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MRI: Acquisition of a GPU-Accelerated Research Cluster

MRI：收购 GPU 加速的研究集群

基本信息

批准号：
1920147
负责人：
Pengyu Hong
金额：
$ 35万
依托单位：
Brandeis 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=1920147&HistoricalAwards=false
关键词：
MRI Acquisition GPU Accelerated Research

项目摘要

Large scale data analysis capability has become the key factor that is driving rapid progress in all fields of science. This project will significantly expand the high-performance computing resources at Brandeis University, by acquiring state of the art GPU computing machines that enable big data driven research. The new capability will support integrated research projects across a variety of disciplines, including the areas of Biology, Biochemistry, Chemistry, Computer Science, Math, Physics, and Psychology. It will also provide a platform at Brandeis University for training the next generation workforce to develop and apply deep learning techniques, thus accelerating discoveries in basic research and technological innovations. The project will enable Brandeis researchers and instructors to utilize modern computing techniques to broaden participation in science, technology, engineering, and mathematics (STEM) fields. Specifically, the new resources will include 16 GPU nodes and 1 storage node on a 10Gbit network fabric to allow rapid internode communications. This project will enable Brandeis researchers to conduct big data driven convergence research that enhances our understanding of the Rules of Life in areas ranging from neuroscience to virus assembly, and addresses the fundamental problems underlying societal needs (e.g., green energy). The emerging interdisciplinary research activities will also create an inclusive environment for developing novel and more powerful big data driven techniques. The project will enable courses and workshops that train faculty, postdocs, graduate students, and undergraduate students to effectively use state of the art GPU computing and deep learning techniques. It will allow Brandeis to further integrate its education and research via a current NSF funded REU site at Brandeis Materials Research Science and Engineering Research Center. In addition, it will enhance Brandeis' ability to broaden participation in STEM, especially by women and underrepresented minorities, through several existing programs, including the NSF funded REU site, the Transitional Year Program for economically disadvantaged students, the local Society for Advancement of Chicanos/Hispanics and Native Americans in Science chapter at Brandeis, the Brandeis Science Posse Program, and the Brandeis Scientists in the Classroom Workshop.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.

大规模数据分析能力已经成为推动所有科学领域快速发展的关键因素。该项目将显著扩展布兰迪斯大学的高性能计算资源，通过收购最先进的GPU计算机器，实现大数据驱动的研究。新的能力将支持跨多种学科的综合研究项目，包括生物学、生物化学、化学、计算机科学、数学、物理和心理学等领域。它还将在布兰迪斯大学提供一个平台，培训下一代劳动力开发和应用深度学习技术，从而加速基础研究和技术创新的发现。该项目将使布兰代斯大学的研究人员和教师能够利用现代计算技术扩大科学、技术、工程和数学（STEM）领域的参与。具体来说，新资源将包括16个GPU节点和1个存储节点在10Gbit网络结构上，以实现节点间的快速通信。该项目将使布兰迪斯大学的研究人员能够开展大数据驱动的融合研究，增强我们对从神经科学到病毒组装等领域的生命规则的理解，并解决潜在社会需求的基本问题（例如，绿色能源）。新兴的跨学科研究活动也将为开发新颖和更强大的大数据驱动技术创造一个包容的环境。该项目将开设课程和研讨会，培训教师、博士后、研究生和本科生有效地使用最先进的GPU计算和深度学习技术。它将允许布兰代斯大学通过目前由美国国家科学基金会资助的布兰代斯大学材料研究科学与工程研究中心的REU站点进一步整合其教育和研究。此外，它将增强布兰代斯大学通过几个现有项目扩大STEM参与的能力，特别是女性和代表性不足的少数民族，包括美国国家科学基金会资助的REU网站、经济困难学生的过渡年计划、当地的奇卡诺人/西班牙裔和美洲原住民促进协会在布兰代斯大学的科学分会、布兰代斯大学科学团队计划和布兰代斯大学科学家在课堂研讨会。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

期刊论文数量（14）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

GlycoDeNovo2: An Improved MS/MS-Based De Novo Glycan Topology Reconstruction Algorithm.

DOI：
10.1021/jasms.1c00288
发表时间：
2022-03-02
期刊：
JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY
影响因子：
3.2
作者：
Chen, Zizhang;Wei, Juan;Tang, Yang;Lin, Cheng;Costello, Catherine E.;Hong, Pengyu
通讯作者：
Hong, Pengyu

A Deep Learning Approach for COVID-19 Trend Prediction

COVID-19 趋势预测的深度学习方法

DOI：
发表时间：
2020
期刊：
International Workshop on Epidemiology Meets Data Mining and Knowledge Discovery
影响因子：
0
作者：
Tong, Yang;Sha, Long;Li, Justin;Hong, Pengyu
通讯作者：
Hong, Pengyu

NagE: Non-Abelian Group Embedding for Knowledge Graphs

DOI：
10.1145/3340531.3411875
发表时间：
2020-05
期刊：
Proceedings of the 29th ACM International Conference on Information & Knowledge Management
影响因子：
0
作者：
Tong Yang;Long Sha;Pengyu Hong
通讯作者：
Tong Yang;Long Sha;Pengyu Hong

Steady states of active Brownian particles interacting with boundaries