A High Performance Research Image Repository (RIR) for the Washington University Center of High Performance Computing (CHPC)

华盛顿大学高性能计算中心 (CHPC) 的高性能研究图像存储库 (RIR)

• 批准号: 10177147
• 负责人: Daniel Scott Marcus
• 金额: $ 192.73万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-25 至 2024-04-24
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10177147
• 关键词: Adolescent; Algorithms; Alzheimer' s Disease; Brain; Clinical; Collection; Computer software; Data; Data Collection; Data Set; Disease; High Performance Computing; Human; Longevity; Performance; Research; System; The Cancer Imaging Archive; Universities; Washington; base; biobank; bioimaging; cognitive development; computerized data processing; connectome; data management; data repository; deep field survey; deep learning; image archival system; image processing; neuro-oncology; neuroimaging; next generation; novel; solid state; young adult

Project Summary/Abstract: We propose to build a Research Image Repository (RIR) to house large collections of biomedical imaging data. The RIR will include datasets produced locally at Washington University: The Connectome Coordination Facility (CCF) (which itself includes the Human Connectome Project (HCP) Young Adult study, The Lifespan related projects, the Disease related projects, and assorted HCP-related projects), The Knight Alzheimer Disease Research Center (ADRC), the Adolescent Brain Cognitive Development (ABCD) Study, The Comprehensive Neuro-Oncology Data Repository (CONDR), and the clinically-based PACS image repository. In addition, copies of external data collections such as the UK Biobank, The Alzheimer's Disease Neuroimaging Initiative (ADNI), and The Cancer Image Archive (TCIA) will be maintained. The RIR includes a data management software solution that will introduce many novel features (such as `data tagging' to enrich datasets, and advanced search features) and will allow us to leverage existing storage including the Center for High Performance Computing's (CHPC) 1.4PB of BeeGFS `scratch' storage, solid-state NVMe drives integrated into the compute nodes, and 10PB of ZFS-based storage. All storage will be presented to the user as a single file-system, while data will be migrated to different performance tiers based on the storage requirements of the datasets or processing algorithms. The RIR will be integrated into the CHPC for data processing. The proposal also includes two NVIDIA DGX A100 GPU servers providing state-of-the-art GPU- based processing power. The combination of high-quality, diverse sets of biomedical imaging data with next- generation computing power will have a transformative effect on biomedical imaging processing pipelines and nowhere will the effects be more profound than in the emerging field of Deep Learning for image processing.

项目摘要/摘要： 我们建议建立一个研究图像存储库（RIR），以容纳大量的生物医学成像数据。 RIR将包括华盛顿大学本地生产的数据集：Connectome协调 设施（CCF）（本身包括人类连接项目（HCP）年轻成人研究，寿命 相关项目，疾病相关项目以及与HCP相关的各种项目），骑士阿尔茨海默氏症 疾病研究中心（ADRC），青少年脑认知发展（ABCD）研究， 全面的神经肿瘤数据存储库（CONDR）和基于临床的PACS图像存储库。 此外，外部数据收集的副本，例如英国生物库，阿尔茨海默氏病 神经影像倡议（ADNI）和癌症图像档案（TCIA）将得以维持。 RIR包括一个 数据管理软件解决方案将介绍许多新颖的功能（例如“数据标记”以丰富 数据集和高级搜索功能），将使我们能够利用现有存储 高性能计算的（CHPC）1.4pb的beegfs``刮擦''存储，固态NVME驱动器 集成到计算节点和基于ZFS的10％的存储中。所有存储将呈现给用户 作为一个文件系统，而数据将根据存储迁移到不同的性能层 数据集或处理算法的要求。 RIR将集成到CHPC中以获取数据 加工。该提案还包括两个NVIDIA DGX A100 GPU服务器，可提供最先进的GPU- 基于处理能力。高质量，多样的生物医学成像数据的组合与接下来的 生成计算能力将对生物医学成像处理管道和 在图像处理深度学习的新兴领域中，这种影响将没有什么比更深刻的了。