This project develops an instrument, called ConFlux, hosted at the University of Michigan (UM), specifically designed to enable High Performance Computing (HPC) clusters to communicate seamlessly and at interactive speeds with data-intensive operations. The project establishes a hardware and software ecosystem to enable large scale data-driven modeling of multiscale physical systems. ConFlux will produce advances in predictive modeling in several disciplines including turbulent flows, materials physics, cosmology, climate science and cardiovascular flow modeling.
A wide range of phenomena exhibit emergent behavior that makes modeling very challenging. In this project, physics-constrained data-driven modeling approaches are pursued to account for the underlying complexity. These techniques require HPC applications (running on external clusters) to interact with large data sets at run time. ConFlux provides low latency communications for in- and out-of-core data, cross-platform storage, as well as high throughput interconnects and massive memory allocations. The file-system and scheduler natively handle extreme-scale machine learning and traditional HPC modules in a tightly integrated workflow---rather than in segregated operations--leading to significantly lower latencies, fewer algorithmic barriers and less data movement.
Course material developed from the usage of ConFlux is being integrated into the educational curriculum via several degree and certificate programs offered by two UM institutes dedicated to computational and data sciences. Use of the ConFlux cluster will be extended to research groups outside of UM utilizing a number of Extreme Science and Engineering Discovery Environment (XSEDE) bridging tools and file-systems. Connections established through UM's Office of Outreach and Diversity are being leveraged to extend the use of ConFlux to minority serving institutions and Historically Black Colleges and Universities. Using the programs developed by the Society of Women Engineers at UM, middle and high school students will be engaged in hands-on educational modules in computing, physics and data.
