A new version of the highly parallelized general-purpose molecular dynamics (MD) simulation program MODYLAS with high performance on the Fugaku computer was developed. A benchmark test using Fugaku indicated highly efficient communication, single instruction, multiple data (SIMD) processing, and on-cache arithmetic operations. The system’s performance deteriorated only slightly, even under high parallelization. In particular, a newly developed minimum transferred data method, requiring a significantly lower amount of data transfer compared to conventional communications, showed significantly high performance. The coordinates and forces of 101 810 176 atoms and the multipole coefficients of the subcells could be distributed to the 32 768 nodes (1 572 864 cores) in 2.3 ms during one MD step calculation. The SIMD effective instruction rates for floating-point arithmetic operations in direct force and fast multipole method (FMM) calculations measured on Fugaku were 78.7% and 31.5%, respectively. The development of a data reuse algorithm enhanced the on-cache processing; the cache miss rate for direct force and FMM calculations was only 2.74% and 1.43%, respectively, on the L1 cache and 0.08% and 0.60%, respectively, on the L2 cache. The modified MODYLAS could complete one MD single time-step calculation within 8.5 ms for the aforementioned large system. Additionally, the program contains numerous functions for material research that enable free energy calculations, along with the generation of various ensembles and molecular constraints.
Skip Nav Destination
,
,
,
,
,
,
,
TurboGenius : Python suite for high-throughput calculations of ab initio quantum Monte Carlo methods
Article navigation
15 May 2023
Research Article|
May 15 2023
An exa-scale high-performance molecular dynamics simulation program: MODYLAS
Special Collection:
High Performance Computing in Chemical Physics
Yoshimichi Andoh
;
Yoshimichi Andoh
(Software, Writing – original draft, Writing – review & editing)
1
National Institute for Materials Science (NIMS)
, 1-2-1, Sengen, Tsukuba, Ibaraki 305-0047, Japan
Search for other works by this author on:
Shin-ichi Ichikawa;
Shin-ichi Ichikawa
(Software, Writing – original draft, Writing – review & editing)
2
Computational Science Division, Technical Computing Business Unit, Fujitsu Limited
, Chiba, Japan
Search for other works by this author on:
Tatsuya Sakashita
;
Tatsuya Sakashita
(Software)
3
Center for Quantum Information and Quantum Biology, Osaka University
, 1-2, Machikaneyama, Toyonaka, Osaka 560-0043, Japan
Search for other works by this author on:
Kazushi Fujimoto
;
Kazushi Fujimoto
(Software, Writing – original draft, Writing – review & editing)
4
Department of Materials Chemistry, Nagoya University
, Chikusa-ku, Nagoya, Aichi 464-8603, Japan
Search for other works by this author on:
Noriyuki Yoshii
;
Noriyuki Yoshii
(Software, Writing – original draft, Writing – review & editing)
5
Center for Computational Science, Graduate School of Engineering, Nagoya University
, Nagoya 464-8603, Japan
Search for other works by this author on:
Tetsuro Nagai
;
Tetsuro Nagai
(Software, Writing – original draft, Writing – review & editing)
6
Department of Chemistry, Faculty of Science, Fukuoka University
, 8-19-1, Nanakuma, Jonan-ku, Fukuoka 814-0180, Japan
Search for other works by this author on:
Zhiye Tang
;
Zhiye Tang
(Software)
7
Department of Theoretical and Computational Molecular Science, Institute for Molecular Science, National Institutes of Natural Sciences
, 38, Nishigonaka, Myodaiji, Okazaki, Aichi 444-8585, Japan
Search for other works by this author on:
Susumu Okazaki
Susumu Okazaki
a)
(Funding acquisition, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing)
8
Department of Advanced Materials Science, The University of Tokyo
, 5-1-5, Kashiwa-no-ha, Kashiwa, Chiba 277-0871, Japan
a)Author to whom correspondence should be addressed: [email protected]
Search for other works by this author on:
Yoshimichi Andoh
1
Shin-ichi Ichikawa
2
Tatsuya Sakashita
3
Kazushi Fujimoto
4
Noriyuki Yoshii
5
Tetsuro Nagai
6
Zhiye Tang
7
Susumu Okazaki
8,a)
1
National Institute for Materials Science (NIMS)
, 1-2-1, Sengen, Tsukuba, Ibaraki 305-0047, Japan
2
Computational Science Division, Technical Computing Business Unit, Fujitsu Limited
, Chiba, Japan
3
Center for Quantum Information and Quantum Biology, Osaka University
, 1-2, Machikaneyama, Toyonaka, Osaka 560-0043, Japan
4
Department of Materials Chemistry, Nagoya University
, Chikusa-ku, Nagoya, Aichi 464-8603, Japan
5
Center for Computational Science, Graduate School of Engineering, Nagoya University
, Nagoya 464-8603, Japan
6
Department of Chemistry, Faculty of Science, Fukuoka University
, 8-19-1, Nanakuma, Jonan-ku, Fukuoka 814-0180, Japan
7
Department of Theoretical and Computational Molecular Science, Institute for Molecular Science, National Institutes of Natural Sciences
, 38, Nishigonaka, Myodaiji, Okazaki, Aichi 444-8585, Japan
8
Department of Advanced Materials Science, The University of Tokyo
, 5-1-5, Kashiwa-no-ha, Kashiwa, Chiba 277-0871, Japan
a)Author to whom correspondence should be addressed: [email protected]
J. Chem. Phys. 158, 194803 (2023)
Article history
Received:
January 29 2023
Accepted:
April 25 2023
Citation
Yoshimichi Andoh, Shin-ichi Ichikawa, Tatsuya Sakashita, Kazushi Fujimoto, Noriyuki Yoshii, Tetsuro Nagai, Zhiye Tang, Susumu Okazaki; An exa-scale high-performance molecular dynamics simulation program: MODYLAS. J. Chem. Phys. 15 May 2023; 158 (19): 194803. https://doi.org/10.1063/5.0144361
Download citation file:
Pay-Per-View Access
$40.00
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
Citing articles via
The Amsterdam Modeling Suite
Evert Jan Baerends, Nestor F. Aguirre, et al.
DeePMD-kit v2: A software package for deep potential models
Jinzhe Zeng, Duo Zhang, et al.
Light–matter interaction at the nano- and molecular scale
Kaifeng Wu, Chufeng Zhang, et al.
Related Content
PluginPlay: Enabling exascale scientific software one module at a time
J. Chem. Phys. (May 2023)
J. Chem. Phys. (December 2023)
TBMaLT, a flexible toolkit for combining tight-binding and machine learning
J. Chem. Phys. (January 2023)
Scalable generalized screening for high-order terms in the many-body expansion: Algorithm, open-source implementation, and demonstration
J. Chem. Phys. (November 2023)
wfl Python toolkit for creating machine learning interatomic potentials and related atomistic simulation workflows
J. Chem. Phys. (September 2023)