A High-memory Supercomputer for Proteomics, Text Mining and Microbiome Research

用于蛋白质组学、文本挖掘和微生物组研究的高内存超级计算机

• 批准号: 8334437
• 负责人: NATALIE G. AHN
• 金额: $ 190万
• 依托单位: UNIVERSITY OF COLORADO
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-04-22 至 2015-04-21
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8334437
• 关键词: Accounting; Area; Biological Sciences; Biomedical Research; Biotechnology; Client; Collaborations; Communities; DNA Sequencing Facility; Data; Databases; Faculty; Funding; Genome; Grant; Growth; High Performance Computing; Housing; Internet; Laboratories; Memory; Occupations; Performance; Proteomics; Research; Research Infrastructure; Resources; Rest; Running; Software Tools; Supercomputing; System; Technology; Time; Training; United States National Institutes of Health; base; end of life; innovation; instrument; knowledge base; meetings; microbiome; outreach; simulation; supercomputer; text searching

DESCRIPTION (provided by applicant): We request funds to purchase an integrated supercomputer to unite 5 highly productive and collaborative laboratories with complementary expertise in the microbiome, proteomics, text mining, and supercomputing, and to extend these capabilities to the broader NIH-funded biomedical research community via cloud and web applications. The critical shared need not met by other systems on campus, unavailable in commercial clouds, and oversubscribed at national labs, is for a system that can run jobs that require high memory (8-32 GB/core) and long duration (>2 weeks wall-time), and is optimized for high-IO tasks that saturate network or storage on other systems. The system will consist of 128 servers, each using 2x8-core 2.93GHz Intel Sandybridge CPUs. 20 large-memory nodes will each have 512GB of RAM (32GB/core), and 100 compute nodes will each have 128GB of RAM (8GB/core). These 120 nodes will each use two 10Gbps Ethernet ports bonded together for a 20Gbps/node (2.5GB/s) connection to the rest of the system, and each node will have 2.4TB raw high- performance local storage. The total aggregate performance of these local disks is over 36GB/s sustained (>300MB/s per node). The remaining 8 nodes will be used for administration, support for advanced software tools and infrastructure, and user interaction. A central high-performance Lustre parallel file system will provide 1.15PB of usable scratch space and sustain 36GB/s to the 128 clients. An archival system of 4 drives/300 tapes will sustain >1GB/s aggregate (accounting for compression), provide 450TB of raw capacity, store ~4.5 PB of user data, and scale to 5x this size. The system, valued at $4.5 million but quoted at $2 million by HP due to the strategic importance of this partnership, will be housed in a state-of-the art machine room in the new Jennie Smoly Caruthers Biotechnology Building on the Boulder campus (opening Feb 2012), and connect to the rest of the campus at 40Gbps. The system will be a key enabling technology for key scientific areas where data growth is exponential and current systems on campus are end-of-life, solely dedicated to other purposes, or optimized for other tasks. The major users will use the instrument largely for time-consuming one-time tasks such as parameter optimization for microbiome and genome assembly workflows, building knowledgebases, and performing simulations and database searches that will provide resources that are re-used by much broader user communities (hundreds of collaborators; thousands of end users) who lack supercomputing access. One key innovative aspect of this proposal is configuration of part of the system as an academic cloud, which will allow us to pilot workflows that can later be deployed by diverse users on commercial clouds (e.g. Amazon EC2) and academic clouds (e.g. Magellan and DIAG) once those clouds are upgraded. The system will also build a broad expertise base in high-performance computing in the life sciences through outreach to promising new faculty and trainees on NIH training grants, and collaborations with new users of the Sequencing Core. The proposed instrument will thus have a profound impact on NIH-funded research.

描述（由申请人提供）：我们请求资金购买一台集成的超级计算机，以联合5个高效协作的实验室，这些实验室在微生物组学，蛋白质组学，文本挖掘和超级计算方面具有互补的专业知识，并通过云和网络应用程序将这些能力扩展到更广泛的NIH资助的生物医学研究社区。园区内的其他系统无法满足、商业云中无法提供且国家实验室超额订购的关键共享需求是一个系统，该系统可以运行需要高内存（8-32 GB/核心）和长时间（>2周的停机时间）的作业，并针对使其他系统上的网络或存储饱和的高IO任务进行了优化。该系统将由128台服务器组成，每台服务器使用2x 8核2.93GHz英特尔Sandybridge CPU。20个大内存节点将分别拥有512 GB的RAM（32 GB/核），100个计算节点将分别拥有128 GB的RAM（8 GB/核）。这120个节点将各自使用两个绑定在一起的10 Gbps以太网端口，以20 Gbps/节点（2. 5 GB/s）连接到系统的其余部分，每个节点将拥有2. 4 TB原始高性能本地存储。这些本地磁盘的总聚合性能超过36 GB/s（每个节点> 300 MB/s）。其余8个节点将用于管理、支持高级软件工具和基础设施以及用户交互。中央高性能Lustre并行文件系统将提供1.15PB的可用暂存空间，并为128个客户端提供36 GB/s的支持。由4个驱动器/300个磁带组成的归档系统可维持> 1 GB/s的聚合（考虑到压缩），提供450 TB的原始容量，存储约4.5 PB的用户数据，并可扩展到该大小的5倍。该系统价值450万美元，但由于这种合作关系的战略重要性，惠普报价为200万美元。 在博尔德校区新落成的Smoly卡鲁瑟斯生物技术大楼（2012年2月开放）中，我们将为您提供一个艺术机房，并以40 Gbps的速度连接到园区的其他部分。该系统将成为关键科学领域的关键使能技术，在这些领域，数据增长呈指数级增长，校园内的当前系统已经报废，仅用于其他目的，或针对其他任务进行了优化。主要用户将使用该仪器主要用于耗时的一次性任务，例如微生物组和基因组组装工作流程的参数优化，构建知识库，以及执行模拟和数据库搜索，这些资源将被更广泛的用户社区（数百名合作者;数千名最终用户）重新使用。该提案的一个关键创新方面是将系统的一部分配置为学术云，这将使我们能够试点工作流程，一旦这些云升级后，这些工作流程可以由不同的用户部署在商业云（例如Amazon EC2）和学术云（例如Magellan和DIAG）上。该系统还将建立一个广泛的专业知识基础，在生命科学的高性能计算，通过推广到有前途的新教师和培训人员在国家卫生研究院的培训赠款，并与测序核心的新用户合作。因此，拟议的工具将对NIH资助的研究产生深远的影响。

项目成果

Low-dimensional representation of genomic sequences

• DOI: 10.1007/s00285-019-01348-1
• 发表时间: 2019-03
• 期刊: Journal of Mathematical Biology
• 影响因子: 1.9
• 作者: Richard C. Tillquist;M. Lladser

Physical determinants of bipolar mitotic spindle assembly and stability in fission yeast.

双极有丝分裂纺锤体组件的物理决定因素和裂变酵母中的稳定性。

• DOI: 10.1126/sciadv.1601603
• 发表时间: 2017-01
• 期刊: Science advances
• 影响因子: 13.6
• 作者: Blackwell R;Edelmaier C;Sweezy-Schindler O;Lamson A;Gergely ZR;O'Toole E;Crapo A;Hough LE;McIntosh JR;Glaser MA;Betterton MD
• 通讯作者: Betterton MD

Alteration of the gut fecal microbiome in children living with HIV on antiretroviral therapy in Yaounde, Cameroon.

• DOI: 10.1038/s41598-021-87368-8
• 发表时间: 2021-04-07
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Abange WB;Martin C;Nanfack AJ;Yatchou LG;Nusbacher N;Nguedia CA;Kamga HG;Fokam J;Kennedy SP;Ndjolo A;Lozupone C;Nkenfou CN
• 通讯作者: Nkenfou CN

A generative model for the behavior of RNA polymerase.

• DOI: 10.1093/bioinformatics/btw599
• 发表时间: 2017-01-15
• 期刊: Bioinformatics (Oxford, England)
• 作者: Azofeifa JG;Dowell RD
• 通讯作者: Dowell RD

RNA Pol II transcription model and interpretation of GRO-seq data.

RNA Pol II 转录模型和 GRO-seq 数据的解释。

• DOI: 10.1007/s00285-016-1014-4
• 发表时间: 2017
• 期刊: Journal of mathematical biology
• 影响因子: 1.9
• 作者: Lladser,ManuelE;Azofeifa,JosephG;Allen,MaryA;Dowell,RobinD
• 通讯作者: Dowell,RobinD