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LS3DF, namely linear scaling three-dimensional fragment method, is an efficient linear scaling ab initio total energy electronic structure calculation code based on a divide-and-conquer strategy. In this paper, we present our GPU implementation of the LS3DF code. Our test results show that the GPU code can calculate systems with about ten thousand atoms fully self-consistently in the order of 10 min using thousands of computing nodes. This makes the electronic structure calculations of 10,000-atom nanosystems routine work. This speed is 4.5–6 times faster than the CPU calculations using the same number of nodes on the Titan machine in the Oak Ridge leadership computing facility (OLCF). Such speedup is achieved by (a) carefully re-designing of the computationally heavy kernels; (b) redesign of the communication pattern for heterogeneous supercomputers. [ABSTRACT FROM AUTHOR]

Abstract: Plane wave pseudopotential (PWP) density functional theory (DFT) calculation is the most widely used material science simulation, and the PWP DFT codes are arguably the most important material science codes. We have implemented a PWP DFT code PEtot on a multi-node GPU machine. Starting from a previous work, we have further improved the speed of the code, and achieved x13-x22 speedups over the CPU calculations for a typical 512 atom system. Such speedups are much higher than other similar works for this important class of material simulation codes on GPU clusters. The current achievement is obtained by (1) moving the calculation fully into the GPU; (2) adopting a new algorithm to reduce the data amount for MPI communication; and (3) using new GPU and CPU numerical libraries. We have also provided a detail quantitative analysis of the computational times for different physical systems and number of GPU units, which helps one to understand the challenges and bottlenecks of the PWP DFT simulations on GPU machines. Based on the analysis, we listed the machine and library requirements in order to further improve the performances of the PWP DFT calculations. [Copyright &y& Elsevier]

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. Program Title: DeePMD-kit CPC Library link to program files: https://doi.org/10.17632/phyn4kgsfx.1 Developer's repository link: https://doi.org/10.5281/zenodo.3961106 Licensing provisions: LGPL Programming language: C++/Python/CUDA Journal reference of previous version: Comput. Phys. Commun. 228 (2018), 178–184. Does the new version supersede the previous version?: Yes. Reasons for the new version: Parallelize and optimize the DeePMD-kit for modern high performance computers. Summary of revisions: The optimized DeePMD-kit is capable of computing 100 million atoms molecular dynamics with ab initio accuracy, achieving 86 PFLOPS in double precision. Nature of problem: Modeling the many-body atomic interactions by deep neural network models. Running molecular dynamics simulations with the models. Solution method: The Deep Potential for Molecular Dynamics (DeePMD) method is implemented based on the deep learning framework TensorFlow. Standard and customized TensorFlow operators are optimized for GPU. Massively parallel molecular dynamics simulations with DeePMD models on high performance computers are supported in the new version. [ABSTRACT FROM AUTHOR]