MRI: Acquisition of a High-Performance Computing Cluster for Research and Teaching at Rutgers University-Newark

MRI：罗格斯大学纽瓦克分校采购高性能计算集群用于研究和教学

• 批准号: 2117429
• 负责人: Michele Pavanello
• 金额: $ 55.93万
• 依托单位: Rutgers University Newark
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2117429&HistoricalAwards=false
• 关键词: MRI; Acquisition; Performance; Computing; Cluster

This award to Rutgers University-Newark supports the acquisition and deployment of a High-Performance Computing (HPC) cluster (named PRICE) dedicated to research, teaching and societal outreach efforts. PRICE will have 60 general compute (CPU) nodes and one graphical processing unit (GPU) node as well as storage appropriate for the planned usage over the lifetime of the machine. The enabled research develops along three main directions: atomistic modeling, neuroscience, and data science. Some atomistic models enabled by PRICE will study the structure and dynamics of proteins to address questions related to diseases such as Alzheimer’s. New materials modeling and design is enabled both by PRICE and by the development of new quantum simulation methods. The enabled simulations will also regard new materials design by way of genetic algorithms. The enabled neuroscience research regards computational analysis of experimental data to understand brain function, connectivity, and human behavior. Enabled data science research includes the formulation of novel cooperative artificial intelligence (AI) algorithms that will improve the outcome of machine learning (ML) models of broad applicability. In addition to enabling new science, the project realizes several societal broader impacts including broadening HPC literacy of underrepresented minorities and training the future NJ workforce using HPC in the classroom and development of new undergraduate and graduate curricula.PRICE will comprise 60 compute nodes (52 cores/node), 700 TB of redundant storage and one GPU node (4 GPUs/node) to be housed at Rutgers University-Newark. PRICE will enable several additional research projects carried out by the PI, co-PIs, and major users at Rutgers-Newark and NJIT. The GPU portion enables state-of-the-art molecular dynamics simulations that elucidate structure and dynamics of proteins for the understanding of diseases, such as Alzheimer’s. GPUs also enable the efficient and timely execution of cooperative AI algorithms aimed at improving predictivity. The CPU nodes will enable quantum simulations aimed at materials engineering through density-functional theory calculations. These simulations facilitate the development of quantum models based on density functional theory, its subsystem formulation (which parallelizes efficiently over PRICE’s low-latency network) as well as quantum mechanical frameworks based on the exact factorization of the Schrödinger equation for multicomponent systems. CPU and GPU nodes will enable data analysis associated with neuroscience experiments aimed at uncovering how the brain modulates behavior and vision-related tasks as well as the study of neuron connectivity to understand brain function. Data from these experiments is growing exponentially due to increased instrument data flow from new fMRI units and improved technology allowing recordings of local field potentials from hundreds on neurons. The project also enables new science as well as the realization of societal broader impacts, such as broadening high-performance computing literacy of underrepresented minorities, training the future NJ workforce and recruitment of new faculty.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.

该奖项授予罗格斯大学纽瓦克分校，支持收购和部署高性能计算（HPC）集群（名为PRICE），致力于研究、教学和社会推广工作。PRICE将拥有60个通用计算（CPU）节点和一个图形处理单元（GPU）节点，以及适合机器生命周期内计划使用的存储。使能的研究沿着三个主要方向发展：原子建模、神经科学和数据科学。PRICE支持的一些原子模型将研究蛋白质的结构和动力学，以解决与阿尔茨海默氏症等疾病相关的问题。新材料的建模和设计都是由PRICE和新的量子模拟方法的发展实现的。启用的模拟也将考虑新材料的设计，通过遗传算法。使神经科学研究涉及对实验数据的计算分析，以了解大脑功能，连通性和人类行为。启用数据科学研究包括制定新的协作人工智能（AI）算法，这些算法将改善具有广泛适用性的机器学习（ML）模型的结果。除了推动新科学之外，该项目还实现了几个更广泛的社会影响，包括扩大未被充分代表的少数民族的HPC素养，在课堂上使用HPC培训未来的新泽西州劳动力，以及开发新的本科和研究生课程。PRICE将包括60个计算节点（52核/节点）、700 TB冗余存储和一个GPU节点（4个GPU /节点），这些节点将位于罗格斯大学-纽瓦克分校。PRICE将使PI、合作PI以及罗格斯-纽瓦克大学和新泽西理工大学的主要用户能够开展几个额外的研究项目。GPU部分支持最先进的分子动力学模拟，阐明蛋白质的结构和动力学，以了解疾病，如阿尔茨海默氏症。gpu还能够高效及时地执行旨在提高预测性的协作AI算法。CPU节点将通过密度泛函理论计算实现针对材料工程的量子模拟。这些模拟促进了基于密度泛函理论的量子模型的发展，它的子系统公式（在PRICE的低延迟网络上有效地并行化）以及基于多组件系统Schrödinger方程的精确分解的量子力学框架。CPU和GPU节点将支持与神经科学实验相关的数据分析，旨在揭示大脑如何调节行为和视觉相关任务，以及研究神经元连接以了解大脑功能。由于新的功能磁共振成像装置的仪器数据流增加，以及改进的技术允许记录数百个神经元的局部场电位，这些实验的数据呈指数级增长。该项目还使新科学以及实现更广泛的社会影响成为可能，例如扩大代表性不足的少数民族的高性能计算素养，培训未来的新泽西州劳动力和招聘新教师。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Entropy is a good approximation to the electronic (static) correlation energy

熵是电子（静态）相关能的良好近似

• DOI: 10.1063/5.0171981
• 发表时间: 2023
• 期刊: The Journal of Chemical Physics
• 作者: Martinez B, Jessica A.;Shao, Xuecheng;Jiang, Kaili;Pavanello, Michele
• 通讯作者: Pavanello, Michele

Which Physical Phenomena Determine the Ionization Potential of Liquid Water?

哪些物理现象决定液态水的电离势？

• DOI: 10.1021/acs.jpcb.2c07639
• 发表时间: 2023
• 期刊: The Journal of Physical Chemistry B
• 作者: Martinez B, Jessica A.;Paetow, Lukas;Tölle, Johannes;Shao, Xuecheng;Ramos, Pablo;Neugebauer, Johannes;Pavanello, Michele
• 通讯作者: Pavanello, Michele

Learning a manifold from a teacher’s demonstrations

从老师的示范中学到很多东西

• 发表时间: 2020
• 期刊: NeurIPS workshop: TDA and beyond
• 作者: Wang, P.;Givchi, A.;Shafto, P.
• 通讯作者: Shafto, P.

Sequential cooperative Bayesian inference

顺序合作贝叶斯推理

• 发表时间: 2020
• 期刊: International Conference on Machine Learning
• 作者: Wang, J.;Wang, P.;Shafto, P.
• 通讯作者: Shafto, P.