A University of Delaware team led by Indian American computer scientist Sunita Chandrasekaran has been given a firsthand role in preparing one of the world’s most powerful supercomputers for its 2029 launch.
Set to be launched in 2029 at the U.S. Department of Energy’s Oak Ridge National Laboratory, Discovery, a next-gen supercomputer is expected to be so powerful it can perform more calculations in a second than a billion people could complete working nonstop for decades. Before it can drive breakthroughs in energy, security, manufacturing and human health, however, Discovery must run a gauntlet of tests.
Led by Chandrasekaran, the David L. and Beverly J.C. Mills Career Development Chair in Computer and Information Sciences and director of the First State AI Institute, the UD team is one of nine groups selected nationwide through a highly competitive process to stress-test the system and prepare it for scientific use, according to UD.
“Our goal is to run thousands of experiments across many different physics scenarios and see what breaks — whether it’s software bugs, performance bottlenecks or system issues under stress,” Chandrasekaran said. “That’s where we learn, and where our team can help fix problems in real time, working directly with the system’s builders.”
READ: Indian American researcher gets $4M NIH grant to study autoimmune disease (July 8, 2026)
The work is part of ORNL’s Discovery Center for Accelerated Application Readiness (CAAR), which is preparing cutting-edge scientific applications under the U.S. Department of Energy’s Genesis Mission.
The initiative aims to build the world’s most powerful scientific platform by combining AI, high-performance computing and emerging quantum technologies to tackle complex problems at unprecedented speed.
Discovery goes beyond today’s exascale machines, which exceed a quintillion calculations per second. But raw speed isn’t enough. The system must sustain that performance across complex, AI-driven workloads. Testing will push every component — processors, memory, networking and data movement — to uncover weaknesses and ensure reliability.
“You’re not just asking, ‘Does it run?’” Chandrasekaran said. “You’re asking, ‘Can it handle the most demanding science continuously, at scale, without failing?’”
Once the initial computing cluster is installed later this year, her team will run thousands of simulations using Particle-in-Cell on GPU (PIConGPU), a software package that models how charged particles, such as electrons and ions, interact in electromagnetic fields.
By using specialized chips called graphics processing units (GPUs) to simulate billions of particles simultaneously, the system will allow researchers to study plasma behavior, with applications in fusion energy, spacecraft propulsion, astrophysics and laser-based medical technologies.
Read: Ajay Thomas chosen chair-elect of Southwestern University board (July 7, 2026)
A key challenge in fusion energy — despite its promise as a clean, nearly limitless energy source — is efficiently transferring laser energy into the tiny fuel pellets that drive reactions. To address this, the team will leverage Discovery’s enhanced performance and memory to pair high-fidelity PIConGPU simulations with AI, identifying optimal fusion target materials and designs.
Nikhil Rao, an Indian American doctoral student in computer science at UD, is developing workflows that connect plasma simulations with machine learning models, without storing the massive data they generate.
“We’re working with simulations that produce data at incredible rates, on the order of petabytes per second,” Rao said. A petabyte — about 1 million gigabytes — is enough to store 250 million photos. “Instead of storing it, we send the data directly to machine learning models while it’s still in memory.”
“We’re preparing for a machine that doesn’t fully exist yet,” Chandrasekaran said. “That’s what makes this work so fascinating and rewarding. We’re not just preparing for what’s next — we’re helping make it possible.”
With a BE degree in Electrical & Electronics from Anna University, Madras, India, Chandrasekaran earned her PhD from Nanyang Technological University (NTU), Singapore before doing postdoctoral work at University of Houston.