Case Study: UIC Utilizes Liqid Composable Infrastructure for Uneven Applications in Scientific Research

Authors: Liqid

Publication: Liqid Case Study, Chicago, IL

Led by EVL Director Maxine Brown, a large team of research faculty and staff from UIC’s College of Engineering applied for and received a Major Research Instrumentation (MRI) grant from the National Science Foundation (NSF) for approximately $1-million to acquire a deep learning and visualization infrastructure.

The design utilizes composable infrastructure architecture from Liqid. The computer’s components (traditional processor, GPU, storage, and networking) are pooled so that different applications with different workflows can be run simultaneously, with each configuring the resources it requires almost instantaneously, at any time.

“EVL resources are traditionally very tailored to meet the requirements of specific applications. For example, on any given day, EVL may address everything from computational fluid dynamics, ecology and neuroimaging to natural language processing, urban engineering, robotics, visualization and visual analytics,” said Lance Long, EVL Senior Research Programmer. “Liqid’s composable infrastructure architecture is interesting because it allows us the flexibility to redefine cyberinfrastructure programmatically based on the applications’ requirements. This enables the EVL team to create a data science platform that presents a full-stack hardware and software solution to the challenges faced by our researchers trying to deploy and scale reproducible experiments.”

Date: April 23, 2020

Document: View PDF
EVL’s COMPaaS DLV system - Photo courtesy of Lance Long, EVL

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