GPU Computing Enabled Linux Computer Cluster

支持 GPU 计算的 Linux 计算机集群

• 批准号: 7792508
• 负责人: Yifan Cheng
• 金额: $ 22.81万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-01-28 至 2012-01-27
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7792508
• 关键词: Biomedical Research; Boxing; Complex; Computer software; Cryoelectron Microscopy; Data Set; Electron Microscope; Funding; Goals; Image; Linux; Macromolecular Complexes; Process; Proteins; Research Project Grants; Resolution; Structure; System; United States National Institutes of Health; Virus; computer cluster; computerized data processing; digital; experience; particle; protein complex; software development; success

DESCRIPTION (provided by applicant): Electron cryomicroscopy (cryoEM), particularly single particle cryoEM, has experienced tremendous successes in term of achievable resolution. It is now possible to determine near atomic structures of large protein assemblies with high symmetry, such as icosahedral virus. However, for large protein complex with low symmetry, achieving the similar resolution is still very difficult. This is partly because of the very tedious process of collecting very large datasets of images, and the tremendous demands of computational power to process such large dataset. We have set up a unique easy-to-operate electron microscope system specifically to facilitate this process (automated acquisition software and a very large format, 8K x 8K pixel digital camera). In this application, we seek funds to set up a GPU computing enabled computer cluster for fast data processing. Together with the electron microscope system, we aim to establish an integrated cryoEM system that will overcome the current resolution limitation of cryoEM in studying large protein complexes without high symmetry. Our goal is to routinely collect and process 105 - 106 particle images. The proposed GPU computer cluster will be a medium sized cluster of GPU-CPU units with each server class headnode connected with the just available Nvidia S1070 GPU box containing four Tesla GPU processors. It will be dedicated for fast processing of cryoEM datasets, particularly very large datasets of ~ 1 million particle images. Establish such a GPU computer cluster is a critical step towards routinely determine near atomic resolution structure of macromolecular complex by single particle cryoEM. Many biomedical research projects funded by NIH, particularly those require structural determination of large complexes; will be benefited from such technological advancement. All developed software will be freely provided.

描述(申请人提供)：电子冷冻显微镜，特别是单粒子冷冻显微镜，在可达到的分辨率方面已经取得了巨大的成功。现在有可能确定具有高度对称性的大型蛋白质组件的近原子结构，如二十面体病毒。然而，对于低对称性的大型蛋白质复合体，要达到相似的分辨率仍然是非常困难的。这在一定程度上是因为收集非常繁琐的图像数据集的过程，以及处理如此大的数据集对计算能力的巨大需求。我们已经建立了一个独特的、易于操作的电子显微镜系统，专门为促进这一过程(自动采集软件和一台非常大的8K x 8K像素数码相机)。在这项申请中，我们寻求资金来建立一个支持GPU计算的计算机集群，以实现快速数据处理。与电子显微镜系统一起，我们的目标是建立一个完整的低温EM系统，该系统将克服目前低温EM在研究不具有高对称性的大型蛋白质复合体时的分辨率限制。我们的目标是定期收集和处理105-106个粒子图像。拟议的GPU计算机集群将是一个中等规模的GPU-CPU单元集群，每个服务器级头节点与刚刚上市的包含四个特斯拉GPU处理器的NVIDIA S1070 GPU盒连接。它将专门用于快速处理CryoEM数据集，特别是具有约100万个粒子图像的超大型数据集。建立这样的GPU计算机集群是用单粒子CryoEM常规确定大分子络合物近原子分辨结构的关键一步。许多由美国国立卫生研究院资助的生物医学研究项目，特别是那些需要确定大型复合体结构的项目；将受益于这种技术进步。所有开发的软件都将免费提供。

项目成果

TRPV1 structures in distinct conformations reveal activation mechanisms.

• DOI: 10.1038/nature12823
• 发表时间: 2013-12-05
• 期刊: Nature
• 影响因子: 64.8

Structure of the TRPV1 ion channel determined by electron cryo-microscopy.

• DOI: 10.1038/nature12822
• 发表时间: 2013-12-05
• 期刊: Nature
• 影响因子: 64.8

Single particle electron cryo-microscopy of a mammalian ion channel.

• DOI: 10.1016/j.sbi.2014.02.005
• 发表时间: 2014-08
• 期刊: Current opinion in structural biology
• 影响因子: 6.8
• 作者: Liao M;Cao E;Julius D;Cheng Y
• 通讯作者: Cheng Y

A distributed multi-GPU system for high speed electron microscopic tomographic reconstruction.

• DOI: 10.1016/j.ultramic.2011.03.015
• 发表时间: 2011-07
• 期刊: ULTRAMICROSCOPY
• 影响因子: 2.2
• 作者: Zheng, Shawn Q.;Branlund, Eric;Kesthelyi, Bettina;Braunfeld, Michael B.;Cheng, Yifan;Sedat, John W.;Agard, David A.
• 通讯作者: Agard, David A.