MRI: Acquisition of Instrument for Data-intensive Applications with Hybrid Cloud Computing Needs

该项目旨在获得一个超级计算机集群，该集群将反过来支持许多不同领域的数据密集型研究，如生物科学、地球科学、成像和视觉。具体地说，此次收购响应了通过联合混合云服务转变校园超级计算机资源配置实践的需求，这些服务可以无缝地协调本地和远程资源(例如，网络科学仪器、公共云)的配置，以满足用户的数据密集型研究和教育需求。该项目的重点是考虑跨学科研究和教育协作所需的连接和通信的应用工作流。超级计算机集群扩大了现有设施(例如，连接到Internet2创新平台的科学DMZ“网络仪器”、传输电子显微镜、Federated-IAM“授权服务”)和校园内核心超级计算机资源。该项目利用用于中央处理单元和图形处理单元(CPU/GPU)的高级工具，以及来自现有资助项目的网络可视化，用于配置该仪器的实验，以开发用于各种数据密集型网络应用程序的“定制模板”。这些自定义模板将抽象数据密集型应用程序的高级策略和性能吞吐量要求，并将其‘个性化’为可由OpenStack和OpenFlow等虚拟化技术以按需方式实现的低级控制规范。此外，调查人员将评估如何构建具有定制模板的下一代超级计算用户服务模型，以使校园IT员工能够可持续且无缝地支持研究和教育中的混合云使用案例。具体地说，仪器服务-混合云计算、生物信息学和计算生物学、多模式数据分析和下一代HPC用户服务。仪器支持生物科学、地球科学、成像和视觉等不同数据密集型科学领域的16名研究人员及其外部合作者。它还支持向该机构及其周围的500多名学生提供高性能计算和大数据分析课程。代表不足和服务不足的群体的参与将利用目前的NSF REU网站以及该机构的EPSCoR活动来完成。将传播简化数据密集型应用程序、技术/工具、政策和服务模型的混合云工程/运营的最佳实践。该工具支持本科生和研究生课程以及各种其他教育和培训活动，包括REU计划。拟议的项目推进了计算科学研究、研究培训和课程开发。

This project aims to acquire a supercomputer cluster that will in turn enable data-intensive research in many diverse fields such as bioscience, geoscience, imaging, and vision. Specifically, the acquisition responds to the need to transform campuses' supercomputer resource provisioning practices with federated, hybrid cloud services that can seamlessly orchestrate the provisioning of local and remote resources (e.g., cyber-enabled scientific instruments, public clouds) to meet data-intensive research and education needs of users. The project focuses on application workflows considering connectivity and communications necessary for interdisciplinary research and education collaborations. The supercomputer cluster augments existing facilities (e.g., Science DMZ 'network instrument' connected to the Internet2 Innovation platform, Transmission Electron Microscope, Federated-IAM 'entitlement service') and the core on-campus supercomputer resources. The project leverages advanced tools for central processing units and graphics processing units (CPU/GPU) as well as network visualization from existing funded projects for configuration of experiments with the instrument to develop 'custom templates' for diverse data intensive web-based applications. These custom templates will abstract the high-level policy and performance throughput requirements of data-intensive applications and 'personalize' them to lower-level control specifications implementable in an on-demand manner by virtualization technologies such as OpenStack and OpenFlow. Furthermore, the investigators will assess how the next-generation supercomputing user service models with custom templates can be composed to allow campus IT staff to sustainably and seamlessly support hybrid cloud use cases in research and education. Specifically, the instrument services- Hybrid Cloud Computing,- Bioinformatics and computational biology, - Multi-modal data analytics, and - Next generation HPC user services.The instrumentation supports 16 researchers and their external collaborators in diverse data-intensive science fields such as bioscience, geoscience, imaging, and vision. It also supports the delivery of high-performance computing and Big Data analytics courses to more than 500 students at this institution and those around it. Participation of underrepresented and underserved groups will be accomplished utilizing the current NSF REU site as well as the institution's EPSCoR activities. Best practices to streamline the engineering/operations of hybrid clouds for data intensive applications, technologies/tools, policies and service models will all be disseminated. The instrument supports undergraduate and graduate courses and various other education and training activities, including REU programs. The proposed projects advances computational science research, research training, and curriculum development.