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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2021

Collaborative Exploration of Scientific Datasets Using Immersive and Statistical Visualization

We discuss the value of collaborative, immersive visualization for the exploration of scientific datasets and review techniques and tools that have been developed and deployed at the National Renewable Energy Laboratory (NREL). We believe that collaborative visualizations linking statistical interfaces and graphics on laptops and high-performance computing (HPC) with 3D visualizations on immersive displays (head-mounted displays and large-scale immersive environments) enable scientific workflows that further rapid exploration of large, high-dimensional datasets by teams of analysts. We present a framework, PlottyVR, that blends statistical tools, general-purpose programming environments, and simulation with 3D visualizations. To contextualize this framework, we propose a categorization and loose taxonomy of collaborative visualization and analysis techniques. Finally, we describe how scientists and engineers have adopted this framework to investigate large, complex datasets.

collaborative visualization↗

DOE OSTI2021

Accelerating Multigrid-based Hierarchical Scientific Data Refactoring on GPUs

Rapid growth in scientific data and a widening gap between computational speed and I/O bandwidth make it increasingly infeasible to store and share all data produced by scientific simulations. Instead, we need methods for reducing data volumes: ideally, methods that can scale data volumes adaptively so as to enable negotiation of performance and fidelity tradeoffs in different situations. Multigrid-based hierarchical data representations hold promise as a solution to this problem, allowing for flexible conversion between different fidelities so that, for example, data can be created at high fidelity and then transferred or stored at lower fidelity via logically simple and mathematically sound operations. However, the effective use of such representations has been hindered until now by the relatively high costs of creating, accessing, reducing, and otherwise operating on such representations. We describe here highly optimized data refactoring kernels for GPU accelerators that enable efficient creation and manipulation of data in multigrid-based hierarchical forms. We demonstrate that our optimized design can achieve up to 250 TB/s aggregated data refactoring throughput—83% of theoretical peak—on 1024 nodes of the Summit supercomputer. We showcase our optimized design by applying it to a large-scale scientific visualization workflow and the MGARD lossy compression software.

Chen, Jieyang↗