Alireza Doostan is leading a major effort for real-time data compression for supercomputer research.

A professor in the Ann and H.J. Smead Department of Aerospace Engineering Sciences at the ����������, Doostan is the principal investigator on a  to change how researchers handle the massive amounts of data that result from complex physics problems like modeling turbulence and aerodynamics for air and space craft.

Compressing data is nothing new when it comes to computing, but advances in high- performance systems are now creating so much data that it becomes impossible to store for later analysis.

“Computing power has increased drastically, but moving and storing that data is becoming a bottleneck. We have to reduce the size of the data generated through large scale simulation codes,” Doostan said.

While some scientific analysis of turbulence flows can be completed faster on ever larger high-performance computing platforms, much of the information must be discarded because the scope of the data is too vast to store, making it impossible to conduct later assessments.

“There is a lot of structure and physics embedded in the data that ideally needs to be preserved to study complex flow physics or develop faster models,” Doostan said.

The goal of the grant is to both maintain accuracy of modeling data while decreasing its complexity, and critically, allowing it to be stored by compressing it in-situ, or in real-time as it is created during modeling. This is not currently possible for large-scale models, as existing technology often requires some or the entire modeling simulation be completed before compression can begin.

Joining Doostan on the project is a team of ���������� faculty, including Ken Jansen and John Evans, both also from Smead Aerospace, and Stephen Becker from applied math.

The team is focused on development of both traditional and deep neural models for massively parallel implementation of novel linear and non-linear dimensionality reduction techniques. It is a major undertaking, bringing together researchers with a broad range of backgrounds, including computational physics and sciences, discretization, machine learning, linear algebra, and statistics.

“This is a very interdisciplinary problem,” Doostan said. “This is not a problem one person can solve. You need a team.”

For Jansen, whose research focuses on turbulence modeling, an advance in compression could lead to significant progress across the spectrum of high-performance computing.

“This data compression research is critically important to provide access to the dynamics of our simulations,” Jansen said. “As simulations have passed petascale and are now exascale, it has become impractical to write the full solution fields to disk at a sufficient frequency and count, owing to the broad range of spatial and temporal scales of turbulence.”

The group has completed soon-to-be-published research showing strong promise for their approach. They are now working to scale up their algorithms to work at scale on supercomputing platforms like ����������’s Blanca cluster as well as Department of Energy systems.

“There is still a lot to be done, but our early work has shown success and only increases the computational load by less than five percent,” Doostan said.

The three-year award runs through fall 2027. Doostan is hopeful their final product will include publicly available next-generation compression software for general use by all simulation practitioners.

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Alireza Doostan
Associate Professor, Smead Aerospace
Friday, Nov. 13 | 12:30 P.M. | Zoom Webinar - Registration Required

Abstract: The increasing power of computing platforms and the recent advances in data science techniques have fostered the development of data-driven computational models of engineering systems with considerably improved prediction accuracies. An important feature of these modeling approaches is the reliance on data to develop reduced-order models of physical phenomena involved and/or the characterization of the uncertainty associated with the models or their parameters.  In the latter case, the quantification of the impact of such uncertainty on the quantities of interest is key to assess the validity of a given model and, potentially, its refinement. However, for complex engineering systems, such as those featuring multi-physics and multi-scale phenomena, data is often high-dimensional and the simulation models are computationally expensive. These, in turn, pose significant challenges to standard data-driven approaches. 

I will start this talk with a brief discussion on the challenges associated with uncertainty quantification (UQ) and data management of complex systems and a high-level introduction to recent work performed by my research group to tackle these challenges. I will then focus on model reduction approaches for efficient UQ and data storage. While seemingly different, I will explain how these two problems can be tackled with similar computational strategies. At the core of these techniques is a systematic use of models with different levels of fidelity, e.g., coarse vs. fine discretization of the same problem, that enables the identification of a lower-dimensional, yet accurate, description of the quantities of interest or data. During the talk, I will present application examples to highlight the efficiency of these multi-fidelity model reduction approaches and their wide applicability to a broad range of problems.

Bio: Alireza Doostan is an H. Joseph Smead Faculty Fellow and Associate Professor of Aerospace Engineering Sciences Department at the ����������. He is also the director of the Center for Aerospace Structures (CAS) and an affiliated faculty of the Applied Mathematics Department. Prior to his appointment at ���������� in 2010, he was an Engineering Research Associate in the Center for Turbulence Research at Stanford University. Alireza received his PhD in Structural Engineering and M.A. in Applied Mathematics and Statistics from the Johns Hopkins University both in 2007. He is a recipient of a DOE (ASCR) and an NSF (Engineering Design) Early Career awards, as well as multiple teaching awards from ���������� and AIAA. His research interests include: Uncertainty quantification, data-driven modeling, optimization under uncertainty, and computational stochastic mechanics.

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����������’s College of Engineering and Applied Science is leading a new Multi-disciplinary Simulation Center funded by the Department of Energy and the National Nuclear Security Administration’s Advanced Simulation and Computing program to model unbonded and bonded particulate materials in support of the stockpile stewardship program. 

It will significantly expand activities in Computational Science and Engineering for particulate materials at ���������� along with multiscale data-driven modeling, machine learning, and uncertainty quantification capabilities. Through it, the university joins a highly select group of Multi-disciplinary Simulation Centers across the country.

The PSAAP program aims to engage the U.S. academic community to make significant advances in predictive modeling and simulation tools through partnerships with the NNSA national laboratories. This relationship also helps recruit and train the next wave of graduate students who will explore high impact interdisciplinary research that requires experience in simulation‐based “predictive science” and advanced experimental and analytical methods. 

For this particular project, ���������� researchers and their collaborators will develop computational tools and conduct experiments with cutting-edge in-situ diagnostics at the Advanced Photon Source at Argonne National Laboratory, that strive to predict how particulate materials respond to different temperature, flow, and strain-rate regimes. These studies will focus on the effects of processing on the thermo-mechanical behavior of mock high explosives – bonded particulate materials that are inert and thus do not explode. The research will broadly advance predictive science for unbonded and bonded particulate materials, and all software developed will be open source and contribute to community advances in reliable predictive simulation of composite/particulate materials.

Professor Richard Regueiro in the Department of Civil, Environmental and Architectural Engineering, along with four other co-directors, will lead the research. He said a technically diverse team of engineering faculty was a big reason their proposal was selected above others for this prestigious center.

"We put together a team that has strength in various areas of interest to the PSAAP program,” he said. “These include exascale computing, community software development, computational multiscale multiphysics, materials experiments with advanced in-situ diagnostics, and – last, but not least – verification, validation and uncertainty quantification.”

Associate Dean for Research Massimo Ruzzene said the new project was one of only a handful awarded, leaving the college and university in prestigious company.

“One of our college goals is leading on interdisciplinary work and this project certainly fits that,” he said. “This is a great opportunity for us to both contribute to a national need with far ranging implications and provide an unparalleled educational opportunity for our students.”

���-�پ����𳦳ٴǰ���:​

• Jed Brown, CS
• Amy Clarke (Colorado School of Mines)
• Alireza Doostan, AES
• Richard Regueiro, CEAE
• Henry Tufo, CS

���������� personnel:

• Ken Jansen, AES
• Shelley Knuth, Research Computing
• Ron Pak, CEAE
• Fatemeh Pourahmadian, CEAE
• JH Song, CEAE
• Franck Vernerey, ME
• Yida Zhang, CEAE

Other collaborators on the project include Khalid Alshibli (University of Tennessee, Knoxville), Christian Linder (Stanford University), Hongbing Lu (University of Texas at Dallas), and Steve Sun (Columbia University) as well as the NNSA national laboratories.

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