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.

摘要：Oberlin学院获得了一项奖励，以支持高性能计算（HPC）集群的收购。作为其教育使命的一部分，奥柏林学院强调本科生的研究，因此努力提供设施和制度支持，以维持有意义的，以学生为中心的教师研究。许多科学学院有外部资助的研究项目，越来越多的项目依赖于高性能计算。该集群将被奥伯林大学自然科学和社会科学院系的十几名教师使用，以支持各种各样的研究，从黑洞碰撞建模到分析有机分子对空气质量的影响，从确定地球上最早生命的蛋白质特征到利用大量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。每年有100多名奥柏林大学的本科生将在生物、化学、计算机科学、物理和心理等系的至少8门课程中，通过指导研究和班级相关项目，对计算集群进行分析。技术摘要：该奖项将资助获得一个大大改进的高性能计算（HPC）集群，以满足Oberlin学院对研究、研究培训和教学的计算支持的迫切需求。为了方便Oberlin科学学院的多样化计算需求，将安装一个集群，该集群可以最大限度地实现并行化，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、新一代测序数据的从头组装和大规模系统发育分析。集群将包括至少38个Intel Xeon E5计算节点，总共544个内核，这些节点的RAM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244tb的长期存储空间。因此，HPC集群将有足够的计算能力和长期的档案存储来推进越来越大的基因组学、化学和物理数据集，这些数据集是奥伯林的一些教师现在在他们的研究中使用的，并且将有灵活性被越来越多的奥伯林教师利用，他们将HPC工作纳入他们的研究，包括天体物理学、基因组学、系统遗传学、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。