Award Abstract # 2200792
CC* Compute: The MSU Data Machine - a high-memory, GPU-enabled compute cluster for data-intensive and AI applications

NSF Org: OAC
Office of Advanced Cyberinfrastructure (OAC)
Recipient: MICHIGAN STATE UNIVERSITY
Initial Amendment Date: April 6, 2022
Latest Amendment Date: April 6, 2022
Award Number: 2200792
Award Instrument: Standard Grant
Program Manager: Amy Apon
awapon@nsf.gov
 (703)292-5184
OAC
 Office of Advanced Cyberinfrastructure (OAC)
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: July 1, 2022
End Date: June 30, 2025 (Estimated)
Total Intended Award Amount: $399,865.00
Total Awarded Amount to Date: $399,865.00
Funds Obligated to Date: FY 2022 = $399,865.00
History of Investigator:
  • Brian O'Shea (Principal Investigator)
    oshea@msu.edu
  • Arika Ligmann-Zielinska (Co-Principal Investigator)
  • Phoebe Zarnetske (Co-Principal Investigator)
  • Matthew Schrenk (Co-Principal Investigator)
  • Junlin Yuan (Co-Principal Investigator)
Recipient Sponsored Research Office: Michigan State University
426 AUDITORIUM RD RM 2
EAST LANSING
MI  US  48824-2600
(517)355-5040
Sponsor Congressional District: 07
Primary Place of Performance: Michigan State University
East Lansing
MI  US  48824-2600
Primary Place of Performance
Congressional District:
07
Unique Entity Identifier (UEI): R28EKN92ZTZ9
Parent UEI: VJKZC4D1JN36
NSF Program(s): Campus Cyberinfrastructure
Primary Program Source: 01002223DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):
Program Element Code(s): 808000
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

The MSU Data Machine addresses the exponential growth of large and complex datasets in many fields of study, particularly those where computing has not been widely used or where the research and teaching approaches needed to work with ?big data?, which present a different set of computing requirements than in traditional high performance computing. The Data Machine facilitates data-intensive research by having computing nodes with large amounts of memory, a high speed file system, graphics processing units that are optimized for machine learning and artificial intelligence-based analysis techniques, and a high speed file system. It also includes software, usage policies, and training that makes it easy for users to interactively analyze and visualize their data.

This project focuses on four specific research areas - in the areas of microbial genomics, social system modeling, spatial and community ecology, and data-driven turbulence modeling - however, the Data Machine broadly enables MSU?s research community to pursue data-intensive research projects by lowering barriers to engaging with these types of resources. The project also provides a valuable computational resource to the nation via the Open Science Grid and MSU?s NSF-funded Science DMZ project, advancing research in a wide spectrum of areas. Furthermore, MSU undergraduate and graduate students are participating in the deployment and administration of the Data Machine as well as using it for research and educational activities, contributing to the development of a globally competitive STEM workforce and promoting the advancement of under-represented groups in computational and data science.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Gerstner, Beth E. and Bills, Patrick and Zarnetske, Phoebe L. "Frugivoria: A trait database for birds and mammals exhibiting frugivory across contiguous Neotropical moist forests" Global Ecology and Biogeography , v.32 , 2023 https://doi.org/10.1111/geb.13716 Citation Details
Gerstner, Beth E and Blair, Mary E and Bills, Patrick and Cruz-Rodriguez, Cristian A and Zarnetske, Phoebe L "The influence of scale-dependent geodiversity on species distribution models in a biodiversity hotspot" Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences , v.382 , 2024 https://doi.org/10.1098/rsta.2023.0057 Citation Details

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