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Two visual aids, one labeled Grid Representation and one labeled Implicit Neural Representations

Researchers are starting a three-year project aimed at improving methods for visual analysis of large heterogeneous data sets as part of a recent DOE funding opportunity.

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A graphic with the text "Exascale Day 10.18.2022"

While LLNL awaits the arrival of El Capitan, physicists and computer scientists running scientific applications on testbeds are getting a taste of what to expect.

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A crowd of people outside an LLNL building adorned with a banner for the laboratory's 70th anniversary

Employees gathered for the Lab’s first-ever Employee Engagement Day, held Oct. 11. The event featured food, drink, informative displays, historical films and more.

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An arm reaching out from a computer screen and protecting the computer with an umbrella

Climate change can bring not only heat, but also increased humidity, reducing the efficiency of the evaporative coolers many HPC centers rely on.

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the red, blue, and green MFEM logo next to the partial derivative symbol

Researchers will address the challenge of efficiently differentiating large-scale applications for the DOE by building on advances in LLNL’s MFEM finite element library and MIT’s Enzyme AD tool.

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A picture of the globe, with the logo for ESGF2 overlaid, and the text "Earth System Grid Federation"

The Earth System Grid Federation, a multi-agency initiative that gathers and distributes data for top-tier projections of the Earth’s climate, is preparing a series of upgrades to make using the data easier and faster while improving how the information is curated.

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A large facility with 6 turbines and a sprawling system of tubes and pipes

Preparing the Livermore Computing Center for El Capitan and the exascale era of supercomputers required an entirely new way of thinking about the facility’s mechanical and electrical capabilities.

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rainbow-colored simulation of turbulent thermonuclear burning

The second article in a series about the Lab's stockpile stewardship mission highlights computational models, parallel architectures, and data science techniques.

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a person works with his arms inside a glove box

The first article in a series about the Lab's stockpile stewardship mission highlights the roles of computer simulations and exascale computing.

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3D cutaway of a lumpy simulation in red and green

The new oneAPI Center of Excellence will involve the Center for Applied Scientific Computing and accelerate ZFP compression software to advance exascale computing.

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Photo of Becky in front of Sierra next to photo of Todd's portrait

The Advanced Technology Development and Mitigation program within the Exascale Computing Project shows that the best way to support the mission is through open collaboration and a sustainable software infrastructure.

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IASC logo

LLNL has signed a memorandum of understanding with HPC facilities in Germany, the United Kingdom, and the U.S., jointly forming the International Association of Supercomputing Centers.

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screen shot of Greg and the host in video chat alongside Spack processes running in a terminal

LLNL's Greg Becker spoke with HPC Tech Shorts to explain how Spack's binary cache works. The video “Get your HPC codes installed and running in minutes using Spack’s Binary Cache” runs 15:11.

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logos for seven open-source projects: Spack, BLT, Caliper, MFEM, Flux, Ascent, and RAJA

Learn how to use LLNL software in the cloud. In August, we will host tutorials in collaboration with AWS on how to install and use these projects on AWS EC2 instances. No previous experience necessary.

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Kathryn's portrait overlaid on a background of teal circles connected like a network

Computer scientist Kathryn Mohror is among LLNL's recipients of the Department of Energy’s Early Career Research Program awards.

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progression of the MuMMI model to predict how RAS and RAF proteins interact with each other

An LLNL team will be among the first researchers to perform work on the world’s first exascale supercomputer—Oak Ridge National Laboratory’s Frontier—when they use the system to model cancer-causing protein mutations.

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Mockup of an autonomous driven vehicle

Livermore’s machine learning experts aim to provide assurances on performance and enable trust in machine-learning technology through innovative validation and verification techniques.

 

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El Capitan mockup

In a presentation delivered to the 79th HPC User Forum at Oak Ridge National Laboratory, LLNL's Terri Quinn revealed that AMD’s forthcoming MI300 APU would be the computational bedrock of El Capitan, which is slated for installation at LLNL in late 2023.

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Some of the LLNL HPSS team in front of one of our tape storage systems: Herb Wartens, Debbie Morford, and Todd Heer

After 30 years, the High Performance Storage System (HPSS) collaboration continues to lead and adapt to the needs of the time while honoring its primary mission of long-term data stewardship of the crown jewels of data for government, academic and commercial organizations around the world.

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HPSS 30th anniversary logo

This year marks the 30th anniversary of the High Performance Storage System (HPSS) collaboration, comprising five DOE HPC national laboratories: LLNL, Lawrence Berkeley, Los Alamos, Oak Ridge, and Sandia, along with industry partner IBM.

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Picture of early career award winners

An update on early and mid-career recognition award recipients, including Livermore Computing's own Todd Gamblin.

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Three El Capitan Early Access Systems: Tioga, Tenaya, and RZVernal

Three testbed machines for Lawrence Livermore National Laboratory’s future exascale El Capitan supercomputer — nicknamed rzVernal, Tioga and Tenaya — all ranked among the top 200 on the latest Top500 List of the world’s most powerful computers.

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LLNL and Amazon Web Services

LLNL and Amazon Web Services (AWS) have signed a memorandum of understanding to define the role of leadership-class HPC in a future where cloud HPC is ubiquitous.

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IPDPS 2022 twitter card

LLNL participates in the International Parallel and Distributed Processing Symposium (IPDPS) on May 30 through June 3.

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square cutaway of rainbow-colored data reconstruction

Winning the best paper award at PacificVis 2022, a research team has developed a resolution-precision-adaptive representation technique that reduces mesh sizes, thereby reducing the memory and storage footprints of large scientific datasets.