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 high performance computing power, resulting in new predictive simulation techniques that promise to expand the limits of scientific knowledge.
The collaboration was announced today 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 high performance computing system, which has the potential to reduce computing costs by accelerating statistical inference and machine learning.
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.
“There is a pressing need for data-driven predictive modeling to help re-envision traditional computing models in our pursuit to bring forth groundbreaking research,” said Karthik Duraisamy, assistant professor in the U-M Department of Aerospace Engineering and director of U-M’s Center for Data-driven Computational Physics. “The recent acceleration in computational power and measurement resolution has made possible the availability of extreme scale simulations and data sets. ConFlux allows us to bring together large scale scientific computing and machine learning for the first time to accomplish research that was previously impossible.”
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 computingSumit Gupta, vice president, high performance computing and data analytics, IBM.
“The explosion of data requires systems and infrastructures based on POWER8 plus accelerators that can both stream and manage the data and quickly synthesize and make sense of data to enable faster insights,” said Sumit Gupta, vice president, high performance computing and data analytics, IBM.
U-M researchers understand the significance of IBM’s shift to data-centric systems, said Michael J. Henesey, vice president business development, data centric systems and innovation centers at IBM.
“They were enthusiastic about the application of this architecture to problems that are essential to the university and to the country,” Henesey said. “We will stay close to U-M to help inform our future system designs.”
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.
The incorporation of OpenPOWER technologies into a modular integrated system will enable U-M to configure the systems for their specific needs. ConFlux incorporates IBM Power Systems LC servers, which were designed based on technologies and development efforts contributed by OpenPOWER Foundation members including Mellanox, NVIDIA, and Tyan. It is also powered by the latest additions to the NVIDIA Tesla Accelerated Computing Platform: NVIDIA Tesla P100 GPU accelerators with the NVLink high-speed interconnect technology.
Additional data-centric solutions U-M is using include IBM Elastic Storage Server, IBM Spectrum Scale software (scale-out, parallel access network attached storage), and IBM Platform Computing software.
In an internal comparison test conducted by U-M, the POWER8 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.
“The ConFlux project aligns with U-M’s comprehensive strategy of investment in research computing and data science across disciplines,” said Eric Michielssen, U-M’s associate vice president for research computing. “For example, our $100 million Data Science Initiative is advancing faculty driven research in engineering and the social and health sciences by building connections between the worlds of Big Data and HPC. ConFlux epitomizes this forward-looking vision.”