High performance IBM iDataPlex/SONAS computing cluster for genomics

用于基因组学的高性能 IBM iDataPlex/SONAS 计算集群

• 批准号: 8334809
• 负责人: JUNHYONG KIM
• 金额: $ 195.49万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 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 2014-04-21
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8334809
• 关键词: Cell physiology; Communities; Complex; Data; Data Analyses; Data Security; Disease; Equilibrium; Equipment; Functional RNA; Genomics; High Performance Computing; Hour; Investments; Modeling; Modification; Pennsylvania; Performance; Postdoctoral Fellow; Process; RNA; Research; Research Infrastructure; Research Personnel; Risk; Science; Source; System; Technology; United States National Institutes of Health; Universities; cluster computing; computing resources; cost; insight; instrument; meetings; next generation sequencing; novel

DESCRIPTION (provided by applicant): Recent dramatic increases in capacity and reduction of costs for Next Generation Sequencing (NGS) is generating revolutionary new information for biomedical sciences. New information from NGS is providing new insights into genomic aberrations, factors underlying complex diseases, and uncovering new non-coding RNAs, as well as novel RNA modifications associated with cell function. However, the NGS data require intensive computing even to put the data in usable form; and, modeling and analysis of these data require even more computing. Simply put, without matching computational resources the NGS machines are useless. The University of Pennsylvania has six installed NGS machines with additional four machines planned, which are being used by a large community of NIH-sponsored investigators. As the costs continue to come down the user community will only increase. Processing the raw data from 10 NGS machines require up to 14.4 million CPU hours of computing per year. Existing computational instruments can only meet 20-30% of this demand. The only other possible source, external commercial cloud computing, has significantly higher costs, data security risks, and still require additional infrastructure to store the resultig data. To meet the critical demand of NGS technologies, we propose to purchase and operate a dedicated high-performance computation instrument. The proposed instrument from IBM (IDataPlex/SONAS) will have 1,440 computing cores and an expandable multi-tier storage with a total capacity of 1.9 petabytes. This instrument features efficient power and cooling, which is critical for extremely large scale computing, and a modular storage system that can be fine-tuned for NGS performance and cost-effectively enlarged using a balance of hard drives and tapes. Unlike most other specialized equipment, this high-performance computing instrument for biomedical data will impact the research of hundreds of investigators, postdoctoral fellows, and graduate trainees. The instrument will remove a significant bottleneck to utilizing NGS technology; potentially alleviate more than $1 million of computing costs per year for NIH-sponsored investigators; and make prior institutional and NIH investment more efficient and useful.

描述（由申请人提供）：最近下一代测序（NGS）的能力和成本的大幅增加正在为生物医学科学产生革命性的新信息。来自NGS的新信息为基因组畸变、复杂疾病的潜在因素、发现新的非编码RNA以及与细胞功能相关的新RNA修饰提供了新的见解。然而，NGS数据甚至需要密集的计算来将数据以可用的形式放置;并且，这些数据的建模和分析需要更多的计算。简单地说，没有匹配的计算资源，NGS机器是无用的。宾夕法尼亚大学已安装了六台NGS机器，并计划增加四台机器，这些机器正在由NIH赞助的大量研究人员使用。随着成本的不断下降，用户群体只会增加。处理来自10台NGS机器的原始数据每年需要高达1440万个CPU小时的计算。现有的计算仪器只能满足这一需求的20-30%。唯一的其他可能的来源，外部商业云计算，具有显着更高的成本，数据安全风险，并且仍然需要额外的基础设施来存储结果数据。为了满足NGS技术的关键需求，我们建议购买和操作专用的高性能计算仪器。IBM（IDataTM/SONAS）提出的仪器将有1，440个计算核心和一个可扩展的多层存储，总容量为1.9 PB。该仪器具有高效的电源和冷却功能，这对于超大规模计算至关重要，还具有模块化存储系统，可以针对NGS性能进行微调，并使用硬盘驱动器和磁带的平衡进行经济高效的扩展。与大多数其他专业设备不同，这种用于生物医学数据的高性能计算仪器将影响数百名研究人员，博士后研究员和研究生的研究。该仪器将消除利用NGS技术的一个重大瓶颈;可能为NIH资助的研究人员每年减少100多万美元的计算成本;并使先前的机构和NIH投资更加有效和有用。