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.

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.

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.

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.

LLNL’s version of Qbox, a first-principles molecular dynamics code, will let researchers accurately calculate bigger systems on supercomputers.

LLNL’s Stack Trace Analysis Tool helps users quickly identify errors in code running on today’s largest machines.

Researchers are developing a standardized and optimized operating system and software for deployment across Linux clusters to enable HPC at a reduced cost.

High-resolution finite volume methods are being developed for solving problems in complex phase space geometries, motivated by kinetic models of fusion plasmas.

GLVis is a lightweight tool for accurate and flexible finite element visualization that provides interactive visualizations of general FE meshes and solutions.

This project's techniques reduce bandwidth requirements for large unstructured data by making use of data compression and optimizing the layout of the data for better locality and cache reuse.

This scalable first-principles MD algorithm with O(N) complexity and controllable accuracy is capable of simulating systems that were previously impossible with such accuracy.

Drawing from data mining, image and video processing, statistics, and pattern recognition, these computational tools improve the way scientists extract useful information from data.

Based on a discretization and time-stepping algorithm, these equations include a local order parameter, a quaternion representation of local orientation, and species composition.

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.

The Extreme Resilient Discretization project (ExReDi) was established to address these challenges for algorithms common for fluid and plasma simulations.

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.