Topic: Open-Source Software

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.

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

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.

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.

SC21's inaugural Best Reproducibility Advancement Award went to an LLNL team for a benchmark suite aimed at simplifying the evaluation process of approximation techniques for scientific applications.

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.

The renowned worldwide competition announced the winners of the 2021 R&D 100 Awards, among them LLNL's Flux workload management software framework in the Software/Services category.

An LLNL-led effort in data compression was one of nine projects recently funded by the DOE for research aimed at shrinking the amount of data needed to advance scientific discovery.

The Livermore-led VisIt visualization and analysis tool has supported scalable, high-quality evaluation of simulation results for over 20 years.

The renowned worldwide competition announced the finalists for the 2021 R&D 100 Awards, among them LLNL's Flux workload management software framework in the Software/Services category.

At the AWS/Arm Cloud Hackathon, Todd Gamblin and Greg Becker discuss the essential skills and concepts needed to understand how to create and deploy Spack recipes to build scientific codes.

A new episode of the Talking Drupal podcast features LLNL developer Shelane French, who discussed how Computing uses Drupal and Docksal in the Lab's web environment.

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.

This video describes Flux, an open-source software framework that manages and schedules computing workflows to maximize available resources to run applications faster and more efficiently.

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.

LLNL, IBM, and Red Hat will develop best practices for interfacing HPC schedulers and cloud orchestrators in preparation for supercomputers that use cloud technologies.

The latest issue of LLNL's Science & Technology Review magazine showcases Computing in the cover story alongside a commentary by Bruce Hendrickson.

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