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.

在农业食品和环境领域，缺乏具有高性能计算（HPC）环境支持的计算到规模能力的专业知识的科学家。农业食品研究人员对HPC功能的低采用率在很大程度上归因于使用HPC的真实的（或感知的）复杂性。此外，植根于CSE科学的传统培训课程往往缺乏情境化的问题焦点和动手访问量身定制的学习数据和问题集，这些数据和问题集是熟悉的，因此对提高这一特定部门的劳动力技能有用。该项目建议开发和部署一个多模块的学习课程，为农业食品科学中的CI应用量身定制，作为同步的虚拟产品提供大量的实践应用学习机会。拟议工作将具有普遍性的挑战，以便寻求利用数据科学和HPC核心进步的其他落后社区可以利用根据该提案开发的方法和基础设施。建议的多模块课程的重点是建立必要的基础，数据驱动的技能，创造一个可持续的社区熟练的CI用户通过定制，学科适当的课程材料，旨在弥合特定领域的科学和计算机科学之间的差距差距的农业食品科学家。该建议旨在开发和部署一个多模块的学习课程，为网络基础设施（CI）的应用，特别是高性能计算（HPC），在农业食品科学，是作为一个同步的，虚拟提供大量的实践应用为基础的学习机会。30人的课程将通过集装箱化的学习环境提供，以确保所有学习者都可以随时使用相同的工具。前三个课程模块为基于HPC的分析提供了基本的构建模块，随后是一系列实践应用模块，使农业食品研究人员能够具备促进HPC分析关键农业食品问题所需的能力水平。该课程将提供给美国和国外的学术（本科生，研究生和教职员工）观众（特别是针对学生人数不足的人群），以及在美国政府机构和农业综合企业工作的个人。为了实现学术和非学术的可访问性，该试点项目将在Microsoft Azure云计算基础设施上举办以CI为重点的农业食品分析课程，但该课程将向学习者介绍可用的私有，学术和基于云的HPC资源组合。项目团队将与内部和外部农业食品网络合作，并利用ACCESS知识库Ask.CI和/或社区亲和团体的功能。该团队将在整个资助期间进行一系列外部和内部内容和交付审计，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提供HPC课程的alpha-，beta-和全课程实例后，该课程将继续通过其GEMS学习组合每年提供2-3次。该奖项反映了NSF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。