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3D visualization is an important data analysis and knowledge discovery tool, however, interactive visualization of large 3D astronomical datasets poses a challenge for many existing data visualization packages. We present a solution to interactively visualize larger-than-memory 3D astronomical data cubes by utilizing a heterogeneous cluster of CPUs and GPUs. The system partitions the data volume into smaller sub-volumes that are distributed over the rendering workstations. A GPU-based ray casting volume rendering is performed to generate images for each sub-volume, which are composited to generate the whole volume output, and returned to the user. Datasets including the HI Parkes All Sky Survey (HIPASS - 12 GB) southern sky and the Galactic All Sky Survey (GASS - 26 GB) data cubes were used to demonstrate our frameworks performance. The framework can render the GASS data cube with a maximum render time < 0.3 second with 1024 x 1024 pixels output resolution using 3 rendering workstations and 8 GPUs. Our framework will scale to visualize larger datasets, even of Terabyte order, if proper hardware infrastructure is available.
We report on an exploratory project aimed at performing immersive 3D visualization of astronomical data, starting with spectral-line radio data cubes from galaxies. This work is done as a collaboration between the Department of Physics and Astronomy
New tools are needed to handle the growth of data in astrophysics delivered by recent and upcoming surveys. We aim to build open-source, light, flexible, and interactive software designed to visualize extensive three-dimensional (3D) tabular data. En
This paper introduces Polyphorm, an interactive visualization and model fitting tool that provides a novel approach for investigating cosmological datasets. Through a fast computational simulation method inspired by the behavior of Physarum polycepha
Upcoming HI surveys will deliver large datasets, and automated processing using the full 3-D information (two positional dimensions and one spectral dimension) to find and characterize HI objects is imperative. In this context, visualization is an es
We present a high-performance, graphics processing unit (GPU)-based framework for the efficient analysis and visualization of (nearly) terabyte (TB)-sized 3-dimensional images. Using a cluster of 96 GPUs, we demonstrate for a 0.5 TB image: (1) volume