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Abstract

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.