Neranjan Edirisinghe Pathirannehe
Georgia State University, GA·Computer Science
Many research areas now depend on powerful computers, including wildfire management, solar event prediction, brain science, financial forecasting and public policy. Fields outside traditional supercomputing, such as game design, economics and biology, increasingly need the same tools for AI and simulation.
- Fields
Computer Science
Engineering
- Topics
high-performance computing
cyberinfrastructure
research computing
- Grants
- 1 known grant, through Oct 2027
What they work on
- 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.
- Support reproducible research using containerization, which packages software to run identically each time, and version control, with science gateways that make data sharing and computing access easier.
- Extend GPU and CPU computing to fields outside traditional supercomputing, such as game design, economics, biology and public policy, and to courses and workshops including generative AI and game design.
More about the research
This project builds shared high-performance computing (HPC) capacity at Georgia State University, which supports 11 identified research projects and many smaller ones across disciplines.
Why it matters
Shared GPU and CPU computing could let researchers at Georgia State, including those in fields with little access to supercomputers, run AI and simulation work on problems such as wildfire management, solar event prediction, brain function, financial forecasting and public policy. Reproducible, easy-to-use tools could also help a diverse student body learn computational methods.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Leif Nelson
Boise State University, ID·Computer Science
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.
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.
Chengming Zhang
University of Houston, TX·Computer Science
Adapt GPU error-bounded lossy compressors such as cuSZ to real scientific workflows, supporting diverse data formats and compression quality targets, with quality checked by tools like QCAT.
The school
Georgia State University: 9 labs on PI Pages, $241M a year on research, 300 research doctorates a year.
Reach out
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