Research Infrastructure: MRI: Acquisition of a Big Data HPC Cluster for Interdisciplinary Research and Training

研究基础设施：MRI：收购大数据 HPC 集群以进行跨学科研究和培训

• 批准号: 2215705
• 负责人: Thomas Girke
• 金额: $ 66万
• 依托单位: University of California-Riverside
• 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: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2215705&HistoricalAwards=false
• 关键词: Research; Infrastructure; MRI; Acquisition; Big

An award is made to the University of California, Riverside (UCR) to acquire a Big Data High-Performance Computing (BD-HPC) cluster designed to enable novel and transformative research, outreach and training activities that are highly relevant to the environment and society. The system will be managed by UCR's HPC Center (HPCC) that serves a broad and diverse user population distributed across colleges and departments. As a highly shared resource, the instrument will enable a large number of NSF-funded programs, including those aiming to improve practices in agriculture, environmental protection, technology development and industry. Extensive educational and training activities are integrated to disseminate multidisciplinary concepts of Big Data Science. These outreach components will educate the public about the impact of Big Data Science on the environment, economy and society. The HPCC supports many undergraduate and graduate classes in a wide range of disciplines. Its resources are also instrumental for the development of new courses and programs in various data science areas. A high percentage of students in these classes and programs are from populations that are traditionally underrepresented in STEM disciplines. The availability of adequate computing and its beneficial impact on educational programs will attract outstanding students to computational and quantitative undergraduate and graduate programs. Combined with UCR’s diverse ethnicity and research mission, this investment will benefit a wide array of translational research directions and technology-based economic development initiatives.The new BD-HPC cluster will enable novel research that cannot be performed on UCR’s current research computing infrastructure or community cyberinfrastructure (CI), while also offering sufficient capacity to ensure support of ongoing research with greatly improved performance. Both current and new research addresses fundamental problems in a highly interdisciplinary environment bridging a broad array of science and engineering disciplines in basic and translational research. Grand challenge questions asked include: How do genetic and population dynamics determine phenotypic and evolutionary diversity? How can large-scale precision data be translated into improved stress and pathogen tolerance to feed a growing world population, and to develop interventions for reducing the rate and impact of environmental changes, natural disasters and climate change? How can quantitative modeling lead to high-performance molecules, materials, and help prevent wildfires and predict earthquakes? UCR researchers working on these problems are from a wide range of research specializations, including environmental science, agriculture, biology, chemistry, physics, engineering, computer science, statistics and applied mathematics. Since their research relies heavily on high-throughput and computational modeling approaches, the BD-HPC cluster will permit new computational approaches for solving these research problems.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.

加州大学河滨分校（UCR）获得了一个大数据高性能计算（BD-HPC）集群，该集群旨在实现与环境和社会高度相关的新颖和变革性研究、推广和培训活动。该系统将由UCR的HPC中心（HPCC）管理，该中心为分布在各学院和院系的广泛而多样化的用户群体提供服务。作为一种高度共享的资源，该仪器将使大量nsf资助的项目成为可能，包括那些旨在改善农业、环境保护、技术开发和工业实践的项目。广泛的教育和培训活动，以传播大数据科学的多学科概念。这些外展内容将教育公众大数据科学对环境、经济和社会的影响。HPCC支持许多本科和研究生班在广泛的学科。它的资源也有助于开发各种数据科学领域的新课程和项目。在这些课程和项目中，有很高比例的学生来自传统上在STEM学科中代表性不足的人群。足够的计算机的可用性及其对教育计划的有益影响将吸引优秀的学生参加计算和定量的本科和研究生课程。结合UCR的多元化种族和研究使命，这项投资将有利于广泛的转化研究方向和基于技术的经济发展计划。新的BD-HPC集群将使UCR目前的研究计算基础设施或社区网络基础设施（CI）无法进行的新研究成为可能，同时还提供足够的容量，以确保对正在进行的研究的支持，并大大提高性能。当前和新的研究都在高度跨学科的环境中解决了基础研究和转化研究中广泛的科学和工程学科的基本问题。提出的重大挑战问题包括：遗传和种群动态如何决定表型和进化多样性？如何将大规模精确数据转化为提高应激和病原体耐受性，以养活不断增长的世界人口，并制定干预措施，以降低环境变化、自然灾害和气候变化的速度和影响？定量建模如何导致高性能分子，材料，并帮助防止野火和预测地震？研究这些问题的UCR研究人员来自广泛的研究专业，包括环境科学、农业、生物学、化学、物理学、工程学、计算机科学、统计学和应用数学。由于他们的研究在很大程度上依赖于高通量和计算建模方法，BD-HPC集群将允许新的计算方法来解决这些研究问题。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

Anterograde signaling controls plastid transcription via sigma factors separately from nuclear photosynthesis genes.

• DOI: 10.1038/s41467-022-35080-0
• 发表时间: 2022-12-02
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Hwang, Youra;Han, Soeun;Yoo, Chan Yul;Hong, Liu;You, Chenjiang;Le, Brandon H.;Shi, Hui;Zhong, Shangwei;Hoecker, Ute;Chen, Xuemei;Chen, Meng
• 通讯作者: Chen, Meng

Characterizing Protoclusters and Protogroups at z ∼ 2.5 Using Lyα Tomography

使用 Lyα 断层扫描表征 z ≤ 2.5 处的原簇和原群

• DOI: 10.3847/1538-4357/ac6259
• 发表时间: 2022
• 期刊: The Astrophysical Journal
• 作者: Qezlou, Mahdi;Newman, Andrew B.;Rudie, Gwen C.;Bird, Simeon
• 通讯作者: Bird, Simeon

The pan-genome of Aspergillus fumigatus provides a high-resolution view of its population structure revealing high levels of lineage-specific diversity driven by recombination.

• DOI: 10.1371/journal.pbio.3001890
• 发表时间: 2022-11
• 期刊: PLoS biology
• 影响因子: 9.8

Mathematical modeling of chemotaxis guided amoeboid cell swimming

趋化引导变形细胞游泳的数学模型

• DOI: 10.1088/1478-3975/abf7d8
• 发表时间: 2021
• 期刊: Physical Biology
• 影响因子: 2
• 作者: Wang, Qixuan;Wu, Hao
• 通讯作者: Wu, Hao

Self-sustained three-dimensional beating of a model eukaryotic flagellum

模型真核鞭毛的自我维持三维跳动

• DOI: 10.1039/d2sm00514j
• 发表时间: 2022
• 期刊: Soft Matter
• 影响因子: 3.4
• 作者: Rallabandi, Bhargav;Wang, Qixuan;Potomkin, Mykhailo
• 通讯作者: Potomkin, Mykhailo