Display Accessibility Tools

Accessibility Tools

Grayscale

Highlight Links

Change Contrast

Increase Text Size

Increase Letter Spacing

Readability Bar

Dyslexia Friendly Font

Increase Cursor Size

MSU lands NSF grant to create an accessible supercomputer

Although the phrase “high-performance computing” traditionally conjures images of a computer scientist more so than a social scientist, the reality is that many diverse areas of research benefit from access to the advanced computational capacity offered by a high-performance computing center (HPCC).

ICER logo on door entrance
The NSF has awarded a $399,865 Campus Cyberinfrastructure Planning Grant to MSU to enable researchers from diverse academic backgrounds to utilize the campus HPCC facilitated by the Institute for Cyber-Enabled Research (ICER). Courtesy photo

The National Science Foundation has awarded a $399,865 Campus Cyberinfrastructure Planning Grant to Michigan State University to enable researchers from diverse academic backgrounds to utilize the campus HPCC facilitated by the Institute for Cyber-Enabled Research (ICER). Several MSU College of Natural Science (NatSci) faculty members—Brian O’Shea, Phoebe Zarnetske and Matt Schrenk—are playing key roles in the project.

Technological advancements and increased availability of data have led to an explosion of data for researchers to employ in machine learning (ML) and artificial intelligence (AI), particularly in fields of study where computing has not been widely used. With the Campus Cyberinfrastructure grant, ICER will create the MSU Data Machine—an accessible supercomputer optimized for such data-intensive research as ML and AI applications.

“This is a very exciting opportunity for NatSci,” said Eric Hegg, NatSci associate dean for budget, operations and research. “The MSU Data Machine will enable researchers from across the college to benefit from the power of high-performance computing by increasing access and providing critical training and expertise, thereby significantly lowering the barriers to using large datasets.  The positive impact of the MSU Data Machine will very likely be felt almost immediately.”

Headshot of Brian O'Shea
Brian O’Shea, ICER director and professor in the Departments of Physics and Astronomy and Computational Mathematics, Science and Engineering, is principal investigator for the grant. Courtesy photo

Brian O’Shea, ICER director and professor in the NatSci Department of Physics and Astronomy and the Department of Computational Mathematics, Science and Engineering, is principal investigator for the grant. O’Shea noted that the technical optimization of the MSU Data Machine is paired with a comprehensive outreach and training program to ensure access to researchers from fields that do not typically use high-performance computing in their workflow.

“The machine will include large amounts of memory to facilitate user-friendly data analysis, low latency solid-state storage that is optimized for working with small files and complex access patterns, and graphics processing units (GPUs) that are well-suited for ML and AI applications,” O’Shea said. “We will ensure that this resource is maximally accessible to researchers and instructors through tools like Open OnDemand, which provides a graphical interface that is much easier to use than the standard command-line interface, modern cloud-informed system and user tools, and usage policies that promote interactive data analysis over a batch queue-based system.”

Illustration of satellite remote sensing and modeling.
The MSU SpaCE Lab led by Phoebe Zarnetske studies what drives biodiversity. Projects include combining data from the National Ecological Observatory Network (NEON) with satellite remote sensing and modeling to explain and predict changes in bird, tree, fish, mammal, and insect biodiversity from local to continental scales. Credit: National Science Foundation

The MSU Data Machine will address the unique needs of researchers in areas such as microbiology, social dynamics, ecology, and remote imaging, and can ultimately lead to substantial scientific advances. Four research groups, led by co-principal investigators of the Campus Cyberinfrastructure grant, will be the first users of the MSU Data Machine. Once efficacy has been established, the machine will be made available to the broader MSU community.

Phoebe Zarnetske, associate professor in the NatSci Department of Integrative Biology and co-PI, has experienced hindered progress due to a lack of resources for big data processing and interactive computing. The MSU Data Machine will provide the resources needed to further her research.

“Big data are essential to help explain and predict natural phenomena including patterns of biodiversity, impacts of climate change on genes to ecosystems, and feedbacks among ecology, evolution, and behavior,” Zarnetske said. “By combining data from satellites, gene sequences, and observations of organisms from public science efforts like iNaturalist or the National Ecological Observatory Network (NEON), we can advance both fundamental knowledge and applied questions that are essential for sustainable management and conservation of Earth’s ecosystems in time and space. The MSU Data Machine enables integrative and conservation biology to expand to bigger scales in research and teaching, facilitating knowledge, discovery, and more robust forecasts of how life is changing on Earth.”

This figure shows work done by the Schrenk Lab looking at microbial species in groundwater and their relationship to environmental characteristics of the water to demonstrate how big data approaches might be used.
This figure shows work done by the Schrenk Lab looking at microbial species in groundwater and their relationship to environmental characteristics of the water to demonstrate how big data approaches might be used. Credit: Schrenk Lab

One of the major challenges faced by Matthew Schrenk, associate professor with joint appointments in the Department of Earth and Environmental Sciences and the Department of Microbiology and Molecular Genetics in NatSci, and co-PI is the variable level of experience students have with coding. The MSU Data Machine’s user-friendly interface will reduce the barrier to entry for high-performance computing.

"The Earth and Environmental Sciences are an area defined by the grand scales that they cover, ranging from nanoseconds to billions of years and from molecules to planets,” Schrenk said. “The new campus cyberinfrastructure award will help us to train our students to work with data across these scales, providing them with new opportunities in the workforce, and potentially new perspectives through the integration of data sets from different disciplines."

The deployment of the MSU Data Machine will provide low-barrier access to computational resources for researchers and instructors across MSU. The technical optimization is joined with user training and support structures that will facilitate data-intensive research and instruction and ultimately contribute to the development of a globally competitive STEM workforce.

In addition to O’Shea, Zarnetske and Schrenk, Arika Ligmann-Zielinska, associate professor in the Department of Geography, Environment, and Spatial Sciences; and Junlin Yuan, an assistant professor in the Department of Mechanical Engineering in the College of Engineering, are co-PIs on the grant. 

 

Banner image: The National Science Foundation has awarded a $399,865 Campus Cyberinfrastructure Planning Grant to Michigan State University to create the MSU Data Machine—an accessible supercomputer optimized for such data-intensive research as machine learning and artificial intelligence applications, which will be housed at MSU Institute for Cyber-Enabled Research. Credit: ICER