Since 2018, the Lab has seen tremendous growth in its data science community and has invested heavily in related research. Five years later, the Data Science Institute has found its stride.
A new component-wise reduced order modeling method enables high-fidelity lattice design optimization.
A new collaboration will leverage advanced LLNL-developed software to create a “digital twin” of the near-net shape mill-products system for producing aerospace parts.
A multidecade, multi-laboratory collaboration evolves scalable long-term data storage and retrieval solutions to survive the march of time.
Combining specialized software tools with heterogeneous HPC hardware requires an intelligent workflow performance optimization strategy.
The 2022 International Conference for High Performance Computing, Networking, Storage, and Analysis (SC22) returned to Dallas as a large contingent of LLNL staff participated in sessions, panels, paper presentations and workshops centered around HPC.
Highlights include MFEM community workshops, compiler co-design, HPC standards committees, and AI/ML for national security.
The Earth System Grid Federation is a web-based tool set that powers most global climate change research.
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.
Presented at the 2022 International Conference on Computational Science, the team’s research introduces metrics that can improve the accuracy of blood flow simulations.
The new oneAPI Center of Excellence will involve the Center for Applied Scientific Computing and accelerate ZFP compression software to advance exascale computing.
The latest generation of Livermore’s workhorse laser physics code promises full integration across research and operations applications.
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.
LLNL has signed a memorandum of understanding with HPC facilities in Germany, the United Kingdom, and the U.S., jointly forming the International Association of Supercomputing Centers.
This year marks the 30th anniversary of the High Performance Storage System (HPSS) collaboration, comprising five DOE HPC national laboratories: LLNL, Lawrence Berkeley, Los Alamos, Oak Ridge, and Sandia, along with industry partner IBM.
After 30 years, the High Performance Storage System (HPSS) collaboration continues to lead and adapt to the needs of the time while honoring its primary mission of long-term data stewardship of the crown jewels of data for government, academic and commercial organizations around the world.
Livermore’s archive leverages a hierarchical storage management application that runs on a cluster architecture that is user-friendly, extremely scalable, and lightning fast.
The Accelerating Therapeutic Opportunities in Medicine (ATOM) consortium is showing “significant” progress in demonstrating that HPC and machine learning tools can speed up the drug discovery process, ATOM co-lead Jim Brase said at a recent webinar.
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.
LLNL and the United Kingdom’s Hartree Centre are launching a new webinar series intended to spur collaboration with industry through discussions on computational science, HPC, and data science.
Technologies developed through the Next-Generation High Performance Computing Network project are expected to support mission-critical applications for HPC, AI and ML, and high performance data analytics. Applications could include stockpile stewardship, fusion research, advanced manufacturing, climate research and other open science on future ASC HPC systems.
El Capitan will have a peak performance of more than 2 exaflops—roughly 16 times faster on average than the Sierra system—and is projected to be several times more energy efficient than Sierra.
The MAPP incorporates multiple software packages into one integrated code so that multiphysics simulation codes can perform at scale on present and future supercomputers.
Upgraded with the C++ programming language, VBL provides high-fidelity models and high-resolution calculations of laser performance predictions.
LLNL will lend its expertise in vaccine research and computing resources to the Human Vaccines Project consortium to aid development of a universal coronavirus vaccine and improve understanding of immune response.