Topic: Scientific Visualization

LLNL held its first-ever Machine Learning for Industry Forum (ML4I) on August 10–12, co-hosted by the Lab’s High-Performance Computing Innovation Center and Data Science Institute.

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The Livermore-led VisIt visualization and analysis tool has supported scalable, high-quality evaluation of simulation results for over 20 years.

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Our use of supercomputers is enabled by the codes developed to model and simulate complex physical phenomena on massively parallel architectures.

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LLNL’s Computing Directorate heads to the 32nd annual Supercomputing Conference (SC20) held virtually on November 9–19. Although the format is different this year, we’re turning out in full force.

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CASC researcher Harsh Bhatia thrives in the Lab’s versatile research environment. “At the Lab, no two problems are the same. Therefore, as a team, researchers deliver hundreds of new data science solutions each year. We are very fortunate to have access to many high-impact projects so we can really make a difference with our data science or data analysis solutions," he says.

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This summer, the Computing Scholar Program welcomed 160 undergraduate and graduate students into virtual internships. The Lab’s open-source community was already primed for student participation.

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Highlights include perspectives on machine learning and artificial intelligence in science, data driven models, autonomous vehicle operations, and the OpenMP standard 5.0.

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Rushil Anirudh describes the machine learning field as undergoing a “gold rush.”

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

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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.

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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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