Enhancing GPU-Acceleration in the Python-Based Simulations of Chemistry Frameworks

IF 16.8 2区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Xiaojie Wu, Qiming Sun, Zhichen Pu, Tianze Zheng, Wenzhi Ma, Wen Yan, Yu Xia, Zhengxiao Wu, Mian Huo, Xiang Li, Weiluo Ren, Sheng Gong, Yumin Zhang, Weihao Gao
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引用次数: 0

Abstract

We describe our contribution as industrial stakeholders to the existing open-source GPU4PySCF project (https://github.com/pyscf/gpu4pyscf), a GPU-accelerated Python quantum chemistry package. We have integrated GPU acceleration into other PySCF functionalities including Density Functional Theory (DFT), geometry optimization, frequency analysis, solvent models, and the density fitting technique. Through these contributions, GPU4PySCF v1.0 can now be regarded as a fully functional and industrially relevant platform, which we demonstrate in this work through a range of tests. When performing DFT calculations with the density fitting scheme on modern GPU platforms, GPU4PySCF delivers a 30 times speedup over a 32-core CPU node, resulting in approximately 90% cost savings for most DFT tasks. The performance advantages and productivity improvements have been found in multiple industrial applications, such as generating potential energy surfaces, analyzing molecular properties, calculating solvation free energy, identifying chemical reactions in lithium-ion batteries, and accelerating neural-network methods. With the improved design that makes it easy to integrate with the Python and PySCF ecosystem, GPU4PySCF is a natural choice that we can now recommend for many industrial quantum chemistry applications.

Abstract Image

在基于 Python 的化学框架模拟中增强 GPU 加速能力
我们描述了我们作为行业利益相关者对现有开源 GPU4PySCF 项目 (https://github.com/pyscf/gpu4pyscf) 的贡献,该项目是一个 GPU 加速 Python 量子化学软件包。我们已将 GPU 加速集成到 PySCF 的其他功能中,包括密度泛函理论(DFT)、几何优化、频率分析、溶剂模型和密度拟合技术。通过这些贡献,GPU4PySCF v1.0 现在可以被视为一个功能齐全且与工业相关的平台,我们在这项工作中通过一系列测试证明了这一点。在现代 GPU 平台上使用密度拟合方案执行 DFT 计算时,GPU4PySCF 的速度比 32 核 CPU 节点快 30 倍,从而为大多数 DFT 任务节省了约 90% 的成本。性能优势和生产率的提高已在多个工业应用中得到体现,例如生成势能面、分析分子性质、计算溶解自由能、识别锂离子电池中的化学反应以及加速神经网络方法。经过改进的设计使其能够轻松与 Python 和 PySCF 生态系统集成,GPU4PySCF 是我们现在可以推荐用于许多工业量子化学应用的自然选择。
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来源期刊
Wiley Interdisciplinary Reviews: Computational Molecular Science
Wiley Interdisciplinary Reviews: Computational Molecular Science CHEMISTRY, MULTIDISCIPLINARY-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
28.90
自引率
1.80%
发文量
52
审稿时长
6-12 weeks
期刊介绍: Computational molecular sciences harness the power of rigorous chemical and physical theories, employing computer-based modeling, specialized hardware, software development, algorithm design, and database management to explore and illuminate every facet of molecular sciences. These interdisciplinary approaches form a bridge between chemistry, biology, and materials sciences, establishing connections with adjacent application-driven fields in both chemistry and biology. WIREs Computational Molecular Science stands as a platform to comprehensively review and spotlight research from these dynamic and interconnected fields.
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