Alexandre-Barff, WelcomeDeleau, HervéSarton, JonathanLedoux, FranckLucas, LaurentSauvage, BasileHasic-Telalovic, Jasminka2022-04-222022-04-222022978-3-03868-171-71017-4656https://doi.org/10.2312/egp.20221014https://diglib.eg.org:443/handle/10.2312/egp20221014Time-varying Adaptive Mesh Refinement (AMR) data have become an essential representation for 3D numerical simulations in many scientific fields. This observation is even more relevant considering that the data volumetry has increased significantly, reaching petabytes, hence largely exceeding the memory capacities of the most recent graphics hardware. Therefore, the question is how to access these massive data - AMR time series in particular - for interactive visualization purposes, without cracks, artifacts or latency. In this paper, we present a time-varying AMR data representation to enable a possible fully GPU-based out-of-core approach. We propose to convert the input data initially expressed as regular voxel grids into a set of AMR bricks uniquely identified by a 3D Hilbert's curve and store them in mass storage.Attribution 4.0 International LicenseCCS Concepts: Human-centered computing --> Scientific visualizationHuman centered computingScientific visualization10.2312/egp.2022101431-322 pages