CC* Data Storage: FASTER Data Infrastructure to Accelerate Computing
- Institution
- Texas A&M University, TX
- Principal investigator
- Lisa M Perez
- Funding
- $500K
- Duration
- 2023–2025
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Abstract
Researchers using computational modeling and data-analytics approaches require access to cutting-edge Cyberinfrastructure (CI) that is coupled with high-performance storage infrastructure. To address this growing need, the shared, parallel, high-performance, and high-capacity storage system is coupled to the state-of-the-art National Science Foundation (NSF) funded Launch and composable FASTER computing clusters for use across 11 universities in The Texas A&M University System (TAMUS). Connected over a state-wide high throughput network, the storage infrastructure offers researchers with seamless access to cutting-edge computing accelerators, support for large datasets, accessible software frameworks, interactive computing approaches, web-applications for advanced computing, and a comprehensive researcher-training program. Research benefiting from this open-source storage system includes: artificial intelligence and machine learning, cybersecurity, health population informatics, bioinformatics, materials design, climate modeling, quantum computing architectures, biomedical imaging, geosciences, and workflows for data collected on instruments such as cryogenic electron microscopes. With an enrollment of over 150,000 students in the TAMUS, the storage system will support researchers at ten emerging research institutions, that includes 6 Hispanic Serving Institutions and the oldest Historically Black College University in Texas. The growing CI ecosystem will foster burgeoning programs in Data Sciences, facilitate new research collaborations around CI, and help build a cohesive vision for regional computing. The national research community will benefit through 20% of the storage space committed as part of the federated data sharing fabric PATh/OSDF, allocations on the FASTER cluster offered by NSF ACCESS, and direct connectivity to most major cloud service providers via Internet2. 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.
Research team
- Lisa M PerezPI
- Honggao Liu
- Dhruva K Chakravorty
- Zhe Zhang
- Theodora Chaspari
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