$997K NSF grant: deep learning and visualization infrastructure (EVL)
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A large UIC team led by Maxine Brown, Director of the Computer Science Department’s Electronic Visualization Lab (EVL), received a new $997,363 NSF Major Research Instrumentation (MRI) grant for deep learning and visualization infrastructure. This grant was obtained by a large interdisciplinary team from many College of Engineering Departments and also Psychiatry.
Title
“MRI: Acquisition of a Composable Platform as a Service Instrument for Deep Learning & Visualization (COMPaaS DLV)”
Principal Investigator
Maxine Brown, UIC Computer Science
Co-PI’s
Andrew Johnson, Robert Kenyon and G. Elisabeta Marai, all Computer Science (CS)
Senior Personnel
Tanya Berger-Wolf (CS), Sybil Derrible (Civil & Materials Eng.), Barbara Di Eugenio (CS), Chris Kanich (CS), Alex Leow (BioEng/Psychiatry, and by courtesy CS), Bing Liu (CS), Lance Long (EVL), Farzad Mashayek (Mechanical & Industrial Eng.), Roberto Paoli (Mechanical & Industrial Eng.), Luc Renambot (CS), Philip Yu (CS), Milos Zefran (Electrical & Computer Eng.), Xinhua Zhang (CS), and Brian D. Ziebart (CS)
Dates
October 2018 to September 2021
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“MRI: Acquisition of a Composable Platform as a Service Instrument for Deep Learning & Visualization (COMPaaS DLV)” – ABSTRACT –This project is about acquiring a much-in-demand Graphics Processing Unit (GPU)-based instrument, to develop a Service Instrument for Deep Learning and Visualization called “COMPaaS DLV: COMposable Platform”. The project aims to complement available campus computing resources via the campus’s research network. It will be able to access local and remote computing and storage facilities funded, including XSEDE, Blue Waters, Deep Learning Instrument and Chameleon. This critical instrumentation will provide a platform to pursue fundamental science and engineering research training in deep learning (data mining and data analytics, computer vision, natural language processing, artificial intelligence), visualization (simulation, rendering, visual analytics, video steaming, image processing), and a combination of deep learning and visualization (e.g., when data is so large that it cannot be easily visualized, then deep learning is used to extract features of interest to be visualized). The instrumentation also enables investigation and contribution to societal issues in disciplines such as anthropology, biology, cybersecurity, data-literacy, fraud detection, healthcare, manufacturing, urban sustainability, and cyber-physical systems (e.g., autonomous cars).Its design utilizes state-of-the-art computer architecture, known as composable architecture, in which the computer’s components (traditional processor, GPU, storage, and networking) form a fluid pool of resources, such that different applications with different workflows can be run simultaneously, with each configuring the resources it requires almost instantaneously, at any time. Given composable infrastructure scalability and agility, it is more beneficial than traditional clouds and clusters that are rigid, overprovisioned, and expensive.