Topic: Open-Source Software

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.

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

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.

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.

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.

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.

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.

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

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.

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.

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.

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

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

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

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.

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

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.

Highlights include power grid challenges, performance analysis, complex boundary conditions, and a novel multiscale modeling approach.

The MFEM software library provides high-order mathematical algorithms for large-scale scientific simulations. An October workshop brought together MFEM’s global user and developer community for the first time.

Held virtually on July 15, our fifth annual Developer Day featured lightning talks, a technical deep dive, “quick takes” on remote-development resources, presentations about career paths, and a career development panel discussion.

A Livermore-developed programming approach helps software to run on different platforms without major disruption to the source code.

Supported by the Advanced Simulation and Computing program, Axom focuses on developing software infrastructure components that can be shared by HPC apps running on diverse platforms.

Computer scientist Vanessa Sochat isn’t afraid to meet new experiences head on. With a Stanford PhD and a jump-right-in attitude, she joined LLNL to work on the BUILD project, Spack package manager, and other open-source initiatives.

Highlights include scalable deep learning, high-order finite elements, data race detection, and reduced order models.

Proxy apps serve as specific targets for testing and simulation without the time, effort, and expertise that porting or changing most production codes would require.