MRI: Acquisition of a high-performance computing cluster to enhance undergraduate research and education across the sciences at Oberlin College

MRI：收购高性能计算集群，以加强欧柏林学院跨科学的本科生研究和教育

• 批准号: 1427949
• 负责人: Michael Moore
• 金额: $ 48.63万
• 依托单位: Oberlin College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1427949&HistoricalAwards=false
• 关键词: MRI; Acquisition; performance; computing; cluster

Non-Technical Abstract:An award is made to Oberlin College to support acquisition of a high-performance computational (HPC) cluster. As part of its educational mission, Oberlin College emphasizes undergraduate research and hence endeavors to provide the facilities and institutional support to sustain meaningful, student-centered faculty research. Numerous science faculty have externally funded research programs, an increasing number of which depend on high-performance computing. The cluster will be used by more than a dozen Oberlin faculty in departments throughout the natural and social sciences to support a diverse array of research, ranging from modeling black hole collisions to analyzing the effects of organic molecules on air quality, and from determining the protein characteristics of the earliest life on Earth to reconstructing the evolution of flowering plants using massive amounts of DNA sequence data. The award will also have a dramatic impact on student research and training. More than 100 Oberlin undergraduates per year will perform analyses on the computing cluster through mentored research and class-associated projects in at least eight courses, in the Departments of Biology, Chemistry, Computer Science, Physics, and Psychology.Technical Abstract:The award will fund the acquisition of a greatly improved high-performance computational (HPC) cluster that will fill a pressing need for computational support of research, research training, and teaching at Oberlin College. To facilitate the diverse computational needs of Oberlin science faculty, a cluster that maximizes parallelization while also offering multiple nodes with relatively large amounts of RAM for protein folding analyses, de novo assembly of next-generation sequencing data, and large-scale phylogenetic analyses will be installed. The cluster will include at least 38 Intel Xeon E5 compute nodes with a total of 544 cores, with RAM varying across these nodes from 32 GB/node to 512 GB/node. The cluster will also include at least two Nvidia Tesla K20 GPUs and at least two Intel Xeon Phi 5110P coprocessors to dramatically increase processing speeds for jobs that are embarrassingly parallel, and will have at least 244 TB of long-term storage space. The HPC cluster will thus have sufficient computational power and long-term archival storage to advance the increasingly large genomics, chemistry, and physics data sets that several Oberlin faculty now use in their research, and will have the flexibility to be utilized by the increasing number of Oberlin faculty who incorporate HPC work in their research, including in astrophysics, genomics, phylogenetics, proteomics, metabolomics, biophysics, atmospheric chemistry, behavioral psychology, cognitive neuroscience, and computer science.

非技术摘要：欧柏林学院获得了一项奖励，以支持购买高性能计算 (HPC) 集群。作为其教育使命的一部分，欧柏林学院强调本科生研究，因此努力提供设施和机构支持，以维持有意义的、以学生为中心的教师研究。许多科学教师都有外部资助的研究项目，其中越来越多的项目依赖于高性能计算。该集群将被奥柏林自然科学和社会科学系的十几名教员使用，以支持各种研究，从模拟黑洞碰撞到分析有机分子对空气质量的影响，再到确定蛋白质地球上最早生命的特征，利用大量 DNA 序列数据重建开花植物的进化。该奖项还将对学生的研究和培训产生巨大影响。每年超过 100 名欧柏林本科生将通过生物、化学、计算机科学、物理和心理学系至少八门课程的指导研究和课堂相关项目对计算集群进行分析。 技术摘要：该奖项将资助收购一个大大改进的高性能计算 (HPC) 集群，该集群将满足欧柏林学院对研究、研究培训和教学计算支持的迫切需求。为了满足 Oberlin 科学学院的多样化计算需求，将建立一个集群，该集群可以最大限度地提高并行化，同时还提供具有相对大量 RAM 的多个节点，用于蛋白质折叠分析、下一代测序数据的从头组装和大规模系统发育分析。安装。 该集群将包括至少 38 个 Intel Xeon E5 计算节点，总共 544 个核心，这些节点的 RAM 范围从 32 GB/节点到 512 GB/节点不等。 该集群还将包括至少两个 Nvidia Tesla K20 GPU 和至少两个 Intel Xeon Phi 5110P 协处理器，以显着提高并行作业的处理速度，并将拥有至少 244 TB 的长期存储空间。因此，HPC 集群将拥有足够的计算能力和长期档案存储，以推进目前奥柏林大学几位教职人员在其研究中使用的日益庞大的基因组学、化学和物理数据集，并且可以灵活地供越来越多的数据集使用。奥柏林学院的教员将 HPC 工作融入到他们的研究中，包括天体物理学、基因组学、系统发育学、蛋白质组学、代谢组学、生物物理学、大气化学、行为心理学、认知神经科学和计算机科学。