ADAPD integrates expertise from DOE national labs to analyze growing global data streams and traditional intelligence data, enabling early warning of nuclear proliferation activities.

Application-level resilience is emerging as an alternative to traditional fault tolerance approaches because it provides fault tolerance at a lower cost than traditional approaches.

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 tool that automatically diagnoses performance and correctness faults in MPI applications. It identifies abnormal MPI tasks and code regions and finds the least-progressed task.

BUILD tackles the complexities of HPC software integration with dependency compatibility models, binary analysis tools, efficient logic solvers, and configuration optimization techniques.

LivIT tackles challenges of workforce safety, telecommuting, cyber security 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.

Cram lets you easily run many small MPI jobs within a single, large MPI job by splitting MPI_COMM_WORLD up into many small communicators to run each job in the cram file independently.

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

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.

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

Fast Global File Status (FGFS) is an open-source package that provides scalable mechanisms and programming interfaces to retrieve global information of a file.

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.

This first-principles simulation method models the interaction of laser light with diffraction gratings, giving scientists a powerful tool to predict the performance of a laser compressor.

These techniques emulate the behavior of anticipated future architectures on current machines to improve performance modeling and evaluation.

Livermore researchers are enhancing HARVEY, an open-source parallel fluid dynamics application designed to model blood flow in patient-specific geometries.

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

hzip 1.0.1 is a C++ library for lossless compression of structured and unstructured meshes composed of cells with hypercube topology.

LLNL's interconnection networks projects improve the communication and overall performance of parallel applications using interconnect topology-aware task mapping.

libMSR provides a convenient interface to access Model Specific Registers and to allow tools to utilize their full functionality.

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.

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.

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

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.