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Merge pull request #697 from LLNL/feb-catchup
February catchup on news, CSS, icon, links, etc.
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_includes/head.html

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<meta name="twitter:site" content="@llnl_opensource" />
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<meta name="twitter:title" content="LLNL Software Catalog" />
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<meta name="twitter:description" content="Software available from LLNL." />
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<meta name="twitter:image" content="https://cdn.llnl.gov/onelab/0.1.1/images/llnl-logo-blue-128x128.jpg" aria-label="LLNL logo" />
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<meta name="twitter:image" content="https://cdn.llnl.gov/onelab/latest/images/llnl-logo-400x400.png" aria-label="LLNL logo" />
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_includes/header.html

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<a class="nav-link text-azure" href="https://github.com/LLNL/llnl.github.io"><i class="fa fa-regular fa-github fa-lg d-none d-lg-inline" aria-hidden="true"></i><span class="d-lg-none">LLNL GitHub</span></a>
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<a class="nav-link text-azure" href="https://twitter.com/LLNL_OpenSource"><i class="fa fa-regular fa-twitter fa-lg d-none d-lg-inline" aria-hidden="true"></i><span class="d-lg-none">LLNL Twitter</span></a>
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<a class="nav-link text-azure" href="https://twitter.com/LLNL_OpenSource"><i class="fa-brands fa-x-twitter fa-lg d-none d-lg-inline" aria-hidden="true"></i><span class="d-lg-none">LLNL Twitter</span></a>
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<a class="nav-link ps-4" href="https://github.com/LLNL/llnl.github.io"><i class="fa fa-regular fa-github fa-lg d-none d-lg-inline" aria-hidden="true"></i><span class="d-lg-none">LLNL GitHub</span></a>
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<a class="nav-link ps-4" href="https://twitter.com/LLNL_OpenSource"><i class="fa fa-regular fa-twitter fa-lg d-none d-lg-inline" aria-hidden="true"></i><span class="d-lg-none">LLNL_OpenSource Twitter</span></a>
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<a class="nav-link ps-4" href="https://twitter.com/LLNL_OpenSource"><i class="fa-brands fa-x-twitter fa-lg d-none d-lg-inline" aria-hidden="true"></i><span class="d-lg-none">LLNL_OpenSource Twitter</span></a>
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_posts/2023-08-28-nedaed-new.md

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[NED_Asteroid_Energy_Deposition](https://github.com/LLNL/NED_Asteroid_Energy_Deposition) is used to initialize a hydrocode simulation of an asteroid deflection/disruption with a nuclear explosive device for planetary defense purposes. It generates an angle/depth-dependent internal energy profile on the surface of the asteroid, which mimics the radiation absorbed from a nuclear explosive device. This code accompanies the paper "X-Ray Energy Deposition Modeling for Nuclear Mitigation Missions" (download the PDF via [*Bulletin of the American Astronomical Society*](https://baas.aas.org/pub/2021n7i303p07/release/1)). Abstract:
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[NED_Asteroid_Energy_Deposition](https://github.com/LLNL/NED_Asteroid_Energy_Deposition) is used to initialize a hydrocode simulation of an asteroid deflection/disruption with a nuclear explosive device for planetary defense purposes. It generates an angle/depth-dependent internal energy profile on the surface of the asteroid, which mimics the radiation absorbed from a nuclear explosive device. This code accompanies the paper "X-Ray Energy Deposition Modeling for Nuclear Mitigation Missions." Abstract:
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> In the event of an imminent Near-Earth Object (NEO) impact, a mitigation mission could be launched to divert the object's orbit or destroy it entirely, provided sufficient warning time and depending on the size. Though a non-nuclear technology, such as a kinetic impactor, is the preferred mitigation strategy within the planetary defense community, a range of threat scenarios exist for which a nuclear device is the only approach capable of preventing an Earth impact. However, the details of asteroid response to a nuclear mitigation mission would depend on the NEO's physical properties, such as size, shape, mass, composition, and structure. Especially if the warning time is limited, all of these properties may be poorly constrained before a mission is launched. The resolution requirements for a simulation of this system span many orders of magnitude and the required physics packages are complex. However, having an efficient and accurate way of modeling an NEO's response to the radiation emitted by a nuclear device (mostly x rays) is necessary for exploring the various NEO properties and their resulting sensitivities. We present a two-part approach for simulating a NEO deflection/disruption via x-ray radiation. The first simulation component includes x rays penetrating into the NEO surface and depositing energy, which happens immediately after the device is detonated and before the heated material has time to move. For this process, we use the Kull multiphysics code, which is a fully-coupled radiation hydrodynamics simulation with Implicit Monte Carlo (IMC) transport to capture an angle-dependent time slice of the energy deposition. The deposition profile can then be used to initialize the second simulation component: a hydrodynamics model. For this process, we utilize the Adaptive SPH code, Spheral, which is well suited for modeling shock propagation and damage in asteroid-like materials. Spheral can be used to track the NEO's response to the device's x rays at long timescales while collecting information on the deflection velocity and any potential fragmentation. We will present an update on the latest x-ray ablation modeling methodology advances.

_posts/2025-01-17-ecpdoe.md

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title: "ECP Leadership Team Recognized with Secretary of Energy’s Honor Award"
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categories: story
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LLNL employees participating in three project teams were recently recognized with Department of Energy (DOE) Secretary’s Honor Awards. Among them was the Exascale Computing Project (ECP) leadership team, which successfully delivered the ECP, a 7-year, $1.8 billion collaboration among six DOE national laboratories to create the world’s first sustainable exascale ecosystem, resulting in the development and enhancement of 25 scientific application codes to provide breakthrough simulation results on exascale computers. Additionally, over 70 software technology products were delivered in an integrated package widely used by the high-performance computing community. This first-of-a-kind software research, development and deployment (RD&D) project was jointly managed by the DOE Office of Science and the National Nuclear Security Administration, with leadership from Argonne, Lawrence Berkeley, Lawrence Livermore, Los Alamos, Oak Ridge and Sandia National Laboratories. ECP also established public-private partnerships by funding computer vendors to prepare the U.S. industry for exascale system procurements, enhancing U.S. competitiveness in the global computing market. The legacy of ECP will be felt for decades through its delivered products, trained staff and best practices in leading large, collaborative RD&D software projects. Visit LLNL News for the [full article](https://www.llnl.gov/article/52386/lab-teams-recognized-secretary-energys-honor-awards).

_posts/2025-02-11-bayesmtl-new.md

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title: "New Repo: BayesMTL"
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[BayesMTL](https://github.com/LLNL/BayesMTL) is an implementation of Bayesian multitask learning from the paper [Hierarchical Sparse Bayesian Multitask Model with Scalable Inference for Microbiome Analysis](https://arxiv.org/abs/2502.02552). Abstract:
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> This paper proposes a hierarchical Bayesian multitask learning model that is applicable to the general multi-task binary classification learning problem where the model assumes a shared sparsity structure across different tasks. We derive a computationally efficient inference algorithm based on variational inference to approximate the posterior distribution. We demonstrate the potential of the new approach on various synthetic datasets and for predicting human health status based on microbiome profile. Our analysis incorporates data pooled from multiple microbiome studies, along with a comprehensive comparison with other benchmark methods. Results in synthetic datasets show that the proposed approach has superior support recovery property when the underlying regression coefficients share a common sparsity structure across different tasks. Our experiments on microbiome classification demonstrate the utility of the method in extracting informative taxa while providing well-calibrated predictions with uncertainty quantification and achieving competitive performance in terms of prediction metrics. Notably, despite the heterogeneity of the pooled datasets (e.g., different experimental objectives, laboratory setups, sequencing equipment, patient demographics), our method delivers robust results.

_posts/2025-02-20-vanguards.md

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title: "Spack Builder Todd Gamblin of LLNL on the Why’s and How’s of Change in HPC"
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categories: story
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In *insideHPC*'s continuing series on current and future leaders of HPC-AI, LLNL’s Todd Gamblin has a well-deserved reputation in the HPC software community as a passionate engineer who enjoys rolling up his sleeves and diving into technical problems. Read the full [Vanguards of HPC-AI](https://insidehpc.com/2025/02/vanguards-of-hpc-ai-spack-builder-todd-gamblin-of-llnl-on-the-whys-and-hows-of-change-in-hpc/) article.

_posts/2025-02-21-gula-new.md

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title: "New Repo: Gula"
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[Gula](https://github.com/LLNL/Gula) a 3D model of the Gula Regional Hospital, a hypothetical facility for studying the protection of high-activity radioactive sources against theft or sabotage. Description:
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> The facility includes 2 stories above ground and a basement. The basement contains two high-activity radiation sources, a blood irradiator (assumed to be using a cesium-137 or cobalt-60 source), and a teletherapy device (assumed to be cobalt-60) for treating cancer patients. These parts of the facility are hardened from a physical security perspective. The high-activity radiation sources are protected in keeping with ORS and international consensus. This includes systems for access control, physical protection, video monitoring, and radiation detection. The combination of these systems ensures a robust detection and response against external and insider threats of theft or sabotage. The facility also contains a security department with a central alarm station (CAS), medical emergency room, treatment facilities, office space, etc.

_visualize/input_lists.json

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"alankaplan/mctfa",
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"boutproject/hermes-3",
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assets/css/bootstrap.min.css

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