论文标题

GFCCLIB:可扩展有效的耦合群集绿色功能库,用于准确解决许多人体电子结构问题

GFCCLib: Scalable and Efficient Coupled-Cluster Green's Function Library for Accurately Tackling Many Body Electronic Structure Problems

论文作者

Peng, Bo, Panyala, Ajay, Kowalski, Karol, Krishnamoorthy, Sriram

论文摘要

近年来,耦合簇Green的功能(GFCC)计算在近年来从许多人体的角度以系统地改进的方式靶向分子和材料电子结构问题。但是,对科学计算群集的GFCC计算通常会在复杂空间中遭受昂贵的更高维度张量收缩,昂贵的分解通信和严重的负载不平衡,这限制了其常规用于解决电子结构问题。在这里,我们提出了专门为大型GFCC计算而设计的数值库原型。图书馆的设计集中在系统上最佳的计算策略上,以提高其可扩展性和效率。通过对远程巨型计算簇运行的GFCC计算的相关分析分析来证明库的性能。通过在GFCCSD水平上首次计算富勒烯C60分子的宽近价带,可以突出显示库的能力,该频带与实验频谱非常吻合。

Coupled cluster Green's function (GFCC) calculation has drawn much attention in the recent years for targeting the molecular and material electronic structure problems from a many body perspective in a systematically improvable way. However, GFCC calculations on scientific computing clusters usually suffer from expensive higher dimensional tensor contractions in the complex space, expensive interprocess communication, and severe load imbalance, which limits it's routine use for tackling electronic structure problems. Here we present a numerical library prototype that is specifically designed for large scale GFCC calculations. The design of the library is focused on a systematically optimal computing strategy to improve its scalability and efficiency. The performance of the library is demonstrated by the relevant profiling analysis of running GFCC calculations on remote giant computing clusters. The capability of the library is highlighted by computing a wide near valence band of a fullerene C60 molecule for the first time at the GFCCSD level that shows excellent agreement with the experimental spectrum.

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