面向二维量子强相关系统的密度矩阵重整群多核平台并行化设计

S. Yamada, Toshiyuki Imamura, M. Machida
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引用次数: 4

摘要

在现代凝聚态物理学中,最吸引人的问题之一是理解高度相关的电子结构,并为减少二氧化碳的未来提出新的设备设计。在高度相关电子的各种数值方法中,密度矩阵重整化群(DMRG)已被广泛接受为与蒙特卡罗和精确对角化相比,在精度和可访问的系统尺寸方面最有前途的数值方案。事实上,DMRG几乎完美地解决了像长量子系统一样的一维链。在本文中,我们建议通过高性能计算技术将其扩展到高维系统。DMRG的计算目标是一个巨大的非均匀稀疏矩阵对角化。为了有效地并行化部件,我们实现了通信步骤加倍,并重用加倍两步之间的中点数据,以避免对角化所必需的所有对所有通信的严重瓶颈。该技术对于超过1000个核心组成的集群也是成功的,并且为二维高相关系统提供了一种可靠的探索方式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Parallelization design on multi-core platforms in density matrix renormalization group toward 2-D Quantum strongly-correlated systems
One of the most fascinating issues in modern condensed matter physics is to understand highly-correlated electronic structures and propose their novel device designs toward the reduced carbon-dioxide future. Among various developed numerical approaches for highly-correlated electrons, the density matrix renormalization group (DMRG) has been widely accepted as the most promising numerical scheme compared to Monte Carlo and exact diagonalization in terms of accuracy and accessible system size. In fact, DMRG almost perfectly resolves one-dimensional chain like long quantum systems. In this paper, we suggest its extended approach toward higher-dimensional systems by high-performance computing techniques. The computing target in DMRG is a huge non-uniform sparse matrix diagonalization. In order to efficiently parallelize the part, we implement communication step doubling together with reuse of the mid-point data between the doubled two steps to avoid severe bottleneck of all-to-all communications essential for the diagonalization. The technique is successful even for clusters composed of more than 1000 cores and offers a trustworthy exploration way for two-dimensional highly-correlated systems.
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