A Compute Cluster for Brain Imaging and Analysis

用于脑成像和分析的计算集群

• 批准号: 9072820
• 负责人: ALLEN W SONG
• 金额: $ 56.82万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-01 至 2017-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9072820
• 关键词: Big Data; Brain; Brain imaging; Collaborations; Data; Data Analyses; Data Set; Diffusion Magnetic Resonance Imaging; Fiber; Funding; High Performance Computing; Image; Image Analysis; Linux; Magnetic Resonance Imaging; Maps; Memory; Methods; Predisposition; Process; Research; Research Personnel; Research Project Grants; Resolution; Running; Scientist; Series; Speed; United States National Institutes of Health; Universities; cluster computing; computer infrastructure; computing resources; cost; experience; improved; medical schools; meetings; neuroimaging

 DESCRIPTION: Neuroimaging research using high-resolution functional and structural MRI (including diffusion tensor imaging, or DTI) involves the acquisition and analysis of very large data sets that then must be transformed through a series of computation-intensive processing tasks into meaningful information. This project, entitled "A Compute Cluster for Brain Imaging and Analysis", is submitted by NIH-supported investigators at Duke University who primarily conduct neuroimaging research at the Duke - UNC Brain Imaging and Analysis Center at Duke University Medical School. The collaborating scientists have found that the sheer volume of data resulting from improved acquisition and analysis methods has made it increasingly difficult to fully analyze data from all the labs in a timely manner. With our existing computational infrastructure, some processes such as the ultrahigh resolution (submillimeter) diffusion MRI through massive parallel imaging (both in-plane and multi-band through-plane) would take over 20 hrs for a typical ten-run whole-brain functional exam. Also, specific large matrix inversions fo our ultrahigh resolution susceptibility mapping would require memory of up to 700 GB per node. Thus, we are requesting funds to purchase a high- performance compute cluster to meet these and other processing requirements. Our proposal builds upon our past experience administering a 60-node, though quite dated, Linux cluster (Dell PowerEdge 1950), with high-speed fiber channel adaptors and connections to our multiple data servers. We anticipate that this much improved computational resource will greatly enhance our ability to meet the much increased MRI acquisition capability and analysis demand in the many NIH-sponsored research projects, and gain maximum efficiency at lower overall cost. In addition to meeting the computational needs of our neuroimaging scientists, this proposal also benefits from the close collaboration that already exists among us in Duke's big data initiative to have a wider impact.

 描述：使用高分辨率功能和结构MRI（包括扩散张量成像或DTI）进行神经影像研究涉及对非常大的数据集的获取和分析，然后必须通过一系列计算密集型处理任务转换为有意义的信息。该项目题为“用于脑成像和分析的计算集群”，由NIH支持的杜克大学的研究人员提交，他在杜克大学医学院的杜克大学UNC脑成像和分析中心进行神经影像学研究。合作的科学家发现，改进的获取和分析方法产生的大量数据使得越来越难以及时地分析所有实验室的数据。借助我们现有的计算基础架构，通过大规模平行成像（平面内和多频段贯穿平面）等一些过程，例如超高分辨率（亚毫米计）扩散MRI，将需要超过20个小时的时间来进行超过20个小时的时间，以进行一项典型的十级全脑功能检查。同样，在我们的超高分辨率易感映射中，特定的大矩阵反转将需要每个节点最多700 GB的内存。这是我们要求资金购买高性能计算集群以满足这些和其他处理要求。我们的提案建立在我们过去的经验基础上管理60个节点的Linux群集（Dell PowerEdge 1950），并具有高速光纤通道适配器，并连接到我们的多个数据服务器。我们预计，这种改进的计算资源将极大地增强我们满足许多NIH赞助的研究项目中MRI获取能力和分析需求大大提高的能力，并以较低的整体成本提高最大效率。除了满足神经影像学科学家的计算需求外，该提案还受益于我们中间已经存在的密切合作，该协作在杜克的大数据计划中具有更大的影响。