Performance Engineering | Argonne National Laboratory

Argonne maintains a wide-ranging science and technology portfolio that seeks to address complex challenges in interdisciplinary and innovative ways. Below is a list of all articles, highlights, profiles, projects, and organizations related specifically to performance engineering.

Quantum network between two national labs achieves record synch

Quantum collaboration demonstrates in Chicagoland the first steps toward functional long-distance quantum networks over deployed telecom fiber optics, opening the door to scalable quantum computing.
The age of exascale and the future of supercomputing

Argonne looks to exascale and beyond, sorting out the relationship between computing and experimental facilities, the need for speed and AI’s role in making it all work.
$1.2 million award helps Argonne steer manufacturers toward supercomputing

Argonne is using its computing power and expertise to help U.S. companies improve energy efficiency in manufacturing and materials development.
Argonne’s new menu of data storage software helps scientists realize findings earlier

Argonne data navigation tool enables scientists to unlock discoveries faster using specialized scientific data services.
Argonne-led team wins technology challenge at SC19

An extensive collaboration led by Argonne recently won the Inaugural SCinet Technology Challenge at the Supercomputing 19 conference by demonstrating real-time analysis of light source data from Argonne’s APS to the ALCF.
Kyle Gerard Felker
Christopher J. Knight

Christopher’s interests include development of large-scale molecular simulation algorithms and understanding scientific application performance on future computational resources.
Timothy J. Williams

Tim specializes in using large-scale supercomputers for computational science.
James C. Osborn

James specializes in quantum field theory and lattice simulation methods, with an emphasis on lattice QCD. He also has interests in quantum computing, HPC, sparse linear algebra, applying ML methods to Hybrid Monte Carlo, and random matrix theory.
Mark Dewing

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