At U-Michigan, mixing supercomputers and big data

By Laura Ascione
September 23rd, 2015

NSF grant will support supercomputer simulations as they interact with large data sets.

data-supercomputerA new way of computing could lead to immediate advances in aerodynamics, climate science, cosmology, materials science and cardiovascular research.

The National Science Foundation will provide $2.42 million to develop a unique facility for refining complex, physics-based computer models with big data techniques at the University of Michigan. The university will provide an additional $1.04 million.

The focal point of the project will be a new computing resource, called ConFlux, which is designed to enable supercomputer simulations to interface with large datasets while running. This capability will close a gap in the U.S. research computing infrastructure and place U-M at the forefront of the emerging field of data-driven physics. The new Center for Data-Driven Computational Physics will build and manage ConFlux.

The project will add supercomputing nodes designed specifically to enable data-intensive operations. The nodes will be equipped with next-generation central and graphics processing units, large memories and ultra-fast interconnects.

A three-petabyte hard drive will seamlessly handle both traditional and big data storage. The project exemplifies the objectives of President Obama’s new National Strategic Computing Initiative, which has called for the use of vast data sets in addition to increasing brute force computing power.

The common challenge among the five main studies in the grant is a matter of scale. The processes of interest can be traced back to the behaviors of atoms and molecules, billions of times smaller than the human-scale or larger questions that researchers want to answer.

Even the most powerful computer in the world cannot handle these calculations without resorting to approximations, said Karthik Duraisamy, U-M assistant professor of aerospace engineering and director of the new center.

“Such a disparity of scales exists in many problems of interest to scientists and engineers,” he said.

But approximate models often aren’t accurate enough to answer many important questions in science, engineering and medicine.

“We need to leverage the availability of past and present data to refine and improve existing models,” Duraisamy said.

This data could come from accurate simulations with a limited scope, small enough to be practical on existing supercomputers, as well as from experiments and measurements. The new computing nodes will be optimized for operations such as feeding data from the hard drive into algorithms that use the data to make predictions, a technique known as machine learning.

“Big data is typically associated with web analytics, social networks and online advertising. ConFlux will be a unique facility specifically designed for physical modeling using massive volumes of data,” said Barzan Mozafari, U-M assistant professor of computer science and engineering, who will oversee the implementation of the new computing technology.

The following projects will be the first to utilize the new computing capabilities:

Cardiovascular disease: Noninvasive imaging such as MRI and CT scans could enable doctors to deduce the stiffness of a patient’s arteries, a strong predictor of diseases such as hypertension. By combining the scan results with a physical model of blood flow, doctors could estimate artery stiffness within an hour of the scan. The study is led by Alberto Figueroa, the Edward B. Diethrich M.D. Research Professor of Biomedical Engineering and Vascular Surgery.

Turbulence: When a flow of air or water breaks up into swirls and eddies, the pure physics equations become too complex to solve. But more accurate turbulence simulation would speed up the 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. Duraisamy leads this project.

Clouds, rainfall and climate: Clouds play a central role in whether the atmosphere retains or releases heat. Wind, temperature, land use and particulates such as smoke, pollen and air pollution all affect cloud formation and precipitation. Derek Posselt, associate professor of climate and space sciences and engineering, and his team plan to use computer models to determine how clouds and precipitation respond to changes in the climate in particular regions and seasons.

Dark matter and dark energy: Dark matter and dark energy are estimated to make up about 96 percent of the universe. Galaxies should trace the invisible structure of dark matter that stretches across the universe, but the formation of galaxies plays by additional rules—it’s not as simple as connecting the dots. Simulations of galaxy formation, informed by data from large galaxy-mapping studies, should better represent the roles of dark matter and dark energy in the history of the universe. August Evrard and Christopher Miller, professors of physics and astronomy, lead this study.

Material property prediction: Material scientists would like to be able to predict a material’s properties based on its chemical composition and structure, but supercomputers aren’t powerful enough to scale atom-level interactions up to bulk qualities such as strength, brittleness or chemical stability. An effort led by Krishna Garikipati and Vikram Gavini, professor and associate professor of mechanical engineering, respectively, will combine existing theories with the help of data on material structure and properties.

“It will enable a fundamentally new description of material behavior—guided by theory, but respectful of the cold facts of the data. Wholly new materials that transcend metals, polymers or ceramics can then be designed with applications ranging from tissue replacement to space travel,” said Garikipati, who is also a professor of mathematics.

Advanced Research Computing-Technology Services at U-M provided critical support in defining the technical requirements of ConFlux. The faculty members spearheading this project come from departments across the university, but all are members of the Michigan Institute for Computational Discovery and Engineering.

Material from a press release was used in this report.

About the Author:

Laura Ascione

Laura Ascione is the Managing Editor, Content Services at eSchool Media. She is a graduate of the University of Maryland's prestigious Philip Merrill College of Journalism. When she isn't wrangling her two children, Laura enjoys running, photography, home improvement, and rooting for the Terps. Find Laura on Twitter: @eSN_Laura

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