In early 2020, the National Energy Research Scientific Computing (NERSC) Center will celebrate the arrival of the Perlmutter supercomputer, designed by Cray Inc., and boasting the ability to hit a whopping 100 million billion floating point operations per second (FLOPS). Carnegie Mellon University’s Zachary Ulissi will be one of the first to use it.
“When this machine comes online, it will be one of the largest open-science machines in the U.S.,” said Ulissi, an assistant professor in chemical engineering. “Our day-to-day work uses machine learning methods and high throughput calculations, but in the past, these tasks often had to be done in separate locations due to limited computational resources. The Perlmutter supercomputer will greatly accelerate both the data generation and the machine learning model development, allowing us to compute many more iterations of our models much, much faster.”
According to NERSC, Perlmutter is the first supercomputing system designed to enable both data analysis and simulation. Participants in this first round are encouraged to explore applications of the Perlmutter’s capabilities in three ways: simulation of complex physical phenomena, real-time data analytics through the supercomputer’s GPU architecture and cutting-edge machine, and deep learning solutions.
Ulissi and his research team will be using Perlmutter’s expanded computing power to accelerate their search for new materials that can serve as active catalysts for renewable energy chemistries. His project was chosen as part of the NERSC Early Science and Application program, and as such, the team will work with high performance computing experts at NERSC to develop and tune GPU-accelerated machine learning methods for this new machine. The project will then be used to demonstrate to future users the impact of the new machine, and to verify that it runs according to its targeted specifications.
“We do hundreds of expensive calculations every day to search for catalysts that can more efficiently split water to produce renewable hydrogen, directly convert waste CO2 into a valuable feedstock chemical and improve the efficiency of hydrogen fuel cell vehicles,” Ulissi said. “All of these technologies are important in an increasingly electrified chemical economy.”
With current computing technology, it takes considerable time and money to analyze each catalyst in search of which will provide fuel cells with greater efficiency and capacity. With supercomputing technology like Perlmutter at their disposal, Ulissi and his team will be able to perform more of these calculations much faster, enabling them to develop technologies that will bring us closer to a zero-emissions transportation sector.