Our research projects vary in size, scope, and duration, but they share a focus on developing tools and methods that help LLNL deliver on its missions to the nation and, more broadly, advance the state of the art in scientific HPC. Projects are organized here in three ways: Active projects are those currently funded and regularly updated. Legacy projects are no longer actively developed. The A-Z option sorts all projects alphabetically, both active and legacy.
ADAPD integrates expertise from DOE national labs to analyze growing global data streams and traditional intelligence data, enabling early warning of nuclear proliferation activities.
The Maestro Workflow Conductor is a lightweight, open-source Python tool that can launch multi-step software simulation workflows in a clear, concise, consistent, and repeatable manner.
The SAMRAI library is the code base in CASC for exploring application, numerical, parallel computing, and software issues associated with structured adaptive mesh refinement.
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.
fpzip is a library for lossless or lossy compression of multidimensional floating-point arrays. It was primarily designed for lossless compression.
Proxy apps serve as specific targets for testing and simulation without the time, effort, and expertise that porting or changing most production codes would require.
StarSapphire is a collection of scientific data mining projects focusing on the analysis of data from scientific simulations, observations, and experiments.
BUILD tackles the complexities of HPC software integration with dependency compatibility models, binary analysis tools, efficient logic solvers, and configuration optimization techniques.
Supported by the Advanced Simulation and Computing program, Axom focuses on developing software infrastructure components that can be shared by HPC apps running on diverse platforms.
A Livermore-developed programming approach helps software to run on different platforms without major disruption to the source code.
This 2021 R&D 100 award-winning software solves data center bottlenecks by enabling resource types, schedulers, and framework services to be deployed as data centers evolve.
libROM is a library designed to facilitate Proper Orthogonal Decomposition (POD) based Reduced Order Modeling (ROM).
The MAPP incorporates multiple software packages into one integrated code so that multiphysics simulation codes can perform at scale on present and future supercomputers.
LLNL is home to the world’s largest Spectra TFinityTM system, which offers the speed, agility, and capacity required to take LLNL into the exascale era.
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.
This project advances research in physics-informed ML, invests in validated and explainable ML, creates an advanced data environment, builds ML expertise across the complex, and more.
LC sited two different AI accelerators in 2020: the Cerebras wafer-scale AI engine attached to Lassen; and an AI accelerator from SambaNova Systems into the Corona cluster.
Upgraded with the C++ programming language, VBL provides high-fidelity models and high-resolution calculations of laser performance predictions.
LLNL’s cyber programs work across a broad sponsor space to develop technologies addressing sophisticated cyber threats directed at national security and civilian critical infrastructure.
The Livermore Information Technology (LivIT) program is the first organization at LLNL to commit to migrating all services and applications to the Amazon Web Services cloud.
LivIT tackles challenges of workforce safety, telecommuting, cybersecurity protocols, National Ignition Facility software updates, and more.
From molecular screening, a software platform, and an online data to the computing systems that power these projects.
The Enabling Technologies for High-Order Simulations (ETHOS) project performs research of fundamental mathematical technologies for next-generation high-order simulations algorithms.
The open-source MFEM library enables application scientists to quickly prototype parallel physics application codes based on PDEs discretized with high-order finite elements.