Using the Miranda code and the Ruby supercomputer, an LLNL team has taken a closer look at how nuclear weapon blasts close to the Earth’s surface create complications in their effects and apparent yields.
On a recent video episode of The Data Standard Podcast, biostatistician Nisha Mulakken discusses the Lawrence Livermore Microbial Detection Array (LLMDA) system, which has detection capability for all variants of SARS-CoV-2.
LLNL was honored by the American Indian Science and Engineering Society (AISES) Winds of Change magazine as one of the Top 50 STEM Workplaces in 2021, as an organization setting the standard for indigenous STEM professionals.
The 2021 Conference on Computer Vision and Pattern Recognition, the premier conference of its kind, will feature two papers co-authored by an LLNL researcher targeted at improving the understanding of robust machine learning models.
The ADAPD program held a two-day virtual meeting to highlight science-based and data-driven analysis work to advance AI innovation and develop AI-enabled systems to enhance the U.S. capability to detect nuclear proliferation activities around the globe.
LLNL, IBM and Red Hat are combining forces to develop best practices for interfacing HPC schedulers and cloud orchestrators, an effort designed to prepare for emerging supercomputers that take advantage of cloud technologies.
LLNL is looking for participants and attendees from industry, research institutions and academia for the first-ever Machine Learning for Industry Forum (ML4I), a three-day virtual event starting Aug. 10. The event is sponsored by LLNL’s High Performance Computing Innovation Center and the Data Science Institute.
LLNL has turned to AMD and Penguin Computing to upgrade a supercomputer to help in the fight against the novel coronavirus. The computer's name is... Corona. The 2018 system, named for the total solar eclipse of 2017, will nearly double in peak performance to 4.5 peak petaflops.
SIAM announced its 2021 Class of Fellows, including LLNL computational mathematician Rob Falgout. Falgout is best known for his development of multigrid methods and for hypre, one of the world’s most popular parallel multigrid codes.
Led by computational scientist Youngsoo Choi, the Data-Driven Physical Simulation reading group has been meeting biweekly since October 2019. The pandemic almost disbanded the group... until it turned into a virtual seminar series.
COVID-19 HPC Consortium scientists and stakeholders met virtually to mark the consortium’s one-year anniversary, discussing the progress of research projects and the need to pursue a broader organization to mobilize supercomputing access for future crises.