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TensorKMC: kinetic Monte Carlo simulation of 50 trillion atoms driven by deep learning on a new generation of Sunway supercomputer

Published: 13 November 2021 Publication History

Abstract

The atomic kinetic Monte Carlo method plays an important role in multi-scale physical simulations because it bridges the micro and macro worlds. However, its accuracy is limited by empirical potentials. We therefore propose herein a triple-encoding algorithm and vacancy-cache mechanism to efficiently integrate ab initio neural network potentials (NNPs) with AKMC and implement them in our TensorKMC codes. We port our program to SW26010-pro and innovate a fast feature operator and a big fusion operator for the NNPs for fully utilizing the powerful heterogeneous computing units of the new-generation Sunway supercomputer. We further optimize memory usage. With these improvements, TensorKMC can simulate up to 54 trillions of atoms and achieve excellent strong and weak scaling performance up to 27,456,000 cores.

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        cover image ACM Conferences
        SC '21: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis
        November 2021
        1493 pages
        ISBN:9781450384421
        DOI:10.1145/3458817
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        Published: 13 November 2021

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        1. kinetic Monte Carlo
        2. many-core processor
        3. neural network potentials
        4. scalability

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