Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA.
J Phys Condens Matter. 2012 Jun 13;24(23):233202. doi: 10.1088/0953-8984/24/23/233202. Epub 2012 May 4.
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.
章鱼是一个通用的密度泛函理论（DFT）代码，特别强调时间相关的 DFT（TDDFT）。在本文中，我们介绍了实现章鱼并行化的持续努力。我们专注于 TDDFT 的实时变体，其中时间相关的 Kohn-Sham 方程直接在时间上传播。这种方法非常适合在现代超级计算机等具有数千个处理器和图形处理单元（GPU）的大规模并行系统中执行。为了挖掘传统超级计算机的潜力，主要策略是一种多级并行化方案，该方案将实时 TDDFT 的固有可扩展性与实空间网格域分区方法相结合。可扩展的泊松求解器对于该方案的效率至关重要。对于 GPU，我们展示了如何使用 Kohn-Sham 状态块提供所需的数据并行性，并且该策略也适用于标准处理器上的代码优化。我们的结果表明，章鱼中实现的实时 TDDFT 可以成为在现代并行架构中研究大分子系统激发态的首选方法。
