Time-Dependent Density-Functional Theory in Massively Parallel Computer Architectures: The Octopus Project
Strubbe, David A.
Oliveira, Micael J. T.
Louie, Steven G.
Marques, Miguel A. L.Note: Order does not necessarily reflect citation order of authors.
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CitationAndrade, Xavier, Joseba Alberdi-Rodriguez, David A. Strubbe, Micael J. T. Oliveira, Fernando Nogueira, Alberto Castro, Javier Muguerza, et al. 2012. "Time-Dependent Density-Functional Theory in Massively Parallel Computer Architectures: The Octopus Project." Journal of Physics: Condensed Matter 24(23): 233202. doi:10.1088/0953-8984/24/23/233202.
AbstractOctopus is a general-purpose density-functional theory (DFT) code, with a particular emphasis on the time-dependent version of DFT (TDDFT). In this paper we present the ongoing efforts to achieve the parallelization 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 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 multi-level parallelization 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 optimization on standard processors. Our results show that real-time TDDFT, as implemented in octopus, can be the method of choice for studying the excited states of large molecular systems in modern parallel architectures.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:12694642
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