86 PFLOPS 1亿个原子的深势分子动力学模拟
86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy
摘要：
我们提出了GPU版本的DeePMD套件，它在使用从头算数据训练深度神经网络模型后，可以以从头算精度驱动超大规模的分子动力学（MD）模拟。我们的测试表明，在相同的功耗下，GPU版本比CPU版本快7倍。代码可以扩展到整个Summit超级计算机。对于113，246，208个原子的铜系统，该代码每天可以执行一纳秒的MD模拟，达到86 PFLOPS的峰值性能（峰值的43%）。这种前所未有的能力，执行MD模拟与从头算精度开辟了研究材料和分子中的许多重要问题的可能性，如非均相催化，电化学电池，辐射损伤，裂纹扩展和生化反应。
英文摘要：
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 the GPU version is 7 times faster than the CPU version with 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.
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