基于CPU-GPU混合架构的大规模并行张量网络状态算法。

IF 5.8 1区 化学 Q2 CHEMISTRY, PHYSICAL Journal of Chemical Theory and Computation Pub Date : 2025-02-25 Epub Date: 2025-02-04 DOI:10.1021/acs.jctc.4c00661
Andor Menczer, Örs Legeza
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引用次数: 0

摘要

量子模拟与经典模拟的相互作用及其微妙的区别是高性能计算(HPC)中大规模并行张量网络状态(TNS)算法的研究重点。在这篇文章中,我们提出了新的算法解决方案以及实现细节,以扩展基于最先进的硬件和软件技术的高性能计算基础设施上TNS算法的当前限制。通过在单节点多gpu NVIDIA A100系统上进行大规模密度矩阵重整化群(DMRG)模拟,给出了所选强相关分子体系在Hilbert空间维度高达4.17 × 1035的问题上的基准测试结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Massively Parallel Tensor Network State Algorithms on Hybrid CPU-GPU Based Architectures.

The interplay of quantum and classical simulation and the delicate divide between them is in the focus of massively parallelized tensor network state (TNS) algorithms designed for high performance computing (HPC). In this contribution, we present novel algorithmic solutions together with implementation details to extend current limits of TNS algorithms on HPC infrastructure building on state-of-the-art hardware and software technologies. Benchmark results obtained via large-scale density matrix renormalization group (DMRG) simulations on single node multiGPU NVIDIA A100 system are presented for selected strongly correlated molecular systems addressing problems on Hilbert space dimensions up to 4.17 × 1035.

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来源期刊
Journal of Chemical Theory and Computation
Journal of Chemical Theory and Computation 化学-物理:原子、分子和化学物理
CiteScore
9.90
自引率
16.40%
发文量
568
审稿时长
1 months
期刊介绍: The Journal of Chemical Theory and Computation invites new and original contributions with the understanding that, if accepted, they will not be published elsewhere. Papers reporting new theories, methodology, and/or important applications in quantum electronic structure, molecular dynamics, and statistical mechanics are appropriate for submission to this Journal. Specific topics include advances in or applications of ab initio quantum mechanics, density functional theory, design and properties of new materials, surface science, Monte Carlo simulations, solvation models, QM/MM calculations, biomolecular structure prediction, and molecular dynamics in the broadest sense including gas-phase dynamics, ab initio dynamics, biomolecular dynamics, and protein folding. The Journal does not consider papers that are straightforward applications of known methods including DFT and molecular dynamics. The Journal favors submissions that include advances in theory or methodology with applications to compelling problems.
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