Topic: Data Science

AIMS (Analytics and Informatics Management Systems) develops integrated cyberinfrastructure for big climate data discovery, analytics, simulations, and knowledge innovation.

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Marisa Torres, software developer with LLNL’s Global Security Computing Applications Division, combines her love of biology with coding.

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Highlights include Computation’s annual external review, machine learning for ALE simulations, CFD modeling for low-carbon solutions, seismic modeling, and an in-line floating point compression tool.

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SOAR (Stateless, One-pass Adaptive Refinement) is a view-dependent mesh refinement and rendering algorithm.

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Highlights include the HYPRE library, recent data science efforts, the IDEALS project, and the latest on the Exascale Computing Project.

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At just 5 years old, Marisol Gamboa, the oldest of six siblings to Mexican immigrants, decided she was definitely going to college.

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

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

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By applying and extending ideas from data mining, image and video processing, statistics, and pattern recognition, we are developing a new generation of computational tools and techniques that are being used to improve the way in which scientists extract useful information from data.

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The sheer size of data poses significant problems in all stages of the visualization pipeline, from offline pre-processing of simulation data, to interactive queries, to real-time rendering. Moreover, visualization data is often unstructured in nature, which further complicates its management and representation. The goal of this project is to develop techniques for reducing bandwidth requirements for large unstructured data, both explicitly, by making use of data compression, and implicitly, by optimizing the layout of the data for better locality and cache reuse.

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

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

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

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

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

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zfp is an open-source C/C++ library for compressed floating-point and integer arrays that support high throughput read and write random access.

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

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The flourishing of simulation-based scientific discovery has also resulted in the emergence of the verification and validation (V&V) and uncertainty quantification (UQ) disciplines. The goal of these emerging disciplines is to enable scientists to make precise statements about the degree of confidence they have in their simulation-based predictions. Here we focus on the UQ discipline, which is essential for validating and verifying computer models.

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The Livermore Metagenomic Analysis Toolkit (LMAT) is a genome sequencing technology that helps accelerate the comparison of genetic fragments with reference genomes and improve the accuracy of the results as compared to previous technologies. It tracks approximately 25 billion short sequences and is currently being evaluated for potential operational use in global biosurveillance and microbial forensics by various federal agencies.

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Jeene Villanueva develops enterprise modeling tools that help DOE decision makers gain insight into the challenging problems faced by the U.S. nuclear weapons complex.

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