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(Clockwise from top left) Alireza Doostan,��
Ken Jansen, Stephen Becker, and John Evans.

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.