Cyber Training: Pilot -- Breaking the Compute Barrier, Upskilling Agri-Food Researchers to Utilize HPC Resources

网络培训：试点 - 打破计算障碍，提高农业食品研究人员利用 HPC 资源的技能

• 批准号: 2320769
• 负责人: Kevin Silverstein
• 金额: $ 30万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2320769&HistoricalAwards=false
• 关键词: Cyber; Training; Pilot; Breaking; Compute

There is a dearth of scientists with expertise in the agri-food and environment domains that have compute-to-scale capabilities enabled by High Performance Computing (HPC) environments. Low adoption of HPC capabilities among agri-food researchers can be largely attributed to the real (or perceived) complexity of using HPC. Moreover, traditional training courses rooted in the CSE sciences often lack the contextualized problem focus and hands-on access to tailor-made learning data and problem sets that are familiar to and thus useful for upskilling this particular sector of the workforce. This project proposes to develop and deploy a multi-module learning curriculum tailored to CI-applications in the agri-food sciences that is provided as a synchronous, virtual offering with substantial hands-on application-based learning opportunities. The challenges that the proposed work will be made generalizable, such that other left-behind communities seeking to capitalize on core advances in data science and HPC can leverage the approaches and infrastructure developed under this proposal. The proposed multi-module course is focused on building the foundational, data-driven skills necessary to create a sustainable community of skilled CI Users through tailored, discipline-appropriate course materials targeted at bridging the gap between domain specific science and computer science for agri-food scientists.This proposal aims to develop and deploy a multi-module learning curriculum tailored to Cyberinfrastructure (CI)-applications, notably High-Performance Computing (HPC), in the agri-food sciences that is provided as a synchronous, virtual offering with substantial hands-on application-based learning opportunities. The 30-person course will be delivered via a containerized learning environment to ensure all learners have ready access to an identical set of tools. The first three course modules provide the basic building blocks for HPC-based analytics, followed by a series of hands-on application modules that enable agri-food researchers with the levels of competency needed to facilitate HPC analyses of critical agri-food problems. The course will be accessible to academic (undergraduate, graduate, and faculty/staff) audiences around the US and abroad (especially targeting underrepresented populations of students), as well as individuals working in US government agencies and agri-business firms. To enable both academic and non-academic accessibility, this pilot project will host the CI-focused agri-food analytics curriculum on Microsoft Azure cloud computing infrastructure, but the course will introduce learners to the portfolio of available private, academic, and cloud-based HPC resources. The project team will work with internal and external agri-food networks and leverage the capabilities of the ACCESS Knowledge Base Ask.CI and/or Community Affinity Groups. The team will engage in a series of external and internal content and delivery audits throughout the grant period to ensure the identification of optimal HPC learning pathways for agri-food researchers. After delivering alpha-, beta- and full-course instances of their HPC for Agri-Food Researchers course, the course will continue to be offered 2-3 times annually through their GEMS Learning portfolio beyond the life of the grant.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.

缺乏在农业食品和环境领域具有专业知识的科学家，这些科学家通过高性能计算（HPC）环境实现了计算到规模的能力。农业食品研究人员中HPC能力的采用率低可能主要归因于使用HPC的实际（或感知）复杂性。此外，植根于CSE科学的传统培训课程通常缺乏上下文化的问题的重点，并动手访问量身定制的学习数据和问题集，这些数据和问题集熟悉，因此对于提高劳动力的这一特定领域而有用。该项目建议开发和部署针对农业食品科学中CI应用程序量身定制的多模块学习课程，该课程作为同步，虚拟产品提供，具有实质性的基于申请的学习机会。拟议的工作将成为可推广的挑战，以便其他寻求利用数据科学和HPC核心进步的左撇子社区可以利用本提案中开发的方法和基础设施。拟议的多模型课程的重点是建立基础，数据驱动的技能，通过量身定制的，纪律 - 适当的课程材料来创建熟练的CI用户社区，该社区旨在弥合针对农业科学的领域特定科学和计算机科学之间的差距高性能计算（HPC），在农业食品科学中，该科学作为同步，虚拟产品提供，具有实质性的基于申请的学习机会。 30人的课程将通过容器化的学习环境进行交付​​，以确保所有学习者都可以访问相同的工具。前三个课程模块为基于HPC的分析提供了基本的构建块，然后是一系列动手应用模块，使农业食品研究人员具有促进关键农业食品问题的HPC分析所需的能力水平。该课程将由美国和国外的学术（本科，研究生和教职员工）以及在美国政府机构和农业企业公司工作的个人以及在美国和农业企业中工作的个人提供学术课程。为了启用学术和非学术可及性，该试点项目将主持有关Microsoft Azure Azure云计算基础架构的以CI为中心的农业食品分析课程，但是该课程将向学习者介绍可用的私人，学术和基于云的HPC资源的投资组合。项目团队将与内部和外部农业食品网络合作，并利用访问知识库Ask.ci和/或社区亲和力小组的功能。该团队将在整个赠款期间进行一系列外部和内部内容和交付审核，以确保为农业食品研究人员确定最佳的HPC学习途径。在为农业食品研究人员的HPC提供了α-，β-和完整的实例之后，该课程将每年通过其GEMS学习组合超出授予的一生，每年将继续提供2-3次，这是NSF的裁定，反映了NSF的立法使命，并认为通过基金会的智力效果进行评估，并值得通过评估来进行评估。