Topic: HPC Systems and Software

To keep employees abreast of the latest tools, two data science–focused projects are under way as part of Lawrence Livermore’s Institutional Scientific Capability Portfolio.

This issue highlights some of CASC’s contributions to the DOE's Exascale Computing Project.

In a milestone for supercomputing-aided drug design, LLNL and BridgeBio Oncology Therapeutics today announced clinical trials have begun for a first-in-class medication that targets specific genetic mutations implicated in many types of cancer.

Bugs, broken codes, or system failures require added time for troubleshooting and increase the risk of data loss. LLNL has addressed failure recovery by developing the Scalable Checkpoint/Restart (SCR) framework.

Collecting variants in low-level hardware features across multiple GPU and CPU architectures.

With SCR, jobs run more efficiently, recover more work upon failure, and reduce load on critical shared resources.

LLNL’s HPC capabilities play a significant role in international science research and innovation, and Lab researchers have won 10 R&D 100 Awards in the Software–Services category in the past decade.

Two LLNL teams have come up with ingenious solutions to a few of the more vexing difficulties. For their efforts, they’ve won awards coveted by scientists in the technology fields.

Learn how to use a modern, open-source HPC software stack! Throughout August, join our tutorials on how to install and use several projects on AWS EC2 instances. No previous experience is necessary, and everyone is welcome.

This open-source file system framework accelerates hierarchical HPC I/O operations with effective, efficient use of node-local storage.

Discover how the software architecture and storage systems that will drive El Capitan’s performance will help LLNL and the NNSA Tri-Labs push the boundaries of computational science.

Unveiled at the International Supercomputing Conference in Germany, the June 2024 Top500 lists three systems with identical components as registering 19.65 petaflops on the High Performance Linpack benchmark, ranking them among the world’s 50 fastest.

In a groundbreaking development for addressing future viral pandemics, a multi-institutional team involving LLNL researchers has successfully combined an AI-backed platform with supercomputing to redesign and restore the effectiveness of antibodies whose ability to fight viruses has been compromi

LLNL participates in the ISC High Performance Conference (ISC24) on May 12–16.

Compilers translate human-programmable source code into machine-readable code. Building a compiler is especially challenging in the exascale era.

The El Capitan Center of Excellence provides a conduit between national labs and commercial vendors, ensuring that the exascale system will meet everyone’s needs.

Backed by Spack’s robust functionality, the Packaging Working Group manages the relationships between user software and system software.

The advent of accelerated processing units presents new challenges and opportunities for teams responsible for network interconnects and math libraries.

The Tools Working Group delivers debugging, correctness, and performance analysis solutions at an unprecedented scale.

As a high school baseball coach, LLNL software developer Chris Moussa understands the value of having teammates you can rely on, which is why he’s especially grateful for his “fantastic” team at work.

The high performance computing publication HPCwire has selected LLNL computer scientist Todd Gamblin as one of its “People to Watch” in HPC for 2024. The program recognized 12 HPC professionals who “play leading roles in driving innovation within their particular fields, making significant contributions to society as a whole.”

The system will enable researchers from the National Nuclear Security Administration weapons design laboratories to create models and run simulations, previously considered challenging, time-intensive or impossible, for the maintenance and modernization of the United States’ nuclear weapons stockpile.

An LLNL-led team has developed a method for optimizing application performance on large-scale GPU systems, providing a useful tool for developers running on GPU-based massively parallel and distributed machines.

Johannes Doerfert, a computer scientist in the Center for Applied Scientific Computing, was one of three researchers awarded the honor at SC23 in Denver.