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

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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.