University Of Arkansas receives $349K Biomedical Research and Research Training grant from the National Institutes of Health

High-performance gpu cluster with nvidia rtx 6000 pro (blackwell) for molecular simulations - project summary this proposal requests funding for the acquisition and deployment of a high-performance gpu computing system to be hosted at the Arkansas high performance computing center (ahpcc), University Of Arkansas. The requested instrumentation comprises thirty-two nvidia rtx 6000 blackwell gpus, distributed across four fully integrated, high-density gpu servers. This system will be incorporated into ahpcc’s pinnacle cluster and will significantly expand computational support for a growing number of NIH-funded research programs that depend on gpu-accelerated molecular dynamics (md), biomedical simulations, and machine learning–driven modeling. Each rtx 6000 blackwell gpu provides 96 gb of high-bandwidth GDDR7 memory and ex- ceptional FP32 performance, optimized for gpu-resident md codes such as namd, gromacs, and amber. The system includes high-speed infiniband networking and robust local storage, enabling large-scale simulations and enhanced sampling protocols needed to investigate complex confor- mational changes in biologically and medically relevant systems, including membrane proteins, therapeutic growth factors, and ion channels. Three NIH-funded major users—drs. Mahmoud moradi (R35), crystal archer (R00), and suresh kumar thallapuranam (R15)—will use the system to advance research in viral glycopro- tein fusion dynamics, phosphorylation-mediated ion channel regulation, and protein-engineered therapeutics for chronic wound healing. These projects collectively account for more than 90% of the projected 6,000 annual gpu hours, satisfying NIH usage criteria. Additional minor users, supported by NSF, DOE, and prior NIH awards, will use the resource for exploratory studies in fluid phase behavior, machine-learned potential development, and membrane separation model- ing—laying the groundwork for future NIH proposals. The instrumentation will accelerate NIH-funded research, expand access for early-stage in- vestigators, and promote cross-disciplinary collaboration in computational biology, chemistry, and engineering. The University Of Arkansas will provide full operational support, including data center integration, power, cooling, and long-term maintenance through ahpcc. This shared resource represents a strategic investment in scientific infrastructure, supporting biomedical discovery, re- searcher training, and translational science over a sustained five-year period and beyond.