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.

项目摘要 贝勒医学院（BCM）的生物医学研究人员越来越依赖高性能 基于计算机群集（HPC）的序列和其他高维数据的基本和集成分析 进行研究。生物统计学和信息学共享资源（BISR），一种共享资源 大学的先进技术核心目前管理着Beowulf风格的集群作为服务 计算研究人员。该簇高度使用但正在老化，没有高内存的类型 有效及时处理单细胞和单核测序实验所需的节点 通常需要每个处理器100-200 GB的内存。在某些情况下，无法进行分析。 尽管BCM还有其他HPC功能，例如在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，这些都不提供满意的工厂解决方案 我们的用户。基于BCM的内部系统不是为需要工作的高内存设计的。没有人开放 一般用户，没有一个作为共享资源运行，可确保高速公路一致 网络连接，可安装的存储和符合法规的数据保护。外部资源是 根本无法为所有者机构以外的一般用户提供，或者他们明确设计 作业类型和对用法的限制，这排除了用户所需的运行类型的使用。 新的BISR HPC将在提供高内存HPC功能方面填补独特的利基市场，作为正式管理的 共享资源，向BCM生物医学研究人员。此外，我们不仅仅是提供原始的CPU小时 不需要任何帮助的计算专家用户。我们为交易的调查人员提供帮助 通过提供中央软件管理和故障排除来进行湿干研究。全部潜力 最近获得的S10支持的超高吞吐量NovaseQ6000 Suequencer和最近获得的CPRIT资助 没有这种计算支持，单细胞测序核心可能无法实现。我们建议建造一个 专门旨在支持调查人员的残破需求的新型高内存GPU系统 他们正在进行大型单细胞和/或单核测序实验。典型的实验 涉及从100至10,000个细胞/每个生物学单元，10至1000个生物学单元的序列。这些 实验代表数十万个基因组，转录组和/或表观基因组序列， 必须处理，对齐和集成。提议的系统将包括前端节点，22个计算 节点每个都有36个处理器和1 TB的存储器，1个带有8个GPU的GPU服务器和1PB直接附加 贮存。主要用户及其项目将占使用的82％。对单细胞的需求 测序正在增长，我们预计将有许多其他用户。此HPC的可用性 将对整个大学的其他基于数据的研究产生积极影响。