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 .
Computational biology is using HPC to rapidly design and develop ways to treat cancer and COVID. LLNL researcher Felice Lightstone discusses ATOM (Accelerated Therapeutic Opportunities in Medicine) in this edition of SC21 TV.
An LLNL-led effort in data compression was one of nine projects recently funded by the DOE for research aimed at shrinking the amount of data needed to advance scientific discovery. Under the project — ComPRESS: Compression and Progressive Retrieval for Exascale Simulations and Sensors — LLNL scientists will seek better understanding of data-compression errors.
LLNL held its first-ever Machine Learning for Industry Forum (ML4I) on August 10–12. Co-hosted by the Lab’s High-Performance Computing Innovation Center and Data Science Institute, the virtual event brought together more than 500 attendees from the Department of Energy (DOE) complex, commercial companies, professional societies, and academia.
A newly funded project involving co-principal investigator and LLNL computer scientist Ignacio Laguna will examine one of the major challenges as supercomputers become increasingly heterogeneous—the numerical aspects of porting scientific applications to different HPC platforms.
From studying radioactive isotope effects to better understanding cancer metastasis, the Laboratory’s relationship with cancer research endures some 60 years after it began, with historical precedent underpinning exciting new research areas.
A new career panel series that kicked off in June continued on August 10 with a session featuring former LLNL interns who converted to full-time employment at the Lab. Moderator Mary Silva was joined by panelists from the Computing and Engineering Directorates.
LLNL and Purdue are partnering to speed up drug design using computational tools under the Accelerating Therapeutic Opportunities in Medicine project. LLNL researcher Jonathan Allen mentored students and two teaching assistants, introducing them to computationally driven drug discovery and designing predictive models for drug candidates.
Held virtually on July 15, our fifth annual Developer Day featured lightning talks, a technical deep dive, “quick takes” on remote-development resources, presentations about career paths, and a career development panel discussion.
In this episode (32:00), LLNL's Jeff Hittinger talks about scientific success, leadership, and the tricks he’s cultivated for communicating science to broader audiences through the Livermore Ambassador Lecture series.