MRI Acquisition of a High Performance Large Memory Computing Cluster for Large Scale Data-Driven Research

用于大规模数据驱动研究的高性能大内存计算集群的 MRI 采集

• 批准号: 1919452
• 负责人: Peter Ramadge
• 金额: $ 49.9万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1919452&HistoricalAwards=false
• 关键词: MRI; Acquisition; Performance; Large; Memory

This project will acquire a state-of-the-art High Performance Computing (HPC) cluster to support large scale, data-driven research. The instrument will support a variety of projects from computer science, electrical engineering, ecology, evolutionary biology, neuroscience and genomics. In neuroscience, the cluster will allow the use of advanced statistical techniques at scale to identify and connect anatomical and functional brain-imaging features of diseased and healthy subjects with specific underlying genetic profiles. In computer science, using machine learning algorithms deployed on the instrument, researchers will to seek new ways to protect the security and privacy of users in large-scale networked systems. Finally, the cluster will also enable research that will improve our understanding of evolutionary history and the molecular complexities of traits through the analysis of multi-animal, large-scale genomic datasets. In addition, through short courses and multiday boot-camps, the instrument will provide valuable opportunities for training postdoctoral fellows, graduate students, and advanced undergraduates in large-scale computational data science. The instrument will also be a valuable asset for certificate programs in statistics and machine learning (one for undergraduate students, the other for graduate students) and for a certificate program in computational science, all of which will support broadening participation of groups underrepresented in STEM. The research and training enabled by the instrument is expected to help improve our understanding of human health and well-being, help create new knowledge that will aid economic competitiveness, and help maintain the country's leadership in science and engineering. The computing cluster will be formed of by nodes with very large memory. The system complements the institution's investments in research cyberinfrastructure and will be managed by the Princeton Institute for Computational Science and Engineering (PICSciE) and the Office of Information Technology (OIT). The instrument would initially be used by five research groups, part of the Center for Statistics and Machine Learning (CSML), which will leverage existing programs and partnerships to increase participation in data science. The initial five specific projects are united under a common theme: machine learning will be used for analyzing big data sets that may not be easily broken into smaller pieces for processing. Specifically, they will examine the following: 1) the use of probabilistic models for large-scale scientific analysis and de novo design in applications areas such as mechanical metamaterials and mixed-signal circuit development; 2) statistical machine learning in genomics, biomedicine, and health biostatistics including the analysis of hospital records to aid doctors in taking early action to improve patient outcomes, the heritability of neuropsychiatric diseases and drug responses, and statistical and experimental examination of cardiovascular disease risk; 3) security and privacy challenges in networked systems using machine learning techniques to detect and isolate attackers in networked systems such as social media; 4) large-scale machine learning for neuroscience such as joint analysis of many large-scale, multi-subject fMRI datasets where the size and number of the datasets; 5) evolutionary genomic and epigenome analyses through collection and analysis of large datasets to investigate the evolutionary history and molecular complexities of traits. Collectively, these research groups are composed of forty graduate students, ten postdocs, and include, on average, thirteen undergrad research projects per year. The instrument will also be used by other researchers engaged in large-scale, data-driven research across a wide variety of disciplines. Hence both the capacity and the capability aspects of the proposed instrument will be highly utilized and will enable the continued advancement of research at the University.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目将获得最先进的高性能计算（HPC）集群，以支持大规模的数据驱动研究。该仪器将支持计算机科学、电气工程、生态学、进化生物学、神经科学和基因组学等各种项目。在神经科学中，该集群将允许大规模使用先进的统计技术，以识别和连接患病和健康受试者的解剖和功能脑成像特征与特定的潜在遗传特征。在计算机科学中，使用部署在仪器上的机器学习算法，研究人员将寻求新的方法来保护大规模网络系统中用户的安全和隐私。最后，该集群还将通过分析多动物、大规模的基因组数据集，使研究能够提高我们对进化历史和性状分子复杂性的理解。此外，通过短期课程和多天的训练营，该仪器将为培训博士后研究员，研究生和大规模计算数据科学的高级本科生提供宝贵的机会。该工具也将是统计和机器学习证书课程（一个针对本科生，另一个针对研究生）以及计算科学证书课程的宝贵资产，所有这些都将支持扩大STEM中代表性不足的群体的参与。该仪器所支持的研究和培训预计将有助于提高我们对人类健康和福祉的理解，有助于创造有助于提高经济竞争力的新知识，并有助于保持国家在科学和工程领域的领导地位。计算集群将由具有非常大内存的节点组成。该系统补充了该机构在研究网络基础设施方面的投资，并将由普林斯顿计算科学与工程研究所（PICSciE）和信息技术办公室（OIT）管理。该工具最初将由统计和机器学习中心（CSML）的五个研究小组使用，这些研究小组将利用现有的计划和合作伙伴关系来增加对数据科学的参与。最初的五个具体项目在一个共同的主题下结合在一起：机器学习将用于分析大数据集，这些数据集可能不容易分解为较小的碎片进行处理。具体而言，他们将研究以下内容：1）在机械超材料和混合信号电路开发等应用领域中使用概率模型进行大规模科学分析和从头设计; 2）基因组学、生物医学和健康生物统计学中的统计机器学习，包括分析医院记录，以帮助医生采取早期行动改善患者预后，神经精神疾病和药物反应的遗传性，以及心血管疾病风险的统计和实验检查; 3）使用机器学习技术检测和隔离社交媒体等网络系统中的攻击者的网络系统中的安全和隐私挑战; 4）大规模的神经科学机器学习，如联合分析许多大规模、多学科的fMRI数据集，其中数据集的大小和数量;第五章）通过收集和分析大型数据集进行进化基因组和表观基因组分析，以研究进化历史和分子生物学特质的复杂性总的来说，这些研究小组由40名研究生，10名博士后组成，平均每年包括13个本科研究项目。该仪器还将被其他研究人员用于跨各种学科的大规模数据驱动研究。因此，无论是能力和能力方面的建议仪器将高度利用，并将使研究在大学的持续进步。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Training Discrete Deep Generative Models via Gapped Straight-Through Estimator

• DOI: 10.48550/arxiv.2206.07235
• 发表时间: 2022-06
• 作者: Ting-Han Fan;Ta-Chung Chi;Alexander I. Rudnicky;P. Ramadge

Experiences Deploying Multi-Vantage-Point Domain Validation at Let’s Encrypt

在 Let’s Encrypt 部署多优势点域验证的经验

• 发表时间: 2021
• 期刊: Proceedings of the 30th USENIX Security Symposium
• 作者: Lee, Henry;Wang, Liang;McCarney, Daniel;Shoemaker, Roland;Rexford, Jennifer;Mittal, Prateek
• 通讯作者: Mittal, Prateek

An Experimental Study of Balance in Matrix Factorization

矩阵分解平衡的实验研究

• DOI: 10.1109/ciss50987.2021.9400232
• 发表时间: 2021
• 期刊: 2021 55th Annual Conference on Information Sciences and Systems
• 作者: Hsia, Jennifer;Ramadge, Peter J.
• 通讯作者: Ramadge, Peter J.