LLNL researchers and a multi-institutional team have developed a highly detailed, machine learning–backed multiscale model revealing the importance of lipids to the signaling dynamics of RAS, a family of proteins whose mutations are linked to numerous cancers.
An LLNL-led collaboration targeted at using machine learning to reduce defects and carbon emissions in steelmaking is one of eight new projects receiving Department of Energy (DOE) funding through the High Performance Computing for Manufacturing (HPC4Mfg) Program.
For the first time ever, the 2021 International Conference for High Performance Computing, Networking, Storage and Analysis (SC21) went hybrid, with dozens of both in-person and virtual workshops, technical paper presentations, panels, tutorials and “birds of a feather” sessions.
The Data Science Institute hosted a career panel on November 3 featuring members of some of LLNL's Employee Resource Groups: Asian Pacific American Council, Amigos Unidos Hispanics in Partnership, Lawrence Livermore Laboratory Women’s Association, and Abilities Champions.
The 2021 International Conference for High Performance Computing, Networking, Storage and Analysis (SC21) on Nov. 18 presented the inaugural Best Reproducibility Advancement Award 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.
“We’re thankful that we’re going to be able to host the conference in-person this year, and we’re very excited about the program content. It’s going to be one of the best SC programs ever,” said SC21 General Chair Bronis R. de Supinski, chief technology officer for Livermore Computing at LLNL.
An LLNL mathematician and collaborators have developed a machine learning–based technique capable of automatically deriving a mathematical model for the motion of binary black holes from raw gravitational wave data.
In a project with U.S. Steel, LLNL computational physicists built models of the hot-rolling process to run on LLNL’s HPC platforms. The models track the steel from reheat-furnace dropout through the subsequent steps of rolling, cooling on the runout table, coiling and, finally, post-rolling cooling.
Computing’s newest internship program focuses on DevOps methodologies. The inaugural class of 2021 built a persistent data services provisioning application that will soon assist real Livermore Computing users.
Though the arrival of the exascale supercomputer El Capitan at LLNL is still almost two years away, teams of code developers are busy working on predecessor systems to ensure critical applications are ready for Day One.
To prepare for the exascale El Capitan and the next generation of power-hungry supercomputers, LLNL construction crews and maintenance workers have been working since late 2019 and throughout the pandemic on a $100 million Exascale Computing Facility Modernization project.
A new version of the Energy Exascale Earth System Model (E3SM) is two times faster than its earlier version released in 2018. E3SM2 was released to the broader scientific community in September. The project is supported by the DOE's Office of Science in the Biological and Environmental Research Office.
The Center for Applied Scientific Computing and Data Science Institute welcomed a new academic partner to the 2021 Data Science Challenge program: the University of California Riverside campus. The intensive program has run for three years with UC Merced, and it tasks students with addressing a real-world scientific problem using data science techniques.
LLNL, in partnership with Los Alamos National Laboratory and Sandia National Laboratories, has awarded a subcontract to Dell Technologies for additional supercomputing systems to support the NNSA's nuclear deterrent mission. The contract will provide at least $40 million for more than 40 petaflops of expanded computing capacity for the NNSA Tri-Labs .