86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy

被引:153
作者
Lu, Denghui [1 ]
Wang, Han [2 ]
Chen, Mohan [1 ]
Lin, Lin [3 ,4 ]
Car, Roberto [5 ]
E, Weinan [5 ]
Jia, Weile [3 ]
Zhang, Linfeng [5 ]
机构
[1] Peking Univ, Coll Engn, CAPT, HEDPS, Beijing, Peoples R China
[2] Inst Appl Phys & Computat Math, Lab Computat Phys, Beijing, Peoples R China
[3] Univ Calif Berkeley, Berkeley, CA 94720 USA
[4] Lawrence Berkeley Natl Lab, Berkeley, CA USA
[5] Princeton Univ, Princeton, NJ 08544 USA
基金
美国国家科学基金会;
关键词
Deep potential; Molecular dynamics; GPU; Heterogeneous architecture; DeePMD-kit; NEURAL-NETWORK; ALGORITHMS; EFFICIENT; MACHINE; PACKAGE; MODEL; AMBER;
D O I
10.1016/j.cpc.2020.107624
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
We present the GPU version of DeePMD-kit, which, upon training a deep neural network model using ab initio data, can drive extremely large-scale molecular dynamics (MD) simulation with ab initio accuracy. Our tests show that for a water system of 12, 582, 912 atoms, the GPU version can be 7 times faster than the CPU version under the same power consumption. The code can scale up to the entire Summit supercomputer. For a copper system of 113, 246, 208 atoms, the code can perform one nanosecond MD simulation per day, reaching a peak performance of 86 PFLOPS (43% of the peak). Such unprecedented ability to perform MD simulation with ab initio accuracy opens up the possibility of studying many important issues in materials and molecules, such as heterogeneous catalysis, electrochemical cells, irradiation damage, crack propagation, and biochemical reactions. (C) 2020 Elsevier B.V. All rights reserved.
引用
收藏
页数:12
相关论文
共 73 条
[1]  
Abadi M, 2016, PROCEEDINGS OF OSDI'16: 12TH USENIX SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION, P265
[2]  
Abbott Adam S, 2019, J CHEM THEORY COMPUT
[3]   General purpose molecular dynamics simulations fully implemented on graphics processing units [J].
Anderson, Joshua A. ;
Lorenz, Chris D. ;
Travesset, A. .
JOURNAL OF COMPUTATIONAL PHYSICS, 2008, 227 (10) :5342-5359
[4]  
Andoh Yoshimichi, 2013, J CHEM THEORY COMPUT
[5]   Free energy of proton transfer at the water-TiO2 interface from ab initio deep potential molecular dynamics [J].
Andrade, Marcos F. Calegari ;
Ko, Hsin-Yu ;
Zhang, Linfeng ;
Car, Roberto ;
Selloni, Annabella .
CHEMICAL SCIENCE, 2020, 11 (09) :2335-2341
[6]  
[Anonymous], 2008, WATER SAV IRRIG
[7]   Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons [J].
Bartok, Albert P. ;
Payne, Mike C. ;
Kondor, Risi ;
Csanyi, Gabor .
PHYSICAL REVIEW LETTERS, 2010, 104 (13)
[8]   Generalized neural-network representation of high-dimensional potential-energy surfaces [J].
Behler, Joerg ;
Parrinello, Michele .
PHYSICAL REVIEW LETTERS, 2007, 98 (14)
[9]   GROMACS - A MESSAGE-PASSING PARALLEL MOLECULAR-DYNAMICS IMPLEMENTATION [J].
BERENDSEN, HJC ;
VANDERSPOEL, D ;
VANDRUNEN, R .
COMPUTER PHYSICS COMMUNICATIONS, 1995, 91 (1-3) :43-56
[10]   Dynamic Topology Aware Load Balancing Algorithms for Molecular Dynamics Applications [J].
Bhatele, Abhinav ;
Kale, Laxmikant V. ;
Kumar, Sameer .
ICS'09: PROCEEDINGS OF THE 2009 ACM SIGARCH INTERNATIONAL CONFERENCE ON SUPERCOMPUTING, 2009, :110-116