UCT CS Research Document Archive

Scalable desktop visualisation of very large radio astronomy data cubes

Perkins, Simon, Jacques Questiaux, Stephen Finniss, Robin Tyler, Sarah Byth and Michelle Kuttel (2014) Scalable desktop visualisation of very large radio astronomy data cubes. New Astronomy 30:1-7.

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Observation data from radio telescopes is typically stored in three (or higher) dimensional data cubes, the resolution, coverage and size of which continues to grow as ever larger radio telescopes come online. The Square Kilometre Array, tabled to be the largest radio telescope in the world, will generate multi-terabyte data cubes – several orders of magnitude larger than the current norm. Despite this imminent data deluge, scalable approaches to file access in Astronomical visualisation software are rare: most current software packages cannot read astronomical data cubes that do not fit into computer system memory, or else provide access only at a serious performance cost. In addition, there is little support for interactive exploration of 3D data.
We describe a scalable, hierarchical approach to 3D visualisation of very large spectral data cubes to enable rapid visualisation of large data files on standard desktop hardware. Our hierarchical approach, embodied in the AstroVis prototype, aims to provide a means of viewing large datasets that do not fit into system memory. The focus is on rapid initial response: our system initially rapidly presents a reduced, coarse-grained 3D view of the data cube selected, which is gradually refined. The user may select subregions of the cube to be explored in more detail, or extracted for use in applications that do not support large files. We thus shift the focus from data analysis informed by narrow slices of detailed information, to analysis informed by overview information, with details on demand. Our hierarchical solution to the rendering of large data cubes reduces the overall time to complete file reading, provides user feedback during file processing and is memory efficient. This solution does not require high performance computing hardware and can be implemented on any platform supporting the OpenGL rendering library.

EPrint Type:Journal (Paginated)
Subjects:E Data: E.0 GENERAL
D Software: D.0 GENERAL
I Computing Methodologies: I.3 COMPUTER GRAPHICS
ID Code:957
Deposited By:Kuttel, Michelle
Deposited On:08 September 2014