Scaling Molecular Dynamics with ab initio Accuracy to 149 Nanoseconds per Day
Physical phenomena such as chemical reactions, bond breaking, and phase transition require molecular dynamics (MD) simulation with ab initio accuracy ranging from milliseconds to microseconds. However, previous state-of-the-art neural network based MD packages such as DeePMD-kit can only reach 4.7 n...
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| Veröffentlicht in: | SC24: International Conference for High Performance Computing, Networking, Storage and Analysis S. 1 - 15 |
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17.11.2024
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| Abstract | Physical phenomena such as chemical reactions, bond breaking, and phase transition require molecular dynamics (MD) simulation with ab initio accuracy ranging from milliseconds to microseconds. However, previous state-of-the-art neural network based MD packages such as DeePMD-kit can only reach 4.7 nanoseconds per day on the Fugaku supercomputer. In this paper, we present a novel node-based parallelization scheme to reduce communication by 81%, then optimize the computationally intensive kernels with sve-gemm and mixed precision. Finally, we implement intra-node load balance to further improve the scalability. Numerical results on the Fugaku supercomputer show that our work has significantly improved the time-to-solution of the DeePMD-kit by a factor of 31.7 x, reaching 149 nanoseconds per day on 12,000 computing nodes. This work has opened the door for millisecond simulation with ab initio accuracy within one week for the first time. |
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| AbstractList | Physical phenomena such as chemical reactions, bond breaking, and phase transition require molecular dynamics (MD) simulation with ab initio accuracy ranging from milliseconds to microseconds. However, previous state-of-the-art neural network based MD packages such as DeePMD-kit can only reach 4.7 nanoseconds per day on the Fugaku supercomputer. In this paper, we present a novel node-based parallelization scheme to reduce communication by 81%, then optimize the computationally intensive kernels with sve-gemm and mixed precision. Finally, we implement intra-node load balance to further improve the scalability. Numerical results on the Fugaku supercomputer show that our work has significantly improved the time-to-solution of the DeePMD-kit by a factor of 31.7 x, reaching 149 nanoseconds per day on 12,000 computing nodes. This work has opened the door for millisecond simulation with ab initio accuracy within one week for the first time. |
| Author | Li, Boyang Yuan, Guojun Li, Mingzhen Li, Enji Jia, Weile Li, Jianxiong Guo, Zhuoqiang Wang, Zhan Liu, Lijun Tan, Guangming |
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| Snippet | Physical phenomena such as chemical reactions, bond breaking, and phase transition require molecular dynamics (MD) simulation with ab initio accuracy ranging... |
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| SubjectTerms | Accuracy Computational efficiency Computational Science DeePMD-kit Distance measurement High performance computing Kernel LAMMPS Molecular Dynamics Neural networks Optimization Scalability Supercomputers Testing |
| Title | Scaling Molecular Dynamics with ab initio Accuracy to 149 Nanoseconds per Day |
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