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U-Michigan, IBM to Develop Data-Centric Predictive Computing System

The University of Michigan is working with IBM to develop data-centric supercomputing systems designed to increase the pace of scientific discovery in a wide variety of fields, such as aircraft and rocket engine design, cardiovascular disease treatment, materials physics, climate modeling and cosmology. The collaboration was announced yesterday in San Jose at the second annual OpenPOWER Summit 2016. The OpenPOWER Foundation, which U-M recently joined, is an open, collaborative, technical community based on IBM's POWER architecture. Several other Foundation members contributed to the development of this new computing system, which has the potential to reduce computing costs by accelerating statistical inference and machine learning.

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. IBM's systems use a GPU-accelerated, data-centric approach, integrating massive datasets seamlessly with computing power, resulting in new predictive simulation techniques that promise to expand the limits of scientific knowledge.

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 establishes 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. ConFlux, funded by a grant from the National Science Foundation, aims to advance predictive modeling in several fields of computational science. IBM is providing servers and software solutions.

ConFlux meshes well with IBM's recent focus on data-centric computing systems.

"Scientific research is now at the crossroads of big data and high performance computing," said Sumit Gupta, vice president, High Performance Computing and Data Analytics, IBM. "The explosion of data requires systems and infrastructures based on... accelerators that can both stream and manage the data and quickly synthesize and make sense of data to enable faster insights."

Progress in a wide spectrum of fields ranging from medicine to transportation relies critically on the ability to gather, store, search and analyze big data and construct truly predictive models of complex, multi-scale systems.

Advanced technologies like data-centric computing systems are at the forefront of tackling these big data challenges and advancing the pace of innovation. By moving computing power to where the data resides, organizations of all sizes can maximize performance and minimize latency in their systems, enabling them to gain deeper insights from research. These data-centric solutions are accelerated through open innovation and IBM's work with other members of the OpenPOWER Foundation.

In an internal comparison test conducted by U-M, the system significantly outperformed a competing architecture by providing low latency networks and a novel architecture that allows for the integrated use of central and graphics processing units.

As one of the first projects U-M will undertake with its advanced supercomputing system, researchers are working with NASA to use cognitive techniques to simulate turbulence around aircraft and rocket engines. They're combining large amounts of data from wind tunnel experiments and simulations to build computing models that are used to predict the aerodynamics around new configurations of an aircraft wing or engine. With ConFlux, U-M can more accurately model and study turbulence, helping to speed development of more efficient airplane designs. It will also improve weather forecasting, climate science and other fields that involve the flow of liquids or gases.

U-M is also studying cardiovascular disease for the National Institutes of Health. By combining noninvasive imaging such as results from MRI and CT scans with a physical model of blood flow, U-M hopes to help doctors estimate artery stiffness within an hour of a scan, serving as an early predictor of diseases such as hypertension.

Studies are also planned to better understand climate science, such as how clouds interact with atmospheric circulation, the origins of the universe and stellar evolution and predictions of the behavior of biologically-inspired materials.

Article published by Cherie Hiland
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