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.
AIMS (Analytics and Informatics Management Systems) develops integrated cyberinfrastructure for big climate data discovery, analytics, simulations, and knowledge innovation.
BLT software supports HPC software development with built-in CMake macros for external libraries, code health checks, and unit testing.
MacPatch provides LLNL with enterprise system management for desktop and laptop computers running Mac OS X.
A new software model helps move million-line codes to various hardware architectures by automating data movement in unique ways.
Apollo, an auto-tuning extension of RAJA, improves performance portability in adaptive mesh refinement, multi-physics, and hydrodynamics codes via machine learning classifiers.
Large Linux data centers require flexible system management. At Livermore Computing, we are committed to supporting our Linux ecosystem at the high end of commodity computing.
PDES focuses on models that can accurately and effectively simulate California’s large-scale electric grid.
Newly developed mathematical techniques reveal important tools for data mining analysis.
GLVis is a lightweight tool for accurate and flexible finite element visualization that provides interactive visualizations of general FE meshes and solutions.
Researchers are developing a standardized and optimized operating system and software for deployment across Linux clusters to enable HPC at a reduced cost.
LLNL’s Stack Trace Analysis Tool helps users quickly identify errors in code running on today’s largest machines.
High-resolution finite volume methods are being developed for solving problems in complex phase space geometries, motivated by kinetic models of fusion plasmas.
ROSE, an open-source project maintained by Livermore researchers, provides easy access to complex, automated compiler technology and assistance.
Master Block List is a service and data aggregation tool that aids Department of Energy facilities in creating filters and blocks to prevent cyber attacks.
New platforms are improving big data computing on Livermore’s high performance computers.
LLNL researchers are finding some factors are more important in determining HPC application performance than traditionally thought.
Researchers are developing enhanced computed tomography image processing methods for explosives identification and other national security applications.
Livermore computer scientists have helped create a flexible framework that aids programmers in creating source code that can be used effectively on multiple hardware architectures.
LLNL computer scientists use machine learning to model and characterize the performance and ultimately accelerate the development of adaptive applications.
Livermore Computing staff is enhancing the high-speed InfiniBand data network used in many of its high performance computing and file systems.
Researchers are testing and enhancing a neutral particle transport code and its algorithm to ensure that they successfully scale to larger and more complex computing systems.
LLNL and University of Utah researchers have developed an advanced, intuitive method for analyzing and visualizing complex data sets.
Testbed Environment for Space Situational Awareness software helps to track satellites and space debris and prevent collisions.
The flourishing of simulation-based scientific discovery has also resulted in the emergence of the UQ discipline, which is essential for validating and verifying computer models.