4.7 Article

Enabling simulation at the fifth rung of DFT: Large scale RPA calculations with excellent time to solution

期刊

COMPUTER PHYSICS COMMUNICATIONS
卷 187, 期 -, 页码 120-129

出版社

ELSEVIER SCIENCE BV
DOI: 10.1016/j.cpc.2014.10.021

关键词

Random phase approximation; RPA; Density functional theory; DFT; Monte Carlo; CP2K

资金

  1. Swiss National Supercomputer Centre (CSCS) [s441, ch5]
  2. Office of Science of the US Department of Energy [DE-AC05-00OR22725, MAT106]
  3. Swiss University Conference through the High Performance and High Productivity Computing (HP2C) programme
  4. Platform for Advanced Scientific Computing (PASC) programme
  5. European Union FP7 in the form of an ERC Starting Grant [277910]

向作者/读者索取更多资源

The Random Phase Approximation (RPA), which represents the fifth rung of accuracy in Density Functional Theory (OFT), is made practical for large systems. Energies of condensed phase systems containing thousands of explicitly correlated electrons and 1500 atoms can now be computed in minutes and less than 1 h, respectively. GPU acceleration is employed for dense and sparse linear algebra, while communication is minimized by a judicious data layout. The performance of the algorithms, implemented in the widely used CP2K simulation package, has been investigated on hybrid Cray XC30 and XK7 architectures, up to 16,384 nodes. Our results emphasize the importance of good network performance, in addition to the availability of GPUs and generous on node memory. A new level of predictivity has thus become available for routine application in Monte Carlo and molecular dynamics simulations. (C) 2014 Elsevier BM. All rights reserved.

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