Project Proposed: This project, acquire an HPC instrument (HPC cluster with large storage and a fast Infiniband network), aims to support 17 projects from 8 departments in a broad range of computational disciplines, including bioscience, ecology, fluid dynamics, earth and atmospheric science, materials science, and energy systems. The inclusion of GPUs and emphasis on large-scale memory units constitutes the key novelty of the proposed instrument. The proposed research involves a mix of algorithm development for parallel architectures and computational modeling, while pursuing compelling applications in biological, material, energy and climate sciences, i.e.: - Biosciences. Bioinformatics tools will be developed to focus on research such as error-correcting algorithms for next-gen sequencers, resequencing, genome assembly, genome-wide association, biological network interference analysis, and metabolomics. - Multiscale methods for grand challenge problems. Methods that can address ?grand challenge? problems, such as simulation of atmospheric aerosol formation and design of new materials. Coarse-graining will be used, starting with high-level quantum mechanics methods that are computationally expensive and mapping the high-level potential onto a new potential is much simpler. - Computational fluid dynamics modeling. The new HPC platform will enable cutting edge research in fluid mechanics and multiphase flows. Particle-resolved direct numerical simulations of multiphase flow with fluid and surface reactions will be first-of-its-kind simulations. Algorithmic developments will have broad applications in sprays, bubbly flows and device-scale simulations of gas-solid flow applications that employ quadrature-based moment methods to treat the solid phase. - Coupled dynamics of land use change and regional climate extremes. The long-term goal is to integrate policy and climate projection models to capture dynamic coupling between policy-driven agricultural land use change and regional climate, including the novel climate and regional agricultural projection systems and simulations. Broader Impacts: The impact should be felt both regionally and nationally. At the national level, the instrument should initiate transformative advances in computational algorithms proposed and will be made available to the broader research community in the form of open-source codes. Simulations made possible by these algorithms will have broad national and societal impact ranging from climate change scenarios to wind power generation to plant biotechnology and improved animal breeding. At the regional level the proposed HPC cluster will greatly enrich the institution?s research infrastructure. Use of the instrument will be incorporated into advanced courses and time will be allocated to train undergraduates, graduate students, and postdoctoral fellows in computational modeling and algorithm development. The HPC cluster will make time available to primarily undergraduate institutions and, coupled with active recruitment plans, should help attract women, underrepresented minorities, and first generation college students, who might otherwise not be encouraged to attend the institution.
