MRI: Acquisition of a High-Performance Computing Cluster for Research and Teaching at Rutgers University-Newark

This award to Rutgers University-Newark supports the acquisition and deployment of a High-Performance Computing (HPC) cluster (named PRICE) dedicated to research, teaching and societal outreach efforts. PRICE will have 60 general compute (CPU) nodes and one graphical processing unit (GPU) node as well as storage appropriate for the planned usage over the lifetime of the machine. The enabled research develops along three main directions: atomistic modeling, neuroscience, and data science. Some atomistic models enabled by PRICE will study the structure and dynamics of proteins to address questions related to diseases such as Alzheimer’s. New materials modeling and design is enabled both by PRICE and by the development of new quantum simulation methods. The enabled simulations will also regard new materials design by way of genetic algorithms. The enabled neuroscience research regards computational analysis of experimental data to understand brain function, connectivity, and human behavior. Enabled data science research includes the formulation of novel cooperative artificial intelligence (AI) algorithms that will improve the outcome of machine learning (ML) models of broad applicability. In addition to enabling new science, the project realizes several societal broader impacts including broadening HPC literacy of underrepresented minorities and training the future NJ workforce using HPC in the classroom and development of new undergraduate and graduate curricula.PRICE will comprise 60 compute nodes (52 cores/node), 700 TB of redundant storage and one GPU node (4 GPUs/node) to be housed at Rutgers University-Newark. PRICE will enable several additional research projects carried out by the PI, co-PIs, and major users at Rutgers-Newark and NJIT. The GPU portion enables state-of-the-art molecular dynamics simulations that elucidate structure and dynamics of proteins for the understanding of diseases, such as Alzheimer’s. GPUs also enable the efficient and timely execution of cooperative AI algorithms aimed at improving predictivity. The CPU nodes will enable quantum simulations aimed at materials engineering through density-functional theory calculations. These simulations facilitate the development of quantum models based on density functional theory, its subsystem formulation (which parallelizes efficiently over PRICE’s low-latency network) as well as quantum mechanical frameworks based on the exact factorization of the Schrödinger equation for multicomponent systems. CPU and GPU nodes will enable data analysis associated with neuroscience experiments aimed at uncovering how the brain modulates behavior and vision-related tasks as well as the study of neuron connectivity to understand brain function. Data from these experiments is growing exponentially due to increased instrument data flow from new fMRI units and improved technology allowing recordings of local field potentials from hundreds on neurons. The project also enables new science as well as the realization of societal broader impacts, such as broadening high-performance computing literacy of underrepresented minorities, training the future NJ workforce and recruitment of new faculty.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.