We live henceforth in a data-driven age where there is a pressing need for data-driven predictive modeling to help re-envision traditional computing models; where
The explosion of data requires streamlined systems and infrastructures that can inter alia stream and manage the data and quickly synthesize and make sense of data to enable faster insights.
The University of Michigan is collaborating with IBM to develop and deliver “data-centric” supercomputing systems designed to increase the pace of scientific discovery in fields as diverse as aircraft and rocket engine design, cardiovascular disease treatment, materials physics, climate modeling and cosmology.
The system is designed to enable high performance computing applications for physics to interact, in real time, with big data in order to improve scientists’ ability to make quantitative predictions.
In fact, working with IBM, U-M researchers have designed a computing resource called ConFlux to enable high performance computing clusters to communicate directly and at interactive speeds with data-intensive operations. Hosted at U-M, the project can establishe a hardware and software ecosystem to enable large-scale data-driven modeling of complex physical problems, such as the performance of an aircraft engine, which consists of trillions of molecular interactions.