Topic: Open-Source Software

The Earth System Grid Federation is a web-based tool set that powers most global climate change research.

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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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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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Livermore builds an open-source community around its award-winning HPC package manager.

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A Sandia National Laboratories team has adapted Livermore’s software.llnl.gov website to showcase their own open-source software. Both projects are developed and hosted on GitHub.

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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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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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zfp is an open-source C/C++ library for compressed floating-point and integer arrays that support high throughput read and write random access.

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More than 100 million smart meters have been installed in the U.S. to record and communicate electric consumption, voltage, and current to consumers and grid operators. LLNL has developed GridDS to help make the most of this data.

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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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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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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.

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LLNL’s Python 3–based ATS tool provides scientific code teams with automated regression testing across HPC architectures.

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This project solves initial value problems for ODE systems, sensitivity analysis capabilities, additive Runge-Kutta methods, DAE systems, and nonlinear algebraic systems.

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The RADIUSS project aims to lower cost and improve agility by encouraging adoption of our core open-source software products for use in institutional applications.

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The Exascale Computing Project (ECP) 2022 Community Birds-of-a-Feather Days will take place May 10–12 via Zoom. The event provides an opportunity for the HPC community to engage with ECP teams to discuss our latest development efforts.

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The open-source MFEM library enables application scientists to quickly prototype parallel physics application codes based on PDEs discretized with high-order finite elements.

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Computational mathematician Julian Andrej began using LLNL-developed, open-source software while in Germany. Now at Livermore, he lends his expertise to the Center for Applied Scientific Computing, developing code for next-generation computing hardware.

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The Livermore Computing–developed Flux project addresses challenges posed by complex scientific research supercomputing workflows.

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The Department of Energy's Office of Science interviewed LLNL computer scientist Peter Lindstrom about his work since receiving the 2011 Early Career Award.

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From molecular screening, a software platform, and an online data to the computing systems that power these projects.

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The MAPP incorporates multiple software packages into one integrated code so that multiphysics simulation codes can perform at scale on present and future supercomputers.

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This project advances research in physics-informed ML, invests in validated and explainable ML, creates an advanced data environment, builds ML expertise across the complex, and more.

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LLNL researchers and collaborators have developed a highly detailed, ML–backed multiscale model revealing the importance of lipids to RAS, a family of proteins whose mutations are linked to many cancers.

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