New parallel computing algorithm of molecular dynamics for extremely huge scale biological systems

In this paper, we address high performance extreme‐scale molecular dynamics (MD) algorithm in the GENESIS software to perform cellular‐scale molecular dynamics (MD) simulations with more than 100,000 CPU cores. It includes (1) the new algorithm of real‐space nonbonded interactions maximizing the per...

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Veröffentlicht in:Journal of computational chemistry Jg. 42; H. 4; S. 231 - 241
Hauptverfasser: Jung, Jaewoon, Kobayashi, Chigusa, Kasahara, Kento, Tan, Cheng, Kuroda, Akiyoshi, Minami, Kazuo, Ishiduki, Shigeru, Nishiki, Tatsuo, Inoue, Hikaru, Ishikawa, Yutaka, Feig, Michael, Sugita, Yuji
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken, USA John Wiley & Sons, Inc 05.02.2021
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ISSN:0192-8651, 1096-987X, 1096-987X
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Zusammenfassung:In this paper, we address high performance extreme‐scale molecular dynamics (MD) algorithm in the GENESIS software to perform cellular‐scale molecular dynamics (MD) simulations with more than 100,000 CPU cores. It includes (1) the new algorithm of real‐space nonbonded interactions maximizing the performance on ARM CPU architecture, (2) reciprocal‐space nonbonded interactions minimizing communicational cost, (3) accurate temperature/pressure evaluations that allows a large time step, and (4) effective parallel file inputs/outputs (I/O) for MD simulations of extremely huge systems. The largest system that contains 1.6 billion atoms was simulated using MD with a performance of 8.30 ns/day on Fugaku supercomputer. It extends the available size and time of MD simulations to answer unresolved questions of biomacromolecules in a living cell. We propose a novel parallelization algorithm of nonbonded interactions such that it can perform cellular‐scale molecular dynamics (MD) with more than 100,000 CPU cores. The new algorithm implemented in GENESIS MD software shows good scalability up to 16,384 nodes for more than 1 billion atoms. Up to our understanding, it is the world best performance for a biomolecule consisting of more than 1 billion atoms.
Bibliographie:Funding information
United States National Institutes of Health, Grant/Award Number: R35 GM126948; MEXT/KAKENHI, Grant/Award Number: 19H05645
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ISSN:0192-8651
1096-987X
1096-987X
DOI:10.1002/jcc.26450