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Q-Next:一种快速、并行且无需对角化的迭代子空间直接求逆替代方法。

Q-Next: A Fast, Parallel, and Diagonalization-Free Alternative to Direct Inversion of the Iterative Subspace.

作者信息

Seidl Christopher, Barca Giuseppe M J

机构信息

School of Computing, The Australian National University, Canberra, Australian Capital Territory 2601, Australia.

出版信息

J Chem Theory Comput. 2022 Jul 12;18(7):4164-4176. doi: 10.1021/acs.jctc.2c00073. Epub 2022 Jun 24.

Abstract

As computer systems dedicated to scientific calculations become massively parallel, the poor parallel performance of the Fock matrix diagonalization becomes a major impediment to achieving larger molecular sizes in self-consistent field (SCF) calculations. In this Article, a novel, highly parallel, and diagonalization-free algorithm for the accelerated convergence of the SCF procedure is presented. The algorithm, called Q-Next, draws on the second-order SCF, quadratically convergent SCF, and direct inversion of the iterative subspace (DIIS) approaches to enable fast convergence while replacing the Fock matrix diagonalization SCF bottleneck with higher parallel efficiency matrix multiplications. Performance results on both parallel multicore CPU and GPU hardware for a variety of test molecules and basis sets are presented, showing that Q-Next achieves a convergence rate comparable to the DIIS method while being, on average, one order of magnitude faster.

摘要

随着用于科学计算的计算机系统变得大规模并行化,福克矩阵对角化较差的并行性能成为在自洽场(SCF)计算中实现更大分子尺寸的主要障碍。在本文中,提出了一种用于加速SCF过程收敛的新颖、高度并行且无需对角化的算法。该算法称为Q-Next,借鉴了二阶SCF、二次收敛SCF和迭代子空间直接反演(DIIS)方法,以实现快速收敛,同时用更高并行效率的矩阵乘法取代福克矩阵对角化这一SCF瓶颈。给出了在并行多核CPU和GPU硬件上针对各种测试分子和基组的性能结果,表明Q-Next实现了与DIIS方法相当的收敛速率,同时平均快一个数量级。

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