High Memory High-Performance Computer Cluster for Biomedical Research

用于生物医学研究的高内存高性能计算机集群

• 批准号: 10414419
• 负责人: SUSAN G. HILSENBECK
• 金额: $ 59.56万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10414419
• 关键词: Aging; Biological; Biomedical Research; Biometry; Cell Nucleus; Cells; Computer software; DNA Sequencing Facility; Data Protection; Ensure; Funding; Genomics; Hour; Individual; Institution; Medicine; Memory; Occupations; Process; Research; Research Personnel; Resource Sharing; Resources; Running; Sequence Analysis; Services; System; Technology; Time; Workplace; base; career networking; college; computer cluster; design; epigenomics; experimental study; high end computer; human genome sequencing; informatics shared resource; multidimensional data; single cell sequencing; transcriptomics

PROJECT SUMMARY Biomedical investigators at Baylor College of Medicine (BCM) are increasingly dependent on high performance computer cluster (HPC) based basic and integrative analysis of sequence and other high-dimensional data to conduct their research. The Biostatistics and Informatics Shared Resource (BISR), a Shared Resource in the College’s Advanced Technology Cores, currently manages a Beowulf style cluster as a service for computational investigators. This cluster is highly used but is aging and does not have the type of high-memory nodes needed for efficient timely processing of single-cell and single nucleus sequencing experiments, which typically require 100-200 GB of memory per processor. In some cases, analyses simply cannot be run. Although there are other HPC capabilities at BCM, for example in the Human Genome Sequencing Center or within individual labs as well as other HPC resources in the region, none of these offer satisfactory solutions to our users. Internal BCM-based systems are not designed for high-memory requiring jobs. None are open to general users, and none are operated as a shared resource that ensures consistent up-times, high-speed network connections, mountable storage and regulatorily compliant data protections. External resources are simply not available to general users outside of the owner institution, or they are expressly designed for certain types of jobs and place limits on usage that preclude their use for the types of runs needed by our users. The new BISR HPC will fill a unique niche in providing high-memory HPC capabilities, as a formally managed shared resource, to BCM biomedical investigators. In addition, we are not simply providing raw CPU hours to computationally expert users who do not need any help. We provide assistance to investigators that straddle wet and dry lab research by offering central software management and troubleshooting. The full potential of a recently acquired S10-supported ultra-high throughput NovaSeq6000 sequencer and a recently CPRIT-funded single-cell sequencing Core may fail to be realized without this computational support. We propose to build a new high-memory GPU-enabled system specifically designed to support the burgeoning need of investigators who are conducting large single-cell and/or single nucleus sequencing experiments. Typical experiments involve sequences from 100’s to 10,000’s of cells/per biologic unit and 10’s to 1000’s of biologic units. These experiments represent hundreds of thousands of genomic, transcriptomic and/or epigenomic sequences that must be processed, aligned and integrated. The proposed system will include a front-end node, 22 compute nodes each with 36 processors and 1 TB of memory, 1 GPU server with 8 GPU’s and 1PB direct attached storage. Major Users and their projects will account for about 82% of usage. Demand for single-cell sequencing is growing and we anticipate that there will be numerous additional users. Availability of this HPC will have a positive impact on other high-dimension data-based research throughout the College.

项目概要 贝勒医学院 (BCM) 的生物医学研究人员越来越依赖高性能 基于计算机集群（HPC）的序列和其他高维数据的基础和综合分析 进行他们的研究。生物统计和信息学共享资源 (BISR)，一个共享资源 学院的先进技术核心，目前管理着 Beowulf 风格的集群作为服务 计算研究者。该集群使用率高，但老化，不具备高内存类型 高效及时处理单细胞和单核测序实验所需的节点， 每个处理器通常需要 100-200 GB 内存。在某些情况下，分析根本无法运行。 尽管 BCM 还具有其他 HPC 功能，例如人类基因组测序中心或 在该地区的各个实验室以及其他 HPC 资源中，这些都没有提供令人满意的解决方案 我们的用户。基于内部 BCM 的系统不适用于需要高内存的作业。没有一个开放给 一般用户，并且没有一个作为共享资源运行，以确保一致的正常运行时间、高速 网络连接、可安装存储和符合法规的数据保护。外部资源有 根本无法供所有者机构之外的一般用户使用，或者它们是专门为某些特定目的而设计的 作业类型并对使用进行限制，以排除将其用于我们用户所需的运行类型。这 新的 BISR HPC 将填补提供高内存 HPC 功能的独特利基，作为正式管理的 与 BCM 生物医学研究人员共享资源。此外，我们不仅仅提供原始 CPU 时间 不需要任何帮助的计算专家用户。我们为跨界调查人员提供帮助 通过提供中央软件管理和故障排除来进行干湿实验室研究。一个人的全部潜力 最近购买了支持 S10 的超高通量 NovaSeq6000 测序仪和最近获得 CPRIT 资助的一台 如果没有这种计算支持，单细胞测序Core可能无法实现。我们建议建立一个 全新高内存 GPU 系统，专为支持调查人员不断增长的需求而设计 正在进行大型单细胞和/或单核测序实验的人。典型实验 涉及每个生物单位 100 到 10,000 个细胞和 10 到 1000 个生物单位的序列。这些 实验代表了数十万个基因组、转录组和/或表观基因组序列 必须进行处理、对齐和集成。拟议的系统将包括一个前端节点、22 个计算节点 每个节点具有 36 个处理器和 1 TB 内存，1 个具有 8 个 GPU 和 1PB 直连的 GPU 服务器 贮存。主要用户及其项目将占使用量的 82% 左右。单细胞需求 测序正在增长，我们预计将会有大量额外的用户。此 HPC 的可用性 将对整个学院其他基于高维数据的研究产生积极影响。