7430

TDDFT in massively parallel computer architectures: the OCTOPUS project

Xavier Andrade, Joseba Alberdi-Rodriguez, David A. Strubbe, Micael J. T. Oliveira, Fernando Nogueira, Alberto Castro, Javier Muguerza, Agustin Arruabarrena, Steven G. Louie, Alan Aspuru-Guzik, Angel Rubio, Miguel A. L. Marques
Department of Chemistry and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, MA 02138, USA
Psi-k Newsletter, 2012

@article{andrade2012tddft,

   title={TDDFT in massively parallel computer architectures: the octopus project},

   author={Andrade, X. and Alberdi-Rodriguez, J. and Strubbe, D.A. and Oliveira, M.J.T. and Nogueira, F. and Castro, A. and Muguerza, J. and Arruabarrena, A. and Louie, S.G. and Aspuru-Guzik, A. and others},

   year={2012}

}

Download Download (PDF)   View View   Source Source   Source codes Source codes

Package:

799

views

OCTOPUS is a general-purpose density-functional theory (DFT) code, with a particular emphasis on the time-dependent version of DFT (TDDFT). In this article we present the ongoing efforts for the parallelisation of OCTOPUS. We focus on the real-time variant of TDDFT, where the time-dependent Kohn-Sham equations are directly propagated in time. This approach has a great potential for execution in massively parallel systems such as modern supercomputers with thousands of processors and graphics processing units (GPUs). For harvesting the potential of conventional supercomputers, the main strategy is a multilevel parallelisation scheme that combines the inherent scalability of real-time TDDFT with a real-space grid domain-partitioning approach. A scalable Poisson solver is critical for the efficiency of this scheme. For GPUs, we show how using blocks of Kohn-Sham states provides the required level of data-parallelism and that this strategy is also applicable for code-optimisation on standard processors. Our results show that real-time TDDFT, as implemented in OCTOPUS, can be the method of choice to study the excited states of large molecular systems in modern parallel architectures.
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2017 hgpu.org

All rights belong to the respective authors

Contact us: