Scaling molecular dynamics beyond 100,000 processor cores for large-scale biophysical simulations

Jaewoon Jung, Wataru Nishima, Marcus Daniels, Gavin Bascom, Chigusa Kobayashi, Adetokunbo Adedoyin, Michael Wall, Anna Lappala, Dominic Phillips, William Fischer, Chang Shung Tung, Tamar Schlick, Yuji Sugita, Karissa Y. Sanbonmatsu

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1919-1930
Number of pages12
JournalJournal of Computational Chemistry
Volume40
Issue number21
DOIs
StatePublished - Aug 5 2019

Keywords

  • 3D modeling
  • GENESIS MD software
  • biomolecular simulation
  • high performance computing

ASJC Scopus subject areas

  • Chemistry(all)
  • Computational Mathematics

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    Jung, J., Nishima, W., Daniels, M., Bascom, G., Kobayashi, C., Adedoyin, A., Wall, M., Lappala, A., Phillips, D., Fischer, W., Tung, C. S., Schlick, T., Sugita, Y., & Sanbonmatsu, K. Y. (2019). Scaling molecular dynamics beyond 100,000 processor cores for large-scale biophysical simulations. Journal of Computational Chemistry, 40(21), 1919-1930. https://doi.org/10.1002/jcc.25840