larger molecular systems [6, 7, 8, 9], further amplifying data production.”
Deep learning insights into β-lactamase dynamics and resistance evolution
Gu, J., Gao, L., Chen, S., Xu, M.-M., Goyal, J., Haider, R., Attana, F., Alzahrani, O. R., Bonomo, R., & Haider, S. (2026) · Biochemical Journal · doi:10.1042/bcj20260196 · Cited by 2 · see citing works
Modulating Coarse-Grained Dynamics by Perturbing Free Energy Landscapes
Nadkarni, I., Jeong, J., Yalcin, B., & Aluru, N. R. (2024) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.4c04530 · Cited by 2 · see citing works
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Kinks in buckled graphene uncompressed and compressed in the longitudinal direction
Yamaletdinov, R. D., & Pershin, Y. V. (2022) · Theoretical and computational chemistry · doi:10.1016/b978-0-12-819514-7.00007-5 · Cited by 2 · see citing works
Mesoscale computational protocols for the design of highly cooperative bivalent macromolecules
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Meeting Experiments at the Diffraction Barrier with In Silico Fluorescence Microscopy
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Optimizing String Method’s Reproducibility Using Generalized Solute Tempering Replica Exchange
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Section Introduction: Molecular Dynamics Simulations and Reaction Rates
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Ma, Y. (2022) · Modern aspects of electrochemistry · doi:10.1007/978-3-030-90899-7_10 · Cited by 1 · see citing works
Sequence context decodes multi-state conformational heterogeneity from crystallographic B-factorsPreprint
Wang, Y., Cai, M., Ma, Y., Shi, T., & Wei, K. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.05.692009 · Cited by 1 · see citing works
“…researchers to revisit over 190,000 static structures and extract quantitative conformational-heterogeneity information without the prohibitive computational cost of molecular dynamics simulations (Jung et al., 2019; Sahil et al., 2023; Praˇsnikar et al., 2024; Lappala, 2024; Cui et al., 2025).”
4D chromosome reconstruction elucidates the spatial reorganization of the mammalian X-chromosomePreprint
Lappala, A., Wang, C., Kriz, A. J., Michalk, H., Tan, K., Lee, J. T., & Sanbonmatsu, K. Y. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.21.427652
Self-citationMeeting experiments at the diffraction barrier: an in-silico widefield fluorescence microscopyPreprint
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Methods for studying nucleic acid structure
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- Authors
- Jung, J., Nishima, W., Daniels, M., Bascom, G., Kobayashi, C., Adedoyin, A., Wall, M., Lappala, A., Phillips, D., Fischer, W., Tung, C., Schlick, T., Sugita, Y., & Sanbonmatsu, K. Y.
- Affiliations
- RIKEN Center for Computational Science (JP)
- Los Alamos National Laboratory (US)
- New York University (US)
- Los Alamos National Laboratory Los Alamos New Mexico
- New York University New York New York
- Corresponding author
- Sanbonmatsu, Karissa Y.
- ORCID
- Venue
- Journal of Computational Chemistry
- Publisher
- Wiley
- Published
- 2019-04-17
- Volume
- 40
- Issue
- 21
- Pages
- 1919-1930
- ISSN
- 0192-8651 (ISSN-L)
- 1096-987X
- PMC
- PMC7153361
- OpenAlex
- W2939975123
- Cited by
- 114
- References
- 29
- Citations by year
- 2026: 5 · 2025: 16 · 2024: 14 · 2023: 19 · 2022: 18 · 2021: 21 · 2020: 13 · 2019: 8
- FWCI
- 15.20
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Journal of Computational Chemistry (publishedVersion, public-domain)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- Los Alamos National Laboratory (CNLS)
- RIKEN
- U.S. Department of Energy
- National Institute of General Medical Sciences (R35GM122562)
- University of Tokyo
- Topics
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
- Keywords
- parallel computing, long-range electrostatic interactions, non-bonded interactions, SIMD acceleration, memory reduction, genetic loci, GATA4, chromatin interactions, atomistic modeling
- MeSH terms
- Algorithms, Chromatin, Software, Biophysical Phenomena, Molecular Dynamics Simulation
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