Leif Nelson
Boise State University, ID·Computer Science
Modern artificial intelligence work, from training language models to simulating physical systems, depends on access to specialized hardware that most campuses do not have in sufficient quantity. This project builds out that shared capacity at Boise State University, adding six compute nodes each holding four NVIDIA L40S graphics processing units, which are the accelerator chips that make large-scale machine learning training and inference practical.
- Fields
Computer Science
Education Research
Engineering
- Topics
cyberinfrastructure
research computing
GPU cluster
- Grants
- 1 known grant, through Jun 2027
What they work on
- Deploy and integrate six compute nodes, each with four NVIDIA L40S GPUs, into the existing campus research computing cluster to support data science, machine learning, and AI workloads.
- Support science drivers on the new hardware, including fundamental large language model research, simulation of energy-efficient hardware, and modeling of weather, oceans, and materials.
- Incorporate the cyberinfrastructure into coursework in data science, natural language processing, and AI, including instructional use of generative AI tools, and build a campus community of practice around AI.
- Extend access to the AI-optimized resource statewide via the C3+3 consortium with Idaho National Laboratory and Idaho's research universities, and nationally through the Open Science Grid Consortium.
More about the research
The new hardware updates infrastructure supporting data science, machine learning, and AI, and is integrated with the university's existing research computing cluster rather than standing alone.
Why it matters
Research and training in machine learning are limited at many institutions by the cost of accelerator hardware. By adding shared GPU capacity and opening it to partners across Idaho and to national users through the Open Science Grid, this project could broaden who can run AI-scale computations, while giving students in data science and natural language processing courses direct experience with the hardware such work requires.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Neranjan Edirisinghe Pathirannehe
Georgia State University, GA·Computer Science
Deploy a campus computing cluster with 12 NVIDIA H100 GPUs, BlueField data processing units, 682 CPU cores and 228 TB of storage to support 11 research projects spanning wildfire management, solar prediction, neuroscience, finance and policy.
Ryan Johnson
University of South Dakota, SD·Computer Science
Add computing capacity, including GPU-accelerated nodes, to the Lawrence high performance computing cluster to speed research in clean energy, sustainability, PTSD and climate change across South Dakota.
Xia Wang
Ohio State University, OH·Computer Science
Build hpcGPT, a question-answering service for academic high-performance computing centers, by combining large language model fine-tuning with retrieval augmented generation so answers reflect center-specific documentation and past support experience.
The school
Boise State University: 10 labs on PI Pages, $58M a year on research, 64 research doctorates a year.
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