The DOE’s National Laboratories will showcase their impactful research at SC18

Visit the DOE Exhibit Booth 2433

The Department of Energy is the nation’s leading provider of high-performance computers. For more than six decades, the DOE national laboratories have helped develop and deploy many of the world’s most powerful supercomputers. Researchers from around the nation use these computing resources to tackle the most important scientific challenges facing us.

Scientific Visualizations

  • Stellar Outburst

    Scientific Visualizations
  • E3SM: Energy Exascale Earth System Model

    Scientific Visualizations
  • Nek5000 Validation Large-Eddy Simulation of the PLANDTL Experiment

    Scientific Visualizations
  • Topological Charge Fluctuations in the Vacuum of Quantum Chromodynamics

    Scientific Visualizations
  • E-field Propagation Through a Human Cheek Cell using FDTD

    Scientific Visualizations
  • Buoyancy-driven homogeneous variable-density turbulence –HVDT

    Scientific Visualizations
  • Birth of Microbubbles in Turbulent Breaking Waves

    Scientific Visualizations

Featured Activities

HPC research and scientific computing insights published

Argonne researchers will be on hand to share their latest research and insights on topics ranging from quantum computing and big data analysis to machine learning and algorithms and applications for exascale.

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IEEE Computer Society Selects Min Si for Early Career Award

The IEEE Computer Society TCHPC Award for Excellence for Early Career Researchers in High Performance Computing recognizes up to 3 individuals who have made outstanding, influential, and potentially long-lasting contributions in the field
 of HPC. Awards will be presented on Thursday, Nov. 15, from 12:45PM–1:30PM in Exhibit Hall B

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Building foundational knowledge in Quantum Computing

Argonne researchers contributed to three papers that will be presented at the Workshop on Post Moore’s Era Supercomputing (PMES18). The papers cover how supercomputers can aid small and noisy quantum computers to simulate large circuits; how a quantum machine learning algorithm combined with a portable, architecture-agnostic hybrid quantum-classical framework can be used to solve real-world

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