首页> 外文期刊>Journal of chemical theory and computation: JCTC >Massively Parallel Implementation of Divide-and-Conquer Jacobi Iterations Using Particle-Mesh Ewald for Force Field Polarization
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Massively Parallel Implementation of Divide-and-Conquer Jacobi Iterations Using Particle-Mesh Ewald for Force Field Polarization

机译:使用粒子网EWALD进行大规模平行的划分和征服Jacobi迭代,用于力场极化

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摘要

To accelerate the evaluation of the self-consistent polarization in large condensed-phase systems with polarizable force fields, the new divide-and-conquer Jacobi iterations (DC-JI) solver is adapted for periodic boundary conditions with particle-mesh Ewald and implemented in a massively parallel fashion within the Tinker-HP software package. DC-JI captures the mutual polarization of close-range interactions within subclusters of atoms using Cholesky decomposition and couples in the polarization effects between these clusters iteratively. Iterative convergence is accelerated with direct inversion of the iterative subspace (DIIS) extrapolation. Compared to widely used preconditioned conjugate gradient (PCG) or conventional Jacobi iterations (JI/DIIS) algorithms, DC-JI/DIIS solves the polarization equations similar to 20-30% faster in protein systems ranging from similar to 10,000-175,000 atoms run on hundreds of processor cores. This translates to similar to 10-15% speed-ups in the number of nanoseconds of simulation time that can be achieved per day. Not only is DC-JI/DIIS faster than PCG, but it also gives more energetically robust solutions for a given convergence threshold. These improvements make numerically robust polarizable force field simulations more computationally tractable for chemical systems of interest.
机译:为了加速具有可极化力场的大冷凝相系统中自一致偏振的评估,新的分行和征服Jacobi迭代(DC-JI)求解器适用于具有粒子网EWALD的周期性边界条件并实施Tinker-HP软件包内的大规模平行方式。 DC-JI使用Cholesky分解和迭代之间的偏振效应中的极化效应中的近距离分解的近距离相互作用的相互偏振。迭代收敛随着迭代子空间(DIIS)推断的直接反演。与广泛使用的预处理缀合物梯度(PCG)或传统的Jacobi迭代(JI / DIIS)算法相比,DC-JI / DIIS在蛋白质系统中求出的偏振方程,范围从类似于10,000-175,000原子的蛋白质系统数以百计的处理器核心。这通常转化为类似于10-15%的速度,以每天可以实现的模拟时间的纳秒数。 DC-JI / DIIS不仅比PCG更快,而且还为给定的收敛阈值提供了更具能力的强大解决方案。这些改进使得数值鲁棒的可极化力场模拟更加计算的感兴趣的化学系统。

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