Journal
COMPUTER PHYSICS COMMUNICATIONS
Volume 254, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.cpc.2020.107314
Keywords
GPU acceleration; High performance computing; Electronic structure; Density functional theory; Localized basis sets; Domain decomposition
Funding
- LDRD Program of ORNL, USA
- U.S. DOE
- Creative Materials Discovery Program through the National Research Foundation of Korea - Ministry of Science, ICT and Future Planning [NRF-2016M3D1A1919181]
- U.S. Department of Energy at Lawrence Livermore National Laboratory [DE-AC52-07NA27344]
- [DE-AC05-00OR22725]
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We present an implementation of all-electron density-functional theory for massively parallel GPU-based platforms, using localized atom-centered basis functions and real-space integration grids. Special attention is paid to domain decomposition of the problem on non-uniform grids, which enables compute- and memory-parallel execution across thousands of nodes for real-space operations, e.g. the update of the electron density, the integration of the real-space Hamiltonian matrix, and calculation of Pulay forces. To assess the performance of our GPU implementation, we performed benchmarks on three different architectures using a 103-material test set. We find that operations which rely on dense serial linear algebra show dramatic speedups from GPU acceleration: in particular, SCF iterations including force and stress calculations exhibit speedups ranging from 4.5 to 6.6. For the architectures and problem types investigated here, this translates to an expected overall speedup between 3-4 for the entire calculation (including non-GPU accelerated parts), for problems featuring several tens to hundreds of atoms. Additional calculations for a 375-atom Bi2Se3 bilayer show that the present GPU strategy scales for large-scale distributed-parallel simulations. (C) 2020 Elsevier B.V. All rights reserved.
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