Award Abstract # 2127188
CC* Compute: A HPC Cluster for Science Research and Education at Tennessee Tech University

NSF Org: OAC
Office of Advanced Cyberinfrastructure (OAC)
Recipient: TENNESSEE TECHNOLOGICAL UNIVERSITY
Initial Amendment Date: July 12, 2021
Latest Amendment Date: February 1, 2023
Award Number: 2127188
Award Instrument: Standard Grant
Program Manager: Kevin Thompson
kthompso@nsf.gov
 (703)292-4220
OAC
 Office of Advanced Cyberinfrastructure (OAC)
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: September 1, 2021
End Date: August 31, 2025 (Estimated)
Total Intended Award Amount: $399,983.00
Total Awarded Amount to Date: $399,983.00
Funds Obligated to Date: FY 2021 = $399,983.00
History of Investigator:
  • Michael Rogers (Principal Investigator)
    mrogers@tntech.edu
  • Alfred Kalyanapu (Co-Principal Investigator)
  • Syed Rafay Hasan (Co-Principal Investigator)
  • Michael Renfro (Co-Principal Investigator)
  • Sheikh Ghafoor (Former Co-Principal Investigator)
Recipient Sponsored Research Office: Tennessee Technological University
1 WILLIAM L JONES DR
COOKEVILLE
TN  US  38505-0001
(931)372-3374
Sponsor Congressional District: 06
Primary Place of Performance: Tennessee Technological University
1 William L Jones Dr
Cookeville
TN  US  38505-0001
Primary Place of Performance
Congressional District:
06
Unique Entity Identifier (UEI): KZNHNMDUTJA5
Parent UEI:
NSF Program(s): Campus Cyberinfrastructure
Primary Program Source: 010V2122DB R&RA ARP Act DEFC V
Program Reference Code(s): 102Z
Program Element Code(s): 808000
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).

Researchers at Tennessee Tech University (TN Tech) are making significant upgrades to the campus computing infrastructure that will significantly improve the university researchers? and students? ability to perform, enhance, and expand their systems-oriented, algorithms-oriented, or applications-oriented research activities. This enhanced new computing infrastructure complements other investments already made, in-progress, or in-planning at TN Tech. This modern campus cluster will enable TN Tech to grow and sustain the HPC culture by expanding on its current NSF-funded CyberTraining activities to include hundreds of faculty and their undergraduate students from resource-limited institutions from across the southeast. This new cluster will help TN Tech to build a regional resource for computational capacity and workforce development expanding opportunities to underrepresented groups in the region.

This award allows TN Tech to procure a state-of-the-art, cost-effective 10 node GPU cluster supporting 20 NVIDIA A100 GPUs, 1280 AMD Epyc2 CPU cores, and 5 TiB of main memory connected with 100 Gbit/s Infiniband. The cluster provides capabilities not previously available on campus. This project team anticipates significant research projects in computational fluid dynamics, biomechanics, and geospatial analysis, which will engage four partner universities. Seventeen ongoing research projects including 3 teaching projects will directly benefit from the improved infrastructure. Additional research projects will be enabled over time as the PIs and the planned internal advisory committee attract additional researchers and students requiring heterogeneous HPC. The project team expects about 100 of College of Engineering Ph.D. students will use the HPC infrastructure for their research.

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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Adeyemo, Adewale A. and Hasan, Syed Rafay "Enhancing the Security of Collaborative Deep Neural Networks: An Examination of the Effect of Low Pass Filters" GLSVLSI '23: Proceedings of the Great Lakes Symposium on VLSI 2023 , 2023 https://doi.org/10.1145/3583781.3590299 Citation Details
Hasan, Eslam and Mahalal, Elmahedi and Ismail, Muhammad and Wu, Zi-Yang and Fouda, Mostafa M and Koketsu_Rodrigues, Tiago and Kato, Nei "Robust Deep Learning-based Indoor mmWave Channel Prediction Under Concept Drift" , 2023 https://doi.org/10.1109/VTC2023-Fall60731.2023.10333513 Citation Details

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