Computational Biophysics Research Team, RIKEN Advanced Institute for Computational Science, 7-1-26 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo Kobe, 640-0047, Japan.
RIKEN Theoretical Molecular Science Laboratory, 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan.
J Comput Chem. 2017 Jun 15;38(16):1410-1418. doi: 10.1002/jcc.24511. Epub 2016 Oct 6.
Parallelization of molecular dynamics (MD) simulation is essential for investigating conformational dynamics of large biological systems, such as ribosomes, viruses, and multiple proteins in cellular environments. To improve efficiency in the parallel computation, we have to reduce the amount of data transfer between processors by introducing domain decomposition schemes. Also, it is important to optimize the computational balance between real-space non-bonded interactions and reciprocal-space interactions for long-range electrostatic interactions. Here, we introduce a novel parallelization scheme for large-scale MD simulations on massively parallel supercomputers consisting of only CPUs. We make use of a multiple program/multiple data (MPMD) approach for separating the real-space and reciprocal-space computations on different processors. We also utilize the r-RESPA multiple time step integrator on the framework of the MPMD approach in an efficient way: when the reciprocal-space computations are skipped in r-RESPA, processors assigned for them are utilized for half of the real-space computations. The new scheme allows us to use twice as many as processors that are available in the conventional single program approach. The best performances of all-atom MD simulations for 1 million (STMV), 8.5 million (8_STMV), and 28.8 million (27_STMV) atom systems on K computer are 65, 36, and 24 ns/day, respectively. The MPMD scheme can accelerate 23.4, 10.2, and 9.2 ns/day from the maximum performance of single-program approach for STMV, 8_STMV, and 27_STMV systems, respectively, which correspond to 57%, 39%, and 60% speed up. This suggests significant speedups by increasing the number of processors without losing parallel computational efficiency. © 2016 Wiley Periodicals, Inc.
并行化分子动力学(MD)模拟对于研究核糖体、病毒和细胞环境中多个蛋白质等大型生物系统的构象动力学至关重要。为了提高并行计算的效率,我们必须通过引入域分解方案来减少处理器之间的数据传输量。此外,优化长程静电相互作用的实空间非键相互作用和倒空间相互作用之间的计算平衡也很重要。在这里,我们引入了一种新颖的并行化方案,用于在仅由 CPU 组成的大规模并行超级计算机上进行大规模 MD 模拟。我们利用多程序/多数据(MPMD)方法在不同的处理器上分离实空间和倒空间计算。我们还在 MPMD 方法的框架中有效地利用 r-RESPA 多重时间步积分器:当在 r-RESPA 中跳过倒空间计算时,分配给它们的处理器将用于完成一半的实空间计算。新方案允许我们使用比传统单程序方法中可用的处理器多一倍。在 K 计算机上对 100 万个(STMV)、850 万个(8_STMV)和 2880 万个(27_STMV)原子系统进行全原子 MD 模拟的最佳性能分别为 65、36 和 24 ns/天。MPMD 方案可将 STMV、8_STMV 和 27_STMV 系统的单程序方法的最大性能分别加速 23.4、10.2 和 9.2 ns/天,分别对应于 57%、39%和 60%的提速。这表明通过增加处理器数量而不损失并行计算效率可以显著提高速度。 © 2016 威利父子公司