Zhang Tianyuan, Liu Xiaolin, Valeev Edward F, Li Xiaosong
Department of Chemistry, University of Washington, Seattle, Washington 98195, United States.
Department of Chemistry, Virginia Tech, Blacksburg, Virginia 24061, United States.
J Phys Chem A. 2021 May 20;125(19):4258-4265. doi: 10.1021/acs.jpca.1c02317. Epub 2021 May 10.
As quantum chemistry calculations deal with molecular systems of increasing size, the memory requirement to store electron-repulsion integrals (ERIs) greatly outpaces the physical memory available in computing hardware. The Cholesky decomposition of ERIs provides a convenient yet accurate technique to reduce the storage requirement of integrals. Recent developments of a two-step algorithm have drastically reduced the memory operation (MOP) count, leaving the floating operation (FLOP) count as the last frontier of cost reduction in the Cholesky ERI algorithm. In this report, we introduce a dynamic integral tracking, reusing, and compression/elimination protocol embedded in the two-step Cholesky ERI method. Benchmark studies suggest that this technique becomes particularly advantageous when the basis set consists of many computationally expensive high-angular-momentum basis functions. With this dynamic-ERI improvement, the Cholesky ERI approach proves to be a highly efficient algorithm with minimal FLOP and MOP count.
随着量子化学计算所处理的分子系统规模不断增大,存储电子排斥积分(ERI)所需的内存需求大幅超过了计算硬件中可用的物理内存。ERI的Cholesky分解提供了一种方便且准确的技术来降低积分的存储需求。两步算法的最新进展已大幅减少了内存操作(MOP)次数,使得浮点操作(FLOP)次数成为Cholesky ERI算法中成本降低的最后一个前沿领域。在本报告中,我们介绍了一种嵌入在两步Cholesky ERI方法中的动态积分跟踪、重用和压缩/消除协议。基准研究表明,当基组由许多计算成本高昂的高角动量基函数组成时,该技术尤为有利。通过这种动态ERI改进,Cholesky ERI方法被证明是一种具有最小FLOP和MOP次数的高效算法。