Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Zeitschriftenartikel

Time-dependent density-functional theory in massively parallel computer architectures: the OCTOPUS project

MPG-Autoren
/persons/resource/persons22028

Rubio,  Angel
Nano-Bio Spectroscopy Group and ETSF Scientific Development Center, Departamento de F´ısica de Materiales, Centro de F´ısica de Materiales CSIC-UPV/EHU and DIPC, University of the Basque Country UPV/EHU;
Theory, Fritz Haber Institute, Max Planck Society;

Externe Ressourcen
Es sind keine externen Ressourcen hinterlegt
Volltexte (beschränkter Zugriff)
Für Ihren IP-Bereich sind aktuell keine Volltexte freigegeben.
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte in PuRe verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Andrade, X., Alberdi-Rodriguez, J., Strubbe, D. A., Oliveira, M. J. T., Nogueira, F., Castro, A., 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.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0010-76DF-E
Zusammenfassung
OCTOPUS 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.