Department of Physics , RWTH Aachen University , 52056 Aachen , Germany.
CaSToRC , The Cyprus Institute , 2121 Aglantzia, Nicosia , Cyprus.
J Chem Theory Comput. 2019 Oct 8;15(10):5601-5613. doi: 10.1021/acs.jctc.9b00424. Epub 2019 Sep 25.
We present a highly scalable DFT-based QM/MM implementation developed within MiMiC, a recently introduced multiscale modeling framework that uses a loose-coupling strategy in conjunction with a multiple-program multiple-data (MPMD) approach. The computation of electrostatic QM/MM interactions is parallelized exploiting both distributed- and shared-memory strategies. Here, we use the efficient CPMD and GROMACS programs as QM and MM engines, respectively. The scalability is demonstrated through large-scale benchmark simulations of realistic biomolecular systems employing non-hybrid and hybrid GGA exchange-correlation functionals. We show that the loose-coupling strategy adopted in MiMiC, with its inherent high flexibility, does not carry any significant computational overhead compared to a tight-coupling scheme. Furthermore, we demonstrate that the adopted parallelization strategy enables scaling up to 13,000 CPU cores with efficiency above 70%, thus making DFT-based QM/MM MD simulations using hybrid functionals at the nanosecond scale accessible.
我们提出了一种高度可扩展的基于 DFT 的QM/MM 实现，该实现是在 MiMiC 中开发的，MiMiC 是最近引入的多尺度建模框架，它使用松散耦合策略结合多程序多数据 (MPMD) 方法。静电 QM/MM 相互作用的计算通过利用分布式和共享内存策略进行并行化。在这里，我们分别使用高效的 CPMD 和 GROMACS 程序作为 QM 和 MM 引擎。通过使用非杂化和杂化 GGA 交换相关泛函对真实生物分子系统进行大规模基准模拟，展示了可扩展性。我们表明，与紧密耦合方案相比，MiMiC 中采用的松散耦合策略并没有带来任何显著的计算开销，因为它具有固有的高灵活性。此外，我们证明所采用的并行化策略能够扩展到 13000 个 CPU 内核，效率超过 70%，从而使使用混合泛函的基于 DFT 的 QM/MM MD 模拟能够在纳秒尺度上进行。
