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Visualization, from simple line plots to complex high-dimensional visual analysis systems, has established itself throughout numerous domains to explore, analyze, and evaluate data. Applying such visualizations in the context of simulation science where High-Performance Computing (HPC) produces ever-growing amounts of data that is more complex, potentially multidimensional, and multimodal, takes up resources and a high level of technological experience often not available to domain experts. In this work, we present DaVE -- a curated database of visualization examples, which aims to provide state-of-the-art and advanced visualization methods that arise in the context of HPC applications. Based on domain- or data-specific descriptors entered by the user, DaVE provides a list of appropriate visualization techniques, each accompanied by descriptions, examples, references, and resources. Sample code, adaptable container templates, and recipes for easy integration in HPC applications can be downloaded for easy access to high-fidelity visualizations. While the database is currently filled with a limited number of entries based on a broad evaluation of needs and challenges of current HPC users, DaVE is designed to be easily extended by experts from both the visualization and HPC communities.

The steady advance of compute hardware is accompanied by an ever-steeper amount of data to be processed for visualization. Limited memory bandwidth provides a significant bottleneck to the runtime performance of visualization algorithms while limited video memory requires complex out-of-core loading techniques for rendering large datasets. Data compression methods aim to overcome these limitations, potentially at the cost of information loss. This work presents an approach to the compression of large data for flow visualization using the BC6H texture compression format natively supported, and therefore effortlessly leverageable, on modern GPUs. We assess the performance and accuracy of BC6H for compression of steady and unsteady vector fields and investigate its applicability to particle advection. The results indicate an improvement in memory utilization as well as runtime performance, at a cost of moderate loss in precision.