Topic: HPC Systems and Software

Two teams led by LLNL computer scientists won Editor’s Awards from HPCwire, a leading high-performance computing industry publication, at the 2024 Supercomputing Conference in Atlanta.

Held for the first time in a hybrid format, the multi-day MFEM workshop drew participants from around the globe.

In a groundbreaking development for computational science, a team of Tri-Lab researchers has unveiled a revolutionary approach to molecular dynamics simulations using the Cerebras Wafer-Scale Engine, the world’s largest computer chip.

On the newest episode of the Big Ideas Lab podcast, listeners will go behind the scenes of LLNL's latest groundbreaking achievement: El Capitan, the world’s most powerful supercomputer.

Verified at 1.742 exaflops (1.742 quintillion calculations per second) on the High Performance Linpack—the standard benchmark used by the Top500 organization to evaluate supercomputing performance—El Capitan is the fastest computing system ever benchmarked.

Oxide Cloud Computer installation at Livermore Computing's HPC center modernizes on-premises cloud computing capabilities for general purpose workloads.

The NNSA’s exascale milestone is possible only through successful industry partnerships. Hewlett Packard Enterprise staff share their experiences working with LLNL.

The latest issue of R&D World's magazine showcases LLNL's 2024 winning technologies, including UnifyFS and UMap software projects.

LLNL is participating in the 36th annual Supercomputing Conference (SC24) in Atlanta on November 17–22, 2024.

Learn about the game-changing potential of El Capitan and discover how it will not only transform HPC and AI but also revolutionize scientific research across multiple domains.

A groundbreaking multidisciplinary team is combining the power of exascale computing with AI, advanced workflows, and GPU acceleration to advance scientific innovation and revolutionize digital design.

Follow along at your own pace through tutorials of several open-source HPC software projects.

Listen to the latest Big Ideas Lab podcast episode on LLNL supercomputing! This article contains links to the podcast on Spotify and Apple.

The event attracted more than 60 attendees from diverse sectors and featured discussions aimed at fostering new collaborations with various DOE offices and national labs.

The Association for Computing Machinery's (ACM) Special Interest Group on High Performance Computing (SIGHPC) has awarded Kathryn Mohror with its prestigious Emerging Woman Leader in Technical Computing (EWLTC) Award.

An optics element team and two open-source software teams (UMap and UnifyFS) are LLNL's winners of this year's awards.

The collaboration has enabled expanding systems of the same architecture as LLNL’s upcoming exascale supercomputer, El Capitan, featuring AMD’s cutting-edge MI300A processors.

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.

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.

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.