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Phase-I IUCRC Virginia Tech: Center for Space, High-performance, and Resilient Computing (SHREC)

Phase-I IUCRC Virginia Tech: Center for Space, High-performance, and Resilient Computing (SHREC)
第一阶段 IUCRC 弗吉尼亚理工大学:空间、高性能和弹性计算中心 (SHREC)
批准号:
1822080
负责人:
Wuchun Feng
金额:
$60.0万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2018
资助国家:
美国
项目状态:
已结题
起止时间:
2018-10-01 至 2024-09-30

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中文摘要
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英文摘要
With this effort, Virginia Tech becomes a research Site of the existing Center for Space, High-performance, and Resilient Computing (SHREC), an NSF Industry-University Collaborative Research Center. SHREC comprises four university Sites: University of Pittsburgh, Brigham Young University, University of Florida, and Virginia Tech. The Center is dedicated to assisting U.S. industrial partners, government agencies, and research organizations in mission-critical computing across three domains: space computing for Earth science, space science, and defense; high-performance computing across a broad range of grand-challenge applications; and resilient computing for dependability in harsh or critical environments. The Center also seeks to address the shortage in the mission-critical computing workforce by training many students with the knowledge and skills necessary to solve the many challenges facing this growing industry.With the complementary nature of expertise at each Site, the Center will address research challenges facing the three aforementioned domains of mission-critical computing by exploiting a variety of existing and emerging computing technologies, including field-programmable gate arrays (FPGAs), graphical processing units (GPUs), and hybrid processors. For space computing, the Center will develop, evaluate, and deploy novel forms of space architectures, systems, and applications while leveraging commercial and radiation-hardened technologies. For high-performance computing, the Center will explore the application and productive use of heterogeneous computing technologies and architectures in support of high-speed, mission-critical computing. For resilient computing, the Center will exploit its expertise in energy-efficient embedded computing for resilience and in fault injection and mitigation and radiation testing to demonstrate reliability concepts and solutions, including adaptive hardware redundancy, fault masking, and software fault tolerance.Research projects in the SHREC IUCRC will significantly benefit society in terms of economic impact, due to the advancement of new ideas and technologies adopted by industry partners and featured in Center publications. Moreover, the new IUCRC will be student-centric, where each industrial affiliate board (IAB) membership funds a graduate student, and each project aims to support a graduate thesis and will employ a diverse body of undergraduate and graduate students. In addition to its research activities, the Center will serve as a catalyst for supporting and broadening the educational and outreach missions of its university partners and members. Artifacts that result from this SHREC IUCRC will be maintained at the Center-wide repository at http://www.chrec.org/ and gradually transitioned to https://nsf-shrec.org/. In addition, as appropriate, code and data will be hosted at an open-source GIT repository such as github, gitlab, or gitea.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.
期刊论文(7)
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科研奖励(0)
会议论文
Fast Stochastic Block Partitioning via Sampling
通过采样进行快速随机块划分
DOI: 10.1109/hpec.2019.8916542
发表时间: 2019
期刊: IEEE High Performance Extreme Computing Conference
影响因子: --
作者: [Wanye, Frank, Gleyzer, Vitaliy, Feng, Wu-chun]
通讯作者: Feng, Wu-chun
DOI: 10.1109/fpl50879.2020.00032
发表时间: 2020-08
期刊: 2020 30th International Conference on Field-Programmable Logic and Applications (FPL)
影响因子: --
作者: [Atharva Gondhalekar;W. Feng]
通讯作者: Atharva Gondhalekar;W. Feng
DOI: 10.1145/3545008.3545058
发表时间: 2022
期刊: International Conference on Parallel Processing
影响因子: --
作者: [Wanye, Frank, Gleyzer, Vitaliy, Kao, Edward, Feng, Wu-chun]
通讯作者: Feng, Wu-chun
MetaCL: Automated “Meta” OpenCL Code Generation for High-Level Synthesis on FPGA
MetaCL:用于 FPGA 上高级综合的自动化 —Meta — OpenCL 代码生成
DOI: 10.1109/hpec43674.2020.9286198
发表时间: 2020
期刊: IEEE High Performance Extreme Computing Conference (HPEC
影响因子: --
作者: [Sathre, Paul, Gondhalekar, Atharva, Hassan, Mohamed, Feng, Wu-chun]
通讯作者: Feng, Wu-chun
6
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    RAPID: A Computational Deep-Learning Approach for Fast, Accurate CT Testing and Monitoring of COVID-19
    NSF XPS Workshop for Exploiting Parallelism and Scalability
    海外基金