Computing at LLNL
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At a Glance

Discovery science and technical innovation

Computing at LLNL advances scientific discovery through foundational and innovative research; mission-driven data science; complex modeling, simulation, and analysis on powerful supercomputers; and creative technologies and software solutions. Everything at Livermore is Team Science. Thus, Computing is at the heart of many of LLNL’s most compelling national security and scientific efforts:

  • Operating one of the world’s largest HPC data centers
  • Deploying research and supercomputers to mitigate COVID-19
  • Preparing for one of the nation’s first exascale-class computers
  • Providing essential IT expertise across LLNL
  • Running one of the world’s largest control systems at NIF
  • Advancing cancer research and treatment
  • Designing AI and machine learning algorithms for science-based pattern discovery

Focus Areas

Computational Math

Powering scientific codes with advanced algebraic methods, algorithms, solvers, and discretizations

Computational Science

Supporting the Lab’s mission-driven programs with scientific modeling and simulation

Cyber Security

Meeting the nation’s top priorities to enhance security in a highly interconnected world

Data Science

Advancing AI, data analytics, machine learning, predictive modeling, statistics, UQ, and more

Emerging Architectures

Innovating in new directions for next-generation hardware designs and platform integrations

HPC Systems & Software

Providing environments, tools, and expertise for vital national security research and development

Information Technology

Meeting the Lab’s computer technology needs every day with enterprise applications and services

Software Engineering

Applying best practices to maximize the efficiency of software development and deployment


Recent Tweet

stylized drawing of atoms, a computer circuit, and labels of temperature readings of 75 degrees, -53 degrees, and -21 degrees Celsius

Source: LLNL News

ML model instantly predicts polymer properties

LLNL researchers have developed a novel machine learning (ML) model that can predict 10 distinct polymer properties more accurately than was possible with previous ML models.

AI/ML | Computational Science | Data Science | Materials Science

SC22 logo sticker on a glass wall at the conference center

Source: LLNL News

LLNL staff returns to Texas-sized Supercomputing Conference

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.

AI/ML | Awards | Collaborations | Compiler Technology | Data Science | Diversity | Emerging Architectures | Events | HPC Architectures | HPC Systems and Software | Outreach | Performance, Portability, and Productivity | Software Build and Installation


Featured Employee
Chen Wang in front of fall-colored trees

Chen Wang

As Computing’s sixth Fernbach Fellow, postdoctoral researcher Chen Wang will work on a new I/O programming paradigm and improve HPC storage consistency models under the mentorship of Kathryn…

Featured Project
abstract rendering of ones, zeros, and interconnected lines

Floating Point Compression

High-precision numerical data from computer simulations, observations, and experiments is often represented in floating point and can easily reach terabytes to petabytes of storage.

Featured Award


The SUNDIALS software team has been awarded the 2023 SIAM/ACM Prize in Computational Science and Engineering, which recognizes outstanding contributions to the development and use of mathematical and computational tools and methods for the solution of science and engineering problems.


Glassdoor Offical Logo 2020

We offer a promising future of discovery science and technical innovation

From software developers and applied math researchers to hardware architects and networking experts, computing at LLNL requires a top-flight workforce with a broad skill set. Check out our internship pages or visit LLNL's careers site to see how you can be a part of the future.