10164

Graphics Processing Unit acceleration of the Random Phase Approximation in the projector augmented wave method

Jun Yan, Lin Li, Christopher O’Grady
SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA 94025, USA
arXiv:1307.8052 [physics.comp-ph], 30 Jul 2013

@article{2013arXiv1307.8052Y,

   author={Yan}, J. and {Li}, L. and {O’Grady}, C.},

   title={"{Graphics Processing Unit acceleration of the Random Phase Approximation in the projector augmented wave method}"},

   journal={ArXiv e-prints},

   archivePrefix={"arXiv"},

   eprint={1307.8052},

   primaryClass={"physics.comp-ph"},

   keywords={Physics – Computational Physics, Condensed Matter – Materials Science},

   year={2013},

   month={jul},

   adsurl={http://adsabs.harvard.edu/abs/2013arXiv1307.8052Y},

   adsnote={Provided by the SAO/NASA Astrophysics Data System}

}

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The Random Phase Approximation (RPA) for correlation energy in the grid-based projector augmented wave (gpaw) code is accelerated by porting to the Graphics Processing Unit (GPU) architecture. The acceleration is achieved by grouping independent vectors/matrices and transforming the implementation from being memory bound to being computation/latency bound. With this approach, both the CPU and GPU implementations have been enhanced. We tested the GPU implementation on a few representative systems: molecules (O2), bulk solids (Li2O and MoO3) and molecules adsorbed on metal surfaces (N2/Ru(0001) and CO/Ni(111)). Improvements from 10+ to 40+ have been achieved (8-GPUs versus 8-CPUs). A realistic RPA calculation for CO/Ni(111) surface can be finished in 5.5 h using 8 GPUs. It is thus promising to employ non-self-consistent RPA for routine surface chemistry simulations.
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