Research Infrastructure: MRI: Acquisition of a GPU Cluster to Advance the Land Grant Mission at Washington State University Using AI-Driven Research
研究基础设施:MRI:收购 GPU 集群,利用人工智能驱动的研究推进华盛顿州立大学的土地授予任务
基本信息
- 批准号:2216108
- 负责人:
- 金额:$ 45万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-10-01 至 2025-09-30
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
This project will enable Washington State University (WSU) to acquire a high-performance computing (HPC) cluster called Camas that incorporates state-of-the-art Graphics Processing Unit (GPU) accelerators. The cluster of GPU-accelerated computers, with their vastly greater density of small compute cores and matrix processors, will radically speed up computationally intensive tasks in machine-learning (ML), artificial intelligence (AI), genomics, and simulation. Through this speedup, Camas will provide new capabilities for scientific computing in research areas ranging from computer hardware design using deep learning to plant genome analysis and climate modeling.Camas will enable GPU-accelerated research that focuses on three key scientific and engineering research pillars. These pillars are software and hardware development using ML, simulation and analysis of natural systems, and big data analytics for infrastructure management, planning, and decision making. Applications that leverage GPU acceleration as well as AI and ML range from astrophysics and quantum information science, to modeling coupled human-natural systems and chemically-reactive transport, to decision making that optimizes demand response in electricity markets. The Camas cluster will strengthen a broad spectrum of research areas, promote the synergistic transfer of knowledge across disciplines, and increase collaborative opportunities both within the University as well as with regional and national partners. Through a breadth of collaboration that spans three of WSU's major campuses and five different Colleges, Camas will have broad impact in nucleating AI-driven research efforts throughout the WSU system as well as fostering training opportunities for students in AI, ML, and data science. Through partnerships with student-led groups including the Python and R Working Groups, as well as the Office of Undergraduate Education, Camas will increase student engagement in research computing including strategic activities that create a pathway for increasing participation of underrepresented groups in STEM. The new computational capabilities and the concomitant development of HPC computational literacy throughout the research community will further enable additional regional partnerships and engagement opportunities for both faculty and students. A project website and repository for Camas that holds links to research artifacts and details of project activities will be housed at https://hpc.wsu.edu/camas. The Camas project website will be maintained under the auspices of the Washington State University Center for Institutional Research Computing.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.
该项目将使华盛顿州立大学(WSU)获得一个名为卡马斯的高性能计算(HPC)集群,该集群集成了最先进的图形处理单元(GPU)加速器。 GPU加速的计算机集群具有更大的小型计算核心和矩阵处理器密度,将从根本上加快机器学习(ML),人工智能(AI),基因组学和模拟中的计算密集型任务。 通过这种加速,卡马斯将为研究领域的科学计算提供新的能力,从使用深度学习的计算机硬件设计到植物基因组分析和气候建模。卡马斯将实现GPU加速的研究,重点关注三个关键的科学和工程研究支柱。这些支柱是使用ML的软件和硬件开发,自然系统的模拟和分析,以及用于基础设施管理,规划和决策的大数据分析。利用GPU加速以及人工智能和机器学习的应用范围从天体物理学和量子信息科学到建模耦合的人类-自然系统和化学反应运输,再到优化电力市场需求响应的决策。 卡马斯集群将加强广泛的研究领域,促进跨学科知识的协同转移,并增加大学内部以及与区域和国家合作伙伴的合作机会。通过跨越WSU三个主要校区和五个不同学院的广泛合作,卡马斯将在整个WSU系统中对人工智能驱动的研究工作产生广泛的影响,并为AI,ML和数据科学的学生提供培训机会。 通过与学生领导的团体,包括Python和R工作组,以及本科教育办公室的合作伙伴关系,卡马斯将增加学生参与研究计算,包括战略活动,创造一个途径,增加在干代表性不足的群体的参与。 新的计算能力和HPC计算素养在整个研究界的伴随发展将进一步为教师和学生提供更多的区域合作伙伴关系和参与机会。卡马斯的项目网站和存储库将位于https://hpc.wsu.edu/camas,其中包含研究工件和项目活动细节的链接。 卡马斯项目网站将在华盛顿州立大学机构研究计算中心的赞助下维护。该奖项反映了NSF的法定使命,并被认为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来支持。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Peter Mills其他文献
Adaptive Gaussian filters for interpolation , classification and retrieval
用于插值、分类和检索的自适应高斯滤波器
- DOI:
- 发表时间:
- 期刊:
- 影响因子:0
- 作者:
Peter Mills;S. Buehler - 通讯作者:
S. Buehler
The patterning of volcanic glass transfer across eastern Oʻahu Island, Hawaiʻi
夏威夷欧胡岛东部的火山玻璃转移图案
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Seth Quintus;Thomas Dye;Peter Mills;Steven Lundblad;Colsen Balai;Timothy M. Rieth;D. Filimoehala;Christopher W. Filimoehala;Alexander E. Morrison;Jon Tulchin;Trever Duarte;Mark D. McCoy;Peng Jiang - 通讯作者:
Peng Jiang
Erratum: Revisiting the Warnock rule
勘误表:重新审视沃诺克规则
- DOI:
10.1038/nbt1217-1211d - 发表时间:
2017 - 期刊:
- 影响因子:46.9
- 作者:
J. Hurlbut;I. Hyun;A. Levine;R. Lovell;J. Lunshof;K. Matthews;Peter Mills;Alison Murdoch;Martin F Pera;C. Scott;J. Tizzard;M. Warnock;M. Zernicka;Q. Zhou;L. Zoloth - 通讯作者:
L. Zoloth
Accelerating kernel classifiers through borders mapping
通过边界映射加速内核分类器
- DOI:
- 发表时间:
2017 - 期刊:
- 影响因子:3
- 作者:
Peter Mills - 通讯作者:
Peter Mills
Tibio-femoral cartilage defects 3-5 years following arthroscopic partial medial meniscectomy.
关节镜部分内侧半月板切除术后 3-5 年胫骨-股骨软骨缺损。
- DOI:
- 发表时间:
2008 - 期刊:
- 影响因子:7
- 作者:
Peter Mills;Yuanyuan Wang;F. Cicuttini;Karl Stoffel;G. Stachowiak;P. Podsiadło;David G. Lloyd - 通讯作者:
David G. Lloyd
Peter Mills的其他文献
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{{ truncateString('Peter Mills', 18)}}的其他基金
MRI: Acquisition of an X-Ray Fluorescence Spectrometer: QuantX EDXRF
MRI:获取 X 射线荧光光谱仪:QuantX EDXRF
- 批准号:
1427950 - 财政年份:2014
- 资助金额:
$ 45万 - 项目类别:
Standard Grant
Roots of Decline? Assembly and Function of the Rhizosphere Microbiome in Relation to Crop Yield
衰落的根源?
- 批准号:
BB/L026201/1 - 财政年份:2014
- 资助金额:
$ 45万 - 项目类别:
Research Grant
Growing risk? The potential impact of plant disease on land use and the UK rural economy
风险越来越大?
- 批准号:
ES/E010873/1 - 财政年份:2007
- 资助金额:
$ 45万 - 项目类别:
Research Grant
MRI/RUI Acquisition of an Energy-Dispersive X-Ray Fluorescence (EDXRF) Analyzer for the Geochemical Characterization of Archaeological Lithics in the Hawaiian Islands
MRI/RUI 获取能量色散 X 射线荧光 (EDXRF) 分析仪,用于夏威夷群岛考古石器的地球化学表征
- 批准号:
0317528 - 财政年份:2003
- 资助金额:
$ 45万 - 项目类别:
Standard Grant
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