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.

Active | A-Z | Legacy

Apollo

Fast, Lightweight, Dynamic Tuning for Data-Dependent Code

Apollo, an auto-tuning extension of RAJA, improves performance portability in adaptive mesh refinement, multi-physics, and hydrodynamics codes via machine learning classifiers.

Cluster Management Tools

Flexible Support for Our Linux Ecosystem

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.

ETHOS

Enabling Technologies for High-Order Simulations

The Enabling Technologies for High-Order Simulations (ETHOS) project performs research of fundamental mathematical technologies for next-generation high-order simulations algorithms.

LibRom

POD-Based Reduced Order Modeling

LibRom is a library designed to facilitate Proper Orthogonal Decomposition (POD) based Reduced Order Modeling (ROM).

Math for Data Mining

Improved Matrix Factorization Algorithms

Newly developed mathematical techniques reveal important tools for data mining analysis.

PDES

Modeling Complex, Asynchronous Systems

PDES focuses on models that can accurately and effectively simulate California’s large-scale electric grid.

GLVis

Finite Element Visualization

GLVis is a lightweight OpenGL-based tool for accurate and flexible finite element visualization. It is based on MFEM, a finite element…

Spack

A Flexible Package Manager for HPC Software

Livermore builds an open-source community around its award-winning HPC package manager.

STAT

Discovering Supercomputers' Code Errors

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

High-Order Finite Volume Methods

Tackling Phase Space Problems in Complex Geometries

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

TOSS

Speeding Up Commodity Cluster Computing

Researchers have been developing a standardized and optimized operating system and software for deployment across a series of Linux clusters to enable high performance computing at a reduced cost…

ROSE Compiler

Robust Analysis, Debugging, and Optimization Capabilities

ROSE, an open-source project maintained by Livermore researchers, provides easy access to complex, automated compiler technology and assistance.

Master Block List

Protecting Against Cyber Threats

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.

RAJA

Managing Application Portability for Next-Generation Platforms

A Livermore-developed programming approach helps software to run on different platforms without major disruption to the source code.

Flux

Building a Framework for Resource Management

Livermore researchers have developed a toolset for solving data center bottlenecks.

Ardra

Scaling Up Transport Sweep Algorithms

LLNL researchers are testing and enhancing a neutral particle transport code and the algorithm on which the code relies to ensure that they successfully scale to larger and more complex computing…

ESGF

Supporting Climate Research Collaboration

The Earth System Grid Federation is a web-based tool set that powers most global climate change research.

Data-Intensive Computing Solutions

Addressing Growing Demands

New platforms are improving big data computing on Livermore’s high performance computers.

HPC Code Performance

Challenges and Solutions

LLNL researchers are finding some factors are more important in determining HPC application performance than traditionally thought.

CT Image Enhancement

Novel Processing Pipeline for Threat Detection

Researchers are developing enhanced computed tomography image processing methods for explosives identification and other national security applications.

Derived Field Generation

Execution Strategies for Multiple Hardware Architectures

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.

Machine Learning

Strengthening Performance Predictions

LLNL computer scientists use machine learning to model and characterize the performance and ultimately accelerate the development of adaptive applications.

InfiniBand

Improving Communications for Large-Scale Computing

Livermore Computing staff is enhancing the high-speed InfiniBand data network used in many of its high performance computing and file systems.

Topological Analysis

Charting Data’s Peaks and Valleys

LLNL and University of Utah researchers have developed an advanced, intuitive method for analyzing and visualizing complex data sets.