• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

泰瑞化学云:用于可扩展分布式 GPU 加速电子结构计算的高性能计算服务。

TeraChem Cloud: A High-Performance Computing Service for Scalable Distributed GPU-Accelerated Electronic Structure Calculations.

机构信息

Department of Chemistry and the PULSE Institute, Stanford University, Stanford, California 94305, United States.

SLAC National Accelerator Laboratory, Menlo Park, California 94305, United States.

出版信息

J Chem Inf Model. 2020 Apr 27;60(4):2126-2137. doi: 10.1021/acs.jcim.9b01152. Epub 2020 Apr 20.

DOI:10.1021/acs.jcim.9b01152
PMID:32267693
Abstract

The encapsulation and commoditization of electronic structure arise naturally as interoperability, and the use of nontraditional compute resources (e.g., new hardware accelerators, cloud computing) remains important for the computational chemistry community. We present TeraChem Cloud, a high-performance computing service (HPCS) that offers on-demand electronic structure calculations on both traditional HPC clusters and cloud-based hardware. The framework is designed using off-the-shelf web technologies and containerization to be extremely scalable and portable. Within the HPCS model, users can quickly develop new methods and algorithms in an interactive environment on their laptop while allowing TeraChem Cloud to distribute calculations across all available resources. This approach greatly increases the accessibility of hardware accelerators such as graphics processing units (GPUs) and flexibility for the development of new methods as additional electronic structure packages are integrated into the framework as alternative backends. Cost-performance analysis indicates that traditional nodes are the most cost-effective long-term solution, but commercial cloud providers offer cutting-edge hardware with competitive rates for short-term large-scale calculations. We demonstrate the power of the TeraChem Cloud framework by carrying out several showcase calculations, including the generation of 300,000 density functional theory energy and gradient evaluations on medium-sized organic molecules and reproducing 300 fs of nonadiabatic dynamics on the B800-B850 antenna complex in LH2, with the latter demonstration using over 50 Tesla V100 GPUs in a commercial cloud environment in 8 h for approximately $1250.

摘要

电子结构的封装和商品化自然而然地随着互操作性而出现,并且非传统计算资源(例如新的硬件加速器、云计算)的使用对于计算化学界仍然很重要。我们提出了 TeraChem Cloud,这是一种高性能计算服务(HPCS),可在传统的 HPC 集群和基于云的硬件上提供按需电子结构计算。该框架使用现成的 Web 技术和容器化设计,具有极高的可扩展性和便携性。在 HPCS 模型中,用户可以在笔记本电脑上的交互式环境中快速开发新的方法和算法,同时允许 TeraChem Cloud 将计算分布到所有可用的资源上。这种方法极大地提高了硬件加速器(如图形处理单元(GPU))的可访问性,并为开发新方法提供了更大的灵活性,因为更多的电子结构软件包被集成到框架中作为替代后端。成本效益分析表明,传统节点是长期最具成本效益的解决方案,但商业云提供商提供具有竞争力价格的前沿硬件,用于短期大规模计算。我们通过进行几个展示性计算来证明 TeraChem Cloud 框架的强大功能,包括在中等大小的有机分子上生成 30 万个密度泛函理论能量和梯度评估,以及在 LH2 中的 B800-B850 天线复合物上再现 300 fs 的非绝热动力学,后者的演示使用了大约 50 个 Tesla V100 GPU 在商业云环境中进行,耗时约 8 小时,成本约为 1250 美元。

相似文献

1
TeraChem Cloud: A High-Performance Computing Service for Scalable Distributed GPU-Accelerated Electronic Structure Calculations.泰瑞化学云:用于可扩展分布式 GPU 加速电子结构计算的高性能计算服务。
J Chem Inf Model. 2020 Apr 27;60(4):2126-2137. doi: 10.1021/acs.jcim.9b01152. Epub 2020 Apr 20.
2
GROMACS in the Cloud: A Global Supercomputer to Speed Up Alchemical Drug Design.GROMACS 在云端:全球超级计算机助力加速药物设计的量子化学计算。
J Chem Inf Model. 2022 Apr 11;62(7):1691-1711. doi: 10.1021/acs.jcim.2c00044. Epub 2022 Mar 30.
3
TeraChem: Accelerating electronic structure and ab initio molecular dynamics with graphical processing units.TeraChem:利用图形处理器加速电子结构和从头算分子动力学
J Chem Phys. 2020 Jun 14;152(22):224110. doi: 10.1063/5.0007615.
4
Massively scalable workflows for quantum chemistry: BigChem and ChemCloud.用于量子化学的大规模可扩展工作流程:BigChem和ChemCloud。
J Chem Phys. 2024 Apr 14;160(14). doi: 10.1063/5.0190834.
5
Edge, Fog, and Cloud Against Disease: The Potential of High-Performance Cloud Computing for Pharma Drug Discovery.边缘计算、雾计算和云计算对抗疾病:高性能云计算在制药药物发现中的潜力。
Methods Mol Biol. 2024;2716:181-202. doi: 10.1007/978-1-0716-3449-3_8.
6
Analytical modeling and feasibility study of a multi-GPU cloud-based server (MGCS) framework for non-voxel-based dose calculations.基于多 GPU 的云服务器 (MGCS) 框架在非体素剂量计算中的分析建模与可行性研究。
Int J Comput Assist Radiol Surg. 2017 Apr;12(4):669-680. doi: 10.1007/s11548-016-1473-5. Epub 2016 Aug 25.
7
Ab initio nonadiabatic dynamics of multichromophore complexes: a scalable graphical-processing-unit-accelerated exciton framework.从头非绝热动力学多发色团复合物:可扩展图形处理单元加速激子框架。
Acc Chem Res. 2014 Sep 16;47(9):2857-66. doi: 10.1021/ar500229p. Epub 2014 Sep 4.
8
High-Performance Statistical Computing in the Computing Environments of the 2020s.2020年代计算环境中的高性能统计计算
Stat Sci. 2022 Nov;37(4):494-518. doi: 10.1214/21-sts835. Epub 2022 Oct 13.
9
Advances in distributed computing with modern drug discovery.现代药物发现中的分布式计算进展。
Expert Opin Drug Discov. 2019 Jan;14(1):9-22. doi: 10.1080/17460441.2019.1552936. Epub 2018 Dec 13.
10
MCX Cloud-a modern, scalable, high-performance and in-browser Monte Carlo simulation platform with cloud computing.MCX 云——一个现代化、可扩展、高性能的网页端蒙特卡罗模拟平台,具有云计算能力。
J Biomed Opt. 2022 Jan;27(8). doi: 10.1117/1.JBO.27.8.083008.

引用本文的文献

1
Search for Correlations Between the Results of the Density Functional Theory and Hartree-Fock Calculations Using Neural Networks and Classical Machine Learning Algorithms.使用神经网络和经典机器学习算法寻找密度泛函理论与哈特里-福克计算结果之间的相关性。
ACS Omega. 2025 Feb 6;10(6):5919-5933. doi: 10.1021/acsomega.4c09861. eCollection 2025 Feb 18.
2
Quantum Chemistry Common Driver and Databases (QCDB) and Quantum Chemistry Engine (QCEngine): Automation and interoperability among computational chemistry programs.量子化学通用驱动程序和数据库 (QCDB) 和量子化学引擎 (QCEngine):计算化学程序之间的自动化和互操作性。
J Chem Phys. 2021 Nov 28;155(20):204801. doi: 10.1063/5.0059356.
3
ChemPix: automated recognition of hand-drawn hydrocarbon structures using deep learning.
ChemPix:利用深度学习对手绘烃类结构进行自动识别
Chem Sci. 2021 Jul 3;12(31):10622-10633. doi: 10.1039/d1sc02957f. eCollection 2021 Aug 11.
4
TeraChem: Accelerating electronic structure and ab initio molecular dynamics with graphical processing units.TeraChem:利用图形处理器加速电子结构和从头算分子动力学
J Chem Phys. 2020 Jun 14;152(22):224110. doi: 10.1063/5.0007615.