LLNL’s Computing & Communications portfolio is a gateway for accessing LLNL’s wide variety of solutions and intellectual property for use in information technologies, communications, quantum sciences, data sciences and applied software/modeling & simulations. LLNL’s long history and strong capabilities in computing underpin our success in research, in developing new solutions for our missions, and in our collaborations with the academic and private sectors. We license solutions via diverse mechanisms suited to the use cases, ranging from open-source software licensing, to nonexclusive end user licenses, to custom proprietary licenses for distributors, startups, and other commercialization licensees. We also collaborate with industry partners interested in applying LLNL’s unique capabilities and computing solutions to their company’s challenges.

Portfolio News and Webcast

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An Open-Source, Data-Science Toolkit for Energy Grids

Lawrence Livermore National Laboratory has developed GridDS — an open-source, data-science toolkit for power and data engineers that will provide an integrated energy data storage and augmentation infrastructure, as well as a flexible and comprehensive set of state-of-the-art machine-learning models.

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Transforming the future: LLNL team explores grid modernization via HPC

An ongoing Technology Commercialization Fund (TCF) project with Eaton Corporation is producing great results for grid planning. IPO is proud to present this private sector collaboration at the Innovation XLab Grid Modernization Summit January 24-25.

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Call for HPC for Energy Innovation proposals

DOE's High Performance Computing for Energy Innovation (HPC4EI) Initiative released a call for proposals seeking American companies interested in collaborating with DOE's national laboratories on one-year projects to apply high-performance computing (HPC) modeling, simulation and data analysis to key challenges in U.S. manufacturing and material development.

IT and Communications Technologies

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The LiDO code combines finite element analysis, design sensitivity analysis and nonlinear programming in a High-Performance Computing (HPC) environment that enables the solution of large-scale structural optimization problems in a computationally efficient manner. Currently, the code uses topology optimization strategies in which a given material is optimally distributed throughout the domain…

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LLNL has developed a new active memory data reorganization engine. In the simplest case, data can be reorganized within the memory system to present a new view of the data. The new view may be a subset or a rearrangement of the original data. As an example, an array of structures might be more efficiently accessed by a CPU as a structure of arrays. Active memory can assemble an alternative…

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Antennas are a foundational component of our global communication and information systems. Cell phones, Wi-Fi networks, and satellite links couldn’t exist without them. LLNL scientists, Gerald Burke, Andrew Poggio, and Edward Miller created the Numerical Electromagnetic Code (NEC), an antenna modeling system for wire and surface antennas. As computer capability to handle heavy calculations…
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The invention utilizes the statistical nature of radiation transport as well as modern processing techniques to implement a physics-based, sequential statistical processor. By this we mean that instead of accumulating a pulse-height spectrum as is done in many other systems, each photon is processed individually upon arrival and then discarded. As each photon arrives, a decision is…

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Lawrence Livermore National Laboratory (LLNL) is offering the opportunity to collaborate in accelerating artificial intelligence (AI) for applied science, including research in key areas such as advanced material design, 3D printing, predictive biology, energy systems, “self-driving” lasers and fusion energy research.…

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CRETIN is a 1D, 2D, and 3D non-local thermodynamic equilibrium (NLTE) atomic kinetics/radiation transport code which follows the time evolution of atomic populations and photon distributions as radiation interacts with a plasma consisting of an arbitrary mix of elements. It can provide detailed spectra for comparing with experimental diagnostics.
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LLNL has developed a method of extending device lifetimes by imprinting into the device a shape that excludes specific vibrational modes, otherwise known as a phononic bandgap. Eliminating these modes prevents one of the primary energy loss pathways in these devices. LLNL’s new method enhances the coherence of superconducting circuits by introducing a phononic bandgap around the system’s…

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LLNL is seeking industry partners to collaborate on quantum science and technology research and development in the following areas: quantum-coherent device physics, quantum materials, quantum–classical interfaces, computing and simulation, and sensing and detection.

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To solve these challenges using new and existing CT system designs, LLNL has developed an innovative software package for CT data processing and reconstruction. Livermore Tomography Tools (LTT) is a modern integrated software package that includes all aspects of CT modeling, simulation, reconstruction, and analysis algorithms based on the latest research in the field. LTT contains the most…

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Some COVID-19 diagnoses are utilizing computed tomography (CT)-scans for triage. CT-scans produce immediate results with high sensitivity. The digital images produced by a CT-scan require physicians to identify objects within the image to determine the presence of disease. Object identification can be done using machine learning (ML) techniques such as deep learning (DL) to improve speed and…

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MimicGAN represents a new generation of methods that can “self-correct” for unseen corruptions in the data out in the field. This is particularly useful for systems that need to be deployed autonomously without needing constant intervention such as Automated Driver Assistance Systems. MimicGAN achieves this by treating every test sample as “corrupt” by default. The goal is to determine (a) the…

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LLNL has developed a new method for securely processing protected data on HPC systems with minimal impact on the existing HPC operations and execution environment. It can be used with no alterations to traditional HPC operations and can be managed locally. It is fully compatible with traditional (unencrypted) processing and can run other jobs, unencrypted or not, on the cluster simultaneously…

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Simrev is a python library imported into a user-generated program. As the program grows in capability and complexity, the engineered product matures. The "software twin" handles all changes to product configuration and is the portal to running supercomputing analysis and managing workflow for engineering simulation codes. Assemblies become program modules; parts, materials, boundary conditions…

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LLNL has developed a new system, called the Segmentation Ensembles System, that provides a simple and general way to fuse high-level and low-level information and leads to a substantial increase in overall performance of digital image analysis. LLNL researchers have demonstrated the effectiveness of the approach on applications ranging from automatic threat detection for airport security, to…

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LLNL's new "Catalyst" supercomputer is now available for collaborative projects with American industry. Developed by a partnership with Cray and Intel, the novel architecture behind this high performance computing (HPC) cluster is intended to serve as a proving ground for new HPC and Big Data technologies and algorithms.

Catalyst boasts nearly a terabyte of addressable memory per…

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The Discriminant Random Forest combines advantages of several methodologies and techniques to produce lower classification error rates.

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LLNL’s technology does not use battery-powered tags. Rather it uses a tag technology that has the same range characteristics of battery-powered tags (approximately 10 m) but without the conventional battery.