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TensorKMC: Kinetic Monte Carlo Simulation of 50 Trillion Atoms Driven by Deep Learning on a New Generation of Sunway Supercomputer
Event Type
Paper
Tags
Algorithms
Applications
Machine Learning and Artificial Intelligence
Numerical Algorithms
Registration Categories
TP
TimeThursday, 18 November 202110:30am - 11am CST
Location227-228
DescriptionThe atomic dynamics Monte Carlo (AKMC) method has played an important role in the multi-scale physical simulation; it is the bridge connecting the micro and macro worlds. Its accuracy is limited, however, by the empirical potentials. In this work, we propose a vacancy-centered tabulation algorithm to efficiently integrate ab initio fitted neural network potentials (NNPs) with AKMC. We port this to SW26010-pro and optimize the neural network work potentials with big fusion strategy for the new Sunway heterogenous computing units. We further optimize the memory usage to make TensorKMC capable of simulating up to 50 trillion atoms. TensorKMC can achieve excellent strong scaling performance using up to 24,960,000 cores, and linear weak scaling using up to 27,456,400 cores.
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